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S. Chen, P. E. Kirstetter, Y. Hong, J. J. Gourley, Y. D. Tian, Y. C. Qi, Q. Cao, J. Zhang, K. Howard, J. J. Hu, and X. W. Xue

1. Introduction Reliable quantitative estimates of the spatial precipitation distribution are critical in the application of satellite-based rainfall in hydrologic modeling and hazards monitoring and forecasting. Because of their global coverage and spatial continuity, satellite-based quantitative precipitation estimates (QPE) products are used for such applications. However, there are many inherent error sources in satellite-based measurements, such as the spatial horizontal

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Dwi Prabowo Yuga Suseno and Tomohito J. Yamada

. A., III , Brooks H. E. , and Maddox R. A. , 1996 : Flash flood forecasting: An ingredient-based methodology . Wea. Forecasting , 11 , 560 – 581 , doi:10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2 . Ebert, E. E. , cited 2007 : Forecast verification: Issues, methods and FAQ. [Available online at ]. Feidas, H. , and Cartalis C. , 2001 : Monitoring mesoscale convective cloud systems asscociated with heavy storms using Meteosat

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Steven M. Martinaitis, Stephen B. Cocks, Andrew P. Osborne, Micheal J. Simpson, Lin Tang, Jian Zhang, and Kenneth W. Howard

WC product was also compared against the NWS Stage IV QPE, a combination of WSR-88D radar QPE and gauge observations generated by NWS river forecast centers ( Lin and Mitchell 2005 ). Statistical analyses of Stage IV QPEs versus CoCoRaHS were not conducted, since CoCoRaHS gauges were not independent to Stage IV QPE. Gridded differences between Stage IV and the MRMS Q WC products were generated for both Harvey and Florence. NWS Stage IV 24-h QPE are generated daily at 1200 UTC; thus, gridded

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Aina Taniguchi, Shoichi Shige, Munehisa K. Yamamoto, Tomoaki Mega, Satoshi Kida, Takuji Kubota, Misako Kachi, Tomoo Ushio, and Kazumasa Aonashi

two successive IR images and a Kalman filter ( Ushio et al. 2009 ). For the comparison with the GSMaP_MVK estimates, we use the TMPA near-real-time version product (3B42RT). Multisatellite MWR rainfall estimates are calibrated by the TRMM estimates, and the geostationary IR rainfall estimates are made by calibrating IR Tbs with the MWR rainfall estimates using a histogram-matching technique ( Huffman et al. 2007 ). The TMPA 3B42RT rain estimates consist of the MWR estimates where available and the

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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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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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