Assessing exposure to climate extremes over the Arabian Peninsula using ERA5 reanalysis data: Spatial distribution and temporal trends

Al-Sakkaf, Ali Salem; Zhang, Jiahua; Yao, Fengmei; Hamed, Mohammed Magdy; Simbi, Claudien Habimana; Ahmed, Arslan; Shahid, Shamsuddin

doi:10.1016/j.atmosres.2024.107224

Atmospheric Research

2024

DOI: 10.1016/j.atmosres.2024.107224

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Assessing exposure to climate extremes over the Arabian Peninsula using ERA5 reanalysis data: Spatial distribution and temporal trends

Ali Salem Al-Sakkaf,

Jiahua Zhang,

Fengmei Yao

et al.

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2024

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Cited by 5 publications

References 85 publications

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Quantifying the stochastic trends of climate extremes over Yemen: a comprehensive assessment using ERA5 data

Al-Sakkaf,

Zhang,

Yao

et al. 2024

Stoch Environ Res Risk Assess

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Quantifying the stochastic trends of climate extremes over Yemen: a comprehensive assessment using ERA5 data

Al-Sakkaf,

Zhang,

Yao

et al. 2024

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models

Merizzi,

Asperti,

Colamonaco

2024

Neural Comput & Applic

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The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution regional reanalysis dataset for the European domain. In recent years, it has shown significant utility across various climate-related tasks, ranging from forecasting and climate change research to renewable energy prediction, resource management, air quality risk assessment, and the forecasting of rare events, among others. Unfortunately, the availability of CERRA is lagging 2 years behind the current date, due to constraints in acquiring the requisite external data and the intensive computational demands inherent in its generation. As a solution, this paper introduces a novel method using diffusion models to approximate CERRA downscaling in a data-driven manner, without additional informations. By leveraging the lower resolution ERA5 dataset, which provides boundary conditions for CERRA, we approach this as a super-resolution task. Focusing on wind speed around Italy, our model, trained on existing CERRA data, shows promising results, closely mirroring the original CERRA. Validation with in-situ observations further confirms the model’s accuracy in approximating ground measurements.

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Spatiotemporal analysis of extreme climate indices in Nineveh Governorate, northern Iraq: four decades study from 1981 to 2022 using NASA-Power dataset

Abdaki,

Alobaydy,

Al-Samman

et al. 2024

Model. Earth Syst. Environ.

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Assessing exposure to climate extremes over the Arabian Peninsula using ERA5 reanalysis data: Spatial distribution and temporal trends

Cited by 5 publications

References 85 publications

Quantifying the stochastic trends of climate extremes over Yemen: a comprehensive assessment using ERA5 data

Quantifying the stochastic trends of climate extremes over Yemen: a comprehensive assessment using ERA5 data

Wind speed super-resolution and validation: from ERA5 to CERRA via diffusion models

Spatiotemporal analysis of extreme climate indices in Nineveh Governorate, northern Iraq: four decades study from 1981 to 2022 using NASA-Power dataset

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