Improving the Assimilation of Scatterometer Wind Observations in Global NWP

This study aims at improving the assimilation of scatterometer wind observations in global Numerical Weather Prediction (NWP) model by refining the background quality control and optimizing the observation sampling strategy. To improve the background quality control, different Huber Norm distribution implementations are tested and compared against the current “Gaussian plus flat” distribution. Sensitivity experiments show that the usage of the Huber Norm distribution improves the analysis and forecasts. The benefit is mainly seen in the lower model levels in the tropics and extra-tropical Southern Hemisphere. The optimal wind sampling is investigated by testing several combinations of thinning scheme and observation error standard deviation. The impact is demonstrated with a large sample and illustrated by a case study. The case study shows the impact of different settings on the analysis and forecast of a tropical cyclone. A revised wind sampling setting, where four times more observations and a higher observation error than the current operational one are used, showed slightly positive impact on the European Centre for Medium-Range Weather Forecasts (ECMWF) global NWP analyses and forecasts.