Finding Missing Clouds in the CONUS404 Using Satellite-Derived Cloud-Radiative Properties

This study investigates the physical mechanisms driving the systematic positive bias in surface solar irradi-ance (SSI) within the high-resolution contiguous United States for 40 years at 4-km grid spacing (CONUS404) simulation, using the satellite-based National Solar Radiation Database (NSRDB) as a reference. Analysis of hourly data under mutually clear-sky conditions for 2018 reveals that the average SSI bias decreases by 99% (from 41.8 to 0.5 W m^-2), indicating that irradiance overestimation is primarily attributable to cloud-related processes. To examine these discrepancies without relying on complex, multilayer atmospheric profiles, a quantitative relationship was established to derive the model’s equiv-alent cloud optical thickness (ECOT) solely from surface SSI variables. This framework enables a consistent, gridpoint-level classification of cloud conditions into mutually exclusive categories: optically thin, medium, and thick. A key finding is that regional SSI overestimation is driven by intercategory misclassification and a resulting deficit of optically medium clouds. Rather than omitting these clouds as false clears, the simulation systematically misclassifies them as optically thin, allowing excessive solar radiation to reach the surface. This misclassification pathway displays clear geographic depen-dence. In cloud-prone regions like the mid-Atlantic and Pacific Northwest, this occurs in 8.6% and 9.3% of total observations, respectively, leading to notable positive SSI biases. Conversely, in relatively clear regions like California, a high baseline frequency of clear-sky conditions buffers the radiation bias. Furthermore, in the Texas Gulf, a lower net SSI bias masks underlying classification discrepancies through an offsetting error mechanism: Solar radiation overestimation is structurally countered by overpredicting thick convective systems associated with transient tropical cyclones.