Observing atmospheric composition and temperature by far infrared and microwave emission sounding

Abstract

A large number of trace species have significant absorption features in the far infrared and microwave regime, which offers opportunities for extensively studying the global/regional atmospheric environment and chemical processes related to ozone depletion and climate change. Furthermore, atmospheric temperature can be derived from thermal emission lines such as CO2 and O2 in the case of infrared and microwave remote sensing, respectively. To reconstruct geophysical parameters (e.g. atmospheric concentrations, temperature) from measurements monitored on different platforms, we have to solve so-called inverse problems. These inverse problems arising in atmospheric remote sensing are nonlinear, and mostly ill-posed that the noise in the data results in large errors in the state vector. In this work, we present the practical treatment of nonlinear inverse problems (“retrieval”) for converting the observation data into estimates of atmospheric profiles, placing an emphasis on analysis of far infrared and microwave emission measurements. In the forward model, the means of automatic differentiation providing a rapid and reliable implementation of exact Jacobians, is a particular optimization feature. The inversion process relies on a variety of Tikhonov-type regularization techniques for stabilizing the solution. The retrievals are carried out to derive vertical distributions of stratospheric trace gases by a balloon-borne spectrometer TELIS (TErahertz and submillimeter LImb Sounder), and atmospheric temperature from airborne radiometer measurements obtained by MTP (Microwave Temperature Profiler).