A local ensemble transform Kalman filter data assimilation system for the NCEP global model

Abstract

The accuracy and computational efficiency of a parallel computer implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme on the model component of the 2004 version of the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) is investigated. Numerical experiments are carried out at model resolution T62L28. All atmospheric observations that were operationally assimilated by NCEP in 2004, except for satellite radiances, are assimilated with the LETKF. The accuracy of the LETKF analyses is evaluated by comparing it to that of the Spectral Statistical Interpolation (SSI), which was the operational global data assimilation scheme of NCEP in 2004. For the selected set of observations, the LETKF analyses are more accurate than the SSI analyses in the Southern Hemisphere extratropics and are comparably accurate in the Northern Hemisphere extratropics and in the Tropics. The computational wall‐clock times achieved on a Beowulf cluster of 3.6 GHz Xeon processors make our implementation of the LETKF on the NCEP GFS a widely applicable analysis‐forecast system, especially for research purposes. For instance, the generation of four daily analyses at the resolution of the NCAR‐NCEP reanalysis (T62L28) for a full season (90 d), using 40 processors, takes less than 4 d of wall‐clock time.

Ensemble data assimilation with the CNMCA regional forecasting system

“…A detailed derivation of the algorithm and its theoretical basis can be found in Hunt et al (2007) and Szunyogh et al (2008).…”

“…This larger value than the one used in the previous study (700 km: Bonavita et al, 2008) has been found to provide more accurate analysis and forecasts. Following Szunyogh et al (2008), the influence of each observation on the analysed grid point is decreased with its geophysical distance r through multiplication of the R −1 observation error matrix entries in Eqs. (1) and (3) with a smoothly decaying function of r (Gaussian with standard deviation equal to half the value of the radius of the local region).…”

The ensemble Kalman filter in an operational regional NWP system: preliminary results with real observations

“…The EnKF has been tested across a large range of forecast scales, from convective (Zhang et al, 2004) and mesoscale applications (Zhang et al, 2006;Meng and Zhang, 2007 and references therein) to regional and global scales (Dirren et al, 2007;Whitaker et al, 2008), with models and observational datasets of different realism. More recently, an efficient variant of the EnKF algorithm called Local Ensemble Transform Kalman Filter (LETKF) has been proposed and tested in global Numerical Weather Prediction (NWP) analysis and forecasting systems (Hunt et al, 2007;Miyoshi and Yamane, 2007;Szunyogh et al, 2008), comparing favourably with currently operational 3D-Var analysis systems.…”

A suboptimal data assimilation algorithm based on the ensemble Kalman filter

“…In particular, there may occur non-zero forecast error covariances at long distances, and the number of leading model operator's eigenvectors may be larger than the number of ensemble members. This is the reason why some authors suggest using the ensemble algorithm locally (Hunt et al, 2007;Szunyogh et al, 2008). As follows from formula (7) for the elements of matrix , this matrix is the same for all grid points where analysis is carried out.…”

“…In the present article the formulae of the ensemble π -algorithm are derived from assumptions that are more general than those in Klimova (2008b). The operation count of the algorithm is close to that of the local ensemble transform Kalman filter (LETKF; Hunt et al, 2007;Szunyogh et al, 2008), but its formulae differ from those of the LETKF. In particular, the ensemble π -algorithm does not require calculating an ensemble that corresponds to the analysis error covariances because it is done automatically.…”