Page 44 - APEC CLIMATE CENTER 2025 Annual Report

APEC CLIMATE CENTER 2025 ANNUAL REPORT

Research 1-4. Improvement of the Verification and Utilization Framework for Research · The framework is designed to diagnose seasonal prediction skill systematically and

to analyze the system’s response characteristics to structural or configuration-related
Projects Climate Prediction Models Projects issues, thereby supporting a comprehensive assessment of forecast performance.
- Diagnosis of Climate Mode Prediction Characteristics in GC3.2 Forecast Data
• Development of an advanced assessment system aimed at improving the performance of
in 2025 the Korea Meteorological Administration’s operational climate forecasting models in 2025 · Prediction characteristics of major climate modes are diagnosed using forecast data,
including summer modes (the Circumglobal Teleconnection (CGT) and Pacific–Japan
(PJ) patterns) and winter modes (the Eurasian pattern (EU) and North Atlantic Oscilla-
Project 7. Development of a Verification Framework and Expansion of Testbeds
tion (NAO)).
for Advancing Climate Prediction Models
· The reliability of forecast data is assessed through a comparative analysis of simula-
tion characteristics between hindcast and forecast datasets.
㉖ Dr. Sun-Hee Shin (ssh222@apcc21.org) · Strategies for the utilization of long-range forecasts are suggested based on the diag-
nosed prediction characteristics of the climate prediction system.
1) Background and Relevance - Development of an Automated Seasonal Verification System Based on Quasi-Opera-
tional Testbed Experiments
- To enhance the predictive performance and operational applicability of the Korea Meteo-
· Diagnostic metrics applicable to quasi-operational testbed experiments are designed
rological Administration (KMA) climate prediction system, timely and systematic verifica-
for Arctic climate variability and East Asian summer and winter monsoon systems,
tion of state-of-the-art climate model development techniques is essential. In particular,
and an automated seasonal verification framework is developed to establish a con-
a robust framework for quantitatively evaluating and assessing the reliability of newly
sistent performance evaluation environment.
developed algorithms and physical parameterizations against existing operational mod-
· In preparation for the introduction of the next-generation climate prediction system,
els is required for their effective transfer into forecasting systems.
hindcast seasonal experiment datasets for the latest model version (GC5.0) are col-
lected and analyzed, and supporting evidence for operational transition is provided
- The climate prediction model testbed serves as a core infrastructure that enables rap-
through the diagnosis of prediction performance and physical processes.
id testing and evaluation of model improvement technologies developed by KMA and
the academic community. It plays a critical role in verifying the predictive skill and phys-
ical consistency of new techniques from an operational perspective. Furthermore, this
project aims to develop diagnostic and evaluation systems using real-time forecast data
while enhancing the versatility and sustainability of the verification framework through
the expansion of verification components and structural improvements.

2) Main Results
A. Testbed Operation for Proactive Verification and Operational Transition of Ad-
vanced Climate Prediction Technologies
- Evaluation of the Operational Utility of High-Resolution Forecast Data from the KMA
Climate Prediction System (GC3.2)
· The climate simulation performance of high-resolution forecast data from the op-
erational climate prediction system is evaluated, and its applicability to operational
forecasting is quantitatively assessed.
· Based on the simulation skill for summer extreme precipitation and winter extreme
Fig 31 Testbed Operation and Expansion of the Verification Framework for Climate Prediction
temperature events, the seasonal prediction utility of high-resolution forecast data is Model Improvement
analyzed, providing a scientific basis for the operational adoption of a high-resolu-
tion ensemble prediction system.
- Assessment of the Operational Applicability of the High-Resolution River Runoff Model (TRIP) 3) Expected Implications
· Quasi-operational experiments are conducted using the climate prediction system - Provision of quantitative scientific bases to support operational decision-making for the
(GC3.2) coupled with the high-resolution TRIP model. adoption of climate model improvements within the KMA climate prediction system.
· Changes in prediction skill associated with freshwater effects and corresponding at- - Enhancement of the versatility and long-term sustainability of the climate model verifi-
mospheric and oceanic responses are analyzed to diagnose operational feasibility. cation framework through flexible application across seasons and climate modes.
- Contribution to improved long-range forecast accuracy and strengthened predictive ca-
pability for extreme climate events through enhanced seasonal climate mode prediction
B. Expansion of the Verification Framework to Enhance Forecast Reliability
skill.
- Development of a Performance Evaluation Framework for Forecast Data of the KMA
- Strengthening collaboration between KMA and related research institutions, facilitating
Climate Prediction System (GC3.2)
rapid operational implementation of research outcomes and advancing APCC’s capacity
· An automated verification system is implemented to support routine and consistent
in climate model diagnostics and evaluation.
performance assessment of forecast data, enabling systematic evaluation of both de-
terministic and probabilistic forecast performance.

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