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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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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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Zhongkun Hong, Zhongying Han, Xueying Li, Di Long, Guoqiang Tang, and Jianhua Wang

to merge high-quality precipitation data globally ( Beck et al. 2017a , 2019 ). However, the effective spatial resolution of MSWEP is relatively coarse in the TP, since the reanalysis (~80–150 km) component is the dominating component ( Beck et al. 2017a ). The Global Precipitation Climatology Centre (GPCP) data combine high-accuracy microwave observations and more frequent geosynchronous infrared observations to provide high-spatiotemporal-resolution precipitation products ( Adler et al. 2003

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Mu Xiao, Sarith P. Mahanama, Yongkang Xue, Fei Chen, and Dennis P. Lettenmaier

, . 10.1029/2002JD003174 Xue , Y. , B. A. Forman , and R. H. Reichle , 2018 : Estimating snow mass in North America through assimilation of advanced microwave scanning radiometer brightness temperature observations using the catchment land surface model and support vector machines . Water Resour. Res. , 54 , 6488 – 6509 , . 10.1029/2017WR022219

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Joseph A. Santanello Jr., Sujay V. Kumar, Christa D. Peters-Lidard, Ken Harrison, and Shujia Zhou

gridded parameter optimization) also relates to the spatial tradeoffs of satellite sensors, while the period of calibration relates to the satellite overpass return time. In the future, simultaneous development of Earth science technologies (e.g., microwave soil moisture sensors) and methodologies (e.g., thermal evapotranspiration retrievals) will warrant the LIS-OPT/UE approach in assessing the impact of observations on coupled forecasts for both calibration and data assimilation studies alike

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Terence J. Pagano, Duane E. Waliser, Bin Guan, Hengchun Ye, F. Martin Ralph, and Jinwon Kim

associated results, including an examination of the veracity of the results that is based on comparisons between reanalysis and satellite observations. Last AR cases within the top and bottom 25% of near-surface stability are examined in the context of understanding the associated frequency of occurrence of extreme wind speeds in ARs. We conclude in section 4 with a summary and discussion. 2. Data and methods a. AR database In this study, the Guan and Waliser (2015) global AR database is used. The

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Mostafa Tarek, François P. Brissette, and Richard Arsenault

for light rainfall and snow precipitation as well as more frequent observations over the medium and high latitudes ( Hou et al. 2014 ). GPM utilizes passive microwave sensors in addition to the infrared measurements from geostationary satellites, providing rainfall monitoring around the globe with higher spatial and temporal resolutions than the previously widely used TMPA products ( Yong et al. 2015 ). These improvements are likely to provide significant advantages for hydrometeorological studies

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