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Patricia Lawston-Parker, Joseph A. Santanello Jr., and Sujay V. Kumar

coupling (surface flux to PBL; Dirmeyer et al. 2014 ), in which atmospheric properties are used to infer land states and fluxes “upstream.” The most common method under the terrestrial approach is calibration of LSM parameters in land-only mode (i.e., offline) to observations of soil moisture or surface fluxes ( Hess 2001 ; Hogue et al. 2005 ; Santanello et al. 2007b , 2013b ; Peters-Lidard et al. 2008 ; Peng et al. 2020 ). For example, Shellito et al. (2016) calibrated soil parameters using

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Rebecca A. Smith and Christian D. Kummerow

regions, satellite-derived precipitation datasets are limited. Microwave precipitation estimates are not as accurate over land (particularly over mountainous regions), and land infrared (IR) precipitation estimates are not provided over regions with snow cover owing to the brightness temperature of snow closely matching the brightness temperature of cloud tops. Over the UCRB, snow cover is an issue for over six months of the year, limiting the amount of satellite precipitation observations. The

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He Sun, Fengge Su, Zhihua He, Tinghai Ou, Deliang Chen, Zhenhua Li, and Yanping Li

1. Introduction Precipitation is the key driver of the terrestrial hydrological system and the most important atmospheric input in land surface hydrological models ( Beven and Hornberger 1982 ; Su et al. 2008 ; Tong et al. 2014b ). However, direct meteorological observations are either sparse or nonexistent in many remote high mountainous areas because of their high elevation, complex terrain, and inaccessibility. This is especially true for the Third Pole (TP) ( Qiu 2008 ), which is the high

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Xinxuan Zhang, Emmanouil N. Anagnostou, Maria Frediani, Stavros Solomos, and George Kallos

different satellite products in the upper Blue Nile area of Ethiopia, Bitew et al. (2012) have indicated that microwave-based satellite rainfall retrievals may have better accuracies over mountainous areas than infrared-based satellite rainfall estimates. Similarly, Dinku et al. (2007) have shown that the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) morphing technique (CMORPH) exhibits the best consistency among 10 different satellite rainfall products with

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Dikra Khedhaouiria, Stéphane Bélair, Vincent Fortin, Guy Roy, and Franck Lespinas

) extended the Clark and Slater (2006) study and proposed ensembles of daily precipitation at approximately 12-km resolution by interpolating site observations. Aalto et al. (2016) employed comprehensive interpolation approaches of seven climate variables observed at climate stations, including daily precipitation, to provide 10-km gridded products and their uncertainties over Finland. Europe-wide 100-member daily precipitation and temperature datasets were proposed by Cornes et al. (2018) on a 25

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Cheng Tao, Yunyan Zhang, Qi Tang, Hsi-Yen Ma, Virendra P. Ghate, Shuaiqi Tang, Shaocheng Xie, and Joseph A. Santanello

boundary layer (PBL), clouds and precipitation, and the limited observations over highly heterogeneous land surfaces with varying vegetation cover, land use, terrain, and soil texture. Previous studies on LA coupling focus on the soil moisture–precipitation (SM–P) feedback and show discrepancies in the coupling strength between models and observations. Based on multiple weather and climate models, the Global Land–Atmosphere Coupling Experiments (GLACE) provided an estimate of the global distribution of

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Hanqing Chen, Bin Yong, Weiqing Qi, Hao Wu, Liliang Ren, and Yang Hong

-only versions of IMERG and GSMaP, respectively ( Chen et al. 2020 ). Besides, these satellite-only precipitation products have not been corrected by ground-based observations relative to ground-adjusted satellite precipitation products (e.g., IMERG-Final and GSMaP-Gauge), avoiding the potential uncertainties caused by the overlap between evaluated SPPs and references. Both IMERG-Late and GSMaP-MVK merged several passive microwave (PMW) and infrared (IR) data for getting high-accuracy global precipitation

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Ning Zhang, Steven M. Quiring, and Trent W. Ford

; Wanders et al. 2014 ), and drought monitoring ( Dai 2013 ; Wang et al. 2011 ). There are three primary sources of soil moisture information: remote sensing (RS) observations, land surface models (LSMs), and in situ measurements. Microwave remote sensing is responsive to surface (~5 cm) soil moisture in regions with sparse to moderate vegetation density. The passive microwave satellites that are currently in orbit include the Soil Moisture and Ocean Salinity (SMOS) satellite (launched 2009; 35-km

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Yixin Wen, Terry Schuur, Humberto Vergara, and Charles Kuster

systems and to reduce unnecessary wear and tear on the antenna pedestal ( Federal Coordinator for Meteorological Services and Supporting Research 2013 ). The ground radars also have limitations in complex terrain where they must rely on scans at higher-elevation angles, and thus observations collected from within the ice region of the clouds, to compute QPE at surface. Since the radar beam broadens with range, it also becomes more difficult to accurately resolve the vertical structure of precipitation

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Martin G. De Kauwe, Christopher M. Taylor, Philip P. Harris, Graham P. Weedon, and Richard. J. Ellis

) and downstream ( Spracklen et al. 2012 ) and on atmospheric circulations on scales of ten ( Anthes 1984 ) to thousands ( Charney 1975 ) of kilometers. In so-called land–atmosphere coupling hotspots ( Koster et al. 2004 ), predictions on daily to centennial time scales rely on a realistic depiction of land surface fluxes within numerical models. Given the strong sensitivity of fluxes to surface properties, in tandem with often substantial spatial variability and a lack of flux observations at the

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