Performance of Using Cascade Forward Back Propagation Neural Networks for Estimating Rain Parameters with Rain Drop Size Distribution

The aim of our study is to estimate the parameters M (water content), R (rain rate) and Z (radar reflectivity) with raindrop size distribution by using the neural network method. Our investigations have been conducted in five African localities: Abidjan (Côte d’Ivoire), Boyele (Congo-Brazzaville), Debuncha (Cameroon), Dakar (Senegal) and Niamey (Niger). For the first time, we have predicted the values of the various parameters in each locality after using neural models (LANN) which have been developed with locally obtained disdrometer data. We have shown that each LANN can be used under other latitudes to get satisfactory results. Secondly, we have also constructed a model, using as train-data, a combination of data issued from all five localities. With this last model called PANN, we could obtain satisfactory estimates forall localities. Lastly, we have distinguished between stratiform and convective rain while building the neural networks. In fact, using simulation data from stratiform rain situations, we have obtained smaller root mean square errors (RMSE) between neural values and disdrometer values than using data issued from convective situations

Comparison between Vegetation and Rainfall of Bioclimatic Ecoregions in Central Africa

This paper investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and extracted rainfall in the Global Precipitation Climatology Project (GPCP) in Central Africa between latitudes 15°S and 20°N and longitudes 0°E and 31°E. Monthly NDVI and GPCP datasets for the period 1982–2000 have been used. The Index of Segmentation of Fourier Components (ISFC) has been applied on the NDVI dataset to segment Central Africa into four bioclimatic ecoregions (BCERs). In order to compare the differential response of vegetation growth to rainfall, an analysis of the inter-annual, intra-annual and seasonal variability for each BCER has been carried out, and the correlations between NDVI and rainfall have been assessed. The plot of the annual cycles of both variables revealed a coherent onset, peak and decay, with a time lag of 1 month for almost all the zones, except the zones, semi-desert and steppe, where a season of short and intense rainfall was observed. The correlation coefficients computed between the two variables are relatively high, especially in brush-grass savannah, where they reach up to 0.90 at a time lag of 1 month. The phenological transition points and phases show that the range between the +1 and -1 time lags corresponds to the duration of the maturity of vegetation. Overall, there is a strong similarity between temporal patterns of NDVI and rainfall, showing that the NDVI can be considered a sensitive indicator of the interannual variability of rainfall