Yulu Yi

Surface solar irradiance (SSI) is a critical factor influencing the power generation capacity of photovoltaic (PV) power plants. Dynamic changes in cloud cover pose significant challenges to the accurate nowcasting of SSI, which in turn directly affects the reliability and stability of renewable energy systems. However, existing research often simplifies or overlooks changes in the optical and morphological characteristics of clouds, leading to considerable errors in SSI nowcasting. To address this limitation and improve the accuracy of ultra-short-term SSI forecasting, this study first forecasts changes in cloud optical thickness (COT) within the next 3 h based on a spatiotemporal long short-term memory model, since COT is the primary factor determining cloud shading effects, and then integrates the zenith and regional averages of COT, along with factors influencing direct solar radiation and scattered radiation, to achieve precise SSI nowcasting. To validate the proposed method, we apply it to the Albuquerque, New Mexico, United States (ABQ) site, where it yielded promising performance, with correlations between predicted and actual surface solar irradiance for the next 1 h, 2 h, and 3 h reaching 0.94, 0.92, and 0.92, respectively. The proposed method effectively captures the temporal trends and spatial patterns of cloud changes, avoiding simplifications of cloud movement trends or interference from non-cloud factors, thus providing a basis for power adjustments in solar power plants.

Surface ozone is a pollutant linked to higher risks of cardiopulmonary diseases with long-term exposure. Timely forecasting of ozone levels helps authorities implement preventive measures to protect public health and safety. However, few studies have been able to reliably provide long-term hourly ozone forecasts due to the complexity of ozone’s diurnal variations. To address this issue, this study constructs a hybrid prediction model integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), bi-directional long short-term memory neural network (BiLSTM), and the persistence model to forecast the hourly ozone concentrations for the next continuous 36 h. The model is trained and tested at the Wanshouxigong site in Beijing. The ICEEMDAN method decomposes the ozone time series data to extract trends and obtain intrinsic mode functions (IMFs) and a residual (Res). Fourier period analysis is employed to elucidate the periodicity of the IMFs, which serves as the basis for selecting the prediction model (BiLSTM or persistence model) for different IMFs. Extensive experiments have shown that a hybrid model of ICEEMDAN, BiLSTM, and persistence model is able to achieve a good performance, with a prediction accuracy of R² = 0.86 and RMSE = 18.70 µg/m³ for the 36th hour, outperforming other models.

Clouds are important for Earth’s energy budget and water cycles, and precisely characterizing their vertical structure is essential for understanding their impact. Although passive remote sensing offers broad coverage and high temporal resolution, sensor and algorithmic limitations impede the accurate depiction of cloud vertical profiles. To improve estimates of their key structural parameters, e.g., cloud top height (CTH) and cloud vertical extent (CVE), we propose a multi-source collaborative optimization algorithm. The algorithm synergizes the wide-coverage FY-4A (FengYun-4A) and DSCOVR (Deep Space Climate Observatory) cloud products with high-precision CloudSat vertical profile data and establishes LightGBM-based CTH/CVE optimization models. The models effectively reduce systematic errors in the FY-4A and DSCOVR cloud products, lowering the CTH Mean Absolute Error (MAE) to 1.8 km for multi-layer clouds, an improvement of 4–8 km over the original. The CVE MAEs for single- and multi-layer clouds are ~2.5 km. Some bias remains in complex cases, e.g., multi-layer thin clouds at low altitudes, and error tracing analysis suggests this may be related to cloud layer number misclassification. The proposed algorithm facilitates daytime near-hourly cloud retrievals over China and neighboring regions.