Initializing Regional Ensemble Forecasts with an Ensemble Partial Cycling Strategy

Abstract An initialization method named ensemble partial cycling (EnPC) is developed for the regional WRF-based ensemble prediction system (EPS) (WEPS) operated by the Central Weather Administration (CWA) of Taiwan. The EnPC method combines partial cycling data assimilation (DA) and the ensemble of DA approach with an additional blending procedure that merges large-scale global features with small-scale regional information, leveraging the DA efforts from the deterministic system of CWA. Ensemble forecasts initialized from EnPC are compared with those from three commonly used initialization methods for regional EPS that include dynamical downscaling, ensemble adjustment Kalman filter (EAKF)-based regional ensemble DA, and a blended combination of the two, the last of which is equivalent to the current operational configuration of WEPS. Several sets of WEPS experiments are conducted over a 5-week period, including five typhoons. EnPC-initialized WEPS forecasts are found to be comparable to the dynamically downscaled forecasts in many evaluation metrics and have more accurate near-surface forecasts over the first 12 h and better precipitation forecast discrimination ability for typhoon events. Compared to the EAKF and the blended methods, forecasts initialized from EnPC have overall smaller errors in most of the evaluation metrics by both deterministic and probabilistic measures and better spread-to-error ratios. As an alternative initialization method, EnPC not only adds some regional benefits on top of downscaling but also shows some advantages over the operational method. With the planned retirement of EAKF and the anticipation of a more unified production suite at CWA, EnPC will replace the current operational method.