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, 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
, 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
1. Introduction Many Monte Carlo methods for geophysical data assimilation and prediction have been developed. The most general methods are particle filters that can represent arbitrary analysis probability distributions ( Van Leeuwen 2003 ). However, the number of particles required increases very rapidly as the size of the prediction model increases. At present, no variant of a particle filter that is practical for large geophysical models is known ( Snyder et al. 2008
1. Introduction Many Monte Carlo methods for geophysical data assimilation and prediction have been developed. The most general methods are particle filters that can represent arbitrary analysis probability distributions ( Van Leeuwen 2003 ). However, the number of particles required increases very rapidly as the size of the prediction model increases. At present, no variant of a particle filter that is practical for large geophysical models is known ( Snyder et al. 2008
1. Introduction A wealth of satellite data have been made available to operational meteorological centers in recent years, and many advances have been made in the techniques for their assimilation, resulting in a dramatic increase in the quantity of both processed and actively used data for numerical weather prediction ( Thépaut 2003 ). The two main approaches developed over the last 2 decades for assimilating remote sensing data into a numerical model are assimilation of retrievals and
1. Introduction A wealth of satellite data have been made available to operational meteorological centers in recent years, and many advances have been made in the techniques for their assimilation, resulting in a dramatic increase in the quantity of both processed and actively used data for numerical weather prediction ( Thépaut 2003 ). The two main approaches developed over the last 2 decades for assimilating remote sensing data into a numerical model are assimilation of retrievals and
these channels, AMSR-E has four low-frequency channels in the microwave region (6.925 and 10.65 GHz, dual polarization) that are sensitive to the sea surface wind speed and the sea surface temperature, and are less affected by the atmosphere. Therefore, these measurements provide useful information on the sea surface wind speed and sea surface temperature under almost all weather conditions. The Japan Meteorological Agency (JMA) has been using AMSR-E radiance data in their global data assimilation
these channels, AMSR-E has four low-frequency channels in the microwave region (6.925 and 10.65 GHz, dual polarization) that are sensitive to the sea surface wind speed and the sea surface temperature, and are less affected by the atmosphere. Therefore, these measurements provide useful information on the sea surface wind speed and sea surface temperature under almost all weather conditions. The Japan Meteorological Agency (JMA) has been using AMSR-E radiance data in their global data assimilation
1. Introduction The advent of modern geostationary satellites ushered in an era of global mesoscale-resolving infrared observations of the atmosphere. Nowadays, operational numerical weather prediction centers include clear-sky infrared radiance observations into their data assimilation (DA). The inclusion of clear-sky observations has improved the analyzed temperature and humidity fields that are used to initiate numerical forecasts ( Köpken et al. 2004 ; Munro et al. 2004 ; Yang et al. 2017
1. Introduction The advent of modern geostationary satellites ushered in an era of global mesoscale-resolving infrared observations of the atmosphere. Nowadays, operational numerical weather prediction centers include clear-sky infrared radiance observations into their data assimilation (DA). The inclusion of clear-sky observations has improved the analyzed temperature and humidity fields that are used to initiate numerical forecasts ( Köpken et al. 2004 ; Munro et al. 2004 ; Yang et al. 2017
1. Introduction In ensemble data assimilation (DA), defining a proper way to update ensemble members when observations are available has been a constant research topic during the two last decades. Starting with the early work of Evensen (1994) about the ensemble Kalman filter (EnKF), a first correction has been proposed by Burgers et al. (1998) and Houtekamer and Mitchell (1998) to ensure that an appropriate ensemble spread is maintained: the perturbation of observations assimilated by
1. Introduction In ensemble data assimilation (DA), defining a proper way to update ensemble members when observations are available has been a constant research topic during the two last decades. Starting with the early work of Evensen (1994) about the ensemble Kalman filter (EnKF), a first correction has been proposed by Burgers et al. (1998) and Houtekamer and Mitchell (1998) to ensure that an appropriate ensemble spread is maintained: the perturbation of observations assimilated by
1. Introduction A diagnostic evaluation of five data types being used by the National Centers for Environmental Prediction (NCEP) operational Global Data Assimilation System (GDAS) is produced in this study. These types of studies help realize the full impact of some of the numerous data sources available today. All types of remotely sensed data are prime examples of such data requiring diagnostic study of their impact in both regional and global models. If these types of experiments are not
1. Introduction A diagnostic evaluation of five data types being used by the National Centers for Environmental Prediction (NCEP) operational Global Data Assimilation System (GDAS) is produced in this study. These types of studies help realize the full impact of some of the numerous data sources available today. All types of remotely sensed data are prime examples of such data requiring diagnostic study of their impact in both regional and global models. If these types of experiments are not
components (i.e., those with smaller eigenvalues). A truncated number of leading principal components (PCs), with significantly fewer variables than the number of channels, can be used to reproduce the original hyperspectral observations with reduced noise ( Huang and Antonelli 2001 ; Antonelli et al. 2004 ; Turner et al. 2006 ). PCA-compressed data have been used in data assimilation approaches in various ways. Collard et al. (2010) assimilated reconstructed IASI radiances from the principal
components (i.e., those with smaller eigenvalues). A truncated number of leading principal components (PCs), with significantly fewer variables than the number of channels, can be used to reproduce the original hyperspectral observations with reduced noise ( Huang and Antonelli 2001 ; Antonelli et al. 2004 ; Turner et al. 2006 ). PCA-compressed data have been used in data assimilation approaches in various ways. Collard et al. (2010) assimilated reconstructed IASI radiances from the principal
1. Introduction Ensemble Kalman filters were developed for data assimilation in oceanic and atmospheric applications during the 1990s ( Evensen 1994 ; Burgers et al. 1998 ). Basic ensemble filters worked well for low-order models, but performed poorly or diverged from the observed system when applied to large geophysical models. Houtekamer and Mitchell (1998) determined that small ensembles could not accurately estimate the small correlations between a state variable and a physically remote
1. Introduction Ensemble Kalman filters were developed for data assimilation in oceanic and atmospheric applications during the 1990s ( Evensen 1994 ; Burgers et al. 1998 ). Basic ensemble filters worked well for low-order models, but performed poorly or diverged from the observed system when applied to large geophysical models. Houtekamer and Mitchell (1998) determined that small ensembles could not accurately estimate the small correlations between a state variable and a physically remote
1. Introduction One of the weakest elements of variational data assimilation (VAR) for numerical weather prediction is the treatment of background state errors (e.g., Zhang and Anderson 2003 ). The background state is the best-guess of the meteorological fields before the assimilation, and is necessary because observations on their own are insufficient to determine the state of the atmosphere uniquely. The best choice of background is found from a short forecast starting from well
1. Introduction One of the weakest elements of variational data assimilation (VAR) for numerical weather prediction is the treatment of background state errors (e.g., Zhang and Anderson 2003 ). The background state is the best-guess of the meteorological fields before the assimilation, and is necessary because observations on their own are insufficient to determine the state of the atmosphere uniquely. The best choice of background is found from a short forecast starting from well
