Accurate photovoltaic (PV) power forecasting is crucial for stable grid integration, particularly under rapidly changing weather conditions. This study presents an ultra-short-term forecasting model that integrates sky imager data and meteorological radar data, achieving significant improvements in forecasting accuracy. By dynamically tracking cloud movement and estimating cloud coverage, the model enhances performance under both clear and cloudy conditions. Over an 8-day evaluation period, the average forecasting accuracy improved from 67.26% to 77.47% (+15%), with MSE reduced by 39.2% (from 481.5 to 292.6), $R^2$ increased from 0.724 to 0.855, NSE improved from 0.725 to 0.851, and Theil’s U reduced from 0.42 to 0.32. Notable improvements were observed during abrupt weather transitions, particularly on 1 July and 3 July, where the combination of MCR and sky imager data demonstrated superior adaptability. This integrated approach provides a robust foundation for advancing ultra-short-term PV power forecasting.

Atmospheric visibility is an important meteorological variable affecting transportation, tourism, and agricultural production. Guizhou Province, located in the eastern Yunnan-Guizhou Plateau, has complex terrain and high humidity and is prone to fog-related low-visibility events. To meet the need for refined forecasts over complex terrain while reducing the cost of direct high-resolution forecasting over large areas, this study integrates high-temporal-resolution WRF (Weather Research and Forecasting) forecasts with deep learning to develop a multiscale model that combines coarse-scale spatiotemporal prediction with fine-scale spatial reconstruction. It provides hourly, spatially continuous visibility forecasts across Guizhou. The following results are established: (1) Multiscale WRF-ConvLSTM (MS-WRF-ConvLSTM) improves low-visibility forecasting compared with other models. For the next 12 h, R ranges from 0.59 to 0.73 and RMSE from 0.81 to 1.01 km, while categorical scores indicate reliable event identification and stable performance across lead times. (2) By integrating multiscale information and learning regional spatial relationships, the model improves spatial continuity and local detail, making it suitable for refined forecasting over complex terrain. (3) Ablation experiments confirm the contributions of the multiscale structure, high-temporal-resolution data, and CVIS spatial constraint. Although CVIS has limited direct predictive accuracy, its spatial prior improves fine-scale reconstruction and balances accuracy with spatial detail. (4) SHAP analysis identifies historical visibility, elevation, slope, wind direction, dew-point temperature, air temperature, and specific humidity as important predictors. Learned spatial relationships among cities further improve regional visibility prediction.