Development of a passive microwave-based satellite precipitation estimation algorithm for Brazil - Lia Martins Costa do Amaral (2019)
In order to develop a passive microwave-based satellite precipitation estimation algorithm optimized for Brazil, this work was divided in two parts. The first part consisted in extending the cloud-radiation database used as a priori information for the Cloud Dynamics and Radiation Database (CDRD) Bayesian algorithm in order to include the cloud resolving model simulations representative of brazilian rainfall regimes. Simulations of microphysical, dynamical and meteorological profiles were then generated using the University of Wisconsin - Nonhydrostatic Modeling System and the brightness temperature (TB) simulations were generated using the Radiative Transfer Equation Modeling System for the CHUVA (Amazon and Vale) golden cases and compared with observed TB. The results demonstrated that the simulations detected perturbations in the TB fields (in space and time) however in terms of the range of temperature values, the model did not reproduce the lowest values of TB that were present in the observations. The model also seemed to struggle with the riming process on graupel formation, providing small amounts of graupel content. These results demonstrated that the models needed adjustments to be able to describe the regional features of TB across a wide range of meteorological systems in Brazil. For these reasons, the second part of the work was developed by making use of an observational database from the sensors GPM Microwave Imager and Dualfrequency Precipitation Radar (GMI/DPR-CMB) in order to develop a screening of precipitation and rainfall retrieval algorithm over Brazil, based on artificial neural networks (ANN) and called Neural Network IMplementation of the Brazilian MUltilayer Perceptron for Screening and precipitation retrieval (NNIMBUS). The precipitation screening proved to be very effective in both detecting larger systems and smaller or isolated systems. Regarding the GMI/DPR-CMB validation dataset, the screening performed well, with an accuracy of 0.95, POD of 0.80, FAR of 0.39 and bias of 1.34. When compared to the Goddard profiling algorithm (GPROF) the screening still had good performance, however with slightly smaller scores. It was observed that through the comparison maps with GPROF the NNIMBUS can detect agglomerates very similarly, however it does not detect the borders of the systems very well. This behavior might be associated with the precipitation thresholds that were configured with the training dataset (0.2 a 60 mm/h), which might be leading more stratiform regions of the systems to go undetected. The rainfall retrieval model also performed well when compared to the GMI/DPR-CMB observations, with an MAE of 4.19, standard deviation of 3.23 and RMSE of 5.59 for the validation dataset. Analyzing the rain rate classes, the retrieval tends to underestimate classes between 0.2 and 1 mm/h, overestimate classes between 1 and 10 mm/h and underestimate classes greater than 10 mm/h. These features can be associated with the input dataset distribution, as well as with the criteria applied in data cleaning process.