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 $\mathrm{k}\mathrm{m}$ altitude, where the mean BIAS, mean RMSE, mean Degrees of Freedom for Signal (DFS), and mean residual reach 0.42 $\mathrm{K}$, 0.80 $\mathrm{K}$, 3.37, and $3.01\times {10}^{-13}$ $\left(\mathrm{W}/{\mathrm{c}\mathrm{m}}^2 \mathrm{s}\mathrm{r} {\mathrm{c}\mathrm{m}}^{-1}\right)$, respectively. In contrast, other regularization methods exhibit over-regularization, leading to degraded information content. For humidity profile retrieval, below 1.5 $\mathrm{k}\mathrm{m}$ 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 $4.36\times {10}^{-12}$ $\left(\mathrm{W}/{\mathrm{c}\mathrm{m}}^2 \mathrm{s}\mathrm{r} {\mathrm{c}\mathrm{m}}^{-1}\right)$, respectively. Conversely, other regularization methods exhibit strong prior dependence, causing retrieval to converge results toward the initial guess.

Accurate classification of aerosol types is crucial for regional aerosol pollution source tracking and optical remote sensing studies. However, conventional classification methods are mostly relying on the single-device observation that provides limited optical parameters, and the use of fixed empirical thresholds that limits the classification accuracy. This study develops a machine learning-enhanced aerosol types classification method that integrates active and passive optical remote sensing data. First, the global multi-parameter aerosol model is built from high-quality aerosol product datasets of 19 representative AERONET sites worldwide, 7 distinct aerosol type classes were established using the K-means clustering algorithm. Then, based on the established aerosol database with multiple characteristic parameters, we propose a random forest-based aerosol types classifier, which uses only active-passive remote sensing directly retrieved parameters as inputs. This classifier is evaluated to achieve an overall accuracy of approximately 80% and successfully classifies the dominant aerosol types at 27 typical AERONET sites. This study offers a practical and reliable method for combining active and passive remote sensing data to automated aerosol types classification.

Accurate classification of aerosol types is crucial for regional aerosol pollution source tracking and optical remote sensing studies. However, conventional classification methods are mostly relying on the single-device observation that provides limited optical parameters, and the use of fixed empirical thresholds that limits the classification accuracy. This study develops a machine learning-enhanced aerosol types classification method that integrates active and passive optical remote sensing data. First, the global multi-parameter aerosol model is built from high-quality aerosol product datasets of 19 representative AERONET sites worldwide, 7 distinct aerosol type classes were established using the K-means clustering algorithm. Then,based on the established aerosol database with multiple characteristic parameters, we propose a random forest-based aerosol types classifier, which uses only active-passive remote sensing directly retrieved parameters as inputs. This classifier is evaluated to achieve an overall accuracy of approximately 80% and successfully classifies the dominant aerosol types at 27 typical AERONET sites. This study offers a practical and reliable method for combining active and passive remote sensing data to automated aerosol types classification.

Accurate classification of aerosol types is crucial for regional aerosol pollution source tracking and optical remote sensing studies. However, conventional classification methods are mostly relying on the single-device observation that provides limited optical parameters, and the use of fixed empirical thresholds that limits the classification accuracy. This study develops a machine learning-enhanced aerosol types classification method that integrates active and passive optical remote sensing data. First, the global multi-parameter aerosol model is built from high-quality aerosol product datasets of 19 representative AERONET sites worldwide, 7 distinct aerosol type classes were established using the K-means clustering algorithm. Then, based on the established aerosol database with multiple characteristic parameters, we propose a random forest-based aerosol types classifier, which uses only active-passive remote sensing directly retrieved parameters as inputs. This classifier is evaluated to achieve an overall accuracy of approximately 80% and successfully classifies the dominant aerosol types at 27 typical AERONET sites. This study offers a practical and reliable method for combining active and passive remote sensing data to automated aerosol types classification.

The infrared spectral features of aero-engine wake serve as a crucial basis for infrared detection, tracking, and identification of aircraft. Their spectral line structure is regulated by the concentration and temperature of the exhaust gases, as well as atmospheric absorption effects. This study employs a high-precision numerical simulation model and conducts field experiments on infrared spectral detection of exhaust plumes to systematically investigate the influence mechanisms of three factors—fuel–air ratio (achieved by varying exhaust gas concentration), exhaust temperature, and atmospheric absorption effects—on key spectral features, including peak intensity, line broadening, shape, and position. Results indicate the following. The fuel–air ratio enhances peak intensity and broadens spectral line width by increasing CO₂ and H₂O concentrations within the fuel-lean operating regime (f = 0.025–0.031); exhaust temperature causes exponential spectral intensity growth and line broadening by elevating molecular energy level populations, while inducing H₂O spectral band redshifts and fine-structure evolution; selective absorption by atmospheric H₂O, CO₂, and O₃ causes severe spectral attenuation near bands at 1041 cm⁻¹, 1590 cm⁻¹, 2349 cm⁻¹, and 3756 cm⁻¹, with transmittance distribution modulated by geographical and seasonal factors. This study provides theoretical support for wake spectral feature extraction, infrared stealth design, and detection/tracking identification.