Incorporating the SSM/I-Derived Precipitable Water and Rainfall Rate into a Numerical Model: A Case Study for the ERICA IOP-4 Cyclone

Abstract

In this paper, a variational data assimilation approach is used to assimilate the rain rate (RR) data together with precipitable water (PW) measurements from the Experiment on Rapidly Intensifying Cyclones over the Atlantic (ERICA) (4?5 January 1989; IOP-4 cyclone). The PW and RR, which are assimilated into the Pennsylvania State University?NCAR Mesoscale Model version 5 (MM5), are computed from the Special Sensor Microwave/Imager (SSM/I) raw data?brightness temperatures?via a statistical regression method. The SSM/I-derived RR and PW at 0000 UTC and/or 0930 UTC are assimilated into the MM5. The data at 2200 UTC are used for verification of the prediction results. Numerical experiments are performed using the MM5. Two horizontal resolutions of 50 km and 25 km are used in the authors? studies. Comparisons are made between the experiments with and without SSM/I-measured PW and RR observations. Results from these experiments showed the following. 1)?The MM5 simulated a well-behaved but slightly less intense, position-shifted cyclogenesis episode based on the NCEP analysis enhanced with only radiosonde and surface observations through a Cressman-type objective analysis. 2)?The satellite-derived PW and RR observations were assimilated successfully into the MM5 model by a variational method. The cost function that measures the distance between the model-predicted and the observed PW and RR decreased by about one order of magnitude. 3)?Assimilation of PW and RR significantly improved the cyclone prediction, reflected mostly in the cyclone?s track, the associated frontal structure and the associated precipitation along the front. The model?s spinup problem during the simulation was greatly reduced after assimilating the PW and RR information into the model initial conditions. 4)?Sensitivity experiments of RR assimilation indicated that the impact on the results of RR assimilation was less sensitive to errors in the magnitude estimate than errors in the RR location. 5)?It was shown that assimilation of RR only was not as effective in producing a satisfactory improvement on the cyclone prediction as the assimilation of both PW and RR. In addition, improvement in the cyclone prediction of RR assimilation was found to depend on the moist parameterization scheme since the Grell cumulus parameterization resulted in a better 24-h cyclone forecast than the Kuo convective parameterization. These results show that the SSM/I-measured PW and RR have great potential to improve the initial conditions for a mesoscale model, especially over the data-sparse oceanic regions. The case study carried out in this paper shows that the variational assimilation of SSM/I-measured PW and RR data produces adjustments in the model states and results in a positive impact on the forecast of the ERICA IOP-4 cyclone. Future experimentation is planned to assimilate the brightness temperature directly into a mesoscale model.