Prediction Of Worldwide Energy Resources

NASA open data project

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The Prediction Of Worldwide Energy Resources (POWER) is a NASA project designed to provide renewable energy and meteorological data to the public. The input data of POWER are from other NASA projects that derive data at the Earth's surface and at various levels of the atmosphere from satellite-based observations. Specifically, the data about solar energy are from GEWEX Surface Radiation Budget (SRB), CERES SYN1deg and FLASHFlux. The solar energy data cover the entire Earth at a 1° latitude by 1° longitude resolution and the time span is from 1984 to near-real time. The meteorological data are from MERRA 2 and GEOS; the spatial resolution is 0.5° latitude by 0.625° longitude and the time span is from 1981 to near-real time.

These data have been validated to varied extents against ground-based measurements from the Baseline Surface Radiation Network (BSRN).[1][2][3]

On top of these data, POWER creates value-added products for its users, such as global tilted irradiances on Equatorward tilted panels with various tilt angles and global irradiances on typical kinds of single-axis tracking panels.

Users can access the data through Data Access Viewer (DAV), Application Programming Interface (API) service, ArcGIS image service, and Amazon Web Services (AWS).

The NASA POWER data has been widely used in analysis of available renewable energy, climate change, siting for solar power plant, crop models in agronomy, public health, etc. Nearly 200 publications can be found.[4]

## Renewable energy

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The Renewable Energy Archive provides solar and wind energy data.

The available parameters for this community include 1.) Solar parameters. Solar irradiances (global, direct and diffuse), surface albedo, photosynthetically active radiation (PAR), ultraviolet radiation (UV), UVA, UVB, cloud amount, cloud optical depth, solar geometry, etc., solar irradiances on variously tilted panels, dual- and single-axis trackers, etc.; 2.) Solar geometry. Solar zenith angle and azimuth angle, etc.; 3.) Temperature/Thermal IR Flux. Temperature at 2 meters above the ground, dew/frost point temperature at 2 meters, wet bult temperature at 2 meters, Earth's skin temperature, etc.; 4.) Humdity/Precipitation. Humidity at 2 meters above the ground, precipitation, etc.; 5.) Wind/Pressure. Wind speed/direction at 10 meters, 50 meters; 6.) Energy-Storage System Sizing. Equivalent no-sun days over various number of consecutive days.

Depending on the parameters, the available temporal resolutions may include hourly, daily, monthly, annual and climatology, and the time span of the data cover 1981 to near present.

The methodology for the development of global tilted irradiances can be found in references.[5][6]

## Sustainable buildings

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The Sustainable Buildings Archive provides data for the architecture industry that aims to make buildings energy-efficient and use solar energy and natural light as much as possible.

In addition to parameters available to the Renewable Energy community, there are parameters for the DOE/ASHRAE Climate Building, and these include cooling/heating degree days above/below various degrees of temperature.

## Agroclimatology

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The Agroclimatology Archive provides data for the agriculture industry, and the data can be used as input for crop models to support decision-making.

In addition to the solar/wind parameters available to the Renewable Energy and Sustainable Buildings communities, there are parameters about soil properties, and these include surface soil wetness, root zone wetness and soil moisture profile.

## Data availability

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The source data for meteorological parameters are from MERRA-2, which covers from 1981-01-01 to a few months within near-real time, and GEOS 5.12.4, which covers from the end of MERRA-2 to near-real time. The spatial resolution of the meteorological parameters is 0.5° latitude by 0.625° longitude.

The source data for solar radiation and related parameters are from the GEWEX SRB R4-IP, which covers 1984-01-01 to 2000-12-31, the CERES SYN1deg Ed4.1, which covers from 2001-01-01 to a few months within near-real time, and FLASHFlux 4, which covers from the end of CERES SYN1deg(Ed4.1) the near-real time. The spatial resolution of the solar radiation data is 1° longitude by 1° latitude.

## Usage

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According to an Op-Ed Explainer of the Solar Today Magazine of American Solar Energy Society, up till June 2024, NASA POWER, which was initiated in 1998, had fulfilled over 480 million data requests, and every month, more than 10 million data requests by over 30,000 unique users are fulfilled.[7]

Numerous researches based on the NASA POWER data have been published in academic journals. Examples of evaluation of available solar energy and siting for solar power plants can be found in references.[8][9][10][11]

Researches about climate, drought, precipitation, phenotypic analysis, evapotranspiration, temperature change, crop yield, etc. can be found in references.[12][13][14][15][16][17][18][19][20][21][22]

Machine learning has also been used in research and application of the NASA POWER data.[23]

Independent effort has also been made to facilitate the downloading of the NASA POWER data.[24]

In addition to the originally intended three communities, the NASA POWER has found application in medical and public health studies.[25]

Many more publications that use NASA POWER data are available.[4]

## References

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1. ^ Rutan, D.A. et al., 2015. CERES Synoptic Product: Methodology and Validation of Surface Radiant Flux. Journal of Atmospheric and Oceanic Technology, 32, 1121-1143. https://doi.org/10.1175/JTECH-D-14-00165.1
2. ^ Zhang, T. et al. 2013. The validation of the GEWEX SRB surface shortwave flux data products using BSRN measurements: A systematic quality control, production and application approach. Journal of Quantitative Spectroscopy & Radiative Transfer, 122, 127-140. http://dx.doi.org/10.1016/j.jqsrt.2012.10.004
3. ^ Zhang, T. et al., 2019. Clear-sky shortwave downward flux at the Earth's surface: Ground-based data vs. satellite-based data. Journal of Quantitative Spectrocopy & Radiative Transfer, 224, 247-260. https://doi.org/10.1016/j.jqsrt.2018.11.015
4. ^ a b Publications using NASA POWER data. https://power.larc.nasa.gov/docs/references/external/publications/
5. ^ Zhang, T., Stackhouse, Jr., P.W., Macpherson, B., and Mikovitz, J.C. 2024. A CERES-based dataset of hourly DNI, DHI and global tilted irradiance (GTI) on equatorward tilted surfaces: Derivation and comparison with the ground-based BSRN data. Solar Energy, 274, 112538. https://doi.org/10.1016/j.solener.2024.112538
6. ^ Zhang, T., Stackhouse, Jr., P.W., Macpherson, B., and Mikovitz, J.C., 2021. A solar azimuth formula that renders circumstantial treatment unnecessary without compromising mathematical regor: Mathematical setup, application and extension of a formula based on the subsolar point and atan2 function. Renewable Energy, 172, 1333-1340. https://doi.org/10.1016/j.renene.2021.03.047
7. ^ Stackhouse, Jr., P.W., Patadia, F., and Waring, Z., 2024. NASA POWER Project Offers Communities Free Solar Data. Solar Today Magazine. Op-Ed Explainer, June 17, 2024. https://ases.org/nasa-power-project/.
8. ^ Rana, S.P., Moniruzzaman, M., Demarcation of suitable site for solar photovoltaic power plant installation in Bangladesh using geospatial techniques. Next Energy, 3, 100109. https://doi.org/10.1016/j.nxener.2024.100109.
9. ^ Halder, B., Banik, P., Almohama, H., Abdullah, A.Z., Dughairi, A.A., Al-Mutiry, M., Falah, H., Shahrani, A., and Abdo, H.G., 2022. Land Suitability Investigation for Solar Power Plant Using GIS, AHP and Multi-Criteria Decision Approach: A Case of Megacity Kolkata, West Bengal, India. Sustainability 14(18). DOI: https://doi.org/10.3390/su141811276.
10. ^ Bandira, P.N.A., Tan, Teh, S.Y., Samat, N., Shaharudin, S.M., Mahamud, M.A., Tangang, F., Juneng, L., Chung, J.X., and Samsudin, M.S., 2022. Optimal Solar Farm Site Selection in the George Town Conurbation Using GIS-Based Multi-Criteria Decision Making (MCDM) and NASA POWER Data. Atmosphere 2022, 13(12), 2105; https://doi.org/10.3390/atmos13122105
11. ^ Quansah, A.D., Dogbey, F., Asilevi, P.J., Boakye, P., Darkwah, L., Oduro-Kwarteng, S., Sokama-Neuyam, Y.A., and Mensah, P., 2022. Assessment of solar radiation resource from the NASA-POWER reanalysis products for tropical climates in Ghana towards clean energy application. Scientific Reports, 12, Article number: 10684. https://doi.org/10.1038/s41598-022-14126-9.
12. ^ Kheyruri, Y., E. Nikaein, and A. Sharafati, 2023: Spatial monitoring of meteorological drought characteristics based on the NASA POWER precipitation product over various regions of Iran. Environ Sci Pollut Res, https://doi.org/10.1007/s11356-023-25283-3.
13. ^ Rockett, P.L., I. L. Campos, C. F. Baes, D. Tulpan, F. Miglior, and F. S. Schenkel, 2023: Phenotypic analysis of heat stress in Holsteins using test-day production records and NASA POWER meteorological data. Journal of Dairy Science, 106, 1142–1158, https://doi.org/10.3168/jds.2022-22370.
14. ^ Hali̇mi̇, A. H., C. Karaca, and D. Büyüktaş, 2022: Evaluation of NASA POWER Climatic Data against Ground-Based Observations in The Mediterranean and Continental Regions of Turkey. JOTAF,https://doi.org/10.33462/jotaf.1073903.
15. ^ Al-Kilani, M. R., M. Rahbeh, J. Al-Bakri, T. Tadesse, and C. Knutson, 2021: Evaluation of Remotely Sensed Precipitation Estimates from the NASA POWER Project for Drought Detection Over Jordan. Earth Syst Environ, 5, 561–573, https://doi.org/10.1007/s41748-021-00245-.
16. ^ Marzouk, O. A., 2021: Assessment of global warming in Al Buraimi, sultanate of Oman based on statistical analysis of NASA POWER data over 39 years, and testing the reliability of NASA POWER against meteorological measurements. Heliyon, 7, e06625, https://doi.org/10.1016/j.heliyon.2021.e06625.
17. ^ Rodrigues, G. and R.Braga, 2021: Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate. Agronomy, 11, 6, https://doi.org/10.3390/agronomy11061207.
18. ^ Rodrigues, G. C., and R. P. Braga, 2021: Estimation of Daily Reference Evapotranspiration from NASA POWER Reanalysis Products in a Hot Summer Mediterranean Climate. Agronomy, 11, 2077, https://doi.org/10.3390/agronomy11102077.
19. ^ Aboelkhair, H., M. Morsy, and G. El Afandi, 2019: Assessment of agroclimatology NASA POWER reanalysis datasets for temperature types and relative humidity at 2 m against ground observations over Egypt. Advances in Space Research, 64, 129–142, https://doi.org/10.1016/j.asr.2019.03.03.
20. ^ Bai, J., X. Chen, A. Dobermann, H. Yang, K. G. Cassman, and F. Zhang, 2010: Evaluation of NASA Satellite- and Model-Derived Weather Data for Simulation of Maize Yield Potential in China. Agron. J., 102, 9–16, https://doi.org/10.2134/agronj2009.0085.
21. ^ Barboza, T.O.C., Ferraz, M.A.J., Pilon, C., Vellidis, G., Valeriano, T.T.B., and dos Santos, A.F., 2024. Advanced Farming Strategies Using NASA POWER Data in Peanut-Producing Regions without Surface Meteorological Stations. AgriEngineering. https://doi.org/10.3390/agriengineering6010027
22. ^ Tan, M.L., et al., 2023. Evaluation of NASA POWER and ERA5-Land for estimating tropical precipitation and temperature extremes. Journal of Hydrology. DOI: https://doi.org/10.1016/j.jhydrol.2023.129940.
23. ^ Saleh, A., Tan, M.L., Yaseen, Z.M., and Zhang, F., 2024. Integrated machine learning models for enhancing tropical rainfall prediction using NASA POWER meteorological data. Journal of Water and Climate Change. DOI: https://doi.org/10.2166/wcc.2024.719
24. ^ Sparks, A.H., 2018. nasapower: A NASA POWER Global Meteorology,Surface Solar Energy and Climatology Data Client for R. The Journal of Open Source Software. DOI: https://doi.org/10.21105/joss.01035.
25. ^ Bauer, M., T. Glenn, et al., P.W. Stackhouse, Jr., et al., 2023, Exploratory study of ultraviolet B (UVB) radiation and age of onset of bipolar disorder. International Journal of Bipolar Disorders, 11(22), https://doi.org/10.1186/s40345-023-00303-w.

## External links

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* Official website

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