A technique to predict hourly potential solar radiation and temperature for a mostly unmonitored area in the Cape Breton Highlands

1998 ◽  
Vol 78 (3) ◽  
pp. 409-420 ◽  
Author(s):  
Charles P.-A. Bourque ◽  
Jeremy J. Gullison
Keyword(s):  
Solar Radiation ◽  
Land Surface ◽  
Time Of Day ◽  
Slope Aspect ◽  
Clear Sky ◽  
Digital Terrain ◽  
Cape Breton ◽  
Grande Anse ◽  
Artificial Neural ◽  
Vegetation Models

A technique was developed to obtain predictions of potential solar radiation and temperature for a prescribed, mostly unmonitored, area in the Cape Breton Highlands region of northeastern Nova Scotia (46°39′N 60°57′W to 46°40′N 60°24′W). Hourly predictions of incoming solar radiation are based on relations of sun-earth geometry, clear-sky atmospheric transmittance, and land-surface attributes resolved from digital terrain and vegetation models. The digital vegetation model characterizes vegetation cover and is used to define the average midday albedoes for the area in question. Hourly albedoes are calculated according to assigned mid-day albedo and sun-illumination angles. Land-surface characteristics (elevation, slope, aspect, horizon angles, terrain configuration factor, and view factor) affect total incident solar radiation by affecting the direct, diffused, and reflected energy components. Hourly spatial variability in above-ground daytime temperature is captured by way of a fully trained artificial neural network (ANN) that describes hourly fluctuations of interior highland temperatures according to i) reference temperatures taken at two lowland locations, one at Ingonish Beach and the other at Grande Anse; ii) distance from a north-south line representing the east coast of the study area and from the Grande Anse location; iii) time of day; and iv) land-surface attributes. Training the ANN involves supplying the network with actual data and having the network adjust its internal weights iteratively so that the output values are sufficiently close to the supplied target values. Comparison of predicted and observed hourly spring-summer (1997) temperatures revealed that the constructed ANN explained over 88% of the variability exhibited in the observed temperatures and that the standard error of estimate was 2.0 °C (mean absolute error = 1.5 °C). Key words: Sun-earth geometry, radiation laws, variable surface albedo, clear-sky atmospheric transmissivity, digital terrain, vegetation models, artificial neural networks

Daily Surface Solar Radiation Prediction Mapping Using Artificial Neural Network: The Case Study of Reunion Island

2019 ◽  
Vol 142 (2) ◽  
Author(s):  
Peng Li ◽  
Miloud Bessafi ◽  
Beatrice Morel ◽  
Jean-Pierre Chabriat ◽  
Mathieu Delsaut ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Solar Radiation ◽  
Computational Time ◽  
Reunion Island ◽  
Surface Solar Radiation ◽  
Clear Sky ◽  
Réunion Island ◽  
Artificial Neural

Abstract This paper focuses on the prediction of daily surface solar radiation maps for Reunion Island by a hybrid approach that combines principal component analysis (PCA), wavelet transform analysis, and artificial neural network (ANN). The daily surface solar radiation over 18 years (1999–2016) from CM SAF (SARAH-E with 0.05 deg × 0.05 deg spatial resolution) is first detrended using the clear sky index. Dimensionality reduction of the detrended dataset is secondly performed through PCA, which results in saving computational time by a factor of eight in comparison to not using PCA. A wavelet transform is thirdly applied onto each of the first 28 principal components (PCs) explaining 95% of the variance. The decomposed nine-wavelet components for each PC are fourthly used as input to an ANN model to perform the prediction of day-ahead surface solar radiation. The predicted decomposed components are finally returned to PCs and clear sky indices, irradiation in the end for re-mapping the surface solar radiation's distribution. It is found that the prediction accuracy is quite satisfying: root mean square error (RMSE) is 30.98 W/m2 and the (1 − RMSE_prediction/RMSE_persistence) is 0.409.

scholarly journals Coupling Diffuse Sky Radiation and Surface Albedo

2005 ◽  
Vol 62 (7) ◽  
pp. 2580-2591 ◽  
Author(s):  
Bernard Pinty ◽  
Alessio Lattanzio ◽  
John V. Martonchik ◽  
Michel M. Verstraete ◽  
Nadine Gobron ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Land Surface ◽  
Surface Albedo ◽  
Climate Models ◽  
Diffuse Radiation ◽  
Atmospheric Model ◽  
Time Of Day ◽  
Mathematical Form ◽  
Diffuse Solar Radiation ◽  
Atmospheric Scattering

Abstract New satellite instruments have been delivering a wealth of information regarding land surface albedo. This basic quantity describes what fraction of solar radiation is reflected from the earth’s surface. However, its concept and measurements have some ambiguity resulting from its dependence on the incidence angles of both the direct and diffuse solar radiation. At any time of day, a surface receives direct radiation in the direction of the sun, and diffuse radiation from the various other directions in which it may have been scattered by air molecules, aerosols, and cloud droplets. This contribution proposes a complete description of the distribution of incident radiation with angles, and the implications in terms of surface albedo are given in a mathematical form, which is suitable for climate models that require evaluating surface albedo many times. The different definitions of observed albedos are explained in terms of the coupling between surface and atmospheric scattering properties. The analytical development in this paper relates the various quantities that are retrieved from orbiting platforms to what is needed by an atmospheric model. It provides a physically simple and practical approach to evaluation of land surface albedo values at any condition of sun illumination irrespective of the current range of surface anisotropic conditions and atmospheric aerosol load. The numerical differences between the various definitions of albedo for a set of typical atmospheric and surface scattering conditions are illustrated through numerical computation.

Study of Land Surface feedback in catalysing the intensity and duration of Heatwave over Indian subcontinent

2020 ◽  
Author(s):  
Shashi Gaurav Kumar ◽  
Ajanta Goswami
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Solar Radiation ◽  
Land Surface ◽  
Indian Subcontinent ◽  
Extreme Heat ◽  
Clear Sky ◽  
Extreme Heat Events ◽  
Drying Up ◽  
Heat Events

<p>Extreme heat events are rising in nature over the Indian subcontinent under the stressed environmental conditions. Unprecedented event of extreme heat is threatening our socio-economic and ecosystem. April marks the start of summer with a clear sky; agricultural harvesting exposes the large land surface to solar heating, so the drying up of the surface causing loss of soil moisture and vegetations. Temperature anomalies become high during May and June, signifying possible role play local land-surface-atmosphere feedbacks involving dried soils in driving the heat extreme. Thus, the study of extreme heat and surface feedback process is conducted using a combination of ERA5 reanalysis data, GLDAS Noah Land Surface Model data, satellite-based observations (TRMM and MODIS), and in-situ India Metrological Department datasets. To address the bias present in datasets, we make use of IMD Datasets for bias correction. We use 2m air temperature to define the extreme heat events as per the IMD definition for the heatwave from 2001 to 2019. Parameters like surface net solar and thermal radiation and (also, clear sky) heat flux, total precipitation, land surface temperature, land use type and vegetation cover, soil moisture used to study the details of land surface conditions during the heatwave events. The examination of the above datasets in space-time provided the general view of heatwaves and suggest that late April and early May with clear sky increased net solar radiation started drying up the surface. Late May and early June with a clear sky and positive net solar radiation anomaly with positive heat flux anomaly and lack of soil moisture and rainfall developed local forcing on air temperature that catalyzed the heatwave events in terms of intensity and duration. The above conclusion is supported by the satellite-derived land surface temperature and heat flux. The results obtained establish the link between the local surface feedback and extreme heat events during the summer over the Indian subcontinent and can enhance in a dry environment with the large agricultural field with no standing crop barren land or land with dead or no shrubs over India leaving northern Himalayan part.</p>

Validation of Solar Radiation Surfaces from MODIS and Reanalysis Data over Topographically Complex Terrain

2009 ◽  
Vol 48 (12) ◽  
pp. 2441-2458 ◽  
Author(s):  
Todd A. Schroeder ◽  
Robbie Hember ◽  
Nicholas C. Coops ◽  
Shunlin Liang
Keyword(s):  
Solar Radiation ◽  
Land Surface ◽  
Forest Productivity ◽  
Reanalysis Data ◽  
Early Summer ◽  
Productive Capacity ◽  
Terrain Effects ◽  
Input Radiation ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Vegetation Models

Abstract The magnitude and distribution of incoming shortwave solar radiation (SW↓) has significant influence on the productive capacity of forest vegetation. Models that estimate forest productivity require accurate and spatially explicit radiation surfaces that resolve both long- and short-term temporal climatic patterns and that account for topographic variability of the land surface. This paper presents a validation of monthly average total (SW↓t) and diffuse ( SW↓df ) incoming solar radiation surfaces taken from North American Regional Reanalysis (NARR) data and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery for a mountainous region of the Pacific northwestern United States and Canada. A topographic solar radiation model based on a regionally defined clearness index was used to downscale the 32-km NARR SW↓t surfaces to 1 km, resulting in surfaces that better matched the spatial resolution of MODIS, as well as accounted for elevation and terrain effects including shadowing. Validation was carried out using a series of ground station measurements (n = 304) collected in 2003. The results indicated that annually, the NARR and MODIS SW↓t surfaces were both in strong agreement with ground measurements (r = 0.98 and 0.97), although the strength and bias of the relationships varied considerably by month. Correlations were highest in winter, early summer, and fall and lowest in spring. The NARR and MODIS SW↓df surfaces displayed poorer agreement with ground measurements (r = 0.89 and 0.79), the result of some months having negative correlations. The correlation and spatial structure between NARR and MODIS SW↓t surfaces was enhanced by topographic correction, resulting in more consistent input radiation surfaces for use in broad-scale forest productivity modeling.

scholarly journals Global Solar Radiation Forecasting using Artificial Neural Network and Support Vector Machine

2021 ◽  
Vol 2129 (1) ◽  
pp. 012079
Author(s):  
Emmanuel Philibus ◽  
Roselina Sallehuddin ◽  
Yusliza Yussof ◽  
Lizawati Mi Yusuf
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Solar Radiation ◽  
Land Surface ◽  
Statistical Tests ◽  
Global Solar Radiation ◽  
Support Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Global solar radiation (GSoR) forecasting involves predicting future energy from the sun based on past and present data. Literature reveals that not all meteorological stations record solar radiation, some equipments are faulty, and are not available in every location due to high cost. Hence, the need to predict and forecast using predictors such as land surface temperature (LST). Satellite data when were used to complement ground-based stations have been yielding good results. Different artificial intelligence (AI) methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) present different forecasting performances. Motivated by existing literature-related contradictions on the performance superiority of ANN and SVM in GSoR forecasting, the two techniques were compared based on several statistical tests. Experimental results show that ANN outperformed SVM by 2.9864% accuracy, making it superior in the forecast of GSoR.

scholarly journals Summertime Continental Shallow Cumulus Cloud Detection Using GOES-16 Satellite and Ground-Based Stereo Cameras at the DOE ARM Southern Great Plains Site

2021 ◽  
Vol 13 (12) ◽  
pp. 2309
Author(s):  
Jingjing Tian ◽  
Yunyan Zhang ◽  
Stephen A. Klein ◽  
Likun Wang ◽  
Rusen Öktem ◽  
...  
Keyword(s):  
Great Plains ◽  
Land Surface ◽  
Detection Threshold ◽  
Department Of Energy ◽  
Surface Reflectance ◽  
Cloud Detection ◽  
Southern Great Plains ◽  
Clear Sky ◽  
Shallow Cumulus ◽  
Stereo Cameras

Summertime continental shallow cumulus clouds (ShCu) are detected using Geostationary Operational Environmental Satellite (GOES)-16 reflectance data, with cross-validation by observations from ground-based stereo cameras at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. A ShCu cloudy pixel is identified when the GOES reflectance exceeds the clear-sky surface reflectance by a reflectance detection threshold of ShCu, ΔR. We firstly construct diurnally varying clear-sky surface reflectance maps and then estimate the ∆R. A GOES simulator is designed, projecting the clouds reconstructed by stereo cameras towards the surface along the satellite’s slanted viewing direction. The dynamic ShCu detection threshold ΔR is determined by making the GOES cloud fraction (CF) equal to the CF from the GOES simulator. Although there are temporal variabilities in ΔR, cloud fractions and cloud size distributions can be well reproduced using a constant ΔR value of 0.045. The method presented in this study enables daytime ShCu detection, which is usually falsely reported as clear sky in the GOES-16 cloud mask data product. Using this method, a new ShCu dataset can be generated to bridge the observational gap in detecting ShCu, which may transition into deep precipitating clouds, and to facilitate further studies on ShCu development over heterogenous land surface.

Landslide Susceptibility Mapping in the Commune of Oudka, Taounate Province, North Morocco: A Comparative Analysis of Logistic Regression, Multivariate Adaptive Regression Spline, and Artificial Neural Network Models

2020 ◽  
Vol 26 (2) ◽  
pp. 185-200
Author(s):  
Said Benchelha ◽  
Hasnaa Chennaoui Aoudjehane ◽  
Mustapha Hakdaoui ◽  
Rachid El Hamdouni ◽  
Hamou Mansouri ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Multivariate Adaptive Regression Spline ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Neural Network Models ◽  
Regression Spline ◽  
Adaptive Regression ◽  
Artificial Neural

ABSTRACT Landslide susceptibility indices were calculated and landslide susceptibility maps were generated for the Oudka, Morocco, study area using a geographic information system. The spatial database included current landslide location, topography, soil, hydrology, and lithology, and the eight factors related to landslides (elevation, slope, aspect, distance to streams, distance to roads, distance to faults, lithology, and Normalized Difference Vegetation Index [NDVI]) were calculated or extracted. Logistic regression (LR), multivariate adaptive regression spline (MARSpline), and Artificial Neural Networks (ANN) were the methods used in this study to generate landslide susceptibility indices. Before the calculation, the study area was randomly divided into two parts, the first for the establishment of the model and the second for its validation. The results of the landslide susceptibility analysis were verified using success and prediction rates. The MARSpline model gave a higher success rate (AUC (Area Under The Curve) = 0.963) and prediction rate (AUC = 0.951) than the LR model (AUC = 0.918 and AUC = 0.901) and the ANN model (AUC = 0.886 and AUC = 0.877). These results indicate that the MARSpline model is the best model for determining landslide susceptibility in the study area.

scholarly journals A Simple Method for Converting 1-km Resolution Daily Clear-Sky LST into Real LST

2020 ◽  
Vol 12 (10) ◽  
pp. 1641
Author(s):  
Yunfei Zhang ◽  
Yunhao Chen ◽  
Jing Li ◽  
Xi Chen
Keyword(s):  
Land Surface ◽  
Missing Values ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Shortwave Radiation ◽  
Simple Method ◽  
The Real ◽  
Independent Variables ◽  
Clear Sky ◽  
Reconstruction Methods

Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.

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