An Observation Validation System for Verification and Diagnosis Data Assimilation of the KIM Model.

The accuracy and reliability of numerical weather prediction (NWP) models are crucial for effective weather forecasting and climate analysis. The Korean Integrated Model (KIM) is the Korea Meteorological Administration (KMA) operational global NWP system that generates analysis fields every 6 hours by assimilating various observational data using the Hybrid 4DEnVar system. To ensure the model's accuracy, it is essential to validate these analysis fields. KIM's outputs have been evaluated using the European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (IFS) as a reference, known for its high-resolution data assimilation and comprehensive global coverage. However, the IFS also has systematic errors and biases, which can lead to skewed interpretations when assessing KIM's performance. To overcome these limitations, we propose a robust observation-based verification system for KIM. This system allows for a more objective evaluation by directly comparing model outputs with diverse observational data, including satellite remote sensing, ground-based meteorological observations, and upper-air radiosonde data. A significant feature is its ability to handle both uncorrected and bias-corrected observational data, which is essential for minimizing the impact of known errors. The verification system conducted spatiotemporal assessments from July 1, 2022, to July 31, 2022, examining model accuracy in both horizontal and vertical dimensions. Statistical evaluations utilize visualization techniques such as vertical profiles, histograms, and horizontal spatial distributions. Vertical profiles display the standard deviation of spatiotemporal data, histograms summarize error distributions to identify biases, and spatial distributions reveal regional performance variations. This system enhances the evaluation of data assimilation processes and serves as a benchmark for KIM's ongoing performance improvement, ultimately contributing to the model's refinement.