A study on optimizing the diabatic initialization of very short-range forecast model in KMA
- Korea Meteorological Administration Numerical Modeling Center, Numerical Data Application Division, Korea, Republic of (lsy0423@korea.kr)
The Korea Meteorological Administration (KMA) operates the Korea Local Analysis and Prediction System (KLAPS), a very short-range forecast model providing real-time and 12-hour forecasts information at 10-minute intervals at 5 km horizontal resolution.
The analysis process of KLAPS, based on the LAPS (Local Analysis and Prediction System) from NOAA, is the data assimilation system that ingests radar, satellite, aircraft, surface observations, and regional atmospheric model data to analyze 3-D atmospheric conditions. After that, to ensure that the momentum and mass fields are consistent with the cloud-derived vertical motions during the diabatic initialization process, optimizing the thermodynamic balance between wind and cloud fields is performed.
This diabatic initialization process is critical to the initial condition of KLAPS for improving performance of model forecasting. In this step, user-defined weights, derived from the known error characteristics of analysis and background models, are utilized to minimize the variational cost function. By fine-tuning the weights of the variational cost function, the gaps between the model’s spin-up and current weather conditions and short-term extrapolation are reduced.
Sensitivity experiments are performed to estimate optimal weights of the variational balance processes, achieving improved initial fields for KLAPS. The predictability of precipitation is also evaluated based on the improved initial fields to verify the effects of weight optimization in adiabatic initialization process. This indicates the significant impacts of improved initial fields on forecast accuracy. The optimization of weights in initialization process of KLAPS is expected that initial state of the model will be improved and these results can contribute to enhancement of predictability performance for precipitation of the KLAPS.
Acknowledgement: This work was supported by Development of Numerical Weather Prediction and Data Application Techniques (KMA2018-00721)
How to cite: Lee, S., Won, H. Y., Lee, K. H., and Kim, S.: A study on optimizing the diabatic initialization of very short-range forecast model in KMA, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-617, https://doi.org/10.5194/ems2024-617, 2024.
