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Identifying and Constraining Ice Effects on Satellite Precipitation Biases Using Reanalysis Data
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DOI：10.1175/JHM-D-25-0070.1.png)
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Abstract
En 中文
Passive microwave satellite precipitation products exhibit highly variable errors which require quantification. These errors can be attributed in part to the ice contents of precipitating systems, which greatly affects cloud radiative properties in the microwave spectrum. Accessing information on atmospheric ice contents, however, is difficult due to the relative lack of measurement techniques and the limited spatiotemporal coverage of existing methods. Since precipitation systems are a product of the large-scale environment, this information should also provide information on the ice formation processes within these systems. This study seeks to establish a link between radar-derived ice content and large-scale meteorological conditions to characterize the effects of atmospheric ice on satellite precipitation errors. Seven years of spaceborne radar and precipitation measurements from the Global Precipitation Measurement (GPM) taken over three tropical land regions were obtained to investigate this hypothesis. Five ice content regimes, as defined by ice-rain ratio (IRR), were identified, with each regime describing environments controlled by system depth, convective capacity, and the source air mass. These arguments were coupled with convective available potential energy (CAPE), total column water vapor (TCWV), and column average temperature (Tavg) information from the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5). Using these variables, it was found that the IRR regimes can be reasonably described and their error characteristics reproduced, showing that knowledge of the environment can provide similar explanatory power for satellite precipitation errors as the ice content information itself. SIGNIFICANCE STATEMENT: Satellite rainfall errors are affected by the amount of ice present in a precipitating system. Identifying the amount of ice in a system, however, is difficult due to limited and inconsistent measurements from radar. This study aims to identify more widely available information which can be used to approximate the effects of ice contents on these errors. It was found that cloud ice content varies with system strength, depth, and moisture content, which can also be identified by reanalysis model data. These data have similar effects on precipitation errors, meaning that they can be used as substitutes for cloud ice content to assess these errors.
Keywords:
Atmosphere
Precipitation
Microwave observations
Satellite observations
Error analysis
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