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Daisuke Hotta, Tse-Chun Chen, Eugenia Kalnay, Yoichiro Ota, and Takemasa Miyoshi

1. Introduction Numerical weather prediction (NWP) has gone through dramatic improvement over the last several decades (e.g., Simmons 2011 ). Despite the very high average forecast skill, however, current operational NWP systems still suffer from abrupt drops in forecast performance (e.g., Alpert et al. 2009 ; Kumar et al. 2009 ; Rodwell et al. 2013 ). Such forecast skill dropouts , or busts , are highly undesirable because they not only degrade the average forecast skills but also taint

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Kazumasa Aonashi, Kozo Okamoto, Tomoko Tashima, Takuji Kubota, and Kosuke Ito

-related variables, we need to use a sampling error damping method whose hypothesis for the forecast error characteristics are satisfied with the forecast errors of these variables. We also need to increase the sample numbers in the forecast error covariance calculation. Hence, the purpose of the present study is twofold: to examine the sampling error properties and forecast error characteristics of the Japan Meteorological Agency (JMA) operational CRM and to develop a CRM sampling error damping method based on

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Jean-François Caron, Thomas Milewski, Mark Buehner, Luc Fillion, Mateusz Reszka, Stephen Macpherson, and Judy St-James

) reported that 4DEnVar can outperform both 4DVar and hybrid 4DVar in the context of the limited-area HIRLAM forecasting system. Therefore, the 4DEnVar approach described in Part I seemed appropriate for the RDPS in order to 1) improve, or at least maintain, the RDPS forecast accuracy obtained using the operational limited-area 4DVar scheme and 2) make more efficient use of limited resources at EC by moving toward a more unified data assimilation approach for deterministic and ensemble forecasting

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Juanzhen Sun, Hongli Wang, Wenxue Tong, Ying Zhang, Chung-Yi Lin, and Dongmei Xu

.1175/BAMS-D-11-00167.1 . Berre , L. , 2000 : Estimation of synoptic and mesoscale forecast error covariances in a limited-area model . Mon. Wea. Rev. , 128 , 644 – 667 , doi: 10.1175/1520-0493(2000)128<0644:EOSAMF>2.0.CO;2 . Courtier , P. , J.-N. Thépaut , and A. Hollingsworth , 1994 : A strategy for operational implementation of 4D-Var, using an incremental approach . Quart. J. Roy. Meteor. Soc. , 120 , 1367 – 1387 , doi: 10.1002/qj.49712051912 . Derber , J. , and F. Bouttier

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Andrew C. Lorenc, Neill E. Bowler, Adam M. Clayton, Stephen R. Pring, and David Fairbairn

summarized in Table 1 . Table 1. Experiments compared in section 3 . The trials were run at slightly lower horizontal resolution than the Met Office operational global NWP system, which for the experimental period in 2013 ran its deterministic forecast with a grid spacing of about 26 km: the grids we used were 640 × 481 (about 42 km) for the deterministic forecast model and 432 × 325 (about 62 km) for the ensemble forecasts and the perturbation forecast model in 4DVar. All used the same 70 levels

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Stefano Migliorini

forward-model uncertainty or with characteristics that make them more sensitive to misspecifications of forecast error uncertainty in observation space (e.g., with multiple gas sensitivities or with Jacobians that have multiple peaks or long tails)—was used at ECMWF ( Collard 2007 ) to determine an optimal set of (currently 373) IASI channels sensitive to atmospheric temperature, water vapor, ozone, and surface conditions in the clear sky for operational monitoring or assimilation. The impact on

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Daryl T. Kleist and Kayo Ide

work are the previous studies that have focused on operational-like or preoperational global numerical weather prediction models. A comparison of the variational, EnKF, and EnVar data assimilation algorithms was carried out for global deterministic prediction using Environment Canada’s operational model in Buehner et al. (2010a , b) . It was found that using ensemble-based covariances in the EnVar system led to significant forecast improvements in the southern extratropics and modest improvements

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Hailing Zhang and Zhaoxia Pu

) operational forecasts or the WRF simulation initialized from GFS analysis. Langland et al. (2009) studied the impacts of Geostationary Operational Environmental Satellite (GOES) rapid-scan wind observations on 24–120-h track forecasts of Hurricane Katrina with a series of data assimilation and forecast experiments using the Navy Operational Global Atmospheric Prediction System (NOGAPS) and the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS). They found that the

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D. J. Lea, I. Mirouze, M. J. Martin, R. R. King, A. Hines, D. Walters, and M. Thurlow

shorter time scales ( Brassington et al. 2015 ). Pellerin et al. (2004) demonstrate significant forecast improvements associated with the changes in sea ice cover when using a coupled atmosphere–ocean–sea ice model in the Gulf of St. Lawrence. Improvements to the modeling of tropical cyclones were shown by Chen et al. (2010) when using coupled models. For the development of coupled forecasting at medium range, and for operational seasonal forecasts [e.g., the Global Seasonal forecast system

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Mark Buehner, Ron McTaggart-Cowan, Alain Beaulne, Cécilien Charette, Louis Garand, Sylvain Heilliette, Ervig Lapalme, Stéphane Laroche, Stephen R. Macpherson, Josée Morneau, and Ayrton Zadra

1. Introduction For more than a decade, numerous operational numerical weather prediction (NWP) centers have had significant improvements in analysis and forecast accuracy by adopting the four-dimensional variational data assimilation (4DVar) approach (e.g., Rabier et al. 2000 ; Rabier 2005 ; Rawlins et al. 2007 ; Gauthier et al. 2007 ). In parallel with these developments, a significant research and development effort has led to the successful application of the ensemble Kalman filter

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