Recorded presentation3A.4
The Ensemble Kalman Filter data assimilation system at the Naval Research Laboratory
Qingyun Zhao, NRL, Monterey, CA; and K. Sashegyi, T. Holt, C. Bishop, F. Zhang, and Q. Xu
An ensemble Kalman Filter (EnKF) data assimilation system has been adapted and is under development at the Naval Research Laboratory (NRL) to provide initial conditions for the US Navy's mesoscale forecasts. The system is designed to share many components such as observational data sources, data processing and quality control algorithms, and operational operators with the NRL Atmospheric Variational Data Assimilation System (NAVDAS) for easy comparisons between the ensemble-based and variational data assimilation methods and for a potential hybrid approach that optimally combines these two systems. Radar data capability is also being added to the system to assimilate high-resolution radar observations into the system to improve mesoscale and storm-scale forecasts. The EnKF has been tested with both simulated and real observations with some encouraging results. An algorithm has been developed to improve the computational efficiency of the EnKF in a distributed parallel computational environment using MPI. The adaptive time-expanded sampling algorithm developed at the National Severe Storms Laboratory (NSSL) will also be adapted and tested to enlarge the ensemble size without increasing the computational cost for the expensive ensemble integration.
Session 3A, Data Assimilation: Overview
Monday, 1 June 2009, 1:30 PM-3:00 PM, Grand Ballroom East
Previous paper Next paper