Contrasting the Environmental Conditions of Tropical MCSs Tracked in Observations and Reanalysis Data

Mesoscale convective systems (MCS) are large, organized thunderstorms that are key to the global hydrologic cycle, but are governed by complex multiscale interactions, highlighting the need for research on how near-storm environments influence MCS development. Previous studies typically track MCSs in observations and spatiotemporally co-locate them with environmental conditions from reanalyses like ERA5. Using 14-years of global MCS tracks identified with PyFLEXTRKR from ERA5 (MCS ERA5 ) and satellite observations (MCS OBS ), we collocate ERA5 environmental variables with both MCS datasets in the tropics. Only a small fraction of MCS OBS and MCS ERA5 tracks spatiotemporally overlap. Results reveal substantial differences in the timing and magnitude of ERA5 near-storm environmental evolution around convection initiation between MCS OBS and MCS ERA5 .The magnitude and peak timing in MCS OBS environments are more like those simulated by global storm-resolving models and observed by satellite products. In contrast, MCS ERA5 shows evidence of data-assimilation-related biases that persist in the co-located near-storm environments.

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Published
2026-11-01
DOI
https://doi.org/10.22541/essoar.15011363/v1
Primary Topic
Meteorological Phenomena and Simulations
Type
preprint
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Contrasting the Environmental Conditions of Tropical MCSs Tracked in Observations and Reanalysis Data

Zhe Feng, Laura Paccini, Stella Heflin, Lai-Yung Ruby Leung et al.
2 links
Meteorological Phenomena and Simulations
preprint

Contrasting the Environmental Conditions of Tropical MCSs Tracked in Observations and Reanalysis Data

Zhe Feng, Laura Paccini, Stella Heflin, Lai-Yung Ruby Leung, Qiang Fu
preprint en

Abstract

Mesoscale convective systems (MCS) are large, organized thunderstorms that are key to the global hydrologic cycle, but are governed by complex multiscale interactions, highlighting the need for research on how near-storm environments influence MCS development. Previous studies typically track MCSs in observations and spatiotemporally co-locate them with environmental conditions from reanalyses like ERA5. Using 14-years of global MCS tracks identified with PyFLEXTRKR from ERA5 (MCS ERA5 ) and satellite observations (MCS OBS ), we collocate ERA5 environmental variables with both MCS datasets in the tropics. Only a small fraction of MCS OBS and MCS ERA5 tracks spatiotemporally overlap. Results reveal substantial differences in the timing and magnitude of ERA5 near-storm environmental evolution around convection initiation between MCS OBS and MCS ERA5 .The magnitude and peak timing in MCS OBS environments are more like those simulated by global storm-resolving models and observed by satellite products. In contrast, MCS ERA5 shows evidence of data-assimilation-related biases that persist in the co-located near-storm environments.

Pacific Northwest National Laboratory (US), University of Washington (US), Pontificia Universidad Católica del Perú (PE)
Climate action
Meteorological Phenomena and Simulations
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