Document Type : Research Article

Authors

1 University of Bu-Ali Sina, Hamedan

2 Hamedan

3 University of Granada, Spain

Abstract

 
Introduction: Surface Solar Radiation (SSR) as the largest source of land-surface energy is an important parameter in meteorological and climatological studies. Limitations in ground-based measurements have encouraged the users to approach low cost and reliable methods to estimate radiation components, for the regions where the ground-based radiation data are sparse. Different methods have been developed for estimating SSR including empirical models, radiative transfer models, semi-empirical models, and models based on satellite and reanalysis products. In most studies in Iran, empirical methods have been investigated. Despite the simplicity of these models, they do not accurately represent SSR variations because of not considering all the parameters affecting radiation variations, at large spatial scales with different climates. The Global Land Data Assimilation System (GLDAS) is a combination of measured and satellite data that uses advanced land surface modeling and data assimilation methods. One of the strengths of this model that makes GLDAS unique is that it has global coverage, high spatial-temporal resolution and is available for free. GLDAS is a terrestrial modeling system uncoupled to the atmosphere. This work was aimed to evaluate SSR derived from GLDAS using ground measurements over Iran from 2012 to 2015 on a daily basis.
Materials and Methods: In this study, measured SSR in 24 radiometer stations of Iran from 2012 to 2015 was extracted. Since the measured data are associated with some errors, the quality of the data must be checked and screened before use. In this study, Moradi's proposed method was used to control data quality. The studied areas were classified into three zones of coastal, arid and semi-arid climates based on Digital Elevation Model (DEM) and UNESCO climate classification approach. The GLDAS SSR outputs were extracted with a spatial and temporal resolution of 0.25° grid cell and 3-hourly from 2012 to 2015. The GLDAS is one of the LDAS projects and has been extended jointly by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP). The purpose of GLDAS is to produce high quality temporal and spatial land surface data. GLDAS drives three land surface models: Mosaic, Noah, and CLM. GLDAS assessments SSR at the land surface using a method and cloud and snow products from the Air Force Weather Agency's (AFWA) Agricultural Meteorology modeling system (AGRMET). Since the GLDAS data are created using the gridded Binary format, the nearest neighborhood interpolation method was used to match these data with ground-based data and GLDAS datasets were generated for station points using CDO software. In this study, GLDAS datasets were compared against measured SSR datasets by four validation metrics. The metrics used are determination coefficient (R2), the mean bias error (MBD), the mean absolute error (MABD), relative mean absolute error (RMABD) and root mean squared error (RMSE).
Results and Discussion: Statistical analysis showed that the performance of GLDAS in SSR evaluation is reasonable in Iran with a high-efficiency coefficient of 0.88. Also, it was shown that the GLDAS has a higher ability to estimate SSR under clear sky (warm seasons) conditions than cloudy conditions (cold seasons). Similar to the obtained results, Träger-Chatterjee et al. (2010); Jia et al. (2013); Boilley and Wild (2015) and Heidary Beni and Yazdanpanah (2017) also showed that the ERA- Interim, NCEP-DOE, RegCM4 and angstrom model are also more capable of estimating SSR in warm seasons. Seasonal bias variations at three studied areas showed that the most changes occurred in summer and least changes in winter. The highest overestimation was also observed in the coastal areas in summer and the lowest overestimation in the semi-arid regions in spring. The evaluation of the GLDAS performance against the site measured SSR data suggests that the GLDAS tends to underestimate in 71% of the studied stations. Moreover, the stations located in the arid region provided a better estimation of SSR as compared with semi-arid and coastal locations. These results were compared with those of Boilley and Wald (2015) that showed ERA-Interim and MERRA reanalysis models have high uncertainty in areas with tropical humid climates, and in regions with arid climates, models perform better in SSR estimation. Our findings were also in good agreement with their results.
Conclusion: GLDAS SSR outputs can be used for agricultural studies. This is due to the facts that arid and semi-arid climates are dominant in Iran and the growing season is mostly in the warm season.

Keywords

1- Bayat K., and Mirlatifi S.M. 2009. Estimation of Daily Global Solar Radiation Using Regression Models and Artificial Neural Networks. Journal of Agricultural Sciences and Natural Resources 16(3): 270-280. (In Persian)
2- Boilley A., and Wald L. 2015. Comparison between meteorological re-analyses from ERA-Interim and MERRA and measurements of daily solar irradiation at surface. Journal of Renewable Energy 75: 135-143.
3- Chen M., Zhuang Q. and He Y. 2014. An efficient method of estimating downward solar radiation based on the MODIS observations for the use of land surface modeling. Journal of Remote Sensing 6(8): 7136-7157.
4- Estevez J., Gavilan P., and Giraldez J. V. 2011. Guidelines on validation procedures for meteorological data from automatic weather stations. Journal of Hydrology 402(1-2): 144-154.
5- Gholamnia A., Mobin M.H., and Alipoor H. 2016. Modeling and Zoning Solar Energy Received at the Earth's Surface in Arid and Semiarid Regions of Central Iran. Journal of Water and Soil 30(4): 1294-1308. (In Persian with English abstract)
6- Heidary Beni M., and Yazdanpanah H.A. 2017. Assessment of RegCM4 model for estimation of total solar radiation (Case study: Chaharmahal and Bakhtiari province). Journal of Agricultural Meteorology 4(2): 27-37. (In Persian with English abstract)
7- Jia B., Xie Z., Dai A., Shi C., and Chen F. 2013. Evaluation of satellite and reanalysis products of downward surface solar radiation over East Asia: Spatial and seasonal variations. Journal of Geophysical Research: Atmospheres 118(9): 3431-3446.
8- Journee M., and Bertrand C. 2010. Improving the spatiotemporal distribution of surface solar radiation data by merging ground and satellite measurements. Journal of Remote Sensing of Environment 114(11): 2692-2704.
9- Khosravi A., Nunes R. O., Assad M. E. H., and Machado L. 2018. Comparison of artificial intelligence methods in estimation of daily global solar radiation. Journal of Cleaner Production 194: 342-358.
10- Laiti L., Andreis D., Zottele F., Giovannini L., Panziera L., Toller G., and Zardi D. 2014. A solar atlas for the Trentino region in the Alps: quality control of surface radiation data. Journal of Energy Procedia 59: 336-343.
11- Liang S., Wang K., Zhang X., and Wild M. 2010. Review on estimation of land surface radiation and energy budgets from ground measurement, remote sensing and model simulations. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 3(3): 225-240.
12- Lotfinejad M., Hafezi R., Khanali M., Hosseini S., Mehrpooya M., and Shamshirband S. 2018. A comparative assessment of predicting daily solar radiation using bat neural network (BNN), generalized regression neural network (GRNN), and neuro-fuzzy (NF) system: A case study. Journal of Energies 11(5): 1188.
13- Majnoni-Heris A., Zand-Parsa S., Sepaskhah A., and Nazemosadat M.J. 2009. Development and Evaluation of Global Solar Radiation Models Based on Sunshine Hours and Meteorological Data. Journal of Water and Soil Science 12(46):491-499. (In Persian)
14- Mobin M.H., Gholamnia A., Soudaiezadeh H., and Mirhosani S.A. 2015. Introducing a new model for estimating solar radiation in arid and semi-arid regions of Iran. Journal of Arid Biome Scientific and Research 5(2): 95-101. (In Persian)
15- Moradi I. 2009. Quality control of global solar radiation using sunshine duration hours. Journal of Energy 34(1): 1-6.
16- Mousavi-Baygi M., Ashraf B., and Miyanabady A. 2010. The Investigation of different Models of Estimating Solar Radiation to Recommend the Suitable Model in a Semi-Arid Climate. Journal of Water and Soil 24(4): 836-844. (In Persian with English abstract)
17- Noory H., Mokhtari A., and Vazifedoust M. 2019. Evaluation of Incoming Solar Radiation Parameter Derived from Empirical and Satellite Models. Iranian Journal of Soil and Water Research 50(2): 353-362. (In Persian with English abstract)
18- Polo J., Wilbert S., Ruiz-Arias J.A., Meyer R., Gueymard C., Suri M., Martin L., Mieslinger T., Blanc P., Grant I., and Boland J. 2016. Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets. Journal of Solar Energy 132: 25-37.
19- Riihelä A., Carlund T., Trentmann J., Müller R., and Lindfors A. 2015. Validation of CM SAF surface solar radiation datasets over Finland and Sweden. Journal of Remote Sensing 7(6): 6663-6682.
20- Rodell M., Houser P.R., Jambor U.E.A., Gottschalck J., Mitchell K., Meng C.J., Arsenault K., Cosgrove B., Radakovich J., Bosilovich M., and Entin J.K. 2004. The global land data assimilation system. Bulletin of the American Meteorological Society 85(3): 381-394.
21- Ruiz-Arias J.A., Quesada-Ruiz S., Fernandez E.F. and Gueymard C.A. 2015. Optimal combination of gridded and ground-observed solar radiation data for regional solar resource assessment. Journal of Solar Energy 112: 411-424.
22- Sabziparvar A.A. and Shetaee H. 2007. Estimation of global solar radiation in arid and semi-arid climates of East and West Iran. Journal of Energy 32(5): 649-655.
23- Sabziparvar A.A. 2008. A simple formula for estimating global solar radiation in central arid deserts of Iran. Journal of Renewable Energy 33(5): 1002-1010.
24- Sanchez-Lorenzo A., Wild M., and Trentmann J. 2013. Validation and stability assessment of the monthly mean CM SAF surface solar radiation dataset over Europe against a homogenized surface dataset (1983–2005). Journal of Remote sensing of environment 134: 355-366.
25- Slater A.G. 2016. Surface solar radiation in North America: A comparison of observations, reanalyses, satellite, and derived products. Journal of Hydrometeorology 17(1): 401-420.
26- Träger-Chatterjee C., Müller R.W., Trentmann J., and Bendix J. 2010. Evaluation of ERA-40 and ERA-interim re-analysis incoming surface shortwave radiation datasets with mesoscale remote sensing data. Journal of Meteorologische Zeitschrift 19(6): 631-640.
27- Urraca R., Gracia-Amillo A.M., Koubli E., Huld T., Trentmann J., Riihelä A., Lindfors A.V., Palmer D., Gottschalg R., and Antonanzas-Torres F. 2017. Extensive validation of CM SAF surface radiation products over Europe. Journal of Remote sensing of environment 199: 171-186.
28- Urraca R., Martinez-de-Pison E., Sanz-Garcia A., Antonanzas J., and Antonanzas-Torres F. 2017. Estimation methods for global solar radiation: Case study evaluation of five different approaches in central Spain. Journal of Renewable and Sustainable Energy Reviews 77: 1098-1113.
29- Urraca R., Huld T., Gracia-Amillo A., Martinez-de-Pison F.J., Kaspar F., and Sanz-Garcia A. 2018. Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data. Journal of Solar Energy 164: 339-354.
30- Wang F., Wang L., Koike T., Zhou H., Yang K., Wang A., and Li W. 2011. Evaluation and application of a fine‐resolution global data set in a semiarid mesoscale river basin with a distributed biosphere hydrological model. Journal of Geophysical Research: Atmospheres 116: D21.
31- Wang Y., Trentmann J., Yuan W., and Wild M. 2018. Validation of CM SAF CLARA-A2 and SARAH-E Surface Solar Radiation Datasets over China. Journal of Remote Sensing 10(12): 1977.
32- Xiao R., He X., Zhang Y., Ferreira V., and Chang L. 2015. Monitoring groundwater variations from satellite gravimetry and hydrological models: a comparison with in-situ measurements in the Mid-Atlantic region of the United States. Journal of Remote Sensing 7(1): 686-703.
33- Yang F., Lu H., Yang K., He J., Wang W., Wright J.S., Li C., Han M., and Li Y. 2017. Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China. Journal of Hydrology and Earth System Sciences 21(11).
34- Yang L., Zhang X., Liang S., Yao Y., Jia K., and Jia A. 2018. Estimating surface downward shortwave radiation over china based on the gradient boosting decision tree method. Journal of Remote Sensing 10(2): 185.
35- Younes S., Claywell R., and Muneer T. 2005. Quality control of solar radiation data: Present status and proposed new approaches. Journal of Energy 30(9): 1533-1549.
36- Zhang T., Stackhouse Jr P.W., Cox S.J., Mikovitz J.C., and Long C.N. 2019. Clear-sky shortwave downward flux at the Earth's surface: Ground-based data vs. satellite-based data. Journal of Quantitative Spectroscopy and Radiative Transfer 224: 247-260.
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