Three-Dimensional Neurointerpolation of Annual Mean Precipitation and Temperature Surfaces for China

Climatic variables such as annual mean precipitation and temperature display complex and nonlinear variation with latitude, longitude, and elevation. Neural networks are universal approximators and very good at detecting and representing nonlinear relationships between dependent and independent variables. In this paper we use resilient backpropagation (Rprop) neural networks to interpolate annual mean precipitation and temperature surfaces for China. Climate surfaces are interpolated from a total of 288 long-term climate station data points using latitude, longitude, and elevation derived from a 5-kilometer resolution digital elevation model. Initial trials of Rprop suggested very fast learning, insensitivity to selection of learning parameters, and a tendency not to overtrain. Cross-validation was used to determine the best network structure and assess the error inherent in climate interpolation. With the error explicit, the final neurointerpolations of annual mean precipitation and temperature were constructed using all 288 climate station data points. Maps of residuals are also presented. The neurointerpolation of temperature was very successful and captures most of the regional trends found in established climate maps of China as well as significant topographically defined detail. For annual mean temperature the Rprop neural network was found to be an accurate and robust global spatial interpolator. However, the precipitation surface captures only the major latitudinally and continentally defined trends and misses many subregional rainfall features probably because of the influence of other nonparameterized atmospheric and topographic factors.