Classification and Evaluation of Stable and Unstable Cloud Forecasts

Abstract

Abstract A physics-based cloud identification scheme, originally developed for a machine-learning forecast system, was applied to verify cloud location and coverage bias errors from two years of 6-h forecasts. The routine identifies stable and unstable environments by assessing the potential for buoyant versus stable cloud formation. The efficacy of the scheme is documented by investigating its ability to identify cloud patterns and systematic forecast errors. Results showed that stable cloud forecasts contained widespread, persistent negative cloud cover biases most likely associated with turbulent, radiative, and microphysical feedback processes. In contrast, unstable clouds were better predicted despite being poorly resolved. This suggests that scale aliasing, while energetically problematic, results in less-severe short-term cloud cover errors. This study also evaluated Geostationary Operational Environmental Satellite (GOES) cloud-base retrievals for their effectiveness at identifying regions of lower-tropospheric cloud cover. Retrieved cloud-base heights were sometimes too high with respect to their actual values in regions of deep-layered clouds, resulting in underestimates of the extent of low cloud cover in these areas. Sensitivity experiments indicate that the most accurate cloud-base estimates existed in regions with cloud tops at or below 8 km. Significance Statement Cloud forecasts are difficult to verify because the height, depth, and type of the clouds are just as important as the spatial location. Satellite imagery and retrievals are good for verifying location, but these measurements are sometimes uncertain because of obscuration from above. Despite these uncertainties, we can learn a lot about specific forecast errors by tracking general areas of clouds based on their physical forcing mechanisms. We chose to sort by atmospheric stability because buoyant and stable processes are physically very distinct. Studies of this nature exist, but they typically assess mean cloud frequencies without considering spatial and temporal displacements. Here, we address displacement error by assessing the direct overlap between the observed and predicted clouds.