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Steven M. Martinaitis, Stephen B. Cocks, Micheal J. Simpson, Andrew P. Osborne, Sebastian S. Harkema, Heather M. Grams, Jian Zhang, and Kenneth W. Howard

fully automated gauge QC algorithm are presented in comparison to manual QC by National Weather Service (NWS) forecasters. The study also describes various characteristics of the gauges ingested into the MRMS system, including data latency, observation times, duplications of observations, impacts of gauge location errors, and gauge network metadata. 2. MRMS gauge ingest The MRMS system IOC ingested gauges from the Hydrometeorological Automated Data System (HADS; Kim et al. 2009 ) network, primarily

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Kevin Werner and Kristen Yeager

1. Introduction This paper describes the 2011 peak streamflows in the Colorado basin and the Great Basin in an attempt to illuminate the forecasting efforts of the NOAA Colorado Basin River Forecast Center (CBRFC). A recent National Research Council (2012) report highlighted the difficulties in transferring research results into operational river forecasting as a major impediment to improving forecasts. The primary goal of this paper is to highlight three areas where research is most needed

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Hector Macian-Sorribes, Ilias Pechlivanidis, Louise Crochemore, and Manuel Pulido-Velazquez

forecasting approaches commonly relying on stochastic modeling techniques ( Foster et al. 2018 ; Pianosi and Soncini-Sessa 2009 ; Pina et al. 2017 ) have recently been complemented by forecasting services driven by seasonal dynamic prediction systems ( Arnal et al. 2018 ; Johnson et al. 2019 ; MacLachlan et al. 2015 ). A number of applications of seasonal forecast information for decision-making can be found in water-related sectors, i.e., urban water supply ( Guo et al. 2018 ), hydropower ( Giuliani

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Ganesh R. Ghimire, Witold F. Krajewski, and Felipe Quintero

phase entails the rainfall-to-rainfall evaluation. We compute errors and uncertainties associated with the QPFs with respect to a surrogate for the perfect QPF, i.e., the MRMS QPE. We use standard verification techniques (see section 3c ) to evaluate the QPFs independent of the hydrologic model. The second phase involves exploring the propagation of QPF uncertainties to streamflow forecasts, which requires the use of a hydrologic forecasting model. A typical real-time streamflow forecasting system

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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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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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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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David A. Lavers, Shaun Harrigan, and Christel Prudhomme

approach. WMO Bulletin , Vol. 69, World Meteorological Organization, Geneva, Switzerland, https://public.wmo.int/en/resources/bulletin/innovating-global-hydrological-prediction-through-earth-system-approach . Hersbach , H. , and Coauthors , 2020 : The ERA5 global reanalysis . Quart. J. Roy. Meteor. Soc. , 146 , 1999 – 2049 , https://doi.org/10.1002/qj.3803 . 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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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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