Short-term probabilistic forecast of cloudiness: a scale-dependent advection approach

Solar energy generation is highly volatile during the day due to the strong dependence on cloud dynamics, which limits its integration into the power grid (Smith et al., 2022). On the other hand, higher utilization of renewable energy is essential to tackle climate change. To increase the share of photovoltaic energy in the grid without jeopardizing grid stability, accurate forecasts are essential to ascertain the balance between energy demand and supply (David et al., 2021).

Photovoltaic energy production mainly depends on downwelling surface solar radiation (). SSR is accurately measured by pyranometers, but their spatial representativeness is limited to a few kilometers. By estimating the SSR from geostationary satellites, we can cover larger areas with high spatial and temporal resolutions, allowing us to track cloud motion.

Previous studies on probabilistic cloud motion focused on optical-flow methods without considering the temporal evolution of clouds as such. We address this issue by presenting a scale-dependent approach to forecast. Our approach is inspired by the works of Bowler et al., 2006 and Pulkkinen et al., 2019 on precipitation nowcasting. The novelty of our study is the utilization of different autoregressive models to forecast the temporal evolution of cloudiness of different spatial scales. Our work is motivated by the scale-dependent predictability of cloud growth and decay. By exploiting more than one autoregressive model, we can predict the noisy evolution of small scales independently of the more deterministic evolution of larger spatial scales.

Our preliminary results over Switzerland indicate that our model outperforms the probabilistic advection model based on Carriere et al., 2021 noise generation by reducing the continuously ranked probability score (CRPS) on the test set by 14%. Moreover, we demonstrate the advantage of cloudiness scale decomposition by comparing our model with the same approach without decomposition. We can reduce the CRPS by 6% and the RMSE by 5% by decomposing the images into multiple cascades

References

Bowler, N., C. Pierce, A. Seed, 2006, “STEPS: A probabilistic precipitation forecasting scheme which merges an extrapolation nowcast with downscaled NWP”, Quarterly Journal of the Royal Meteorological Society, 132, 620, pp. 2127–2155, doi:10.1256/qj.04.100.

Carriere, T., R. Amaro e Silva, F. Zhuang, Y. Saint-Drenan, P. Blanc, 2021, “A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors”, Energies, 14, doi:10.3390/en14164951.

David, M., M. Luis, P. Lauret, 2018, “Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data”, International Journal of Forecasting, 34, doi:10.1016/j.ijforecast.2018.02.003.

Pulkkinen, S., D. Nerini, A. Perez Hortal, C. Velasco-Forero, A. Seed, U. Germann, L. Foresti, 2019, “Systems: an open-source Python library for probabilistic precipitation nowcasting (v1.0)”, Geoscientific Model Development, 12, 10, pp. 4185–4219, doi:10.5194/gmd-12-4185-2019.

Smith, O., O. Cattell, E. Farcot, R. D. O’Dea, K. I. Hopcraft, 2022, “The effect of renewable energy incorporation on power grid stability and resilience”, Science Advances,  https://www.science.org/doi/abs/10.1126/sciadv.abj6734.