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Zhangkang Shu, Jianyun Zhang, Junliang Jin, Lin Wang, Guoqing Wang, Jie Wang, Zhouliang Sun, Ji Liu, Yanli Liu, Ruimin He, Cuishan Liu, and Zhenxin Bao

only be improved through certain bias correction and data fusion techniques. Figure 6c shows the spatial distribution of the discrepancies in the forecast performance of the models. The areas with large discrepancies in forecast performance are centered on the Northwest Rivers Region, the upper reaches of the Yangtze River Region, the middle reaches of the Yellow River Region, the Liaohe River Region, and the Haihe River Region. The forecast performance of the models in no-rain events in these

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Eric A. Rosenberg, Andrew W. Wood, and Anne C. Steinemann

needs are least satisfied and identify sites with the best potential to offer skill improvements. This paper presents a hydrometric network design approach toward the objective of enhancing statistical prediction models. The specific focus of the paper is the development of a forecast skill-oriented technique for informing NRCS SNOTEL network expansion decisions. We employ a hybrid dynamical–statistical approach that combines the dimension-reducing power of the NRCS PCR methodology with the

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Tirthankar Roy, Xiaogang He, Peirong Lin, Hylke E. Beck, Christopher Castro, and Eric F. Wood

feedbacks, improper model calibration, etc. A better understanding of the predictability issues and proper diagnostics and accounting of the model deficiencies can lead to significant improvement in the seasonal forecasts. Statistical postprocessing techniques are the alternative ways to improve the forecasting skill. We already discussed about model averaging and its caveats in section 3c . In our case, the arithmetic mean showed good skill in many cases, but they did not necessarily outperform all

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Martyn P. Clark and Lauren E. Hay

basins where the surface hydrology is dominated by rainfall, the NCEP temperature forecasts do provide useful predictions of streamflow in river basins dominated by snowmelt. 5. Improvement of raw NCEP NWP output a. Background Given the large systematic biases in the NCEP model and the poor skill in precipitation and 2-m air temperature forecasts in some regions, it is necessary to use methods that may improve upon the raw forecasts. The technique of model output statistics (MOS; e.g., Glahn and

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David A. Lavers, Shaun Harrigan, and Christel Prudhomme

approach. WMO Bulletin , Vol. 69, World Meteorological Organization, Geneva, Switzerland, . Hersbach , H. , and Coauthors , 2020 : The ERA5 global reanalysis . Quart. J. Roy. Meteor. Soc. , 146 , 1999 – 2049 , . 10.1002/qj.3803 Hewson , T. D. , and F. M. Pillosu , 2020 : A new low-cost technique improves weather forecasts across the world

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Paul W. Miller and Craig A. Ramseyer

than the ERA5-based method. Because the CFS demonstrated a persistent high bias, any effort to compare the 2015 CFS GDI to the ERA5 dataset necessarily involves a calibration technique like the one performed in Fig. 9 . Instead, computing the percentile rank of the 2015 CFS GDI within the 37-yr reforecast/operational dataset neutralizes the CFS’s high bias by allowing it to equally affect all forecasts in the distribution. Over the eastern Caribbean, the 2015 GDI predicted by the CFS with a 0

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Hisham Eldardiry and Faisal Hossain

technique that has recently gained increasing attention in the reservoir operation literature ( Breckpot et al. 2013 ; Galelli et al. 2014 ). MPC is typically implemented in reservoir operations with a rolling horizon decision approach. This approach updates forecasts and decisions with each time step leading to more reliable operation ( Zhao et al. 2012 ; Wan et al. 2016 ). The steps to derive optimal reservoir operation using MPC are as follows: At each decision time instant t , a control problem

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Mohammadvaghef Ghazvinian, Yu Zhang, and Dong-Jun Seo

distribution (CSGD; Scheuerer and Hamill 2015 ; Baran and Nemoda 2016 ). These techniques represent the discontinuous–continuous nature of precipitation using left-censored distributions and rely on heteroscedastic distributional regression to derive distribution parameters. Unlike logistic regression and its extended version ( Wilks 2009 ), EMOS variants offer the flexibility of incorporating ensemble attributes such as spread, as well as other forecast variables as additional predictors. Perhaps the

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Hernan A. Moreno, Enrique R. Vivoni, and David J. Gochis

. 2004 ; Verbunt et al. 2007 ; Anagnostou et al. 2010 ; Moreno et al. 2012 ) and thus in the use of radar nowcasting techniques for predicting the timing, location, and magnitude of precipitation as input to hydrologic models. Uncertainties inherent in radar nowcasting QPFs are a consequence of the difficulty to forecast rainfall fields for extended periods given that extrapolation functions lose their correlation structures at large lead times (e.g., Sharif et al. 2006 ; Vivoni et al. 2007b

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Qian Cao, Shraddhanand Shukla, Michael J. DeFlorio, F. Martin Ralph, and Dennis P. Lettenmaier

forecasting by providing the basis for postprocessing methods (e.g., bias correction and calibration techniques) that provide adjustments to real-time predictions. Primary factors that impact forecast quality and ability to evaluate the performance of the hindcast in a forecast system configuration include hindcast period, ensemble size and ensemble strategy (e.g., initial times) ( Merryfield et al. 2020 ). However, there are tradeoffs in the system configuration due to practical constraints. A few SubX

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