To explore the information that the “polarization crossfire” technique can provide for multi-layer cloud identification, we use the Monte Carlo radiative transfer model to simulate and calculate the top-of-atmosphere radiative properties of single-layer ice clouds, single-layer water clouds, and multi-layer clouds under different cloud microphysical and optical properties. We then establish a multi-layer cloud identification algorithm based on the threshold method, which provides theoretical references for the subsequent operational application of multi-layer cloud identification using the “polarization crossfire” scheme.

Single-layer clouds and multi-layer clouds exhibit significant radiative differences, and accurately identifying them is crucial for understanding their role in the radiation balance of the Earth-atmosphere system. We accurately simulate the top-of-atmosphere radiation characteristics of both single-layer and multi-layer clouds under different conditions using the Monte Carlo vector radiative transfer model. We also provide simulation data of the top-of-atmosphere radiation characteristics of cloud-containing layers, which supports the sensitivity analysis of multi-layer clouds’ radiation properties through polarization remote sensing. Through sensitivity analysis of the top-of-atmosphere radiation characteristics, we examine the polarization channel (865 nm), the oxygen A absorption band (763 nm and 765 nm), and the shortwave infrared channels (1380, 1610, and 2250 nm). This analysis yields feature information useful for identifying multi-layer clouds. Based on these findings, we propose a multi-layer cloud identification algorithm using a thresholding method and validate it with actual remote sensing data. The results show that the algorithm’s accuracy in identifying multi-layer clouds when compared to the moderate-resolution imaging spectroradiometer (MODIS) cloud products, achieves a consistency of 88.3%.

We use the Monte Carlo vector radiative transfer model in the libRadtran software to simulate and calculate the top-of-atmosphere radiative properties under different cloud microphysical and optical properties across various channels. We analyze the sensitivity of the polarization channel, the oxygen A absorption band channel, and the short-wave infrared channel to single-layer and multi-layer clouds in the lower part of the cloud. We also establish a new set of algorithms for identifying multi-layer clouds, as shown in Fig. 5. The core idea of the algorithm is as follows: First, the polarization characteristics of the polarization channel are employed to identify the multi-layer cloud. Then, for the cloud image elements that the polarization channel fails to identify, the reflectivity ratio of the oxygen A absorption band channels (763 nm and 765 nm) is used to identify the upper-layer ice cloud. Once the upper-layer ice cloud is identified, the reflectivity difference between the short-wave infrared channels (2250 nm and 1610 nm) is applied to distinguish between single-layer and multi-layer clouds. When using the oxygen A channel to recognize the upper ice cloud, multi-layer clouds with a thin optical thickness in the upper ice cloud may be misclassified as water clouds. To resolve this, the 1380 nm channel is employed to correctly identify these misclassified elements as multi-layer clouds, thereby improving the accuracy of multi-layer cloud recognition. The experimental results show that the algorithm identifies multi-layer clouds with 92.0% consistency with MODIS cloud products, identifies single-layer clouds with 83.0% consistency with MODIS, and achieves an overall cloud identification accuracy of 88.3%. This demonstrates that the algorithm is more effective at identifying multi-layer clouds.

Based on the characteristics of the “polarization crossfire” observation scheme, we use the Monte Carlo vector radiative transfer model in libRadtran software to simulate and analyze the sensitivity of the polarization channel, the oxygen A absorption band channel, and the short-wave infrared channel to single-layer and multi-layer clouds under different cloud microphysical and optical properties, as well as their effect on top-of-atmosphere radiative properties. The sensitivity of the polarization channel, the short-wave infrared channel, and the oxygen A absorption channel are then used to establish a new multi-layer cloud recognition algorithm, which improves the accuracy of multi-layer cloud identification.