Estimation of solar radiation over Cambodia from long-term satellite data

Janjai, Serm; Pankaew, P.; Laksanaboonsong, J.; Kitichantaropas, P.

doi:10.1016/j.renene.2010.09.023

Cited by 65 publications

(24 citation statements)

References 25 publications

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“…Using Meteosat retrievals, Pereira et al (1996) reported a PE of 13 % during a 2-yr period (1985)(1986)) and using 22 meteorological stations. Finally, using data from GOES, GMS and MT-SAT from 1995, Janjai et al (2011 found a PE of 6.3 % with 5 meteorological stations in flat areas in Cambodia. According to Pinker et al (2005), several attempts to compute R s with remote sensing data at a monthly time step and at a global scale yielded RMSE between 11.7 W m −2 and 31.5 W m −2 .…”

Section: Monthly Evaluationmentioning

confidence: 92%

“…R s evaluation with ground data showed a PE of 28.3 % and 9.9 % for 30-min and daily R s time steps, yielding an ET error at 30-min time steps of around 30 % and a R 2 of 0.67 but lower error of 3.1 % and higher R 2 of 0.90 in daily ET retrievals. When comparing four potential ET methods at a daily time step over a wetland area in Florida, USA, Jacobs et al (2004) found dramatic improvements in the efficiency of ET-radiation based models using GOES R s , with a RMSE of 19.4 W m −2 . During the soil-moisture atmospheric coupling experiment (SMACEX) carried out in Iowa, USA, in 2002, Su et al (2005) reported good agreement between modeled ET using GOES R s data and in situ observations of instantaneous ET, with a RMSE of 60 W m −2 in 8 corn and soybean plots.…”

Section: Using Dssf As Input Data In Et Modelingmentioning

confidence: 99%

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Validation of a Meteosat Second Generation solar radiation dataset over the northeastern Iberian Peninsula

Cristóbal

Anderson

2013

Hydrol. Earth Syst. Sci.

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Abstract. Solar radiation plays a key role in the Earth's energy balance and is used as an essential input data in radiation-based evapotranspiration (ET) models. Accurate gridded solar radiation data at high spatial and temporal resolution are needed to retrieve ET over large domains. In this work we present an evaluation at hourly, daily and monthly time steps and regional scale (Catalonia, NE Iberian Peninsula) of a satellite-based solar radiation product developed by the Land Surface Analysis Satellite Application Facility (LSA SAF) using data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Product performance and accuracy were evaluated for datasets segmented into two terrain classes (flat and hilly areas) and two atmospheric conditions (clear and cloudy sky), as well as for the full dataset as a whole. Evaluation against measurements made with ground-based pyranometers yielded good results in flat areas with an averaged model RMSE of 65 W m −2 (19 %), 34 W m −2 (9.7 %) and 21 W m −2 (5.6 %), for hourly, daily and monthly-averaged solar radiation and including clear and cloudy sky conditions and snow or ice cover. Hilly areas yielded intermediate results with an averaged model RMSE (root mean square error) of 89 W m −2 (27 %), 48 W m −2 (14.5 %) and 32 W m −2 (9.3 %), for hourly, daily and monthly time steps, suggesting the need of further improvements (e.g., terrain corrections) required for retrieving localized variability in solar radiation in these areas. According to the literature, the LSA SAF solar radiation product appears to have sufficient accuracy to serve as a useful and operative input to evaporative flux retrieval models.

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Section: Monthly Evaluationmentioning

confidence: 92%

Section: Using Dssf As Input Data In Et Modelingmentioning

confidence: 99%

Validation of a Meteosat Second Generation solar radiation dataset over the northeastern Iberian Peninsula

Cristóbal

Anderson

2013

Hydrol. Earth Syst. Sci.

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“…The H2 has been developed by changing some inputs to the model in several studies [12][13][14]. Other studies also used satellite imagery with different techniques for GHI estimations [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

et al. 2018

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Several sectors need global horizontal irradiance (GHI) data for various purposes. However, the availability of a long-term time series of high quality in situ GHI measurements is limited. Therefore, several studies have tried to estimate GHI by re-analysing climate data or satellite images. Validation is essential for the later use of GHI data in the regions with a scarcity of ground-recorded data. This study contributes to previous studies that have been carried out in the past to validate HelioClim-3 version 5 (HC3v5) and the Copernicus Atmosphere Monitoring Service, using radiation service version 3 (CRSv3) data of hourly GHI from satellite-derived datasets (SDD) with nine ground stations in northeast Iraq, which have not been used previously. The validation is carried out with station data at the pixel locations and two other data points in the vicinity of each station, which is something that is rarely seen in the literature. The temporal and spatial trends of the ground data are well captured by the two SDDs. Correlation ranges from 0.94 to 0.97 in all-sky and clear-sky conditions in most cases, while for cloudy-sky conditions, it is between 0.51–0.72 and 0.82–0.89 for the clearness index. The bias is negative for most of the cases, except for three positive cases. It ranges from −7% to 4%, and −8% to 3% for the all-sky and clear-sky conditions, respectively. For cloudy-sky conditions, the bias is positive, and differs from one station to another, from 16% to 85%. The root mean square error (RMSE) ranges between 12–20% and 8–12% for all-sky and clear-sky conditions, respectively. In contrast, the RMSE range is significantly higher in cloudy-sky conditions: above 56%. The bias and RMSE for the clearness index are nearly the same as those for the GHI for all-sky conditions. The spatial variability of hourly GHI SDD differs only by 2%, depending on the station location compared to the data points around each station. The variability of two SDDs is quite similar to the ground data, based on the mean and standard deviation of hourly GHI in a month. Having station data at different timescales and the small number of stations with GHI records in the region are the main limitations of this analysis.

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“…A number of methods have been proposed to model spatio-temporal solar radiation, such as retrieval from satellite data [76], interpolation from surrounding weather stations [77,78], empirical estimation from other meteorological observations (e.g., diurnal air temperature range) [79,80], generation from weather models [81], and geometrical simulation accounting for shadows and clouds [82]. However, satellite-retrieved solar radiation has a coarse spatial resolution [76] and we lack appropriate data to calculate precise solar radiation (specifically, we lack solar radiation observations from weather stations, spatio-temporal meteorological data such as air temperature, water vapor, clouds, total optical length, and atmospheric transmittance [1], and 3D urban structures for shadow determination [82]). Alternatively, DDL was used as a substitute for daily solar radiation in calm and clear days considering their significant correlation [48], the simplicity of DDL calculation, and the spatio-temporal variance in DDL.…”

Section: The Seven Predictors and Complementary Predictorsmentioning

confidence: 99%

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

et al. 2016

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Near surface air temperature (Ta) is one of the most critical variables in climatology, hydrology, epidemiology, and environmental health. In situ measurements are not efficient for characterizing spatially heterogeneous Ta, while remote sensing is a powerful tool to break this limitation. This study proposes a mapping framework for daily mean Ta using an enhanced empirical regression method based on remote sensing data. It differs from previous studies in three aspects. First, nighttime light data is introduced as a predictor (besides land surface temperature, normalized difference vegetation index, impervious surface area, black sky albedo, normalized difference water index, elevation, and duration of daylight) considering the urbanization-induced Ta increase over a large area. Second, independent components are extracted using principal component analysis considering the correlations among the above predictors. Third, a composite sinusoidal coefficient regression is developed considering the dynamic Ta-predictor relationship. This method was performed at 333 weather stations in China during 2001-2012. Evaluation shows overall mean error of −0.01 K, root mean square error (RMSE) of 2.53 K, correlation coefficient (R 2 ) of 0.96, and average uncertainty of 0.21 K. Model inter-comparison shows that this method outperforms six additional empirical regressions that have not incorporated nighttime light data or considered predictor independence or coefficient dynamics (by 0.18-2.60 K in RMSE and 0.00-0.15 in R 2 ).

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Estimation of solar radiation over Cambodia from long-term satellite data

Cited by 65 publications

References 25 publications

Validation of a Meteosat Second Generation solar radiation dataset over the northeastern Iberian Peninsula

Validation of a Meteosat Second Generation solar radiation dataset over the northeastern Iberian Peninsula

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

Enhanced Statistical Estimation of Air Temperature Incorporating Nighttime Light Data

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