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Wan-Shu Wu, David F. Parrish, Eric Rogers, and Ying Lin

1. Introduction Lorenc (2003) provided a thorough comparison between ensemble Kalman filter (EnKF) and four-dimensional variational data assimilation (4DVar) and proposed a hybrid method with extended control variable formulation for regional mesoscale numerical weather prediction (NWP) systems. Kalnay et al. (2007) and Gustafsson (2007) discussed the advantages and the disadvantages of these two approaches and also concluded that a hybrid method would be beneficial to meteorological data

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Redouane Lguensat, Pierre Tandeo, Pierre Ailliot, Manuel Pulido, and Ronan Fablet

1. Introduction The reconstruction of the spatiotemporal dynamics of geophysical systems from noisy and/or partial observations is a major issue in geosciences. Variational and stochastic data assimilation schemes are the two main categories of methods considered to address this issue [see Evensen (2007) for more details]. A key feature of these data assimilation schemes is that they rely on repeated forward integrations of an explicitly known dynamical model. This may greatly limit their

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Qingyun Zhao, Fuqing Zhang, Teddy Holt, Craig H. Bishop, and Qin Xu

1. Introduction An ensemble Kalman filter (EnKF; Evensen 1994 ) was recently developed at the Naval Research Laboratory (NRL) for use as an advanced high-resolution data assimilation system for the U.S. Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS; 1 Hodur 1997 ). The objectives of the EnKF development at NRL are twofold: (i) to investigate the impact of flow-dependent background error covariance on mesoscale and storm-scale data assimilation, especially when applied

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Jingzhe Sun, Zhengyu Liu, Feiyu Lu, Weimin Zhang, and Shaoqing Zhang

1. Introduction Coupled data assimilation (CDA) is considered an effective initialization approach for coupled Earth system models ( Zhang et al. 2007 ; Sugiura et al. 2008 ; Saha et al. 2010 ; Dee et al. 2011 ). CDA assimilates observations into one or more model components and allows the exchange of information between different model components dynamically and statistically. Therefore, it is expected to produce more self-consistent state estimation for coupled models ( Zhang et al. 2005

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Jidong Gao and David J. Stensrud

1. Introduction The operational Weather Surveillance Radar-1988 Doppler (WSR-88D) network is a valuable source of data for storm-scale numerical weather prediction (NWP). However, the assimilation of radar reflectivity into storm-scale NWP remains a challenge. One of the greatest difficulties is the uncertainty in the reflectivity forward operators that link model hydrometeor variables with radar reflectivity observations. These uncertainties occur because of the complexity of numerical model

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Monique Tanguay, Luc Fillion, Ervig Lapalme, and Manon Lajoie

1. Introduction The Canadian Meteorological Center’s (CMC) operational regional data assimilation and forecasting system was upgraded on 20 October 2010. The previous global variable-resolution forecasting approach was replaced by a limited-area nested forecasting approach. Both systems are based on the Global Environmental Multiscale (GEM) model ( Côté et al. 1998 ), which can be run either in a global uniform, a limited-area, or a global variable-grid configuration. Under the constraints of

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Weiwei Fu and Jiang Zhu

1. Introduction By combining existing observations and theoretical knowledge obtained from general circulation models (GCM), ocean data assimilation is able to help in providing a better estimation of ocean state. This is very important for both operational purposes and climate variability assessments. In ocean data assimilation, satellite observation has spatial and temporal coverage that cannot be achieved in current in situ observations. Previous studies find that assimilation of the

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Ming Hu, Stanley G. Benjamin, Therese T. Ladwig, David C. Dowell, Stephen S. Weygandt, Curtis R. Alexander, and Jeffrey S. Whitaker

1. Introduction The Rapid Refresh (RAP; Benjamin et al. 2016 , hereafter B16 ) was developed as an hourly updated data assimilation–model forecast cycling system to meet the growing requirements for increased accuracy in short-range weather guidance for aviation, energy, severe weather, hydrology, agriculture, and other sectors. The RAP replaced the Rapid Update Cycle (RUC; Benjamin et al. 2004a , b ) within the operational model suite at NOAA’s National Centers for Environmental Prediction

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Roland Potthast, Klaus Vobig, Ulrich Blahak, and Clemens Simmer

, turbulence, orographic influence, and microphysics. These model system are initialized by the use of data assimilation techniques, such as variational data assimilation (3D-VAR or 4D-VAR) or ensemble data assimilation, e.g., the ensemble Kalman filter (EnKF) or, more recently, particle filters; for a detailed introduction we refer to Lorenc et al. (2000) , Kalnay (2003) , Evensen (2009) , Anderson and Moore (2012) , van Leeuwen et al. (2015) , Reich and Cotter (2015) , Kleist et al. (2009

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Sabrina Rainwater and Brian Hunt

1. Introduction Numerical weather prediction uses a numerical model of atmospheric physics to predict the future states of the atmosphere given an estimate for the current atmospheric state. Uncertainty about the current state, along with inaccuracies in the model dynamics, leads to (greater) uncertainty in the forecast. Quantifying this uncertainty is important for interpreting the forecast and for data assimilation. In data assimilation, information from a short-term forecast is combined with

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