31. Appendix A FAQs & How do I … ?

31.1.1. File-IO

31.1.1.1. Q. How do I improve the speed of MET tools using Gen-Vx-Mask?

A. The main reason to run gen_vx_mask is to make the MET statistics tools (e.g. point_stat, grid_stat, or ensemble_stat) run faster. The verification masking regions in those tools can be specified as Lat/Lon polyline files or the NetCDF output of gen_vx_mask. However, determining which grid points are inside/outside a polyline region can be slow if the polyline contains many points or the grid is dense. Running gen_vx_mask once to create a binary mask is much more efficient than recomputing the mask when each MET statistics tool is run. If the polyline only contains a small number of points or the grid is sparse running gen_vx_mask first would only save a second or two.

31.1.1.2. Q. How do I use map_data?

A. The MET repository includes several map data files. Users can modify which map datasets are included in the plots created by modifying the configuration files for those tools. The default map datasets are defined by the map_data dictionary in the ConfigMapData file.

map_data = {

line_color = [ 25, 25, 25 ]; // rgb triple values, 0-255
line_width = 0.5;
line_dash  = "";

source = [
{ file_name = "MET_BASE/map/country_data"; },
{ file_name = "MET_BASE/map/usa_state_data"; },
{ file_name = "MET_BASE/map/major_lakes_data"; }
];
}


Users can modify the ConfigMapData contents prior to running ‘make install’. This will change the default map data for all of the MET tools which plots. Alternatively, users can copy/paste/modify the map_data dictionary into the configuration file for a MET tool. For example, you could add map_data to the end of the MODE configuration file to customize plots created by MODE.

Here is an example of running plot_data_plane and specifying the map_data in the configuration string on the command line:

plot_data_plane
'name="TMP"; level="Z2"; \
map_data = { source = [ { file_name = \
"${MET_BUILD_BASE}/data/map/admin_by_country/admin_China_data"; } \ ]; }'  31.1.1.3. Q. How can I understand the number of matched pairs? A. Statistics are computed on matched forecast/observation pairs data. For example, if the dimension of the grid is 37x37 up to 1369 matched pairs are possible. However, if the forecast or observation contains bad data at a point, that matched pair would not be included in the calculations. There are a number of reasons that observations could be rejected - mismatches in station id, variable names, valid times, bad values, data off the grid, etc. For example, if the forecast field contains missing data around the edge of the domain, then that is a reason there may be 992 matched pairs instead of 1369. Users can use the ncview tool to look at an example netCDF file or run their files through plot_data_plane to help identify any potential issues. One common support question is “Why am I getting 0 matched pairs from Point-Stat?”. As mentioned above, there are many reasons why point observations can be excluded from your analysis. If running point_stat with at least verbosity level 2 (-v 2, the default value), zero matched pairs will result in the following type of log messages to be printed: DEBUG 2: Processing TMP/Z2 versus TMP/Z2, for observation type ADPSFC, over region FULL, for interpolation method UW_MEAN(1), using 0 pairs. DEBUG 2: Number of matched pairs = 0 DEBUG 2: Observations processed = 1166 DEBUG 2: Rejected: station id = 0 DEBUG 2: Rejected: obs var name = 1166 DEBUG 2: Rejected: valid time = 0 DEBUG 2: Rejected: bad obs value = 0 DEBUG 2: Rejected: off the grid = 0 DEBUG 2: Rejected: topography = 0 DEBUG 2: Rejected: level mismatch = 0 DEBUG 2: Rejected: quality marker = 0 DEBUG 2: Rejected: message type = 0 DEBUG 2: Rejected: masking region = 0 DEBUG 2: Rejected: bad fcst value = 0 DEBUG 2: Rejected: bad climo mean = 0 DEBUG 2: Rejected: bad climo stdev = 0 DEBUG 2: Rejected: mpr filter = 0 DEBUG 2: Rejected: duplicates = 0  This list of the rejection reason counts above matches the order in which the filtering logic is applied in the code. In this example, none of the point observations match the variable name requested in the configuration file. So all of the 1166 observations are rejected for the same reason. 31.1.1.4. Q. What types of NetCDF files can MET read? A. There are three flavors of NetCDF that MET can read directly. 1. Gridded NetCDF output from one of the MET tools 2. Output from the WRF model that has been post-processed using the wrf_interp utility 3. NetCDF data following the climate-forecast (CF) convention Lastly, users can write python scripts to pass data that’s gridded to the MET tools in memory. If the data doesn’t fall into one of those categories, then it’s not a gridded dataset that MET can handle directly. Satellite data, in general, will not be gridded. Typically it contains a dense mesh of data at lat/lon points, but typically those lat/lon points are not evenly spaced onto a regular grid. While MET’s point2grid tool does support some satellite data inputs, it is limited. Using python embedding is another option for handling new datasets not supported natively by MET. 31.1.1.5. Q. How do I choose a time slice in a NetCDF file? A. When processing NetCDF files, the level information needs to be specified to tell MET which 2D slice of data to use. The index is selected from a value when it starts with “@” for vertical level (pressure or height) and time. The actual time, @YYYYMMDD_HHMM, is allowed instead of selecting the time index. Let’s use plot_data_plane as an example: plot_data_plane \ MERGE_20161201_20170228.nc \ obs.ps \ 'name="APCP"; level="(5,*,*)";' plot_data_plane \ gtg_obs_forecast.20130730.i00.f00.nc \ altitude_20000.ps \ 'name = "edr"; level = "(@20130730_0000,@20000,*,*)";'  Assuming that the first array is the time, this will select the 6-th time slice of the APCP data and plot it since these indices are 0-based. 31.1.1.6. Q. How do I use the UNIX time conversion? A. Regarding the timing information in the NetCDF variable attributes: APCP_24:init_time_ut = 1306886400 ;  “ut” stands for UNIX time, which is the number of seconds since Jan 1, 1970. It is a convenient way of storing timing information since it is easy to add/subtract. The UNIX date command can be used to convert back/forth between unix time and time strings: To convert unix time to ymd_hms date: date -ud '1970-01-01 UTC '1306886400' seconds' +%Y%m%d_%H%M%S 20110601_000000  To convert ymd_hms to unix date: date -ud ''2011-06-01' UTC '00:00:00'' +%s 1306886400  Regarding TRMM data, it may be easier to work with the binary data and use the trmm2nc.R script described on this page under observation datasets. Follow the TRMM binary links to either the 3 or 24-hour accumulations, save the files, and run them through that script. That is faster and easier than trying to get an ASCII dump. That Rscript can also subset the TRMM data if needed. Look for the section of it titled “Output domain specification” and define the lat/lon’s that needs to be included in the output. 31.1.1.7. Q. Does MET use a fixed-width output format for its ASCII output files? A. MET does not use the Fortran-like fixed width format in its ASCII output file. Instead, the column widths are adjusted for each run to insert at least one space between adjacent columns. The header columns of the MET output contain user-defined strings which may be of arbitrary length. For example, columns such as MODEL, OBTYPE, and DESC may be set by the user to any string value. Additionally, the amount of precision written is also configurable. The “output_precision” config file entry can be changed from its default value of 5 decimal places to up to 12 decimal places, which would also impact the column widths of the output. Due to these issues, it is not possible to select a reasonable fixed width for each column ahead of time. The AsciiTable class in MET does a lot of work to line up the output columns, to make sure there is at least one space between them. If a fixed-width format is needed, the easiest option would be writing a script to post-process the MET output into the fixed-width format that is needed or that the code expects. 31.1.1.8. Q. Do the ASCII output files created by MET use scientific notation? A. By default, the ASCII output files created by MET make use of scientific notation when appropriate. The formatting of the numbers that the AsciiTable class writes is handled by a call to printf. The “%g” formatting option can result in scientific notation: http://www.cplusplus.com/reference/cstdio/printf/ It has been recommended that a configuration option be added to MET to disable the use of scientific notation. That enhancement is planned for a future release. 31.1.2. Gen-Vx-Mask 31.1.2.1. Q. I have a list of stations to use for verification. I also have a poly region defined. If I specify both of these should the result be a union of them? A. These settings are defined in the “mask” section of the Point-Stat configuration file. You can define masking regions in one of 3 ways, as a “grid”, a “poly” line file, or a “sid” list of station ID’s. If you specify one entry for “poly” and one entry for “sid”, you should see output for those two different masks. Note that each of these settings is an array of values, as indicated by the square brackets “[]” in the default config file. If you specify 5 grids, 3 poly’s, and 2 SID lists, you’d get output for those 10 separate masking regions. Point-Stat does not compute unions or intersections of masking regions. Instead, they are each processed separately. Is it true that you really want to use a polyline to define an area and then use a SID list to capture additional points outside of that polyline? If so, your options are: 1. Define one single SID list which include all the points currently inside the polyline as well as the extra ones outside. 2. Continue verifying using one polyline and one SID list and write partial sums and contingency table counts. Then aggregate the results together by running a Stat-Analysis job. 31.1.2.2. Q. How do I define a masking region with a GFS file? A. Grab a sample GFS file: wget http://www.ftp.ncep.noaa.gov/data/nccf/com/gfs/prod/gfs/2016102512/gfs.t12z.pgrb2.0p50.f000  Use the MET regrid_data_plane tool to put some data on a lat/lon grid over Europe: regrid_data_plane gfs.t12z.pgrb2.0p50.f000 \ 'latlon 100 100 25 0 0.5 0.5' gfs_euro.nc -field 'name="TMP"; level="Z2";'  Run the MET gen_vx_mask tool to apply your polyline to the European domain: gen_vx_mask gfs_euro.nc POLAND.poly POLAND_mask.nc  Run the MET plot_data_plane tool to display the resulting mask field: plot_data_plane POLAND_mask.nc POLAND_mask.ps 'name="POLAND"; level="(*,*)";'  In this example, the mask is in roughly the right spot, but there are obvious problems with the latitude and longitude values used to define that mask for Poland. 31.1.3. Grid-Stat 31.1.3.1. Q. How do I define a complex masking region? A. A user can define intersections and unions of multiple fields to define masks. Prior to running Grid-Stat, the user can run the Gen-VX-Mask tool one or more times to define a more complex masking area by thresholding multiple fields. For example, using a forecast GRIB file (fcst.grb) which contains 2 records, one for 2-m temperature and a second for 6-hr accumulated precip. The only grid points that are desired are grid points below freezing with non-zero precip. The user should run Gen-Vx-Mask twice - once to define the temperature mask and a second time to intersect that with the precip mask: gen_vx_mask fcst.grb fcst.grb tmp_mask.nc \ -type data \ -mask_field 'name="TMP"; level="Z2"' -thresh le273 gen_vx_mask tmp_mask.nc fcst.grb tmp_and_precip_mask.nc \ -type data \ -input_field 'name="TMP_Z2"; level="(*,*)";' \ -mask_field 'name="APCP"; level="A6";' -thresh gt0 \ -intersection -name "FREEZING_PRECIP"  The first one is pretty straight-forward. 1. The input field (fcst.grb) defines the domain for the mask. 2. Since we’re doing data masking and the data we want lives in fcst.grb, we pass it in again as the mask_file. 3. Lastly “-mask_field” specifies the data we want from the mask file and “-thresh” specifies the event threshold. The second call is a bit tricky. 1. Do data masking (-type data) 2. Read the NetCDF variable named “TMP_Z2” from the input file (tmp_mask.nc) 3. Define the mask by reading 6-hour precip from the mask file (fcst.grb) and looking for values > 0 (-mask_field) 4. Apply intersection logic when combining the “input” value with the “mask” value (-intersection). 5. Name the output NetCDF variable as “FREEZING_PRECIP” (-name). This is totally optional, but convenient. A user can write a script with multiple calls to Gen-Vx-Mask to apply complex masking logic and then pass the output mask file to Grid-Stat in its configuration file. 31.1.3.2. Q. How do I use neighborhood methods to compute fraction skill score? A. A common application of fraction skill score (FSS) is comparing forecast and observed thunderstorms. When computing FSS, first threshold the fields to define events and non-events. Then look at successively larger and larger areas around each grid point to see how the forecast event frequency compares to the observed event frequency. Applying this method to rainfall (and monsoons) is also reasonable. Keep in mind that Grid-Stat is the tool that computes FSS. Grid-Stat will need to be run once for each evaluation time. As an example, evaluating once per day, run Grid-Stat 122 times for the 122 days of a monsoon season. This will result in 122 FSS values. These can be viewed as a time series, or the Stat-Analysis tool could be used to aggregate them together into a single FSS value, like this: stat_analysis -job aggregate -line_type NBRCNT \ -lookin out/grid_stat  Be sure to pick thresholds (e.g. for the thunderstorms and monsoons) that capture the “events” that are of interest in studying. Also be aware that MET uses the “vld_thresh” setting in the configuration file to decide how to handle data along the edge of the domain. Let us say it is computing a fractional coverage field using a 5x5 neighborhood and it is at the edge of the domain. 15 points contain valid data and 10 points are outside the domain. Grid-Stat computes the valid data ratio as 15/25 = 0.6. Then it applies the valid data threshold. Suppose vld_thresh = 0.5. Since 0.6 > 0.5 MET will compute a fractional coverage value for that point using the 15 valid data points. Next suppose vld_thresh = 1.0. Since 0.6 is less than 1.0, MET will just skip that point by setting it to bad data. Setting vld_thresh = 1.0 will ensure that FSS will only be computed at points where all NxN values contain valid data. Setting it to 0.5 only requires half of them. 31.1.3.3. Q. Is an example of verifying forecast probabilities? A. There is an example of verifying probabilities in the test scripts included with the MET release. Take a look in: ${MET_BUILD_BASE}/scripts/config/GridStatConfig_POP_12


The config file should look something like this:

fcst = {
wind_thresh = [ NA ];
field = [
{
name = "LCDC";
level = [ "L0" ];
prob = TRUE;
cat_thresh = [ >=0.0, >=0.1, >=0.2, >=0.3, >=0.4, >=0.5, >=0.6, >=0.7, >=0.8, >=0.9];
}
];
};

obs = {
wind_thresh = [ NA ];
field = [
{
name = "WIND";
level = [ "Z2" ];
cat_thresh = [ >=34 ];
}
];
};


The PROB flag is set to TRUE to tell grid_stat to process this as probability data. The cat_thresh is set to partition the probability values between 0 and 1. Note that if the probability data contains values from 0 to 100, MET automatically divides by 100 to rescale to the 0 to 1 range.

31.1.3.4. Q. What is an example of using Grid-Stat with regridding and masking turned on?

A. Run Grid-Stat using the following commands and the attached config file

mkdir out
grid_stat \
gfs_4_20160220_0000_012.grb2 \
ST4.2016022012.06h \
GridStatConfig \
-outdir out


Note the following two sections of the Grid-Stat config file:

regrid = {
to_grid = OBS;
vld_thresh = 0.5;
method = BUDGET;
width = 2;
}


This tells Grid-Stat to do verification on the “observation” grid. Grid-Stat reads the GFS and Stage4 data and then automatically regrids the GFS data to the Stage4 domain using budget interpolation. Use “FCST” to verify the forecast domain. And use either a named grid or a grid specification string to regrid both the forecast and observation to a common grid. For example, to_grid = “G212”; will regrid both to NCEP Grid 212 before comparing them.

mask = { grid = [ "FULL" ];
poly = [ "MET_BASE/poly/CONUS.poly" ]; }


This will compute statistics over the FULL model domain as well as the CONUS masking area.

To demonstrate that Grid-Stat worked as expected, run the following commands to plot its NetCDF matched pairs output file:

plot_data_plane \
out/grid_stat_120000L_20160220_120000V_pairs.nc \
out/DIFF_APCP_06_A06_APCP_06_A06_CONUS.ps \
'name="DIFF_APCP_06_A06_APCP_06_A06_CONUS"; level="(*,*)";'


Examine the resulting plot of that difference field.

Lastly, there is another option for defining that masking region. Rather than passing the ascii CONUS.poly file to grid_stat, run the gen_vx_mask tool and pass the NetCDF output of that tool to grid_stat. The advantage to gen_vx_mask is that it will make grid_stat run a bit faster. It can be used to construct much more complex masking areas.

31.1.3.5. Q. How do I use one mask for the forecast field and a different mask for the observation field?

A. You can’t define different masks for the forecast and observation fields in MET tools. MET only lets you define a single mask (a masking grid or polyline) and then you choose whether you want to apply it to the FCST, OBS, or BOTH of them.

Nonetheless, there is a way you can accomplish this logic using the gen_vx_mask tool. You run it once to pre-process the forecast field and a second time to pre-process the observation field. And then pass those output files to your desired MET tool.

Below is an example using sample data that is included with the MET release tarball. To illustrate, this command will read 3-hour precip and 2-meter temperature, and resets the precip at any grid point where the temperature is less than 290 K to a value of 0:

gen_vx_mask \
data/sample_fcst/2005080700/wrfprs_ruc13_12.tm00_G212 \
data/sample_fcst/2005080700/wrfprs_ruc13_12.tm00_G212 \
APCP_03_where_2m_TMPge290.nc \
-type data \
-input_field 'name="APCP"; level="A3";' \
-thresh 'lt290&&ne-9999' -v 4 -value 0


So this is a bit confusing. Here’s what is happening:

• The first argument is the input file which defines the grid.

• The second argument is used to define the masking region and since I’m reading data from the same input file, I’ve listed that file twice.

• The third argument is the output file name.

• The type of masking is “data” masking where we read a 2D field of data and apply a threshold.

• By default, gen_vx_mask initializes each grid point to a value of 0. Specifying “-input_field” tells it to initialize each grid point to the value of that field (in my example 3-hour precip).

• The “-mask_field” option defines the data field that should be thresholded.

• The “-thresh” option defines the threshold to be applied.

• The “-value” option tells it what “mask” value to write to the output, and I’ve chosen 0.

The example threshold is less than 290 and not -9999 (which is MET’s internal missing data value). So any grid point where the 2 meter temperature is less than 290 K and is not bad data will be replaced by a value of 0.

To more easily demonstrate this, I changed to using “-value 10” and ran the output through plot_data_plane:

plot_data_plane \
APCP_03_where_2m_TMPge290.nc \
APCP_03_where_2m_TMPge290.ps \


In the resulting plot, anywhere you see the pink value of 10, that’s where gen_vx_mask has masked out the grid point.

31.1.4. Pcp-Combine

31.1.4.1. Q. How do I add and subtract with Pcp-Combine?

A. An example of running the MET pcp_combine tool to put NAM 3-hourly precipitation accumulations data into user-desired 3 hour intervals is provided below.

If the user wanted a 0-3 hour accumulation, this is already available in the 03 UTC file. Run this file through pcp_combine as a pass-through to put it into NetCDF format:

pcp_combine -add 03_file.grb 03 APCP_00_03.nc


If the user wanted the 3-6 hour accumulation, they would subtract 0-6 and 0-3 accumulations:

pcp_combine -subtract 06_file.grb 06 03_file.grb 03 APCP_03_06.nc


Similarly, if they wanted the 6-9 hour accumulation, they would subtract 0-9 and 0-6 accumulations:

pcp_combine -subtract 09_file.grb 09 06_file.grb 06 APCP_06_09.nc


And so on.

Run the 0-3 and 12-15 through pcp_combine even though they already have the 3-hour accumulation. That way, all of the NAM files will be in the same file format, and can use the same configuration file settings for the other MET tools (grid_stat, mode, etc.). If the NAM files are a mix of GRIB and NetCDF, the logic would need to be a bit more complicated.

31.1.4.2. Q. How do I combine 12-hour accumulated precipitation from two different initialization times?

A. The “-sum” command assumes the same initialization time. Use the “-add” option instead.

pcp_combine -add \
WRFPRS_1997-06-03_APCP_A12.nc 'name="APCP_12"; level="(*,*)";' \
WRFPRS_d01_1997-06-04_00_APCP_A12.grb 12 \
Sum.nc


For the first file, list the file name followed by a config string describing the field to use from the NetCDF file. For the second file, list the file name followed by the accumulation interval to use (12 for 12 hours). The output file, Sum.nc, will contain the combine 12-hour accumulated precipitation.

Here is a small excerpt from the pcp_combine usage statement:

Note: For “-add” and “-subtract”, the accumulation intervals may be substituted with config file strings. For that first file, we replaced the accumulation interval with a config file string.

Here are 3 commands you could use to plot these data files:

plot_data_plane WRFPRS_1997-06-03_APCP_A12.nc \
WRFPRS_1997-06-03_APCP_A12.ps 'name="APCP_12"; level="(*,*)";'

plot_data_plane WRFPRS_d01_1997-06-04_00_APCP_A12.grb \
WRFPRS_d01_1997-06-04_00_APCP_A12.ps 'name="APCP" level="A12";'

plot_data_plane sum.nc sum.ps 'name="APCP_24"; level="(*,*)";'


31.1.4.3. Q. How do I correct a precipitation time range?

A. Typically, accumulated precipitation is stored in GRIB files using an accumulation interval with a “time range” indicator value of 4. Here is a description of the different time range indicator values and meanings: http://www.nco.ncep.noaa.gov/pmb/docs/on388/table5.html

For example, take a look at the APCP in the GRIB files included in the MET tar ball:

wgrib ${MET_BUILD_BASE}/data/sample_fcst/2005080700/wrfprs_ruc13_12.tm00_G212 | grep APCP 1:0:d=05080700:APCP:kpds5=61:kpds6=1:kpds7=0:TR=4:P1=0: \ P2=12:TimeU=1:sfc:0- 12hr acc:NAve=0 2:31408:d=05080700:APCP:kpds5=61:kpds6=1:kpds7=0:TR=4: \ P1=9:P2=12:TimeU=1:sfc:9- 12hr acc:NAve=0  The “TR=4” indicates that these records contain an accumulation between times P1 and P2. In the first record, the precip is accumulated between 0 and 12 hours. In the second record, the precip is accumulated between 9 and 12 hours. However, the GRIB data uses a time range indicator of 5, not 4. wgrib rmf_gra_2016040600.24 | grep APCP 291:28360360:d=16040600:APCP:kpds5=61:kpds6=1:kpds7=0: \ TR=5:P1=0:P2=24:TimeU=1:sfc:0-24hr diff:NAve=0  pcp_combine is looking in “rmf_gra_2016040600.24” for a 24 hour accumulation, but since the time range indicator is no 4, it doesn’t find a match. If possible switch the time range indicator to 4 on the GRIB files. If this is not possible, there is another workaround. Instead of telling pcp_combine to look for a particular accumulation interval, give it a more complete description of the chosen field to use from each file. Here is an example: pcp_combine -add rmf_gra_2016040600.24 'name="APCP"; level="L0-24";' \ rmf_gra_2016040600_APCP_00_24.nc  The resulting file should have the accumulation listed at 24h rather than 0-24. 31.1.4.4. Q. How do I use Pcp-Combine as a pass-through to simply reformat from GRIB to NetCDF or to change output variable name? A. The pcp_combine tool is typically used to modify the accumulation interval of precipitation amounts in model and/or analysis datasets. For example, when verifying model output in GRIB format containing runtime accumulations of precipitation, run the pcp_combine -subtract option every 6 hours to create 6-hourly precipitation amounts. In this example, it is not really necessary to run pcp_combine on the 6-hour GRIB forecast file since the model output already contains the 0 to 6 hour accumulation. However, the output of pcp_combine is typically passed to point_stat, grid_stat, or mode for verification. Having the 6-hour forecast in GRIB format and all other forecast hours in NetCDF format (output of pcp_combine) makes the logic for configuring the other MET tools messy. To make the configuration consistent for all forecast hours, one option is to choose to run pcp_combine as a pass-through to simply reformat from GRIB to NetCDF. Listed below is an example of passing a single record to the pcp_combine -add option to do the reformatting: $MET_BUILD/bin/pcp_combine -add forecast_F06.grb \
'name="APCP"; level="A6";' \
forecast_APCP_06_F06.nc -name APCP_06


Reformatting from GRIB to NetCDF may be done for any other reason the user may have. For example, the -name option can be used to define the NetCDF output variable name. Presuming this file is then passed to another MET tool, the new variable name (CompositeReflectivity) will appear in the output of downstream tools:

$MET_BUILD/bin/pcp_combine -add forecast.grb \ 'name="REFC"; level="L0"; GRIB1_ptv=129; lead_time="120000";' \ forecast.nc -name CompositeReflectivity  31.1.4.5. Q. How do I use “-pcprx” to run a project faster? A. To run a project faster, the “-pcprx” option may be used to narrow the search down to whatever regular expression you provide. Here are a two examples: # Only using Stage IV data (ST4) pcp_combine -sum 00000000_000000 06 \ 20161015_18 12 ST4.2016101518.APCP_12_SUM.nc -pcprx "ST4.*.06h" # Specify that files starting with pgbq[number][number]be used: pcp_combine \ -sum 20160221_18 06 20160222_18 24 \ gfs_APCP_24_20160221_18_F00_F24.nc \ -pcpdir /scratch4/BMC/shout/ptmp/Andrew.Kren/pre2016c3_corr/temp \ -pcprx 'pgbq[0-9][0-9].gfs.2016022118' -v 3  31.1.4.6. Q. How do I enter the time format correctly? A. Here is an incorrect example of running pcp_combine with sub-hourly accumulation intervals: # incorrect example: pcp_combine -subtract forecast.grb 0055 \ forecast2.grb 0005 forecast.nc -field APCP  The time signature is entered incorrectly. Let’s assume that “0055” meant 0 hours and 55 minutes and “0005” meant 0 hours and 5 minutes. Looking at the usage statement for pcp_combine (just type pcp_combine with no arguments): “accum1” indicates the accumulation interval to be used from in_file1 in HH[MMSS] format (required). The time format listed “HH[MMSS]” means specifying hours or hours/minutes/seconds. The incorrect example is using hours/minutes. Below is the correct example. Add the seconds to the end of the time strings, like this: # correct example: pcp_combine -subtract forecast.grb 005500 \ forecast2.grb 000500 forecast.nc -field APCP  31.1.4.7. Q. How do I use Pcp-Combine when my GRIB data doesn’t have the appropriate accumulation interval time range indicator? A. Run wgrib on the data files and the output is listed below: 279:503477484:d=15062313:APCP:kpds5=61:kpds6=1:kpds7=0:TR= 10:P1=3:P2=247:TimeU=0:sfc:1015min \ fcst:NAve=0 \ 279:507900854:d=15062313:APCP:kpds5=61:kpds6=1:kpds7=0:TR= 10:P1=3:P2=197:TimeU=0:sfc:965min \ fcst:NAve=0  Notice the output which says “TR=10”. TR means time range indicator and a value of 10 means that the level information contains an instantaneous forecast time, not an accumulation interval. Here’s a table describing the TR values: http://www.nco.ncep.noaa.gov/pmb/docs/on388/table5.html The default logic for pcp_combine is to look for GRIB code 61 (i.e. APCP) defined with an accumulation interval (TR = 4). Since the data doesn’t meet that criteria, the default logic of pcp_combine won’t work. The arguments need to be more specific to tell pcp_combine exactly what to do. Try the command: pcp_combine -subtract \ forecast.grb 'name="APCP"; level="L0"; lead_time="165500";' \ forecast2.grb 'name="APCP"; level="L0"; lead_time="160500";' \ forecast.nc -name APCP_A005000  Some things to point out here: 1. Notice in the wgrib output that the forecast times are 1015 min and 965 min. In HHMMSS format, that’s “165500” and “160500”. 2. An accumulation interval can’t be specified since the data isn’t stored that way. Instead, use a config file string to describe the data to use. 3. The config file string specifies a “name” (APCP) and “level” string. APCP is defined at the surface, so a level value of 0 (L0) was specified. 4. Technically, the “lead_time” doesn’t need to be specified at all, pcp_combine would find the single APCP record in each input GRIB file and use them. But just in case, the lead_time option was included to be extra certain to get exactly the data that is needed. 5. The default output variable name pcp_combine would write would be “APCP_L0”. However, to indicate that its a 50-minute “accumulation interval” use a different output variable name (APCP_A005000). Any string name is possible. Maybe “Precip50Minutes” or “RAIN50”. But whatever string is chosen will be used in the Grid-Stat, Point-Stat, or MODE config file to tell that tool what variable to process. 31.1.4.8. Q. How do I use “-sum”, “-add”, and “-subtract“ to achieve the same accumulation interval? A. Here is an example of using pcp_combine to put GFS into 24- hour intervals for comparison against 24-hourly StageIV precipitation with GFS data through the pcp_combine tool. Be aware that the 24-hour StageIV data is defined as an accumulation from 12Z on one day to 12Z on the next day: http://www.emc.ncep.noaa.gov/mmb/ylin/pcpanl/stage4/ Therefore, only the 24-hour StageIV data can be used to evaluate 12Z to 12Z accumulations from the model. Alternatively, the 6- hour StageIV accumulations could be used to evaluate any 24 hour accumulation from the model. For the latter, run the 6-hour StageIV files through pcp_combine to generate the desired 24-hour accumulation. Here is an example. Run pcp_combine to compute 24-hour accumulations for GFS. In this example, process the 20150220 00Z initialization of GFS. pcp_combine \ -sum 20150220_00 06 20150221_00 24 \ gfs_APCP_24_20150220_00_F00_F24.nc \ -pcprx "gfs_4_20150220_00.*grb2" \ -pcpdir /d1/model_data/20150220  pcp_combine is looking in the /d1/SBU/GFS/model_data/20150220 directory at files which match this regular expression “gfs_4_20150220_00.*grb2”. That directory contains data for 00, 06, 12, and 18 hour initializations, but the “-pcprx” option narrows the search down to the 00 hour initialization which makes it run faster. It inspects all the matching files, looking for 6-hour APCP data to sum up to a 24-hour accumulation valid at 20150221_00. This results in a 24-hour accumulation between forecast hours 0 and 24. The following command will compute the 24-hour accumulation between forecast hours 12 and 36: pcp_combine \ -sum 20150220_00 06 20150221_12 24 \ gfs_APCP_24_20150220_00_F12_F36.nc \ -pcprx "gfs_4_20150220_00.*grb2" \ -pcpdir /d1/model_data/20150220  The “-sum” command is meant to make things easier by searching the directory. But instead of using “-sum”, another option would be the “- add” command. Explicitly list the 4 files that need to be extracted from the 6-hour APCP and add them up to 24. In the directory structure, the previous “-sum” job could be rewritten with “-add” like this: pcp_combine -add \ /d1/model_data/20150220/gfs_4_20150220_0000_018.grb2 06 \ /d1/model_data/20150220/gfs_4_20150220_0000_024.grb2 06 \ /d1/model_data/20150220/gfs_4_20150220_0000_030.grb2 06 \ /d1/model_data/20150220/gfs_4_20150220_0000_036.grb2 06 \ gfs_APCP_24_20150220_00_F12_F36_add_option.nc  This example explicitly tells pcp_combine which files to read and what accumulation interval (6 hours) to extract from them. The resulting output should be identical to the output of the “-sum” command. 31.1.4.9. Q. What is the difference between “-sum” vs. “-add”? A. The -sum and -add options both do the same thing. It’s just that ‘-sum’ could find files more quickly with the use of the -pcprx flag. This could also be accomplished by using a calling script. 31.1.4.10. Q. How do I select a specific GRIB record? A. In this example, record 735 needs to be selected. pcp_combine -add 20160101_i12_f015_HRRR_wrfnat.grb2 \ 'name="APCP"; level="R735";' \ -name "APCP_01" HRRR_wrfnat.20160101_i12_f015.nc  Instead of having the level as “L0”, tell it to use “R735” to select grib record 735. 31.1.5. Plot-Data-Plane 31.1.5.1. Q. How do I inspect Gen-Vx-Mask output? A. Check to see if the call to Gen-Vx-Mask actually did create good output with Plot-Data-Plane. The following commands assume that the MET executables are found in your path. plot_data_plane \ out/gen_vx_mask/CONUS_poly.nc \ out/gen_vx_mask/CONUS_poly.ps \ 'name="CONUS"; level="(*,*)";'  View that postscript output file, using something like “gv” for ghostview: gv out/gen_vx_mask/CONUS_poly.ps  Please review a map of 0’s and 1’s over the USA to determine if the output file is what the user expects. It always a good idea to start with plot_data_plane when working with data to make sure MET is plotting the data correctly and in the expected location. 31.1.5.2. Q. How do I specify the GRIB version? A. When MET reads Gridded data files, it must determine the type of file it’s reading. The first thing it checks is the suffix of the file. The following are all interpreted as GRIB1: .grib, .grb, and .gb. While these mean GRIB2: .grib2, .grb2, and .gb2. There are 2 choices to control how MET interprets a grib file. Renaming the files to use a particular suffix, or keep them named and explicitly tell MET to interpret them as GRIB1 or GRIB2 using the “file_type” configuration option. The examples below use the plot_data_plane tool to plot the data. Set "file_type = GRIB2;"  To keep the files named this as they are, add “file_type = GRIB2;” to all the MET configuration files (i.e. Grid-Stat, MODE, and so on) that you use: plot_data_plane \ test_2.5_prog.grib \ test_2.5_prog.ps \ 'name="TSTM"; level="A0"; file_type=GRIB2;' \ -plot_range 0 100  31.1.5.3. Q. How do I test the variable naming convention? (Record number example.) A. Make sure MET can read GRIB2 data. Plot the data from that GRIB2 file by running: plot_data_plane LTIA98_KWBR_201305180600.grb2 tmp_z2.ps 'name="TMP"; level="R2";  “R2” tells MET to plot record number 2. Record numbers 1 and 2 both contain temperature data and 2-meters. Here’s some wgrib2 output: 1:0:d=2013051806:TMP:2 m above ground:anl:analysis/forecast error 2:3323062:d=2013051806:TMP:2 m above ground:anl:  The GRIB id info has been the same between records 1 and 2. 31.1.5.4. Q. How do I compute and verify wind speed? A. Here’s how to compute and verify wind speed using MET. Good news, MET already includes logic for deriving wind speed on the fly. The GRIB abbreviation for wind speed is WIND. To request WIND from a GRIB1 or GRIB2 file, MET first checks to see if it already exists in the current file. If so, it’ll use it as is. If not, it’ll search for the corresponding U and V records and derive wind speed to use on the fly. In this example the RTMA file is named rtma.grb2 and the UPP file is named wrf.grb, please try running the following commands to plot wind speed: plot_data_plane wrf.grb wrf_wind.ps \ 'name"WIND"; level="Z10";' -v 3 plot_data_plane rtma.grb2 rtma_wind.ps \ 'name"WIND"; level="Z10";' -v 3  In the first call, the log message should be similar to this: DEBUG 3: MetGrib1DataFile::data_plane_array() -> Attempt to derive winds from U and V components.  In the second one, this won’t appear since wind speed already exists in the RTMA file. 31.1.6. Stat-Analysis 31.1.6.1. Q. How does ‘-aggregate_stat’ work? A. In Stat-Analysis, there is a “-vx_mask” job filtering option. That option reads the VX_MASK column from the input STAT lines and applies string matching with the values in that column. Presumably, all of the MPR lines will have the value of “FULL” in the VX_MASK column. Stat-Analysis has the ability to read MPR lines and recompute statistics from them using the same library code that the other MET tools use. The job command options which begin with “-out” are used to specify settings to be applied to the output of that process. For example, the “-fcst_thresh” option filters strings from the input “FCST_THRESH” header column. The “-out_fcst_thresh” option defines the threshold to be applied to the output of Stat-Analysis. So reading MPR lines and applying a threshold to define contingency table statistics (CTS) would be done using the “-out_fcst_thresh” option. Stat-Analysis does have the ability to filter MPR lat/lon locations using the “-mask_poly” option for a lat/lon polyline and the “-mask_grid” option to define a retention grid. However, there is currently no “-mask_sid” option. With met-5.2 and later versions, one option is to apply column string matching using the “-column_str” option to define the list of station ID’s you would like to aggregate. That job would look something like this: stat_analysis -lookin path/to/mpr/directory \ -job aggregate_stat -line_type MPR -out_line_type CNT \ -column_str OBS_SID SID1,SID2,SID3,...,SIDN \ -set_hdr VX_MASK SID_GROUP_NAME \ -out_stat mpr_to_cnt.stat  Where SID1…SIDN is a comma-separated list of the station id’s in the group. Notice that a value for the output VX_MASK column using the “-set_hdr” option has been specified. Otherwise, this would show a list of the unique values found in that column. Presumably, all the input VX_MASK columns say “FULL” so that’s what the output would say. Use “-set_hdr” to explicitly set the output value. 31.1.6.2. Q. What is the best way to average the FSS scores within several days or even several months using ‘Aggregate to Average Scores’? A. Below is the best way to aggregate together the Neighborhood Continuous (NBRCNT) lines across multiple days, specifically the fractions skill score (FSS). The Stat-Analysis tool is designed to do this. This example is for aggregating scores for the accumulated precipitation (APCP) field. Run the “aggregate” job type in stat_analysis to do this: stat_analysis -lookin directory/file*_nbrcnt.txt \ -job aggregate -line_type NBRCNT -by FCST_VAR,FCST_LEAD,FCST_THRESH,INTERP_MTHD,INTERP_PNTS -out_stat agg_nbrcnt.txt  This job reads all the files that are passed to it on the command line with the “-lookin” option. List explicit filenames to read them directly. Listing a top-level directory name will search that directory for files ending in “.stat”. In this case, the job running is to “aggregate” the “NBRCNT” line type. In this case, the “-by” option is being used and lists several header columns. Stat-Analysis will run this job separately for each unique combination of those header column entries. The output is printed to the screen, or use the “-out_stat” option to also write the aggregated output to a file named “agg_nbrcnt.txt”. 31.1.6.3. Q. How do I use ‘-by’ to capture unique entries? A. Here is a stat-analysis job that could be used to run, read the MPR lines, define the probabilistic forecast thresholds, define the single observation threshold, and compute a PSTD output line. Using “-by FCST_VAR” tells it to run the job separately for each unique entry found in the FCST_VAR column. stat_analysis \ -lookin point_stat_model2_120000L_20160501_120000V.stat \ -job aggregate_stat -line_type MPR -out_line_type PSTD \ -out_fcst_thresh ge0,ge0.1,ge0.2,ge0.3,ge0.4,ge0.5,ge0.6,ge0.7,ge0.8,ge0.9,ge1.0 \ -out_obs_thresh eq1.0 \ -by FCST_VAR \ -out_stat out_pstd.txt  The output statistics are written to “out_pstd.txt”. 31.1.6.4. Q. How do I use ‘-filter’ to refine my output? A. Here is an example of running a Stat-Analysis filter job to discard any CNT lines (continuous statistics) where the forecast rate and observation rate are less than 0.05. This is an alternative way of tossing out those cases without having to modify the source code. stat_analysis \ -lookin out/grid_stat/grid_stat_120000L_20050807_120000V.stat \ -job filter -dump_row filter_cts.txt -line_type CTS \ -column_min BASER 0.05 -column_min FMEAN 0.05 DEBUG 2: STAT Lines read = 436 DEBUG 2: STAT Lines retained = 36 DEBUG 2: DEBUG 2: Processing Job 1: -job filter -line_type CTS -column_min BASER 0.05 -column_min FMEAN 0.05 -dump_row filter_cts.txt DEBUG 1: Creating STAT output file "filter_cts.txt" FILTER: -job filter -line_type CTS -column_min BASER 0.05 -column_min FMEAN 0.05 -dump_row filter_cts.txt DEBUG 2: Job 1 used 36 out of 36 STAT lines.  This job reads find 56 CTS lines, but only keeps 36 of them where both the BASER and FMEAN columns are at least 0.05. 31.1.6.5. Q. How do I use the “-by” flag to stratify results? A. Adding “-by FCST_VAR” is a great way to associate a single value, of say RMSE, with each of the forecast variables (UGRD,VGRD and WIND). Run the following job on the output from Grid-Stat generated when the “make test” command is run: stat_analysis -lookin out/grid_stat \ -job aggregate_stat -line_type SL1L2 -out_line_type CNT \ -by FCST_VAR,FCST_LEV \ -out_stat cnt.txt  The resulting cnt.txt file includes separate output for 6 different FCST_VAR values at different levels. 31.1.6.6. Q. How do I speed up run times? A. By default, Stat-Analysis has two options enabled which slow it down. Disabling these two options will create quicker run times: 1. The computation of rank correlation statistics, Spearman’s Rank Correlation and Kendall’s Tau. Disable them using “-rank_corr_flag FALSE”. 2. The computation of bootstrap confidence intervals. Disable them using “-n_boot_rep 0”. Two more suggestions for faster run times. 1. Instead of using “-fcst_var u”, use “-by fcst_var”. This will compute statistics separately for each unique entry found in the FCST_VAR column. 2. Instead of using “-out” to write the output to a text file, use “-out_stat” which will write a full STAT output file, including all the header columns. This will create a long list of values in the OBTYPE column. To avoid the long, OBTYPE column value, manually set the output using “-set_hdr OBTYPE ALL_TYPES”. Or set its value to whatever is needed. stat_analysis \ -lookin diag_conv_anl.2015060100.stat \ -job aggregate_stat -line_type MPR -out_line_type CNT -by FCST_VAR \ -out_stat diag_conv_anl.2015060100_cnt.txt -set_hdr OBTYPE ALL_TYPES \ -n_boot_rep 0 -rank_corr_flag FALSE -v 4  Adding the “-by FCST_VAR” option to compute stats for all variables and runs quickly. 31.1.7. TC-Stat 31.1.7.1. Q. How do I use the “-by” flag to stratify results? A. To perform tropical cyclone evaluations for multiple models use the “-by AMODEL” option with the tc_stat tool. Here is an example. In this case the tc_stat job looked at the 48 hour lead time for the HWRF and H3HW models. Without the “-by AMODEL” option, the output would be all grouped together. tc_stat \ -lookin d2014_vx_20141117_reset/al/tc_pairs/tc_pairs_H3WI_* \ -lookin d2014_vx_20141117_reset/al/tc_pairs/tc_pairs_HWFI_* \ -job summary -lead 480000 -column TRACK -amodel HWFI,H3WI \ -by AMODEL -out sample.out  This will result in all 48 hour HWFI and H3WI track forecasts to be aggregated (statistics and scores computed) for each model separately. 31.1.7.2. Q. How do I use rapid intensification verification? A. To get the most output, run something like this: tc_stat \ -lookin path/to/tc_pairs/output \ -job rirw -dump_row test \ -out_line_type CTC,CTS,MPR  By default, rapid intensification (RI) is defined as a 24-hour exact change exceeding 30kts. To define RI differently, modify that definition using the ADECK, BDECK, or both using -rirw_time, -rirw_exact, and -rirw_thresh options. Set -rirw_window to something larger than 0 to enable false alarms to be considered hits when they were “close enough” in time. tc_stat \ -lookin path/to/tc_pairs/output \ -job rirw -dump_row test \ -rirw_time 36 -rirw_window 12 \ -out_line_type CTC,CTS,MPR  To evaluate Rapid Weakening (RW) by setting “-rirw_thresh <=-30”. To stratify your results by lead time, you could add the “-by LEAD” option. tc_stat \ -lookin path/to/tc_pairs/output \ -job rirw -dump_row test \ -rirw_time 36 -rirw_window 12 \ -rirw_thresh <=-30 -by LEAD \ -out_line_type CTC,CTS,MPR  31.1.8. Utilities 31.1.8.1. Q. What would be an example of scripting to call MET? A. The following is an example of how to call MET from a bash script including passing in variables. This shell script is listed below to run Grid-Stat, call Plot-Data-Plane to plot the resulting difference field, and call convert to reformat from PostScript to PNG. #!/bin/sh for case in echo "FCST OBS"; do export TO_GRID=${case}
grid_stat gfs.t00z.pgrb2.0p25.f000 \
nam.t00z.conusnest.hiresf00.tm00.grib2 GridStatConfig
plot_data_plane \
*TO_GRID_${case}*_pairs.nc TO_GRID_${case}.ps 'name="DIFF_TMP_P500_TMP_P500_FULL"; \
level="(*,*)";'
convert -rotate 90 -background white -flatten TO_GRID_${case}.ps TO_GRID_${case}.png
done


31.1.8.2. Q. How do I convert TRMM data files?

A. Here is an example of NetCDF that the MET software is not expecting. Here is an option for accessing that same TRMM data, following links from the MET website: http://dtcenter.org/community-code/model-evaluation-tools-met/input-data

# Pull binary 3-hourly TRMM data file
wget
ftp://disc2.nascom.nasa.gov/data/TRMM/Gridded/3B42_V7/201009/3B42.100921.00z.7.
precipitation.bin
# Pull Rscript from MET website
wget http://dtcenter.org/sites/default/files/community-code/met/r-scripts/trmmbin2nc.R
# Edit that Rscript by setting
out_lat_ll = -50
out_lon_ll = 0
out_lat_ur = 50
out_lon_ur = 359.75
# Run the Rscript
Rscript trmmbin2nc.R 3B42.100921.00z.7.precipitation.bin \
3B42.100921.00z.7.precipitation.nc
# Plot the result
plot_data_plane 3B42.100921.00z.7.precipitation.nc \
3B42.100921.00z.7.precipitation.ps 'name="APCP_03"; level="(*,*)";'


It may be possible that the domain of the data is smaller. Here are some options:

1. In that Rscript, choose different boundaries (i.e. out_lat/lon_ll/ur) to specify the tile of data to be selected.

2. As of version 5.1, MET includes support for regridding the data it reads. Keep TRMM on it’s native domain and use the MET tools to do the regridding. For example, the Regrid-Data-Plane” tool reads a NetCDF file, regrids the data, and writes a NetCDF file. Alternatively, the “regrid” section of the configuration files for the MET tools may be used to do the regridding on the fly. For example, run Grid-Stat to compare to the model output to TRMM and say

"regrid = { field = FCST;
...}"


That tells Grid-Stat to automatically regrid the TRMM observations to the model domain.

31.1.8.3. Q. How do I convert a PostScript to png?

A. Use the linux “convert” tool to convert a Plot-Data-Plane PostScript file to a png:

convert -rotate 90 -background white plot_dbz.ps plot_dbz.png


To convert a MODE PostScript to png

convert mode_out.ps mode_out.png


Will result in all 6-7 pages in the PostScript file be written out to a seperate .png with the following naming convention:

mode_out-0.png, mode_out-1.png, mode_out-2.png, etc.

31.1.8.4. Q. How does pairwise differences using plot_tcmpr.R work?

A. One necessary step in computing pairwise differences is “event equalizing” the data. This means extracting a subset of cases that are common to both models.

While the tc_stat tool does not compute pairwise differences, it can apply the “event_equalization” logic to extract the cases common to two models. This is done using the config file “event_equal = TRUE;” option or setting “-event_equal true” on the command line.

Most of the hurricane track analysis and plotting is done using the plot_tcmpr.R Rscript. It makes a call to the tc_stat tool to track data down to the desired subset, compute pairwise differences if needed, and then plot the result.

Rscript ${MET_BUILD_BASE}/scripts/Rscripts/plot_tcmpr.R \ -lookin tc_pairs_output.tcst \ -filter '-amodel AHWI,GFSI' \ -series AMODEL AHWI,GFSI,AHWI-GFSI \ -plot MEAN,BOXPLOT  The resulting plots include three series - one for AHWI, one for GFSI, and one for their pairwise difference. It’s a bit cumbersome to understand all the options available, but this may be really useful. If nothing else, it could be adapted to dump out the pairwise differences that are needed. 31.1.9. Miscellaneous 31.1.9.1. Q. Regrid-Data-Plane - How do I define a LatLon grid? A. Here is an example of the NetCDF variable attributes that MET uses to define a LatLon grid: :Projection = "LatLon" ; :lat_ll = "25.063000 degrees_north" ; :lon_ll = "-124.938000 degrees_east" ; :delta_lat = "0.125000 degrees" ; :delta_lon = "0.125000 degrees" ; :Nlat = "224 grid_points" ; :Nlon = "464 grid_points" ;  This can be created by running the Regrid-Data-Plane” tool to regrid some GFS data to a LatLon grid: regrid_data_plane \ gfs_2012040900_F012.grib G110 \ gfs_g110.nc -field 'name="TMP"; level="Z2";'  Use ncdump to look at the attributes. As an exercise, try defining these global attributes (and removing the other projection-related ones) and then try again. 31.1.9.2. Q. Pre-processing - How do I use wgrib2, pcp_combine regrid and reformat to format NetCDF files? A. If you are extracting only one or two fields from a file, using MET’s Regrid-Data-Plane can be used to generate a Lat-Lon projection. If regridding all fields, the wgrib2 utility may be more useful. Here’s an example of using wgrib2 and pcp_combine to generate NetCDF files MET can read: wgrib2 gfsrain06.grb -new_grid latlon 112:131:0.1 \ 25:121:0.1 gfsrain06_regrid.grb2  And then run that GRIB2 file through pcp_combine using the “-add” option with only one file provided: pcp_combine -add gfsrain06_regrid.grb2 'name="APCP"; \ level="A6";' gfsrain06_regrid.nc  Then the output NetCDF file does not have this problem: ncdump -h 2a_wgrib2_regrid.nc | grep "_ll" :lat_ll = "25.000000 degrees_north" ; :lon_ll = "112.000000 degrees_east" ;  31.1.9.3. Q. TC-Pairs - How do I get rid of WARNING: TrackInfo Using Specify Model Suffix? A. Below is a command example to run: tc_pairs \ -adeck aep142014.h4hw.dat \ -bdeck bep142014.dat \ -config TCPairsConfig_v5.0 \ -out tc_pairs_v5.0_patch \ -log tc_pairs_v5.0_patch.log \ -v 3  Below is a warning message: WARNING: TrackInfo::add(const ATCFLine &) -> skipping ATCFLine since the valid time is not increasing (20140801_000000 < 20140806_060000): WARNING: AL, 03, 2014080100, 03, H4HW, 000, 120N, 547W, 38, 1009, XX, 34, NEQ, 0084, 0000, 0000, 0083, -99, -99, 59, 0, 0, , 0, , 0, 0,  As a sanity check, the MET-TC code makes sure that the valid time of the track data doesn’t go backwards in time. This warning states that this is occurring. The very likely reason for this is that the data being used are probably passing tc_pairs duplicate track data. Using grep, notice that the same track data shows up in “aal032014.h4hw.dat” and “aal032014_hfip_d2014_BERTHA.dat”. Try this: grep H4HW aal*.dat | grep 2014080100 | grep ", 000," aal032014.h4hw.dat:AL, 03, 2014080100, 03, H4HW, 000, 120N, 547W, 38, 1009, XX, 34, NEQ, 0084, 0000, 0000, 0083, -99, -99, 59, 0, 0, , 0, , 0, 0, , , , , 0, 0, 0, 0, THERMO PARAMS, -9999, -9999, -9999, Y, 10, DT, -999 aal032014_hfip_d2014_BERTHA.dat:AL, 03, 2014080100, 03, H4HW, 000, 120N, 547W, 38, 1009, XX, 34, NEQ, 0084, 0000, 0000, 0083, -99, -99, 59, 0, 0, , 0, , 0, 0, , , , , 0, 0, 0, 0, THERMOPARAMS, -9999 ,-9999 , -9999 ,Y ,10 ,DT ,-999  Those 2 lines are nearly identical, except for the spelling of “THERMO PARAMS” with a space vs “THERMOPARAMS” with no space. Passing tc_pairs duplicate track data results in this sort of warning. The DTC had the same sort of problem when setting up a real-time verification system. The same track data was making its way into multiple ATCF files. If this really is duplicate track data, work on the logic for where/how to store the track data. However, if the H4HW data in the first file actually differs from that in the second file, there is another option. You can specify a model suffix to be used for each ADECK source, as in this example (suffix=_EXP): tc_pairs \ -adeck aal032014.h4hw.dat suffix=_EXP \ -adeck aal032014_hfip_d2014_BERTHA.dat \ -bdeck bal032014.dat \ -config TCPairsConfig_match \ -out tc_pairs_v5.0_patch \ -log tc_pairs_v5.0_patch.log -v 3  Any model names found in “aal032014.h4hw.dat” will now have _EXP tacked onto the end. Note that if a list of model names in the TCPairsConfig file needs specifying, include the _EXP variants to get them to show up in the output or it won’t show up. That’ll get rid of the warnings because they will be storing the track data from the first source using a slightly different model name. This feature was added for users who are testing multiple versions of a model on the same set of storms. They might be using the same ATCF ID in all their output. But this enables them to distinguish the output in tc_pairs. 31.1.9.4. Q. Why is the grid upside down? A. The user provides a gridded data file to MET and it runs without error, but the data is packed upside down. Try using the “file_type” entry. The “file_type” entry specifies the input file type (e.g. GRIB1, GRIB2, NETCDF_MET, NETCDF_PINT, NETCDF_NCCF) rather than letting the code determine it itself. For valid file_type values, see “File types” in the data/config/ConfigConstants file. This entry should be defined within the “fcst” or “obs” dictionaries. Sometimes, directly specifying the type of file will help MET figure out what to properly do with the data. Another option is to use the Regrid-Data-Plane tool. The Regrid-Data-Plane tool may be run to read data from any gridded data file MET supports (i.e. GRIB1, GRIB2, and a variety of NetCDF formats), interpolate to a user-specified grid, and write the field(s) out in NetCDF format. See the Regrid-Data-Plane tool Section 8.2 in the MET User’s Guide for more detailed information. While the Regrid-Data-Plane tool is useful as a stand-alone tool, the capability is also included to automatically regrid data in most of the MET tools that handle gridded data. This “regrid” entry is a dictionary containing information about how to handle input gridded data files. The “regird” entry specifies regridding logic and has a “to_grid” entry that can 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. See the regrid entry in the Configuration File Overview in the MET User’s Guide for a more detailed description of the configuration file entries that control automated regridding. A single model level can be plotted using the plot_data_plane utility. This tool can assist the user by showing the data to be verified to ensure that times and locations matchup as expected. 31.1.9.5. Q. Why was the MET written largely in C++ instead of FORTRAN? A. MET relies upon the object-oriented aspects of C++, particularly in using the MODE tool. Due to time and budget constraints, it also makes use of a pre-existing forecast verification library that was developed at NCAR. 31.1.9.6. Q. How does MET differ from the previously mentioned existing verification packages? A. MET is an actively maintained, evolving software package that is being made freely available to the public through controlled version releases. 31.1.9.7. Q. Will the MET work on data in native model coordinates? A. No - it will not. In the future, we may add options to allow additional model grid coordinate systems. 31.1.9.8. Q. How do I get help if my questions are not answered in the User’s Guide? A. First, look on our MET User’s Guide website. If that doesn’t answer your question, create a post in the METplus GitHub Discussions Forum. 31.1.9.9. Q. What graphical features does MET provide? A. MET provides some plotting and graphics support. The plotting tools, including plot_point_obs, plot_data_plane, and plot_mode_field, can help users visualize the data. MET is intended to be a set of command line tools for evaluating forecast quality. So, the development effort is focused on providing the latest, state of the art verification approaches, rather than on providing nice plotting features. However, the ASCII output statistics of MET may be plotted with a wide variety of plotting packages, including R, NCL, IDL, and GNUPlot. METViewer is also currently being developed and used by the DTC and NOAA It creates basic plots of MET output verification statistics. The types of plots include series plots with confidence intervals, box plots, x-y scatter plots and histograms. R is a language and environment for statistical computing and graphics. It’s a free package that runs on most operating systems and provides nice plotting features and a wide array of powerful statistical analysis tools. There are sample scripts on the MET website that you can use and modify to perform the type of analysis you need. If you create your own scripts, we encourage you to submit them to us through the METplus GitHub Discussions Forum so that we can post them for other users. 31.1.9.10. Q. How do I find the version of the tool I am using? A. Type the name of the tool followed by -version. For example, type “pb2nc -version”. 31.1.9.11. Q. What are MET’s conventions for latitude, longitude, azimuth and bearing angles? A. MET considers north latitude and east longitude positive. Latitudes have range from $$-90^\circ$$ to $$+90^\circ$$. Longitudes have range from $$-180^\circ$$ to $$+180^\circ$$. Plane angles such as azimuths and bearing (example: horizontal wind direction) have range $$0^\circ$$ to $$360^\circ$$ and are measured clockwise from the north. 31.2. Troubleshooting The first place to look for help with individual commands is this User’s Guide or the usage statements that are provided with the tools. Usage statements for the individual MET tools are available by simply typing the name of the executable in MET’s bin/ directory. Example scripts available in the MET’s scripts/ directory show examples of how one might use these commands on example datasets. Here are suggestions on other things to check if you are having problems installing or running MET. 31.2.1. MET won’t compile • Have you specified the locations of NetCDF, GNU Scientific Library, and BUFRLIB, and optional additional libraries using corresponding MET_ environment variables prior to running configure? • Have these libraries been compiled and installed using the same set of compilers used to build MET? • Are you using NetCDF version 3.4 or version 4? Currently, only NetCDF version 3.6 can be used with MET. 31.2.2. BUFRLIB Errors during MET installation error message: /usr/bin/ld: cannot find -lbufr The linker can not find the BUFRLIB library archive file it needs. export MET_BUFRLIB=/home/username/BUFRLIB_v10.2.3:$MET_BUFRLIB


It isn’t making it’s way into the configuration because BUFRLIB_v10.2.3 isn’t showing up in the output of make. This may indicate the wrong shell type. The .bashrc file sets the environment for the Bourne shell, but the above error could indicate that the c- shell is being used instead.

Try the following 2 things:

1. Check to make sure this file exists:

ls /home/username/BUFRLIB_v10.2.3/libbufr.a

1. Rerun the MET configure command using the following option on the command line:

MET_BUFRLIB=/home/username/BUFRLIB_v10.2.3


After doing that, please try recompiling MET. If it fails, please submit the following log files: “make_install.log” as well as “config.log” with a new post in the METplus GitHub Discussions Forum.

31.2.3. Command line double quotes

Single quotes, double quotes, and escape characters can be difficult for MET to parse. If there are problems, especially in Python code, try breaking the command up like the below example.

['regrid_data_plane',
'/h/data/global/WXQC/data/umm/1701150006',
'G003', '/h/data/global/WXQC/data/met/nc_mdl/umm/1701150006', '- field',
'\'name="HGT"; level="P500";\'', '-v', '6']


31.2.4. Environment variable settings

In the below incorrect example for many environment variables have both the main variable set and the INC and LIB variables set:

export MET_GSL=$MET_LIB_DIR/gsl export MET_GSLINC=$MET_LIB_DIR/gsl/include/gsl
export MET_GSLLIB=$MET_LIB_DIR/gsl/lib  only MET_GSL *OR *MET_GSLINC *AND *MET_GSLLIB need to be set. So, for example, either set: export MET_GSL=$MET_LIB_DIR/gsl


or set:

export MET_GSLINC=$MET_LIB_DIR/gsl/include/gsl export MET_GSLLIB=$MET_LIB_DIR/gsl/lib


Additionally, MET does not use MET_HDF5INC and MET_HDF5LIB. It only uses MET_HDF5.

Our online tutorial can help figure out what should be set and what the value should be: https://met.readthedocs.io/en/latest/Users_Guide/installation.html

31.2.5. NetCDF install issues

This example shows a problem with NetCDF in the make_install.log file:

/usr/bin/ld: warning: libnetcdf.so.11,
needed by /home/zzheng25/metinstall/lib/libnetcdf_c++4.so,
may conflict with libnetcdf.so.7


Below are examples of too many MET_NETCDF options:

MET_NETCDF='/home/username/metinstall/'


Either MET_NETCDF OR MET_NETCDFINC AND MET_NETCDFLIB need to be set. If the NetCDF include files are in /home/username/local/include and the NetCDF library files are in /home/username/local/lib, unset the MET_NETCDF environment variable, then run “make clean”, reconfigure, and then run “make install” and “make test” again.

• Add the lib dir to your LD_LIBRARY_PATH. For example, if you receive the following error: “./mode_analysis: error while loading shared libraries: libgsl.so.19: cannot open shared object file: No such file or directory”, you should add the path to the gsl lib (for example, /home/user/MET/gsl-2.1/lib) to your LD_LIBRARY_PATH.

31.2.7. General troubleshooting

• For configuration files used, make certain to use empty square brackets (e.g. [ ]) to indicate no stratification is desired. Do NOT use empty double quotation marks inside square brackets (e.g. [“”]).

• Have you designated all the required command line arguments?

• Try rerunning with a higher verbosity level. Increasing the verbosity level to 4 or 5 prints much more diagnostic information to the screen.

31.3. Where to get help

If none of the above suggestions have helped solve your problem, help is available through the METplus GitHub Discussions Forum.

31.4. How to contribute code

If you have code you would like to contribute, we will gladly consider your contribution. Please create a post in the METplus GitHub Discussions Forum.