Supplement that indicates where to find the source data sets on the EPA system. This dataset is associated with the following publication: Bowden, J., K.D. Talgo, T. Spero , and C. Nolte. Assessing the Added Value of Dynamical Downscaling Using the Standardized Precipitation Index. ADVANCES IN METEOROLOGY. Hindawi Publishing Corporation, New York, NY, USA, 2016(8432064): 14 pages, (2016).

This is a README file to indicate the locations of codes, data sets, and other supporting materials. This dataset is associated with the following publication: Spero , T., C. Nolte , J.H. Bowden, M.S. Mallard, and J. Herwehe. The Impact of Incongruous Lake Temperatures on Regional Climate Extremes Downscaled from the CMIP5 Archive Using the WRF Model. Journal of Climate. American Meteorological Society, Boston, MA, USA, 29(2): 839-853, (2016).

This is a README file to indicate the locations of codes, data sets, and other supporting materials. This dataset is associated with the following publication: Spero , T., C. Nolte , J.H. Bowden, M.S. Mallard, and J. Herwehe. The Impact of Incongruous Lake Temperatures on Regional Climate Extremes Downscaled from the CMIP5 Archive Using the WRF Model. Journal of Climate. American Meteorological Society, Boston, MA, USA, 29(2): 839-853, (2016).

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Data used to produce figures for the manuscript. This dataset is associated with the following publication: Jalowska, A., and T. Spero. Developing PIDF Curves From Dynamically Downscaled WRF Model Fields to Examine Extreme Precipitation Events in Three Eastern U.S. Metropolitan Areas. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 124(24): 13895-13913, (2019).

Data used to produce figures for the manuscript. This dataset is associated with the following publication: Jalowska, A., and T. Spero. Developing PIDF Curves From Dynamically Downscaled WRF Model Fields to Examine Extreme Precipitation Events in Three Eastern U.S. Metropolitan Areas. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES. American Geophysical Union, Washington, DC, USA, 124(24): 13895-13913, (2019).

The EPA Dynamically Downscaled Ensemble (EDDE) Version 2 is a collection of physics-based projections of future conditions, as well as historical simulations, dynamically downscaled from global climate models within the Sixth Coupled Model Intercomparison Project (CMIP6) using the Weather Research and Forecasting (WRF) model. EDDE V2 contains simulations over the historical period 1985-2014 and projections of a future period 2025-2100 under multiple Shared Socioeconomic Pathways (SSPs) downscaled on a 12-km domain over the contiguous U.S. EDDE datasets are subset from WRF's output and then prepared by EPA/ORD staff and by contract staff who worked under the technical guidance of EPA/ORD staff. Data are in Network Common Data Form (netCDF) version 4, which is used in atmospheric modeling. The EDDE data in netCDF are further written to adhere to principles of Climate and Forecasting System (CF) Compliance, as outlined at https://cfconventions.org. The files are self-describing with metadata included in the netCDF header.

This is an hourly future weather dataset for energy modeling applications. The dataset is primarily based on the output of a regional climate model (RCM), i.e., the Weather Research and Forecasting (WRF) model version 3.3.1. The WRF simulations are driven by the output of a general circulation model (GCM), i.e., the Community Climate System Model version 4 (CCSM4). This dataset is in the EPW format, which can be read or translated by more than 25 building energy modeling programs (e.g., EnergyPlus, ESP-r, and IESVE), energy system modeling programs (e.g., System Advisor Model (SAM)), indoor air quality analysis programs (e.g., CONTAM), and hygrothermal analysis programs (e.g., WUFI). It contains 13 weather variables, which are the Dry-Bulb Temperature, Dew Point Temperature, Relative Humidity, Atmospheric Pressure, Horizontal Infrared Radiation Intensity from Sky, Global Horizontal Irradiation, Direct Normal Irradiation, Diffuse Horizontal Irradiation, Wind Speed, Wind Direction, Sky Cover, Albedo, and Liquid Precipitation Depth. The weather data is created for two emissions scenarios: RCP4.5 and RCP8.5 and spans two 10-year time slices in the future: 2045 - 2054 and 2085 - 2094. It offers a spatial resolution of 12 km by 12 km with extensive coverage across most of North America. Due to the enormous size of the entire dataset, in the first stage of its distribution, we provide 20 years of future weather data for the centroid of each Public Use Microdata Area (PUMA), excluding Hawaii. PUMAs are non-overlapping, statistical geographic areas that partition each state or equivalent entity into geographic areas containing no fewer than 100,000 people each. The 2,378 PUMAs as a whole cover the entirety of the U.S. The weather data can be utilized alongside the large-scale energy analysis tools, ResStock and ComStock, developed by National Renewable Energy Laboratory, whose smallest resolution is at the PUMA scale. The data for RCP4.5 is still being processed and will be published soon.

The dataset consists of projections of 1-12 months Standardized Precipitation Evapotranspiration Index (SPEI) between 1950-2099 for the contiguous United States from 20 climate models and 2 emission scenarios at a 4km spatial resolution. The SPEI dataset was developed using the SPEI package in R (Beguería & Vicente-Serrano, 2023). SPEI quantifies standardized departures in the balance between precipitation and potential evapotranspiration (PET) across varying timescales, making it highly suitable for assessing drought and water availability (Vicente-Serrano et al., 2010). Monthly precipitation and PET data were sourced from the MACAv2-METDATA dataset for climate projections between 1950-2099 based on 20 global climate models under RCP 4.5 and RCP 8.5 emission scenarios (Abatzoglou, 2013). Projected SPEI values were calculated relative to the 1981-2020 reference period, with SPEI computed using a log-logistic distribution fitted to the difference between precipitation and PET values. This methodology standardizes SPEI values as z-scores, allowing for comparative evaluations of drought and wetness across different regions and timescales (1 to 12 months).