Evaluation of the RainFARM Statistical Downscaling Technique Applied to IMERG over Global Oceans Using Passive Aquatic Listener In Situ Rain Measurements

Janice L. Bytheway aNOAA/Physical Sciences Lab, Boulder, Colorado

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Elizabeth J. Thompson aNOAA/Physical Sciences Lab, Boulder, Colorado

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Jie Yang bApplied Physics Lab, University of Washington, Seattle, Washington

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Haonan Chen cDepartment of Electrical Engineering, Colorado State University, Fort Collins, Colorado

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Abstract

High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean–atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in situ oceanic rain-rate observations collected by passive aquatic listeners (PALs) in 11 different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristics or combinations thereof lead to the most improvement or consistent improvement when applying RainFARM to IMERG.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Janice L. Bytheway, janice.bytheway@noaa.gov

Abstract

High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean–atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in situ oceanic rain-rate observations collected by passive aquatic listeners (PALs) in 11 different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristics or combinations thereof lead to the most improvement or consistent improvement when applying RainFARM to IMERG.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Janice L. Bytheway, janice.bytheway@noaa.gov

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