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Konstantinos M. Andreadis, Ding Liang, Leung Tsang, Dennis P. Lettenmaier, and Edward G. Josberger

microwave satellite observations have been available since the late 1970s and have been used extensively for snow depth and cover extent estimation ( Tait 1998 ; Grody and Basist 1996 ; Josberger and Mognard 2002 ). Nonetheless, retrievals of snow parameters from passive microwave satellite observations are hindered by several factors. Hardware configurations on current operational satellites produce relatively coarse spatial resolutions (dependent on frequency, 27 km × 17 km for 18.7 GHz, and 14 km

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T. J. Bellerby

microwave overpass but update estimates once they are bracketed by both a previous and subsequent observation. For the general Lagrangian algorithm described by Eq. (3) , the conditional distribution P u ( R ; x , t ) of observed rainfall with respect to the satellite information used to derive the estimate is given by Here P ( R| …) is a conditional distribution of rainfall characterized only by the previous and subsequent microwave rain-rate observations and their respective lags t − t 1 and t

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Maheshwari Neelam, Rajat Bindlish, Peggy O’Neill, George J. Huffman, Rolf Reichle, Steven Chan, and Andreas Colliander

-based estimates and (ii) the PMW adjustment to the IR depends on adjustments interpolated from surrounding areas to the areas where PMW observations have been screened out due to snowy/icy surfaces ( Huffman 2019 ). The IMERG algorithm utilizes a combination of PERSIANN, CMORPH, and CORRA algorithms. It is worth mentioning that PERSIANN estimates the precipitation based on infrared brightness temperature image (as input) and artificial neural network (as a model), while CMORPH is mainly based on microwave

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Shanshui Yuan, Laiyin Zhu, and Steven M. Quiring

: Estimation of tropical cyclone intensity in the North Atlantic and northeastern Pacific basins using TRMM satellite passive microwave observations . J. Appl. Meteor. Climatol. , 58 , 185 – 197 , https://doi.org/10.1175/JAMC-D-18-0094.1 . 10.1175/JAMC-D-18-0094.1 Jones , P. W. , 1999 : First- and second-order conservative remapping schemes for grids in spherical coordinates . Mon. Wea. Rev. , 127 , 2204 – 2210 , https://doi.org/10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2 . 10

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Michael Durand and Steven A. Margulis

the usefulness of these methodologies, most snow data assimilation has been limited to direct-insertion schemes that do not take into account the uncertainty in the observations. Assimilation of passive microwave remote sensing observations to update a modeled estimate of SWE has not been reported in the literature. d. Motivation and science questions General application of a data assimilation methodology requires only assumptions about the measurement and model input uncertainty, and implicitly

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M. Tugrul Yilmaz and Wade T. Crow

nature of soil moisture in land surface models . J. Climate , 22 , 4322 – 4335 . Parinussa, R. M. , Holmes T. R. H. , Yilmaz M. T. , and Crow W. T. , 2011 : The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations . Hydrol. Earth Syst. Sci. , 15 , 3135 – 3151 . Reichle, R. H. , and Koster R. D. , 2004 : Bias reduction in short records of satellite soil moisture. Geophys. Res. Lett., 31, L19501, doi:10.1029/2004GL020938

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Robert F. Adler, George J. Huffman, Alfred Chang, Ralph Ferraro, Ping-Ping Xie, John Janowiak, Bruno Rudolf, Udo Schneider, Scott Curtis, David Bolvin, Arnold Gruber, Joel Susskind, Philip Arkin, and Eric Nelkin

key feature of the GPCP merge technique has centered on combining the superior physical basis of the microwave-based observations from a low-orbit satellite and the frequent time sampling of the geosynchronous IR observations. Adler et al. (1991 , 1993 ) described a technique for using precipitation estimates from low-orbit microwave data to “adjust” GPI precipitation estimates made from geosynchronous IR data. The resulting “microwave-adjusted IR” estimates provide an objective means of

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Nasrin Nasrollahi, Kuolin Hsu, and Soroosh Sorooshian

1. Introduction Reliable estimation of precipitation is important to predict and manage water resources, hazard preparedness, and climate studies ( Ajami et al. 2008 ; AghaKouchak and Nakhjiri 2012 ; Anderson et al. 2008 ). However, spatial and temporal variability of precipitation makes it difficult to rely on sparse gauge point measurements, especially for remote regions. Higher spatial and temporal resolutions as well as global coverage of satellite observations are the main advantages of

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Carlo Montes, Nachiketa Acharya, S. M. Quamrul Hassan, and Timothy J. Krupnik

observations ( Xie et al. 2003 ). Some of the most commonly used operational satellite-derived precipitation products include the microwave-based Climate Prediction Center (CPC) morphing technique (CMORPH) ( Joyce et al. 2004 ), the microwave- and infrared-based Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) ( Ashouri et al. 2014 ), the National Aeronautics and Space Administration’s (NASA) Tropical Rainfall Measuring Mission (TRMM) ( Huffman et al

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Wade T. Crow, Concepcion Arroyo Gomez, Joaquín Muñoz Sabater, Thomas Holmes, Christopher R. Hain, Fangni Lei, Jianzhi Dong, Joseph G. Alfieri, and Martha C. Anderson

). This approach combines best-possible estimates of land surface states based on available observations and short-range atmospheric forecasts provided by the NWP system. In this regard, the European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) mission ( Kerr et al. 2012 ), specifically designed to measure surface SM and ocean salinity from space, provides a unique opportunity to assimilate L-band microwave brightness temperature (Tb) observations that are highly sensitive to surface SM

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