Harmful exposure to erythemally-effective ultraviolet radiation (UVR) poses high health risks such as malignant keratinocyte cancers and eye-related diseases. Delivering short-term forecasts of the solar ultraviolet index (UVI) is an effective way to advise UVR exposure information to the public at risk. This research reports on a novel framework built to forecast UVI, integrating antecedent lagged memory of cloud statistical properties and the solar zenith angle (SZA). To produce the forecasts at multi-step horizon we design a 3-phase hybrid convolutional long short-term memory network (W-O-convLSTM) model, validated with Queensland-based datasets in near real-time ( i.e ., 10-minute, 20-minute, 30-minute and 1 hour forecast horizon). Our approach in optimizing the performance also entails a robust selective filtering method using the BorutaShap algorithm, data decomposition with stationary wavelet transformation and hyperparameter optimization using the Optuna algorithm. We assess the performance of the proposed W-O-convLSTM model alongside the baseline and benchmark models. The captured results, through statistical metrics and visual infographics, elucidate the superior performance of the objective model in short-term UVI forecasting. For instance, at a 10-minute forecast horizon, our objective model yields a relatively high correlation coefficient of ~0.961 in the autumn, 0.909 in the summer, 0.926 in the spring and 0.936 in the winter season. Overall, the proposed O-convLSTM model outperforms its competing counterpart models for all forecast horizons with the lowest absolute forecast error. The robustness of our newly proposed model avers its practical utility in delivering sun-protection behavior recommendations that can mitigate UV-exposure-related public health risk. We also recommend that future integration of aerosol and ozone effects with cloud cover data can enhance our forecasting framework for wider applications in solar energy or skin health monitoring systems.

Excessive exposure to solar ultraviolet (UV) radiation has damaging effects on life on Earth. High-energy short-wavelength ultraviolet B (UVB) is biologically effective, influencing a range of dermal processes, including the potentially beneficial production of vitamin D. In addition to the damaging effects of UVB, the longer wavelength and more abundant ultraviolet A (UVA) has been shown to be linked to an increased risk of skin cancer. To evaluate this risk requires the monitoring of the solar UVA globally on a time repetitive basis in order to provide an understanding of the environmental solar UVA irradiance and resulting exposures that humans may receive during their normal daily activities. Satellite-based platforms, with the appropriate validation against ground-based instrumentation, can provide global monitoring of the solar UVA environment. Two satellite platforms that currently provide data on the terrestrial UVA environment are the ozone monitoring instrument (OMI) and the global ozone monitoring experiment (GOME-2). The objectives of this review are to provide a summary of the OMI and GOME-2 satellite-based platforms for monitoring the terrestrial UVA environment and to compare the remotely sensed UVA data from these platforms to that from ground-based instrumentation.

Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m⁻² s⁻¹, mean absolute error MAE = 150.24 μ mol of photons m⁻² s⁻¹, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient E_NS = 0.85, and Legate and McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems.