Evaluation of Different Methods for Retrieving Temperature and Humidity Profiles in the Lower Atmosphere Using the Atmospheric Sounder Spectrometer by Infrared Spectral Technology

The temperature and humidity profiles within the planetary boundary layer (PBL) are crucial for Earth’s climate research. The Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) measures downward thermal radiation in the atmosphere with high temporal and spectral resolution continuously during day and night. The physics-based retrieval method, utilizing iterative optimization, can obtain solutions that align with the true atmospheric state. However, the retrieval is typically an ill-posed problem and is affected by noise, necessitating the introduction of regularization. To achieve high-precision detection, a systematic evaluation was conducted on the retrieval performance of temperature and humidity profiles using ASSIST by regularization methods based on the Gauss–Newton framework, which include Fixed regularization factor (FR), L-Curve (LC), Generalized Cross-Validation (GCV), Maximum Likelihood Estimation (MLE), and Iterative Regularized Gauss–Newton (IRGN) methods, and the Levenberg–Marquardt (LM) method based on a damping least squares strategy. A five-day validation experiment was conducted under clear-sky conditions at the Anqing radiosonde station in China. The results indicate that for temperature profile retrieval, the IRGN method demonstrates superior performance, particularly below 1.5 km altitude, where the mean BIAS, mean RMSE, mean Degrees of Freedom for Signal (DFS), and mean residual reach 0.42 K, 0.80 K, 3.37, and 3.01×10−13 W/cm2 sr cm−1, respectively. In contrast, other regularization methods exhibit over-regularization, leading to degraded information content. For humidity profile retrieval, below 1.5 km altitude, the LM method outperforms all regularization-based methods, with the mean BIAS, mean RMSE, mean DFS, and mean residual of 3.65%, 5.62%, 2.05, and 4.36×10−12 W/cm2 sr cm−1, respectively. Conversely, other regularization methods exhibit strong prior dependence, causing retrieval to converge results toward the initial guess.