24. TC-Pairs Tool

24.1. Introduction

The TC-Pairs tool provides verification for tropical cyclone forecasts in ATCF file format. It matches an ATCF format tropical cyclone (TC) forecast with a second ATCF format reference TC dataset (most commonly the Best Track analysis). The TC-Pairs tool processes both track and intensity adeck data and probabilistic edeck data. The adeck matched pairs contain position errors, as well as values for each TC dataset, including wind speed, wind radii, minimum sea level pressure, and distance to land. The edeck matched pairs contain probabilistic forecast values and the verifying observation values. The pair generation can be subset based on user-defined filtering criteria. Practical aspects of the TC-Pairs tool are described in Section 24.3.

24.2. Scientific and Statistical Aspects

24.2.1. TC Diagnostics

TC diagnostics provide information about a TC’s structure or its environment. Each TC diagnostic is a single-valued measure that corresponds to some aspect of the storm itself or the surrounding large-scale environment. TC diagnostics can be derived from observational analyses, model fields, or even satellite observations. Examples include:

  • Inner core diagnostics provide information about the structure of the storm near the storm center. Examples include the intensity of the storm and the radius of maximum winds.

  • Large scale diagnostics provide information about quantities that characterize its environment. Examples include environmental vertical wind shear, total precipitable water, relative humidity, convective instability, and the upper bound of intensity that a storm may be expected to achieve in its current environment. These diagnostics are typically derived from model fields as an average of the quantity of interest over either a circular area or an annulus centered on the storm center. Often, the storm center is taken to be the underlying model’s storm center. In other cases, the diagnostics may be computed along some other specified track.

  • Ocean-based diagnostics provide information about the sea’s thermal characteristics in the vicinity of the storm center. Examples include the sea surface temperature, ocean heat content, and the depth of warm water of a given temperature.

  • Satellite-based diagnostics provide information about the storm structure as observed by geostationary satellite infrared imagery. Examples include information about the shape and extent of the cold-cirrus canopy of the TC and whether patterns are present that may portend intensification.

Diagnostics are critically important for training and running statistical-dynamical models that predict a TC’s intensity or size. One of the most well-known diagnostics sets is that of the Statistical Hurricane Intensity Prediction Scheme (SHIPS), which supports predictions of TC intensity. A large 30-year development dataset of TC diagnostics has been retrospectively derived to support the training of the SHIPS intensity model as well as other related models such as the Logistic Growth Equation Model (LGEM), SHIPS Rapid Intensification Index (SHIPS-RII), and others. These diagnostics, called lsdiag for “large scale” environment, are computed using a perfect prog approach in which the diagnostics are computed on the reference model’s verifying analyses to generate a set of time-dependent diagnostics from t=0 out to the desired maximum forecast lead time. This is repeated for each initialization, building up a full history of diagnostics for each storm. By using the subsequent verifying analysis for later lead times, the model is taken to be “perfect”, removing the impact of model forecast errors. The resulting developmental dataset is ideal for training statistical-dynamical models such as SHIPS. To generate forecasts in real-time, the diagnostics are computed along a forecast track (often taken to be the National Hurricane Center’s official forecast) using the fields of the underlying NWP model (e.g, the Global Forecast System, or GFS model). The resulting diagnostics are then used as predictors in models like SHIPS and LGEM to predict a TC’s future intensity or probability of undergoing rapid intensification.

Beside their use in TC prediction, TC diagnostics can be very useful to forecasters to understand the forecast scenario. They are also useful to model developers for evaluation of model errors and understanding model performance under different environmental conditions. For instance, a modeler may wish to understand their model’s track biases under conditions of high vertical wind shear. TC diagnostics can also be used to understand the sensitivity of the model’s intensity predictions to oceanic conditions such as upwelling. The TC-Pairs tool allows filtering and subsetting based on the values of one or several TC diagnostics.

As of MET v11.0.0, two types of TC diagnostics are supported in TC-Pairs:

  • SHIPS_DIAG_RT: Real-time SHIPS diagnostics computed from a NWP model such as the Global Forecast System (GFS) model along a GFS forecast track defined by a SHIPS-specific tracker. Note that these SHIPS-derived forecast tracks do not appear in the NHC adeck data.

  • CIRA_DIAG_RT: Real-time model-based diagnostics computed along the model’s predicted track.

Diagnostics from the SHIPS Development Datasets (SHIPS_DIAG_DEV) will be supported in a future release of MET.

A future version of MET will also allow the CIRA model diagnostics to be computed directly from model forecast fields. Until then, users may obtain the SHIPS diagnostics at the following locations:

24.3. Practical Information

This section describes how to configure and run the TC-Pairs tool. The TC-Pairs tool is used to match a tropical cyclone model forecast to a corresponding reference dataset. Both tropical cyclone forecast/reference data must be in ATCF format. Output from the TC-dland tool (NetCDF gridded distance file) is also a required input for the TC-Pairs tool. It is recommended to run tc_pairs on a storm-by-storm basis, rather than over multiple storms or seasons to avoid memory issues.

24.3.1. tc_pairs Usage

The usage statement for tc_pairs is shown below:

Usage: tc_pairs
       -adeck path and/or -edeck path
       -bdeck path
       -config file
       [-diag source path]
       [-out base]
       [-log file]
       [-v level]

tc_pairs has required arguments and can accept several optional arguments.

24.3.1.1. Required Arguments for tc_pairs

  1. The -adeck path argument indicates the adeck TC-Pairs acceptable format data containing tropical cyclone model forecast (output from tracker) data to be verified. Acceptable data formats are limited to the standard ATCF format and the one column modified ATCF file, generated by running the tracker in genesis mode. It specifies the name of a TC-Pairs acceptable format file or top-level directory containing TC-Pairs acceptable format files ending in “.dat” to be processed. The -adeck or -edeck option must be used at least once.

  2. The -edeck path argument indicates the edeck ATCF format data containing probabilistic track data to be verified. It specifies the name of an ATCF format file or top-level directory containing ATCF format files ending in “.dat” to be processed. The -adeck or -edeck option must be used at least once.

  3. The -bdeck path argument indicates the TC-Pairs acceptable format data containing the tropical cyclone reference dataset to be used for verifying the adeck data. This data is typically the NHC Best Track Analysis, but could be any TC-Pairs acceptable formatted reference. The acceptable data formats for bdecks are the same as those for adecks. This argument specifies the name of a TC-Pairs acceptable format file or top-level directory containing TC-Pairs acceptable format files ending in “.dat” to be processed.

  4. The -config file argument indicates the name of the configuration file to be used. The contents of the configuration file are discussed below.

24.3.1.2. Optional Arguments for tc_pairs

  1. The -diag source path argument indicates the TC-Pairs acceptable format data containing the tropical cyclone diagnostics dataset corresponding to the adeck tracks. The source can be set to CIRA_DIAG_RT or SHIPS_DIAG_RT to indicate the input diagnostics data source. The path argument specifies the name of a TC-Pairs acceptable format file or top-level directory containing TC-Pairs acceptable format files ending in “.dat” to be processed. Support for additional diagnostic sources will be added in future releases.

  2. The -out base argument indicates the path of the output file base. This argument overrides the default output file base (./out_tcmpr).

  3. The -log file option directs output and errors to the specified log file. All messages will be written to that file as well as standard out and error. Thus, users can save the messages without having to redirect the output on the command line. The default behavior is no log file.

  4. The -v level option indicates the desired level of verbosity. The contents of “level” will override the default setting of 2. Setting the verbosity to 0 will make the tool run with no log messages, while increasing the verbosity above 1 will increase the amount of logging.

This tool currently only supports the rapid intensification (RI) edeck probability type but support for additional edeck probability types will be added in future releases.

At least one -adeck or -edeck option must be specified. The -adeck, -edeck, and -bdeck options may optionally be followed with suffix=string to append that string to all model names found within that data source. This option may be useful when processing track data from two different sources which reuse the same model names.

An example of the tc_pairs calling sequence is shown below:

tc_pairs -adeck aal092010.dat -bdeck bal092010.dat -config TCPairsConfig

In this example, the TC-Pairs tool matches the model track (aal092010.dat) and the best track analysis (bal092010.dat) for the 9th Atlantic Basin storm in 2010. The track matching and subsequent error information is generated with configuration options specified in the TCPairsConfig file.

The TC-Pairs tool implements the following logic:

  • Parse the adeck, edeck, and bdeck data files and store them as track objects.

  • Parse diagnostics data files and add the requested diagnostics to the existing adeck track objects.

  • Apply configuration file settings to filter the adeck, edeck, and bdeck track data down to a subset of interest.

  • Apply configuration file settings to derive additional adeck track data, such as interpolated tracks, consensus tracks, time-lagged tracks, and statistical track and intensity models.

  • For each adeck track that was parsed or derived, search for a matching bdeck track with the same basin and cyclone number and overlapping valid times. If not matching against the BEST track, also ensure that the model initialization times match.

  • For each adeck/bdeck track pair, match up their track points in time, lookup distances to land, compute track location errors, and write an output TCMPR line for each track point.

  • For each set of edeck probabilities that were parsed, search for a matching bdeck track.

  • For each edeck/bdeck pair, write paired edeck probabilities and matching bdeck values to output PROBRIRW lines.

24.3.2. tc_pairs Configuration File

The default configuration file for the TC-Pairs tool named TCPairsConfig_default can be found in the installed share/met/config/ directory. Users are encouraged to copy these default files before modifying their contents. The contents of the configuration file are described in the subsections below.

The contents of the tc_pairs configuration file are described below.

storm_id     = [];
basin        = [];
cyclone      = [];
storm_name   = [];
init_beg     = "";
init_end     = "";
init_inc     = [];
init_exc     = [];
valid_beg    = "";
valid_end    = "";
valid_inc    = [];
valid_exc    = [];
init_hour    = [];
init_mask    = "";
valid_mask   = "";
lead_req     = [];
match_points = TRUE;
version      = "VN.N";

The configuration options listed above are common to multiple MET tools and are described in Section 6.

model = [ "DSHP", "LGEM", "HWRF" ];

The model variable contains a list of comma-separated models to be used. Each model is identified with an ATCF TECH ID (normally four unique characters). This model identifier should match the model column in the ATCF format input file. An empty list indicates that all models in the input file(s) will be processed. Note that when reading ATCF track data, all instances of the string AVN are automatically replaced with GFS.

write_valid = [ "20101231_06" ];

The write_valid entry specifies a comma-separated list of valid time strings in YYYYMMDD[_HH[MMSS]] format for which output should be written. An empty list indicates that data for all valid times should be written. This option may be useful when verifying track forecasts in realtime. If evaluating performance for a single valid time, this option can limit the output to that time and skip output for earlier track points.

check_dup = FALSE;

The check_dup flag expects either TRUE and FALSE, indicating whether the code should check for duplicate ATCF lines when building tracks. Setting check_dup to TRUE will check for duplicated lines, and produce output information regarding the duplicate. Any duplicated ATCF line will not be processed in the tc_pairs output. Setting check_dup to FALSE, will still exclude tracks that decrease with time, and will overwrite repeated lines, but specific duplicate log information will not be output. Setting check_dup to FALSE will make parsing the track quicker.

interp12 = NONE;

The interp12 flag expects the entry NONE, FILL, or REPLACE, indicating whether special processing should be performed for interpolated forecasts. The NONE option indicates no changes are made to the interpolated forecasts. The FILL and REPLACE (default) options determine when the 12-hour interpolated forecast (normally indicated with a “2” or “3” at the end of the ATCF ID) will be renamed with the 6-hour interpolated ATCF ID (normally indicated with the letter “I” at the end of the ATCF ID). The FILL option renames the 12-hour interpolated forecasts with the 6-hour interpolated forecast ATCF ID only when the 6-hour interpolated forecasts is missing (in the case of a 6-hour interpolated forecast which only occurs every 12-hours (e.g. EMXI, EGRI), the 6-hour interpolated forecasts will be “filled in” with the 12-hour interpolated forecasts in order to provide a record every 6-hours). The REPLACE option renames all 12-hour interpolated forecasts with the 6-hour interpolated forecasts ATCF ID regardless of whether the 6-hour interpolated forecast exists. The original 12-hour ATCF ID will also be retained in the output file (all modified ATCF entries will appear at the end of the TC-Pairs output file). This functionality expects both the 12-hour and 6-hour early (interpolated) ATCF IDs to be listed in the model field.

consensus = [
   {
      name          = "CON1";
      members       = [ "MOD1", "MOD2", "MOD3" ];
      required      = [   true,  false, false  ];
      min_req       = 2;
      diag_required = [   false, false, false  ];
      min_diag_req  = 0;
      write_members = TRUE;
   }
];

The consensus array allows users to derive consensus forecasts from any number of models. A consensus forecast is computed as the average intensity and location of the members which comprise it. TC-Pairs attempts to derive consensus forecasts for each unique storm ID and initialization time found in the input track data. Each array entry is a dictionary which defines the consensus name, membership, and requirements:

  • The name field is a string defining the consensus model name to be written.

  • The members field is a comma-separated array of model ID stings which define the members of the consensus.

  • The required field is a comma-separated array of true/false values associated with each consensus member. If a member is designated as true, that member must be present in order for the consensus to be generated. If a member is false, the consensus will be generated regardless of whether or not the member is present. The required array can either be empty or have the same length as the members array. If empty, it defaults to all false.

  • The min_req field is the number of members required in order for the consensus to be computed. The required and min_req field options are applied at each forecast lead time. If any member of the consensus has a non-valid position or intensity value, the consensus for that valid time will not be generated.

  • Tropical cyclone diagnostics, if provided on the command line, are included in the computation of consensus tracks. The consensus diagnostics are computed as the mean of the diagnostics for the members. The diag_required and min_diag_req entries apply the same logic described above, but to the computation of each consensus diagnostic value rather than the consensus track location and intensity. If diag_required is missing or an empty list, it defaults to all false. If min_diag_req is missing, it default to 0.

  • The write_members field is a boolean that indicates whether or not to write track output for the individual consensus members. If set to true, standard output will show up for all members. If set to false, output for the consensus members is excluded from the output, even if they are used to define other consensus tracks in the configuration file.

Users should take care to avoid filtering out track data for the consensus members with the model field, described above. Either set model to an empty list to process all input track data or include all of the consensus members in the model list. Use the write_members field, not the model field, to suppress track output for consensus members.

lag_time = [ "06", "12" ];

The lag_time field is a comma-separated list of forecast lag times to be used in HH[MMSS] format. For each adeck track identified, a lagged track will be derived for each entry. In the tc_pairs output, the original adeck record will be retained, with the lagged entry listed as the adeck name with “_LAG_HH” appended.

best_technique = [ "BEST" ];
best_baseline  = [ "BCLP", "BCD5", "BCLA" ];

The best_technique field specifies a comma-separated list of technique name(s) to be interpreted as BEST track data. The default value (BEST) should suffice for most users. The best_baseline field specifies a comma-separated list of CLIPER/SHIFOR baseline forecasts to be derived from the best tracks. Specifying multiple best_technique values and at least one best_baseline value results in a warning since the derived baseline forecast technique names may be used multiple times.

The following are valid baselines for the best_baseline field:

BTCLIP: Neumann original 3-day CLIPER in best track mode. Used for the Atlantic basin only. Specify model as BCLP.

BTCLIP5: 5-day CLIPER (Aberson, 1998)/SHIFOR (DeMaria and Knaff, 2003) in best track mode for either Atlantic or eastern North Pacific basins. Specify model as BCS5.

BTCLIPA: Sim Aberson’s recreation of Neumann original 3-day CLIPER in best-track mode. Used for Atlantic basin only. Specify model as BCLA.

oper_technique = [ "CARQ" ];
oper_baseline  = [ "OCLP", "OCS5", "OCD5" ];

The oper_technique field specifies a comma-separated list of technique name(s) to be interpreted as operational track data. The default value (CARQ) should suffice for most users. The oper_baseline field specifies a comma-separated list of CLIPER/SHIFOR baseline forecasts to be derived from the operational tracks. Specifying multiple oper_technique values and at least one oper_baseline value results in a warning since the derived baseline forecast technique names may be used multiple times.

The following are valid baselines for the oper_baseline field:

OCLIP: Merrill modified (operational) 3-day CLIPER run in operational mode. Used for Atlantic basin only. Specify model as OCLP.

OCLIP5: 5-day CLIPER (Aberson, 1998)/ SHIFOR (DeMaria and Knaff, 2003) in operational mode, rerun using CARQ data. Specify model as OCS5.

OCLIPD5: 5-day CLIPER (Aberson, 1998)/ DECAY-SHIFOR (DeMaria and Knaff, 2003). Specify model as OCD5.

anly_track = BDECK;

Analysis tracks consist of multiple track points with a lead time of zero for the same storm. An analysis track may be generated by running model analysis fields through a tracking algorithm. The anly_track field specifies which datasets should be searched for analysis track data and may be set to NONE, ADECK, BDECK, or BOTH. Use BOTH to create pairs using two different analysis tracks.

match_points = TRUE;

The match_points field specifies whether only those track points common to both the adeck and bdeck tracks should be written out. If match_points is selected as FALSE, the union of the adeck and bdeck tracks will be written out, with “NA” listed for unmatched data.

dland_file = "MET_BASE/tc_data/dland_global_tenth_degree.nc";

The dland_file string specifies the path of the NetCDF format file (default file: dland_global_tenth_degree.nc) to be used for the distance to land check in the tc_pairs code. This file is generated using tc_dland (default file provided in installed share/met/tc_data directory).

watch_warn = {
    file_name   = "MET_BASE/tc_data/wwpts_us.txt";
    time_offset = -14400;
 }

The watch_warn field specifies the file name and time applied offset to the watch_warn flag. The file_name string specifies the path of the watch/warning file to be used to determine when a watch or warning is in effect during the forecast initialization and verification times. The default file is named wwpts_us.txt, which is found in the installed share/met/tc_data/ directory within the MET build. The time_offset string is the time window (in seconds) assigned to the watch/warning. Due to the non-uniform time watches and warnings are issued, a time window is assigned for which watch/warnings are included in the verification for each valid time. The default watch/warn file is static, and therefore may not include warned storms beyond the current MET code release date; therefore users may wish to create a post in the METplus GitHub Discussions Forum in order to obtain the most recent watch/warning file if the static file does not contain storms of interest.

diag_info_map = [
   {
      diag_source    = "CIRA_DIAG_RT";
      track_source   = "GFS";
      field_source   = "GFS_0p50";
      match_to_track = [];
      diag_name      = [];
   },
   {
      diag_source    = "SHIPS_DIAG_RT";
      track_source   = "SHIPS_TRK";
      field_source   = "GFS_0p50";
      match_to_track = [ "OFCL" ];
      diag_name      = [];
   }
];

A TCMPR line is written to the output for each track point. If diagnostics data is also defined for that track point, a TCDIAG line is written immediately after the corresponding TCMPR line. The contents of that TCDIAG line is determined by the diag_info_map entry.

The diag_info_map entries define how the diagnostics read with the -diag command line option should be used. Each array element is a dictionary consisting of entries for diag_source, track_source, field_source, match_to_track, and diag_name.

  • The diag_source entry is one of the supported diagnostics data sources.

  • The track_source entry is a string defining the ATCF ID of the track data used to define the locations at which diagnostics are computed. This string is written to the TRACK_SOURCE column of the TCDIAG output line.

  • The field_source entry is a string describing the gridded model data from which the diagnostics are computed. This string is written to the FIELD_SOURCE column of the TCDIAG output line type.

  • The match_to_track entry specifies a comma-separated list of strings defining the ATCF ID(s) of the tracks to which these diagnostic values should be matched. For the SHIPS_DIAG_RT source, this is required since it is the only way to associate diagnostics with track ATCF IDs. For the CIRA_DIAG_RT source, this is optional. If a non-zero list is provided, the diagnostics will be matched to tracks for the specified ATCF ID(s). If defined as an empty list (default), the ATCF ID will be extracted from each CIRA diagnostic file and used to match the diagnostics to track data.

  • The diag_name entry specifies a comma-separated list of strings for the tropical cyclone diagnostics of interest. If a non-zero list of diagnostic names is specified, only those diagnostics appearing in the list are written to the TCDIAG output line type. If defined as an empty list (default), all diagnostics found in the input are written to the TCDIAG output lines.

diag_convert_map = [
   {
      diag_source = "CIRA_DIAG";
      key         = [ "(10C)", "(10KT)", "(10M/S)" ];
      convert(x)  = x / 10;
   },
   {
      diag_source = "SHIPS_DIAG";
      key         = [ "LAT",  "LON",  "CSST", "RSST", "DSST", "DSTA", "XDST", "XNST", "NSST", "NSTA",
                      "NTMX", "NTFR", "U200", "U20C", "V20C", "E000", "EPOS", "ENEG", "EPSS", "ENSS",
                      "T000", "TLAT", "TLON", "TWAC", "TWXC", "G150", "G200", "G250", "V000", "V850",
                      "V500", "V300", "SHDC", "SHGC", "T150", "T200", "T250", "SHRD", "SHRS", "SHRG",
                      "HE07", "HE05", "PW01", "PW02", "PW03", "PW04", "PW05", "PW06", "PW07", "PW08",
                      "PW09", "PW10", "PW11", "PW12", "PW13", "PW14", "PW15", "PW16", "PW17", "PW18",
                      "PW20", "PW21" ];
      convert(x)  = x / 10;
   },
   {
      diag_source = "SHIPS_DIAG";
      key         = [ "VVAV", "VMFX", "VVAC" ];
      convert(x)  = x / 100;
   },
   {
       diag_source = "SHIPS_DIAG";
       key         = [ "TADV" ];
       convert(x)  = x / 1000000;
   },
   {
      diag_source = "SHIPS_DIAG";
      key         = [ "Z850", "D200", "TGRD", "DIVC" ];
      convert(x)  = x / 10000000;
   },
   {
      diag_source = "SHIPS_DIAG";
      key         = [ "PENC", "PENV" ];
      convert(x)  = x / 10 + 1000;
   }
];

The diag_convert_map entries define conversion functions to be applied to diagnostics data read with the -diag command line option. Each array element is a dictionary consisting of a diag_source, key, and convert(x) entry.

The diag_source entry is one of the supported diagnostics data sources. Partial string matching logic is applied, so SHIPS_DIAG entries are matched to both SHIPS_DIAG_RT and SHIPS_DIAG_DEV diagnostic sources. The key entry is an array of strings. The strings can specify diagnostic names or units, although units are only checked for CIRA_DIAG sources. If both the name and units are specified, the conversion function for the name takes precedence. The convert(x) entry is a function of one variable which defines how the diagnostic data should be converted. The defined function is applied to any diagnostic value whose name or units appears in the key.

basin_map = [
   { key = "SI"; val = "SH"; },
   { key = "SP"; val = "SH"; },
   { key = "AU"; val = "SH"; },
   { key = "AB"; val = "IO"; },
   { key = "BB"; val = "IO"; }
];

The basin_map entry defines a mapping of input names to output values. Whenever the basin string matches “key” in the input ATCF files, it is replaced with “val”. This map can be used to modify basin names to make them consistent across the ATCF input files.

Many global modeling centers use ATCF basin identifiers based on region (e.g., ‘SP’ for South Pacific Ocean, etc.), however the best track data provided by the Joint Typhoon Warning Center (JTWC) use just one basin identifier ‘SH’ for all of the Southern Hemisphere basins. Additionally, some modeling centers may report basin identifiers separately for the Bay of Bengal (BB) and Arabian Sea (AB) whereas JTWC uses ‘IO’.

The basin mapping allows MET to map the basin identifiers to the expected values without having to modify your data. For example, the first entry in the list below indicates that any data entries for ‘SI’ will be matched as if they were ‘SH’. In this manner, all verification results for the Southern Hemisphere basins will be reported together as one basin.

An empty list indicates that no basin mapping should be used. Use this if you are not using JTWC best tracks and you would like to match explicitly by basin or sub-basin. Note that if your model data and best track do not use the same basin identifier conventions, using an empty list for this parameter will result in missed matches.

24.3.3. tc_pairs Output

TC-Pairs produces output in TCST format. The default output file name can be overwritten using the -out file argument in the usage statement. The TCST file output from TC-Pairs may be used as input into the TC-Stat tool. The header column in the TC-Pairs output is described in Table 24.1.

Table 24.1 Header information for TC-Pairs TCST output.

HEADER

Column Number

Header Column Name

Description

1

VERSION

Version number

2

AMODEL

User-provided text string designating the forecast ATCF ID

3

BMODEL

User-provided text string designating the reference ATCF ID

4

DESC

User-provided description text string

5

STORM_ID

BBCCYYYY designation of storm

6

BASIN

Basin (BB in STORM_ID)

7

CYCLONE

Cyclone number (CC in STORM_ID)

8

STORM_NAME

Name of Storm

9

INIT

Initialization time of forecast in YYYYMMDD_HHMMSS format.

10

LEAD

Forecast lead time in HHMMSS format.

11

VALID

Forecast valid time in YYYYMMDD_HHMMSS format.

12

INIT_MASK

Initialization time masking grid applied

13

VALID_MASK

Valid time masking grid applied

14

LINE_TYPE

Output line types described below
Table 24.2 Format information for TCMPR (Tropical Cyclone Matched Pairs) output line type.

TCMPR OUTPUT FORMAT

Column Number

Header Column Name

Description

14

TCMPR

Tropical Cyclone Matched Pair line type

15

TOTAL

Total number of pairs in track

16

INDEX

Index of the current track pair

17

LEVEL

Level of storm classification

18

WATCH_WARN

HU or TS watch or warning in effect

19

INITIALS

Forecaster initials

20

ALAT

Latitude position of adeck model

21

ALON

Longitude position of adeck model

22

BLAT

Latitude position of bdeck model

23

BLON

Longitude position of bdeck model

24

TK_ERR

Track error of adeck relative to bdeck (nm)

25

X_ERR

X component position error (nm)

26

Y_ERR

Y component position error (nm)

27

ALTK_ERR

Along track error (nm)

28

CRTK_ERR

Cross track error (nm)

29

ADLAND

adeck distance to land (nm)

30

BDLAND

bdeck distance to land (nm)

31

AMSLP

adeck mean sea level pressure

32

BMSLP

bdeck mean sea level pressure

33

AMAX_WIND

adeck maximum wind speed

34

BMAX_WIND

bdeck maximum wind speed

35, 36

A/BAL_WIND_34

a/bdeck 34-knot radius winds in full circle or the mean of the non-zero 34-knot wind quadrants

37, 38

A/BNE_WIND_34

a/bdeck 34-knot radius winds in NE quadrant

39, 40

A/BSE_WIND_34

a/bdeck 34-knot radius winds in SE quadrant

41, 42

A/BSW_WIND_34

a/bdeck 34-knot radius winds in SW quadrant

43, 44

A/BNW_WIND_34

a/bdeck 34-knot radius winds in NW quadrant

45, 46

A/BAL_WIND_50

a/bdeck 50-knot radius winds in full circle or the mean of the non-zero 50-knot wind quadrants

47, 48

A/BNE_WIND_50

a/bdeck 50-knot radius winds in NE quadrant

49, 50

A/BSE_WIND_50

a/bdeck 50-knot radius winds in SE quadrant

51, 52

A/BSW_WIND_50

a/bdeck 50-knot radius winds in SW quadrant

53, 54

A/BNW_WIND_50

a/bdeck 50-knot radius winds in NW quadrant

55, 56

A/BAL_WIND_64

a/bdeck 64-knot radius winds in full circle or the mean of the non-zero 64-knot wind quadrants

57, 58

A/BNE_WIND_64

a/bdeck 64-knot radius winds in NE quadrant

59, 60

A/BSE_WIND_64

a/bdeck 64-knot radius winds in SE quadrant

61, 62

A/BSW_WIND_64

a/bdeck 64-knot radius winds in SW quadrant

63, 64

A/BNW_WIND_64

a/bdeck 64-knot radius winds in NW quadrant

65, 66

A/BRADP

pressure in millibars of the last closed isobar, 900 - 1050 mb

67, 68

A/BRRP

radius of the last closed isobar in nm, 0 - 9999 nm

69, 70

A/BMRD

radius of max winds, 0 - 999 nm

71, 72

A/BGUSTS

gusts, 0 through 995 kts

73, 74

A/BEYE

eye diameter, 0 through 999 nm

75, 76

A/BDIR

storm direction in compass coordinates, 0 - 359 degrees

77, 78

A/BSPEED

storm speed, 0 - 999 kts

79, 80

A/BDEPTH

system depth, D-deep, M-medium, S-shallow, X-unknown

81

NUM_MEMBERS

consensus variable: number of models (or ensemble members) that were used to build the consensus track

82

TRACK_SPREAD

consensus variable: the mean of the distances from the member location to the consensus track location (nm)

83

TRACK_STDEV

consensus variable: the standard deviation of the distances from the member locations to the consensus track location (nm)

84

MSLP_STDEV

consensus variable: the standard deviation of the member’s mean sea level pressure values

85

MAX_WIND_STDEV

consensus variable: the standard deviation of the member’s maximum wind speed values
Table 24.3 Format information for TCDIAG (Tropical Cyclone Diagnostics) output line type.

TCDIAG OUTPUT FORMAT

Column Number

Header Column Name

Description

14

TCDIAG

Tropical Cyclone Diagnostics line type

15

TOTAL

Total number of pairs in track

16

INDEX

Index of the current track pair

17

DIAG_SOURCE

Diagnostics data source indicated by the -diag command line option

18

TRACK_SOURCE

ATCF ID of the track data used to define the diagnostics

19

FIELD_SOURCE

Description of gridded field data source used to define the diagnostics

20

N_DIAG

Number of storm diagnostic name and value columns to follow

21

DIAG_i

Name of the of the ith storm diagnostic (repeated)

22

VALUE_i

Value of the ith storm diagnostic (repeated)
Table 24.4 Format information for PROBRIRW (Probability of Rapid Intensification/Weakening) output line type.

PROBRIRW OUTPUT FORMAT

Column Number

Header Column Name

Description

14

PROBRIRW

Probability of Rapid Intensification/Weakening line type

15

ALAT

Latitude position of edeck model

16

ALON

Longitude position of edeck model

17

BLAT

Latitude position of bdeck model

18

BLON

Longitude position of bdeck model

19

INITIALS

Forecaster initials

20

TK_ERR

Track error of adeck relative to bdeck (nm)

21

X_ERR

X component position error (nm)

22

Y_ERR

Y component position error (nm)

23

ADLAND

adeck distance to land (nm)

24

BDLAND

bdeck distance to land (nm)

25

RI_BEG

Start of RI time window in HH format

26

RI_END

End of RI time window in HH format

27

RI_WINDOW

Width of RI time window in HH format

28

AWIND_END

Forecast maximum wind speed at RI end

29

BWIND_BEG

Best track maximum wind speed at RI begin

30

BWIND_END

Best track maximum wind speed at RI end

31

BDELTA

Exact Best track wind speed change in RI window

32

BDELTA_MAX

Maximum Best track wind speed change in RI window

33

BLEVEL_BEG

Best track storm classification at RI begin

34

BLEVEL_END

Best track storm classification at RI end

35

N_THRESH

Number of probability thresholds

36

THRESH_i

The ith probability threshold value (repeated)

37

PROB_i

The ith probability value (repeated)