ArticleData in brief2025
The Environmental Protection Agency (EPA) Dynamically Downscaled Ensemble (EDDE) version 2: A 3D physics-based dataset for projections of future extreme weather events.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The U.S. Environmental Protection Agency (EPA) Dynamically Downscaled Ensemble version 2 (EDDEv2) contains 3D physics-based projections of future conditions and extreme events over a model domain with 12-km grid spacing covering most of North America and focusing on the contiguous U.S. (CONUS). The Weather Research and Forecasting (WRF) model is used to downscale global projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over a 30-year historical period (1985-2014) and a 75-year future period (2025-2099) under multiple Shared Socioeconomic Pathways. The output from WRF is further processed to create self-describing netCDF files conforming to the Climate and Forecasting System (CF) standards. A subset of EDDEv2 data is available with free egress via Amazon Web Services (AWS) Open Data Project at temporal frequencies ranging from 5 min to monthly. In addition to key variables like precipitation and 2-m temperature, EDDEv2 also contains other dynamically consistent atmospheric and soil fields that can support subsequent modeling applications, including humidity, winds, radiative variables, heat fluxes, and soil temperature and moisture, among others. The continuous, high-frequency data through the end of the century make EDDEv2 well-suited to explore potential changes to localized extreme events that can occur over a range of timescales, including heat extremes and flooding. The use of a large suite of variables facilitates modeling potential impacts on agriculture, infrastructure, and ecosystems, among other applications.
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