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Nobuyuki Utsumi, F. Joseph Turk, Ziad S. Haddad, Pierre-Emmanuel Kirstetter, and Hyungjun Kim

1. Introduction Global precipitation products capitalize upon the long period of record of satellite-based passive microwave (MW) radiometer observations ( Aonashi and Ferraro 2020 ). The passive MW brightness temperature (TB) represents the net top-of-atmosphere upwelling radiation, after taking into consideration the emission and scattering properties of hydrometeors within the top-to-bottom profile, including the contribution from the surface emissivity. The surface precipitation represents

Open access
Shruti A. Upadhyaya, Pierre-Emmanuel Kirstetter, Jonathan J. Gourley, and Robert J. Kuligowski

surprisingly better detection rate (21%) than its calibrator MWCOMB (10%). Similar observations are made with the Snow type, with overall better detection with SCaMPR (23%) than MWCOMB (9%). A possible explanation is that surface emissivity affects microwave observations in the range [10–37] GHz more significantly than infrared observations. Surface emissivity variability associated with surface snow is particularly challenging for microwave observations (e.g., Takbiri et al. 2019 ; Gebregiorgis et al

Free access
Alberto Ortolani, Francesca Caparrini, Samantha Melani, Luca Baldini, and Filippo Giannetti

from multiple instruments are presented in Haese et al. (2017) . They use a stochastic approach called random mixing to generate precipitation fields from a set of rain gauge observations and path-averaged rain rates estimated using commercial microwave links. They apply their method to both synthetic (generated via the COSMO model) and real data in a study area in Germany, adopting an hourly time step. Bianchi et al. (2013) also present a technique to combine measurements from rain gauges

Open access
Andrea Camplani, Daniele Casella, Paolo Sanò, and Giulia Panegrossi

to the five different 10° latitude bins indicated in the legend. The extremely variable snow-cover extent and snow radiative properties in the MW are one of the main issues in the detection and quantification of snowfall by passive microwave observations, which remain among the most challenging tasks in global precipitation retrieval ( Bennartz and Bauer 2003 ; Skofronick-Jackson et al. 2004 , 2019 ; Noh et al. 2009 ; Levizzani et al. 2011 ; Kongoli and Helfrich 2015 ; Chen et al. 2016

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Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph

small-scale convective storm rainfall variability . J. Hydrol. , 173 , 283 – 308 , . 10.1016/0022-1694(95)02703-R Grecu , M. , W. S. Olson , and E. N. Anagnostou , 2004 : Retrieval of precipitation profiles from multiresolution, multifrequency active and passive microwave observations . J. Appl. Meteor. , 43 , 562 – 575 ,<0562:ROPPFM>2.0.CO;2 . 10.1175/1520-0450(2004)043<0562:ROPPFM>2.0.CO;2

Open access
Clement Guilloteau, Efi Foufoula-Georgiou, Pierre Kirstetter, Jackson Tan, and George J. Huffman

assimilate a higher number of microwave observations than the Early product as all microwave observations are not always available with the 4-h latency. The “uncalibrated” precipitation estimates that do not include gauge adjustment from IMERG-E and IMERG-F products are used in the present study. The January 2018–April 2020 period is selected for the evaluation of the satellite products. The March 2018 and March 2019 months are excluded from the analysis because of a high rate of missing MRMS data (or

Open access
Samantha H. Hartke, Daniel B. Wright, Dalia B. Kirschbaum, Thomas A. Stanley, and Zhe Li

of ARI and in the conditional CSGDs of ARI ( Fig. 3d ) are unsurprisingly much lower than that for daily precipitation. It has been previously shown that IMERG error depends on the amount and source of passive microwave and infrared data used ( Tan et al. 2016 ). Since this data availability varies over relatively short time scales (generally subhourly), it is not feasible to consider it when modeling multiday ARI. Since both Stage IV and IMERG observations of ARI are available over the entire

Free access
Thomas C. van Leth, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet

1. Introduction Quantification of small-scale rainfall variability is important for the design and operation of small-scale sensor networks for flood prediction, particularly in urban areas ( ten Veldhuis et al. 2018 ). It is also important for the assessment of the spatial representativeness of path- or area-averaged remote rainfall measurement methods such as microwave links ( Berne and Uijlenhoet 2007 ; van Leth et al. 2020 ), weather radar ( Jaffrain et al. 2011 ; Peleg et al. 2013 ), and

Open access