Ingesting microwave sounder radiances from SAPHIR of Megha–Tropiques has been attempted. A local ensemble transform Kalman filter assimilation algorithm is adopted to ingest radiances directly into the ARW–WRF model. The forward radiative transfer calculations were surrogated with an artificial neural network (ANN) based on the fast radiative transfer model. Raining pixels from the observations were removed using a threshold test on the observed brightness temperatures. Following this, corrections of both scan and air mass biases were accomplished using a predictor-based approach. The bias characteristics of each channel were calculated from ab initio clear-sky profiles from European reanalysis Interim reanalysis data. The vertical localization functions required for the radiance observations were chosen to be similar to the weighting function of the respective channel. The overall performance of the SAPHIR radiance assimilation in terms of the average error over the forecast period showed a positive impact on the cyclone track prediction when compared with the control run and the best track data from the Indian Meteorological Department. The effect of assimilation is observed to improve the minimum sea-level pressure values, whereas the improvements in the maximum sustainable wind speed are not significant. An assimilation experiment was set up to ingest channel-wise radiances independently, and it was concluded that the assimilation of channel 5 radiances results in the least error in the track forecast. The effect of using ensembles generated by initial perturbations in (i) temperature and (ii) both temperature and humidity was studied. The ensembles generated from perturbations in both humidity and temperature resulted in a better 72-h track compared with perturbation of only one of them. The overall performance of the assimilation of all the six channels for both 48- and 72-h forecast lead times showed a considerable improvement against the control run without any assimilation. Furthermore, the results show a degradation of the forecast of cyclone track in the first 24 h. The sensitivity toward channel-wise radiances showed a positive impact on the precipitation forecast when compared with global precipitation mission rainfall estimates. Threat and bias scores were used for quantitative assessment of precipitation, which indicated improvements in skill after assimilating all six channel radiances from SAPHIR. Finally, a sequential assimilation experiment was set up, and the improvements in the analysis fields were computed.
In the present study, the effect of horizontal and vertical localization scales on the assimilation of direct SAPHIR radiances is studied. An Artificial Neural Network (ANN) has been used as a surrogate for the forward radiative calculations. The training input dataset for ANN consists of vertical layers of atmospheric pressure, temperature, relative humidity and other hydrometeor profiles with 6 channel Brightness Temperatures (BTs) as output. The best neural network architecture has been arrived at, by a neuron independence study. Since vertical localization of radiance data requires weighting functions, a ANN has been trained for this purpose. The radiances were ingested into the NWP using the Ensemble Kalman Filter (EnKF) technique. The horizontal localization has been taken care of, by using a Gaussian localization function centered around the observed coordinates. Similarly, the vertical localization is accomplished by assuming a function which depends on the weighting function of the channel to be assimilated. The effect of both horizontal and vertical localizations has been studied in terms of ensemble spread in the precipitation. Aditionally, improvements in 24 hr forecast from assimilation are also reported.