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F. Joseph Turk, Sarah E. Ringerud, Yalei You, Andrea Camplani, Daniele Casella, Giulia Panegrossi, Paolo Sanò, Ardeshir Ebtehaj, Clement Guilloteau, Nobuyuki Utsumi, Catherine Prigent, and Christa Peters-Lidard

. In this manuscript, the term “complex surfaces” refers to land surfaces whose emissivity is controlled by a large number of factors such as vegetation conditions, soil type and moisture ( Harrison et al. 2015 ), and (for frozen surfaces) the snow and/or ice properties ( Hirahara et al. 2020 ), but also surfaces whose geophysical properties are not homogeneous over the resolution of the PMW radiometer field of view including mixed land–water ( Gouweleeuw et al. 2012 ) and coastal boundaries ( Mega

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Yingzhao Ma, V. Chandrasekar, Haonan Chen, and Robert Cifelli

several meteorological forcing variables, including surface precipitation rate, incoming shortwave and longwave radiations, air temperature, specific humidity, surface air pressure, and wind speeds in both horizontal and vertical directions. Using these forcing inputs, the LSM simulates runoff production (e.g., soil infiltration, surface and subsurface overflow, and channel inflow), soil moisture and temperature, canopy energy exchange, and soil moisture versus groundwater interaction, etc. ( Niu et

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Allison E. Goodwell

. Climate conditions, topography, and regional land–atmosphere feedbacks drive these aspects of temporal persistence and spatial synchronicity of precipitation, which in turn influence soil moisture, flows, and vegetation. For example, the direction, speed, and size of a storm event moving across a basin can impact downstream flows and ecohydrologic processes. Goodwell and Kumar (2019) explored temporal precipitation persistence and predictability, addressing the extent to which the knowledge of past

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

producing error estimates in the first place, another challenge is enabling environmental models to ingest such estimates. One approach is to generate ensembles consisting of multiple realizations of precipitation time series or space–time fields and then use each ensemble member to drive a prediction model. This approach allows prediction models to be used without any particular modification, as demonstrated in a number of studies using stochastic rainfall input for soil moisture and landslide hazard

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Chandra Rupa Rajulapati, Simon Michael Papalexiou, Martyn P. Clark, Saman Razavi, Guoqiang Tang, and John W. Pomeroy

, . 10.1002/2017RG000574 Trenberth , K. E. , A. Dai , R. M. Rasmussen , and D. B. Parsons , 2003 : The changing character of precipitation . Bull. Amer. Meteor. Soc. , 84 , 1205 – 1218 , . 10.1175/BAMS-84-9-1205 Trenberth , K. E. , J. T. Fasullo , and J. Mackaro , 2011 : Atmospheric moisture transports from ocean to land and global energy flows in reanalyses . J. Climate , 24 , 4907 – 4924 , https

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Yagmur Derin, Pierre-Emmanuel Kirstetter, and Jonathan J. Gourley

snow and ice properties ( Takbiri et al. 2019 ; Kummerow 2020 ). Over the ocean, PMW algorithms can separate the surface radiation signal from the emission signal for liquid hydrometeors. Over land, microwave emissivity is sensitive to soil moisture, particularly at lower frequencies, which obscures the liquid hydrometeor emission signals. Hence, coastal areas present high emissivity gradients that challenge GPROF2017 precipitation retrieval. These retrievals are then intercalibrated to CORRA (the

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

or soil moisture retrievals into a land surface model . Hydrol. Earth Syst. Sci. , 20 , 4895 – 4911 , . 10.5194/hess-20-4895-2016 Evensen , G. , 2003 : The ensemble Kalman filter: Theoretical formulation and practical implementation . Ocean Dyn. , 53 , 343 – 367 , . 10.1007/s10236-003-0036-9 Gaspari , G. , and S. E. Cohn , 1999 : Construction of correlation functions in two and three dimensions

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