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Elizabeth Satterfield and Istvan Szunyogh

varying adaptive covariance inflation technique, such as described in Anderson (2009) or a localized version of Li et al. (2009) may lead to an improvement of the analyses and the short-term ensemble forecasts. Fig . 7. The time average of the ratio d k in the leading direction for the temperature at 850 hPa. Results are shown for (left) the analysis time and (right) the 5-day forecast for experiments that assimilate (top) randomly distributed simulated observations, (middle) simulated

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Ronald Gelaro, Rolf H. Langland, Simon Pellerin, and Ricardo Todling

be a flexible and computationally efficient way to estimate the impacts of all assimilated observations on a selected measure of short-range forecast error. Although subject to assumptions and limitations inherent in the use of adjoint models, the technique efficiently estimates the impacts of all observations simultaneously, and produces results that can be easily aggregated by data type, location, channel, etc. The technique has gained popularity as a an alternative or complement to

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Benoît Vié, Olivier Nuissier, and Véronique Ducrocq

sampling the probability density function for the initial state with different techniques. Global EPSs were first implemented at the European Centre for Medium-Range Weather Forecasts (ECMWF) using the computation of singular vectors ( Molteni et al. 1996 ), and at the National Centers for Environmental Prediction (NCEP) through the breeding modes technique. The Meteorological Service of Canada used an ensemble data assimilation technique and, more recently, an ensemble Kalman filter to generate the

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John E. Janowiak, Peter Bauer, Wanqiu Wang, Phillip A. Arkin, and Jon Gottschalck

1. Introduction Numerical models evolve constantly due to the ever-present demand for more accurate weather and climate forecasts. Similarly, observational datasets improve with time largely due to advances in observing and communication systems, data storage capacity, and computing power. These circumstances warrant continued validation activities to assess potential model improvements with state-of-the-art observing systems. Several investigations of model precipitation

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William A. Komaromi, Sharanya J. Majumdar, and Eric D. Rappin

perturbation technique, a range of positive and negative vorticity perturbations is selected. Target S1 is perturbed using α max = ±0.75 and ±0.23, generating maximum vertically averaged deep-layer (850–200 hPa) wind perturbations of 15.6 and 4.9 m s −1 , respectively, at the target, and 0.6 and 0.2 m s −1 , respectively, within 300 km of the center of Sinlaku. The WRF forecast track of Sinlaku is found to be highly sensitive to the perturbations with α max = ±0.75 ( Fig. 4a ). The substantial weakening

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Thomas M. Hamill, Jeffrey S. Whitaker, Michael Fiorino, and Stanley G. Benjamin

, another advanced data assimilation and ensemble initialization technique has emerged: the ensemble Kalman filter (EnKF; Evensen 1994 ; Houtekamer and Mitchell 1998 ; Lorenc 2003 ; Hamill 2006 ; Ehrendorfer 2007 ; Evensen 2009 ). Ensembles of short-term forecasts are used to estimate the background-error covariances in the update step, where forecasts are adjusted to newly available observations. This update step produces an ensemble of analyses that are specifically constructed to sample the

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Mio Matsueda, Masayuki Kyouda, Zoltan Toth, H. L. Tanaka, and Tadashi Tsuyuki

and improvements in blocking prediction in medium-range forecasts, for which the initial-value problem is of greater concern than the boundary-value problem. Advances in these areas may lead to improvements not only in medium-range forecasting skill but also in model performance in climate projections. The NWP technique has progressed rapidly with advances in computer science. A 5-day weather forecast today is as reliable as a 2-day weather forecast 20 years ago, which represents a major

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Warren J. Tennant, Glenn J. Shutts, Alberto Arribas, and Simon A. Thompson

technique suggested by Epstein (1969) . This shifted the focus from a purely deterministic view of forecasting the weather to the idea of trying to quantify the amount of uncertainty in a forecast. However, forecast uncertainty was not only attributed to initial condition errors. A second cause of forecast uncertainty was ascribed to upscaling errors that occurred because of the limited variability in the model phase space. To address this problem, Leith (1978) suggested using empirical correction

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Sharanya J. Majumdar, Kathryn J. Sellwood, Daniel Hodyss, Zoltan Toth, and Yucheng Song

. Forecast error variance, or forecast uncertainty, may be predicted by a multimodel ensemble of forecasts, aimed at capturing a wide range of likely scenarios. The ETKF is philosophically different from adjoint-based methods in this regard, since the latter methods (e.g., Palmer et al. 1998 ) are aimed at reducing the forecast error in a given model . On the other hand, theoretical connections between ensemble- and adjoint-based techniques have been identified ( Leutbecher 2003 ; Majumdar et al

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Munehiko Yamaguchi and Sharanya J. Majumdar

: Ensemble forecasting at NCEP and the breeding method. Mon. Wea. Rev. , 125 , 3297 – 3319 . Wei , M. , Z. Toth , R. Wobus , and Y. Zhu , 2008 : Initial perturbations based on the ensemble transform (ET) technique in the NCEP global operational forecast system. Tellus

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