Delineation of Convective and Stratiform Rainy Clouds Based on Their Spectral and Textural Features on Meteosat Data
In the present study two schemes were developed for the delineation of convective and stratiform clouds based on the high spectral resolution of the Meteosat Second Generation (MSG). Two classification methods were proposed that use spectral cloud parameters along with textural cloud parameters. The first model is an empirical algorithm based on the estimation of the probability of convective rainfall (PCR) for each pixel of the satellite data and the second is a statistical approach (Artificial Neural Network, ANN) based on the correlation of spectral and textural parameters with convective and stratiform rain. It was found that the introduction of textural parameters as additional information tends to improve the discrimination between convective and stratiform clouds for the models in the training and validation dataset. The PCR algorithm based on spectral and textural parameters shows the best performance among all the rain classification models for the training dataset. When evaluating against the independent dataset, the ANN model based on both spectral and textural parameters produces scores significantly better than the other rain classification algorithms. All algorithms overestimate the convective rain occurrences detected by the rain stations network.
KeywordsBrightness Temperature Textural Parameter Stratiform Cloud MLP2 Model Rain Gauge Data
This research has been co-financed by the European Union (European Social Fund – ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) – Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund. The authors also wish to thank the National Observatory of Athens for providing the precipitation data.
- Inoue T, Wu X, Bessho K (2001) Life cycle of convective activity in terms of cloud type observed by split window. In: Proceedings of 11th conference on satellite meteorology and oceanography, Madison, WI, USAGoogle Scholar