National Climate Database (NCDB)

Yang, Jaemo; Sengupta, Manajit; Habte, Aron; Xie, Yu

doi:10.25984/2460455

Title: National Climate Database (NCDB)

Dataset
Other Related Research

Abstract

The National Climate Database (NCDB) is a high resolution, bias-corrected climate dataset consisting of the three most widely used variables of solar radiation- global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI)- as well as other meteorological data. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling. The NCDB is modeled using a statistical downscaling approach with Regional Climate Model (RCM)-based climate projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX; linked below). Daily climate projections simulated by the Canadian Regional Climate Model 4 (CanRCM4) forced by the second-generation Canadian Earth System Model (CanESM2) for two Representative Concentration Pathways (RCP4.5 or moderate emissions scenario and RCP8.5 or highest baseline emission scenario) are selected as inputs to the statistical downscaling models. The National Solar Radiation Database (NSRDB) is used to build and calibrate statistical models.

Authors:

Yang, Jaemo; Sengupta, Manajit; Habte, Aron; Xie, Yu

National Renewable Energy Laboratory (NREL)

Publication Date:: Mon Sep 30 04:00:00 UTC 2024

Other Number(s):: 6163

Research Org.:: DOE Open Energy Data Initiative (OEDI); National Renewable Energy Laboratory (NREL)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Multiple Programs (EE)

Collaborations:: National Renewable Energy Laboratory (NREL)

Subject:: Array; CONUS; DHI; DNI; GHI; NCDB; RCP; climate; climate scenarios; code; data; dataset; downscaling; emissions; energy; high-resolution; meteorology; model; power; python; solar; solar energy; solar resource; statistical downscaling; temperature; wind

OSTI Identifier:: 2460455

DOI:: https://doi.org/10.25984/2460455

Citation Formats


                    Yang, Jaemo, Sengupta, Manajit, Habte, Aron, and Xie, Yu. National Climate Database (NCDB).  United States: N. p., 2024. 
        Web.  doi:10.25984/2460455.

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                    Yang, Jaemo, Sengupta, Manajit, Habte, Aron, & Xie, Yu. National Climate Database (NCDB).  United States.  doi:https://doi.org/10.25984/2460455

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                    Yang, Jaemo, Sengupta, Manajit, Habte, Aron, and Xie, Yu. 2024.  
"National Climate Database (NCDB)".  United States.  doi:https://doi.org/10.25984/2460455.  https://www.osti.gov/servlets/purl/2460455. Pub date:Mon Sep 30 04:00:00 UTC 2024

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@article{osti_2460455,

  title        = {National Climate Database (NCDB)},

  author       = {Yang, Jaemo and Sengupta, Manajit and Habte, Aron and Xie, Yu},

  abstractNote = {The National Climate Database (NCDB) is a high resolution, bias-corrected climate dataset consisting of the three most widely used variables of solar radiation- global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI)- as well as other meteorological data. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling. The NCDB is modeled using a statistical downscaling approach with Regional Climate Model (RCM)-based climate projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX; linked below). Daily climate projections simulated by the Canadian Regional Climate Model 4 (CanRCM4) forced by the second-generation Canadian Earth System Model (CanESM2) for two Representative Concentration Pathways (RCP4.5 or moderate emissions scenario and RCP8.5 or highest baseline emission scenario) are selected as inputs to the statistical downscaling models. The National Solar Radiation Database (NSRDB) is used to build and calibrate statistical models.},

  doi          = {10.25984/2460455},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Mon Sep 30 04:00:00 UTC 2024},

  month        = {Mon Sep 30 04:00:00 UTC 2024}

}

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DOI: https://doi.org/10.25984/2460455

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