Application of Ensemble Forecasts in Predicting and Assessing the Effectiveness of Rain Enhancement using Cloud Seeding

In countries where drought is a serious threat to agriculture, water resources and increases risk of wildfires, there has been an increasing interest in using weather modification techniques to improve local precipitation.  Despite the fact that cloud seeding is used in many places around the world, there is still not a clear consensus on its effectiveness. Various evaluation methods, including aircraft and ground-based measurements, remote sensing, statistical analysis, and numerical simulation have been widely used to evaluate the effect of cloud seeding. While each of these methods has its own benefits, they also come with limitations. For example, numerical models can simulate cases with and without cloud seeding and these scenarios can be then compared to determine how much rainfall increases after the seeding. Predictions provided by such scenarios can help with decision-making before conducting a cloud seeding experiment. The downside of numerical simulations is the presence of both systematic and random errors that originate from uncertainties initial conditions and numerical approximations. Ensemble forecasts can capture some of these uncertainties and provide a range of possible outcomes. The main goal of this study is to explore the potential of an ensemble forecasting system in evaluating the efficacy of cloud seeding. We used a limited-area ensemble forecasting system which is based on Met Office Unified Model coupled with Weather Research and Forecasting Model (WRF). The initial conditions of 13 ensemble members were created by downscaling of global ensemble model that was perturbed using the Ensemble transform Kalman filter. The WRF model was used to downscale the ensemble members to a finer resolution and simulate the seeding effect using an algorithm that was added to the Morrison microphysics scheme. As study cases, we utilized several cloud seeding experiments that were conducted during the weather modification campaign of 2022 and 2023 by the National Institute of Meteorological Sciences and the Korea Meteorological Administration. Ground-based hygroscopic cloud seeding was conducted in the mountainous region of South Korea using calcium chloride flares. Simulations were conducted for scenarios with and without seeding, and the difference in rainfall we assessed using ensemble mean, ensemble spread, and probabilities of various rainfall increment thresholds. The overall ensemble performance in rain predictability of the ensemble forecasts was evaluated using commonly used techniques, including the Brier score, reliability diagrams and ROC curves.

Acknowledgments: This work was funded by the Korea Meteorological Administration Research and Development Program “Research on Weather Modification and Cloud Physics” under Grant (KMA2018-00224).