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Philippe Lucas-Picher, Fredrik Boberg, Jens H. Christensen, and Peter Berg

observationally based datasets. Therefore, systematic errors from simulated climate datasets are often reduced with postprocessing using bias correction techniques (e.g., Piani et al. 2010 ; Berg et al. 2012 ) before being provided to CIMs. However, deviations in the sequence of events cannot be corrected with such postprocessing. A method that is not widely adopted by the RCM community, but is popular in the numerical weather prediction and data assimilation communities to generate small-scale predictions

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

initialization? The answer would provide insight as to the first-order influence of the land surface on ambient weather (e.g., temperature, humidity, and precipitation) and coupled LA components of a prediction system [e.g., planetary boundary layer (PBL) growth and convective initiation]. This question is addressed here by combining LSM calibration and spinup approaches to produce best estimates of land surface fluxes for coupling with the Advanced Research Weather Research and Forecasting Model (WRF

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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
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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