Noise filtering in a variational wind field retrieval from Doppler radar

Two different concepts for noise filtering in a DOPPLER radar wind field re trieval are compared. The first technique is a smoothing on the raw radar d ata. The second method uses a smoothness constraint within a variational te chnique in which a model wind field is adjusted to radar data by minimizing a cost function. The wind field retrieval algorithm is described by PROTAT and ZAWADZKI(1999) and is further extended here by adding a topography con straint for its application in mountainous terrain. The experiments are carried out with analytic wind fields artificially affe cted by noise. In a first step only the horizontal wind is retrieved. In a second step the continuity equation serves to calculate the three-dimension al wind field. Power spectra and an error analysis of the retrieved wind fi elds as well as the performance of the algorithm lead to the following resu lts. An efficient noise filtering by a mere data smoothing is accompanied b y a lack of accuracy in the retrieved wind components. The smoothness const raint eliminates the noise more efficiently than smoothing of raw data whil e producing an accurate wind field. For the retrieval of the vertical veloc ity wind field, the smoothness constraint is indispensable.