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Y. Ourmières, J-M. Brankart, L. Berline, P. Brasseur, and J. Verron

any oscillations, when a free run started from an ocean state obtained after a similar period but with intermittent assimilation will exhibit spurious oscillations lasting several weeks. Then it appears that the ocean states obtained after several months of IAU integration are more balanced. This feature could be valuable for operational forecasting, where getting a model trajectory free of oscillations can be of major importance. d. Additional diagnostic: The EKE distribution EKE is a diagnostic

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Emmanouil N. Anagnostou and Witold F. Krajewski

National Weather Service River Forecasting System operational system within the Office of Hydrology of the National Weather Service. The LFM grid is a limited polar-stereographic projection and covers essentially the coterminous (i.e., lower 48) United States, plus some surrounding territory. g. Mean-field systematic error adjustment The mean-field systematic error (bias) adjustment is applied to the final (i.e., HRAP grid) hourly rainfall products. The decision of applying range correction (4

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Neville R. Smith

statisticalinterpolation and is illustrated using analyses for the 20-C isotherm depth over the Pacific and Indian Oceans.A new monthly climatology is derived by exploiting the statistical interpolation method to provide an improvedweighted estimate. The new climatology is shown to better represent key aspects of the tropical ocean thermalstructure. A statistical forecast based on the previous analysis and climatology significantly improves the analysis product,both in a qualitative sense and as judged by

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Yu Zhang, Min Chen, and Jiqin Zhong

, their use in meteorological NWP studies in China is still highly restricted because the error characteristics of the profiler data in terms of assimilation are still unclear, and no quality control procedure for wind profiler data tailored to meet the requirements of future assimilation is available. To incorporate the profiler data from the operational profiling network into the local data assimilation and forecasting system (BJRUC), a two-step quality control procedure is proposed in this study

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Sijie Pan, Jidong Gao, David J. Stensrud, Xuguang Wang, and Thomas A. Jones

studied. Thus, we study the impact of assimilating CWP and TPW (both of which will be GOES-16 products) on the analysis and forecast of an idealized supercell storm together with the assimilation of radar data using the En3DVar method ( Gao et al. 2016 ). A concept of the ensemble of DAs (EDA) used by Météo-France and ECMWF ( Berre et al. 2007 ; Bonavita et al. 2012 ) was adopted in Gao et al. (2016) . The only difference between the operational approaches is that Gao et al. used extended control

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Gary A. Wick, Terrence F. Hock, Paul J. Neiman, Holger Vömel, Michael L. Black, and J. Ryan Spackman

high-vertical-resolution atmospheric profile measurements over the oceans, particularly in stormy environments, dropsondes are the most widely employed observing system. Operational programs for collecting dropsonde measurements for hurricane surveillance in support of forecasting are conducted within the United States by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Air Force. Beyond their forecast value, the utility of the dropsonde observations for research studies of

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Timothy J. Wagner and Ralph A. Petersen

1. Introduction Upper-air observations are critical for many aspects of operational meteorology. Forecasters, for example, rely on atmospheric profiles to assess atmospheric stability or the potential of hazardous precipitation types, while analyses of data assimilation (D/A) systems used in numerical weather prediction (NWP) models show that in situ profiles continue to play a prominent role in facilitating accurate forecast simulations ( Eyre and Reid 2014 ). Since their operational

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Ross N. Hoffman, Rui M. Ponte, Eric J. Kostelich, Alan Blumberg, Istvan Szunyogh, Sergey V. Vinogradov, and John M. Henderson

1. Introduction Large advances in data gathering and ocean modeling capabilities in the last decade have started to make “operational oceanography” more than just a concept. In this new reality in oceanography, it is becoming more and more important that nowcasting and forecasting methods be both fast and accurate, provide information about uncertainties, make best use of disparate in situ and satellite datasets, and be implemented under different data constraints and dynamical regimes. Such

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Godelieve Deblonde

are, respectively, the background, the instrumental, and the representativeness (includes errors of the forward model) error covariance matrices. Here J SAT is a cubic function that limits the supersaturation and acts as a weak constraint ( Phalippou 1996 ). The satellite observations are denoted by y o , and x b are the collocated background profiles, which are Global Environmental Multiscale Model (GEM, Côté et al. 1998 ) 6-h forecasts. GEM has been operational at the Canadian

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Kanghui Zhou, Yongguang Zheng, Wansheng Dong, and Tingbo Wang

regression models to predict hail and thunderstorms based on radiosonde data, and their results showed that the forecasting models had a high probability of detection (POD) and a low false alarm ratio (FAR). Mecikalski et al. (2015) combined Geostationary Operational Environmental Satellite (GOES) data with NWP data using the RF and logistic regression (LR) methods, and they extracted 25 related elements (variables) for CI and achieved CI nowcasting results for North America that showed a better

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