Open AccessValidation of Improved TAMANN Neural Network for Operational Satellite-Derived Rainfall Estimation in Africa
Author(s) -
Erika Coppola,
D. I. F. Grimes,
Marco Verdecchia,
Guido Visconti
Publication year - 2006
Publication title -
journal of applied meteorology and climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.079
H-Index - 134
eISSN - 1558-8432
pISSN - 1558-8424
DOI - 10.1175/jam2426.1
Subject(s) - artificial neural network , calibration , computer science , satellite , context (archaeology) , rain gauge , meteorology , linear regression , remote sensing , data mining , algorithm , environmental science , machine learning , statistics , geography , mathematics , archaeology , engineering , aerospace engineering , precipitation
Real-time rainfall monitoring in Africa is of great practical importance for operational applications in hydrology and agriculture. Satellite data have been used in this context for many years because of the lack of surface observations. This paper describes an improved artificial neural network algorithm for operational applications. The algorithm combines numerical weather model information with the satellite data. Using this algorithm, daily rainfall estimates were derived for 4 yr of the Ethiopian and Zambian main rainy seasons and were compared with two other algorithms—a multiple linear regression making use of the same information as that of the neural network and a satellite-only method. All algorithms were validated against rain gauge data. Overall, the neural network performs best, but the extent to which it does so depends on the calibration/validation protocol. The advantages of the neural network are most evident when calibration data are numerous and close in space and time to the validation data. This result emphasizes the importance of a real-time calibration system.