METEOROLOGICAL DROUGHT PREDICTION USING ADVANCED SPATIO-TEMPORAL DOWNSCALING OF GLOSEA6 CLIMATE FORECAST DATA

Jeongwoo Lee; Minwoo Park; Gihyun Park; Hyun Han Kwon

METEOROLOGICAL DROUGHT PREDICTION USING ADVANCED SPATIO-TEMPORAL DOWNSCALING OF GLOSEA6 CLIMATE FORECAST DATA

Jeongwoo Lee
, Minwoo Park
, Gihyun Park
, Hyun Han Kwon

Department of Civil Engineering

Sejong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Accurate hydro-meteorological predictions are essential for effective water resource management and drought mitigation. This study refines GloSea6 climate data using a multivariate Non-stationary Hidden Markov Model (MNHMM) to enhance its spatial and temporal resolution for hydrological modeling. GloSea6 Hindcast data were downscaled with observations from the Automated Surface Observing System (ASOS) in South Korea, and the derived MNHMM parameters were applied to GloSea6 Forecast data to generate daily rainfall and temperature sequences across multiple weather stations. The MNHMM effectively captures temporal and regional climate variations through dynamic transition probabilities, overcoming the limitations of stationary models. This approach improves rainfall projections, streamflow simulations, and hydrological drought indices, enabling more accurate drought predictions on a daily scale over a three-month forecast horizon.

Original language	English
Title of host publication	Book of Extended Abstracts of the 41st IAHR World Congress, 2025
Editors	Adrian Wing-Keung Law, Jenn Wei Er
Publisher	International Association for Hydro-Environment Engineering and Research
Pages	767-769
Number of pages	3
ISBN (Print)	9789083558950
State	Published - 2025
Event	Book of Extended Abstracts of the 41st IAHR World Congress, 2025 - Singapore, Singapore Duration: 22 Jun 2025 → 27 Jun 2025

Publication series

Name	Proceedings of the IAHR World Congress
ISSN (Print)	2521-7119
ISSN (Electronic)	2521-716X

Conference

Conference	Book of Extended Abstracts of the 41st IAHR World Congress, 2025
Country/Territory	Singapore
City	Singapore
Period	22/06/25 → 27/06/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 13 Climate Action

Keywords

Drought Prediction
GloSea6
Spatio-temporal Downscaling

Cite this

Lee, J., Park, M., Park, G., & Kwon, H. H. (2025). METEOROLOGICAL DROUGHT PREDICTION USING ADVANCED SPATIO-TEMPORAL DOWNSCALING OF GLOSEA6 CLIMATE FORECAST DATA. In A. Wing-Keung Law, & J. W. Er (Eds.), Book of Extended Abstracts of the 41st IAHR World Congress, 2025 (pp. 767-769). (Proceedings of the IAHR World Congress). International Association for Hydro-Environment Engineering and Research.

Lee, Jeongwoo ; Park, Minwoo ; Park, Gihyun et al. / METEOROLOGICAL DROUGHT PREDICTION USING ADVANCED SPATIO-TEMPORAL DOWNSCALING OF GLOSEA6 CLIMATE FORECAST DATA. Book of Extended Abstracts of the 41st IAHR World Congress, 2025. editor / Adrian Wing-Keung Law ; Jenn Wei Er. International Association for Hydro-Environment Engineering and Research, 2025. pp. 767-769 (Proceedings of the IAHR World Congress).

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title = "METEOROLOGICAL DROUGHT PREDICTION USING ADVANCED SPATIO-TEMPORAL DOWNSCALING OF GLOSEA6 CLIMATE FORECAST DATA",

abstract = "Accurate hydro-meteorological predictions are essential for effective water resource management and drought mitigation. This study refines GloSea6 climate data using a multivariate Non-stationary Hidden Markov Model (MNHMM) to enhance its spatial and temporal resolution for hydrological modeling. GloSea6 Hindcast data were downscaled with observations from the Automated Surface Observing System (ASOS) in South Korea, and the derived MNHMM parameters were applied to GloSea6 Forecast data to generate daily rainfall and temperature sequences across multiple weather stations. The MNHMM effectively captures temporal and regional climate variations through dynamic transition probabilities, overcoming the limitations of stationary models. This approach improves rainfall projections, streamflow simulations, and hydrological drought indices, enabling more accurate drought predictions on a daily scale over a three-month forecast horizon.",

keywords = "Drought Prediction, GloSea6, Spatio-temporal Downscaling",

author = "Jeongwoo Lee and Minwoo Park and Gihyun Park and Kwon, \{Hyun Han\}",

note = "Publisher Copyright: {\textcopyright} 2025 IAHR.; Book of Extended Abstracts of the 41st IAHR World Congress, 2025 ; Conference date: 22-06-2025 Through 27-06-2025",

year = "2025",

language = "English",

isbn = "9789083558950",

series = "Proceedings of the IAHR World Congress",

publisher = "International Association for Hydro-Environment Engineering and Research",

pages = "767--769",

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}

Lee, J, Park, M, Park, G & Kwon, HH 2025, METEOROLOGICAL DROUGHT PREDICTION USING ADVANCED SPATIO-TEMPORAL DOWNSCALING OF GLOSEA6 CLIMATE FORECAST DATA. in A Wing-Keung Law & JW Er (eds), Book of Extended Abstracts of the 41st IAHR World Congress, 2025. Proceedings of the IAHR World Congress, International Association for Hydro-Environment Engineering and Research, pp. 767-769, Book of Extended Abstracts of the 41st IAHR World Congress, 2025, Singapore, Singapore, 22/06/25.

METEOROLOGICAL DROUGHT PREDICTION USING ADVANCED SPATIO-TEMPORAL DOWNSCALING OF GLOSEA6 CLIMATE FORECAST DATA. / Lee, Jeongwoo; Park, Minwoo; Park, Gihyun et al.
Book of Extended Abstracts of the 41st IAHR World Congress, 2025. ed. / Adrian Wing-Keung Law; Jenn Wei Er. International Association for Hydro-Environment Engineering and Research, 2025. p. 767-769 (Proceedings of the IAHR World Congress).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - METEOROLOGICAL DROUGHT PREDICTION USING ADVANCED SPATIO-TEMPORAL DOWNSCALING OF GLOSEA6 CLIMATE FORECAST DATA

AU - Lee, Jeongwoo

AU - Park, Minwoo

AU - Park, Gihyun

AU - Kwon, Hyun Han

PY - 2025

Y1 - 2025

N2 - Accurate hydro-meteorological predictions are essential for effective water resource management and drought mitigation. This study refines GloSea6 climate data using a multivariate Non-stationary Hidden Markov Model (MNHMM) to enhance its spatial and temporal resolution for hydrological modeling. GloSea6 Hindcast data were downscaled with observations from the Automated Surface Observing System (ASOS) in South Korea, and the derived MNHMM parameters were applied to GloSea6 Forecast data to generate daily rainfall and temperature sequences across multiple weather stations. The MNHMM effectively captures temporal and regional climate variations through dynamic transition probabilities, overcoming the limitations of stationary models. This approach improves rainfall projections, streamflow simulations, and hydrological drought indices, enabling more accurate drought predictions on a daily scale over a three-month forecast horizon.

AB - Accurate hydro-meteorological predictions are essential for effective water resource management and drought mitigation. This study refines GloSea6 climate data using a multivariate Non-stationary Hidden Markov Model (MNHMM) to enhance its spatial and temporal resolution for hydrological modeling. GloSea6 Hindcast data were downscaled with observations from the Automated Surface Observing System (ASOS) in South Korea, and the derived MNHMM parameters were applied to GloSea6 Forecast data to generate daily rainfall and temperature sequences across multiple weather stations. The MNHMM effectively captures temporal and regional climate variations through dynamic transition probabilities, overcoming the limitations of stationary models. This approach improves rainfall projections, streamflow simulations, and hydrological drought indices, enabling more accurate drought predictions on a daily scale over a three-month forecast horizon.

KW - Drought Prediction

KW - GloSea6

KW - Spatio-temporal Downscaling

UR - https://www.scopus.com/pages/publications/105026281074

M3 - Conference contribution

AN - SCOPUS:105026281074

SN - 9789083558950

T3 - Proceedings of the IAHR World Congress

SP - 767

EP - 769

BT - Book of Extended Abstracts of the 41st IAHR World Congress, 2025

A2 - Wing-Keung Law, Adrian

A2 - Er, Jenn Wei

PB - International Association for Hydro-Environment Engineering and Research

T2 - Book of Extended Abstracts of the 41st IAHR World Congress, 2025

Y2 - 22 June 2025 through 27 June 2025

ER -

Lee J, Park M, Park G, Kwon HH. METEOROLOGICAL DROUGHT PREDICTION USING ADVANCED SPATIO-TEMPORAL DOWNSCALING OF GLOSEA6 CLIMATE FORECAST DATA. In Wing-Keung Law A, Er JW, editors, Book of Extended Abstracts of the 41st IAHR World Congress, 2025. International Association for Hydro-Environment Engineering and Research. 2025. p. 767-769. (Proceedings of the IAHR World Congress).

METEOROLOGICAL DROUGHT PREDICTION USING ADVANCED SPATIO-TEMPORAL DOWNSCALING OF GLOSEA6 CLIMATE FORECAST DATA

Abstract

Publication series

Conference

UN SDGs

Keywords

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