Abstract
Precipitation rate from various products of the integrated multisatellite retrievals for GPM (IMERG) and passive microwave (PMW) sensors are assessed with respect to near-surface wet-bulb temperature (Tw), precipitation intensity, and surface type (i.e., with and without snow and ice on the surface) over the contiguous United States (CONUS) and using ground radar product as reference precipitation. IMERG products include precipitation estimates from infrared (IR), combined PMW, and combination of PMW and IR. It was found that precipitation estimates from PMW products generally have higher skills than IR over snow- and ice-free surfaces. Over snow- and ice-covered surfaces: (1) most PMW products show higher correlation coefficients than IR, (2) at cold temperatures (e.g., Tw < −10 °C), PMW products tend to underestimate and IR product shows large overestimations, and (3) PMW sensors show higher overall skill in detecting precipitation occurrence, but not necessarily at very cold Tw. The results suggest that the current approach of IMERG (i.e., replacing PMW with IR precipitation estimates over snow- and ice-surfaces) may need to be revised.
Highlights
passive microwave (PMW) products generally have higher skills than IR over snow- and ice-free surfaces
The results of this study are presented under three main sections: (1) general characteristics and differences of integrated multisatellite retrievals for GPM (IMERG) and stage IV products including spatial distribution and seasonality accompanied by a case study, (2) analysis of IMERG components versus stage IV data over snow-and ice-covered and snow- and ice-free surfaces with different intensities and, (3) investigating the performance of individual PMW sensor types
This is observed in spring (MAM) and fall (SON), but not in summer (JJA), the underestimation is more noticeable for IMERG-HQ than
Summary
PMW products generally have higher skills than IR over snow- and ice-free surfaces. Over snowand ice-covered surfaces: (1) most PMW products show higher correlation coefficients than IR, (2) at cold temperatures (e.g., Tw < −10 ◦ C), PMW products tend to underestimate and IR product shows large overestimations, and (3) PMW sensors show higher overall skill in detecting precipitation occurrence, but not necessarily at very cold Tw. Rain gauges and ground radars have enabled high-quality observation and estimation of precipitation at a point or at regional scale and satellite observations have enabled precipitation estimates with global coverage at subdaily temporal sampling, which is important for hydrologic applications [1]. PMW sensors often provide more information about the hydrometeors, tend to result in more accurate precipitation estimates than precipitation retrieval based on IR data. PMW-based precipitation estimation may face large uncertainties due to several factors including errors related to the poor understanding of precipitation microphysics, difficulties in distinguishing between light rain and cloud [9,10,11], and challenges in determining surface emissivity, especially over snow and ice [12,13]. Precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN) [14] and PERSIANN cloud classification system (PERSIANN-CCS) [15] are mainly based on IR brightness
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