Performance Simulation of Spaceborne Range-resolved Differential Absorption Lidar System For CO2 Profile Detection At 1.57μm and 2.05μm Wavelength Scenarios

The increasing atmospheric carbon dioxide (CO₂) is the most important factor forcing climate change. However, due to lack of observation data about large-scale range-resolved CO₂, there remains substantial uncertainty in current global atmospheric CO₂ budget, which hinders giving insight into CO₂ cycle and modeling its forcing to climate change. Space-based range-resolved differential absorption lidar (range-resolved DIAL), is a promising and powerful means for obtaining large-scale range-resolved CO₂ data, but has been rarely studied. Prior to developing spaceborne range-resolved DIAL, a preliminary study on optimization of on/off-line wavelengths must be performed to ensure high signal-to-noise (SNR), high sensitivity to near surface region and minimize the interference of atmospheric factors. This study aims to find the optimum wavelength scenarios in terms of random errors determined by SNR, weighting functions used to assess sensitivity to near-surface region, and systematic errors affected by atmospheric factors. Firstly, we find the optimal on/off-line wavelengths at 1.57μm and 2.05μm, which are widely used and show good results for measuring CO₂ concentration, after estimating on-line and off-line wavenumbers separately using evaluation indexes called  and . Furthermore, we get the optimum wavelength scenarios of spaceborne range-resolved DIAL by comparing the random, systematic errors and weighting functions of optimal on-line and off-line wavelengths at 1.57μm and 2.05μm. Results show that the wavelength scenario at 2.05μm is the optimal for spaceborne range-resolved CO₂ detection. To satisfy the requirement that the relative random errors are smaller than 0.01 (<1%), systems at 2.05μm wavelength scenario with vertical resolution of 0.5 km, 0.7 km, 0.8 km, 0.9 km separately require that SNR values of on-line wavelength at 0 km height are larger than 10, 9, 8, 7.