Potential impact of all-sky assimilation of visible and infrared satellite observations compared with radar reflectivity for convective-scale numerical weather prediction

Although cloud-affected satellite observations are heavily used for nowcasting applications, their use in regional data assimilation is very limited despite possible benefits for convective-scale forecasts. In this article, we estimate the potential impact of assimilating cloud-affected satellite observations of visible (0.6 (Formula presented.) m) and near thermal infrared wavelengths (6.2 and 7.3 (Formula presented.) m) relative to the impact of assimilating radar reflectivity observations. We employed observing-system simulation experiments with a perfect-model forecast for two cases of strong convective summertime precipitation. Observations are simulated using the radiative transfer model RTTOV/MFASIS and assimilated by the ensemble adjustment Kalman filter in the Data Assimilation Research Testbed. The Weather Research and Forecasting model at 2-km grid resolution was used for forecasts. Results show that satellite observations can be nearly as beneficial as three-dimensional radar reflectivity observations. Under favorable conditions, where the prior contains no error in the stage of storm development but only in horizontal position and strength, the assimilation of visible observations leads to 88% of the radar impact. Under more difficult conditions, the impact of visible and infrared observations still reached 50 and 79%, respectively.