Merging ELMs with Satellite Data and Clear-Sky Models for Effective Solar Radiation Estimation

2018 ◽  
pp. 163-170
Author(s):  
L. Cornejo-Bueno ◽  
C. Casanova-Mateo ◽  
J. Sanz-Justo ◽  
S. Salcedo-Sanz
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Clear Sky

scholarly journals Estimations of Global Horizontal Irradiance and Direct Normal Irradiance by Using Fengyun-4A Satellite Data in Northern China

2021 ◽  
Vol 13 (4) ◽  
pp. 790 ◽  
Author(s):  
Dongyu Jia ◽  
Jiajia Hua ◽  
Liping Wang ◽  
Yitao Guo ◽  
Hong Guo ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Satellite Data ◽  
Weather Conditions ◽  
Northern China ◽  
Energy Utilization ◽  
Mean Bias Error ◽  
Clear Sky ◽  
Direct Normal Irradiance ◽  
Autumn And Winter

Accurate solar radiation estimation is very important for solar energy systems and is a precondition of solar energy utilization. Due to the rapid development of new energy sources, the demand for surface solar radiation estimation and observation has grown. Due to the scarcity of surface radiation observations, high-precision remote sensing data are trying to fill this gap. In this paper, a global solar irradiance estimation method (in different months, seasons, and weather conditions), using data from the advanced geosynchronous radiation imager (AGRI) sensor onboard the FengYun-4A satellite with cloud index methodology (CSD-SI), was tested. It was found that the FengYun-4A satellite data could be used to calculate the clear sky index through the Heliosat-2 method. Combined with McClear, the global horizontal irradiance (GHI) and the direct normal irradiance (DNI) in northeast China could be accurately obtained. The estimated GHI accuracy under clear sky was slightly affected by the seasons and the normalized root mean square error (nRMSE) values (in four sites) were higher in summer and autumn (including all weather conditions). Compared to the estimated GHI, the estimated DNI was less accurate. It was found that the estimated DNI in October had the best performance. In the meantime, the nRMSE, the normalized mean absolute error (nMAE), and the normalized mean bias error (nMBE) of Zhangbei were 35.152%, 27.145%, and −8.283%, while for Chengde, they were 43.150%, 28.822%, and −13.017%, respectively. In addition, the estimated DNI at ground level was significantly higher than the actual observed value in autumn and winter. Considering that the error mainly came from the overestimation of McClear, a new DNI radiation algorithm during autumn and winter is proposed for northern China. After applying the new algorithm, the nRMSE decreased from 49.324% to 48.226% for Chengde and from 48.342% to 41.631% for Zhangbei. Similarly, the nMBE decreased from −32.351% to −18.823% for Zhangbei and from −26.211% to −9.107% for Chengde.

Rotifer dynamics in three shallow lakes from the Salado river watershed (Argentina): the potential modulating role of incident solar radiation

2015 ◽  
Vol 14 (11) ◽  
pp. 2007-2013 ◽  
Author(s):  
Nadia Diovisalvi ◽  
Armando M. Rennella ◽  
Horacio E. Zagarese
Keyword(s):  
Solar Radiation ◽  
Shallow Lakes ◽  
Seasonal Cycle ◽  
Schematic Representation ◽  
Incident Solar Radiation ◽  
River Watershed ◽  
Clear Sky ◽  
Rotifer Species ◽  
Salado River

A schematic representation of the seasonal cycle of rotifer in L. Chascomús. In this figure the relative abundances of the three dominant rotifer species are expressed as fractions of the estimated clear-sky mean daily incident solar radiation.

scholarly journals Prediction of Solar Power Using Near-Real Time Satellite Data

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5865
Author(s):  
Abhnil Amtesh Prasad ◽  
Merlinde Kay
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Solar Power ◽  
Short Term ◽  
Motion Vectors ◽  
Clear Sky ◽  
Cloud Motion ◽  
Visible Images ◽  
Grid Operators ◽  
Short Term Prediction

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

scholarly journals Inter-comparison of CO measurements from TROPOMI, ACE-FTS, and a high-Arctic ground-based FTS

2021 ◽  
Author(s):  
Tyler Wizenberg ◽  
Kimberly Strong ◽  
Kaley Walker ◽  
Erik Lutsch ◽  
Tobias Borsdorff ◽  
...  
Keyword(s):  
Satellite Data ◽  
Polar Region ◽  
High Arctic ◽  
Equatorial Region ◽  
The Arctic ◽  
Data Sets ◽  
Positive Bias ◽  
Clear Sky ◽  
Global Comparison ◽  
The Mean

Abstract. ACE/TROPOMI Abstract for AMT submission The TROPOspheric Monitoring Instrument (TROPOMI) provides a daily, spatially-resolved (initially 7 × 7 km2, upgraded to 7 × 5.6 km2 in August 2019) global data set of CO columns, however, due to the relative sparseness of reliable ground-based data sources, it can be challenging to characterize the validity and accuracy of satellite data products in remote regions such as the high Arctic. In these regions, satellite inter-comparisons can supplement model- and ground-based validation efforts and serve to verify previously observed differences. In this paper, we compare the CO products from TROPOMI, the Atmospheric Chemistry Experiment (ACE) Fourier Transform Spectrometer (FTS), and a high-Arctic ground-based FTS located at the Polar Environment Atmospheric Research Laboratory (PEARL) in Eureka, Nunavut (80.05° N, 86.42° W). A global comparison of TROPOMI reference profiles scaled by the retrieved total column with ACE-FTS CO partial columns for the period from 10 November 2017 to 31 May 2020 displays excellent agreement between the two data sets (R = 0.93), and a small relative bias of −0.68 ± 0.25 % (bias ± standard error). Additional comparisons were performed within five latitude bands; the north Polar region (60° N to 90° N), northern Mid-latitudes (20° N to 60° N), the Equatorial region (20° S to 20° N), southern Mid-latitudes (60° S to 20° S), and the south Polar region (90° S to 60° S). Latitudinal comparisons of the TROPOMI and ACE-FTS CO datasets show strong correlations ranging from R = 0.93 (southern Mid-latitudes) to R = 0.85 (Equatorial region) between the CO products, but display a dependence of the mean differences on latitude. Positive mean biases of 7.92 ± 0.58 % and 7.98 ± 0.51 % were found in the northern and southern Polar regions, respectively, while a negative bias of −9.16 ± 0.55 % was observed in the Equatorial region. To investigate whether these differences are introduced by cloud contamination which is reflected in the TROPOMI averaging kernel shape, the latitudinal comparisons were repeated for cloud-covered pixels and clear-sky pixels only, and for the unsmoothed and smoothed cases. Clear-sky pixels were found to be biased higher with poorer correlations on average than clear+cloudy scenes and cloud-covered scenes only. Furthermore, the latitudinal dependence on the biases was observed in both the smoothed and unsmoothed cases. To provide additional context to the global comparisons of TROPOMI with ACE-FTS in the Arctic, both satellite data sets were compared against measurements from the ground-based PEARL-FTS. Comparisons of TROPOMI with smoothed PEARL-FTS total columns in the period of 3 March 2018 to 27 March 2020 display a strong correlation (R = 0.88), however a positive mean bias of 14.3 ± 0.16 % was also found. A partial column comparison of ACE-FTS with the PEARL-FTS in the period from 25 February 2007 to 18 March 2020 shows good agreement (R = 0.82), and a mean positive bias of 9.83 ± 0.22 % in the ACE-FTS product relative to the ground-based FTS. The magnitude and sign of the mean relative differences are consistent across all inter-comparisons in this work, as well as with recent ground-based validation efforts, suggesting that current TROPOMI CO product exhibits a positive bias in the high-Arctic region. However, the observed bias is within the TROPOMI mission accuracy requirement of ±15 %, providing further confirmation that the data quality in these remote high-latitude regions meets this specification.

A simple model to predict solar radiation under clear sky conditions

2014 ◽  
Vol 53 (8) ◽  
pp. 1239-1245 ◽  
Author(s):  
Qiumin Dai ◽  
Xiande Fang
Keyword(s):  
Simple Model ◽  
Solar Radiation ◽  
Clear Sky ◽  
Sky Conditions

Estimation of global solar radiation under clear sky radiation in Turkey

2000 ◽  
Vol 21 (2) ◽  
pp. 271-287 ◽  
Author(s):  
Inci Turk Toğrul ◽  
Hasan Toğrul ◽  
Duygu Evin
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Clear Sky
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