Filtering algorithm of airborne Doppler lidar measurements for improved wind estimation

In this paper, we describe a filtering algorithm for removing the error of wind velocity arises in airborne Doppler lidar measurements. The algorithm is based on the Kalman filter with a simplified Kalman gain, which assumes zero variance for correct wind velocity and infinite variance for incorrect wind velocity. The algorithm is applied to 17,487 seconds of airborne Doppler lidar measurements, where a sequence of measurements along the lidar's measurement range is obtained every one second. The reduction of incorrect wind velocity is evaluated at the distance where correct wind velocity exists at least 20-30% of the time out of all measurements. The average standard deviation of filtered wind velocity at the above mentioned distance results in 17.2% of the original value, which is similar magnitude to correct measurements.