A simple model for the calculation of global solar radiation using geostationary satellite data

1995 ◽  
Vol 39 (1-3) ◽  
pp. 79-90 ◽  
Author(s):  
P. Illera ◽  
A. Fernández ◽  
A. Pérez
BIBECHANA ◽  
2021 ◽  
Vol 18 (1) ◽  
pp. 193-200
Author(s):  
Ganesh Kumar Shrestha ◽  
Binod Pandey ◽  
Usha Joshi ◽  
Khem N. Poudyal

This study proposes to find the regression coefficient of the modified Angstrom type model for the estimation of global solar radiation (GSR) in lowland Biratnagar (Lat. 26.5º N, Long. 87.3º E and Alt. 72m) using relative sunshine duration and satellite data of GSR. Using the regression technique, the empirical constants 0.29 and 0.56 are found in the modified Angstrom model. Furthermore, Modified Angstrom model along with other linear models such as Glover and McCulloch model, Page model, Rietveld model, and Turton's model are statistically assessed to evaluate the significance of models. Statistical tests like MPE, MBE, RMSE, and CC reveal that all these models are statistically significant. These findings can be utilized for other locations with a high confidence level at the similar climatic locations of Nepal. BIBECHANA 18 (2021) 193-200


2019 ◽  
Author(s):  
Hou Jiang ◽  
Ning Lu ◽  
Jun Qin ◽  
Ling Yao

Abstract. Surface solar radiation drives the water cycle and energy exchange on the earth's surface, being an indispensable parameter for many numerical models to estimate soil moisture, evapotranspiration and plant photosynthesis, and its diffuse component can promote carbon uptake in ecosystems as a result of improvements of plant productivity by enhancing canopy light use efficiency. To reproduce the spatial distribution and spatiotemporal variations of solar radiation over China, we generate the high-accuracy radiation datasets, including global solar radiation (GSR) and the diffuse radiation (DIF) with spatial resolution of 1/20 degree, based on the observations from the China Meteorology Administration (CMA) and Multi-functional Transport Satellite (MTSAT) satellite data, after tackling the integration of spatial pattern and the simulation of complex radiation transfer that the existing algorithms puzzle about by means of the combination of convolutional neural network (CNN) and multi-layer perceptron (MLP). All data cover a period from 2007 to 2018 in hourly, daily total and monthly total scales. The validation in 2008 shows that the root mean square error (RMSE) between our datasets and in-situ measurements approximates 73.79 W/m2 (0.27 MJ/m2) and 58.22 W/m2 (0.21 MJ/m2) for GSR and DIF, respectively. Besides, the spatially continuous hourly estimates properly reflect the regional differences and restore the diurnal cycles of solar radiation in fine scales. Such accurate knowledge is useful for the prediction of agricultural yield, carbon dynamics of terrestrial ecosystems, research on regional climate changes, and site selection of solar power plants etc. The datasets are freely available from Pangaea at https://doi.org/10.1594/PANGAEA.904136 (Jiang and Lu, 2019).


2020 ◽  
Vol 12 (15) ◽  
pp. 2472
Author(s):  
Hideaki Takenaka ◽  
Taiyou Sakashita ◽  
Atsushi Higuchi ◽  
Teruyuki Nakajima

This study describes a high-speed correction method for geolocation information of geostationary satellite data for accurate physical analysis. Geostationary satellite observations with high temporal resolution provide instantaneous analysis and prompt reports. We have previously reported the quasi real-time analysis of solar radiation at the surface and top of the atmosphere using geostationary satellite data. Estimating atmospheric parameters and surface albedo requires accurate geolocation information to estimate the solar radiation accurately. The physical analysis algorithm for Earth observations is verified by the ground truth. In particular, downward solar radiation at the surface is validated by pyranometers installed at ground observation sites. The ground truth requires that the satellite observation data pixels be accurately linked to the location of the observation equipment on the ground. Thus, inaccurate geolocation information disrupts verification and causes complex problems. It is difficult to determine whether error in the validation of physical quantities arises from the estimation algorithm, satellite sensor calibration, or a geolocation problem. Geolocation error hinders the development of accurate analysis algorithms; therefore, accurate observational information with geolocation information based on latitude and longitude is crucial in atmosphere and land target analysis. This method provides the basic data underlying physical analysis, parallax correction, etc. Because the processing speed is important in geolocation correction, we used the phase-only correlation (POC) method, which is fast and maintains the accuracy of geolocation information in geostationary satellite observation data. Furthermore, two-dimensional fast Fourier transform allowed the accurate correction of multiple target points, which improved the overall accuracy. The reference dataset was created using NASA’s Shuttle Radar Topography Mission 1-s mesh data. We used HIMAWARI-8/Advanced HIMAWARI Imager data to demonstrate our method, with 22,709 target points for every 10-min observation and 5826 points for every 2.5 min observation. Despite the presence of disturbances, the POC method maintained its accuracy. Column offset and line offset statistics showed stability and characteristic error trends in the raw HIMAWARI standard data. Our method was sufficiently fast to apply to quasi real-time analysis of solar radiation every 10 and 2.5 min. Although HIMAWARI-8 is used as an example here, our method is applicable to all geostationary satellites. The corrected HIMAWARI 16 channel gridded dataset is available from the open database of the Center for Environmental Remote Sensing (CEReS), Chiba University, Japan. The total download count was 50,352,443 on 8 July 2020. Our method has already been applied to NASA GeoNEX geostationary satellite products.


Solar Energy ◽  
1986 ◽  
Vol 37 (1) ◽  
pp. 31-39 ◽  
Author(s):  
D. Cano ◽  
J.M. Monget ◽  
M. Albuisson ◽  
H. Guillard ◽  
N. Regas ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document