A comparative study of libRadtran and RTTOV predicted satellite images using the WRF forecasting output

Abstract

ABSTRACTThe assimilation of cloudy radiances remains important in improving precipitation and severe weather forecasting. In practice, Numerical Weather Prediction (NWP) Models frequently do not predict meso-scale phenomena, so the phenomenon is either predicted but not realised, or is well predicted but not where it is observed. Radiative Transfer Models such as TIROS-Television and Infrared Observation Satellite Operational Vertical Sounder (RTTOV) and libRadtran are the mathematical operators used in the simulation of satellite data. In the data assimilation process, an effective reproduction of the mesoscale convective phenomenon leads to high quality data analysis. Therefore, we are looking for an operator that reproduces the NWP model’s behaviour in a realistic way. Several cloud parameterisation schemes are available in RTTOV and libRadtran to simulate the satellite cloudy radiances. Each selected scheme may result in different simulated brightness temperature data compared to those observed by satellite. However, the source of errors is still unknown: are they generated by the RT model, are they coming from the predicted fields of the NWP Model used as input, or from both? This study aims to investigate the impact of libRadtran or RTTOV operators on the quality of the predicted satellite image. The same mandatory forecasted fields are used as input for both models and derive from the Weather Research and Forecasting (WRF) Limited-Area Model. In this study, we did not investigate the total capability of RT models, but we have focused on a standard and specific physical parameterisation scheme. As reference data, Meteosat Second Generation (MSG) images have been used to compute the deterministic and probabilistic scores. The results for the deterministic scores show that libRadtran reproduces the cold temperatures predicted by WRF well, but these are sometimes slightly distant from their geographical location. Conversely, for the RTTOV, there is a tendency to miss more cases of good detection of the events predicted by WRF. Probabilistic analyses confirm an improvement in libRadtran scores when the neighbourhood size is increased, and a boxplot analysis of the bootstrap method confirms the stability of scores for both models.