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H. Lievens, A. Al Bitar, N. E. C. Verhoest, F. Cabot, G. J. M. De Lannoy, M. Drusch, G. Dumedah, H.-J. Hendricks Franssen, Y. Kerr, S. K. Tomer, B. Martens, O. Merlin, M. Pan, M. J. van den Berg, H. Vereecken, J. P. Walker, E. F. Wood, and V. R. N. Pauwels

studies have used actual SMOS TB data ( De Lannoy et al. 2013 ; Montzka et al. 2013 ). This study proposes a method for optimizing a coupled land surface and radiative transfer model framework to decrease the amount of biases in the simulation of multiangular and multipolarization SMOS TB observations. Therefore, the Community Microwave Emission Modelling platform (CMEM; Holmes et al. 2008 ; Drusch et al. 2009 ; de Rosnay et al. 2009 ) is coupled to the Variable Infiltration Capacity model (VIC

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Ali Behrangi, Bisher Imam, Kuolin Hsu, Soroosh Sorooshian, Timothy J. Bellerby, and George J. Huffman

.1175/1525-7541(2003)004<1088:SREUCP>2.0.CO;2 Kuligowski, R. J. , 2002 : A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. J. Hydrometeor. , 3 , 112 – 130 . 10.1175/1525-7541(2002)003<0112:ASCRTG>2.0.CO;2 Kummerow, C. , and Giglio L. , 1995 : A method for combining passive microwave and infrared rainfall observations. J. Atmos. Oceanic Technol. , 12 , 33 – 45 . 10.1175/1520-0426(1995)012<0033:AMFCPM>2.0.CO;2 Kummerow, C. , and Coauthors , 2001 : The evolution of the

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E. A. Zakharova, A. V. Kouraev, S. Biancamaria, M. V. Kolmakova, N. M. Mognard, V. A. Zemtsov, S. N. Kirpotin, and B. Decharme

in situ data with satellite microwave remote sensing techniques. Satellite observations of spatial and temporal distribution of various snowpack parameters, once calibrated with the in situ data, provide continuous, reliable, regular, and weather-independent data over large regions. Western Siberia is a large flat region located between the Ural Mountains and the middle Siberian Highlands. Most of its territory comprises the watershed of the Ob’ River (almost 3 million km 2 ); while its northern

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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

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Thomas J. Jackson, Ann Y. Hsu, and Peggy E. O'Neill

recognized as the best direction for future soil moisture measurement systems, there is still a good reason in the meantime to consider the use of higher frequencies: the vast quantity of global systematic high-frequency microwave data that have been collected for the past 15 yr by the Special Sensor Microwave Imager (SSM/I). Despite the fact that high-frequency microwave sensors will have limited retrieval capabilities, there are some conditions under which these observations can provide useful soil

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Gabriëlle J. M. De Lannoy, Rolf H. Reichle, and Valentijn R. N. Pauwels

1. Introduction Assimilating low-frequency (1–10 GHz) passive microwave observations into land surface models is expected to improve estimates of land surface conditions and, hence, weather and climate predictions. Global observations of brightness temperatures (Tb) are available from the (late) Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), the Soil Moisture Ocean Salinity (SMOS; Kerr et al. 2010 ) mission, and Aquarius ( Le Vine et al. 2007 ). Soil moisture has a

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Roongroj Chokngamwong and Long S. Chiu

included in the comparison. The V5 3B42 is an IR estimated rain rate calibrated to TRMM Combined Instrument (TCI), whereas the V6 is mainly based on microwave rain estimate from all available sensors, all calibrated to the TCI, and then merged with gauge measurements at the submonthly scale. Because of their design, each methodology has different spatial and temporal characteristics. The comparison of TRMM rainfall products with independent gauge measurement would aid in the refinement of rainfall

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Abishek Adhikari, Chuntao Liu, and Lindsey Hayden

the clouds. Based on the Special Sensor Microwave Imager (SSM/I) observations, Grody (1991) purposed the scattering index method, which uses scattering signatures to obtain rain rate, snow equivalent water content, and other parameters. The scattering index method was further expanded by Ferraro et al. (1994) by developing a more expansive set of screens that could be used to separate rainfall signal from various surface backgrounds, which was later used in the Tropical Rainfall Measuring

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F. Joseph Turk, R. Sikhakolli, P. Kirstetter, and S. L. Durden

1. Introduction The joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Global Precipitation Mission (GPM) core satellite was successfully launched on 28 February 2014. GPM is a constellation mission, whereby the observations and precipitation profile estimates from the core satellite dual-frequency (Ku and Ka band) precipitation radar (DPR) and 13-channel (10–183 GHz) passive microwave (PMW) GPM imager (GMI) act as a reference for the other

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Pingping Xie, Robert Joyce, Shaorong Wu, Soo-Hyun Yoo, Yelena Yarosh, Fengying Sun, and Roger Lin

North American Land Data Assimilation System (NLDAS) project . J. Geophys. Res. , 108 , 8842 , doi: 10.1029/2002JD003118 . Ebert , E. E. , J. E. Janowiak , and C. Kidd , 2007 : Comparison of near-real-time precipitation estimates from satellite observations and numerical models . Bull. Amer. Meteor. Soc. , 88 , 47 – 64 , doi: 10.1175/BAMS-88-1-47 . 10.1175/BAMS-88-1-47 Ferraro , R. , and Coauthors , 2005 : NOAA operational hydrological products derived from the Advanced Microwave

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