Classifying precipitation from GEO satellite observations: Prognostic model
Corresponding Author
Shruti A. Upadhyaya
Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma, USA
Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma, USA
Correspondence
P.-E. Kirstetter, School of Meteorology, University of Oklahoma, 120 David L Boren Blvd, Room 4616, Norman, OK, USA.
Email: [email protected]
S.A. Upadhyaya, Cooperative Institute for Mesoscale Meteorological Studies, 120 David L Boren Blvd, Room 4603, Norman, OK, USA.
Email: [email protected]
Contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft
Search for more papers by this authorCorresponding Author
Pierre-Emmanuel Kirstetter
Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma, USA
School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA
School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma, USA
NOAA/National Severe Storms Laboratory, Norman, Oklahoma, USA
Correspondence
P.-E. Kirstetter, School of Meteorology, University of Oklahoma, 120 David L Boren Blvd, Room 4616, Norman, OK, USA.
Email: [email protected]
S.A. Upadhyaya, Cooperative Institute for Mesoscale Meteorological Studies, 120 David L Boren Blvd, Room 4603, Norman, OK, USA.
Email: [email protected]
Search for more papers by this authorRobert J. Kuligowski
NOAA/NESDIS/Center for Satellite Applications and Research, College Park, Maryland, USA
Contribution: Data curation, Resources, Writing - review & editing
Search for more papers by this authorJonathan J. Gourley
NOAA/National Severe Storms Laboratory, Norman, Oklahoma, USA
Contribution: Funding acquisition, Writing - review & editing
Search for more papers by this authorHeather M. Grams
NOAA/National Severe Storms Laboratory, Norman, Oklahoma, USA
Contribution: Investigation, Methodology, Writing - review & editing
Search for more papers by this authorCorresponding Author
Shruti A. Upadhyaya
Cooperative Institute for Mesoscale Meteorological Studies, Norman, Oklahoma, USA
Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma, USA
Correspondence
P.-E. Kirstetter, School of Meteorology, University of Oklahoma, 120 David L Boren Blvd, Room 4616, Norman, OK, USA.
Email: [email protected]
S.A. Upadhyaya, Cooperative Institute for Mesoscale Meteorological Studies, 120 David L Boren Blvd, Room 4603, Norman, OK, USA.
Email: [email protected]
Contribution: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft
Search for more papers by this authorCorresponding Author
Pierre-Emmanuel Kirstetter
Advanced Radar Research Center, University of Oklahoma, Norman, Oklahoma, USA
School of Meteorology, University of Oklahoma, Norman, Oklahoma, USA
School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma, USA
NOAA/National Severe Storms Laboratory, Norman, Oklahoma, USA
Correspondence
P.-E. Kirstetter, School of Meteorology, University of Oklahoma, 120 David L Boren Blvd, Room 4616, Norman, OK, USA.
Email: [email protected]
S.A. Upadhyaya, Cooperative Institute for Mesoscale Meteorological Studies, 120 David L Boren Blvd, Room 4603, Norman, OK, USA.
Email: [email protected]
Search for more papers by this authorRobert J. Kuligowski
NOAA/NESDIS/Center for Satellite Applications and Research, College Park, Maryland, USA
Contribution: Data curation, Resources, Writing - review & editing
Search for more papers by this authorJonathan J. Gourley
NOAA/National Severe Storms Laboratory, Norman, Oklahoma, USA
Contribution: Funding acquisition, Writing - review & editing
Search for more papers by this authorHeather M. Grams
NOAA/National Severe Storms Laboratory, Norman, Oklahoma, USA
Contribution: Investigation, Methodology, Writing - review & editing
Search for more papers by this authorFunding information: NOAA GOES-R Series Risk Reduction program, NA16OAR4320115; NASA Global Precipitation Measurement Ground Validation program, NNX16AL23G; NASA Precipitation Measurement Missions program, 80NSSC19K0681
Abstract
Precipitation is one of the most important components of the global water and energy cycles, which together regulate the climate system. Future changes in precipitation patterns related to climate change are likely to have the greatest impacts on society. The new generation of geostationary Earth orbit (GEO) satellites provide high-resolution observations and opportunities to improve our understanding of precipitation processes. This study contributes to improved precipitation characterization and retrievals from space by identifying precipitation types (e.g., convective and stratiform) with multispectral observations from the Advanced Baseline Imager (ABI) sensor onboard the GOES-16 satellite. A machine-learning-based classification model is developed by deriving a comprehensive set of features using five ABI channels and numerical weather prediction observations, and trained with the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system as a benchmark. The developed prognostic model shows skillful performance in identifying the occurrence/nonoccurrence of precipitation (accuracy of 97%; Kappa coefficient of 0.9) and precipitation processes, with overall classification accuracy of 76% and Kappa coefficient of 0.56. Challenges exist in separating convective and tropical from other precipitation types. It is suggested to utilize probabilities instead of deterministically separating precipitation types, especially in regions with uncertain classifications.
Supporting Information
| Filename | Description |
|---|---|
| qj4134-sup-0001-Figures.pdfPDF document, 282 KB |
Figure S1 Estimated probabilities for each precipitation type on July 26, 2018 at 0900 UTC. Figure S2. Estimated probabilities for each precipitation type on August 12, 2018 at 2300 UTC. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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