Assimilation of simulated wind lidar data with a Kalman filter

Abstract

The assimilation experiments focused solely on the time evolution of the forecast error covariances that are influenced by two factors: 1) their time integration performed here with the tangent linear model obtained from a linearization around the true trajectory and 2) the accuracy and distribution of the observations. Data from a simulated radiosonde network have been assimilated over a 24-h period. The results show that even though no model error has been considered, there can be a substantial forecast error growth, especially in regions where the flow is unstable and no data are available. The impact of different initial conditions for the forecast error covariance is also looked at. In an experiment where the time integration of the forecast error covariance is suppressed, the results show that error growth is suppressed, causing the analysis error variance to differ substantially from the variance field obtained with the EKF. A mini-observing system simulation experiment has been conducted for which wind data from a proposed satellite-based lidar instrument have been simulated and added to the radiosonde data of the previous experiments. Compared to the results obtained with the radiosonde data alone, the global data coverage leads to an improvement in the analysis, especially in the Southern Hemisphere. Data being available in the regions of instability, the assimilation is now capable of putting a stop to the unlimited error growth observed in the previous experiments. -from Authors