README

This is a description of the pre-digested observational data for the TC-MSE POD. Our POD reads a set of files (TC_MSE.20230223.tar) that should be placed in the obs_data directory.

DESCRIPTION OF FILES IN TC_MSE.20230223.tar
-CFSR_Binned_Composites_with_normFeedbacks.nc
-CFSR_Binned_STDEVS_of_BoxAvgs.nc
-ERA5_Binned_Composites_with_normFeedbacks.nc
-ERA5_Binned_STDEVS_of_BoxAvgs.nc
-ERAINT_Binned_Composites_with_normFeedbacks.nc
-ERAINT_Binned_STDEVS_of_BoxAvgs.nc
-JRA55_Binned_Composites_with_normFeedbacks.nc
-JRA55_Binned_STDEVS_of_BoxAvgs.nc
-MERRA2_Binned_Composites_with_normFeedbacks.nc
-MERRA2_Binned_STDEVS_of_BoxAvgs.nc
-sftlf_fx_GFDL-CM4_amip_r1i1p1f1_gr1.nc: contains the landmask for the CMIP6 GFDL CM4 AMIP simulation that the POD needs to run
-trackdata.txt: tropical cyclone track data from the CMIP6 GFDL CM4 AMIP simulation (providing to us by John Krasting at GFDL) that the POD needs to run

The other netcdf files contain the reference version of the diagnostic in 5 reanalysis dataset that is then plotted in the POD. These files were generated by the sequence of scripts contained in TC_MSE_predigested_code.tar

DESCRIPTION OF FILES IN TC_MSE_predigested_code.tar
-calc_mse_$reanalysis.py: Requires sys, numpy, matplotlib, cartopy, matplotlib, scipy, xarray, math, datetime, Nio, cfgrib. Calcluates the column-integrated MSE from the raw reanalysis temperature, specific humidity, and geopotential height data on pressure leveles, saves to files as a function of latitude, longitude, and time.
-tempest_%reanalysis_AAW.py: Requires numpy, pandas, xarray, sys, netCDF4. Reads the reanalysis TC track files (from Zarzycki et al. 2021), the column-integrated MSE calcluated from reanalysis in calc_mse_$reanalysis.py, and the raw reanalysis radiative and surface flux variables. Extracts the relevant variables along the tracks of TCs from the reanalysis data and computes the MSE variance budget in 10 x 10 degree boxes around the tracked TCs. Saves this data in a netcdf file for each year for each reanalysis dataset.
-new_lsmvars_%reanalysis.py: Requires numpy, xarray, pandas. Reads in the files created by tempest_%reanalysis_AAW.py, removes grid points over land, removes snapshots after the time of a TC's lifetime maximum intensity, shifts the track data to align about the time of each TC's lifetime maximum intensity, and concatenates all the years together. Saves this data in a netcdf file for each reanalysis (AAW.%reanalysis.lmconcats.nc)
-Reanalysis_Binning_Averaging.py: Requires re, numpy, xarray, pandas, matplotlib. Reads in the AAW.%reanalysis.lmconcats.nc files and bins the MSE variance budget around the tracked TCs based o nintensity and computes the necessary composite statistics. Creates the reanalysis composite MSE variance budget files that are in TC_MSE.20230223.tar  and used in the plotting within the POD.

The 5 %reanalysis datasets are:
CFSR (cfsr)
ERA-Interim (eraint)
ERA-5 (era5)
JRA-55 (jra55 - native is what is used in the POD)
MERRA-2 (merra2)

DATA SOURCES

Reanalysis data from CFSR (doi:10.5065/D6513W89, doi:10.5065/D69K487J, Saha et al. 2010a,b), CFSv2 (doi:10.5065/D61C1TXF, Saha et al. 2011), JRA-55 (doi:10.5065/D6HH6H41, Japan Meteorological Agency 2013), ERA-Interim (doi:10.5065/D6CR5RD9, doi:10.5065/D64747WN, ECMWF 2009, 2012), and ERA-5 (doi:10.5065/BH6N-5N20, ECMWF 2019) were provided by the Research Data Archive (RDA) of the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NCAR) available at https://rda.ucar.edu. NCAR is supported by grants from the National Science Foundation. Reanalysis data from MERRA-2 (doi:10.5067/7MCPBJ41Y0K6, doi:10.5067/A7S6XP56VZWS, GMAO 2015a,b) were provided by the Goddard Earth Sciences Data and Information Services Center (GES DISC) available at https://disc.gsfc.nasa.gov. Reanalysis data from ERA-5 (Copernicus Climate Change Service 2017) were were provided by the Copernicus Climate Change Service Climate Data Store (CDS) available at https://cds.climate.copernicus.eu. Calculations with ERA-5 and JRA-55 3D data were made with high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. Tropical cyclone tracks in the reanalysis data are from Zarzycki et al. (2021), derived from the Cyclone Metrics Package (CyMeP; https://github.com/zarzycki/cymep) which utilizes the TempestExtremes detection and tracking algorithm (doi:10.5281/zenodo.4546658).  

Copernicus Climate Change Service, 2017: ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form.

ECMWF, 2009: ERA-Interim Project. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, doi:10.5065/D6CR5RD.

ECWMF, 2012: ERA-Interim Project, Single Parameter 6-Hourly Surface Analysis and Surface Forecast Analysis and Surface Forecast Time Series. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, doi:10.5065/D64747WN.

ECMWF, 2019: ERA5 Reanalysis (0.25 Degree Latitude-Longitude Grid). Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, doi:10.5065/BH6N-5N20.

GMAO, 2015a: MERRA-2 inst6 3d ana Np: 3d,6-Hourly, Instantaneous, Pressure-Level, Analysis, Analyzed Meteorological Fields V5.12.4. Goddard Earth Sciences Data and Information Services Center (GES DISC), doi:10.5067/A7S6XP56VZWS.

GMAO, 2015b: MERRA-2 tavg1 2d flx Nx: 2d,1-Hourly, Time-Averaged, Single-Level, Assimilation,Surface Flux Diagnostics V5.12.4. Goddard Earth Sciences Data and Information Services Center (GES DISC), doi:10.5067/7MCPBJ41Y0K6.

Japan Meteorological Agency, 2013: JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, doi:10.5065/D6HH6H41.

Saha, S., and Coauthors, 2010a: NCEP Climate Forecast System Reanalysis (CFSR) 6-hourly Products, January 1979 to December 2010. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, doi:10.5065/D69K487J.

Saha, S., and Coauthors, 2010b: NCEP Climate Forecast System Reanalysis (CFSR) Selected Hourly Time-Series Products, January 1979 to December 2010. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, doi:10.5065/D6513W89.

Saha, S., and Coauthors, 2011: NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products. Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, doi:10.5065/D61C1TXF.

Zarzycki, C.M., P.A. Ullrich, and K.A. Reed, 2021: Metrics for evaluating tropical cyclones in climate data. J. Appl. Meteor. and Clim., 60, 643-660, doi:10.1175/JAMC-D-20-0149.1.
