NASA Prediction of Worldwide Energy Resources (POWER)

Stream data with DDA:

from dagshub.streaming import DagsHubFilesystem

fs = DagsHubFilesystem(".", repo_url="https://dagshub.com/DagsHub-Datasets/nasa-power-dataset")

fs.listdir("s3://power-analysis-ready-datastore")

Description:

NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program serves NASA and Society by expanding and accelerating the realization of societal and economic benefits from Earth science, information, and technology research and development.

The NASA Prediction Of Worldwide Energy Resources (POWER) Project, a NASA Applied Sciences program, improves the accessibility and usage NASA Earth Observations (EO) supporting community research in three focus areas: 1) renewable energy development, 2) building energy efficiency, and 3) agroclimatology applications. POWER can help communities be resilient amid observed climate variability through the easy access of solar and meteorological data via a verity of access methods.

The latest POWER version includes hourly-based source Analysis Ready Data (ARD), in addition to enhanced daily, monthly, annual, and climatology ARD. The daily time-series spans 40 years for meteorology available from 1981 and solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning 20 years from 2001. The hourly data will provide users the ARD needed to model the energy performance of building systems, providing information directly amenable to decision support tools introducing the industry standard EPW (EnergyPlus Weather file).

POWER also provides parameters at daily, monthly, annual, and user-defined time periods, spanning from 1984 through to within a week of real time. Additionally, POWER provides are user-defined analytic capabilities, including custom climatologies and climatological-based reports for parameter anomalies, ASHRAE® compatible climate design condition statistics, and building climate zones.

The ARD and climate analytics will be readily accessible through POWER's integrated services suite, including the Data Access Viewer (DAV). The DAV has recently been improved to incorporate updated parameter groupings, new analytical capabilities, and the new data formats. POWER also provides a complete API (Application Programming Interface) that allows uses to obtain all the parameters in the DAV plus additional parameters for larger repetitive orders or from within the user's own analytic tools.

Contact:

NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program serves NASA and Society by expanding and accelerating the realization of societal and economic benefits from Earth science, information, and technology research and development.

The NASA Prediction Of Worldwide Energy Resources (POWER) Project, a NASA Applied Sciences program, improves the accessibility and usage NASA Earth Observations (EO) supporting community research in three focus areas: 1) renewable energy development, 2) building energy efficiency, and 3) agroclimatology applications. POWER can help communities be resilient amid observed climate variability through the easy access of solar and meteorological data via a verity of access methods.

The latest POWER version includes hourly-based source Analysis Ready Data (ARD), in addition to enhanced daily, monthly, annual, and climatology ARD. The daily time-series spans 40 years for meteorology available from 1981 and solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning 20 years from 2001. The hourly data will provide users the ARD needed to model the energy performance of building systems, providing information directly amenable to decision support tools introducing the industry standard EPW (EnergyPlus Weather file).

POWER also provides parameters at daily, monthly, annual, and user-defined time periods, spanning from 1984 through to within a week of real time. Additionally, POWER provides are user-defined analytic capabilities, including custom climatologies and climatological-based reports for parameter anomalies, ASHRAE® compatible climate design condition statistics, and building climate zones.

The ARD and climate analytics will be readily accessible through POWER's integrated services suite, including the Data Access Viewer (DAV). The DAV has recently been improved to incorporate updated parameter groupings, new analytical capabilities, and the new data formats. POWER also provides a complete API (Application Programming Interface) that allows uses to obtain all the parameters in the DAV plus additional parameters for larger repetitive orders or from within the user's own analytic tools.

Update Frequency:

Near Real Time (NRT); as soon as source data becomes available from our source data providers.

• Meteorological: ~2-3 days
  
• Solar ~5-7 days
  
  For more information see the NASA POWER Availability Dashboards

Managed By:

NASA

Collabs:

• ASDI:
  • Tags: energy

Resources:

1. resource:
   • Description: POWER's Zarr Analysis Ready Data (ARD) Datasets
   • ARN: arn:aws:s3:::power-analysis-ready-datastore
   • Region: us-west-2
   • Type: S3 Bucket
   • RequesterPays: False
   • Explore: Browse Bucket
2. resource:
   • Description: POWER's NetCDF Datastore
   • ARN: arn:aws:s3:::power-datastore
   • Region: us-west-2
   • Type: S3 Bucket
   • RequesterPays: False
   • Explore: Browse Bucket

Tags:

agriculture, air quality, analytics, archives, atmosphere, climate, climate model, data assimilation, deep learning, earth observation, energy, environmental, forecast, geoscience, geospatial, global, netcdf, history, imaging, industry, machine learning, machine translation, metadata, meteorological, model, opendap, radiation, satellite imagery, solar, statistics, sustainability, time series forecasting, water, weather, zarr, aws-pds

Tutorials:

1. tutorial:

   • Title: Accessing and Subsetting POWER data, from AWS S3 using Python.
   • URL: https://power.larc.nasa.gov/docs/tutorials/service-data-request/aws/
   • AuthorName: The POWER Project

2. tutorial:

   • Title: About the Prediction Of Worldwide Energy Resources (POWER) Project ArcGIS StoryMap.
   • URL: https://arcg.is/1PG5jH
   • AuthorName: The POWER Project

Tools & Applications:

1. tools & applications:

   • Title: POWER Data Access Viewer (DAV)
   • URL: https://power.larc.nasa.gov/data-access-viewer/
   • AuthorName: The POWER Project
   • AuthorURL: https://power.larc.nasa.gov/

2. tools & applications:

   • Title: POWER Application Programming Interface (API)
   • URL: https://power.larc.nasa.gov/api/pages/
   • AuthorName: The POWER Project
   • AuthorURL: https://power.larc.nasa.gov/

3. tools & applications:

   • Title: NASA POWER NetCDF Data Download
   • URL: https://power.larc.nasa.gov/data/
   • AuthorName: The POWER Project
   • AuthorURL: https://power.larc.nasa.gov/

Publication:

1. publication:

   • Title: Variations in seasonal solar insolation are associated with a history of suicide attempts in bipolar I disorder
   • URL: https://doi.org/10.1186/s40345-021-00231-7
   • AuthorName: Bauer, M., and Coauthors

2. publication:

   • Title: A solar azimuth formula that renders circumstantial treatment unnecessary without compromising mathematical rigor: Mathematical setup, application and extension of a formula based on the subsolar point and atan2 function
   • URL: https://doi.org/10.1016/j.renene.2021.03.047
   • AuthorName: Zhang, T., P. W. Stackhouse, B. Macpherson, and J. C. Mikovitz

3. publication:

   • Title: Association between solar insolation and a history of suicide attempts in bipolar I disorder
   • URL: https://doi.org/10.1016/j.jpsychires.2019.03.001
   • AuthorName: Bauer M, et al., Stackhouse PW Jr., et al.

4. publication:

   • Title: The Contribution of Solar Brightening to the US Maize Yield Trend
   • URL: https://doi.org/10.1038/nclimate3234
   • AuthorName: Tollenaar, T., J. Fridgen, P. Tyagi, P. W. Stackhouse Jr., and S. Kumudini

5. publication:

   • Title: Application of a global-to-beam irradiance model to the NASA GEWEX SRB dataset: An extension of the NASA Surface meteorology and Solar Energy datasets
   • URL: https://doi.org/10.1016/j.solener.2014.09.006
   • AuthorName: Zhang, T., P. W. Stackhouse, W. S. Chandler, and D. J. Westberg

6. publication:

   • Title: Enhancing Climate Resilience at NASA Centers: A Collaboration between Science and Stewardship
   • URL: https://doi.org/10.1175/BAMS-D-12-00169.1
   • AuthorName: Rosenzweig, C., and Coauthors

7. publication:

   • Title: CEOS contributions to informing energy management and policy decision making using space-based Earth observations
   • URL: https://doi.org/10.1016/j.apenergy.2011.03.001
   • AuthorName: Eckman, R. S., and P. W. Stackhouse

8. publication:

   • Title: Evaluation of Satellite-Based, Modeled-Derived Daily Solar Radiation Data for the Continental United States
   • URL: https://doi.org/10.2134/agronj2011.0038
   • AuthorName: White, J. W., G. Hoogenboom, P. W. Wilkens, P. W. Stackhouse, and J. M. Hoel

9. publication:

   • Title: Modeling the potential for thermal concentrating solar power technologies
   • URL: https://doi.org/10.1016/j.enpol.2010.09.008
   • AuthorName: Zhang, Y., S. J. Smith, G. P. Kyle, and P. W. Stackhouse

10. publication:

    • Title: Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US
    • URL: https://doi.org/10.1016/j.agrformet.2008.05.017
    • AuthorName: White, J. W., G. Hoogenboom, P. W. Stackhouse, and J. M. Hoell

11. publication:

    • Title: Application of Satellite Sensor Data and Models for Energy Management
    • URL: https://doi.org/10.1109/JSTARS.2008.2001142
    • AuthorName: Zell, E., J. Engel-Cox, R. Eckman, and P. Stackhouse

12. publication:

    • Title: NASA Climatological Data for Renewable Energy Assessment
    • URL: https://doi.org/10.1115/1.1748466
    • AuthorName: Chandler, W. S., C. H. Whitlock, and P. W. Stackhouse

13. publication:

    • Title: Global Surface Solar Energy Anomalies Including El Nino and La Nina Years.
    • URL: https://doi.org/10.1115/1.1384570
    • AuthorName: C. H., and Coauthors