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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
1. Introduction In Part I of this two-part paper ( Sondergaard and Lermusiaux 2013 ), we derived the GMM-DO filter: data assimilation with Gaussian Mixture Models (GMMs) using the Dynamically Orthogonal (DO) field equations. The result was an efficient, rigorous, data-driven assimilation scheme preserving non-Gaussian statistics and respecting nonlinear dynamics. In the present study, we evaluate its performance against contemporary filters in a dynamical systems setting, including ocean and
1. Introduction In Part I of this two-part paper ( Sondergaard and Lermusiaux 2013 ), we derived the GMM-DO filter: data assimilation with Gaussian Mixture Models (GMMs) using the Dynamically Orthogonal (DO) field equations. The result was an efficient, rigorous, data-driven assimilation scheme preserving non-Gaussian statistics and respecting nonlinear dynamics. In the present study, we evaluate its performance against contemporary filters in a dynamical systems setting, including ocean and
1. Introduction The good performance of numerical weather forecasts highly depends on the accuracy of initial conditions. In modern numerical weather prediction (NWP), optimal analysis fields are estimated from previous model forecasts and multisource meteorological observations by data assimilation (DA). Compared with conventional observational data, satellite measurements can observe atmospheric structures with better spatial and temporal coverages. Sensitive to the temperature and humidity
1. Introduction The good performance of numerical weather forecasts highly depends on the accuracy of initial conditions. In modern numerical weather prediction (NWP), optimal analysis fields are estimated from previous model forecasts and multisource meteorological observations by data assimilation (DA). Compared with conventional observational data, satellite measurements can observe atmospheric structures with better spatial and temporal coverages. Sensitive to the temperature and humidity
