ABSTRACT:The ensemble Kalman filter (EnKF) has been widely tested as a possible candidate for the next generation of meteorological and oceanographic data assimilation algorithms. While a number of tests with models of varying realism have been successfully performed, the EnKF has been seldom evaluated in an operational regional NWP environment at realistic spatial resolution. In this work one particular EnKF implementation (Local Ensemble Transform Kalman Filter, LETKF) has been implemented and its performance evaluated in comparison with CNMCA operational 3D-Var.One of the most important issues in EnKF implementations lies in the filter tendency to become underdispersive for practical ensemble sizes. While multiplicative (or additive) covariance inflation has been used to deal with this problem, tuning its values is an expensive and possibly never-ending task. Following ideas from linear estimation theory, we test an adaptive estimation procedure to evaluate forecast covariance inflation factors and observation errors. Our results show that, differently from previous experiences, the online estimation technique can be successfully employed in a realistic state-ofthe-art NWP system. More generally the LETKF analysis is shown to be of superior quality with respect to the operational 3D-Var and a likely candidate for its replacement in the not-too-distant future.
The Ensemble Kalman Filter (EnKF) is likely to become a viable alternative to variational methods for the next generation of meteorological and oceanographic data assimilation systems. In this work we present results from real-data assimilation experiments using the CNMCA regional numerical weather prediction (NWP) forecasting system and compare them to the currently operational variational-based analysis. The set of observations used is the same as the one ingested in the operational data stream, with the exception of satellite radiances and scatterometer winds. Results show that the EnKF-based assimilation cycle is capable of producing analyses and forecasts of consistently superior skill in the root mean square error metric than CNMCA operational 3D-Var.One of the most important issues in EnKF implementations lies in the filter tendency to become underdispersive for practical ensemble sizes. To combat this problem a number of different parametrizations of the model error unaccounted for in the assimilation cycle have been proposed. In the CNMCA system a combination of adaptive multiplicative and additive background covariance inflations has been found to give adequate results and to be capable of avoiding filter divergence in extended assimilation trials. The additive component of the covariance inflation has been implemented through the use of scaled forecast differences.Following suggestions that ensemble square-root filters can violate the gaussianity assumption when used with nonlinear prognostic models, the statistical distribution of the forecast and analysis ensembles has been studied. No sign of the ensemble collapsing onto one or a few model states has been found, and the forecast and analysis ensembles appear to stay remarkably close to the assumed probability distribution functions.
An example of wavelet transforms applied to data from EGRET and data of the GLAST pre-launch simulations is reported. 1D wavelet transform can be an useful tool in the analysis of gamma-ray source variability, while 2D/3D wavelet transform is a potential partner tool of the standard Likelihood analysis, in the frame of gamma-ray source detection.
The Gamma-ray Large Area Space telescope (GLAST) is a gamma-ray satellite scheduled for launch in 2008. Before the assembly of the Tracker subsystem of the Large Area Telescope (LAT) science instrument of GLAST, every component (tray) and module (tower) has been subjected to extensive ground testing required to ensure successful launch and on-orbit operation. This paper describes the sequence and results of the environmental tests performed on an engineering model and all the flight hardware of the GLAST LAT Ã Corresponding author.
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