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How Accurately Can Warm Rain Realistically Be Retrieved with Satellite Sensors? Part 2: Horizontal and Vertical Heterogeneities

Richard M. Schulte1Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Christian D. Kummerow1Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Stephen M. Saleeby1Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Gerald G. Mace2Department of Atmospheric Science, University of Utah, Salt Lake City, Utah

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Abstract

There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud resolving model simulation of warm, shallow clouds is used to simulate satellite observations from 3 theoretical satellite architectures – one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beam filling are quite large, with retrieved rain rates biased low by as much as 40-50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of −19% compared to −28%, a reduced spread of retrieval errors, and an additional 17.5 % of cases falling within desired uncertainty limits. The results speak to the need for additional high resolution modeling simulations of warm rain in order to better characterize the uncertainties in satellite precipitation retrievals.

Corresponding author: Richard Schulte, rick.schulte@colostate.edu

Abstract

There are many sources of uncertainty in satellite precipitation retrievals of warm rain. In this paper, the second of a two-part study, we focus on uncertainties related to spatial heterogeneity and surface clutter. A cloud resolving model simulation of warm, shallow clouds is used to simulate satellite observations from 3 theoretical satellite architectures – one similar to the Global Precipitation Measurement Core Observatory, one similar to CloudSat, and one similar to the planned Atmosphere Observing System (AOS). Rain rates are then retrieved using a common optimal estimation framework. For this case, retrieval biases due to nonuniform beam filling are quite large, with retrieved rain rates biased low by as much as 40-50% (depending on satellite architecture) at 5 km horizontal resolution. Surface clutter also acts to negatively bias retrieved rain rates. Combining all sources of uncertainty, the theoretical AOS satellite is found to outperform CloudSat in terms of retrieved surface rain rate, with a bias of −19% compared to −28%, a reduced spread of retrieval errors, and an additional 17.5 % of cases falling within desired uncertainty limits. The results speak to the need for additional high resolution modeling simulations of warm rain in order to better characterize the uncertainties in satellite precipitation retrievals.

Corresponding author: Richard Schulte, rick.schulte@colostate.edu
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