---
title: "Weather, climate and disaster data datasets"
description: "Datasets in the category “Weather, climate and disaster data” were mentioned in 15 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #28 of the 58 data categories mentioned."
url: https://scholar4ai.com/datasets/weather-climate-and-disaster-data
markdown_url: https://scholar4ai.com/datasets/weather-climate-and-disaster-data.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Datasets in the category “Weather, climate and disaster data” were mentioned in 15 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #28 of the 58 data categories mentioned."
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T14:52:47+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Weather, climate and disaster data

Weather observations, climate reanalyses and projections, and disaster records.

- 2 papers in the last 7 days
- 6 of its 7 datasets mentioned

Back to the ranking: https://scholar4ai.com/rankings/datasets.md

## Key findings

- Datasets in the category “Weather, climate and disaster data” were mentioned in 15 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #28 of the 58 data categories mentioned.
- The most-mentioned were ERA5 reanalysis (5), NOAA (5), CMIP climate projections (3), FEMA disaster data (2) and CRU climate data (1).
- By journal category: Economics (7), AI (4), Operations Research (2), Management (1) and Finance (1).
- Most of them appeared in Energy Economics (2), Neural Computing and Applications (2) and Technological Forecasting and Social Change (1).

## Datasets

*Datasets in this category · 2026-09-27 – 2026-10-26 (30 days)*

| Dataset | Papers | Last 7 days | Names searched for |
|---|---:|---:|---|
| ERA5 reanalysis | 5 | 0 | ERA5 |
| NOAA | 5 | 1 | NOAA, National Oceanic and Atmospheric Administration |
| CMIP climate projections | 3 | 0 | CMIP6, CMIP5 |
| FEMA disaster data | 2 | 0 | FEMA |
| CRU climate data | 1 | 1 | Climatic Research Unit, CRU TS |
| EM-DAT disaster database | 1 | 0 | EM-DAT, Emergency Events Database |
| NOAA Storm Events Database | 0 | 0 | Storm Events Database |

## Papers

*Papers mentioning these datasets, newest first.*

| Date | Title | Journal | Authors | Affiliations | Datasets mentioned |
|---|---|---|---|---|---|
| 2026-10-23 | [Public support for regulatory research on the potential ecological effects of RF-EMF](https://doi.org/10.1016/j.techfore.2026.124938) | [Technological Forecasting and Social Change](https://scholar4ai.com/journals/technological-forecasting-and-social-change.md) | Laura Recuero Virto, Peter Bou Saba, Arno Thielens et al. | Centre Européen de Recherche en Economie Financière et Gestion des Entreprises; ICN Business School Nancy-Paris-Berlin; Université de Lorraine; … | NOAA |
| 2026-10-22 | [Asymmetric decentralization and inequality](https://doi.org/10.1007/s00168-026-01586-4) | [The Annals of Regional Science](https://scholar4ai.com/journals/the-annals-of-regional-science.md) | Melissa Ziegler Rogers, Melissa Ziegler Rogers | Duke University; Claremont Graduate University | CRU climate data |
| 2026-10-14 | [Modeling and simulating human behavior in large-scale emergency evacuations: A comprehensive review](https://doi.org/10.1016/j.trb.2026.103611) | [Transportation Research Part B Methodological](https://scholar4ai.com/journals/transportation-research-part-b-methodological.md) | Shiva Moslemi, Ada Arıkanoğlu, Vedat Bayram et al. |  | EM-DAT disaster database, FEMA disaster data |
| 2026-10-10 | [Too little too late? Nationwide carbon trading in China](https://doi.org/10.1016/j.jpubeco.2026.105798) | [Journal of Public Economics](https://scholar4ai.com/journals/journal-of-public-economics.md) | Shuang Zhang, Douglas Almond | Centre for Economic Policy Research; Imperial College London; National Bureau of Economic Research; … | NOAA |
| 2026-10-10 | [When heat hits: Short-run saving and withdrawal behavior among microfinance clients in Benin](https://doi.org/10.1016/j.worlddev.2026.107578) | [World Development](https://scholar4ai.com/journals/world-development.md) | Anna Mader, Oliver Mußhoff | Humboldt-Universität zu Berlin | NOAA |
| 2026-10-08 | [Forecasting the S&P 500 materials volatility via the dual climate risk: A hybrid machine learning approach](https://doi.org/10.1016/j.frl.2026.110887) | [Finance research letters](https://scholar4ai.com/journals/finance-research-letters.md) | Jinzuo He, Chi Xie, Weiyiqing Wang et al. | Hunan University | ERA5 reanalysis |
| 2026-10-08 | [Cross-border risk co-movement and multidimensional network resilience in global clean energy markets: Evidence from a higher-order downside co-movement network](https://doi.org/10.1016/j.eneco.2026.109626) | [Energy Economics](https://scholar4ai.com/journals/energy-economics.md) | Yaoxun Deng, LAI Yongzeng, Tianlei Zhu et al. | Hunan Normal University; Wilfrid Laurier University; Beijing Jiaotong University | ERA5 reanalysis |
| 2026-10-07 | [Future in- and out-of-state air pollutant emissions under decarbonization pathways for Massachusetts](https://doi.org/10.1016/j.enpol.2026.115645) | [Energy Policy](https://scholar4ai.com/journals/energy-policy.md) | Kaixin Huang, Emma K Walter, Daniel H. Loughlin et al. | Northeastern University; Environmental Protection Agency; Research Triangle Park Foundation; … | NOAA |
| 2026-10-06 | [Improving runoff forecasting in data-scarce basins on the Loess Plateau: a physics-guided meta-learning approach with feature-wise linear modulation](https://doi.org/10.1016/j.eswa.2026.134653) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Zhongfeng Zhao, Xueni Wang, Zhang Leike et al. | Taiyuan University of Technology | ERA5 reanalysis |
| 2026-10-01 | [A machine learning model for the prediction of the US news & world report ranking of the mechanical engineering graduate programs in the United States](https://doi.org/10.1007/s00521-026-12636-5) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Faeze Khademi, Nikith Rachakonda, Matias Salazar Ismodes et al. | University of Illinois Chicago | CMIP climate projections |
| 2026-10-01 | [Cold shocks in a warming world: Regional disparities in household climate adaptation and electricity demand](https://doi.org/10.1016/j.eneco.2026.109656) | [Energy Economics](https://scholar4ai.com/journals/energy-economics.md) | Zihan He, Jiayang Shi, Qi Wang et al. | State Key Laboratory of Pollution Control and Resource Reuse; Hankou University; Nanjing University of Finance and Economics | CMIP climate projections |
| 2026-10-01 | [HydraML: a hybrid damaged roofs automated machine learning classifier for cultural heritage](https://doi.org/10.1007/s00521-026-12499-w) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Francesco Colace, Massimo De Santo, Rosario Gaeta et al. | University of Salerno; Ministero della cultura | FEMA disaster data |
| 2026-10-01 | [The resilience dividend: A tractable climate-economy model](https://doi.org/10.1016/j.econmod.2026.107872) | [Economic Modelling](https://scholar4ai.com/journals/economic-modelling.md) | Andrea Bacchiocchi, Fabio Tramontana, Federico Favaretto | University of Urbino; University of Ferrara; Bocconi University | NOAA |
| 2026-09-30 | [Impact of wind-assistance on the efficiency of a tramp fleet](https://doi.org/10.1016/j.trc.2026.106043) | [Transportation Research Part C Emerging Technologies](https://scholar4ai.com/journals/transportation-research-part-c-emerging-technologies.md) | Alberto Tamburini, Aurore Wendling, Benjamin Lagemann et al. | Technical University of Denmark; Norwegian University of Science and Technology | ERA5 reanalysis |
| 2026-09-28 | [Regional climate risk assessment from climate models using probabilistic machine learning](https://doi.org/10.1038/s42256-026-01308-7) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Zhong Yi Wan, Ignacio Lopez‐Gomez, Robert W. Carver et al. | Google (United States); California Institute of Technology; General Motors (United States); … | ERA5 reanalysis, CMIP climate projections |

## Cite this page

Scholar4AI. (2026). Weather, climate and disaster data. Journal Research Rankings. Retrieved October 26, 2026, from https://scholar4ai.com/datasets/weather-climate-and-disaster-data

```bibtex
@misc{scholar4ai-datasets-weather-climate-and-disaster-data,
  author = {{Scholar4AI}},
  title = {Weather, climate and disaster data},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/weather-climate-and-disaster-data}},
  urldate = {2026-10-26},
  note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).