The synergy of assimilating visible and infrared radiances and radar observations

Cloud-affected satellite observations in the visible and infrared spectrum contain vast and largely complementary information on clouds and atmospheric convection and thereby constitute a promising data source for convective-scale data assimilation. Preceding studies have demonstrated that the assimilation of either one of these observation types can lead to improved convective-scale weather forecasts, but research on the combined assimilation of cloud-affected visible and infrared satellite data as well as radar observations is still very scarce. In this article, we investigate the combined assimilation of multiple infrared and visible satellite channels as well as radar observations, and evaluate their analysis and forecast impact with a primary focus on clouds and precipitation. We assimilate visible (0.6- (Formula presented.)) and thermal infrared (6.2- and 7.3- (Formula presented.)) satellite channels in observing-system simulation experiments (OSSEs) with a perfect-model forecast for an idealized weather scenario. Observations are simulated synthetically and assimilated by the ensemble adjustment Kalman filter (EAKF). The forecasts used the Weather Research and Forecasting (WRF) model at 2-km grid resolution. Results show that assimilating satellite channels in addition to radar reflectivity can improve forecasts of cloudiness and precipitation, while improvements in temperature, humidity, and wind fields are about the same as in the radar experiment. The evaluation of the analysis error for different cloud conditions revealed that the combined assimilation can mitigate the ambiguity of individual visible and infrared channels. Assimilating visible reflectance before infrared brightness temperature can remove pre-existing erroneous water clouds and avoid the introduction of erroneous clouds by the following assimilation of infrared channels. It follows that the combined assimilation of visible and infrared radiances could be crucial to avoid shortcomings of assimilating only visible or infrared radiances in convective-scale numerical weather prediction.