Optimization of Atmospheric Radiative Transfer Model LBLRTM Based on Measured CO₂ Data

According to the CO2 observation data of TIMED/SABER from 2002 to 2018, analyze the variation characteristics of CO2 concentration, gives the monthly average fitting formula of CO2 concentration varying with time, height and latitude according to the various characteristics, and uses the nonlinear least square fitting method to fit the CO2 concentration data of different heights and latitudes respectively to generate corresponding fitting parameters, Then, summarize all fitting parameters and generate the fitting parameter file. Combined with the fitting formula, the empirical calculation module of global CO2 concentration is constructed, and the module is applied to the atmospheric radiative transfer model LBLRTM to optimize LBLRTM. Comparing the simulation results of the optimized LBLRTM model with the TIMED/SABER observation data, the root mean square error between the simulation results of the not optimized LBLRTM model and the observation value is 15.4%, while the root mean square error between the simulation results of the optimized LBLRTM model and the observation value is reduced to 8.91%. The results show that this optimization method can further improve the radiation simulation accuracy of the LBLRTM model in the infrared band.