Page 19 - APEC CLIMATE CENTER 2025 Annual Report

APEC CLIMATE CENTER 2025 ANNUAL REPORT

Highlighted 3. Bridging the Gap in Seasonal Prediction: Development Highlighted cal for subseasonal prediction, we developed a soil moisture initialization technique for

SCoPS and quantitatively evaluated its impact. We also examined how improvements
Achievements of Integrated Subseasonal to Seasonal Forecasting Achievements in oceanic and atmospheric initial conditions influence prediction performance on both
Approaches
in 2025 in 2025 subseasonal and seasonal time scales. On the basis of these results, we have initiated pi-
lot operations of the newly established APCC subseasonal prediction system to provide
enhanced forecast information. We plan to develop display systems for weekly prediction
㉖ Dr. Suryun Ham (suryun01@apcc21.org) Dr. Young-Mi Min (ymmin@apcc21.org) products and select highly useful content for operational dissemination.
Dr. Bong-Geun Song (songbg@apcc21.org) Mr. Soonjo Yoon (sjyoon@apcc21.org)
Dr. Sinil Yang (siyang@apcc21.org) Subseasonal prediction provides forecast information on timescales ranging from 2 to 8
weeks and is rapidly gaining value for applications across a wide range of sectors, includ-
ing disaster prevention, agricultural management, and hydrological and energy opera-
Since 2017, the APEC Climate Center (APCC) has established a robust collaborative frame- tions. However, due to the inherent characteristics of the forecast lead time, subseasonal
work to support the Korea Meteorological Administration (KMA)'s operational one-month prediction remains highly challenging, as both sensitivity to initial conditions and signals
forecast by collecting, processing, and providing the latest subseasonal prediction infor- from external forces are relatively weak. The newly developed APCC subseasonal predic-
mation. This study describes the stable operation of the subseasonal prediction system tion system is expected to play a vital role in climate information services for the Asia-Pa-
and the deep learning-based probabilistic temperature forecasting system tailored for cific region through sustained operation and improvement. In particular, the expanded
this purpose. In addition, the migration and improvement of data collection and forecast subseasonal forecast information is anticipated to support disaster prevention efforts and
production servers were carried out to enhance operational efficiency. Through a case climate risk management that relies on forecast-based decision-making..
study, the system's performance in predicting detailed weather variability—which cannot
be identified through monthly mean values alone—was verified. The results demonstrate
the high utility of subseasonal prediction information in capturing intra-monthly variabil-
ity. To address the limitations of seasonal prediction information in a rapidly changing cli-
mate, APCC has pursued the development of a subseasonal prediction system designed
to enhance its utilization value.
To develop the APCC subseasonal prediction system, a wide range of global subseason-
al-to-seasonal (S2S) prediction datasets were systematically collected and standardized.
Using these datasets, we confirmed that the Multi-Model Ensemble (MME) provides stable
and superior performance within the subseasonal prediction range. We also confirmed
that the APCC’s own model, with its diverse component model composition, can further
increase the overall contribution to the MME. Furthermore, we evaluated parametric prob-
abilistic prediction methods for temperature and precipitation in order to identify meth-
odologies optimized for the statistical characteristics of each variable. The results indicate
that parametric methods outperform non-parametric approaches in terms of quantitative
predictive skill and can effectively reduce spatial noise arising from an insufficient ensem-
ble size. Building on these research findings, we established the model configuration and Fig 12 Probabilisic forecasts of 3-4 week mean temperature and precipitation produced by the
subseasonal prediction system
probabilistic methods required for the APCC subseasonal prediction system. The system
ingests weekly forecast data from 10 models, including APCC's SCoPS, BOM, NCEP, and
HMC, to generate subseasonal prediction products. For the final MME production, the
Simple Composite Method (SCM) is used for deterministic forecasts, while the Hybrid
Gamma method is employed for probabilistic forecasts. This integrated system allows for
the production and display of MME prediction results, initialized to start every Monday. To
diversify and enhance the usability of subseasonal information, the study also explored
and identified seamless content that integrates subseasonal weekly and monthly forecast
information. We identified products that can be delivered immediately, such as integrat-
ed monthly/weekly probability distributions and weekly variability information, as well as
more advanced products requiring additional technical development, including probabil-
ities of intra-monthly extreme events.

Meanwhile, to include SCoPS as one of the contributing models, we developed a re-
al-time subseasonal forecast system that produces 60-day forecasts every week, based
on monthly seasonal forecasts. However, given the relatively old development baseline
of SCoPS and its lower skill compared to other MME participating models, there is a clear
Fig 13 Example of potential contents to bridge subseasonal and seasonal (monthly) forecasts.
need to reduce its systematic errors. To reduce land-related initial errors, which are criti-

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