A novel atmospheric segmentation model for improving the accuracy of radiative cooling potential prediction

Radiative cooling offers a wide range of potential applications in the current promotion of clean energy. However, the radiative cooling potential is generally underestimated, accounting for ignoring the effect of aerosols on atmospheric longwave downward radiation. This paper proposes an atmospheric segmentation model to improve the accuracy of radiative cooling potential prediction. The heat transfer on the radiative cooling surface is analyzed, and the accuracy of the proposed atmospheric segmentation model is verified with experiments and compared with the models in existing studies. Then, the effects of aerosol optical depth, precipitable water vapor, and relative humidity on the radiative cooling potential are discussed in detail based on the atmospheric segmentation model. Finally, the map of radiative cooling potential in China is also derived from the proposed atmospheric segmentation model. The results show that the relative error of radiative cooling power between the atmospheric segmentation model prediction and experiment is only 2.4%. Furthermore, the measured and calculated atmospheric longwave downward radiations match well with each other, in which the Pearson correlation coefficients and root mean square errors are 0.9834 and 11.34 W/m² under clear sky conditions and 0.94211 and 21.32 W/m² under cloudy sky conditions.