5. Configuration File Overview
The configuration files that control many of the MET tools contain formatted ASCII text. This format has been updated for MET version 11.1.0 and continues to be used in subsequent releases.
Settings common to multiple tools are described in the top part of this file and settings specific to individual tools are described beneath the common settings. Please refer to the MET User’s Guide for more details about the settings if necessary.
A configuration file entry is an entry name, followed by an equal sign (=), followed by an entry value, and is terminated by a semicolon (;). The configuration file itself is one large dictionary consisting of entries, some of which are dictionaries themselves.
The configuration file language supports the following data types:
Dictionary:
Grouping of one or more entries enclosed by curly braces {}.
Array:
List of one or more entries enclosed by square braces [].
Array elements are separated by commas.
String:
A character string enclosed by double quotation marks “”.
Integer:
A numeric integer value.
Float:
A numeric float value.
Boolean:
A boolean value (TRUE or FALSE).
Threshold:
A threshold type (<, <=, ==, !-, >=, or >) followed by a numeric value.
The threshold type may also be specified using two letter abbreviations (lt, le, eq, ne, ge, gt).
Multiple thresholds may be combined by specifying the logic type of AND (&&) or OR (||). For example, “>=5&&<=10” defines the numbers between 5 and 10 and “==1||==2” defines numbers exactly equal to 1 or 2.
Percentile Thresholds:
A threshold type (<, <=, ==, !=, >=, or >), followed by a percentile type description (SFP, SOP, SCP, USP, CDP, or FBIAS), followed by a numeric value, typically between 0 and 100.
Note that the two letter threshold type abbreviations (lt, le, eq, ne, ge, gt) are not supported for percentile thresholds.
Thresholds may be defined as percentiles of the data being processed in several places:
In Point-Stat and Grid-Stat when setting “cat_thresh”, “wind_thresh” and “cnt_thresh”.
In Wavelet-Stat when setting “cat_thresh”.
In MODE when setting “conv_thresh” and “merge_thresh”.
In Ensemble-Stat when setting “obs_thresh”.
When using the “censor_thresh” config option.
In the Stat-Analysis “-out_fcst_thresh” and “-out_obs_thresh” job command options.
In the Gen-Vx-Mask “-thresh” command line option.
The following percentile threshold types are supported:
“SFP” for a percentile of the sample forecast values. e.g. “>SFP33.3” means greater than the 33.3-rd forecast percentile.
“SOP” for a percentile of the sample observation values. e.g. “>SOP75” means greater than the 75-th observation percentile.
“SCP” for a percentile of the sample climatology values. e.g. “>SCP90” means greater than the 90-th climatology percentile.
“USP” for a user-specified percentile threshold. e.g. “<USP90(2.5)” means less than the 90-th percentile values which the user has already determined to be 2.5 outside of MET.
“==FBIAS” for a user-specified frequency bias value. e.g. “==FBIAS1” to automatically de-bias the data, “==FBIAS0.9” to select a low-bias threshold, or “==FBIAS1.1” to select a high-bias threshold. This option must be used in conjunction with a simple threshold in the other field. For example, when “obs.cat_thresh = >5.0” and “fcst.cat_thresh = ==FBIAS1;”, MET applies the >5.0 threshold to the observations and then chooses a forecast threshold which results in a frequency bias of 1. The frequency bias can be any float value > 0.0.
“CDP” for climatological distribution percentile thresholds. These thresholds require that the climatological mean and standard deviation be defined using the climo_mean and climo_stdev config file options, respectively. The categorical (cat_thresh), conditional (cnt_thresh), or wind speed (wind_thresh) thresholds are defined relative to the climatological distribution at each point. Therefore, the actual numeric threshold applied can change for each point. e.g. “>CDP50” means greater than the 50-th percentile of the climatological distribution for each point.
When percentile thresholds of type SFP, SOP, SCP, or CDP are requested for continuous filtering thresholds (cnt_thresh), wind speed thresholds (wind_thresh), or observation filtering thresholds (obs_thresh in ensemble_stat), the following special logic is applied. Percentile thresholds of type equality are automatically converted to percentile bins which span the values from 0 to 100. For example, “==CDP25” is automatically expanded to 4 percentile bins: >=CDP0&&<CDP25,>=CDP25&&<CDP50,>=CDP50&&<CDP75,>=CDP75&&<=CDP100
When sample percentile thresholds of type SFP, SOP, SCP, or FBIAS are requested, MET recomputes the actual percentile that the threshold represents. If the requested percentile and actual percentile differ by more than 5%, a warning message is printed. This may occur when the sample size is small or the data values are not truly continuous.
When percentile thresholds of type SFP, SOP, SCP, or USP are used, the actual threshold value is appended to the FCST_THRESH and OBS_THRESH output columns. For example, if the 90-th percentile of the current set of forecast values is 3.5, then the requested threshold “<=SFP90” is written to the output as “<=SFP90(3.5)”.
When parsing FCST_THRESH and OBS_THRESH columns, the Stat-Analysis tool ignores the actual percentile values listed in parentheses.
Piecewise-Linear Function (currently used only by MODE):
A list of (x, y) points enclosed in parenthesis ().
The (x, y) points are NOT separated by commas.
User-defined function of a single variable:
Left side is a function name followed by variable name in parenthesis.
Right side is an equation which includes basic math functions (+,-,*,/), built-in functions (listed below), or other user-defined functions.
Built-in functions include: sin, cos, tan, sind, cosd, tand, asin, acos, atan, asind, acosd, atand, atan2, atan2d, arg, argd, log, exp, log10, exp10, sqrt, abs, min, max, mod, floor, ceil, step, nint, sign
The context of a configuration entry matters. If an entry cannot be found in the expected dictionary, the MET tools recursively search for that entry in the parent dictionaries, all the way up to the top-level configuration file dictionary. If you’d like to apply the same setting across all cases, you can simply specify it once at the top-level. Alternatively, you can specify a setting at the appropriate dictionary level to have finer control over the behavior.
In order to make the configuration files more readable, several descriptive integer types have been defined in the ConfigConstants file. These integer names may be used on the right-hand side for many configuration file entries.
Each of the configurable MET tools expects a certain set of configuration entries. Examples of the MET configuration files can be found in data/config and scripts/config.
When you pass a configuration file to a MET tool, the tool actually parses up to four different configuration files in the following order:
Reads share/met/config/ConfigConstants to define constants.
If the tool produces PostScript output, it reads share/met/config/ConfigMapData to define the map data to be plotted.
Reads the default configuration file for the tool from share/met/config.
Reads the user-specified configuration file from the command line.
Many of the entries from step (3) are overwritten by the user-specified entries from step (4). Therefore, the configuration file you pass in on the command line really only needs to contain entries that differ from the defaults.
Any of the configuration entries may be overwritten by the user-specified configuration file. For example, the map data to be plotted may be included in the user-specified configuration file and override the default settings defined in the share/met/config/ConfigMapData file.
The configuration file language supports the use of environment variables. They are specified as ${ENV_VAR}, where ENV_VAR is the name of the environment variable. When scripting up many calls to the MET tools, you may find it convenient to use them. For example, when applying the same configuration to the output from multiple models, consider defining the model name as an environment variable which the controlling script sets prior to verifying the output of each model. Setting MODEL to that environment variable enables you to use one configuration file rather than maintaining many very similar ones.
An error in the syntax of a configuration file will result in an error from the MET tool stating the location of the parsing error.
5.1. Runtime Environment Variables
5.1.1. User-Specified Environment Variables
When editing configuration files, environment variables may be used for setting the configurable parameters if convenient. The configuration file parser expands environment variables to their full value before proceeding. Within the configuration file, environment variables must be specified in the form ${VAR_NAME}.
For example, using an environment variable to set the message_type (see below) parameter to use ADPUPA and ADPSFC message types might consist of the following.
Setting the environment variable in a Bash Shell:
export MSG_TYP='"ADPUPA", "ADPSFC"'
Referencing that environment variable inside a MET configuration file:
message_type = [ ${MSG_TYP} ];
In addition to supporting user-specified environment variables within configuration files, the environment variables listed below have special meaning if set at runtime.
5.1.2. MET_AIRNOW_STATIONS
The MET_AIRNOW_STATIONS environment variable can be used to specify a file that will override the default file. If set, it should be the full path to the file. The default table can be found in the installed share/met/table_files/airnow_monitoring_site_locations_v2.dat. This file contains ascii column data that allows lookups of latitude, longitude, and elevation for all AirNow stations based on stationId and/or AqSid.
Additional information and updated site locations can be found at the EPA AirNow website. While some monitoring stations are permanent, others are temporary, and theirs locations can change. When running the ascii2nc tool with the -format airnowhourly option, users should download the Monitoring_Site_Locations_V2.dat data file data file corresponding to the date being processed and set the MET_AIRNOW_STATIONS envrionment variable to define its location.
5.1.3. MET_NDBC_STATIONS
The MET_NDBC_STATIONS environment variable can be used to specify a file that will override the default file. If set it should be a full path to the file. The default table can be found in the installed share/met/table_files/ndbc_stations.xml. This file contains XML content for all stations that allows lookups of latitude, longitude, and, in some cases, elevation for all stations based on stationId.
This set of stations comes from 2 online sources: the active stations website and the complete stations website.
As these lists can change as a function of time, a script can be run to pull down the contents of both websites and merge any changes with the existing stations file content, creating an updated stations file locally. The MET_NDBC_STATIONS environment variable can be then set to refer to this newer stations file. Also, the MET development team will periodically run this script and update share/met/table_files/ndbc_stations.xml.
To run this utility:
build_ndbc_stations_from_web.py <-d> <-p> <-o OUTPUT_FILE> <-e EXISTING_FILE>
Usage: build_ndbc_stations_from_web.py [options]
Options:
-h, --help show this help message and exit
-d, --diagnostic Rerun using downlaoded files, skipping download step (optional, default: False)
-p, --prune Prune files that are no longer online (optional, default: False)
-o OUT_FILE, --out=OUT_FILE
Save the text into the named file (optional, default: merged.txt)
-e EXISTING_FILE, --existing=EXISTING_FILE
Save the text into the named file (optional, default: ../../../data/table_files/ndbc_stations.xml)
NOTE: The downloaded files are written to a subdirectory ndbc_temp_data which can be deleted once the final output file is created.
5.1.4. MET_BASE
The MET_BASE variable is defined in the code at compilation time as the path to the MET shared data. These are things like the default configuration files, common polygons and color scales. MET_BASE may be used in the MET configuration files when specifying paths and the appropriate path will be substituted in. If MET_BASE is defined as an environment variable, its value will be used instead of the one defined at compilation time.
5.1.5. MET_OBS_ERROR_TABLE
The MET_OBS_ERROR_TABLE environment variable can be set to specify the location of an ASCII file defining observation error information. The default table can be found in the installed share/met/table_files/obs_error_table.txt. This observation error logic is applied in Ensemble-Stat to perturb ensemble member values and/or define observation bias corrections.
When processing point and gridded observations, Ensemble-Stat searches the table to find the entry defining the observation error information. The table consists of 15 columns and includes a header row defining each column. The special string “ALL” is interpreted as a wildcard in these files. The first 6 columns (OBS_VAR, MESSAGE_TYPE, PB_REPORT_TYPE, IN_REPORT_TYPE, INSTRUMENT_TYPE, and STATION_ID) may be set to a comma-separated list of strings to be matched. In addition, the strings in the OBS_VAR column are interpreted as regular expressions when searching for a match. For example, setting the OBS_VAR column to ‘APCP_[0-9]+’ would match observations for both APCP_03 and APCP_24. The HGT_RANGE, VAL_RANGE, and PRS_RANGE columns should either be set to “ALL” or “BEG,END” where BEG and END specify the range of values to be used. The INST_BIAS_SCALE and INST_BIAS_OFFSET columns define instrument bias adjustments which are applied to the observation values. The DIST_TYPE and DIST_PARM columns define the distribution from which random perturbations should be drawn and applied to the ensemble member values. See the obs_error description below for details on the supported error distributions. The last two columns, MIN and MAX, define the bounds for the valid range of the bias-corrected observation values and randomly perturbed ensemble member values. Values less than MIN are reset to the mimimum value and values greater than MAX are reset to the maximum value. A value of NA indicates that the variable is unbounded.
5.1.6. MET_GRIB_TABLES
The MET_GRIB_TABLES environment variable can be set to specify the location of custom GRIB tables. It can either be set to a specific file name or to a directory containing custom GRIB tables files. These file names must begin with a “grib1” or “grib2” prefix and end with a “.txt” suffix. Their format must match the format used by the default MET GRIB table files, described below. The custom GRIB tables are read prior to the default tables and their settings take precedence.
At runtime, the MET tools read default GRIB tables from the installed share/met/table_files directory, and their file formats are described below:
GRIB1 table files begin with “grib1” prefix and end with a “.txt” suffix. The first line of the file must contain “GRIB1”. The following lines consist of 4 integers followed by 3 strings:
References:
GRIB2 table files begin with “grib2” prefix and end with a “.txt” suffix. The first line of the file must contain “GRIB2”. The following lines consist of 8 integers followed by 3 strings.
References:
5.1.7. OMP_NUM_THREADS
Introduction
There are a number of different ways of parallelizing code. OpenMP offers parallelism within a single shared-memory workstation or supercomputer node. The programmer writes OpenMP directives into the code to parallelize particular code regions.
When a parallelized code region is reached, which we shall hereafter call a parallel region, a number of threads are spawned and work is shared among them. Running on different cores, this reduces the execution time. At the end of the parallel region, the code returns to single-thread execution.
A limited number of code regions are parallelized in MET. As a consequence, there are limits to the overall speed gains acheivable. Only the parallel regions of code will get faster with more threads, leaving the remaining serial portions to dominate the runtime.
Not all top-level executables use parallelized code. If OpenMP is available, a log message will appear inviting the user to increase the number of threads for faster runtimes.
Setting the number of threads
The number of threads is controlled by the environment variable OMP_NUM_THREADS . For example, on a quad core machine, the user might choose to run on 4 threads:
export OMP_NUM_THREADS=4
Alternatively, the variable may be specified as a prefix to the executable itself. For example:
OMP_NUM_THREADS=4 <exec>
The case where this variable remains unset is handled inside the code, which defaults to a single thread.
There are choices when deciding how many threads to use. To perform a single run as fast as possible, it would likely be appropriate to use as many threads as there are (physical) cores available on the specific system. However, it is not a cast-iron guarantee that more threads will always translate into more speed. In theory, there is a chance that running across multiple non-uniform memory access (NUMA) regions may carry negative performance impacts. This has not been observed in practice, however.
A lower thread count is appropriate when time-to-solution is not so critical, because cores remain idle when the code is not inside a parallel region. Fewer threads typically means better resource utilization.
Which code is parallelized?
Regions of parallelized code are:
fractional_coverage (data_plane_util.cc)
Only the following top-level executables can presently benefit from OpenMP parallelization:
grid_stat
ensemble_stat
grid_ens_prod
Thread Binding
It is normally beneficial to bind threads to particular cores, sometimes called affinitization. There are a few reasons for this, but at the very least it guarantees that threads remain evenly distributed across the available cores. Otherwise, the operating system may migrate threads between cores during a run.
OpenMP provides some environment variables to handle this: OMP_PLACES
and OMP_PROC_BIND. We anticipate that the effect of setting only
OMP_PROC_BIND=true would be neutral-to-positive.
However, there are sometimes compiler-specific environment variables. Instead, thread affinitization is sometimes handled by MPI launchers, since OpenMP is often used in MPI codes to reduce intra-node communications.
Where code is running in a production context, it is worth being familiar with the binding / affinitization method on the particular system and building it into any relevant scripting.
5.2. Settings common to multiple tools
5.2.1. exit_on_warning
The “exit_on_warning” entry in ConfigConstants may be set to true or false. If set to true and a MET tool encounters a warning, it will immediately exit with bad status after writing the warning message.
exit_on_warning = FALSE;
5.2.2. nc_compression
The “nc_compression” entry in ConfigConstants defines the compression level for the NetCDF variables. Setting this option in the config file of one of the tools overrides the default value set in ConfigConstants. The environment variable MET_NC_COMPRESS overrides the compression level from configuration file. The command line argument “-compress n” for some tools overrides it. The range is 0 to 9.
0 is to disable the compression.
1 to 9: Lower number is faster, higher number for smaller files.
WARNING: Selecting a high compression level may slow down the reading and writing of NetCDF files within MET significantly.
nc_compression = 0;
5.2.3. output_precision
The “output_precision” entry in ConfigConstants defines the precision (number of significant decimal places) to be written to the ASCII output files. Setting this option in the config file of one of the tools will override the default value set in ConfigConstants.
output_precision = 5;
5.2.4. tmp_dir
The “tmp_dir” entry in ConfigConstants defines the directory for the temporary files. The directory must exist and be writable. The environment variable MET_TMP_DIR overrides the default value at the configuration file. Some tools override the temporary directory by the command line argument “-tmp_dir <diretory_name>”.
tmp_dir = "/tmp";
5.2.5. message_type_group_map
The “message_type_group_map” entry is an array of dictionaries, each containing a “key” string and “val” string. This defines a mapping of message type group names to a comma-separated list of values. This map is defined in the config files for PB2NC, Point-Stat, or Ensemble-Stat. Modify this map to define sets of message types that should be processed together as a group. The “SURFACE” entry defines message types for which surface verification logic should be applied. If not defined, the default values listed below are used.
mesage_type_group_map = [
{ key = "SURFACE"; val = "ADPSFC,SFCSHP,MSONET"; },
{ key = "ANYAIR"; val = "AIRCAR,AIRCFT"; },
{ key = "ANYSFC"; val = "ADPSFC,SFCSHP,ADPUPA,PROFLR,MSONET"; },
{ key = "ONLYSF"; val = "ADPSFC,SFCSHP"; }
];
5.2.6. message_type_map
The “message_type_map” entry is an array of dictionaries, each containing a “key” string and “val” string. This defines a mapping of input strings to output message types. This mapping is applied in ASCII2NC when converting input little_r report types to output message types. This mapping is also supported in PBN2NC as a way of renaming input PREPBUFR message types.
message_type_map = [
{ key = "FM-12 SYNOP"; val = "ADPSFC"; },
{ key = "FM-13 SHIP"; val = "SFCSHP"; },
{ key = "FM-15 METAR"; val = "ADPSFC"; },
{ key = "FM-18 BUOY"; val = "SFCSHP"; },
{ key = "FM-281 QSCAT"; val = "ASCATW"; },
{ key = "FM-32 PILOT"; val = "ADPUPA"; },
{ key = "FM-35 TEMP"; val = "ADPUPA"; },
{ key = "FM-88 SATOB"; val = "SATWND"; },
{ key = "FM-97 ACARS"; val = "AIRCFT"; }
];
5.2.7. model
The “model” entry specifies a name for the model being verified. This name is written to the MODEL column of the ASCII output generated. If you’re verifying multiple models, you should choose descriptive model names (no whitespace) to distinguish between their output. e.g. model = “GFS”;
model = "WRF";
5.2.8. desc
The “desc” entry specifies a user-specified description for each verification task. This string is written to the DESC column of the ASCII output generated. It may be set separately in each “obs.field” verification task entry or simply once at the top level of the configuration file. If you’re verifying the same field multiple times with different quality control flags, you should choose description strings (no whitespace) to distinguish between their output. e.g. desc = “QC_9”;
desc = "NA";
5.2.9. obtype
The “obtype” entry specifies a name to describe the type of verifying gridded observation used. This name is written to the OBTYPE column in the ASCII output generated. If you’re using multiple types of verifying observations, you should choose a descriptive name (no whitespace) to distinguish between their output. When verifying against point observations the point observation message type value is written to the OBTYPE column. Otherwise, the configuration file obtype value is written.
obtype = "ANALYS";
5.2.10. regrid
The “regrid” entry is a dictionary containing information about how to handle input gridded data files. The “regrid” entry specifies regridding logic using the following entries:
The “to_grid” entry may be set to NONE, FCST, OBS, a named grid, the path to a gridded data file defining the grid, or an explicit grid specification string.
to_grid = NONE; To disable regridding.
to_grid = FCST; To regrid observations to the forecast grid.
to_grid = OBS; To regrid forecasts to the observation grid.
to_grid = “G218”; To regrid both to a named grid.
to_grid = “path”; To regrid both to a grid defined by a file.
to_grid = “spec”; To define a grid specification string, as described in Appendix B Map Projections, Grids, and Polylines.
The “vld_thresh” entry specifies a proportion between 0 and 1 to define the required ratio of valid data points. When regridding, compute a ratio of the number of valid data points to the total number of points in the neighborhood. If that ratio is less than this threshold, write bad data for the current point.
The “method” entry defines the regridding method to be used.
Valid regridding methods:
MIN for the minimum value
MAX for the maximum value
MEDIAN for the median value
UW_MEAN for the unweighted average value
DW_MEAN for the distance-weighted average value (weight = distance^-2)
AW_MEAN for an area-weighted mean when regridding from high to low resolution grids (width = 1)
LS_FIT for a least-squares fit
BILIN for bilinear interpolation (width = 2)
NEAREST for the nearest grid point (width = 1)
BUDGET for the mass-conserving budget interpolation
FORCE to compare gridded data directly with no interpolation as long as the grid x and y dimensions match.
UPPER_LEFT for the upper left grid point (width = 1)
UPPER_RIGHT for the upper right grid point (width = 1)
LOWER_RIGHT for the lower right grid point (width = 1)
LOWER_LEFT for the lower left grid point (width = 1)
MAXGAUSS to compute the maximum value in the neighborhood and apply a Gaussian smoother to the result
The BEST, GEOG_MATCH, and HIRA options are not valid for regridding.
The “width” entry specifies a regridding width, when applicable. - width = 4; To regrid using a 4x4 box or circle with diameter 4.
The “shape” entry defines the shape of the neighborhood. Valid values are “SQUARE” or “CIRCLE”
The “gaussian_dx” entry specifies a delta distance for Gaussian smoothing. The default is 81.271. Ignored if not Gaussian method.
The “gaussian_radius” entry defines the radius of influence for Gaussian smoothing. The default is 120. Ignored if not Gaussian method.
The “gaussian_dx” and “gaussian_radius” settings must be in the same units, such as kilometers or degress. Their ratio (sigma = gaussian_radius / gaussian_dx) determines the Guassian weighting function.
The “convert”, “censor_thresh”, and “censor_val” entries are described below. When specified, these operations are applied to the output of the regridding step. The conversion operation is applied first, followed by the censoring operation. Note that these operations are limited in scope. They are only applied if defined within the regrid dictionary itself. Settings defined at higher levels of config file context are not applied.
regrid = {
to_grid = NONE;
method = NEAREST;
width = 1;
vld_thresh = 0.5;
shape = SQUARE;
gaussian_dx = 81.271;
gaussian_radius = 120;
convert(x) = x;
censor_thresh = [];
censor_val = [];
}
5.2.11. fcst
The “fcst” entry is a dictionary containing information about the field(s) to be verified. This dictionary may include the following entries:
The “field” entry is an array of dictionaries, each specifying a verification task. Each of these dictionaries may include:
The “name” entry specifies a name for the field.
The “level” entry specifies level information for the field.
Setting “name” and “level” is file-format specific. See below.
The “prob” entry in the forecast dictionary defines probability information. It may either be set as a boolean (i.e. TRUE or FALSE) or as a dictionary defining probabilistic field information.
When set as a boolean to TRUE, it indicates that the “fcst.field” data should be treated as probabilities. For example, when verifying the probabilistic NetCDF output of Ensemble-Stat, one could configure the Grid-Stat or Point-Stat tools as follows:
fcst = { field = [ { name = "APCP_24_A24_ENS_FREQ_gt0.0"; level = "(*,*)"; prob = TRUE; } ]; }Setting “prob = TRUE” indicates that the “APCP_24_A24_ENS_FREQ_gt0.0” data should be processed as probabilities.
When set as a dictionary, it defines the probabilistic field to be used. For example, when verifying GRIB files containing probabilistic data, one could configure the Grid-Stat or Point-Stat tools as follows:
fcst = { field = [ { name = "PROB"; level = "A24"; prob = { name = "APCP"; thresh_lo = 2.54; } }, { name = "PROB"; level = "P850"; prob = { name = "TMP"; thresh_hi = 273; } } ]; }The example above selects two probabilistic fields. In both, “name” is set to “PROB”, the GRIB abbreviation for probabilities. The “level” entry defines the level information (i.e. “A24” for a 24-hour accumulation and “P850” for 850mb). The “prob” dictionary defines the event for which the probability is defined. The “thresh_lo” (i.e. APCP > 2.54) and/or “thresh_hi” (i.e. TMP < 273) entries are used to define the event threshold(s).
Probability fields should contain values in the range [0, 1] or [0, 100]. However, when MET encounters a probability field with a range [0, 100], it will automatically rescale it to be [0, 1] before applying the probabilistic verification methods.
Set “prob_as_scalar = TRUE” to override the processing of probability data. When the “prob” entry is set as a dictionary to define the field of interest, setting “prob_as_scalar = TRUE” indicates that this data should be processed as regular scalars rather than probabilities. For example, this option can be used to compute traditional 2x2 contingency tables and neighborhood verification statistics for probability data. It can also be used to compare two probability fields directly. When this flag is set, probability values are automatically rescaled from the range [0, 100] to [0, 1].
The “convert” entry is a user-defined function of a single variable for processing input data values. Any input values that are not bad data are replaced by the value of this function. The convert function is applied prior to regridding or thresholding. This function may include any of the built-in math functions (e.g. sqrt, log10) described above. Several standard unit conversion functions are already defined in data/config/ConfigConstants. Examples of user-defined conversion functions include:
convert(x) = 2*x; convert(x) = x^2; convert(a) = log10(a); convert(a) = a^10; convert(t) = max(1, sqrt(abs(t))); convert(x) = K_to_C(x); where K_to_C(x) is defined in ConfigConstantsThe “censor_thresh” entry is an array of thresholds to be applied to the input data. The “censor_val” entry is an array of numbers and must be the same length as “censor_thresh”. These arguments must appear together in the correct format (threshold and number). For each censor threshold, any input values meeting the threshold criteria will be reset to the corresponding censor value. An empty list indicates that no censoring should be performed. The censoring logic is applied prior to any regridding but after the convert function. All statistics are computed on the censored data. These entries may be used to apply quality control logic by resetting data outside of an expected range to the bad data value of -9999. These entries are not indicated in the metadata of any output files, but the user can set the “desc” entry accordingly.
Examples of user-defined data censoring operations include:
censor_thresh = [ >12000 ]; censor_val = [ 12000 ];
Several configuration options are provided to override and correct the metadata read from the input file. The supported options are listed below:
// Data attributes set_attr_name = "string"; set_attr_level = "string"; set_attr_units = "string"; set_attr_long_name = "string"; // Time attributes set_attr_init = "YYYYMMDD[_HH[MMSS]]"; set_attr_valid = "YYYYMMDD[_HH[MMSS]]"; set_attr_lead = "HH[MMSS]"; set_attr_accum = "HH[MMSS]"; // Grid definition (must match the actual data dimensions) set_attr_grid = "named grid or grid specification string"; // Flags is_precipitation = boolean; is_specific_humidity = boolean; is_u_wind = boolean; is_v_wind = boolean; is_grid_relative = boolean; is_wind_speed = boolean; is_wind_direction = boolean; is_prob = boolean;
The “mpr_column” and “mpr_thresh” entries are arrays of strings and thresholds to specify which matched pairs should be included in the statistics. These options apply to the Point-Stat and Grid-Stat tools. They are parsed seperately for each “obs.field” array entry. The “mpr_column” strings specify MPR column names (“FCST”, “OBS”, “CLIMO_MEAN”, “CLIMO_STDEV”, or “CLIMO_CDF”), differences of columns (“FCST-OBS”), or the absolute value of those differences (“ABS(FCST-OBS)”). The number of “mpr_thresh” thresholds must match the number of “mpr_column” entries, and the n-th threshold is applied to the n-th column. Any matched pairs which do not meet any of the specified thresholds are excluded from the analysis. For example, the following settings exclude matched pairs where the observation value differs from the forecast or climatological mean values by more than 10:
mpr_column = [ "ABS(OBS-FCST)", "ABS(OBS-CLIMO_MEAN)" ]; mpr_thresh = [ <=10, <=10 ];
The “cat_thresh” entry is an array of thresholds to be used when computing categorical statistics.
The “cnt_thresh” entry is an array of thresholds for filtering data prior to computing continuous statistics and partial sums.
The “cnt_logic” entry may be set to UNION, INTERSECTION, or SYMDIFF and controls the logic for how the forecast and observed cnt_thresh settings are combined when filtering matched pairs of forecast and observed values.
The “file_type” entry specifies the input gridded data file type rather than letting the code determine it. MET determines the file type by checking for known suffixes and examining the file contents. Use this option to override the code’s choice. The valid file_type values are listed the “data/config/ConfigConstants” file and are described below. This entry should be defined within the “fcst” and/or “obs” dictionaries. For example:
fcst = { file_type = GRIB1; GRIB version 1 file_type = GRIB2; GRIB version 2 file_type = NETCDF_MET; NetCDF created by another MET tool file_type = NETCDF_PINT; NetCDF created by running the p_interp or wrf_interp utility on WRF output. May be used to read unstaggered raw WRF NetCDF output at the surface or a single model level. file_type = NETCDF_NCCF; NetCDF following the Climate Forecast (CF) convention. file_type = PYTHON_NUMPY; Run a Python script to load data into a NumPy array. file_type = PYTHON_XARRAY; Run a Python script to load data into an xarray object. }The “wind_thresh” entry is an array of thresholds used to filter wind speed values when computing VL1L2 vector partial sums. Only those U/V pairs that meet this wind speed criteria will be included in the sums. Setting this threshold to NA will result in all U/V pairs being used.
The “wind_logic” entry may be set to UNION, INTERSECTION, or SYMDIFF and controls the logic for how the forecast and observed wind_thresh settings are combined when filtering matched pairs of forecast and observed wind speeds.
The “eclv_points” entry specifies the economic cost/loss ratio points to be evaluated. For each cost/loss ratio specified, the relative value will be computed and written to the ECLV output line. This entry may either be specified as an array of numbers between 0 and 1 or as a single number. For an array, each array entry will be evaluated. For a single number, all evenly spaced points between 0 and 1 will be evaluated, where eclv_points defines the spacing. Cost/loss values are omitted for ratios of 0.0 and 1.0 since they are undefined.
The “init_time” entry specifies the initialization time in YYYYMMDD[_HH[MMSS]] format. This entry can be included in the “fcst” entry as shown below or included in the “field” entry if the user would like to use different initialization times for different fields.
The “valid_time” entry specifies the valid time in YYYYMMDD[_HH[MMSS]] format. This entry can be included in the “fcst” entry as shown below or included in the “field” entry if the user would like to use different valid times for different fields.
The “lead_time” entry specifies the lead time in HH[MMSS] format. This entry can be included in the “fcst” entry as shown below or included in the “field” entry if the user would like to use different lead times for different fields.
It is only necessary to use the “init_time”, “valid_time”, and/or “lead_time” settings when verifying a file containing data for multiple output times. For example, to verify a GRIB file containing data for many lead times, you could use “lead_time” to specify the record to be verified.
File-format specific settings for the “field” entry:
GRIB1 and GRIB2:
For custom GRIB tables, see note about MET_GRIB_TABLES.
The “name” entry specifies a GRIB code number or abbreviation.
The “level” entry specifies a level type and value:
ANNN for accumulation interval NNN
ZNNN for vertical level NNN
ZNNN-NNN for a range of vertical levels
PNNN for pressure level NNN in hPa
PNNN-NNN for a range of pressure levels in hPa
LNNN for a generic level type
RNNN for a specific GRIB record number
The “GRIB_lvl_typ” entry is an integer specifying the level type.
The “GRIB_lvl_val1” and “GRIB_lvl_val2” entries are floats specifying the first and second level values.
The “GRIB_ens” entry is a string specifying NCEP’s usage of the extended PDS for ensembles. Set to “hi_res_ctl”, “low_res_ctl”, “+n”, or “-n”, for the n-th ensemble member.
The “GRIB1_ptv” entry is an integer specifying the GRIB1 parameter table version number.
The “GRIB1_code” entry is an integer specifying the GRIB1 code (wgrib kpds5 value).
The “GRIB1_center” is an integer specifying the originating center.
The “GRIB1_subcenter” is an integer specifying the originating subcenter.
The “GRIB1_tri” is an integer specifying the time range indicator.
The “GRIB2_mtab” is an integer specifying the master table number.
The “GRIB2_ltab” is an integer specifying the local table number.
The “GRIB2_disc” is an integer specifying the GRIB2 discipline code.
The “GRIB2_parm_cat” is an integer specifying the parameter category code.
The “GRIB2_parm” is an integer specifying the parameter code.
The “GRIB2_pdt” is an integer specifying the product definition template (Table 4.0).
The “GRIB2_process” is an integer specifying the generating process (Table 4.3).
The “GRIB2_cntr” is an integer specifying the originating center.
The “GRIB2_ens_type” is an integer specifying the ensemble type (Table 4.6).
The “GRIB2_der_type” is an integer specifying the derived product type (Table 4.7).
The “GRIB2_stat_type” is an integer specifying the statistical processing type (Table 4.10).
The “GRIB2_perc_val” is an integer specifying the requested percentile value (0 to 100) to be used. This applies only to GRIB2 product definition templates 4.6 and 4.10.
The “GRIB2_ipdtmpl_index” and “GRIB2_ipdtmpl_val” entries are arrays of integers which specify the product description template values to be used. The indices are 0-based. For example, use the following to request a GRIB2 record whose 9-th and 27-th product description template values are 1 and 2, respectively:
GRIB2_ipdtmpl_index=[8, 26]; GRIB2_ipdtmpl_val=[1, 2];
NetCDF (from MET tools, CF-compliant, p_interp, and wrf_interp):
The “name” entry specifies the NetCDF variable name.
The “level” entry specifies the dimensions to be used:
(i,…,j,*,*) for a single field, where i,…,j specifies fixed dimension values and , specifies the two dimensions for the gridded field. @ specifies the vertical level value or time value instead of offset, (i,…,@NNN,*,*). For example:
field = [ { name = "QVAPOR"; level = "(0,5,*,*)"; }, { name = "TMP_P850_ENS_MEAN"; level = [ "(*,*)" ]; } ]; field = [ { name = "QVAPOR"; level = "(@20220601_1200,@850,*,*)"; }, { name = "TMP_P850_ENS_MEAN"; level = [ "(*,*)" ]; } ];Python (using PYTHON_NUMPY or PYTHON_XARRAY):
The Python interface for MET is described in Appendix F of the MET User’s Guide.
Two methods for specifying the Python command and input file name are supported. For tools which read a single gridded forecast and/or observation file, both options work. However, only the second option is supported for tools which read multiple gridded data files, such as Ensemble-Stat, Series-Analysis, and MTD.
Option 1:
On the command line, replace the path to the input gridded data file with the constant string PYTHON_NUMPY or PYTHON_XARRAY.
Specify the configuration “name” entry as the Python command to be executed to read the data.
The “level” entry is not required for Python.
For example:
field = [ { name = "read_ascii_numpy.py data/python/fcst.txt FCST"; } ];Option 2:
On the command line, leave the path to the input gridded data as is.
Set the configuration “file_type” entry to the constant PYTHON_NUMPY or PYTHON_XARRAY.
Specify the configuration “name” entry as the Python command to be executed to read the data, but replace the input gridded data file with the constant MET_PYTHON_INPUT_ARG.
The “level” entry is not required for Python.
For example:
file_type = PYTHON_NUMPY; field = [ { name = "read_ascii_numpy.py MET_PYTHON_INPUT_ARG FCST"; } ];
fcst = {
censor_thresh = [];
censor_val = [];
cnt_thresh = [ NA ];
cnt_logic = UNION;
wind_thresh = [ NA ];
wind_logic = UNION;
eclv_points = 0.05;
message_type = [ "ADPSFC" ];
init_time = "20120619_12";
valid_time = "20120620_00";
lead_time = "12";
field = [
{
name = "APCP";
level = [ "A03" ];
cat_thresh = [ >0.0, >=5.0 ];
}
];
}
5.2.12. obs
The “obs” entry specifies the same type of information as “fcst”, but for the observation data. It will often be set to the same things as “fcst”, as shown in the example below. However, when comparing forecast and observation files of different format types, this entry will need to be set in a non-trivial way. The length of the “obs.field” array must match the length of the “fcst.field” array. For example:
obs = fcst;
or
fcst = {
censor_thresh = [];
censor_val = [];
cnt_thresh = [ NA ];
cnt_logic = UNION;
wind_thresh = [ NA ];
wind_logic = UNION;
field = [
{
name = "PWAT";
level = [ "L0" ];
cat_thresh = [ >2.5 ];
}
];
}
obs = {
censor_thresh = [];
censor_val = [];
mpr_column = [];
mpr_thresh = [];
cnt_thresh = [ NA ];
cnt_logic = UNION;
wind_thresh = [ NA ];
wind_logic = UNION;
field = [
{
name = "IWV";
level = [ "L0" ];
cat_thresh = [ >25.0 ];
}
];
}
The “message_type” entry is an array of point observation message types to be used. This only applies to the tools that verify against point observations. This may be specified once at the top-level “obs” dictionary or separately for each “field” array element. In the example shown above, this is specified in the “fcst” dictionary and copied to “obs”.
Simplified vertical level matching logic is applied for surface message types. Observations for the following message types are assumed to be at the surface, as defined by the default message_type_group_map: ADPSFC, SFCSHP, MSONET
The “message_type” would be placed in the “field” array element if more than one “message_type” entry is desired within the config file. For example:
fcst = { censor_thresh = []; censor_val = []; cnt_thresh = [ NA ]; cnt_logic = UNION; wind_thresh = [ NA ]; wind_logic = UNION; field = [ { message_type = [ "ADPUPA" ]; sid_inc = []; sid_exc = []; name = "TMP"; level = [ "P250", "P500", "P700", "P850", "P1000" ]; cat_thresh = [ <=273.0 ]; }, { message_type = [ "ADPSFC" ]; sid_inc = []; sid_exc = [ "KDEN", "KDET" ]; name = "TMP"; level = [ "Z2" ]; cat_thresh = [ <=273.0 ]; } ]; }
The “sid_inc” entry is an array of station ID groups indicating which station ID’s should be included in the verification task. If specified, only those station ID’s appearing in the list will be included. Note that filtering by station ID may also be accomplished using the “mask.sid” option. However, when using the “sid_inc” option, statistics are reported separately for each masking region.
The “sid_exc” entry is an array of station ID groups indicating which station ID’s should be excluded from the verification task.
Each element in the “sid_inc” and “sid_exc” arrays is either the name of a single station ID or the full path to a station ID group file name. A station ID group file consists of a name for the group followed by a list of station ID’s. All of the station ID’s indicated will be concatenated into one long list of station ID’s to be included or excluded.
As with “message_type” above, the “sid_inc” and “sid_exc” settings can be placed in the in the “field” array element to control which station ID’s are included or excluded for each verification task.
obs = fcst;
5.2.13. climo_mean
The “climo_mean” dictionary specifies climatology mean data to be read by the Grid-Stat, Point-Stat, Ensemble-Stat, and Series-Analysis tools. It consists of several entires defining the climatology file names and fields to be used.
The “file_names” entry specifies one or more file names containing the gridded climatology data to be used.
The “field” entry is an array of dictionaries, specified the same way as those in the “fcst” and “obs” dictionaries. If the array has length zero, not climatology data will be read and all climatology statistics will be written as missing data. Otherwise, the array length must match the length of “field” in the “fcst” and “obs” dictionaries.
The “regrid” dictionary defines how the climatology data should be regridded to the verification domain.
The “time_interp_method” entry specifies how the climatology data should be interpolated in time to the forecast valid time:
NEAREST for data closest in time
UW_MEAN for average of data before and after
DW_MEAN for linear interpolation in time of data before and after
The “day_interval” entry is an integer specifying the spacing in days of the climatology data. Use 31 for monthly data or 1 for daily data. Use “NA” if the timing of the climatology data should not be checked.
The “hour_interval” entry is an integer specifying the spacing in hours of the climatology data for each day. This should be set between 0 and 24, with 6 and 12 being common choices. Use “NA” if the timing of the climatology data should not be checked.
The “day_interval” and “hour_interval” entries replace the deprecated entries “match_month”, “match_day”, and “time_step”.
climo_mean = {
file_name = [ "/path/to/climatological/mean/files" ];
field = [];
regrid = {
method = NEAREST;
width = 1;
vld_thresh = 0.5;
}
time_interp_method = DW_MEAN;
day_interval = 31;
hour_interval = 6;
}
5.2.14. climo_stdev
The “climo_stdev” dictionary specifies climatology standard deviation data to be read by the Grid-Stat, Point-Stat, Ensemble-Stat, and Series-Analysis tools. The “climo_mean” and “climo_stdev” data define the climatological distribution for each grid point, assuming normality. These climatological distributions are used in two ways:
To define climatological distribution percentile (CDP) thresholds which can be used as categorical (cat_thresh), continuous (cnt_thresh), or wind speed (wind_thresh) thresholds.
To subset matched pairs into climatological bins based on where the observation value falls within the climatological distribution. See the “climo_cdf” dictionary.
This dictionary is identical to the “climo_mean” dictionary described above but points to files containing climatological standard deviation values rather than means. In the example below, this dictionary is set by copying over the “climo_mean” setting and then updating the “file_name” entry.
climo_stdev = climo_mean;
climo_stdev = {
file_name = [ "/path/to/climatological/standard/deviation/files" ];
}
5.2.15. climo_cdf
The “climo_cdf” dictionary specifies how the the climatological mean (“climo_mean”) and standard deviation (“climo_stdev”) data are used to evaluate model performance relative to where the observation value falls within the climatological distribution. This dictionary consists of the following entries:
The “cdf_bins” entry defines the climatological bins either as an integer or an array of floats between 0 and 1.
The “center_bins” entry may be set to TRUE or FALSE.
The “write_bins” entry may be set to TRUE or FALSE.
The “direct_prob” entry may be set to TRUE or FALSE.
MET uses the climatological mean and standard deviation to construct a normal PDF at each observation location. The total area under the PDF is 1, and the climatological CDF value is computed as the area of the PDF to the left of the observation value. Since the CDF is a value between 0 and 1, the CDF bins must span that same range.
When “cdf_bins” is set to an array of floats, they explicitly define the climatological bins. The array must begin with 0.0 and end with 1.0. For example:
cdf_bins = [ 0.0, 0.10, 0.25, 0.75, 0.90, 1.0 ];
When “cdf_bins” is set to an integer, it defines the number of bins to be used. The “center_bins” flag indicates whether or not the bins should be centered on 0.5. An odd number of bins can be centered or uncentered while an even number of bins can only be uncentered. For example:
4 uncentered bins (cdf_bins = 4; center_bins = FALSE;) yields:
0.0, 0.25, 0.50, 0.75, 1.0
5 uncentered bins (cdf_bins = 5; center_bins = FALSE;) yields:
0.0, 0.2, 0.4, 0.6, 0.8, 0.9, 1.0
5 centered bins (cdf_bins = 5; center_bins = TRUE;) yields:
0.0, 0.125, 0.375, 0.625, 0.875, 1.0
When multiple climatological bins are used for Point-Stat and Grid-Stat, statistics are computed separately for each bin, and the average of the statistics across those bins is written to the output. When “write_bins” is true, the statistics for each bin are also written to the output. The bin number is appended to the contents of the VX_MASK output column.
Setting the number of bins to 1 effectively disables this logic by grouping all pairs into a single climatological bin.
climo_cdf = {
cdf_bins = 11; or an array of floats
center_bins = TRUE; or FALSE
write_bins = FALSE; or TRUE
direct_prob = FALSE; or TRUE
}
5.2.16. climate_data
When specifying climatology data for probability forecasts, either supply a probabilistic “climo_mean” field or non-probabilistic “climo_mean” and “climo_stdev” fields from which a normal approximation of the climatological probabilities should be derived.
When “climo_mean” is set to a probability field with a range of [0, 1] and “climo_stdev” is unset, the MET tools use the “climo_mean” probability values directly to compute Brier Skill Score (BSS).
When “climo_mean” and “climo_stdev” are both set to non-probability fields, the MET tools use the mean, standard deviation, and observation event threshold to derive a normal approximation of the climatological probabilities.
The “direct_prob” option controls the derivation logic. When “direct_prob” is true, the climatological probability is computed directly from the climatological distribution at each point as the area to the left of the event threshold value. For greater-than or greater-than-or-equal-to thresholds, 1.0 minus the area is used. When “direct_prob” is false, the “cdf_bins” values are sampled from climatological distribution. The probability is computed as the proportion of those samples which meet the threshold criteria. In this way, the number of bins impacts the resolution of the climatological probabilities. These derived probability values are used to compute the climatological Brier Score and Brier Skill Score.
5.2.17. seeps_p1_thresh
The “seeps_p1_thresh” option controls the threshold of p1 (probability of being dry) values. The default setting is >=0.1&&<=0.85.
seeps_p1_thresh = >=0.1&&<=0.85;
5.2.18. mask_missing_flag
The “mask_missing_flag” entry specifies how missing data should be handled in the Wavelet-Stat and MODE tools:
“NONE” to perform no masking of missing data
“FCST” to mask the forecast field with missing observation data
“OBS” to mask the observation field with missing forecast data
“BOTH” to mask both fields with missing data from the other
mask_missing_flag = BOTH;
5.2.19. obs_window
The “obs_window” entry is a dictionary specifying a beginning (“beg” entry) and ending (“end” entry) time offset values in seconds. It defines the time window over which observations are retained for scoring. These time offsets are defined relative to a reference time t, as [t+beg, t+end]. In PB2NC, the reference time is the PREPBUFR files center time. In Point-Stat and Ensemble-Stat, the reference time is the forecast valid time.
obs_window = {
beg = -5400;
end = 5400;
}
5.2.20. mask
The “mask” entry is a dictionary that specifies the verification masking regions to be used when computing statistics. Each mask defines a geographic extent, and any matched pairs falling inside that area will be used in the computation of statistics. Masking regions may be specified in the following ways:
The “grid” entry is an array of named grids. It contains a comma-separated list of pre-defined NCEP grids over which to perform verification. An empty list indicates that no masking grids should be used. The standard NCEP grids are named “GNNN” where NNN indicates the three digit grid number. Supplying a value of “FULL” indicates that the verification should be performed over the entire grid on which the data resides. See: ON388 - TABLE B, GRID IDENTIFICATION (PDS Octet 7), MASTER LIST OF NCEP STORAGE GRIDS, GRIB Edition 1 (FM92). The “grid” entry can be the gridded data file defining grid.
The “poly” entry contains a comma-separated list of files that define verification masking regions. These masking regions may be specified in two ways: in an ASCII file containing lat/lon points defining the mask polygon, or using a gridded data file such as the NetCDF output of the Gen-Vx-Mask tool. Some details for each of these options are described below:
If providing an ASCII file containing the lat/lon points defining the mask polygon, the file must contain a name for the region followed by the latitude (degrees north) and longitude (degrees east) for each vertex of the polygon. The values are separated by whitespace (e.g. spaces or newlines), and the first and last polygon points are connected. The general form is “poly_name lat1 lon1 lat2 lon2… latn lonn”. Here is an example of a rectangle consisting of 4 points:
RECTANGLE 25 -120 55 -120 55 -70 25 -70
Several masking polygons used by NCEP are predefined in the installed share/met/poly directory. Creating a new polygon is as simple as creating a text file with a name for the polygon followed by the lat/lon points which define its boundary. Adding a new masking polygon requires no code changes and no recompiling. Internally, the lat/lon polygon points are converted into x/y values in the grid. The lat/lon values for the observation points are also converted into x/y grid coordinates. The computations performed to check whether the observation point falls within the polygon defined is done in x/y grid space.
The NetCDF output of the gen_vx_mask tool. Please see Section 10 for more details.
Any gridded data file that MET can read may be used to define a verification masking region. Users must specify a description of the field to be used from the input file and, optionally, may specify a threshold to be applied to that field. Once this threshold is applied, any grid point where the resulting field is 0, the mask is turned off. Any grid point where it is non-zero, the mask is turned on. For example, “sample.grib {name = "TMP"; level = "Z2";} >273”
The “sid” entry is an array of strings which define groups of observation station ID’s over which to compute statistics. Each entry in the array is either a filename of a comma-separated list.
For a filename, the strings are whitespace-separated. The first string is the mask “name” and the remaining strings are the station ID’s to be used.
For a comma-separated list, optionally use a colon to specify a name. For “MY_LIST:SID1,SID2”, name = MY_LIST and values = SID1 and SID2.
For a comma-separated list of length one with no name specified, the mask “name” and value are both set to the single station ID string. For “SID1”, name = SID1 and value = SID1.
For a comma-separated list of length greater than one with no name specified, the name is set to MASK_SID and the values are the station ID’s to be used. For “SID1,SID2”, name = MASK_SID and values = SID1 and SID2.
The “name” of the station ID mask is written to the VX_MASK column of the MET output files.
The “llpnt” entry is either a single dictionary or an array of dictionaries. Each dictionary contains three entries, the “name” for the masking region, “lat_thresh”, and “lon_thresh”. The latitude and longitude thresholds are applied directly to the point observation latitude and longitude values. Only observations whose latitude and longitude values meet this threshold criteria are used. A threshold set to “NA” always evaluates to true.
The masking logic for processing point observations in Point-Stat and Ensemble-Stat fall into two cateogries. The “sid” and “llpnt” options apply directly to the point observations. Only those observations for the specified station id’s are included in the “sid” masks. Only those observations meeting the latitude and longitude threshold criteria are included in the “llpnt” masks.
The “grid” and “poly” mask options are applied to the grid points of the verification domain. Each grid point is determined to be inside or outside the masking region. When processing point observations, their latitude and longitude values are rounded to the nearest grid point of the verification domain. If the nearest grid point is inside the mask, that point observation is included in the mask.
mask = {
grid = [ "FULL" ];
poly = [ "MET_BASE/poly/LMV.poly",
"MET_BASE/out/gen_vx_mask/CONUS_poly.nc",
"MET_BASE/sample_fcst/2005080700/wrfprs_ruc13_12.tm00_G212 \
{name = \"TMP\"; level = \"Z2\";} >273"
];
sid = [ "CONUS.stations" ];
llpnt = [ { name = "LAT30TO40";
lat_thresh = >=30&&<=40;
lon_thresh = NA; },
{ name = "BOX";
lat_thresh = >=20&&<=40;
lon_thresh = >=-110&&<=-90; } ];
}
5.2.21. ci_alpha
The “ci_alpha” entry is an array of floats specifying the values for alpha to be used when computing confidence intervals. Values of alpha must be between 0 and 1. The confidence interval computed is 1 minus the alpha value. Therefore, an alpha value of 0.05 corresponds to a 95% confidence interval.
ci_alpha = [ 0.05, 0.10 ];
5.2.22. boot
The “boot” entry defines the parameters to be used in calculation of bootstrap confidence intervals. The interval variable indicates what method should be used for computing bootstrap confidence intervals:
The “interval” entry specifies the confidence interval method:
“BCA” for the BCa (bias-corrected percentile) interval method is highly accurate but computationally intensive.
“PCTILE” uses the percentile method which is somewhat less accurate but more efficient.
The “rep_prop” entry specifies a proportion between 0 and 1 to define the replicate sample size to be used when computing percentile intervals. The replicate sample size is set to boot_rep_prop * n, where n is the number of raw data points.
When computing bootstrap confidence intervals over n sets of matched pairs, the size of the subsample, m, may be chosen less than or equal to the size of the sample, n. This variable defines the size of m as a proportion relative to the size of n. A value of 1 indicates that the size of the subsample, m, should be equal to the size of the sample, n.
The “n_rep” entry defines the number of subsamples that should be taken when computing bootstrap confidence intervals. This variable should be set large enough so that when confidence intervals are computed multiple times for the same set of data, the intervals do not change much. Setting this variable to zero disables the computation of bootstrap confidence intervals, which may be necessary to run MET in realtime or near-realtime over large domains since bootstrapping is computationally expensive. Setting this variable to 1000 indicates that bootstrap confidence interval should be computed over 1000 subsamples of the matched pairs.
The “rng” entry defines the random number generator to be used in the computation of bootstrap confidence intervals. Subsamples are chosen at random from the full set of matched pairs. The randomness is determined by the random number generator specified. Users should refer to detailed documentation of the GNU Scientific Library for a listing of the random number generators available for use.
The “seed” entry may be set to a specific value to make the computation of bootstrap confidence intervals fully repeatable. When left empty the random number generator seed is chosen automatically which will lead to slightly different bootstrap confidence intervals being computed each time the data is run. Specifying a value here ensures that the bootstrap confidence intervals will be reproducable over multiple runs on the same computing platform.
boot = {
interval = PCTILE;
rep_prop = 1.0;
n_rep = 0;
rng = "mt19937";
seed = "";
}
5.2.23. interp
The “interp” entry is a dictionary that specifies what interpolation or smoothing (for the Grid-Stat tool) methods should be applied. This dictionary may include the following entries:
The “field” entry specifies to which field(s) the interpolation method should be applied. This does not apply when doing point verification with the Point-Stat or Ensemble-Stat tools:
“FCST” to interpolate/smooth the forecast field.
“OBS” to interpolate/smooth the observation field.
“BOTH” to interpolate/smooth both the forecast and the observation.
The “vld_thresh” entry specifies a number between 0 and 1. When performing interpolation over some neighborhood of points the ratio of the number of valid data points to the total number of points in the neighborhood is computed. If that ratio is less than this threshold, the matched pair is discarded. Setting this threshold to 1, which is the default, requires that the entire neighborhood must contain valid data. This variable will typically come into play only along the boundaries of the verification region chosen.
The “shape” entry may be set to SQUARE or CIRCLE to specify the shape of the smoothing area.
The “type” entry is an array of dictionaries, each specifying one or more interpolation methods and widths. Interpolation is performed over an N by N box centered on each point, where N is the width specified. Each of these dictionaries must include:
The “width” entry is an array of integers to specify the size of the interpolation area. The area is either a square or circle containing the observation point. The width value specifies the width of the square or diameter of the circle. A width value of 1 is interpreted as the nearest neighbor model grid point to the observation point. For squares, a width of 2 defines a 2 x 2 box of grid points around the observation point (the 4 closest model grid points), while a width of 3 defines a 3 x 3 box of grid points around the observation point, and so on. For odd widths in grid-to-point comparisons (i.e. Point-Stat), the interpolation area is centered on the model grid point closest to the observation point. For grid-to-grid comparisons (i.e. Grid-Stat), the width must be odd.
The “method” entry is an array of interpolation procedures to be applied to the points in the box:
MIN for the minimum value
MAX for the maximum value
MEDIAN for the median value
UW_MEAN for the unweighted average value
DW_MEAN for the distance-weighted average value where weight = distance^-2
LS_FIT for a least-squares fit
BILIN for bilinear interpolation (width = 2)
NEAREST for the nearest grid point (width = 1)
BEST for the value closest to the observation
UPPER_LEFT for the upper left grid point (width = 1)
UPPER_RIGHT for the upper right grid point (width = 1)
LOWER_RIGHT for the lower right grid point (width = 1)
LOWER_LEFT for the lower left grid point (width = 1)
GAUSSIAN for the Gaussian kernel
MAXGAUSS for the maximum value followed by a Gaussian smoother
GEOG_MATCH for the nearest grid point where the land/sea mask and geography criteria are satisfied
HIRA for all neighborhood points to define a spatial ensemble (only in Ensemble-Stat)
The BUDGET, FORCE, GAUSSIAN, and MAXGAUSS methods are not valid for interpolating to point locations. For grid-to-grid comparisons, the only valid smoothing methods are MIN, MAX, MEDIAN, UW_MEAN, and GAUSSIAN, and MAXGAUSS.
If multiple “method” and “width” options are specified, all possible permutations of their values are applied.
interp = {
field = BOTH;
vld_thresh = 1.0;
shape = SQUARE;
type = [
{
method = [ NEAREST ];
width = [ 1 ];
}
];
}
5.2.24. land_mask
The “land_mask” dictionary defines the land/sea mask field used when verifying at the surface. The “flag” entry enables/disables this logic. When enabled, the “message_type_group_map” dictionary must contain entries for “LANDSF” and “WATERSF”. For point observations whose message type appears in the “LANDSF” entry, only use forecast grid points where land = TRUE. For point observations whose message type appears in the “WATERSF” entry, only use forecast grid points where land = FALSE. If the “file_name” entry is left empty, the land/sea is assumed to exist in the input forecast file. Otherwise, the specified file(s) are searched for the data specified in the “field” entry. The “regrid” settings specify how this field should be regridded to the verification domain. Lastly, the “thresh” entry is the threshold which defines land (threshold is true) and water (threshold is false).
The “land_mask.flag” entry may be set separately in each “obs.field” entry.
land_mask = {
flag = FALSE;
file_name = [];
field = { name = "LAND"; level = "L0"; }
regrid = { method = NEAREST; width = 1; }
thresh = eq1;
}
5.2.25. topo_mask
The “topo_mask” dictionary defines the model topography field used when verifying at the surface. The flag entry enables/disables this logic. When enabled, the “message_type_group_map” dictionary must contain an entry for “SURFACE”. This logic is applied to point observations whose message type appears in the “SURFACE” entry. Only use point observations where the topo minus station elevation difference meets the “use_obs_thresh” threshold entry. For the observations kept, when interpolating forecast data to the observation location, only use forecast grid points where the topo minus station difference meets the “interp_fcst_thresh” threshold entry. If the “file_name” is left empty, the topography data is assumed to exist in the input forecast file. Otherwise, the specified file(s) are searched for the data specified in the “field” entry. The “regrid” settings specify how this field should be regridded to the verification domain.
The “topo_mask.flag” entry may be set separately in each “obs.field” entry.
topo_mask = {
flag = FALSE;
file_name = [];
field = { name = "TOPO"; level = "L0"; }
regrid = { method = BILIN; width = 2; }
use_obs_thresh = ge-100&&le100;
interp_fcst_thresh = ge-50&&le50;
}
5.2.26. hira
The “hira” entry is a dictionary that is very similar to the “interp” and “nbrhd” entries. It specifies information for applying the High Resolution Assessment (HiRA) verification logic in Point-Stat. HiRA is analogous to neighborhood verification but for point observations. The HiRA logic interprets the forecast values surrounding each point observation as an ensemble forecast. These ensemble values are processed in two ways. First, the ensemble continuous statistics (ECNT) and ranked probability score (RPS) line types are computed directly from the ensemble values. Second, for each categorical threshold specified, a fractional coverage value is computed as the ratio of the nearby forecast values that meet the threshold criteria. Point-Stat evaluates those fractional coverage values as if they were a probability forecast. When applying HiRA, users should enable the matched pair (MPR), probabilistic (PCT, PSTD, PJC, or PRC), or ensemble statistics (ECNT or PRS) line types in the output_flag dictionary. The number of probabilistic HiRA output lines is determined by the number of categorical forecast thresholds and HiRA neighborhood widths chosen. This dictionary may include the following entries:
The “flag” entry is a boolean which toggles “hira” on (TRUE) and off (FALSE).
The “width” entry specifies the neighborhood size. Since HiRA applies to point observations, the width may be even or odd.
The “vld_thresh” entry is as described above.
The “cov_thresh” entry is an array of probabilistic thresholds used to populate the Nx2 probabilistic contingency table written to the PCT output line and used for computing probabilistic statistics.
The “shape” entry defines the shape of the neighborhood. Valid values are “SQUARE” or “CIRCLE”
The “prob_cat_thresh” entry defines the thresholds which define ensemble probabilities from which to compute the ranked probability score output. If left empty but climatology data is provided, the climo_cdf thresholds will be used instead. If left empty but no climatology data is provided, the obs.cat_thresh thresholds will be used instead.
hira = {
flag = FALSE;
width = [ 2, 3, 4, 5 ];
vld_thresh = 1.0;
cov_thresh = [ ==0.25 ];
shape = SQUARE;
prob_cat_thresh = [];
}
5.2.27. output_flag
The “output_flag” entry is a dictionary that specifies what verification methods should be applied to the input data. Options exist for each output line type from the MET tools. Each line type may be set to one of:
“NONE” to skip the corresponding verification method
“STAT” to write the verification output only to the “.stat” output file
“BOTH” to write to the “.stat” output file as well the optional “_type.txt” file, a more readable ASCII file sorted by line type.
output_flag = {
fho = NONE; Forecast, Hit, Observation Rates
ctc = NONE; Contingency Table Counts
cts = NONE; Contingency Table Statistics
mctc = NONE; Multi-category Contingency Table Counts
mcts = NONE; Multi-category Contingency Table Statistics
cnt = NONE; Continuous Statistics
sl1l2 = NONE; Scalar L1L2 Partial Sums
sal1l2 = NONE; Scalar Anomaly L1L2 Partial Sums when climatological data
is supplied
vl1l2 = NONE; Vector L1L2 Partial Sums
val1l2 = NONE; Vector Anomaly L1L2 Partial Sums when climatological data
is supplied
pct = NONE; Contingency Table Counts for Probabilistic Forecasts
pstd = NONE; Contingency Table Statistics for Probabilistic Forecasts
with Dichotomous outcomes
pjc = NONE; Joint and Conditional Factorization for Probabilistic
Forecasts
prc = NONE; Receiver Operating Characteristic for Probabilistic
Forecasts
eclv = NONE; Economic Cost/Loss Value derived from CTC and PCT lines
mpr = NONE; Matched Pair Data
nbrctc = NONE; Neighborhood Contingency Table Counts
nbrcts = NONE; Neighborhood Contingency Table Statistics
nbrcnt = NONE; Neighborhood Continuous Statistics
isc = NONE; Intensity-Scale
ecnt = NONE; Ensemble Continuous Statistics
rps = NONE; Ranked Probability Score Statistics
rhist = NONE; Rank Histogram
phist = NONE; Probability Integral Transform Histogram
orank = NONE; Observation Rank
ssvar = NONE; Spread Skill Variance
grad = NONE; Gradient statistics (S1 score)
}
5.2.28. nc_pairs_flag
The “nc_pairs_flag” can be set either to a boolean value or a dictionary in either Grid-Stat, Wavelet-Stat or MODE. The dictionary (with slightly different entries for the various tools … see the default config files) has individual boolean settings turning on or off the writing out of the various fields in the netcdf output file for the tool. Setting all dictionary entries to false means the netcdf file will not be generated.
“nc_pairs_flag” can also be set to a boolean value. In this case, a value of true means to just accept the default settings (which will turn on the output of all the different fields). A value of false means no netcdf output will be generated.
nc_pairs_flag = {
latlon = TRUE;
raw = TRUE;
diff = TRUE;
climo = TRUE;
climo_cdp = FALSE;
weight = FALSE;
nbrhd = FALSE;
fourier = FALSE;
gradient = FALSE;
distance_map = FLASE;
apply_mask = TRUE;
}
5.2.29. nc_pairs_var_name
The “nc_pairs_var_name” entry specifies a string for each verification task in Grid-Stat. This string is parsed from each “obs.field” dictionary entry and is used to construct variable names for the NetCDF matched pairs output file. The default value of an empty string indicates that the “name” and “level” strings of the input data should be used. If the input data “level” string changes for each run of Grid-Stat, using this option to define a constant string may make downstream processing more convenient.
For example:
nc_pairs_var_name = "";
5.2.30. nc_pairs_var_suffix
The “nc_pairs_var_suffix” entry is similar to the “nc_pairs_var_name” entry described above. It is also parsed from each “obs.field” dictionary entry. However, it defines a suffix to be appended to the output variable name. This enables the output variable names to be made unique. For example, when verifying height for multiple level types but all with the same level value, use this option to customize the output variable names.
For example:
NOTE: This option was previously named “nc_pairs_var_str”, which is now deprecated.
nc_pairs_var_suffix = "";
5.2.31. ps_plot_flag
The “ps_plot_flag” entry is a boolean value for Wavelet-Stat and MODE indicating whether a PostScript plot should be generated summarizing the verification.
ps_plot_flag = TRUE;
5.2.32. grid_weight_flag
The “grid_weight_flag” specifies how grid weighting should be applied during the computation of continuous statistics and partial sums. It is meant to account for grid box area distortion and is often applied to global Lat/Lon grids. It is only applied for grid-to-grid verification in Grid-Stat and Ensemble-Stat and is not applied for grid-to-point verification. Three grid weighting options are currently supported:
“NONE” to disable grid weighting using a constant weight (default).
“COS_LAT” to define the weight as the cosine of the grid point latitude. This an approximation for grid box area used by NCEP and WMO.
“AREA” to define the weight as the true area of the grid box (km^2).
The weights are ultimately computed as the weight at each grid point divided by the sum of the weights for the current masking region.
grid_weight_flag = NONE;
5.2.33. hss_ec_value
The “hss_ec_value” entry is a floating point number used in the computation of the HSS_EC statistic in the CTS and MCTS line types. It specifies the expected correct (EC) rate by chance for multi-category contingency tables. If set to its default value of NA, it will automatically be replaced with 1.0 divided by the CTC or MCTC table dimension. For example, for a 2x2 CTC table, the default hss_ec_value is 1.0 / 2 = 0.5. For a 4x4 MCTC table, the default hss_ec_value is 1.0 / 4 = 0.25.
If set, it must greater than or equal to 0.0 and less than 1.0. A value of 0.0 produces an HSS_EC statistic equal to the Accuracy statistic.
hss_ec_value = NA;
5.2.34. rank_corr_flag
The “rank_corr_flag” entry is a boolean to indicate whether Kendall’s Tau and Spearman’s Rank Correlation Coefficients (in the CNT line type) should be computed. Computing them over large datasets is computationally intensive and slows down the runtime significantly.
rank_corr_flag = FALSE;
5.2.35. duplicate_flag
The “duplicate_flag” entry specifies how to handle duplicate point observations in Point-Stat and Ensemble-Stat:
“NONE” to use all point observations (legacy behavior)
“UNIQUE” only use a single observation if two or more observations match. Matching observations are determined if they contain identical latitude, longitude, level, elevation, and time information. They may contain different observation values or station IDs
The reporting mechanism for this feature can be activated by specifying a verbosity level of three or higher. The report will show information about where duplicates were detected and which observations were used in those cases.
duplicate_flag = NONE;
5.2.36. obs_summary
The “obs_summary” entry specifies how to compute statistics on observations that appear at a single location (lat,lon,level,elev) in Point-Stat and Ensemble-Stat. Eight techniques are currently supported:
“NONE” to use all point observations (legacy behavior)
“NEAREST” use only the observation that has the valid time closest to the forecast valid time
“MIN” use only the observation that has the lowest value
“MAX” use only the observation that has the highest value
“UW_MEAN” compute an unweighted mean of the observations
“DW_MEAN” compute a weighted mean of the observations based on the time of the observation
“MEDIAN” use the median observation
“PERC” use the Nth percentile observation where N = obs_perc_value
The reporting mechanism for this feature can be activated by specifying a verbosity level of three or higher. The report will show information about where duplicates were detected and which observations were used in those cases.
obs_summary = NONE;
5.2.37. obs_perc_value
Percentile value to use when obs_summary = PERC
obs_perc_value = 50;
5.2.38. obs_quality_inc
The “obs_quality_inc” entry specifies the quality flag values that are to be retained and used for verification. An empty list signifies that all point observations should be used, regardless of their quality flag value. The quality flag values will vary depending on the original source of the observations. The quality flag values to retain should be specified as an array of strings, even if the values themselves are numeric. Note “obs_quality_inc” replaces the older option “obs_quality”.
obs_quality_inc = [ "1", "2", "3", "9" ];
5.2.39. obs_quality_exc
The “obs_quality_exc” entry specifies the quality flag values that are to be ignored and not used for verification. An empty list signifies that all point observations should be used, regardless of their quality flag value. The quality flag values will vary depending on the original source of the observations. The quality flag values to ignore should be specified as an array of strings, even if the values themselves are numeric.
obs_quality_exc = [ "1", "2", "3", "9" ];
5.2.40. met_data_dir
The “met_data_dir” entry specifies the location of the internal MET data sub-directory which contains data files used when generating plots. It should be set to the installed share/met directory so the MET tools can locate the static data files they need at run time.
met_data_dir = "MET_BASE";
5.2.41. many_plots
The “fcst_raw_plot” entry is a dictionary used by Wavelet-Stat and MODE containing colortable plotting information for the plotting of the raw forecast field:
The “color_table” entry specifies the location and name of the colortable file to be used.
The “plot_min” and “plot_max” entries specify the range of data values. If they are both set to 0, the MET tools will automatically rescale the colortable to the range of values present in the data. If they are not both set to 0, the MET tools will rescale the colortable using their values.
When applicable, the “colorbar_flag” enables the creation of a colorbar for this plot.
fcst_raw_plot = {
color_table = "MET_BASE/colortables/met_default.ctable";
plot_min = 0.0;
plot_max = 0.0;
colorbar_flag = TRUE;
}
The “obs_raw_plot”, “wvlt_plot”, and “object_plot” entries are dictionaries similar to the “fcst_raw_plot” described above.
5.2.42. output_prefix
The “output_prefix” entry specifies a string to be included in the output file name. The MET statistics tools construct output file names that include the tool name and timing information. You can use this setting to modify the output file name and avoid naming conflicts for multiple runs of the same tool.
output_prefix = "";
5.2.43. version
The “version” entry specifies the version number of the configuration file. The configuration file version number should match the version number of the MET code being run. This value should generally not be modified.
version = "VN.N";
5.2.44. time_summary
This feature was implemented to allow additional processing of observations with high temporal resolution. The “flag” entry toggles the “time_summary” on (TRUE) and off (FALSE). Obs may be summarized across the user specified time period defined by the “beg” and “end” entries. The “step” entry defines the time between intervals in seconds. The “width” entry specifies the summary interval in seconds. It may either be set as an integer number of seconds for a centered time interval or a dictionary with beginning and ending time offsets in seconds.
For example:
beg = "00";
end = "235959";
step = 300;
width = 600;
width = { beg = -300; end = 300; }
This example does a 10-minute time summary every 5 minutes throughout the day. The first interval will be from 23:55:00 the previous day through 00:04:59 of the current day. The second interval will be from 0:00:00 through 00:09:59. And so on.
The two “width” settings listed above are equivalent. Both define a centered 10-minute time interval. Use the “beg” and “end” entries to define uncentered time intervals. The following example requests observations for one hour prior:
width = { beg = -3600; end = 0; }
The summaries will only be calculated for the specified GRIB codes or observation variable (“obs_var”) names.
When determining which observations fall within a time interval, data for the beginning timestamp is included while data for the ending timestamp is excluded. Users may need to adjust the “beg” and “end” settings in the “width” dictionary to include the desired observations in each time interval.
The supported time summaries are “min” (minimum), “max” (maximum), “range”, “mean”, “stdev” (standard deviation), “median”, “sum”, and “p##” (percentile, with the desired percentile value specified in place of ##).
The “vld_freq” and “vld_thresh” options may be used to require that a certain ratio of observations must be present and contain valid data within the time window in order for a summary value to be computed. The “vld_freq” entry defines the expected observation frequency in seconds. For example, when summarizing 1-minute data (vld_freq = 60) over a 30 minute time window, setting “vld_thresh = 0.5” requires that at least 15 of the 30 expected observations be present and valid for a summary value to be written. The default “vld_thresh = 0.0” setting will skip over this logic.
When using the “sum” option, users should specify “vld_thresh = 1.0” to avoid missing data values from affecting the resulting sum value.
The variable names are saved to NetCDF file if they are given instead of grib_codes which are not available for non GRIB input. The “obs_var” option was added and works like “grib_code” option (string value VS. int value). They are inclusive (union). All variables are included if both options are empty. Note: grib_code 11 is equivalent to obs_var “TMP”.
time_summary = {
flag = FALSE;
beg = "000000";
end = "235959";
step = 300;
width = 600;
width = { beg = -300; end = 300; }
grib_code = [ 11, 204, 211 ];
obs_var = [];
type = [ "min", "max", "range", "mean", "stdev", "median", "p80" ];
vld_freq = 0;
vld_thresh = 0.0;
}
5.3. Settings specific to individual tools
5.3.1. GenEnsProdConfig_default
5.3.1.1. ens
The “ens” entry is a dictionary that specifies the fields for which ensemble products should be generated. This is very similar to the “fcst” and “obs” entries. This dictionary may include the following entries:
The “censor_thresh” and “censor_val” entries are described above.
The “ens_thresh” entry specifies a proportion between 0 and 1 to define the required ratio of valid input ensemble member files. If the ratio of valid input ensemble files to expected ones is too low, the tool will error out.
The “vld_thresh” entry specifies a proportion between 0 and 1 to define the required ratio of valid data points. When computing ensemble products, if the ratio of valid data values is too low, the ensemble product will be set to bad data for that point.
The “field” entry is as described above. However, in this case, the cat_thresh entry is used for calculating probabilities of exceeding the given threshold. In the default shown below, the probability of accumulated precipitation > 0.0 mm and > 5.0 mm will be calculated from the member accumulated precipitation fields and stored as an ensemble field.
ens = {
censor_thresh = [];
censor_val = [];
ens_thresh = 1.0;
vld_thresh = 1.0;
field = [
{
name = "APCP";
level = "A03";
cat_thresh = [ >0.0, >=5.0 ];
}
];
}
5.3.1.2. nbrhd_prob
The nbrhd_prob dictionary defines the neighborhoods used to compute NEP and NMEP output. The neighborhood shape is a SQUARE or CIRCLE centered on the current point, and the width array specifies the width of the square or diameter of the circle as an odd integer. The vld_thresh entry is a number between 0 and 1 specifying the required ratio of valid data in the neighborhood for an output value to be computed.
If ensemble_flag.nep is set to TRUE, NEP output is created for each combination of the categorical threshold (cat_thresh) and neighborhood width specified.
nbrhd_prob = {
width = [ 5 ];
shape = CIRCLE;
vld_thresh = 0.0;
}
5.3.1.3. nmep_smooth
Similar to the interp dictionary, the nmep_smooth dictionary includes a type array of dictionaries to define one or more methods for smoothing the NMEP data. Setting the interpolation method to nearest neighbor (NEAREST) effectively disables this smoothing step.
If ensemble_flag.nmep is set to TRUE, NMEP output is created for each combination of the categorical threshold (cat_thresh), neighborhood width (nbrhd_prob.width), and smoothing method (nmep_smooth.type) specified.
nmep_smooth = {
vld_thresh = 0.0;
shape = CIRCLE;
gaussian_dx = 81.27;
gaussian_radius = 120;
type = [
{
method = GAUSSIAN;
width = 1;
}
];
}
5.3.1.4. ensemble_flag
The “ensemble_flag” entry is a dictionary of boolean value indicating which ensemble products should be generated:
“latlon” for a grid of the Latitude and Longitude fields
“mean” for the simple ensemble mean
“stdev” for the ensemble standard deviation
“minus” for the mean minus one standard deviation
“plus” for the mean plus one standard deviation
“min” for the ensemble minimum
“max” for the ensemble maximum
“range” for the range of ensemble values
“vld_count” for the number of valid ensemble members
“frequency” for the ensemble relative frequency meeting a threshold
“nep” for the neighborhood ensemble probability
“nmep” for the neighborhood maximum ensemble probability
“rank” to write the rank for the gridded observation field to separate NetCDF output file.
“weight” to write the grid weights specified in grid_weight_flag to the rank NetCDF output file.
ensemble_flag = {
latlon = TRUE;
mean = TRUE;
stdev = TRUE;
minus = TRUE;
plus = TRUE;
min = TRUE;
max = TRUE;
range = TRUE;
vld_count = TRUE;
frequency = TRUE;
nep = FALSE;
nmep = FALSE;
rank = TRUE;
weight = FALSE;
}
5.3.2. EnsembleStatConfig_default
5.3.2.1. fcst, obs
The fcst and obs entries define the fields for which Ensemble-Stat should compute rank histograms, probability integral transform histograms, spread-skill variance, relative position histograms, economic value, and other statistics.
The “ens_ssvar_bin_size” entry sets the width of the variance bins. Smaller bin sizes provide the user with more flexibility in how data are binned during analysis. The actual variance of the ensemble data will determine the number of bins written to the SSVAR output lines.
The “ens_phist_bin_size” is set to a value between 0 and 1. The number of bins for the probability integral transform histogram in the PHIST line type is defined as the ceiling of 1.0 / ens_phist_bin_size. For example, a bin size of 0.05 results in 20 PHIST bins.
The “prob_cat_thresh” entry is an array of thresholds to be applied in the computation of the ranked probability score. If left empty, but climatology data is provided, the climo_cdf thresholds will be used instead.
fcst = {
message_type = [ "ADPUPA" ];
ens_ssvar_bin_size = 1;
ens_phist_bin_size = 0.05;
prob_cat_thresh = [];
field = [
{
name = "APCP";
level = [ "A03" ];
}
];
}
5.3.2.2. nc_var_str
The “nc_var_str” entry specifies a string for each ensemble field and verification task in Ensemble-Stat. This string is parsed from each “ens.field” and “obs.field” dictionary entry and is used to customize the variable names written to the NetCDF output file. The default is an empty string, meaning that no customization is applied to the output variable names. When the Ensemble-Stat config file contains two fields with the same name and level value, this entry is used to make the resulting variable names unique. e.g. nc_var_str = “MIN”;
nc_var_str = "";
5.3.2.3. obs_thresh
The “obs_thresh” entry is an array of thresholds for filtering observation values prior to applying ensemble verification logic. They specify the values to be included in the verification, not excluded. The default setting of NA, which always evaluates to true, means that all observations should be used. Verification output will be computed separately for each threshold specified. This option may be set separately for each obs.field entry.
obs_thresh = [ NA ];
5.3.2.4. skip_const
Setting “skip_const” to true tells Ensemble-Stat to exclude pairs where all the ensemble members and the observation have a constant value. For example, exclude points with zero precipitation amounts from all output line types. This option may be set separately for each obs.field entry. When set to false, constant points are included and the observation rank is chosen at random.
skip_const = FALSE;
5.3.2.5. obs_error
Observation error options
Set dist_type to NONE to use the observation error table instead. May be set separately in each “obs.field” entry. The obs_error dictionary controls how observation error information should be handled. Observation error information can either be specified directly in the configuration file or by parsing information from an external table file. By default, the MET_BASE/data/table_files/obs_error_table.txt file is read but this may be overridden by setting the $MET_OBS_ERROR_TABLE environment variable at runtime.
The flag entry toggles the observation error logic on (TRUE) and off (FALSE). When flag is TRUE, random observation error perturbations are applied to the ensemble member values. No perturbation is applied to the observation values but the bias scale and offset values, if specified, are applied.
The dist_type entry may be set to NONE, NORMAL, EXPONENTIAL, CHISQUARED, GAMMA, UNIFORM, or BETA. The default value of NONE indicates that the observation error table file should be used rather than the configuration file settings.
The dist_parm entry is an array of length 1 or 2 specifying the parameters for the distribution selected in dist_type. The NORMAL, EXPONENTIAL, and CHISQUARED distributions are defined by a single parameter. The GAMMA, UNIFORM, and BETA distributions are defined by two parameters. See the GNU Scientific Library Reference Manual for more information on these distributions.
The inst_bias_scale and inst_bias_offset entries specify bias scale and offset values that should be applied to observation values prior to perturbing them. These entries enable bias-correction on the fly.
Defining the observation error information in the configuration file is convenient but limited. If defined this way, the random perturbations for all points in the current verification task are drawn from the same distribution. Specifying an observation error table file instead (by setting dist_type = NONE;) provides much finer control, enabling the user to define observation error distribution information and bias-correction logic separately for each observation variable name, message type, PREPBUFR report type, input report type, instrument type, station ID, range of heights, range of pressure levels, and range of values.
obs_error = {
flag = FALSE; TRUE or FALSE
dist_type = NONE; Distribution type
dist_parm = []; Distribution parameters
inst_bias_scale = 1.0; Instrument bias scale adjustment
inst_bias_offset = 0.0; Instrument bias offset adjustment
min = NA; Valid range of data
max = NA;
}
5.3.2.6. rng
See: Random Number Generator Performance used for random assignment of ranks when they are tied.
rng = {
type = "mt19937";
seed = "";
}
5.3.3. MODEAnalysisConfig_default
MODE line options are used to create filters that determine which MODE output lines are read in and processed. The MODE line options are numerous. They fall into seven categories: toggles, multiple set string options, multiple set integer options, integer max/min options, date/time max/min options, floating-point max/min options, and miscellaneous options. In order to be applied, the options must be uncommented (i.e. remove the “//” marks) before running. These options are described in subsequent sections. Please note that this configuration file is processed differently than the other config files.
Toggles: The MODE line options described in this section are shown in pairs. These toggles represent parameters that can have only one (or none) of two values. Any of these toggles may be left unspecified. However, if neither option for toggle is indicated, the analysis will produce results that combine data from both toggles. This may produce unintended results.
This toggle indicates whether forecast or observed lines should be used for analysis.
fcst = FALSE;
obs = FALSE;
This toggle indicates whether single object or object pair lines should be used.
single = FALSE;
pair = FALSE;
This toggle indicates whether simple object or object cluster object lines should be used.
simple = FALSE;
cluster = FALSE;
This toggle indicates whether matched or unmatched object lines should be used.
matched = FALSE;
unmatched = FALSE;
Multiple-set string options: The following options set various string attributes. They can be set multiple times on the command line but must be separated by spaces. Each of these options must be indicated as a string. String values that include spaces may be used by enclosing the string in quotation marks.
This options specifies which model to use
// model = [];
These two options specify thresholds for forecast and observations objects to be used in the analysis, respectively.
// fcst_thr = [];
// obs_thr = [];
These options indicate the names of variables to be used in the analysis for forecast and observed fields.
// fcst_var = [];
// obs_var = [];
These options indicate vertical levels for forecast and observed fields to be used in the analysis.
// fcst_lev = [];
// obs_lev = [];
Multiple-set integer options: The following options set various integer attributes. Each of the following options may only be indicated as an integer.
These options are integers of the form HH[MMSS] specifying the lead_time.
// fcst_lead = [];
//obs_lead = [];
These options are integers of the form HH[MMSS] specifying the valid hour.
// fcst_valid_hour = [];
// obs_valid_hour = [];
These options are integers of the form HH[MMSS] specifying the model initialization hour.
// fcst_init_hour = [];
// obs_init_hour = [];
These options are integers of the form HHMMSS specifying the accumulation time.
// fcst_accum = [];
// obs_accum = [];
These options indicate the convolution radius used for forecast of observed objects, respectively.
// fcst_rad = [];
// obs_rad = [];
Integer max/min options: These options set limits on various integer attributes. Leaving a maximum value unset means no upper limit is imposed on the value of the attribute. The option works similarly for minimum values.
These options are used to indicate minimum/maximum values for the area attribute to be used in the analysis.
// area_min = 0;
// area_max = 0;
These options are used to indicate minimum/maximum values accepted for the area thresh. The area thresh is the area of the raw field inside the object that meets the threshold criteria.
// area_thresh_min = 0;
// area_thresh_max = 0;
These options refer to the minimum/maximum values accepted for the intersection area attribute.
// intersection_area_min = 0;
// intersection_area_max = 0;
These options refer to the minimum/maximum union area values accepted for analysis.
// union_area_min = 0;
// union_area_max = 0;
These options refer to the minimum/maximum values for symmetric difference for objects to be used in the analysis.
// symmetric_diff_min = 0;
// symmetric_diff_max = 0;
Date/time max/min options: These options set limits on various date/time attributes. The values can be specified in one of three ways: First, the options may be indicated by a string of the form YYYMMDD_HHMMSS. This specifies a complete calendar date and time. Second, they may be indicated by a string of the form YYYYMMMDD_HH. Here, the minutes and seconds are assumed to be zero. The third way of indicating date/time attributes is by a string of the form YYYMMDD. Here, hours, minutes, and seconds are assumed to be zero.
These options indicate minimum/maximum values for the forecast valid time.
// fcst_valid_min = "";
// fcst_valid_max = "";
These options indicate minimum/maximum values for the observation valid time.
// obs_valid_min = "";
// obs_valid_max = "";
These options indicate minimum/maximum values for the forecast initialization time.
// fcst_init_min = "";
// fcst_init_max = "";
These options indicate minimum/maximum values for the observation initialization time.
// obs_init_min = "";
// obs_init_max = "";
Floating-point max/min options: Setting limits on various floating-point attributes. One may specify these as integers (i.e., without a decimal point), if desired. The following pairs of options indicate minimum and maximum values for each MODE attribute that can be described as a floating- point number. Please refer to “The MODE Tool” section on attributes in the MET User’s Guide for a description of these attributes.
// centroid_x_min = 0.0;
// centroid_x_max = 0.0;
// centroid_y_min = 0.0;
// centroid_y_max = 0.0;
// centroid_lat_min = 0.0;
// centroid_lat_max = 0.0;
// centroid_lon_min = 0.0;
// centroid_lon_max = 0.0;
// axis_ang_min = 0.0;
// axis_ang_max = 0.0;
// length_min = 0.0;
// length_max = 0.0;
// width_min = 0.0;
// width_max = 0.0;
// aspect_ratio_min = 0.0;
// aspect_ratio_max = 0.0;
// curvature_min = 0.0;
// curvature_max = 0.0;
// curvature_x_min = 0.0;
// curvature_x_max = 0.0;
// curvature_y_min = 0.0;
// curvature_y_max = 0.0;
// complexity_min = 0.0;
// complexity_max = 0.0;
// intensity_10_min = 0.0;
// intensity_10_max = 0.0;
// intensity_25_min = 0.0;
// intensity_25_max = 0.0;
// intensity_50_min = 0.0;
// intensity_50_max = 0.0;
// intensity_75_min = 0.0;
// intensity_75_max = 0.0;
// intensity_90_min = 0.0;
// intensity_90_max = 0.0;
// intensity_user_min = 0.0;
// intensity_user_max = 0.0;
// intensity_sum_min = 0.0;
// intensity_sum_max = 0.0;
// centroid_dist_min = 0.0;
// centroid_dist_max = 0.0;
// boundary_dist_min = 0.0;
// boundary_dist_max = 0.0;
// convex_hull_dist_min = 0.0;
// convex_hull_dist_max = 0.0;
// angle_diff_min = 0.0;
// angle_diff_max = 0.0;
// area_ratio_min = 0.0;
// area_ratio_max = 0.0;
// intersection_over_area_min = 0.0;
// intersection_over_area_max = 0.0;
// complexity_ratio_min = 0.0;
// complexity_ratio_max = 0.0;
// percentile_intensity_ratio_min = 0.0;
// percentile_intensity_ratio_max = 0.0;
// interest_min = 0.0;
// interest_max = 0.0;
5.3.4. MODEConfig_default
5.3.4.1. quilt
The “quilt” entry is a boolean to indicate whether all permutations of convolution radii and thresholds should be run. If set to false, the number of forecast and observation convolution radii and thresholds must all match. One configuration of MODE will be run for each group of settings in those lists. If set to true, the number of forecast and observation convolution radii must match and the number of forecast and observation convolution thresholds must match. For N radii and M thresholds, NxM configurations of MODE will be run.
quilt = false;
5.3.4.2. fcst, obs
The object definition settings for MODE are contained within the “fcst” and “obs” entries:
The “censor_thresh” and “censor_val” entries are described above. The entries replace the previously supported “raw_thresh” entry.
The “conv_radius” entry specifies the convolution radius in grid squares. The larger the convolution radius, the smoother the objects. Multiple convolution radii may be specified as an array. For example:
conv_radius = [ 5, 10, 15 ];
The “conv_thresh” entry specifies the convolution threshold used to define MODE objects. The lower the threshold, the larger the objects. Multiple convolution thresholds may be specified as an array. For example:
conv_thresh = [ >=5.0, >=10.0, >=15.0 ];
The “vld_thresh” entry is described above.
The “filter_attr_name” and “filter_attr_thresh” entries are arrays of the same length which specify object filtering criteria. By default, no object filtering criteria is defined.
The “filter_attr_name” entry is an array of strings specifying the MODE output header column names for the object attributes of interest, such as “AREA”, “LENGTH”, “WIDTH”, and “INTENSITY_50”. In addition, “ASPECT_RATIO” specifies the aspect ratio (width/length), “INTENSITY_101” specifies the mean intensity value, and “INTENSITY_102” specifies the sum of the intensity values.
The “filter_attr_thresh” entry is an array of thresholds for the object attributes. Any simple objects not meeting all of these filtering criteria are discarded.
Note that the “area_thresh” and “inten_perc_thresh” entries form earlier versions of MODE are replaced by these options and are now deprecated.
The “merge_thresh” entry specifies a lower convolution threshold used when the double-threshold merging method is applied. The number of merge thresholds must match the number of convolution thresholds. Multiple merge thresholds may be specified as an array. For example:
merge_thresh = [ >=1.0, >=2.0, >=3.0 ];
The “merge_flag” entry specifies the merging methods to be applied:
“NONE” for no merging
“THRESH” for the double-threshold merging method. Merge objects that would be part of the same object at the lower threshold.
“ENGINE” for the fuzzy logic approach comparing the field to itself
“BOTH” for both the double-threshold and engine merging methods
fcst = {
field = {
name = "APCP";
level = "A03";
}
censor_thresh = [];
censor_val = [];
conv_radius = 60.0/grid_res; in grid squares
conv_thresh = >=5.0;
vld_thresh = 0.5;
filter_attr_name = [];
filter_attr_thresh = [];
merge_thresh = >=1.25;
merge_flag = THRESH;
}
5.3.4.3. grid_res
The “grid_res” entry is the nominal spacing for each grid square in kilometers. The variable is not used directly in the code, but subsequent variables in the configuration files are defined in terms of it. Therefore, setting the appropriately will help ensure that appropriate default values are used for these variables.
grid_res = 4;
5.3.4.4. match_flag
The “match_flag” entry specifies the matching method to be applied:
“NONE” for no matching between forecast and observation objects
“MERGE_BOTH” for matching allowing additional merging in both fields. If two objects in one field match the same object in the other field, those two objects are merged.
“MERGE_FCST” for matching allowing only additional forecast merging
“NO_MERGE” for matching with no additional merging in either field
match_flag = MERGE_BOTH;
5.3.4.5. max_centroid_dist
The “max_centroid_dist” entry specifies the maximum allowable distance in grid squares between the centroids of objects for them to be compared. Setting this to a reasonable value speeds up the runtime enabling MODE to skip unreasonable object comparisons.
max_centroid_dist = 800.0/grid_res;
5.3.4.6. weight
The weight variables control how much weight is assigned to each pairwise attribute when computing a total interest value for object pairs. The weights need not sum to any particular value but must be non-negative. When the total interest value is computed, the weighted sum is normalized by the sum of the weights listed.
weight = {
centroid_dist = 2.0;
boundary_dist = 4.0;
convex_hull_dist = 0.0;
angle_diff = 1.0;
area_ratio = 1.0;
int_area_ratio = 2.0;
complexity_ratio = 0.0;
inten_perc_ratio = 0.0;
inten_perc_value = 50;
}
5.3.4.7. interest_function
The set of interest function variables listed define which values are of interest for each pairwise attribute measured. The interest functions may be defined as a piecewise linear function or as an algebraic expression. A piecewise linear function is defined by specifying the corner points of its graph. An algebraic function may be defined in terms of several built-in mathematical functions.
interest_function = {
centroid_dist = (
( 0.0, 1.0 )
( 60.0/grid_res, 1.0 )
( 600.0/grid_res, 0.0 )
);
boundary_dist = (
( 0.0, 1.0 )
( 400.0/grid_res, 0.0 )
);
convex_hull_dist = (
( 0.0, 1.0 )
( 400.0/grid_res, 0.0 )
);
angle_diff = (
( 0.0, 1.0 )
( 30.0, 1.0 )
( 90.0, 0.0 )
);
corner = 0.8;
ratio_if = (
( 0.0, 0.0 )
( corner, 1.0 )
( 1.0, 1.0 )
);
area_ratio = ratio_if;
int_area_ratio = (
( 0.00, 0.00 )
( 0.10, 0.50 )
( 0.25, 1.00 )
( 1.00, 1.00 )
);
complexity_ratio = ratio_if;
inten_perc_ratio = ratio_if;
}
5.3.4.8. total_interest_thresh
The total_interest_thresh variable should be set between 0 and 1. This threshold is applied to the total interest values computed for each pair of objects and is used in determining matches.
total_interest_thresh = 0.7;
5.3.4.9. print_interest_thresh
The print_interest_thresh variable determines which pairs of object attributes will be written to the output object attribute ASCII file. The user may choose to set the print_interest_thresh to the same value as the total_interest_thresh, meaning that only object pairs that actually match are written to the output file. When set to zero, all object pair attributes will be written as long as the distance between the object centroids is less than the max_centroid_dist variable.
print_interest_thresh = 0.0;
5.3.4.10. plot_valid_flag
When applied, the plot_valid_flag variable indicates that only the region containing valid data after masking is applied should be plotted. TRUE indicates the entire domain should be plotted; FALSE indicates only the region containing valid data after masking should be plotted.
plot_valid_flag = FALSE;
5.3.4.11. plot_gcarc_flag
When applied, the plot_gcarc_flag variable indicates that the edges of polylines should be plotted using great circle arcs as opposed to straight lines in the grid.
plot_gcarc_flag = FALSE;
5.3.4.12. ct_stats_flag
The ct_stats_flag can be set to TRUE or FALSE to produce additional output, in the form of contingency table counts and statistics.
ct_stats_flag = TRUE;
5.3.4.13. shift_right
When MODE is run on global grids, this parameter specifies how many grid squares to shift the grid to the right. MODE does not currently connect objects from one side of a global grid to the other, potentially causing objects straddling that longitude to be cut in half. Shifting the grid by some amount enables the user to control where that longitude cut line occurs. This option provides a very specialized case of automated regridding. The much more flexible “regrid” option may be used instead.
shift_right = 0;
5.3.5. PB2NCConfig_default
The PB2NC tool filters out observations from PREPBUFR or BUFR files using the following criteria:
by message type: supply a list of PREPBUFR message types to retain
by station id: supply a list of observation stations to retain
by valid time: supply the beginning and ending time offset values in the obs_window entry described above.
by location: use the “mask” entry described below to supply either an NCEP masking grid, a masking lat/lon polygon or a file to a mask lat/lon polygon
by elevation: supply min/max elevation values
by report type: supply a list of report types to retain using pb_report_type and in_report_type entries described below
by instrument type: supply a list of instrument type to retain
by vertical level: supply beg/end vertical levels using the level_range entry described below
by variable type: supply a list of observation variable types to retain using the obs_bufr_var entry described below
by quality mark: supply a quality mark threshold
Flag to retain values for all quality marks, or just the first quality mark (highest): use the event_stack_flag described below
by data level category: supply a list of category types to retain.
0 - Surface level (mass reports only)
1 - Mandatory level (upper-air profile reports)
2 - Significant temperature level (upper-air profile reports)
2 - Significant temperature and winds-by-pressure level (future combined mass and wind upper-air reports)
3 - Winds-by-pressure level (upper-air profile reports)
4 - Winds-by-height level (upper-air profile reports)
5 - Tropopause level (upper-air profile reports)
6 - Reports on a single level (e.g., aircraft, satellite-wind, surface wind, precipitable water retrievals, etc.)
7 - Auxiliary levels generated via interpolation from spanning levels (upper-air profile reports)
5.3.5.1. message_type
In the PB2NC tool, the “message_type” entry is an array of message types to be retained. An empty list indicates that all should be retained.
For example:
Current Table A Entries in PREPBUFR mnemonic table
message_type = [];
5.3.5.2. station_id
The “station_id” entry is an array of station ids to be retained or the filename which contains station ids. An array of station ids contains a comma-separated list. An empty list indicates that all stations should be retained.
For example: station_id = [ “KDEN” ];
station_id = [];
5.3.5.3. elevation_range
The “elevation_range” entry is a dictionary which contains “beg” and “end” entries specifying the range of observing locations elevations to be retained.
elevation_range = {
beg = -1000;
end = 100000;
}
5.3.5.4. pb_report_type
The “pb_report_type” entry is an array of PREPBUFR report types to be retained. The numeric “pb_report_type” entry allows for further stratification within message types. An empty list indicates that all should be retained.
See: Code table for PREPBUFR report types used by Regional NAM GSI analyses
For example:
pb_report_type = [];
5.3.5.5. in_report_type
The “in_report_type” entry is an array of input report type values to be retained. The numeric “in_report_type” entry provides additional stratification of observations. An empty list indicates that all should be retained.
See: Code table for input report types
For example:
in_report_type = [];
5.3.5.6. instrument_type
The “instrument_type” entry is an array of instrument types to be retained. An empty list indicates that all should be retained.
instrument_type = [];
5.3.5.7. level_range
The “level_range” entry is a dictionary which contains “beg” and “end” entries specifying the range of vertical levels (1 to 255) to be retained.
level_range = {
beg = 1;
end = 255;
}
5.3.5.8. level_category
The “level_category” entry is an array of integers specifying which level categories should be retained:
An empty list indicates that all should be retained.
See: Current Table A Entries in PREPBUFR mnemonic table
level_category = [];
5.3.5.9. obs_bufr_var
The “obs_bufr_var” entry is an array of strings containing BUFR variable names to be retained or derived. This replaces the “obs_grib_code” setting from earlier versions of MET. Run PB2NC on your data with the “-index” command line option to see the list of available observation variables.
obs_bufr_var = [ "QOB", "TOB", "ZOB", "UOB", "VOB" ];
5.3.5.10. obs_bufr_map
Mapping of input BUFR variable names to output variables names. The default PREPBUFR map, obs_prepbufr_map, is appended to this map. Users may choose to rename BUFR variables to match the naming convention of the forecast the observation is used to verify.
obs_bufr_map = [];
5.3.5.11. obs_prepbufr_map
Default mapping for PREPBUFR. Replace input BUFR variable names with GRIB abbreviations in the output. This default map is appended to obs_bufr_map. This should not typically be overridden. This default mapping provides backward-compatibility for earlier versions of MET which wrote GRIB abbreviations to the output.
obs_prepbufr_map = [
{ key = "POB"; val = "PRES"; },
{ key = "QOB"; val = "SPFH"; },
{ key = "TOB"; val = "TMP"; },
{ key = "ZOB"; val = "HGT"; },
{ key = "UOB"; val = "UGRD"; },
{ key = "VOB"; val = "VGRD"; },
{ key = "D_DPT"; val = "DPT"; },
{ key = "D_WDIR"; val = "WDIR"; },
{ key = "D_WIND"; val = "WIND"; },
{ key = "D_RH"; val = "RH"; },
{ key = "D_MIXR"; val = "MIXR"; },
{ key = "D_PRMSL"; val = "PRMSL"; }
];
5.3.5.12. quality_mark_thresh
The “quality_mark_thresh” entry specifies the maximum quality mark value to be retained. Observations with a quality mark LESS THAN OR EQUAL TO this threshold will be retained, while observations with a quality mark GREATER THAN this threshold will be discarded.
See Code table for observation quality markers
quality_mark_thresh = 2;
5.3.5.13. event_stack_flag
The “event_stack_flag” entry is set to “TOP” or “BOTTOM” to specify whether observations should be drawn from the top of the event stack (most quality controlled) or the bottom of the event stack (most raw).
event_stack_flag = TOP;
5.3.6. SeriesAnalysisConfig_default
5.3.6.1. block_size
Computation may be memory intensive, especially for large grids. The “block_size” entry sets the number of grid points to be processed concurrently (i.e. in one pass through a time series). Smaller values require less memory but increase the number of passes through the data. If set less than or equal to 0, it is automatically reset to the number of grid points, and they are all processed concurrently.
block_size = 1024;
5.3.6.2. vld_thresh
Ratio of valid matched pairs to total length of series for a grid point. If valid threshold is exceeded at that grid point the statistics are computed and stored. If not, a bad data flag is stored. The default setting requires all data in the series to be valid.
vld_thresh = 1.0;
5.3.6.3. output_stats
Statistical output types need to be specified explicitly. Refer to User’s Guide for available output types. To keep output file size reasonable, it is recommended to process a few output types at a time, especially if the grid is large.
output_stats = {
fho = [];
ctc = [];
cts = [];
mctc = [];
mcts = [];
cnt = [ "RMSE", "FBAR", "OBAR" ];
sl1l2 = [];
pct = [];
pstd = [];
pjc = [];
prc = [];
}
5.3.7. STATAnalysisConfig_default
5.3.7.1. jobs
The “jobs” entry is an array of STAT-Analysis jobs to be performed. Each element in the array contains the specifications for a single analysis job to be performed. The format for an analysis job is as follows:
Where “job_name” is set to one of the following:
“filter”
To filter out the STAT or TCMPR lines matching the job filtering criteria specified below and using the optional arguments below. The output STAT lines are written to the file specified using the “-dump_row” argument. Required Args: -dump_row
“summary”
To compute summary information for a set of statistics. The summary output includes the mean, standard deviation, percentiles (0th, 10th, 25th, 50th, 75th, 90th, and 100th), range, and inter-quartile range. Also included are columns summarizing the computation of WMO mean values. Both unweighted and weighted mean values are reported, and they are computed using three types of logic:
simple arithmetic mean (default)
square root of the mean of the statistic squared (applied to columns listed in “wmo_sqrt_stats”)
apply fisher transform (applied to columns listed in “wmo_fisher_stats”)
The columns of data to be summarized are specified in one of two ways:
Specify the -line_type option once and specify one or more column names.
Format the -column option as LINE_TYPE:COLUMN.
Use the -derive job command option to automatically derive statistics on the fly from input contingency tables and partial sums.
Use the -column_union TRUE/FALSE job command option to compute summary statistics across the union of input columns rather than processing them separately.
For TCStat, the “-column” argument may be set to:
“TRACK” for track, along-track, and cross-track errors.
“WIND” for all wind radius errors.
“TI” for track and maximum wind intensity errors.
“AC” for along-track and cross-track errors.
“XY” for x-track and y-track errors.
“col” for a specific column name.
“col1-col2” for a difference of two columns.
“ABS(col or col1-col2)” for the absolute value.
Required Args: -line_type, -column
Optional Args:
-by column_name to specify case information -out_alpha to override default alpha value of 0.05 -derive to derive statistics on the fly -column_union to summarize multiple columns
“aggregate”
To aggregate the STAT data for the STAT line type specified using the “-line_type” argument. The output of the job will be in the same format as the input line type specified. The following line types may be aggregated:
-line_type FHO, CTC, MCTC, SL1L2, SAL1L2, VL1L2, VAL1L2, PCT, NBRCNT, NBRCTC, GRAD, ISC, ECNT, RPS, RHIST, PHIST, RELP, SSVARRequired Args: -line_type
“aggregate_stat”
To aggregate the STAT data for the STAT line type specified using the “-line_type” argument. The output of the job will be the line type specified using the “-out_line_type” argument. The valid combinations of “-line_type” and “-out_line_type” are listed below.
-line_type FHO, CTC, -out_line_type CTS, ECLV -line_type MCTC -out_line_type MCTS -line_type SL1L2, SAL1L2, -out_line_type CNT -line_type VL1L2 -out_line_type VCNT -line_type VL1L2, VAL1L2, -out_line_type WDIR (wind direction) -line_type PCT, -out_line_type PSTD, PJC, PRC, ECLV -line_type NBRCTC, -out_line_type NBRCTS -line_type ORANK, -out_line_type ECNT, RPS, RHIST, PHIST, RELP, SSVAR -line_type MPR, -out_line_type FHO, CTC, CTS, MCTC, MCTS, CNT, SL1L2, SAL1L2, VL1L2, VCNT, PCT, PSTD, PJC, PRC, ECLV, WDIR (wind direction)Required Args: -line_type, -out_line_type
Additional Required Args for -line_type MPR:
-out_thresh or -out_fcst_thresh and -out_obs_thresh When -out_line_type FHO, CTC, CTS, MCTC, MCTS, PCT, PSTD, PJC, PRCAdditional Optional Args for -line_type MPR:
-mask_grid, -mask_poly, -mask_sid -out_thresh or -out_fcst_thresh and -out_obs_thresh -out_cnt_logic -out_wind_thresh or -out_fcst_wind_thresh and -out_obs_wind_thresh -out_wind_logic When -out_line_type WDIR
Additional Optional Arg for:
-line_type ORANK -out_line_type PHIST, SSVAR ... -out_bin_size
Additional Optional Args for:
-out_line_type ECLV ... -out_eclv_points
“ss_index”
The skill score index job can be configured to compute a weighted average of skill scores derived from a configurable set of variables, levels, lead times, and statistics. The skill score index is computed using two models, a forecast model and a reference model. For each statistic in the index, a skill score is computed as:
SS = 1 - (S[model]*S[model])/(S[reference]*S[reference])
Where S is the statistic.
Next, a weighted average is computed over all the skill scores.
Lastly, an index value is computed as:
Index = sqrt(1/(1-SS[avg]))
Where SS[avg] is the weighted average of skill scores.
Required Args:
Exactly 2 entries for -model, the forecast model and reference For each term of the index: -fcst_var, -fcst_lev, -fcst_lead, -line_type, -column, -weight Where -line_type is CNT or CTS and -column is the statistic. Optionally, specify other filters for each term, -fcst_thresh.
“go_index”
The GO Index is a special case of the skill score index consisting of a predefined set of variables, levels, lead times, statistics, and weights.
For lead times of 12, 24, 36, and 48 hours, it contains RMSE for:
- Wind Speed at the surface(b), 850(a), 400(a), 250(a) mb - Dew point Temperature at the surface(b), 850(b), 700(b), 400(b) mB - Temperature at the surface(b), 400(a) mB - Height at 400(a) mB - Sea Level Pressure(b) Where (a) means weights of 4, 3, 2, 1 for the lead times, and (b) means weights of 8, 6, 4, 2 for the lead times.Required Args: None
“ramp”
The ramp job operates on a time-series of forecast and observed values and is analogous to the RIRW (Rapid Intensification and Weakening) job supported by the tc_stat tool. The amount of change from one time to the next is computed for forecast and observed values. Those changes are thresholded to define events which are used to populate a 2x2 contingency table.
Required Args:
-ramp_thresh (-ramp_thresh_fcst or -ramp_thresh_obs) For DYDT, threshold for the amount of change required to define an event. For SWING, threshold the slope. -swing_width val Required for the swinging door algorithm width.
Optional Args:
-ramp_type str Overrides the default ramp definition algorithm to be used. May be set to DYDT (default) or SWING for the swinging door algorithm. -line_type str Overrides the default input line type, MPR. -out_line_type str Overrides the default output line types of CTC and CTS. Set to CTC,CTS,MPR for all possible output types. -column fcst_column,obs_column Overrides the default forecast and observation columns to be used, FCST and OBS. -ramp_time HH[MMSS] (-ramp_time_fcst or -ramp_time_obs) Overrides the default ramp time interval, 1 hour. -ramp_exact true/false (-ramp_exact_fcst or -ramp_exact_obs) Defines ramps using an exact change (true, default) or maximum change in the time window (false). -ramp_window width in HH[MMSS] format -ramp_window beg end in HH[MMSS] format Defines a search time window when attempting to convert misses to hits and false alarms to correct negatives. Use 1 argument to define a symmetric time window or 2 for an asymmetric window. Default window is 0 0, requiring an exact match.
Job command FILTERING options to further refine the STAT data:
Each optional argument may be used in the job specification multiple times unless otherwise indicated. When multiple optional arguments of the same type are indicated, the analysis will be performed over their union:
"-model name" "-fcst_lead HHMMSS" "-obs_lead HHMMSS" "-fcst_valid_beg YYYYMMDD[_HH[MMSS]]" (use once) "-fcst_valid_end YYYYMMDD[_HH[MMSS]]" (use once) "-obs_valid_beg YYYYMMDD[_HH[MMSS]]" (use once) "-obs_valid_end YYYYMMDD[_HH[MMSS]]" (use once) "-fcst_init_beg YYYYMMDD[_HH[MMSS]]" (use once) "-fcst_init_end YYYYMMDD[_HH[MMSS]]" (use once) "-obs_init_beg YYYYMMDD[_HH[MMSS]]" (use once) "-obs_init_end YYYYMMDD[_HH[MMSS]]" (use once) "-fcst_init_hour HH[MMSS]" "-obs_init_hour HH[MMSS]" "-fcst_valid_hour" HH[MMSS] "-obs_valid_hour" HH[MMSS] "-fcst_var name" "-obs_var name" "-fcst_lev name" "-obs_lev name" "-obtype name" "-vx_mask name" "-interp_mthd name" "-interp_pnts n" "-fcst_thresh t" "-obs_thresh t" "-cov_thresh t" "-thresh_logic UNION, or, || INTERSECTION, and, && SYMDIFF, symdiff, * "-alpha a" "-line_type type" "-column name" "-weight value"Job command FILTERING options that may be used only when -line_type has been listed once. These options take two arguments: the name of the data column to be used and the min, max, or exact value for that column. If multiple column eq/min/max/str/exc options are listed, the job will be performed on their intersection:
"-column_min col_name value" e.g. -column_min BASER 0.02 "-column_max col_name value" "-column_eq col_name value" "-column_thresh col_name threshold" e.g. -column_thresh FCST '>273' "-column_str col_name string" separate multiple filtering strings with commas "-column_str_exc col_name string" separate multiple filtering strings with commasJob command options to DEFINE the analysis job. Unless otherwise noted, these options may only be used ONCE per analysis job:
"-dump_row path"
"-mask_grid name" "-mask_poly file" "-mask_sid file|list" see description of "sid" entry above
"-out_line_type name" "-out_thresh value" sets both -out_fcst_thresh and -out_obs_thresh "-out_fcst_thresh value" multiple for multi-category contingency tables and probabilistic forecasts "-out_obs_thresh value" multiple for multi-category contingency tables "-out_cnt_logic value""-out_wind_thresh value" "-out_fcst_wind_thresh value" "-out_obs_wind_thresh value" "-out_wind_logic value"
"-out_bin_size value"
"-out_eclv_points value" see description of "eclv_points" config file entry"-out_alpha value"
"-boot_interval value" "-boot_rep_prop value" "-n_boot_rep value" "-boot_rng value" "-boot_seed value"
"-hss_ec_value value" "-rank_corr_flag value" "-vif_flag value"
For aggregate and aggregate_stat job types:
"-out_stat path" to write a .stat output file for the job including the .stat header columns. Multiple values for each header column are written as a comma-separated list. "-set_hdr col_name value" may be used multiple times to explicity specify what should be written to the header columns of the output .stat file.When using the “-by” job command option, you may reference those columns in the “-set_hdr” job command options. For example, when computing statistics separately for each station, write the station ID string to the VX_MASK column of the output .stat output file:
-job aggregate_stat -line_type MPR -out_line_type CNT \ -by OBS_SID -set_hdr VX_MASK OBS_SID -stat_out out.stat When using mulitple "-by" options, use "CASE" to reference the full string: -by FCST_VAR,OBS_SID -set_hdr DESC CASE -stat_out out.stat
jobs = [
"-job filter -line_type SL1L2 -vx_mask DTC165 \
-dump_row job_filter_SL1L2.stat",
"-job summary -line_type CNT -alpha 0.050 -fcst_var TMP \
-dump_row job_summary_ME.stat -column ME",
"-job aggregate -line_type SL1L2 -vx_mask DTC165 -vx_mask DTC166 \
-fcst_var TMP -dump_row job_aggregate_SL1L2_dump.stat \
-out_stat job_aggregate_SL1L2_out.stat \
-set_hdr VX_MASK CONUS",
"-job aggregate_stat -line_type SL1L2 -out_line_type CNT -vx_mask DTC165 \
-vx_mask DTC166 -fcst_var TMP \
-dump_row job_aggregate_stat_SL1L2_CNT_in.stat",
"-job aggregate_stat -line_type MPR -out_line_type CNT -vx_mask DTC165 \
-vx_mask DTC166 -fcat_var TMP -dump_row job_aggregate_stat_MPR_CNT_in.stat",
"-job aggregate -line_type CTC -fcst_thresh <300.000 -vx_mask DTC165 \
-vx_mask DTC166 -fcst_var TMP -dump_row job_aggregate_CTC_in.stat",
"-job aggregate_stat -line_type CTC -out_line_type CTS \
-fcst_thresh <300.000 -vx_mask DTC165 -vx_mask DTC166 -fcst_var TMP \
-dump_row job_aggregate_stat_CTC_CTS_in.stat",
"-job aggregate -line_type MCTC -column_eq N_CAT 4 -vx_mask DTC165 \
-vx_mask DTC166 -fcst_var APCP_24 -dump_row job_aggregate_MCTC_in.stat",
"-job aggregate_stat -line_type MCTC -out_line_type MCTS \
-column_eq N_CAT 4 -vx_mask DTC165 -vx_mask DTC166 -fcst_var APCP_24 \
-dump_row job_aggregate_stat_MCTC_MCTS_in.stat",
"-job aggregate -line_type PCT -vx_mask DTC165 -vx_mask DTC166 \
-dump_row job_aggregate_PCT_in.stat",
"-job aggregate_stat -line_type PCT -out_line_type PSTD -vx_mask DTC165 \
-vx_mask DTC166 -dump_row job_aggregate_stat_PCT_PSTD_in.stat",
"-job aggregate -line_type ISC -fcst_thresh >0.000 -vx_mask TILE_TOT \
-fcst_var APCP_12 -dump_row job_aggregate_ISC_in.stat",
"-job aggregate -line_type RHIST -obtype MC_PCP -vx_mask HUC4_1605 \
-vx_mask HUC4_1803 -dump_row job_aggregate_RHIST_in.stat",
"-job aggregate -line_type SSVAR -obtype MC_PCP -vx_mask HUC4_1605 \
-vx_mask HUC4_1803 -dump_row job_aggregate_SSVAR_in.stat",
"-job aggregate_stat -line_type ORANK -out_line_type RHIST -obtype ADPSFC \
-vx_mask HUC4_1605 -vx_mask HUC4_1803 \
-dump_row job_aggregate_stat_ORANK_RHIST_in.stat"
];
List of statistics by the logic that should be applied when computing their WMO mean value in the summary job. Each entry is a line type followed by the statistic name. Statistics using the default arithemtic mean method do not need to be listed.
wmo_sqrt_stats = [];
wmo_fisher_stats = [];
The “vif_flag” entry is a boolean to indicate whether a variance inflation factor should be computed when aggregating a time series of contingency table counts or partial sums. The VIF is used to adjust the normal confidence intervals computed for the aggregated statistics.
vif_flag = FALSE;
5.3.8. WaveletStatConfig_default
5.3.8.1. grid_decomp_flag
The “grid_decomp_flag” entry specifies how the grid should be decomposed in Wavelet-Stat into dyadic (2^n x 2^n) tiles:
“AUTO” to tile the input data using tiles of dimension n by n where n is the largest integer power of 2 less than the smallest dimension of the input data. Center as many tiles as possible with no overlap.
“TILE” to use the tile definition specified below.
“PAD” to pad the input data out to the nearest integer power of 2.
grid_decomp_flag = AUTO;
5.3.8.2. tile
The “tile” entry is a dictionary that specifies how tiles should be defined in Wavelet-Stat when the “grid_decomp_flag” is set to “TILE”:
The “width” entry specifies the dimension for all tiles and must be an integer power of 2.
The “location” entry is an array of dictionaries where each element consists of an “x_ll” and “y_ll” entry specifying the lower-left (x,y) coordinates of the tile.
tile = {
width = 0;
location = [
{
x_ll = 0;
y_ll = 0;
}
];
}
5.3.8.3. wavelet
The “wavelet” entry is a dictionary in Wavelet-Stat that specifies how the wavelet decomposition should be performed:
The “type” entry specifies which wavelet should be used.
The “member” entry specifies the wavelet shape. See: Discrete Wavelet Transforms (DWT) initialization
Valid combinations of the two are listed below:
“HAAR” for Haar wavelet (member = 2)
“HAAR_CNTR” for Centered-Haar wavelet (member = 2)
“DAUB” for Daubechies wavelet (member = 4, 6, 8, 10, 12, 14, 16, 18, 20)
“DAUB_CNTR” for Centered-Daubechies wavelet (member = 4, 6, 8, 10, 12, 14, 16, 18, 20)
“BSPLINE” for Bspline wavelet (member = 103, 105, 202, 204, 206, 208, 301, 303, 305, 307, 309)
“BSPLINE_CNTR” for Centered-Bspline wavelet (member = 103, 105, 202, 204, 206, 208, 301, 303, 305, 307, 309)
wavelet = {
type = HAAR;
member = 2;
}
5.3.8.4. obs_raw_wvlt_object_plots
The “obs_raw_plot”, “wvlt_plot”, and “object_plot” entries are dictionaries similar to the “fcst_raw_plot” described in the “Settings common to multiple tools” section.
5.3.9. WWMCARegridConfig_default
5.3.9.1. to_grid
Please see the description of the “to_grid” entry in the “regrid” dictionary above.
5.3.9.2. NetCDF output information
Supply the NetCDF output information. For example:
variable_name = "Cloud_Pct";
units = "percent";
long_name = "cloud cover percent";
level = "SFC";
variable_name = "";
units = "";
long_name = "";
level = "";
5.3.9.3. max_minutes (pixel age)
Maximum pixel age in minutes
max_minutes = 120;
5.3.9.4. swap_endian
The WWMCA pixel age data is stored in binary data files in 4-byte blocks. The swap_endian option indicates whether the endian-ness of the data should be swapped after reading.
swap_endian = TRUE;
5.3.9.5. write_pixel_age
By default, wwmca_regrid writes the cloud percent data specified on the command line to the output file. This option writes the pixel age data, in minutes, to the output file instead.
write_pixel_age = FALSE;