LAUSR

CO2 column-weighted dry-air mixing ratio (XCO2) products with high precision and spatial resolution are essential for inverting CO2 fluxes and promoting our understanding of global climate change. Compared with passive remote sensing methods, IPDA LIDAR, as an active remote sensing technique, offers many advantages in measuring XCO2. However, a significant random error in IPDA LIDAR measurements causes XCO2 values calculated directly from LIDAR signals to be unqualified as the final XCO2 products. Hence, we propose an efficient particle filter-based inversion of CO2 for single observation (EPICSO) algorithm to precisely retrieve the XCO2 of every LIDAR observation while preserving the high spatial resolution of LIDAR measurements. The EPICSO algorithm adopts the sliding average results as the first estimate of the local XCO2; subsequently, it estimates the difference between two adjacent XCO2 points and calculates the posterior probability of XCO2 based on particle filter theory. To evaluate the performance of the EPICSO algorithm numerically, we perform an EPICSO to process pseudo-observation data. The simulation results show that the results retrieved by the EPICSO algorithm satisfy the required high precision and that the algorithm is robust to a significant amount of random errors. In addition, we utilize LIDAR observation data from actual experiments in Hebei, China, to validate the performance of the EPICSO algorithm. The results retrieved by the EPICSO algorithm are more consistent with the actual local XCO2 than those of the conventional method, indicating that the EPICSO algorithm is efficient and practical for retrieving XCO2 with high precision and spatial resolution.