Search

We present a neural network algorithm for spectroscopic retrievals of concentrations of trace gases. Using synthetic data we demonstrate that a neural network is well suited for filtering etalon fringes and provides superior performance to conventional least squares minimization techniques. This novel method can improve the accuracy of atmospheric retrievals and minimize biases.

Trace gases and their isotopic ratios in planetary atmospheres offer important but subtle clues as to the origins of a planet's atmosphere, hydrology, geology, and potential for biology. An orbiting laser remote sensing instrument capable of measuring trace gases on global scales with unprecedented accuracy and high spatial resolution can provide valuable measurements for planetary and earth science missions.