Validation Method for Spaceborne IPDA LIDAR ${{\mathrm{X}}_{\mathrm{C}{{\mathrm{O}}_2}}}$ Products via TCCON
Hongyuan Zhang, Ge Han, Weibiao Chen, Zhipeng Pei, Boming Liu, Jiqiao Liu, Tianhao Zhang, Siwei Li, Wei Gong
Abstract
The successful launch of the first spaceborne CO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> IPDA LIDAR onboard Daqi-1 (DQ-1) in April 2022 marks a milestone in advancing scientific research. However, a notable discrepancy in the physical definitions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\mathrm{X}}_{\mathrm{C}{{\mathrm{O}}_2}}}$</tex-math></inline-formula> products between the IPDA LIDAR and TCCON presents a challenge for directly using TCCON for verifying and evaluating the performance of DQ-1’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\mathrm{X}}_{\mathrm{C}{{\mathrm{O}}_2}}}$</tex-math></inline-formula> products. To address this, we propose a method based on statistical hypothesis testing to globally validate the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\mathrm{X}}_{\mathrm{C}{{\mathrm{O}}_2}}}$</tex-math></inline-formula> products derived from the spaceborne LIDAR. Our validation method does not compare DQ-1’s observations with the TCCON observations. We only utilize the useful information from TCCON to simulate the probability distribution of the true <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\mathrm{X}}_{\mathrm{C}{{\mathrm{O}}_2}}}$</tex-math></inline-formula> value in the LIDAR definition, and then hypothesis testing is adopted for deriving the systematic error. Our method improves the accuracy of computing the systematic error of DQ-1 by over 50% compared to the traditional approach. Up to now, we have produced the first four months (June 2022–September 2022) of DQ-1’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\mathrm{X}}_{\mathrm{C}{{\mathrm{O}}_2}}}$</tex-math></inline-formula> products. In this context, we present preliminary validation results based on the four months of data. Based on our method, we have validated that the accuracy of DQ-1’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\mathrm{X}}_{\mathrm{C}{{\mathrm{O}}_2}}}$</tex-math></inline-formula> products is 0.1 ± 1 ppm. This approach not only sets the stage for future official global validations of DQ-1’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\mathrm{X}}_{\mathrm{C}{{\mathrm{O}}_2}}}$</tex-math></inline-formula> products but also holds promise for application in upcoming similar missions such as MERLIN and DQ-2.