Global track dataset of monsoon low pressure systems
- 1. Department of Earth and Planetary Science, University of California, Berkeley, Berkeley, California, USA
- 2. Department of Earth and Planetary Science, University of California, Berkeley, Berkeley, California, USA & Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
- 3. Department of Land, Air, and Water Resources, University of California, Davis, Davis, California, USA
- 4. Earth and Atmospheric Sciences Department, Indiana University, Bloomington, Indiana, USA &. Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA
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)
- 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
- Run_tempest_lps.sh: TempestExtremes script to track LPS in reanalysis dataset.
Additionally, four python scripts are available to subset the dataset:
- Python_Moist_LPS_heat_low.py: Python script to create two separate files for moist LPS and heat lows
- Python_Low_Depression.py: Python script to create two separate files for monsoon lows and monsoon depressions
- Python_Region.py: Python script to create a separate file for a region
- 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 (firstname.lastname@example.org) or William R Boos (email@example.com).
- 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.
- 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.