Next, we'll run Shift-Data-Plane on the command line using the following command:

This command shifts the surface pressure field from Denver, CO to Chicago, IL and is meant to simply illustrate the functionality of this tool. On the next page we'll plot the result.

Run the following commands to plot the input and output of Shift-Data-Plane:

Display the output images by running the following commands:

Notice the rigid shift that has been applied. Why would anyone want to shift data in this way? This tool has been applied when doing feature-relative verification. For example when evaluating hurricane forecasts, select the forecast storm track location and the corresponding location of the BEST track. Prior to computing grid-to-grid statistics, first shift the data from the forecast track location to the BEST track location. Then compute statistics near the storm. This is a simple way of removing the effect of track error when evaluating hurricane forecasts.

Since MODIS-Regrid simply performs a reformatting step, no configuration file is needed.

Run MODIS-Regrid on the command line using the following command:

MODIS-Regrid is now processing the input field from modis_file into NetCDF format and writing it to the output m_sfc_temp.nc file on the grid specified with -data_file option using the appropriate scale, offset, and fill values.

Open up and examine the following output files we just generated:

• Conventional MPR data:

• Radiance MPR data:

• Conventional ORANK data:

• Radiance ORANK data:

Look at the header lines for the GSI2MPR output files and notice that the conventional and radiance headers are the same through the OBS_QC column. However, the remaining columns differ and are specific to the data type. The GSIDENS2ORANK output files include the standard ORANK header columns followed by extra columns specific to the data type. Next, we'll run STAT-Analysis aggregate_stat jobs to derive statistics from this data.

GSID2MPR Statistics

Run the following STAT-Analysis jobs to compute statistics using the GSID2MPR output:

• Read MPR lines and compute continuous statistics for each variable present. Write the output to the specified .stat file:

• Open up $MET_TUTORIAL_DATA/output/gsid2mpr/conv.mem001_CNT.stat and notice that multiple observation type values are written as a comma-separated list in the OBTYPE. Rerun this command by use the -set_hdr option to define the output for that column:

• The OBTYPE column in the output should now be set to ALL. With the right set of options, STAT-Analysis may be used to filter this data in any way you would like and derive many different types of statistics. For example, process only temperature data (-fcst_var t), using only the pairs that were actually assimilated (-column_thresh ANLY_USE eq1), and threshold them to define a contingency table (-out_line_type CTC -out_thresh ge273):

GSIDENS2ORANK Statistics

As we saw above, STAT-Analysis can read MPR lines and derive a variety of output line types, such as SL1L2, CNT, CTC, CTS, MCTC, MCTS, and so on). Similarly, STAT-Analysis can read the ORANK lines generated by GSIDENS2ORANK and derive ensemble output line types, such as RHIST, PHIST, and SSVAR. Run the following STAT-Analysis jobs to compute statistics using the GSIDENS2ORNK output:

• Read radiance ORANK lines and compute ranked histograms for each variable present and write the output to the screen:

• Now process conventional ORANK lines and derive PHIST output with a 0.10 bin size for each unique combination of FCST_VAR and N_USE column:

Next, we'll run GSID2MPR on the command line using the following command:

Here, we've reformatted five conventional GSI diagnostic files. The -swap option indicates that the Endianness of the data must be switched. For each input file, a .stat output file is written to the directory specified by the -outdir option. Notice in the log messages that it checks for and skips duplicate entries in the data. The -no_check_dup option disables the checking for duplicate data. Next, run a similar command but for satellite radiance data:

Next, run very similar commands using the GSIDENS2ORANK tool to process the conventional and satellite radiance data as a 5-member ensemble, rather than as indivual files. This generates ORANK output lines instead of MPR lines:

Open up and examine the following output files we just generated:

• Conventional MPR data:

• Radiance MPR data:

• Conventional ORANK data:

• Radiance ORANK data:

Look at the header lines for the GSI2MPR output files and notice that the conventional and radiance headers are the same through the OBS_QC column. However, the remaining columns differ and are specific to the data type. The GSIDENS2ORANK output files include the standard ORANK header columns followed by extra columns specific to the data type. Next, we'll run STAT-Analysis aggregate_stat jobs to derive statistics from this data.

GSID2MPR Statistics

Run the following STAT-Analysis jobs to compute statistics using the GSID2MPR output:

• Read MPR lines and compute continuous statistics for each variable present. Write the output to the specified .stat file:

• Open up $MET_TUTORIAL_DATA/output/gsid2mpr/conv.mem001_CNT.stat and notice that multiple observation type values are written as a comma-separated list in the OBTYPE. Rerun this command by use the -set_hdr option to define the output for that column:

• The OBTYPE column in the output should now be set to ALL. With the right set of options, STAT-Analysis may be used to filter this data in any way you would like and derive many different types of statistics. For example, process only temperature data (-fcst_var t), using only the pairs that were actually assimilated (-column_thresh ANLY_USE eq1), and threshold them to define a contingency table (-out_line_type CTC -out_thresh ge273):

GSIDENS2ORANK Statistics

As we saw above, STAT-Analysis can read MPR lines and derive a variety of output line types, such as SL1L2, CNT, CTC, CTS, MCTC, MCTS, and so on). Similarly, STAT-Analysis can read the ORANK lines generated by GSIDENS2ORANK and derive ensemble output line types, such as RHIST, PHIST, and SSVAR. Run the following STAT-Analysis jobs to compute statistics using the GSIDENS2ORNK output:

• Read radiance ORANK lines and compute ranked histograms for each variable present and write the output to the screen:

• Now process conventional ORANK lines and derive PHIST output with a 0.10 bin size for each unique combination of FCST_VAR and N_USE column: