Application of Neural Networks for Retrieval of the CO2 Concentration at Aerospace Sensing by IPDA-DIAL lidar

Abstract

Greenhouse gas concentrations are increasing over the past few decades, creating the need to measure their concentration with high accuracy, including for determining their trends, sources, and sinks. In this regard, various methods of regional and global control are being developed. One of the measuring methods is passive satellite method, but they allow for you to get data mainly during the day and outside the poles of the Earth. Another method is active lidar; they require the consideration of various aspects that are related to the technical characteristics of the lidar and methods for solving inverse problems. This article discusses the possibility of using lidars for sensing carbon dioxide from space (orbit 450 km) and from a height of 10 km and 23 km, which presumably corresponds to the aircrafts and balloons. As a method of solving the inverse problem, the method of fully connected neural networks with three layers and pre-training of first layer is considered, allowing for the application of additional data, including the IPDA (Integrated Path Differential Absorption) signal, the scattered DIAL (Differential Absorption Lidar) signal, temperature, and pressure profiles. These estimates show the possibility of measuring the average concentration from an orbit height of 450 km with an error of 0.16%, a resolution of 60 km, with a 50 mJ laser pulse energy, and 1 m diameter telescope. It is also shown that it is possible to obtain the concentration profile, including the near-surface concentration with an error of 2 ppm.