Adaptive selection of members for convective-permitting regional ensemble prediction over the western Maritime Continent - Sharma, Kuldeep; Lee, Joshua Chun Kwang; Porson, Aurore; Chandramouli, Krishnamoorthy; Roberts, Nigel; Boyd, Douglas; Zhang, Huqiang; Barker, Dale Melvyn (Frontiers in Environmental Science, 2023)
In downscaled regional ensemble prediction systems, a common issue is the under-dispersiveness of the ensemble forecasts, often attributed to the lack of spread in the initial conditions from the global ensemble. In this study, a novel method which adopts an adaptive approach to select global ensemble members for regional downscaling has been developed.Instead of using a fixed set of pre-selected global ensemble members, the adaptive selection performs a sampling algorithm and selects the global ensemble members which maximise a Fractions Skill Score (FSS)-based displacement between ensemble members. The method is applied to a convective-permitting ensemble prediction system over the western Maritime Continent, referred to as SINGV-EPS. SINGV-EPS has a grid spacing of 4.5km and is a 12member ensemble that is driven by the European Centre for Medium-range Weather Forecasts (ECMWF) 51-member global ensemble. Month-long trials in June 2020 were conducted to assess the impact of adaptive selection on the ensemble forecast spread and rainfall verification scores. In both fixed pre-selection and adaptive selection experiments, SINGV-EPS was still under-dispersive. However, adaptive selection improved the ensemble spread and reduced the root-mean-square error of the ensemble mean in winds, temperature and precipitation fields.Further verification of the rainfall forecasts showed that there was a reduction in the Brier Score and a higher hit rate in the Relative Operating Characteristic curve for all rainfall thresholds when adaptive selection was applied. Additionally,, the ensemble mean forecasts from adaptive selection experiments are more accurate beyond 24 hours, with a higher FSS for all rainfall thresholds and neighborhood lengths. These results suggest that the adaptive selection is superior to the fixed pre-selection of global ensemble members for downscaled regional ensemble prediction.