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Lucas Bohne, Courtenay Strong, and W. James Steenburgh

forecasts, many weather models still have biases in interior complex terrain areas of the western United States ( Gowan et al. 2018 ). Several research projects have investigated the relationship between precipitation and elevation in the western United States. Lull and Ellison (1950) used precipitation observations in central Utah to develop a linear relationship between precipitation and elevation. Others have used more complex combinations of precipitation observations and terrain features to

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Erin Dougherty and Kristen L. Rasmussen

-7-955 . 10.1175/BAMS-85-7-955 Funk , T. W. , 1991 : Forecasting techniques utilized by the forecast branch of the national meteorological center during a major convective rainfall event . Wea. Forecasting , 6 , 548 – 564 ,<0548:FTUBTF>2.0.CO;2 . 10.1175/1520-0434(1991)006<0548:FTUBTF>2.0.CO;2 Gutmann , E. D. , and Coauthors , 2018 : Changes in hurricanes from a 13-yr convection-permitting pseudo–global warming simulation . J. Climate , 31 , 3643

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Liqing Peng, Zhongwang Wei, Zhenzhong Zeng, Peirong Lin, Eric F. Wood, and Justin Sheffield

-scale R sd directly from these records because the traditional interpolation techniques for upscaling point observations are not appropriate for the sparsely distributed radiation network over a large domain. Machine-learning is a powerful tool to draw information from both ground observations and satellite products of surface radiation ( Mellit et al. 2010 ; Wang et al. 2012 ; Yang et al. 2018 ; Wei et al. 2019 ). The model tree ensemble (MTE) technique ( Jung et al. 2010 ) is one of the machine

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Nergui Nanding, Huan Wu, Jing Tao, Viviana Maggioni, Hylke E. Beck, Naijun Zhou, Maoyi Huang, and Zhijun Huang

, N. , M. A. Rico-Ramirez , and D. Han , 2015 : Comparison of different radar-raingauge rainfall merging techniques . J. Hydroinform. , 17 , 422 – 445 , . 10.2166/hydro.2015.001 Nash , J. , and J. Sutcliffe , 1970 : River flow forecasting through conceptual models part I—A discussion of principles . J. Hydrol. , 10 , 282 – 290 , . 10.1016/0022-1694(70)90255-6 Nelson , B. R. , O. P. Prat

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Mostafa Tarek, François P. Brissette, and Richard Arsenault

represent vital sources of data in weather and climate studies. A typical reanalysis system consists of two main components: the forecast model and the data assimilation system. The role of the data assimilation system is to integrate many sources of observations to provide the forecast model with the most accurate representation of initial atmospheric states. Then, the numerical weather forecast models are executed for a given time step to produce consistent gridded datasets ( Di Luzio et al. 2008

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Maoyi Huang, Zhangshuan Hou, L. Ruby Leung, Yinghai Ke, Ying Liu, Zhufeng Fang, and Yu Sun

data, prescribed land surface properties, and initial and boundary conditions). Another source of uncertainty arises from model structure and parameterization based on our understanding of hydrological processes and how they should be parameterized. Reductions of such uncertainty rely on improved understanding of the physics and its effective representation in models. Such uncertainties can be reduced by optimizing model parameter sets using model calibration techniques with available historical

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Yali Luo, Weimiao Qian, Renhe Zhang, and Da-Lin Zhang

are much needed to aid in the understanding of complicated precipitation processes and the verification of satellite precipitation products and numerical weather prediction (NWP) models. Recently, the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA) started providing the 0.1°-resolution gridded hourly precipitation product across China from 2008 onward ( Pan et al. 2012 ). This product is developed using the optimum interpolation technique by

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Trent W. Ford, Steven M. Quiring, Chen Zhao, Zachary T. Leasor, and Christian Landry

, influencing the climate on monthly to seasonal time scales ( Dirmeyer et al. 2009 ; Lorenz et al. 2010 ; Orth and Seneviratne 2014 ). Therefore, accurate soil moisture information is critical for subseasonal-to-seasonal climate prediction as well as forecasting extreme events at those time scales ( Mahanama et al. 2008 ; Guo et al. 2011 ; Ford et al. 2018 ). In addition to playing an integral role in the global climate system, soil moisture is often used as an indicator of agricultural drought

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Naoki Mizukami, Victor Koren, Michael Smith, David Kingsmill, Ziya Zhang, Brian Cosgrove, and Zhengtao Cui

using BBH Ptype data may be more clearly identified for each event. Techniques for streamflow data assimilation with the lumped model (e.g., Seo et al. 2009 ) and distributed model (e.g., Lee et al. 2011 ) are progressing. Anticipating that further analyses would lead to more consistent improvements, use of the BBH data (spatially constant or variable) for precipitation typing in real time hydrologic forecasting would be computationally feasible. Gridded data such as multisensor QPE have been used

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Yabin Gou and Haonan Chen

1. Introduction Radar quantitative precipitation estimation (QPE) is critical for extreme rainfall monitoring, forecast, and decision-making during contingent flash floods, mudslides, and debris flows. However, weather radars often suffer from complete or partial beam blockage (PBB) induced by surrounding terrains ( Giangrande and Ryzhkov 2005 ; Zhang et al. 2013 ; Chen et al. 2020 ). The PBB effect is more evident in mountainous areas and/or complex urban environments because of the

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