A radial basis function neural network was successfully applied to an area which is relatively new for neural networks: a remote sensing application that provides estimates of water vapor content, an important parameter for climate modeling. The neural network provided results which are up to 32% better than had been previously obtained using conventional statistical methods on the same data. These results have implications for improved short-term weather forecasting and for long-term global climate modeling. The neural network approach is compared with the past and present operating algorithms at the National Oceanic and Atmospheric Administration. The radial basis function network's performance is compared with sigmoidal backpropagation network. Low-power electronic implementations of the neural methodology were explored to demonstrate the feasibility of placing the network on a remote sensing platform. This would permit processing the raw sensor data into information on the platform, eliminating the need to store the raw data, and helping to contain the expected explosion of climate data.