Quantifying and Mitigating Errors in Estimating Downward Surface Shortwave Radiation Caused by Cloud Mask Data

Cloud mask (CM) data are indispensable for estimating downward surface shortwave radiation (DSSR). Most DSSR products are generated using CM data derived from passive satellite observations through the threshold methods. Some uncertainty exists in these CM data, yet the impact of CM quality on the DSSR estimates has received limited attention. To address this gap, this study proposed a method to quantify and mitigate errors in DSSR estimates resulting from CM data error, using the Himawari-8 (H8) products as a case study. First, machine learning (ML) models were constructed for CM, DSSR, and needed atmospheric parameters for DSSR estimation. Then, high-reliability CM estimates were utilized to update the H8 CM. The missing atmospheric parameters resulting from the CM updates were filled by the constructed models. Subsequently, DSSR data were estimated based on the updated CM. Results show that the updated CM effectively corrects misclassifications in the H8 CM, and the differences are more than 600 Wm−2 between DSSR estimates and H8 DSSR for some pixels. Cloud-aerosol Lidar and infrared pathfinder satellite observations (CALIPSO) CM and in situ DSSR were used as truth references for validation. The improved accuracy of the updated CM compared to H8 CM is mainly observed for snow/ice, with clear-sky and cloudy-sky hit rates (HRs) increasing by 0.1 and 0.3, respectively. Besides, when the H8 CM is consistent with the updated CM, the estimated DSSR exhibits a slightly lower root mean square error (RMSE) compared to the H8 DSSR, with a difference of no more than 3 Wm−2. However, in cases where the two CM data are inconsistent, the reduction in RMSE for the estimated DSSR compared to the H8 DSSR is more significant, exceeding 9 Wm−2.

I. Introduction

Accurately estimating downward surface shortwave radiation (DSSR) plays a pivotal role in various domains, including vegetation growth, the solar energy industry, and hydrological modeling [1], [2], [3]. Shortwave radiation interacts with various atmospheric factors as it traverses the atmosphere [4], [5]. Under cloudy-sky conditions, clouds reflect a significant amount of shortwave radiation back into space, resulting in substantial attenuation [6], [7]. In clear-sky (cloudless) conditions, DSSR is primarily influenced by aerosols, water vapor, and ozone [8], [9]. Due to the distinct differences in shortwave radiation transfer processes between cloudy-sky and clear-sky conditions, it is common to estimate DSSR separately for each condition [10]. To achieve accurate classification of clear-sky and cloudy-sky conditions, a reliable cloud mask (CM) product is crucial. As satellite remote sensing data have become increasingly abundant and accessible, it has supported substantial research on estimating CM and DSSR [4], [11], [12]. Satellite observation techniques are broadly categorized into passive remote sensing and active remote sensing approaches. Active remote sensing provides higher measurement precision than passive remote sensing but typically has lower temporal and spatial observational continuity.