The Prediction of the Worldwide Energy Resources (POWER) Project, initiated under the NASA Science Mission Directorate Applied Science Energy Management Program, synthesizes and analyzes data on a global scale that are invaluable to the renewable energy industries, especially to the solar and wind energy sectors. The POWER project derives its data primarily from NASA's World Climate Research Programme (WCRP)/Global Energy and Water cycle Experiment (GEWEX) Surface Radiation Budget (SRB) project (Version 2.9) and the Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System (GEOS) assimilation model (Version 4). The latest development of the NASA POWER Project and its plans for the future are presented in this paper

Chandler, William S.

Hoell, James M.

Stackhouse, Paul W., Jr.

Westberg, David

Whitlock, Charles H.

Zhang, Taiping

NASA Technical Reports Server

 1
A GLOBAL PERSPECTIVE: NASA's Prediction of Worldwide  
Energy Resources (POWER) Project 
 
Taiping Zhang1, Paul W. Stackhouse, Jr2, William S. Chandler1,  
James M. Hoell1, David Westberg1, Charles H. Whitlock1   
 
1SSAI/NASA Langley Research Center, Mail Stop 936, Hampton, VA 23681 USA Taiping.Zhang-1@nasa.gov 
2NASA Langley Research Center, Mail Stop 420, Hampton, VA 23681 USA Paul.W.Stackhouse@nasa.gov 
 
 
 
ABSTRACT 
 
The Prediction of the Worldwide Energy Resources 
(POWER) Project, initiated under the NASA Science 
Mission Directorate Applied Science Energy Management 
Program, synthesizes and analyzes data on a global scale 
that are invaluable to the renewable energy industries, 
especially to the solar and wind energy sectors. The 
POWER project derives its data primarily from NASA's 
World Climate Research Programme (WCRP)/Global 
Energy and Water cycle Experiment (GEWEX) Surface 
Radiation Budget (SRB) project (Version 2.9) and the 
Global Modeling and Assimilation Office (GMAO) 
Goddard Earth Observing System (GEOS) assimilation 
model (Version 4). 
 
The latest development of the NASA POWER Project  and 
its plans for the future are presented in this paper. 
 
 
1.  INTRODUCTION 
 
The POWER data are available to users through NASA's 
Surface meteorology and Solar Energy (SSE, Version 6.0) 
website (http://earth-www.larc.nasa.gov/power/). The 
number of parameters available is over 200 and the 
resolution is 1 degree by 1 degree. The time span now 
covers 22 years from July, 1983 to June, 2005 and continues 
to grow, and the data are presented as 3-hourly, daily and 
monthly means. The SSE website has now had over 5 
million hits and 1 million data document downloads. 
 
The radiation data are systematically validated against data 
from the Baseline Surface Radiation Network (BSRN), the 
World Radiation Data Centre (WRDC), the Global Energy 
Balance Archive (GEBA), and National Solar Radiation 
Data Base (NSRD). The GEOS-4 data are results of 
reanalyses that have incorporated land/ocean surface- and 
satellite-based observations[1][2]. Other meteorological 
parameters, such as minimum, maximum, daily mean and 
dew point temperatures, relative humidity, and surface 
pressure, are validated against the National Climate Data 
Center (NCDC) data. SSE feeds data through an interface 
directly to the National Renewable Energy Laboratory's 
(NREL) Hybrid Optimization Model for Electric 
Renewables (HOMER) and the RETSCreen International. 
 
The POWER data, for its high-resolution global coverage 
and long continuous record, are not only of immediate value 
to industrialists, architects of sustainable buildings, and 
agriculturists, but have great potential to facilitate analysis 
and prediction of worldwide energy from the climatological 
as well as economic points of view. 
 
 
2. DATA AND SOURCE 
 
The POWER project derives its data mainly from two 
sources: the GEWEX/SRB project for radiation data; 
GEOS-4 for temperature and humidity data [3][4]. The time 
span of the currently available data is from July 1983 to 
June 2005, and the resolution is 1ox1o. 
 
Figures 1 and 2 show the the 22-year average of monthly 
means of surface solar insolation for July and October, 
respectively, from 1983 to 2004. And Figure 3 is the 22-
year average of annual means of the surface solar insolation. 
Figure 4 shows the seasonally (JJA for June-July-August; 
DJF for December-January-Februrary) and annually 
averaged zonal means of surface solar insolation. 
 
Solar insolation, as well as the longwave radiation, at the 
Earth’s surface and the top of the atmosphere make it 
possible to derive the global northward meridional transport 
of thermal energy by the atmosphere. Figure 5 shows the 
results in 1014 W. Previous estimates of the transport can be 
found in [5][6]. 
https://ntrs.nasa.gov/search.jsp?R=20070031737 2019-08-30T01:36:12+00:00Z
 2
 
Fig. 1: 22-year average of July surface solar insolation from 
1983 to 2004 at 1ox1o resolution. (The color bar ranges from 
0 to 400 W m-2.) 
 
Fig. 2: 22-year average of October surface solar insolation 
from 1983 to 2004 at 1ox1o resolution. . (The color bar 
ranges from 0 to 400 W m-2.) 
 
3.  VALIDATION 
In order to establish the validity of the SSE data, massive 
validation has been conducted. The ground observations 
used for the validation include the BSRN, WRDC, GEBA 
and NSRDB databases.  
Figure 6 shows the scatter plot of the SSE monthly mean 
surface solar insolation  along with its BSRN counterpart. 
The statistics are computed globally, 60o poleward, and 60o 
equatorward. As the figure indicates, the global bias based 
on 2981 site-months of data is about –8 W m-2, and the 
RMS is about 24 W m-2.  
 
Fig. 3: 22-year average of the annual mean of surface solar 
insolation from July 1983 to June 2005. (The color bar is 
from 60 to 300 W m-2.) 
Fig. 4:  22-year averages of seasonally (JJA for December-
January-February ; DJF for  December-January-February) 
and annually averaged  zonal means of surface solar 
insolation. 
 
Figure 7 shows the monthly mean SSE surface solar 
insolation in comparison with the WRDC data from 1983 to 
1993. The total number of site-months is 39,343, and the 
bias is as small as about 3 W m-2. Though the data points are 
widely spread, the scatter density shows that the majority of 
the SEE data are in good agreement with the WRDC data. 
 
Figure 8 compares the SSE monthly surface solar insolation  
with that of the GEBA. The time span is about 20 years 
from July 1983 to September 2003.  The scatter density plot 
shows that the 82,977 site-months of SSE-GEBA pairs  
compared favorably with each other.  
 3
 
Fig. 5:Total northward transport of thermal energy by the 
atmosphere based on both shortwave and longwave 
radiation at the top of the atmosphere and the Earth’s 
surface. (The unit is 1014 W.) 
 
 
Fig. 6: The SSE surface solar insolation in comparison with 
its BSRN counterpart from January 1992 to June 2005.. The 
overall bias based on 2981 site-months of data is about –8 
W m-2. 
 
The availability of the solar energy varies as the global 
climate system varies. The prediction of the solar energy is 
thus closely related to the understanding and simulation of 
the dynamics of the global climate system. Figure 9 shows 
the coefficient of the first empirical orthogonal function 
(EOF) of the SSE monthly mean surface solar insolation 
from July 1983 to June 2005 over the Pacific region in 
comparison with the Southern Oscillation Index (SOI) of the 
same period. The data has been deseasonalized before 
computing  
 
Fig. 7:The SSE surface solar insolation in comparison with 
its WRDC counterpart from July 1983 to June 1993. The 
overall bias based on 39,343  site-months of data is about 3 
W m-2. 
T
 
Fig. 8: The SSE  surface solar insolation in comparison with 
its GEBA counterpart from July 1983 to September 2003. 
The bias based on 82977 site-months of data is 3 W m-2. 
 
the EOF.The range of the region is from  120oE to 180o to 
120oW and from 20oS to 20oN. The corresponding EOF 
represents 21% of the total variance of the deseasonalized 
solar insolation. The correlation between the EOF 
coefficient and the SOI is 0.6978. 
 
 4
 
Fig. 9: The coefficient of the first EOF of the monthly 
mean surface solar insolation from July 1983 to June 
2005 over the Pacific region in comparison with the 
Southern Oscillation Index. The correlation coefficient is 
as high as 0.6978. 
 
 
4.  VISION OF THE POWER PROJECT 
 
So far, the POWER project has made available to users 22 
years of historical record from 1983 to 2005. The record for 
the past 25 years is being actively worked on. This needs 
improvement on historic representation of cloud and aerosol 
properties, which involves cooperation with the GOCART 
models (Georgia Tech/Goddard Ozone, Chemistry, Aerosol 
and Radiation Transport model [7]. 
 
Additionally, the NASA GMAO’s upcoming GEOS-5 will 
feature a horizontal resolution of (1/2)ox(1/15)o. 
Consequently, the SSE resolution can be increased to 
(1/2)ox(1/2)o 
 
The POWER has also developed new prototypes  more 
specifically designed to meet the needs of the sustainable 
building engineers and architects as well as agricultural 
applications, included in which are clear-sky data in 
building design. 
 
FLASHFlux (Fast Longwave and Shortwave Radiative 
Fluxes from CERES and MODIS) is another project that 
POWER is involved in. This project produces global 
gridded solar irradiance estimates within one week of 
observation fro NASA’s Terra and Aqua satellites [8].  
 
POWER is now also collaborating with others for short-
term forecast of solar insolation..    
] 
5.CONCLUSION 
The POWER project and its latest development is reviewed 
in this paper. POWER has produced 22 years of solar 
radiation and other  related meteorological data that of great 
values, especially to the renewable energy, architectural and 
agricultural industries. POWER is also actively working 
toward short-term forecasting  of solar irradiance.  
 
More information can be found at 
http://eosweb.larc.nasa.gov/sse/ and http://earth-
www.larc.nasa.gov/solar/power. 
 
 
 
6.  REFERENCES 
 
(1)Schubert, S.D., R.B. Rood, and J.W. Pfaendtner, (1993). 
An Assimilated Dataset for Earth Science. Bull. Amer. 
Meteor. Soc., 74, 2331-2342. 
(2) Bloom, S.A. da Silva, and D.Dee, (2005). 
Documentation and Validation of the Goddard Earth 
Observing System (GEOS) Data Assimilation System—
Version 4. NASA Technical Report Series on Global 
Modeling and Data Assimilation, Max J. Suarea, Editor, 
NASA/TM-2005-104606, 26.  
(3) Stackhouse, P.W., Jr., R.S. Eckman, C.H. Whitlock, W.S. 
Chandler, J.M. Hoell, T. Zhang, J. C. Mikovitz, G.S. Leng, P. 
Lilienthal: Supporting Energy-Related Societal Applications 
Using NASA’s Satellite and Modeling Data, IEEE 
International Geoscience and Remote Sensing Symposium, 
Geoscience and Remote Sensing, Denver, CO, 2006. 
(4).Cox, S.J., P.W. Stackhouse, Jr., S.K. Gupta, J.C. 
Mikovitz, M.Chiacchio and T. Zhang: The NASA/GEWEX 
Surface Radiation Budget Project: Results and Analysis, 
International Radiation Symposium, Busan, Korea, 2004. 
(5). Kann, D.K., S. Yang, and Miller, A.J., 1994. Mean 
meridional transport of energy in the earth-atmosphere 
system using NMC global analyses and ERBE radiation 
data. Tellus, 46A, 553-565. 
(6) Peixoto, J.P. and A.H. Oort, 1992. Physics of Climate. 
American Institute of Physics, New York, New York.  
(7) Chin, M., P. Ginoux, S. Kinne, O. Torres, B. Holben, 
B.N. Duncan, R.V. Martin, J.A. Logan, A. Higurashi, T. 
Nakajima, 2002. Tropospheric Aerosol Optical Thickness 
from the GOCART Model and Comparisons with Satellite 
and Sun Photometer Measurements. J. Atmos. Sci., 59, 461-
483. 
(8) Stackhouse Jr., P.W., D.P. Kratz, G.R. McGarragh, S.K. 
Gupta and E.B.Geier, 2006: Fast Longwave and Shortwave 
Radiative Flux (FLASHFlux) Products from CERES and 
MODIS Measurements. Proceedings of the 12th Conference 
on Atmospheric Radiation. Madison, WI, July 10-14, 
American Meteorological Society. 


A Global Perspective: NASA's Prediction of Worldwide Energy Resources (POWER) Project

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