scholarly journals The Ångström–Prescott Regression Coefficients for Six Climatic Zones in South Africa

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5418
Author(s):  
Brighton Mabasa ◽  
Meena D. Lysko ◽  
Henerica Tazvinga ◽  
Sophie T. Mulaudzi ◽  
Nosipho Zwane ◽  
...  
Keyword(s):  
South Africa ◽  
Sunshine Duration ◽  
Absolute Error ◽  
Regression Coefficients ◽  
Solar Irradiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Observation Data ◽  
Statistical Validation ◽  
Worst Case

The South African Weather Service (SAWS) manages an in situ solar irradiance radiometric network of 13 stations and a very dense sunshine recording network, located in all six macroclimate zones of South Africa. A sparsely distributed radiometric network over a landscape with dynamic climate and weather shifts is inadequate for solar energy studies and applications. Therefore, there is a need to develop mathematical models to estimate solar irradiation for a multitude of diverse climates. In this study, the annual regression coefficients, a and b, of the Ångström–Prescott (AP) model, which can be used to estimate global horizontal irradiance (GHI) from observed sunshine hours, were calibrated and validated with observed station data. The AP regression coefficients were calibrated and validated for each of the six macroclimate zones of South Africa using the observation data that span 2013 to 2019. The predictive effectiveness of the calibrated AP model coefficients was evaluated by comparing estimated and observed daily GHI. The maximum annual relative Mean Bias Error (rMBE) was 0.371%, relative Mean Absolute Error (rMAE) was 0.745%, relative Root Mean Square Error (rRMSE) was 0.910%, and the worst-case correlation coefficient (R2) was 0.910. The statistical validation metrics results show that there is a strong correlation and linear relation between observed and estimated GHI values. The AP model coefficients calculated in this study can be used with quantitative confidence in estimating daily GHI data at locations in South Africa where daily observation sunshine duration data are available.

scholarly journals The Ångström-Prescott Regression Coefficients for Six Climatic Zones in South Africa

2020 ◽  
Author(s):  
Brighton Mabasa ◽  
Meena D. Lysko ◽  
Henerica Tazvinga ◽  
Sophie T. Mulaudzi ◽  
Nosipho Zwane ◽  
...  
Keyword(s):  
South Africa ◽  
Sunshine Duration ◽  
Regression Coefficients ◽  
Solar Irradiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Observation Data ◽  
Statistical Validation ◽  
Worst Case ◽  
Climate Zones

The South African Weather Service (SAWS) manages an in-situ solar irradiance radiometric network of 13 stations and a very dense sunshine recording network; located in all six macro-climate zones of South Africa. A sparsely distributed radiometric network and over a landscape with dynamic climate and weather shifts is inadequate for solar energy studies and applications. Therefore, there is a need to develop mathematical models to estimate solar irradiation for a multitude of diverse climates. In this study, the annual regression coefficients, a and b, of the Ångström-Prescott (AP) model that can be used to estimate global horizontal irradiance from observed sunshine hours were calibrated and validated with observed station data. The AP regression coefficients were calibrated and validated for each of the six macro-climate zones of South Africa using the observation data that spans 2013 to 2019. The predictive effectiveness of the calibrated AP model coefficients was evaluated by comparing estimated and observed daily global horizontal irradiance. The maximum annual relative Mean Bias Error (rMBE) was 0.371 %, relative Mean Absolute Error (rMAE) was 0.745 %, relative Root Mean Square Error (rRMSE) was 0.910 % and the worst-case correlation coefficient (R2) was 0.910. The statistical validation metrics results show that there is a strong correlation and linear relation between observed and estimated solar radiation values. The AP model coefficients calculated in this study can be used with quantitative confidence in estimating daily GHI data at locations in South Africa where the daily observation sunshine duration data is available.

scholarly journals A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

scholarly journals Validating Hourly Satellite Based and Reanalysis Based Global Horizontal Irradiance Datasets over South Africa

Geomatics ◽  
2021 ◽  
Vol 1 (4) ◽  
pp. 429-450
Author(s):  
Brighton Mabasa ◽  
Meena D. Lysko ◽  
Sabata J. Moloi
Keyword(s):  
South Africa ◽  
South African ◽  
Absolute Error ◽  
Surface Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Forecasts ◽  
In Situ Data

This study validates the hourly satellite based and reanalysis based global horizontal irradiance (GHI) for sites in South Africa. Hourly GHI satellite based namely: SOLCAST, Copernicus Atmosphere Monitoring Service (CAMS), and Satellite Application Facility on Climate Monitoring (CMSAF SARAH) and two reanalysis based, namely, Fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) and Modern-Era Retrospective Analysis for Research and Applications (MERRA2) were assessed by comparing in situ measured data from 13 South African Weather Service radiometric stations, located in the country’s six macro climatological regions, for the period 2013–2019. The in situ data were first quality controlled using the Baseline Surface Radiation Network methodology. Data visualization and statistical metrics relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) were used to evaluate the performance of the datasets. There was very good correlation against in situ GHI for the satellite based GHI, all with R2 above 0.95. The R2 correlations for the reanalysis based GHI were less than 0.95 (0.931 for ERA5 and 0.888 for MERRA2). The satellite and reanalysis based GHI showed a positive rMBE (SOLCAST 0.81%, CAMS 2.14%, CMSAF 2.13%, ERA5 1.7%, and MERRA2 11%), suggesting consistent overestimation over the country. SOLCAST satellite based GHI showed the best rRMSE (14%) and rMAE (9%) combinations. MERRA2 reanalysis based GHI showed the weakest rRMSE (37%) and rMAE (22%) combinations. SOLCAST satellite based GHI showed the best overall performance. When considering only the freely available datasets, CAMS and CMSAF performed better with the same overall rMBE (2%), however, CAMS showed slightly better rRMSE (16%), rMAE(10%), and R2 (0.98) combinations than CMSAF rRMSE (17%), rMAE (11%), and R2 (0.97). CAMS and CMSAF are viable freely available data sources for South African locations.

An Improved Model of Estimation Global Solar Irradiation From in Situ Data: Case of Algerian Oranie’s Region

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Houcine Naim ◽  
Redouane Fares ◽  
Abed Bouadi ◽  
Abdelatif Hassini ◽  
Benabadji Noureddine
Keyword(s):  
Meteorological Parameter ◽  
Absolute Error ◽  
Solar Irradiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
In Situ Data ◽  
Experimental Values ◽  
Improved Model

Abstract The total monthly average of daily radiation on a horizontal surface at the site of Oran (35.38 deg N, 0.37 deg W) is achieved by applying two models. We present a comparison between the first one which is a regression equation of the Angstrom type and the second model, developed by the present authors: Some modifications were recommended using relative humidity as the input meteorological parameter) and longitude, latitude, and altitude as the astronomical parameters. The process of examining similarities is made using root mean square error (RMSE), the mean bias error (MBE), mean absolute error (MAE), and mean absolute percentage error (MAPE). This comparison shows that the second model is closer to the experimental values of the Angstrom model.

scholarly journals Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

2015 ◽  
Vol 8 (1) ◽  
pp. 183-194 ◽  
Author(s):  
A. Sanchez-Romero ◽  
J. A. González ◽  
J. Calbó ◽  
A. Sanchez-Lorenzo
Keyword(s):  
Image Processing ◽  
Temporal Resolution ◽  
Solar Irradiance ◽  
Atmospheric Aerosol ◽  
Mean Squared Error ◽  
Sunshine Duration ◽  
Strong Relationship ◽  
Mean Bias Error ◽  
Bias Error ◽  
High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas), but the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI) and to prove whether this relationship depends on the type of CSSR and burning card. A method of analysis based on image processing of digital scanned images of burned cards is used. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e., visual) determination. The method tends to slightly overestimate SD, but the thresholds that are used in the image processing could be adjusted to obtain an improved estimation. Regarding the burn width, experimental results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error is 24 and 30%, respectively; mean bias error is −0.6 and −30.0 W m−2, respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

scholarly journals Using digital image processing to characterize the Campbell–Stokes sunshine recorder and to derive high-temporal resolution direct solar irradiance

2014 ◽  
Vol 7 (9) ◽  
pp. 9537-9571
Author(s):  
A. Sanchez-Romero ◽  
J. A. González ◽  
J. Calbó ◽  
A. Sanchez-Lorenzo
Keyword(s):  
Image Processing ◽  
Temporal Resolution ◽  
Solar Irradiance ◽  
Atmospheric Aerosol ◽  
Mean Squared Error ◽  
Sunshine Duration ◽  
Mean Bias Error ◽  
Unbiased Estimation ◽  
Bias Error ◽  
High Temporal Resolution

Abstract. The Campbell–Stokes sunshine recorder (CSSR) has been one of the most commonly used instruments for measuring sunshine duration (SD) through the burn length of a given CSSR card. Many authors have used SD to obtain information about cloudiness and solar radiation (by using Ångström–Prescott type formulas). Contrarily, the burn width has not been used systematically. In principle, the burn width increases for increasing direct beam irradiance. The aim of this research is to show the relationship between burn width and direct solar irradiance (DSI), and to prove whether this relationship depends on the type of CSSR and burning card. A semi-automatic method based on image processing of digital scanned images of burnt cards is presented. With this method, the temporal evolution of the burn width with 1 min resolution can be obtained. From this, SD is easily calculated and compared with the traditional (i.e. visual) determination. The method tends to slightly overestimate SD but the thresholds that are used in the image processing could be adjusted to obtain an unbiased estimation. Regarding the burn width, results show that there is a high correlation between two different models of CSSRs, as well as a strong relationship between burn widths and DSI at a high-temporal resolution. Thus, for example, hourly DSI may be estimated from the burn width with higher accuracy than based on burn length (for one of the CSSR, relative root mean squared error 24 and 30% respectively; mean bias error −0.6 and −30.0 W m−2 respectively). The method offers a practical way to exploit long-term sets of CSSR cards to create long time series of DSI. Since DSI is affected by atmospheric aerosol content, CSSR records may also become a proxy measurement for turbidity and atmospheric aerosol loading.

scholarly journals Estimates of daily global solar radiation based on the daily temperature extremes and precipitation sums

2004 ◽  
Vol 52 (5) ◽  
pp. 13-28
Author(s):  
Miroslav Trnka
Keyword(s):  
Solar Radiation ◽  
Sunshine Duration ◽  
Regression Line ◽  
Global Solar Radiation ◽  
Daily Temperature ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Daily Maximum ◽  
Bias Error ◽  
Temperature Extremes

Two methods for estimating daily global solar radiation (RG) based on the daily temperature extremes and precipitation sum are compared in the study. All parameters necessary for application of both methods were derived either from literature or from climatic characteristics easily available at the given meteorological stations excluding need for measured RG data. The performance of both methods was assessed with a help of meteorological database including 4 stations in the Czech Republic (data were provided by the Czech Hydrometeorological Institute) and 6 in Austria (data provided by the Austrian Weather Service) containing in total 41 640 observational day. For each day in the database observed daily sum of RG, daily maximum and minimum temperatures and precipitation sum were available. Coefficient of determination, slope of regression line forced through origin, mean bias error (MBE) and root mean square error (RMSE) were used as performance indicators. The first method proposed by Winslow et al. (2001) – Eq. (1) is capable to explain 86% of daily RG variability, with systematic error represented by MBE equaling to 0.19 MJ.m–2.day-1 and random error indicated by RMSE reaching up to 3.09. The second method published by Thornton and Running (1999)-Eq. (2) was found to be in almost all parameters inferior to the Eq. (1) and thus the Eq. (1) is recommended to be used in the Central European region (up to 600 m above the sea level). This method might be recommended for stations where neither measured RG or sunshine duration hours exist. However, one should take into consideration that relative MBE and RMSE are in some months higher than 10% and 30% respectively, which may compromise results of subsequent calculations made with use of estimated solar radiation data and alter the order of the method suitability.

scholarly journals An assessment of IMERG rainfall products over Bali at multiple time scale

2020 ◽  
Vol 153 ◽  
pp. 02001
Author(s):  
I Wayan Andi Yuda ◽  
Rakhmat Prasetia ◽  
Abd. Rahman As-syakur ◽  
Takahiro Osawa ◽  
Masahiko Nagai
Keyword(s):  
Time Scale ◽  
Rain Gauge ◽  
Probability Of Detection ◽  
Mean Bias Error ◽  
Bias Error ◽  
Correction Coefficient ◽  
Multiple Time ◽  
Observation Data ◽  
Wet Season ◽  
Rainfall Variation

Evaluation of first five years of the Global Precipitation Measurement - Integrated Multi-satellitE Retrievals for GPM (IMERG) final preciptitation product was performed over Bali – Indonesia using surface observation data which derived from The Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG) as a reference. This study evaluated IMERG’s performance in describing the temporal characteristics of rainfall variation over various time periods (including daily, monthly, and seasonal). The analysis concentrated on the period of April 2014 to April 2019. The results of statistical measurements consisted Probability of Detection (POD), linear correction coefficient (r), Mean Bias Error (MBE), and Root Mean Square Error (RMSE). In general, the results showed that IMERG rainfall estimation value was lower than rain gauges data. The statistical assesment indicated IMERG data was highly accurate on monthly to seasonal timescales. However, a moderate correlation was shown between the daily data comparison from IMERG to ground references. IMERG Performed better in wet season period (November -April) than in dry season period (May – Oktober). The probability of detection rain events on daily time scale was good. Overall, data from IMERG has the potential to be useful as a complement to rain gauge data in areas where rainfall observations are not available in the field.

Solar radiation analysis and regression coefficients for the Vhembe Region, Limpopo Province, South Africa

2013 ◽  
Vol 24 (3) ◽  
pp. 2-7 ◽  
Author(s):  
Sophie T. Malaudzi ◽  
Vaithianathaswami Sankaran ◽  
Meena D. Lysko
Keyword(s):  
South Africa ◽  
Solar Radiation ◽  
Sunshine Duration ◽  
Correlation Coefficients ◽  
Correlation Equation ◽  
Reliable Data ◽  
Limpopo Province ◽  
Regression Coefficients ◽  
Linear Type ◽  
Vhembe District

Given the limited observed and reliable data for solar irradiance in rural parts in South Africa, a correlation equation of the Angström-Prescott linear type has been used to estimate the regression coefficients in the Vhembe District, Limpopo Province, South Africa. Five stations were selected for the study, with the greatest distance between stations less than 180 km. Monthly regression coefficients were derived for each station based on an observation dataset of sunshine duration hours and global horizontal irradiance. The correlation coefficients appear to be above 0.9. The representative Angström-Prescott model for the Vhembe Region was found by collating the data for each station and then averaging the respective correlation coefficients. This paper presents the generated regression coefficients for each station and for the Vhembe Region.

scholarly journals A Comparative Study of Some Regression Models to Estimate the Global Solar Radiation on a Horizontal Surface from Sunshine Duration and Meteorological Parameters for Ghardaïa Site, Algeria

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Kacem Gairaa ◽  
Yahia Bakelli
Keyword(s):  
Solar Radiation ◽  
Horizontal Plane ◽  
Regression Models ◽  
Meteorological Parameters ◽  
Sunshine Duration ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Quadratic Models

A comparison between some regression correlations for predicting the global solar radiation received on a horizontal plane has been processed. Seven models for estimating the global solar radiation from sunshine duration and two meteorological parameters (air temperature and relative humidity) are presented. The root mean square error (RMSE), mean bias error (MBE), correlation coefficient (CC), and percentage error () have been also computed to test the accuracy of the proposed models. Comparisons between the measured and the calculated values have been made. The results obtained show that the linear and quadratic models are the most suitable for estimating the global solar radiation from sunshine duration, and for the models based on meteorological parameters, Abdalla and Ojosu's models give the best performance with a CC of 0.898 and 0.892, respectively.

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