Characteristics of sub-seasonal prediction performance revealed on hindcast of the Korean Integrated Model

This study introduces the prediction performance of the Korean Integrated Model (KIM) associated with medium-long range or sub-seasonal timescales. KIM has been developed as the Korea Meteorological Administration’s operational numerical weather prediction (NWP) system by the Korea Institute of Atmospheric Prediction Systems (KIAPS). KIM is a newly introduced global atmospheric model system, consisting of a spectral-element non-hydrostatic dynamical core on a cubed sphere grid and an advanced physics parameterization package (Kim et al., 2021; Hong et al., 2018). We’ll conduct hindcast sets and assess the general characteristics and systematic biases of KIM focused on a sub-seasonal timescale.

Through analyzing KIM’s hindcast experiments, we try to examine the prediction performance and predictability in the lead time range between 2 weeks and sub-seasonal timescales. This timescale is one of the important issues in terms of a seamless forecast that links NWP and climate prediction. A seamless forecast means bridging discrete short-term weather forecasts valid at a specific time and time-averaged forecast at longer periods. Sub-seasonal predictions span this time range and this transition period determines forecasting skills.

We plan to perform KIM’s ensemble reforecast simulation for the boreal winter and summer cases for the period of 2001 – 2020 (20 years). The hindcast results are compared with reanalysis data (ERA5). In order to evaluate KIM’s sub-seasonal performance, several skill scores are calculated, which are Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), Ratio of Predictable Components (RPC), and Root Mean Square Skill Score (RMSSS). Finally, it is expected that these multi-year simulations will contribute to improving sub-seasonal and seasonal predictabilities.