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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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Global track dataset of monsoon low pressure systems

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

Global track dataset of monsoon low pressure systems

Abstract

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 Extented Data for ERA5: ERA5 tracks for 1940-2022 are available on https://boos.berkeley.edu in the "Data & Tools" section. Script: 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 (vishnuedv@gmail.com) or William R Boos (billboos@alum.mit.edu).

Keywords

TempestExtremes, Tropical storm, Low Pressure System, Track data, Heat low, Monsoon depression

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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5
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