Neural network Jacobian analysis for high-resolution profiling of the atmosphere

Abstract

Neural networks have been widely used to provide retrievals of geophysical parameters from spectral radiance measurements made remotely by air-, ground-, and space-based sensors. The advantages of retrievals based on neural networks include speed of execution, simplicity of the trained algorithm, and ease of error analysis, and the proliferation of high quality training data sets derived from models and/or operational measurements has further facilitated their use. In this article, we provide examples of geophysical retrieval algorithms based on neural networks with a focus on Jacobian analysis. We examine a hypothetical 80-channel hyperspectral microwave atmospheric sounder (HyMAS) and construct examples comparing neural network water vapor retrieval performance with simple regressions. Jacobians (derivatives of the outputs with respect to the network weights and with respect to the inputs) are also presented and discussed. Finally, a discussion of the Jacobian operating points is provided.