A Kalman filter for a two-dimensional shallow-water model

摘要: A two-dimensional Kalman filter is described for data assimilation making weather forecasts. The regarded as superior to the optimal interpolation method because determines forecast error covariance matrix exactly instead of using an approximation. generalized time step defined which includes expressions one model, matrix, gain and evolution matrix. Subsequent steps are achieved by quantifying variables or employing a linear extrapolation from current variable set, assuming dynamics linear. Calculations banded, i.e., performed only with elements significantly different zero. Experimental results provided application shallow-water simulation covering 6000 x km grid.