scholarly journals Adaptation of an empirical model for erythemal ultraviolet irradiance

2007 ◽  
Vol 25 (7) ◽  
pp. 1499-1508 ◽  
Author(s):  
I. Foyo-Moreno ◽  
I. Alados ◽  
L. Alados-Arboledas
Keyword(s):  
Empirical Model ◽  
Independent Set ◽  
Mean Bias Error ◽  
Bias Error ◽  
Data Set ◽  
Uv Index ◽  
Ultraviolet Irradiance ◽  
Sky Conditions ◽  
Mean Square Errors ◽  
Radiation Measurements

Abstract. In this work we adapt an empirical model to estimate ultraviolet erythemal irradiance (UVER) using experimental measurements carried out at seven stations in Spain during four years (2000–2003). The measurements were taken in the framework of the Spanish UVB radiometric network operated and maintained by the Spanish Meteorological Institute. The UVER observations are recorded as half hour average values. The model is valid for all-sky conditions, estimating UVER from the ozone columnar content and parameters usually registered in radiometric networks, such as global broadband hemispherical transmittance and optical air mass. One data set was used to develop the model and another independent set was used to validate it. The model provides satisfactory results, with low mean bias error (MBE) for all stations. In fact, MBEs are less than 4% and root mean square errors (RMSE) are below 18% (except for one location). The model has also been evaluated to estimate the UV index. The percentage of cases with differences of 0 UVI units is in the range of 61.1% to 72.0%, while the percentage of cases with differences of ±1 UVI unit covers the range of 95.6% to 99.2%. This result confirms the applicability of the model to estimate UVER irradiance and the UV index at those locations in the Iberian Peninsula where there are no UV radiation measurements.

scholarly journals Drying of MalaysianCapsicum annuumL. (Red Chili) Dried by Open and Solar Drying

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ahmad Fudholi ◽  
Mohd Yusof Othman ◽  
Mohd Hafidz Ruslan ◽  
Kamaruzzaman Sopian
Keyword(s):  
Root Mean Square ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Solar Drying ◽  
Mean Square ◽  
Drying Time ◽  
Sun Drying ◽  
65 H ◽  
Mean Square Errors

This study evaluated the performance of solar drying in the Malaysian red chili (Capsicum annuumL.). Red chilies were dried down from approximately 80% (wb) to 10% (wb) moisture content within 33 h. The drying process was conducted during the day, and it was compared with 65 h of open sun drying. Solar drying yielded a 49% saving in drying time compared with open sun drying. At the average solar radiation of 420 W/m2and air flow rate of 0.07 kg/s, the collector, drying system, and pickup demonstrated efficiency rates of approximately 28%, 13%, and 45%, respectively. Evaporative capacity ranged from 0.13 to 2.36 kg/h, with an average of 0.97 kg/h. The specific moisture extraction rate (SMER) of 0.19 kg/kWh was obtained. Moreover, the drying kinetics ofC. annuumL. were investigated. A nonlinear regression procedure was used to fit three drying models. These models were compared with experimental data on red chilies dried by open sun drying and those dried by solar drying. The fit quality of the models was evaluated using their coefficient of determination (R2), mean bias error, and root-mean-square error values. The Page model resulted in the highestR2and the lowest mean bias and root-mean-square errors.

scholarly journals Empirical Model for Estimating Global Solar Radiation on Horizontal Surfaces for Selected Cities in the Six Geopolitical Zones in Nigeria

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
M. S. Okundamiya ◽  
A. N. Nzeako
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Data Set ◽  
Ambient Temperatures ◽  
Performance Accuracy ◽  
Daily Data ◽  
Proposed Model ◽  
Regression Theory

This study proposes a temperature-based model of monthly mean daily global solar radiation on horizontal surfaces for selected cities, representing the six geopolitical zones in Nigeria. The modelling was based on linear regression theory and was computed using monthly mean daily data set for minimum and maximum ambient temperatures. The results of three statistical indicators: Mean Bias Error (MBE), Root Mean Square Error (RMSE), andt-statistic (TS), performed on the model along with practical comparison of the estimated and observed data, validate the excellent performance accuracy of the proposed model.

scholarly journals Atmospheric Transmittance Model Validation for CSP Tower Plants

2019 ◽  
Vol 11 (9) ◽  
pp. 1083 ◽  
Author(s):  
Natalie Hanrieder ◽  
Abdellatif Ghennioui ◽  
Ahmed Alami Merrouni ◽  
Stefan Wilbert ◽  
Florian Wiesinger ◽  
...  
Keyword(s):  
Measurement Data ◽  
Barometric Pressure ◽  
Resource Assessment ◽  
Mean Bias Error ◽  
Bias Error ◽  
Plant Design ◽  
Data Set ◽  
Yield Analysis ◽  
Boundary Layer Height ◽  
Atmospheric Transmittance

In yield analysis and plant design of concentrated solar power (CSP) tower plants, increased uncertainties are caused by the mostly unknown solar attenuation between the concentrating heliostat field and the receiver on top of the tower. This attenuation is caused mainly by aerosol particles and water vapor. Various on-site measurement methods of atmospheric extinction in solar tower plants have been developed during recent years, but during resource assessment for distinct tower plant projects in-situ measurement data sets are typically not available. To overcome this lack of information, a transmittance model (TM) has been previously developed and enhanced by the authors to derive the atmospheric transmittance between a heliostat and receiver on the basis of common direct normal irradiance (DNI), temperature, relative humidity and barometric pressure measurements. Previously the model was only tested at one site. In this manuscript, the enhanced TM is validated for three sites (CIEMAT’s Plataforma Solar de Almería (PSA), Spain, Missour, Morocco (MIS) and Zagora, Morocco (ZAG)). As the strongest assumption in the TM is the vertical aerosol particle profile, three different approaches to describe the vertical profile are tested in the TM. One approach assumes a homogeneous aerosol profile up to 1 kilometer above ground, the second approach is based on LIVAS profiles obtained from Lidar measurements and the third approach uses boundary layer height (BLH) data of the European Centre for Medium-Range Weather Forecasts (ECMWF). The derived broadband transmittance for a slant range of 1 km ( T 1 k m ) time series is compared with a reference data set of on-site absorption- and broadband corrected T 1 k m derived from meteorological optical range (MOR) measurements for the temporal period between January 2015 and November 2017. The absolute mean bias error (MBE) for the TM’s T 1 k m using the three different aerosol profiles lies below 5% except for ZAG and one profile assumption. The MBE is close to 0 for PSA and MIS assuming a homogeneous extinction coefficient up to 1 km above ground. The root mean square error (RMSE) is around 5–6% for PSA and ZAG and around 7–8% for MIS. The TM performs better during summer months, during which more data points have been evaluated. This validation proves the applicability of the transmittance model for resource assessment at various sites. It enables the identification of a clear site with high T 1 k m with a high accuracy and provides an estimation of the T 1 k m for hazy sites. Thus it facilitates the decision if on-site extinction measurements are necessary. The model can be used to improve the accuracy of yield analysis of tower plants and allows the site adapted design.

scholarly journals A Novel Hybrid Model of WRF and Clearness Index-Based Kalman Filter for Day-Ahead Solar Radiation Forecasting

2019 ◽  
Vol 9 (19) ◽  
pp. 3967 ◽  
Author(s):  
Yuzhang Che ◽  
Lewei Chen ◽  
Jiafeng Zheng ◽  
Liang Yuan ◽  
Feng Xiao
Keyword(s):  
Kalman Filter ◽  
Solar Radiation ◽  
Hybrid Model ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Energy Utilization ◽  
Mean Bias Error ◽  
Bias Error ◽  
Clearness Index ◽  
Sky Conditions

Day-ahead forecasting of solar radiation is essential for grid balancing, real-time unit dispatching, scheduling and trading in the solar energy utilization system. In order to provide reliable forecasts of solar radiation, a novel hybrid model is proposed in this study. The hybrid model consists of two modules: a mesoscale numerical weather prediction model (WRF: Weather Research and Forecasting) and Kalman filter. However, the Kalman filter is less likely to predict sudden changes in the forecasting errors. To address this shortcoming, we develop a new framework to implement a Kalman filter based on the clearness index. The performance of this hybrid model is evaluated using a one-year dataset of solar radiation taken from a photovoltaic plant located at Maizuru, Japan and Qinghai, China, respectively. The numerical results reveal that the proposed hybrid model performs much better in comparison with the WRF-alone forecasts under different sky conditions. In particular, in the case of clear sky conditions, the hybrid model can improve the forecasting accuracy by 95.7% and 90.9% in mean bias error (MBE), and 42.2% and 26.8% in root mean square error (RMSE) for Maizuru and Qinghai sites, respectively.

scholarly journals Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

2019 ◽  
Vol 11 (17) ◽  
pp. 1984 ◽  
Author(s):  
Yang ◽  
Gao ◽  
Li ◽  
Jia ◽  
Jiang
Keyword(s):  
Solar Irradiance ◽  
Satellite Images ◽  
Motion Vector ◽  
Northwest China ◽  
Mean Bias Error ◽  
Bias Error ◽  
Random Mutation ◽  
Time Horizons ◽  
Desert Areas ◽  
Sky Conditions

The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively.

scholarly journals Comparison of hourly surface downwelling solar radiation estimated from MSG/SEVIRI and forecast by RAMS model with pyranometers over Italy

2017 ◽  
Author(s):  
Stefano Federico ◽  
Rosa Claudia Torcasio ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Monica Campanelli ◽  
...  
Keyword(s):  
Mesoscale Model ◽  
Horizontal Resolution ◽  
Climatic Conditions ◽  
Atmospheric Modeling ◽  
Mean Bias Error ◽  
Bias Error ◽  
Hourly Data ◽  
Sky Conditions ◽  
The Impact ◽  
Rams Model

Abstract. In this paper, we evaluate the performance of two Global Horizontal solar Irradiance (GHI) estimates, one derived from Meteosat Second Generation (MSG) and another from one-day forecast of the Regional Atmospheric Modeling System (RAMS) mesoscale model. The horizontal resolution of the MSG-GHI is 3*5 km2 over Italy, which is the focus area of this study. For this paper, RAMS has the horizontal resolution of 4 km. The performance of MSG-GHI estimate and RAMS-GHI one-day forecast are evaluated for one year (1 June 2013–31 May 2014) against data of twelve ground based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate. Statistics on hourly GHI and daily integrated GHI are presented for the four seasons and the whole year for all the measurement sites. Different sky conditions are considered in the analysis. Results on hourly data show an evident dependence on the sky conditions, with the Root Mean Square Error (RMSE) increasing from clear to contaminated, and to overcast conditions. The RMSE increases substantially for Alpine stations in all the seasons, mainly because of the increase of the cloud coverage for these stations, which is not well represented at the satellite and model resolutions. Considering the yearly statistics for the RAMS model, the RMSE ranges from 152 W/m2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W/m2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W/m2 , 14 %), while the maximum is for Aosta (181 W/m2 , 51 %). The Mean Bias Error (MBE) shows the tendency of RAMS to over forecast the GHI, while no specific tendency if found for MSG-GHI. Results for daily integrated GHI show a reduction of the RMSE of at least 10 %, compared to hourly GHI evaluation, for both RAMS-GHI one-day forecast and MSG-GHI estimate. A partial compensation of underestimation and overestimation of the GHI contributes to the RMSE reduction. Furthermore, a post-processing technique, namely Model Output Statistics (MOS), is applied to hourly and daily integrated GHI. The application of MOS shows an improvement for RAMS-GHI up to 24 %, depending on the site considered, while the impact of MOS on MSG-GHI RMSE is small (2–3 %).

scholarly journals An empirical model to estimate ultraviolet erythemal transmissivity

2009 ◽  
Vol 27 (4) ◽  
pp. 1387-1398 ◽  
Author(s):  
M. Antón ◽  
A. Serrano ◽  
M. L. Cancillo ◽  
J. A. García
Keyword(s):  
Empirical Model ◽  
Weather Conditions ◽  
Bias Error ◽  
Clearness Index ◽  
Uv Index ◽  
The Public ◽  
Independent Variables ◽  
Absolute Bias ◽  
Western Spain ◽  
Ozone Column

Abstract. An empirical model to estimate the solar ultraviolet erythemal irradiance (UVER) for all-weather conditions is presented. This model proposes a power expression with the UV transmissivity as a dependent variable, and the slant ozone column and the clearness index as independent variables. The UVER were measured at three stations in South-Western Spain during a five year period (2001–2005). A dataset corresponding to the period 2001–2004 was used to develop the model and an independent dataset (year 2005) for validation purposes. For all three locations, the empirical model explains more than 95% of UV transmissivity variability due to changes in the two independent variables. In addition, the coefficients of the models show that when the slant ozone amount decreases 1%, UV transmissivity and, therefore, UVER values increase approximately 1.33%–1.35%. The coefficients also show that when the clearness index decreases 1%, UV transmissivity increase 0.75%–0.78%. The validation of the model provided satisfactory results, with low mean absolute bias error (MABE), about 7%–8% for all stations. Finally, a one-day ahead forecast of the UV Index for cloud-free cases is presented, assuming the persistence in the total ozone column. The percentage of days with differences between forecast and experimental UVI lower than ±0.5 unit and ±1 unit is within the range of 28% to 37%, and 60% to 75%, respectively. Therefore, the empirical model proposed in this work provides reliable forecast cloud-free UVI in order to inform the public about the possible harmful effects of UV radiation over-exposure.

scholarly journals Satellite Retrieval of Downwelling Shortwave Surface Flux and Diffuse Fraction under All Sky Conditions in the Framework of the LSA SAF Program (Part 2: Evaluation)

2019 ◽  
Vol 11 (22) ◽  
pp. 2630 ◽  
Author(s):  
Dominique Carrer ◽  
Suman Moparthy ◽  
Chloé Vincent ◽  
Xavier Ceamanos ◽  
Sandra C. Freitas ◽  
...  
Keyword(s):  
Land Surface ◽  
Surface Radiation ◽  
Wave Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Separate Analysis ◽  
European Organization ◽  
Diffuse Fraction ◽  
Sky Conditions

High frequency knowledge of the spatio-temporal distribution of the downwelling surface shortwave flux (DSSF) and its diffuse fraction (fd) at the surface is nowadays essential for understanding climate processes at the surface–atmosphere interface, plant photosynthesis and carbon cycle, and for the solar energy sector. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility for Land Surface Analysis operationally delivers estimation of the MDSSFTD (MSG Downwelling Surface Short-wave radiation Fluxes—Total and Diffuse fraction) product with an operational status since the year 2019. The method for retrieval was presented in a companion paper. Part 2 now focuses on the evaluation of the MDSSFTD algorithm and presents a comparison of the corresponding outputs, i.e., total DSSF and diffuse fraction (fd) components, against in situ measurements acquired at four Baseline Surface Radiation Network (BSRN) stations over a seven-month period. The validation is performed on an instantaneous basis. We show that the satellite estimates of DSSF and fd meet the target requirements defined by the user community for all-sky (clear and cloudy) conditions. For DSSF, the requirements are 20 Wm−2 for DSSF < 200 Wm−2, and 10% for DSSF ≥ 200 Wm−2. The mean bias error (MBE) and relative mean bias error (rMBE) compared to the ground measurements are 3.618 Wm−2 and 0.252%, respectively. For fd, the requirements are 0.1 for fd < 0.5, and 20% for fd ≥ 0.5. The MBE and rMBE compared to the ground measurements are −0.044% and −17.699%, respectively. The study also provides a separate analysis of the product performances for clear sky and cloudy sky conditions. The importance of representing the cloud–aerosol radiative coupling in the MDSSFTD method is discussed. Finally, it is concluded that the quality of the aerosol optical depth (AOD) forecasts currently available is accurate enough to obtain reliable diffuse solar flux estimates. This quality of AOD forecasts was still a limitation a few years ago.

scholarly journals Correcting Daytime Thermal Offset in Unventilated Pyranometers

2015 ◽  
Vol 32 (11) ◽  
pp. 2088-2099 ◽  
Author(s):  
A. Serrano ◽  
G. Sanchez ◽  
M. L. Cancillo
Keyword(s):  
Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
General Validity ◽  
Absolute Value ◽  
Complex Models ◽  
Independent Variable ◽  
Source Of Error ◽  
Radiation Measurements

AbstractA main source of error in solar radiation measurements is the thermal offset inherent to pyranometers. Despite acknowledgment of its importance, its correction has been widely ignored for several decades. This neglect may have caused a generalized underestimation in solar radiation measurements. This study focuses on the correction of this error in solar irradiance measurements. For this aim a plethora of correction models built as a linear combination of several environmental variables related to the ambient temperature and to the incoming radiation were proposed. The models are fitted to experimental measurements obtained during capping events and, finally, their performance is evaluated and compared. The main results indicate that models with only one independent variable moderately correct the thermal offset error. These simple models are useful when no additional instrumentation other than the pyranometer is available. On the other hand, the more complex models show the best performance, with a coefficient of determination R2 over 0.8, an RMSE under 2 W m−2, and an absolute value of mean bias error (MBE) under 0.5 W m−2. Additionally, these models are used to study the differences between nighttime and daytime correction, revealing the unsuitability of using nighttime-fitted models to correct the daytime thermal offset. The general validity of the models is tested by their application to two different pyranometers. Results indicate that, whereas the factors involved in the best-performing models are the same, the values of the loading coefficients differ and therefore must be specifically calculated for each pyranometer.

scholarly journals Testing longwave radiation parameterizations under clear and overcast skies at Storglaciären, Sweden

2009 ◽  
Vol 3 (1) ◽  
pp. 75-84 ◽  
Author(s):  
J. Sedlar ◽  
R. Hock
Keyword(s):  
Meteorological Data ◽  
Water Vapor Pressure ◽  
Longwave Radiation ◽  
Cloud Fraction ◽  
Shortwave Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Near Surface ◽  
Radiation Parameterizations ◽  
Sky Conditions

Abstract. Energy balance based glacier melt models require accurate estimates of incoming longwave radiation but direct measurements are often not available. Multi-year near-surface meteorological data from Storglaciären, Northern Sweden, were used to evaluate commonly used longwave radiation parameterizations in a glacier environment under clear-sky and all-sky conditions. Parameterizations depending solely on air temperature performed worse than those which include water vapor pressure. All models tended to overestimate incoming longwave radiation during periods of low longwave radiation, while incoming longwave was underestimated when radiation was high. Under all-sky conditions root mean square error (RMSE) and mean bias error (MBE) were 17 to 20 W m−2 and −5 to 1 W m−2, respectively. Two attempts were made to circumvent the need of cloud cover data. First cloud fraction was parameterized as a function of the ratio, τ, of measured incoming shortwave radiation and calculated top of atmosphere radiation. Second, τ was related directly to the cloud factor (i.e. the increase in sky emissivity due to clouds). Despite large scatter between τ and both cloud fraction and the cloud factor, resulting calculations of hourly incoming longwave radiation for both approaches were only slightly more variable with RMSE roughly 3 W m−2 larger compared to using cloud observations as input. This is promising for longwave radiation modeling in areas where shortwave radiation data are available but cloud observations are not.

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