Prediction of Worldwide Energy Resource
NASA’s Prediction of Worldwide Energy Resources (NASA-POWER; power.larc.nasa.gov) project estimates daily solar radiation based on satellite observations and atmospheric parameters obtained from
satellite observations and assimilation models.
POWER also provides satellite and model-derived agrometeorological data on a 1° latitude by 1° longitude grid with global coverage.POWER Data Access Interface
- Daily total solar radiation from July 1, 1983 through near real-time
- Daily averaged dew point and air temperatures from January 1, 1983 through near real-time
- Daily maximum and minimum air temperatures from January 1, 1983 through near real-time
- Daily averaged precipitation from January 1, 1997 – current with two month delay
For DSSAT crop models, users can download the data with all variables in ICASA Standards format.
Jeffrey W. White, Gerrit Hoogenboom, Paul W. Wilkens, Paul W Stackhouse, James M. Hoel,Evaluation of Satellite-Based, Modeled-Derived Daily Solar Radiation Data for the Continental United States, Agron. J., 2011 103: 1242–1251, 10.2134/agronj2011.0038
Decision support tools for agriculture often require meteorological data as inputs, but data availability and quality are often problematic. Difficulties arise with daily solar radiation (SRAD) because the instruments require electronic integrators, accurate sensors are expensive, and calibration standards are seldom available. NASA’s Prediction of Worldwide Energy Resources (NASA/POWER; power.larc.nasa.gov) project estimates SRAD based on satellite observations and atmospheric parameters obtained from satellite observations and assimilation models. These data are available for a global 1° × 1° coordinate grid. The SRAD can also be generated from atmospheric attenuation of extraterrestrial radiation (Q0). We compared daily solar radiation data from NASA/POWER (SRADNP) with instrument readings from 295 stations (observed values of daily solar radiation, SRADOB) and values estimated by Weather Generator for Solar Radiation (WGENR) generator. Two sources of air temperature and precipitation records provided inputs to WGENR: the stations reporting solar data and the NOAA Cooperative Observer Program (COOP) stations. The resulting data were identified as solar radiation valaues obtained using the Weather Generator for Solar Radiation software in conjunction with daily weather data from the stations providing values of observed values of daily solar radiation (SRADWG) and solar radiation values obtained using the Weather Generator for Solar Radiation software in conjunction with daily weather data from NOAA COOP stations (SRADCO), respectively. Values of SRADNP for individual grid cells consistently showed higher correlations (typically 0.85–0.95) with SRADOB than did SRADWG or SRADCO. Mean values of SRADOB, SRADWG, and SRADNP for a grid cell usually were within 1 MJ m?2 d?1 of each other, but NASA/POWER values averaged 1.1 MJ m?2 d?1 lower than SRADOB. This bias increased at lower latitudes and during summer months and is partially explained by assumptions about ambient aerosol properties. The NASA/POWER solar data are a promising resource for studies requiring realistic accounting of historic variation.
Jeffrey W. White, Gerrit Hoogenboom, Paul W. Stackhouse Jr., James M. Hoell, Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US, Agricultural and Forest Meteorology, Volume 148, Issue 10, 3 September 2008, Pages 1574-1584, ISSN 0168-1923, 10.1016/j.agrformet.2008.05.017.
Agricultural research increasingly is expected to provide precise, quantitative information with an explicit geographic coverage. Limited availability of daily meteorological records often constrains efforts to provide such information through use of simulation models, spatial analysis, and related decision support tools. The Prediction Of Worldwide Energy Resources (NASA/POWER) project at the NASA Langley Research Center provides daily data globally for maximum and minimum temperatures and other weather variables on a 1° latitude–longitude grid. The data are assembled from a range of products derived from satellite imagery, ground observations, windsondes, modeling and data assimilation. Daily temperature data from NASA/POWER for 1983 to 2004 for the continental US were compared with data of 855 individual ground stations from the National Weather Service Cooperative Observer Program (COOP). Additionally, a wheat (Triticum aestivum L.) simulation model was used to compare predicted time to anthesis using the two data sources. Comparisons of daily maximum temperatures (Tmax) gave an r2-value of 0.88 (P < 0.001) and root-mean-squared error (RMSE) of 4.1 °C. For minimum temperature (Tmin), the r2-value was 0.88 (P < 0.001) and RMSE, 3.7 °C. Mean values of Tmax, and Tmin from NASA/POWER were, respectively, 2.4 °C cooler and 1.1 °C warmer than the COOP data. Differences in temperature were least during summer months. When data were aggregated over periods of 8 days or more, the RMSE values declined to below 2.7 °C for Tmax and Tmin. Simulations of time to anthesis with the two data sources were also strongly correlated (r2 = 0.92, P < 0.001, RMSE = 14.5 d). Anthesis dates of winter wheat regions showed better agreement than southern, winter-grown spring wheat regions. The differences between the data sources were associated with differences in elevation, which in large part resulted from NASA/POWER data being based on mean elevations over a 1° grid cells vs. COOP data corresponding to the elevation of specific stations. Additional sources of variation might include proximity to coastlines and differences in observation time, although these factors were not quantified. Overall, if mountainous and coastal regions are excluded, the NASA/POWER data appeared promising as a source of continuous daily temperature data for the USA for research and management applications concerned with scales appropriate to the 1° coordinate grid. It further appeared that the POWER data could be improved by adjusting for elevation (lapse rate) effects, reducing seasonal bias, and refining estimation of actual maximum and minimum temperatures in diurnal cycles.
- NASA-POWER Homepage: http://power.larc.nasa.gov
- Data access interface: http://email@example.com
- Documentation: Agroclimatology Methodology (Update: 1 March 2012)
- Acknowledgment guideline: http://power.larc.nasa.gov/common/php/POWER_Acknowledgments.php