Enhancing 1-Month Forecasts in South Korea with the Combination of Time-Lagged Ensemble and Dynamical Downscaling

In South Korea, daily weather forecasts are currently limited to a 10-day range, which is insufficient for adequately preparing weather-dependent sectors like agriculture for the impact of meteorological conditions over longer periods. To meet the growing demand for subseasonal-to-seasonal predictions, organizations worldwide provide GCM-driven forecasts that extend several weeks or even months ahead. One example is the Climate Forecast System version 2 (CFSv2) operational forecast by NCEP, which is initialized every 6 hours and extends up to 9 months. Its large pool of available forecast members with different initialization times enables the construction of a time-lagged ensemble, which can improve forecasting accuracy by offering a range of potential future meteorological conditions and accounting for the inherent uncertainty in a single deterministic forecast. In this regard, this study aims to build an optimal time-lagged ensemble for 1-month forecasts in South Korea by employing a systematic method to select suitable members from a pool of hundreds of CFSv2 forecast members. Furthermore, the selected ensemble members will be dynamically downscaled to address limitations arising from their coarse resolution. The integration of the time-lagged ensemble and dynamical downscaling methods will be evaluated from both quantitative and qualitative perspectives to assess their added value in enhancing forecasts. By evaluating the performance of the optimized time-lagged ensemble combined with dynamical downscaling, this study will provide valuable insights into the improvement and practical application of subseasonal-to-seasonal forecasts in South Korea, benefiting various sectors that can leverage the enhanced long-term weather predictions.

 

Acknowledgments

This study was carried out with the support of “Research Program for Agricultural Science & Technology Development (Project No. PJ014882)”, National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea.