Overview of Korea Precipitation Observation Program(KPOP) in the Seoul Metropolitan Area
Bibliographic record
Abstract
The Seoul metropolitan area is a residential area with a population of over 20 million and Korea’s major industrial facilities are closely located, so when hazardous weather events such as heavy rain and snow occur in this area, the social and economic damage is very significant. The region's localized heavy rainfall is challenging to observe and predict due to heterogeneity in surface conditions and the complexity of the terrain. This study introduces the Korea Meteorological Administration's (KMA) efforts in constructing an intensive observation network and utilizing the data to analyze rainfall mechanisms, and improve the accuracy of numerical weather models..Since 2021, the KMA has conducted annual intensive observations during the summer in the Seoul metropolitan area. In 2023, international collaborative intensive observations were conducted in partnership with USA, Canada, Spain, and Korean universities, utilizing radar, storm trackers, and other tools. Furthermore, the National Institute of Meteorological Sciences (NIMS) established a supersite near Incheon International Airport, equipped with X and C-band radars, wind profiler, wind lidar, and rain gauges to collect real-time observation data and support operational forecasting.Summer heavy rainfall in the Korean Peninsula is related to low-level jets. For instance, during heavy rainfall in July 2024 in the Seoul metropolitan area, wind lidar observations near Incheon International Airport showed that low-level jets extended vertically and contributed to the development of heavy rainfall through moisture fluxes. Additionally, to analyze the impact of intensive observation data on improving the accuracy of heavy rainfall predictions, the WRF mesoscale numerical model was used for simulations and analysis. The observation data collected by KMA's intensive observation network is shared and served through the KPOP-MS web page system. Detailed results on the observation network, heavy rainfall case studies, and numerical simulations will be presented at the conference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".