Senegal is a country of the Sahel. In this region, most of the populations live from agro-pastoral activities. The northern zone of Senegal is strongly influenced by river cultures. And the dynamics of the Senegal Riv...Senegal is a country of the Sahel. In this region, most of the populations live from agro-pastoral activities. The northern zone of Senegal is strongly influenced by river cultures. And the dynamics of the Senegal River are dependent on rainfall. The rainfall in the area is very closely linked to the dynamics of the atmosphere. The study of the spatio-temporal variability of rainfall in the northern region of Senegal requires quality rainfall observation data. It includes the Ferlo and the Senegal River valley, in particular the regions of Louga (department of Linguère included), Saint-Louis (departments of Dagana and Podor included) and Matam. These stations have been defined since Le Borgne (1988). The difficulty of having quality rain observation data can be resolved by using more accessible and good quality satellite data. Using satellite data, namely MSWEP, CRU, TAMSAT, ARC and PERSIANN, we showed the return of precipitation that appeared in 2000 and the unimodal cycle of precipitation in our study area. These data were validated using the correlation coefficient, the bias, the RMSE and the Nash index with observation data from the Regional Study Center for the Improvement of Adaptation to Drought (CERASS). The CRU data is then retained. Thus, this study made it possible to show the zonal distribution of rainfall in the northern zone of Senegal.展开更多
An analysis of a large number of cases of 500 hPa height monthly prediction shows that systematic errors exist in the zonal mean components which account for a large portion of the total forecast errors, and such erro...An analysis of a large number of cases of 500 hPa height monthly prediction shows that systematic errors exist in the zonal mean components which account for a large portion of the total forecast errors, and such errors are commonly seen in other prediction models. To overcome the difficulties of the numerical model, the authors attempt a 'hybrid' approach to improving the dynamical extended-range (monthly) prediction. The monthly pentad-mean nonlinear dynamical regional prediction model of the zonal-mean geopotential height (wave number 0) based on a large amount of data is constituted by employing the reconstruction of phase-space theory and the spatio-temporal series predictive method. The dynamical prediction of the numerical model is then combined with that of the nonlinear model, i.e., the pentadmean zonal-mean height produced by the nonlinear model is transformed to its counterpart in the numerical model by nudging during the time integration. The forecast experiment results show that the above hybrid approach not only reduces the systematic error in zonal mean height by the numerical model, but also makes an improvement in the non-axisymmetric components due to the wave-flow interaction.展开更多
文摘Senegal is a country of the Sahel. In this region, most of the populations live from agro-pastoral activities. The northern zone of Senegal is strongly influenced by river cultures. And the dynamics of the Senegal River are dependent on rainfall. The rainfall in the area is very closely linked to the dynamics of the atmosphere. The study of the spatio-temporal variability of rainfall in the northern region of Senegal requires quality rainfall observation data. It includes the Ferlo and the Senegal River valley, in particular the regions of Louga (department of Linguère included), Saint-Louis (departments of Dagana and Podor included) and Matam. These stations have been defined since Le Borgne (1988). The difficulty of having quality rain observation data can be resolved by using more accessible and good quality satellite data. Using satellite data, namely MSWEP, CRU, TAMSAT, ARC and PERSIANN, we showed the return of precipitation that appeared in 2000 and the unimodal cycle of precipitation in our study area. These data were validated using the correlation coefficient, the bias, the RMSE and the Nash index with observation data from the Regional Study Center for the Improvement of Adaptation to Drought (CERASS). The CRU data is then retained. Thus, this study made it possible to show the zonal distribution of rainfall in the northern zone of Senegal.
基金The study was financed by theNational Key Project for Development of Science and Tech-nology(96-908-02),by the National Natural Science Foun-dation of China under Grant No.40175013,and partly bythe Project of the Chinese Academy of Sciences (ZKC)
文摘An analysis of a large number of cases of 500 hPa height monthly prediction shows that systematic errors exist in the zonal mean components which account for a large portion of the total forecast errors, and such errors are commonly seen in other prediction models. To overcome the difficulties of the numerical model, the authors attempt a 'hybrid' approach to improving the dynamical extended-range (monthly) prediction. The monthly pentad-mean nonlinear dynamical regional prediction model of the zonal-mean geopotential height (wave number 0) based on a large amount of data is constituted by employing the reconstruction of phase-space theory and the spatio-temporal series predictive method. The dynamical prediction of the numerical model is then combined with that of the nonlinear model, i.e., the pentadmean zonal-mean height produced by the nonlinear model is transformed to its counterpart in the numerical model by nudging during the time integration. The forecast experiment results show that the above hybrid approach not only reduces the systematic error in zonal mean height by the numerical model, but also makes an improvement in the non-axisymmetric components due to the wave-flow interaction.
