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

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In this paper, a variational data assimilation approach is used to assimilate the rain rate RR data together with precipitable water PW measurements from 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 MM5 model, are computed from the Special Sensor MicrowaveImager SSMI raw data-brightness temperatures, via a statistical regression method. The SSMI-derived RR and PW at 0000 UTC andor 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 Penn StateNCAR mesoscale model version 5 MM5. Two horizontal resolutions of 50 km and 25 km are used in our studies. Comparisons are made between the experiments with and without SSMI-measured PW and RR observations. Results from these experiments showed that 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 of objective analysis. 2 The satellite-derived PW and RR observations were assimilated successfully into the MM5 model by a variational method. The cost function which 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 cyclones track, the associated frontal structure and the associated precipitation along the front. The models spin-up 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.

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  • Meteorology

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