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Steven M. Martinaitis, Andrew P. Osborne, Micheal J. Simpson, Jian Zhang, Kenneth W. Howard, Stephen B. Cocks, Ami Arthur, Carrie Langston, and Brian T. Kaney

1. Introduction Accurate, high spatiotemporal resolution quantitative precipitation estimates (QPEs) are crucial for flood and flash flood operations, hydrologic forecasting, long-term climatological evaluations, and water resource management. One common source of measuring precipitation are rain gauges, which provide direct surface measurements; however, a single gauge observation based on an orifice of 80–325 cm 2 typically covers a region spanning many square kilometers. Large distances

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A. Msilini, P. Masselot, and T. B. M. J. Ouarda

, . 10.1097/00001648-200301000-00009 Rounaghi , M. M. , M. R. Abbaszadeh , and M. Arashi , 2015 : Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique . Physica , 438A , 625 – 633 , . 10.1016/j.physa.2015.07.021 Roy , S. S. , R. Roy , and V. E. Balas , 2018 : Estimating heating load

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Ioannis Sofokleous, Adriana Bruggeman, Silas Michaelides, Panos Hadjinicolaou, George Zittis, and Corrado Camera

simulations over South America. Lo et al. (2008) , used the Weather Research and Forecasting (WRF) Model at 36-km horizontal resolution and found that weekly initializations gave a higher skill in simulated precipitation over the United States than monthly initializations. Lucas-Picher et al. (2013) found that dynamical downscaling with the HIRHAM RCM at 12-km resolution over Europe with daily initialization resulted in improved temporal and spatial correlation of precipitation, relative to continuous

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Brian R. Nelson, Olivier P. Prat, and Ronald D. Leeper

Mosaic Quantitative Precipitation Estimation (Q2) product to the NCEP Stage IV project over the conterminous United States (CONUS). Chen et al. (2013) present results by season and location [i.e., River Forecast Center (RFC)] as well as striating the error based on the radar quality indicator by location. In this study we approach the method of describing errors using rain gauges as the reference, but we also use variables that are available as ancillary information to try and characterize the

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Rebecca A. Smith and Christian D. Kummerow

); therefore, spatially gridding the data first may be needlessly and computationally intensive. Additionally, the weighting technique (which captures the elevation dependence) removes the need for linear interpolation and simply uses the actual precipitation values from the COOP stations. Figure 3 shows the comparison of annual precipitation using a simple basin average compared to using the weighting technique. The weighting technique results in annual totals that are 10%–25% higher than the basin

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Kian Abbasnezhadi, Alain N. Rousseau, Étienne Foulon, and Stéphane Savary

1. Introduction The spatiotemporal representativeness of liquid and solid precipitation data is among the most crucial factors in every flow simulation practice. Sporadic meteorological observations, among other data constraints, can result in uncertainties in many hydrological modeling practices performed for flow and inflow forecasting. This is also the case with the “HYDROTEL” system ( Bouda et al. 2012 , 2014 ; Fortin et al. 2001a ; Turcotte et al. 2003 , 2007 ) set up for the

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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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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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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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