A Kalman filter for a two-dimensional shallow-water modelA two-dimensional Kalman filter is described for data assimilation for making weather forecasts. The filter is regarded as superior to the optimal interpolation method because the filter determines the forecast error covariance matrix exactly instead of using an approximation. A generalized time step is defined which includes expressions for one time step of the forecast model, the error covariance matrix, the gain matrix, and the evolution of the covariance matrix. Subsequent time steps are achieved by quantifying the forecast variables or employing a linear extrapolation from a current variable set, assuming the forecast dynamics are linear. Calculations for the evolution of the error covariance matrix are banded, i.e., are performed only with the elements significantly different from zero. Experimental results are provided from an application of the filter to a shallow-water simulation covering a 6000 x 6000 km grid.
Document ID
19870024403
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Parrish, D. F. (NOAA, National Meteorological Center Washington, DC, United States)
Cohn, S. E. (New York University NY, United States)