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Ben S. Pickering, Ryan R. Neely III, Judith Jeffery, David Dufton, and Maryna Lukach

1. Introduction All meteorologists agree that precipitation must be recorded accurately, yet there is no consensus on the best method to do so. There are many ways to measure precipitation, both in situ or remote sensing. For remote sensing techniques, the sample volume of any single remote sensing measurement contains a population of hydrometeors that must be derived statistically from the measurement. As such, spatial variability smaller than the measurement scale is lost and important

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Lu Yi, Bin Yong, Junxu Chen, Ziyan Zheng, and Ling Li

1. Introduction The coupled land–atmosphere model based on the regional climate model and hydrological model is an important tool to extend the forecast period of local flood ( Bosilovich and Sun 1999 ; Wu and Zhang 2013 ). In a coupled land–atmosphere model, the regional climate model can provide a hydrological model with continuous spatiotemporal variation fields of hydrological variables such as precipitation, evaporation, temperature, and radiation. The hydrological model has more refined

Open access
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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Zhi Zhang, Dagang Wang, Jianxiu Qiu, Jinxin Zhu, and Tingli Wang

forecasts in the North American Multimodel Ensemble (NMME). Turini et al. (2019) delineated the rainfall area and assigned the rainfall rate by using the RF models to improve the IMERG based on the infrared bands of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG SEVIRI). Kumar et al. (2019) proposed the SVM-based regression (SVR) approach to improve TRMM-3B42RT, an NRT satellite-based rainfall product. Extreme gradient boosting (XGB), a powerful ML technique that

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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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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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Shibo Yao, Dabang Jiang, and Zhongshi Zhang

Xinjiang and understand whether the moisture source varies when heavy precipitation events are characterized by different meteorological patterns. This study attempts to answer these questions by first identifying heavy precipitation days based on daily reanalysis and observation data in the wet seasons (April–September) of 1979–2018. Then, a neural network technique, called self-organizing maps (SOM; Kohonen 1998 ), is used to obtain the main meteorological patterns of heavy precipitation days in

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