scholarly journals A semi-empirical model for estimating surface solar radiation from satellite data

2013 ◽  
Author(s):  
Serm Janjai ◽  
Somjet Pattarapanitchai ◽  
Rungrat Wattan ◽  
Itsara Masiri ◽  
Sumaman Buntoung ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Empirical Model ◽  
Surface Solar Radiation ◽  
Semi Empirical

scholarly journals Outdoor Air Temperature Measurement: A Semi-Empirical Model to Characterize Shelter Performance

Climate ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 26
Author(s):  
Jérémy Bernard ◽  
Pascal Kéravec ◽  
Benjamin Morille ◽  
Erwan Bocher ◽  
Marjorie Musy ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Response Time ◽  
Air Temperature ◽  
Empirical Model ◽  
Regression Coefficient ◽  
Radiation Sensitivity ◽  
Temperature Sensors ◽  
Outdoor Air ◽  
The One ◽  
Semi Empirical

Shelters used to protect air temperature sensors from solar radiation induce a measurement error. This work presents a semi-empirical model based on meteorological variables to evaluate this error. The model equation is based on the analytical solution of a simplified energy balance performed on a naturally ventilated shelter. Two main physical error causes are identified from this equation: one is due to the shelter response time and the other is due to its solar radiation sensitivity. A shelter intercomparison measurement campaign performed by the World Meteorological Organization (WMO) is used to perform a non-linear regression of the model coefficients. The regression coefficient values obtained for each shelter are found to be consistent with their expected physical behavior. They are then used to simply classify shelters according to their response time and radiation sensitivity characteristics. Finally, the ability of the model to estimate the temperature error within a given shelter is assessed and compared to the one of two existing models (proposed by Cheng and by Nakamura). For low-response-time shelters, our results reduce the root mean square error by about 15% (0.07 K) on average when compared with other compensation schemes.

scholarly journals Surface global and diffuse solar radiation over China acquired from geostationary Multi-functional Transport Satellite data

2019 ◽  
Author(s):  
Hou Jiang ◽  
Ning Lu ◽  
Jun Qin ◽  
Ling Yao
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Power Plants ◽  
Numerical Models ◽  
Regional Climate ◽  
Diffuse Radiation ◽  
Terrestrial Ecosystems ◽  
Global Solar Radiation ◽  
Surface Solar Radiation ◽  
Diffuse Solar Radiation

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).

Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data

2020 ◽  
Vol 270 ◽  
pp. 115178 ◽  
Author(s):  
Hou Jiang ◽  
Ning Lu ◽  
Guanghui Huang ◽  
Ling Yao ◽  
Jun Qin ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Spatial Scale ◽  
Satellite Data ◽  
Scale Effects ◽  
Surface Solar Radiation ◽  
Retrieval Accuracy

scholarly journals An Improved Model for the Estimation of Solar Radiation from Satellite Data for Thailand

1970 ◽  
Vol 8 (3) ◽  
pp. 130-139
Author(s):  
Serm Janjai ◽  
Itsara Masiri ◽  
Somjet Pattarapanitchai ◽  
Jarungsaeng Laksanaboonsong
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Global Solar Radiation ◽  
Original Model ◽  
Digital Data ◽  
Multiple Reflections ◽  
Surface Solar Radiation ◽  
Main Input ◽  
Mean Square Difference ◽  
Improved Model

This paper presents an improved model for estimating surface solar radiation from satellite data for Thailand. Digital data from the visible channel of the GOES9 and MTSAT-1R satellites were used as the main input data of the model. This model accounted for the scattering of solar radiation by clouds, absorption of solar radiation by water vapour, ozone and gases and solar radiation depletion by aerosols. Additionally, the multiple reflections between the atmosphere and the ground in satellite band, which were ignored in the original model, were included in the improved model. For testing its validity, the model was employed to calculate monthly average daily global solar radiation at 38 solar monitoring stations in Thailand. It was found that the solar radiation calculated from the model and that obtained from the measurements were in good agreement, with a root mean square difference (RMSD) of 6.1% and mean bias difference (MBD) of 0.3%. The performance of the improved model was better than that of the original model. DOI: http://dx.doi.org/10.3126/jie.v8i3.5939 JIE 2011; 8(3): 130-139

scholarly journals Merging of satellite and ground measurements of hourly surface solar radiation variables in Germany

2021 ◽  
Author(s):  
Sven Brinckmann ◽  
Anna Klameth ◽  
Jörg Trentmann
Keyword(s):  
Solar Radiation ◽  
Satellite Data ◽  
Sunshine Duration ◽  
Data Sources ◽  
Visible Radiation ◽  
High Quality ◽  
Surface Solar Radiation ◽  
Station Data ◽  
First Results ◽  
Radiation Measurements

<p>Measurements of the surface solar radiation have a high importance for the fields of meteorology, climatology, solar energy, agriculture, forestry and other applications. Radiation measurements at ground stations using high quality instruments such as pyranometers began in the second half of the nineteenth century. From the 1980s onward, satellite imagery in the visible radiation spectrum has been used to calculate gridded data of cloud information and, subsequently, of solar radiation at the Earth's surface. Compared to station data, satellite data have the advantage of spatial continuity, but have disadvantages in temporal resolution and data accuracy.</p><p>As part of a restructuring of the radiation measurement network, the German Meteorological Service (DWD) is pursuing the goal of expanding its high-quality surface measurements using pyranometers (to 42 stations) and largely discontinuing other radiation measurements, such as direct measurements of sunshine duration. At the same time, surface solar radiation products from satellite data are progressively improving in quality and can be used to compensate for the reduction of ground measurements and increase the spatial coverage of radiation information over Germany. For this purpose, the project DUETT aims at a merging between solar radiation data from the 42 pyranometer stations and near-real-time data based on measurements from METEOSAT-SEVIRI. As products, hourly values of the parameters global horizontal irradiance (GHI) and sunshine duration (SDU) will be provided on a 1x1km grid for Germany with a time delay of 15 minutes after each full hour.</p><p>Merging is performed in three main steps, which are described in the following for the parameter GHI. First, the hourly mean values of both data sources are calculated. In the case of the satellite data, this step involves the use of an 'optical flow' technique to generate intermediate images to increase the original time resolution from 15 minutes to virtually 1 minute. Using this technique, the displacement of fast-moving clouds is better reflected. In the second step, systematic deviations between the two data sources are determined and corrected for by using predictors. Preliminary research suggests that cloudiness (or clearness index) is one such appropriate predictor. In the final step, the local differences between the corrected satellite data and the station data are interpolated to the target grid using Universal Kriging and the results are added to the corrected satellite data.</p><p>We present the first results of the merging procedure to be developed for both radiation parameters GHI and SDU. Analyses of the systematically occurring radiation differences between the two data sources are shown as well as the related correction functions. Furthermore, first results of the validation of the combined radiation products will be presented. This includes comparisons with measurements at validation stations as well as analyses based on cross-validation.</p>

scholarly journals A Semi-Empirical Model for Predicting Frost Properties

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 412
Author(s):  
Shao-Ming Li ◽  
Kai-Shing Yang ◽  
Chi-Chuan Wang
Keyword(s):  
Thermal Conductivity ◽  
Linear Programming ◽  
Numerical Model ◽  
Empirical Model ◽  
Quantitative Method ◽  
Predictive Ability ◽  
Dimensionless Temperature ◽  
Crystal Shape ◽  
Proposed Model ◽  
Semi Empirical

In this study, a quantitative method for classifying the frost geometry is first proposed to substantiate a numerical model in predicting frost properties like density, thickness, and thermal conductivity. This method can recognize the crystal shape via linear programming of the existing map for frost morphology. By using this method, the frost conditions can be taken into account in a model to obtain the corresponding frost properties like thermal conductivity, frost thickness, and density for specific frost crystal. It is found that the developed model can predict the frost properties more accurately than the existing correlations. Specifically, the proposed model can identify the corresponding frost shape by a dimensionless temperature and the surface temperature. Moreover, by adopting the frost identification into the numerical model, the frost thickness can also be predicted satisfactorily. The proposed calculation method not only shows better predictive ability with thermal conductivities, but also gives good predictions for density and is especially accurate when the frost density is lower than 125 kg/m3. Yet, the predictive ability for frost density is improved by 24% when compared to the most accurate correlation available.

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