Detection and tracking of tropical convective storms based on globally gridded precipitation measurements: Algorithm and Survey over the Tropics

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  • 1 Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, California
  • | 2 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
  • | 3 Department of Earth and Atmospheric Sciences, City University of New York, City College, New York
  • | 4 Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan
  • | 5 Program in Atmospheric and Oceanic Sciences, Princeton University
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Abstract

This paper is the first attempt to document a simple convection tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection tracking algorithms have traditionally been developed based on brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in moist environment, which cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more complete lifecycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 hours after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics.

Corresponding author: Hanii Takahashi (Hanii.Takahashi@jpl.nasa.gov)

Abstract

This paper is the first attempt to document a simple convection tracking method based on the IMERG precipitation product to generate an IMERG-based Convection Tracking (IMERG-CT) dataset. Up to now precipitation datasets have been Eulerian accumulations. Now with IMERG-CT, we can estimate total rainfall based on Lagrangian accumulations, which is a very important step in diagnosing cloud-precipitation process following the evolution of air masses. Convection tracking algorithms have traditionally been developed based on brightness temperature (Tb) from satellite infrared (IR) retrievals. However, vigorous rainfall can be produced by warm-topped systems in moist environment, which cannot be captured by traditional IR-based tracking but is observed in IMERG-CT. Therefore, an advantage of IMERG-CT is its ability to include the previously missing information of shallow clouds that grow into convective storms, which provides us more complete lifecycle records of convective storms than traditional IR-based tracking does. This study also demonstrates the utility of IMERG-CT through investigating various properties of convective systems in terms of the evolution before and after peak precipitation rate and amount. For example, composite analysis reveals a link between evolution of precipitation and convective development: the signature of stratiform anvils remaining after the storm has produced the maximum rainfall, as average Tb stays almost constant for 5 hours after the peak of precipitation. Our study highlights the importance of joint analysis of cloud and precipitation data in time sequence, which helps elucidate the underlying dynamic processes producing tropical rainfall and its resultant effects on the atmospheric thermodynamics.

Corresponding author: Hanii Takahashi (Hanii.Takahashi@jpl.nasa.gov)
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