Assessment of Satellite Precipitation Products in Relation With Orographic Enhancement Over the Western United States

Abstract

Satellite-based precipitation estimates have been widely used in many research and application areas, including short-term weather and long-term climate prediction (Arkin & Ardanuy, 1989;Huffman et al., 1995). Precipitation estimates from satellite measurements are unique due to their global coverage, including oceanic and high terrain mountainous regions where ground-based observations are not always possible (Kidd et al., 2017). It is difficult to get broad spatial coverage in mountainous areas using rain gauges, and ground-based radar observation has limitations (Sapiano & Arkin, 2009). Despite the great coverage of satellite products, their precipitation retrieval accuracy in mountain regions is still a challenge (Mei et al., 2014;Shige et al., 2013). The primary types of sensors used to estimate precipitation from satellite measurements are radar, passive microwave imager/ sounder, and Infrared radiometers. The space-borne radar onboard satellites such as the Tropical Rainfall Measuring Mission (TRMM; Kummerow et al., 1998) and the Global Precipitation Measurement (GPM; Hou et al., 2014;Skofronick-Jackson et al., 2018) have shown success in measuring precipitations globally, but they still suffer from uncertainties especially in mountainous regions (Adhikari et al., 2019). One major source of uncertainty associated with space-borne radars is the contamination of near-surface reflectivity profiles due to ground-clutter (Arulraj & Barros, 2019;Liao et al., 2014). Passive microwave sensors (PMW) are popular and have been widely used in estimating precipitation for decades (Prigent, 2010;Wilheit, 1986). The benefit of using PMW sensors compared to radar is their higher sampling rate, although they can misclassify rainfall over mountainous regions (Yamamoto & Shige, 2015). For example, snow and ice-covered surfaces over the mountains could be classified wrongly as precipitating clouds resulting in an overestimation of precipitation. The PMW precipitation estimates mainly rely on the cumulative signal from the vertical column of cloud, hydrometeors and their emission or scattering properties. The heavy rainfall associated with mountainous regions can be generated from clouds with no or little ice aloft (You & Liu, 2012), resulting in an underestimation in PMW estimates (Dinku et al., 2010;Houze, 2012;Shige et al., 2013). Studies have shown that satellite-based precipitation products underestimate precipitation rate over several mountainous regions of the globe such as Japan (Kubota et al., 2009;Shige et al., 2013), Africa

Near Real-Time Estimation of High Spatiotemporal Resolution Rainfall from Cloud Top Properties of the Msg Satellite and Commercial Microwave Link Rainfall Intensities

“…IMERG is a multisensor technique; its estimates represent a spatiotemporally averaged rainfall from multiple microwave estimates. Also, the accuracy of PMW rainfall retrievals over mountainous areas is affected by the orographic effect on cloud and rainfall formation (Adhikari and Behrangi 2022;Kumah et al 2021b;Petković and Kummerow 2017), which may explain the high differences observed between the RF versus IMERG and RF versus MPE estimates for areas with complex topography. Furthermore, the high spatiotemporal rainfall variability in the study area may also contribute to the differences in the rainfall estimates by the gauge, RF, IMERG and MPE (Wakachala et al 2015).…”

Smart Data Blending Framework to Enhance Precipitation Estimation through Interconnected Atmospheric, Satellite, and Surface Variables

“…Examining in detail the major inconsistencies and errors between the gauge measurements, IMERG daily products, and daily estimations corrected by applying blending models, it is possible to notice worse performances over the eastern region, which is poorly gauged (especially in the southeast corner) and characterized by high intensity and depth of rainfall [28] and diverse mechanisms of precipitation (i.e., orographic rather than cyclonic) that can be challenging to estimate accurately due to interactions with complex orography causing large spatial variations [81][82][83].…”

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

“…Sun et al, 2018), regional (Bytheway et al, 2023;Hu et al, 2016;Satgé et al, 2020), national (Aksu & Akgül, 2020;Du et al, 2022;Li et al, 2022;Liu et al, 2022;Nepal et al, 2021;W. Xie et al, 2022), and basin scales (Adhikari & Behrangi, 2022;Dangol et al, 2022;Gu et al, 2022;J. Chen et al, 2020;Khadka et al, 2022;Kumar et al, 2021;.…”

“…Until now, many evaluation studies of such datasets have been conducted at global (Beck et al., 2017; Derin et al., 2019; Li et al., 2021; Q. Sun et al., 2018), regional (Bytheway et al., 2023; Hu et al., 2016; Satgé et al., 2020), national (Aksu & Akgül, 2020; Y. Chen et al., 2021; Du et al., 2022; Li et al., 2022; Liu et al., 2022; Nepal et al., 2021; Sharma, Khadka, et al., 2020; W. Xie et al., 2022), and basin scales (Adhikari & Behrangi, 2022; Dangol et al., 2022; Gu et al., 2022; J. Chen et al., 2020; Khadka et al., 2022; Kumar et al., 2021; W. Wang et al., 2020). Most of them have uncovered uncertainties in the measurements, particularly when dealing with complex terrain.…”