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Statistical and Machine Learning Methods Applied to the Prediction of Different Tropical Rainfall Types
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  • Jiayi Wang,
  • Raymond K. W. Wong,
  • Mikyoung Jun,
  • Courtney Schumacher,
  • R Saravanan,
  • Chunmei Sun
Jiayi Wang
Texas A&M University, Texas A&M University, Texas A&M University

Corresponding Author:[email protected]

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Raymond K. W. Wong
Texas A&M University, Texas A&M University, Texas A&M University
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Mikyoung Jun
University of Houston, University of Houston, University of Houston
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Courtney Schumacher
Texas A&M University, Texas A&M University, Texas A&M University
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R Saravanan
Department of Atmospheric Sciences, Texas A & M University, Department of Atmospheric Sciences, Texas A & M University, Department of Atmospheric Sciences, Texas A & M University
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Chunmei Sun
University of Houston, University of Houston, University of Houston
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Abstract

We explore the use of three advanced statistical and machine learning methods (a generalized linear model, random forest, and neural network) to predict the occurrence and rain rate distribution of three tropical rain types (deep convective, stratiform, and shallow convective) observed by the radar onboard the GPM satellite over the West Pacific at three-hourly, 0.5-degree resolution. Temperature and moisture profiles from MERRA-2 were used as predictors. All three methods perform reasonably well at predicting the occurrence and rain rate distribution of each rain type. However, none of the methods obviously distinguish themselves from one another and each method still has issues with predicting rain too often and not fully capturing the high end of the rain rate distributions, both of which are common problems in climate models.
01 Nov 2021Published in Environmental Research Communications volume 3 issue 11 on pages 111001. 10.1088/2515-7620/ac371f