Predictability of U.S. Regional Extreme Precipitation Occurrence Based on Large-Scale Meteorological Patterns (LSMPs)
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clim-JCLI-D-21-0137.1.pdf
Description
Published version
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3.22 MB
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Adobe PDF
Checksum (MD5)
0584de8f9aea5938acd14cde8029477b
Author(s)
Gao, Xiang
Mathur, Shray
Date Issued
July 29, 2021
Journal
Journal of Climate
Publisher
American Meteorological Society
Citation
Gao, X., and S. Mathur, 2021: Predictability of U.S. Regional Extreme Precipitation Occurrence Based on Large-Scale Meteorological Patterns (LSMPs). J. Climate, 34, 7181–7198.
Version
Final published version
Abstract
In this study, we use analogue method and convolutional neural networks (CNNs) to assess the potential predictability of extreme precipitation occurrence based on large-scale meteorological patterns (LSMPs) for the winter (DJF) of “Pacific Coast California” region (PCCA) and the summer (JJA) of the midwestern United States (MWST). We evaluate the LSMPs constructed with a large set of variables at multiple atmospheric levels and quantify the prediction skill with a variety of complementary performance measures. Our results suggest that LSMPs provide useful predictability of daily extreme precipitation occurrence and its interannual variability over both regions. The 14-yr (2006–19) independent forecast shows Gilbert skill scores (GSS) in PCCA ranging from 0.06 to 0.32 across 24 CNN schemes and from 0.16 to 0.26 across four analogue schemes, in contrast from 0.1 to 0.24 and from 0.1 to 0.14 in MWST. Overall, CNN is shown to be more powerful in extracting the relevant features associated with extreme precipitation from the LSMPs than analogue method, with several single-variate CNN schemes achieving more skillful prediction than the best multivariate analogue scheme in PCCA and more than half of CNN schemes in MWST. Nevertheless, both methods highlight that the integrated vapor transport (IVT, or its zonal and meridional components) enables higher skills than other atmospheric variables over both regions. Warm-season extreme precipitation in MWST presents a forecast challenge with overall lower prediction skill than in PCCA, attributed to the weak synoptic-scale forcing in summer.
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DOI of Published Version
https://doi.org/10.1175/JCLI-D-21-0137.1
