The atmospheric profile is a critical component in radiative transfer calculations, and constructing an atmospheric model that accurately reflects regional atmospheric conditions is essential to ensure the precision of these calculations. In this paper, we aim to explore atmospheric profile variations and improve the accuracy of radiative transfer calculations by proposing a novel method for constructing atmospheric models.

We analyze the vertical distribution and variation patterns of key atmospheric parameters, including temperature, water vapor, pressure, carbon dioxide, ozone, and methane. A new approach based on K-means clustering and random forest regression is developed to construct atmospheric profiles. Data sources include ERA5, WACCM, and CarbonTracker, covering historical atmospheric profile data over the past two decades. To address the resolution differences among these data sources, spatiotemporal interpolation, and height normalization methods are applied. We focus on the eastern region of China, where temperature, pressure, water vapor, and ozone profiles are clustered to reveal their seasonal and regional variation patterns. Subsequently, carbon dioxide and methane profiles are reconstructed using newly processed data.

The self-developed atmospheric model is compared with the 1976 US standard atmosphere using MODTRAN software to simulate spectral data. The simulated spectra are then compared with actual measurements from the FengYun satellite. The results show that the self-developed model improves simulation accuracy by 11.2% in January and 10.5% in July compared to 1976 US standard atmosphere model, indicating that the proposed model better approximates real atmospheric conditions (Fig. 5). This method offers a new approach for constructing atmospheric profiles for radiative transfer calculations.

The proposed method, which combines K-means clustering and random forest regression, significantly improves the accuracy of radiative transfer calculations by better capturing regional and seasonal variations in atmospheric profiles. This approach not only enhances the precision of radiative transfer simulations but also provides a valuable tool for atmospheric research and applications.