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MATCHA, a novel regional hydroclimate-chemical reanalysis: System description and evaluation

C. Roychoudhury, R. Kumar, C. He, W. Y. Y. Cheng et al. • Earth System Science Data (2026)

We present MATCHA (Model for Atmospheric Transport and Chemistry in Asia), a 17-year (2003–2019) regional hydroclimate-chemical reanalysis for Asia (58–140° E, 4– 40° N) at 12 km resolution. MATCHA couples the Weather Research and Forecasti...

DOAJ Open Access (DOAJ) DOI: 10.5194/essd-18-5209-2026

Machine Learning for Climate Precipitation Prediction Modeling over South America

Juliana Aparecida Anochi, Vinícius Albuquerque de Almeida, Haroldo Fraga de Campos Velho • Remote Sensing (2021)

Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learni...

DOAJ Open Access (DOAJ) DOI: 10.3390/rs13132468

Machine learning approach for the estimation of missing precipitation data: a case study of South Korea

Heechan Han, Boran Kim, Kyunghun Kim, Donghyun Kim et al. • Water Science and Technology (2023)

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an ...

DOAJ Open Access (DOAJ) DOI: 10.2166/wst.2023.237

Towards instance-wise calibration: local amortized diagnostics and reshaping of conditional densities (LADaR)

Biprateep Dey, David Zhao, Brett H Andrews, Jeffrey A Newman et al. • Machine Learning: Science and Technology (2025)

Key science questions, such as galaxy distance estimation and weather forecasting, often require knowing the full predictive distribution of a target variable Y given complex inputs X . Despite recent advances in machine learning and physic...

DOAJ Open Access (DOAJ) DOI: 10.1088/2632-2153/ae1f05

Artificial Intelligence for Enhancing Resilience

Nitin Rane, Mallikarjuna Paramesha, Saurabh Choudhary, Jayesh Rane • Journal of Applied Artificial Intelligence (2024)

In an increasingly complex and unpredictable world, resilience-the ability to withstand and recover from adverse conditions is essential across various sectors. This research paper investigates the transformative potential of artificial int...

OPENALEX cc-by DOI: https://doi.org/10.2139/ssrn.4831911

A Generative Super‐Resolution Model for Enhancing Tropical Cyclone Wind Field Intensity and Resolution

Joseph W. Lockwood, Avantika Gori, Pierre Gentine • Journal of Geophysical Research: Machine Learning and Computation (2024)

Abstract Extreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coa...

DOAJ Open Access (DOAJ) DOI: 10.1029/2024JH000375