Dataset Open Access

Global track dataset of monsoon low pressure systems

S, Vishnu; W. R. Boos; P. A. Ullrich; T. A. O'Brien

This dataset contains the tracks and intensities of low pressure system (LPS) in the global tropics (35ºS-35ºN), as identified in five atmospheric reanalyses (ERA5, ERA-Interim, JRA55, MERRA2, and CFSR) using the algorithm described in the paper titled Assessing historical variability of South Asian monsoon lows and depressions with an optimized tracking algorithm.  Tracking of LPS was performed using an automated Lagrangian pointwise feature tracker, TempestExtremes (Ullrich & Zarzycki, 2017), with criteria chosen to best match a subjectively analyzed LPS dataset while minimizing disagreement  between four atmospheric reanalyses. A full description of the algorithm and dataset is described in the preprint *(https://doi.org/10.1029/2020JD032977)

 

Files:

  • Header.txt: contains the names of columns of the LPS dataset files
  • LPS_Global_ERA5.dat: LPS track file for ERA5, 1979-2019, hourly resolution
  • LPS_Global_ERA-Interim.dat: LPS track file for ERA-Interim, 1979-2018, six-hourly resolution
  • LPS_Global_JRA55.dat: LPS track file for JRA55, 1958-2019, six-hourly resolution
  • LPS_Global_CFSR.dat: LPS track file for CFSR, 1979-2010, six-hourly resolution
  • LPS_Global_MERRA2.dat: LPS track file for MERRA2, 1980-2019, three-hourly resolution

Script:

  • Run_tempest_lps.sh: TempestExtremes script to track LPS in reanalysis dataset. 

Additionally, four python scripts are available to subset the dataset:

  1. Python_Moist_LPS_heat_low.py: Python script to create two separate files for  moist LPS and heat lows
  2. Python_Low_Depression.py: Python script to create two separate files for monsoon lows and monsoon depressions
  3. Python_Region.py: Python script to create a separate file for a region
  4. Python_Season.py: Python script to create a separate file for a season

Note: The TempestExtremes software can be obtained from GitHub at https://github.com/ClimateGlobalChange/tempestextremes.

For further details, contact S. Vishnu (vishnuedv@gmail.com) or William R Boos  (billboos@alum.mit.edu).

Files (613.8 MB)
Name Size
Header.dat
md5:77f021b6305682c27a54e7edcb6eb15c
1.5 kB Download
LPS_Global_CFSR.dat
md5:586dcb11c5c16ca1ff549ab3ae92f8b5
44.4 MB Download
LPS_Global_ERA-Interim.dat
md5:2b8c3354a2053b5ed89a118959dceef8
46.5 MB Download
LPS_Global_ERA5.dat
md5:cb3dc01b0b26b8d4b452a7ec572a2b06
327.7 MB Download
LPS_Global_JRA55.dat
md5:6e44baa94f22c571c06979e12baabbbd
83.9 MB Download
LPS_Global_MERRA2.dat
md5:9ad97692cb8935be0739b2f490ea26e2
111.3 MB Download
Python_Low_Depression.py
md5:d186393e464b2ee61c29c926a7ea1259
1.6 kB Download
Python_Moist_LPS_heat_low.py
md5:ec1509a6a3ebec2ab98b4dde7b74a366
1.2 kB Download
Python_Region.py
md5:12421da6cb24bc64c863d7b772114599
962 Bytes Download
Python_Season.py
md5:961bfc437c4fbeb371df4ed0f61cd879
902 Bytes Download
Run_tempest_lps.sh
md5:372c675a1e10c0fdfee1054bc7190f6c
4.5 kB Download
  • Ullrich, P. A., & Zarzycki, C. M. (2017). TempestExtremes: A framework for scale- insensitive pointwise feature tracking on unstructured grids. Geoscientific Model De- velopment, 10(3), 1069.

  • Vishnu, S., Boos, W.R., Ullrich, P.A.& O'Brien, T.A., 2020. Assessing Historical Variability of South Asian Monsoon Lows and Depressions With an Optimized Tracking Algorithm. Journal of Geophysical Research: Atmospheres, 125(15), p.e2020JD032977.

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