Classifying precipitation from GEO Satellite Observations: Diagnostic Model

Details

• Journal Title:
  Quarterly Journal of the Royal Meteorological Society
• Personal Author:
  Upadhyaya, Shruti A. ; Kirstetter, Pierre‐Emmanuel ; Kuligowski, Robert J. ; Searls, Maresa
• NOAA Program & Office:
  NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research) OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies)
• Description:
  Improvements in remote-sensing capability and improvements in artificial intelligence have created significant opportunities to advance understanding of precipitation processes. While highly advanced Machine Learning (ML) techniques improve the accuracy of precipitation retrievals, how these observations contribute to our understanding of precipitation processes remains an underexplored research question. In a companion manuscript, a precipitation-type prognostic ML model is developed by deriving predictors from the Advanced Baseline Imager (ABI) sensor on board Geostationary Observing Environmental Satellite (GOES)-16. In this study, these predictors are linked to different precipitation processes. It is observed that satellite observations are important in separating Rain and No-Rain areas. For stratiform precipitation types, predictors related to atmospheric moisture content, such as relative humidity and precipitable water, are the most important predictors, while for convective types, predictors such as 850–500 hPa lapse rate and convective available potential energy (CAPE) are more important. The diagnostic analysis confirms the benefit of spatial textures derived from ABI observations to improve the classification accuracy. It is recommended to combine the heritage water vapour channel T6.2 with the infrared T11.2 channel for improved precipitation classification. Overall, this study provides guidance to atmospheric and remote-sensing scientists on a large array of predictors that can be used from geostationary satellites and multispectral sensors for precipitation studies.
• Keywords:
  Classification Geostationary Satellites Machine Learning Numerical Weather Forecasting Geostationary Satellites Machine Learning Numerical Weather Forecasting GOES-16 Interpretable Machine Learning Numerical Weather Prediction Precipitation

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• Source:
  Q J R Meteorol Soc, 147 (739), 3318– 3334
• DOI:
  https://doi.org/10.1002/qj.4130
• Document Type:
  Journal Article
• Funding:
  Grant no. NA16OAR4320115
• Rights Information:
  Accepted Manuscript
• Rights Statement:
  The NOAA IR provides access to this content under the authority of the government's retained license to distribute publications and data resulting from federal funding. While users may legally access this content, the copyright owners retain rights that govern the reproduction, redistribution, and re-use of this work. The user is solely responsible for complying with applicable copyright law.
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha-512:203428dd5f216cf3542a07a6964bb8ed926b657b00214eb4c17264245b3caadf2326ad4601dc763cd2894f091ba484c3333a6f49e67431b09be2215a7e7e1b62
• Download URL:
  https://repository.library.noaa.gov/view/noaa/45096/noaa_45096_DS1.pdf
• File Type:
  [PDF - 1.38 MB ]