Assessment of Radiative Effect of Hydrometeors in Rapid Radiative Transfer Model in Support of Satellite Cloud and Precipitation Microwave Data Assimilation

Abstract

To date, various satellite observations have been assimilated in numerical weather prediction (NWP) , making a dramatic contribution to the improvement of forecast accuracy. However, the focus has mainly been on satellite measurements in a clear atmosphere, with relatively little attention paid to exploring the use of satellite observations under cloudy and rainy conditions. One of the key issues related to cloud and precipitation data assimilation is the radiative effect of hydrometeors in the rapid radiative transfer model (RRTM ). This paper presents a detailed assessment of the radiative effect of hydrometeors in the RRTM, in support of satellite cloud and precipitation microwave data assimilation. Using the output of hydrometeors from the Weather Research and Forecasting (WRF ) numerical forecast model as the input for the Community Radiative Transfer Model (CRTM) , the radiative effect of hydrometeors on the simulation of Advanced Microwave Sounding Unit (A and B) (AMSU-A and AMSU-B , respectively) satellite observations are analyzed. Then, the sensitivity of the satellite simulation to hydrometeors’ properties, including the water content, particle size and vertical distribution, are investigated. Finally, the result of CRTM is compared and discussed with that of Radiative Transfer for TOVS (RTTOV )—another popular RRTM used in the numerical weather prediction community. The results show that the inclusion of the radiative effect of hydrometeors in the RRTM makes the satellite brightness temperature simulation match up, reasonably successfully, with the observation. Overall, the radiative effects of hydrometeors have diverse influences in most of the channels of the satellite microwave observations, except the AMSU-A high-level temperature sounding channels 10–14. Investigation of the radiative effect of the individual hydrometeors verifies that the cloud and rain water mainly have a warming effect, owing to radiative emission. This effect dominates three of the AMSU-A window channels, 1–3, but is weakened both in the other AMSU-A window channel, 15, and in the AMSU-B window channel 1. The effect of cloud and rain water in the other channels is one of scattering, which decreases the brightness temperature. Ice, snow and graupel all present a cooling effect, owing to scattering. The variation produced by ice is extremely small, but is obvious in AMSU-B. The effect of both snow and graupel is notable in all AMSU-A and AMSU-B channels. The sensitivity of satellite microwave remote sensing to the water content of hydrometeors corresponds well with their radiative effect. The brightness temperature is not sensitive to the effective radius size of cloud and ice, and the sensitivity of satellite observations to the particle size of rain, snow and graupel is strong. Also, the sensitivity is complicated by the frequency. The sensitivity of the satellite simulation to the vertical distribution of hydrometeors is presented by the transfer of the particular channel affected. Additionally, the results of RTTOV and CRTM are generally consistent. The main discrepancy is the magnitude of the response function of hydrometeors and the corresponding deviation of brightness temperature produced by the radiative effect of hydrometeors. The result in CRTM appears to be at least double the magnitude of that in RTTOV.