An Efficient Method of Estimating Downward Solar Radiation Based on the MODIS Observations for the Use of Land  Surface Modeling

Chen, Min; Zhuang, Qianlai; He, Yujie

doi:10.3390/rs6087136

Cited by 36 publications

(17 citation statements)

References 56 publications

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“…The model is now used in many solar-related applications, including the benchmarking of the radiative output of the Weather Research and Forecasting (WRF) model (Ruiz-Arias et al 2012), operational derivation of surface irradiance components using MODIS satellite observations (Chen, Zhuang, and He 2014), improvement in GHI to DNI separation modeling (Vindel, Polo, and Antonanzas-Torres 2013), and development of future climate scenarios (Fatichi, Ivanov, and Caporali et al 2011). REST2 is also being used at NREL and is integrated into its suite of algorithms that will produce future versions of the NSRDB.…”

Section: Rest2 Modelmentioning

confidence: 99%

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Sengupta¹,

Kurtz²,

Dobos³

et al. 2015

152

121

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ForewordThe first version of this handbook was developed in response to a growing need by the solar energy industry for a single document addressing the key aspects of solar resource characterization. The solar energy industry has developed rapidly throughout the last few years, and there have been significant enhancements in the body of knowledge in the areas of solar resource assessment and forecasting. Thus, this second version of the handbook was developed from the need to update and enhance the initial version and present the state of the art in a condensed form for all of its users.Although the first version of this handbook was developed by only researchers from the National Renewable Energy Laboratory, this version has additional contributions from an international group of experts primarily from the knowledge that has been gained through participation in the International Energy Agency's Solar Heating and Cooling Programme Task 36 and Task 46.As in the first version, this material was assembled by scientists and engineers who have many decades of combined experience in atmospheric science, radiometry, meteorological data processing, and renewable energy technology development.Readers are encouraged to provide feedback to the authors for future revisions and an expansion of the handbook's scope and content. iv This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications. PrefaceAs the world looks for low-carbon sources of energy, solar power stands out as the single most abundant energy resource on Earth. Harnessing this energy is the challenge for this century. Photovoltaics, solar heating and cooling, and concentrating solar power (CSP) are primary forms of energy applications using sunlight. These solar energy systems use different technologies, collect different fractions of the solar resource, and have different siting requirements and production capabilities. Reliable information about the solar resource is required for every solar energy application. This holds true for small installations on a rooftop as well as for large solar power plants. However, solar resource information is of particular interest for large installations, because they require a substantial investment, sometimes exceeding $1 billion in construction costs. Before such a project is undertaken, the best possible information about the quality and reliability of the fuel source must be made available. That is, project developers need to have reliable data about the solar resource available at specific locations, including historic trends with seasonal, daily, hourly, and (preferably) subhourly variability to predict the daily and annual performance of a proposed power plant. Without this data, an accurate financial analysis is not possible.In September 2008, the U.S. Department of Energy (DOE) hosted a meeting of prominent CSP developers and stakeholders. One purpose was to identify areas in which the DOE's CSP program should focus its efforts to help the industry develop an...

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Section: Rest2 Modelmentioning

confidence: 99%

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Sengupta¹,

Kurtz²,

Dobos³

et al. 2015

152

121

View full text Add to dashboard Cite

show abstract

“…Here we conduct a global‐scale study to understand the aerosols' radiative effects on terrestrial ecosystem heat fluxes using a process‐based modeling approach. We first apply an atmospheric radiative transfer model [ Chen et al , ] that uses MODIS‐based global aerosol properties. We then integrate the model with a terrestrial ecosystem model (the integrated Terrestrial ecosystem model, iTem; Chen , ; Chen and Zhuang , ) to estimate aerosol direct radiative effects on the energy fluxes of the global terrestrial ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Aerosol effects on global land surface energy fluxes during 2003–2010

Liu

Chen

Zhuang

2014

Geophysical Research Letters

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Aerosols affect downward solar radiation, impacting the terrestrial ecosystem carbon dynamics and energy budget. Here we apply a coupled modeling framework of a terrestrial ecosystem model and an atmospheric radiative transfer model to evaluate aerosol direct radiative effects on the surface heat fluxes of global terrestrial ecosystems during 2003-2010. We find that aerosol loadings decrease the mean latent heat flux by 2.4 Wm À2 (or evapotranspiration by 28 mm) and sensible heat flux by 16 Wm À2. As a result, global mean soil moisture and water evaporative fraction have increased by 0.5% and 4%, respectively. Spatially, aerosol effects are significant in tropical forests and temperate broadleaf evergreen forests. This study is among the first quantifications of aerosols' effects on the heat fluxes of the global terrestrial ecosystems. The study further suggests that both direct and indirect aerosol radiative effects through aerosol-cloud interactions should be considered to quantify the energy budget of the global terrestrial ecosystems.

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“…Chen et al (2014) evaluated the estimated daily average DSSR from MODIS L3 products at 48 BSRN sites under clear-sky conditions. The statistics of daily average DSSR and BSRN observations were R 2 = 0.87, RMSE = 29.57 W/m 2 , and bias = 12.09 W/m 2 .…”

Section: Comparison Of the Estimated Dssrs With Evaluations From Othementioning

confidence: 99%

“…MODIS data products have temporal resolutions of 1 day to 1 year and spatial resolutions such as 1 km (e.g., geolocation properties, surface reflectance, and water vapor) and 5 km (e.g., ozone amount), which are appropriate for mapping landscapes from the regional to global scale (MODIS 2016). Many algorithms that do not require such heavy computational power and detailed inputs as the radiative transfer models have been developed to estimate DSSR based on MODIS products (Bisht et al 2005;Chen et al 2012Chen et al , 2014Huang et al 2012;Niu and Pinker 2015;Ryu et al 2008;Wang et al 2012).…”

Section: Introductionmentioning

confidence: 99%

A MODIS-based modeling scheme for the estimation of downward surface shortwave radiation under cloud-free conditions

2017

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Atmospheric aerosol optical depth (AOD) plays an important role in radiation modeling and partly determines the accuracy of estimated downward surface shortwave radiation (DSSR). In this study, Iqbal's model C was used to estimate DSSR under cloud-free conditions over the Koohin and Chitgar sites in Tehran, Iran; the estimated DSSR was based on (1) our proposed hybrid modeling scheme where the AOD is retrieved using the Simplified Aerosol Retrieval Algorithm (SARA), ground-based measurements at the AERONET site in Zanjan and (2) the AOD from the Terra MODerateresolution Imaging Spectroradiometer (MODIS) sensor. Several other Terra MODIS land and atmospheric products were also used as input data, including geolocation properties, water vapor, total ozone, surface reflectance, and top-ofatmosphere (TOA) radiance. SARA-based DSSR and MODIS-based DSSR were evaluated with ground-based DSSR measurements at the Koohin and Chitgar sites in 2011 and 2013, respectively; the averaged statistics for SARA-based DSSR [R 2 ≈ 0.95, RMSE ≈ 22 W/m 2 (2.5% mean value), and bias ≈ 3 W/m 2 ] were stronger than those for MODIS-based DSSR [R 2 ≈ 0.79, RMSE ≈ 51 W/m 2 (5.8% mean value), and bias ≈ 34 W/m 2 ]. These results show that the proposed hybrid scheme can be used at regional to global scales under the assumption of future access to spatially distributed AERONET sites. Additionally, the robustness of this modeling scheme was exemplified by estimating the aerosol radiative forcing (ARF) during a dust storm in Southwest Asia. The results were comparable to those of previous studies and showed the strength of our modeling scheme.

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An Efficient Method of Estimating Downward Solar Radiation Based on the MODIS Observations for the Use of Land Surface Modeling

Cited by 36 publications

References 56 publications

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications

Aerosol effects on global land surface energy fluxes during 2003–2010

A MODIS-based modeling scheme for the estimation of downward surface shortwave radiation under cloud-free conditions

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