Current status and future plans for stochastic representations of model uncertainties in the Korean Integrated Model

The Korea Institute for Atmospheric Prediction Systems (KIAPS) was established in 2011 with the objective of developing a global atmosphere-only numerical weather prediction system for operational use at the Korea Meteorological Administration. The system was completed in accordance with the established timeline and became operational at KMA in April 2020, demonstrating immediate and exceptional performance. The system is based on a novel atmospheric model called the "Korean Integrated Model" (KIM), which is structured on a cubed-sphere grid and employs the spectral element method within its dynamical core. The deterministic data assimilation (DA) process is based on a hybrid-4DEnVar algorithm, while the forecast uncertainties are modelled by a 50-member Ensemble Prediction System (EPS). The EPS is based on a local ensemble transform Kalman filter (LETKF) data assimilation (DA) algorithm, and further schemes are included to account for deficiencies and uncertainties in the DA process and the forecast model. Ensemble forecasts are contingent upon the manner in which initial uncertainties and model uncertainties are represented. This study introduces the stochastic representation schemes of model uncertainties in KIM and presents the results of numerical experiments. We will provide a preliminary examination of the results, with a particular focus on the sensitivities of the model to stochastically perturbed physical tendencies (SPPT) and stochastically perturbed parameterizations (SPP) schemes. To reduce computational costs, sensitivity tests were performed in a low-resolution framework to analyze the impact of these schemes on the ensemble mean error and spread in temperature, geopotential height, and specific humidity in extended medium-range forecasts. This will be followed by a discussion of possible future plans to further enhance the methods used to perturb the forecast model.