3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

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

Applied Sciences ◽

10.3390/app9193967 ◽

2019 ◽

Vol 9 (19) ◽

pp. 3967 ◽

Cited By ~ 4

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.

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Terrestrial Solar Radiation

Economical and Technical Considerations for Solar Tracking - Advances in Environmental Engineering and Green Technologies ◽

10.4018/978-1-5225-2950-7.ch004 ◽

2017 ◽

pp. 191-293

Keyword(s):

Solar Radiation ◽

Horizontal Plane ◽

Cloud Cover ◽

Weather Conditions ◽

Clear Sky ◽

Solar Tracking ◽

Sky Conditions ◽

The Impact ◽

Estimation Models

After shading a light on the extraterrestrial solar radiation in the chapter 3 it is important to evaluate the global terrestrial solar radiation and its components. The information on terrestrial solar radiation is required in several different forms depending on the kinds of calculations and kind of application that are to be done. Of course, terrestrial solar radiation on the horizontal plane depends on the different weather conditions such as cloud cover, relative humidity, and ambient temperature. Therefore, the impact of the atmosphere on solar radiation should be considered. One of the most important points of terrestrial solar radiation evaluation is its determination during clear sky conditions. Therefore, in this chapter, the equations that determine the air mass basing on available theories are given and the clear sky conditions are introduced with shading a light on the previous work in identifying clear sky conditions. Taking into consideration that, clear sky solar radiation estimation is of great importance for solar tracking, a detailed review of main available models is given in this chapter. As daily, monthly, seasonally, biannually and yearly mean daily solar radiations are required information for designing and installing long term tracking systems, different available methods are commented regarding their applicability for the estimation of solar radiation information in the desired format from the data that are available. An important accent is paid also on the assessment and comparison of monthly mean daily solar radiation estimation models.

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

Remote Sensing ◽

10.3390/rs11171984 ◽

2019 ◽

Vol 11 (17) ◽

pp. 1984 ◽

Cited By ~ 4

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.

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Regression Model to Predict Global Solar Irradiance in Malaysia

International Journal of Photoenergy ◽

10.1155/2015/347023 ◽

2015 ◽

Vol 2015 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Hairuniza Ahmed Kutty ◽

Muhammad Hazim Masral ◽

Parvathy Rajendran

Keyword(s):

Regression Model ◽

Solar Irradiance ◽

Cloud Cover ◽

Prediction Models ◽

Coefficient Of Determination ◽

Mean Bias Error ◽

Bias Error ◽

Proposed Model ◽

Multiple Parameter ◽

Gust Speed

A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R2) with other models available from literature studies. Seven models based on single parameters (PM1 to PM7) and five multiple-parameter models (PM7 to PM12) are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%,R2ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.

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

10.5194/amt-2017-9 ◽

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 %).

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Rooftop Photovoltaic Energy Production Management in India Using Earth-Observation Data and Modeling Techniques

Remote Sensing ◽

10.3390/rs12121921 ◽

2020 ◽

Vol 12 (12) ◽

pp. 1921

Author(s):

Akriti Masoom ◽

Panagiotis Kosmopoulos ◽

Yashwant Kashyap ◽

Shashi Kumar ◽

Ankit Bansal

Keyword(s):

Solar Irradiance ◽

Energy Production ◽

Production Management ◽

Earth Observation ◽

Observation Data ◽

Energy Potential ◽

Clear Sky ◽

Percentage Difference ◽

Sky Conditions ◽

The Impact

This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations.

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Increased UV radiation due to polar ozone chemical depletion and vortex occurrences at southern sub-polar latitudes in the period (1997–2005)

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-8-6501-2008 ◽

2008 ◽

Vol 8 (2) ◽

pp. 6501-6537

Author(s):

A. F. Pazmino ◽

S. Godin-Beekmann ◽

E. A. Luccini ◽

R. D. Piacentini ◽

E. J. Quel ◽

...

Keyword(s):

Uv Radiation ◽

Total Ozone ◽

Cloud Cover ◽

Transport Model ◽

Austral Spring ◽

Latitude Band ◽

Ozone Loss ◽

Clear Sky ◽

Sky Conditions ◽

The Impact

Abstract. The variability of total ozone and UV radiation from Total Ozone Mapping Spectrometer (TOMS) measurements is analyzed as a function of polar vortex occurrences over the southern subpolar regions during the 1997–2005 period. The analysis of vortex occurrences showed high interannual variability in the 40° S–60° S latitude band with a longitudinal asymmetry showing the largest frequencies over the 90° W–90° E region. The impact of vortex occurrences on UV radiation and ozone in clear sky conditions was determined from the comparison between the measurements inside the vortex and a climatology obtained from data outside the vortex over the studied period. Clear sky conditions were determined from TOMS reflectivity data. For measurements outside the vortex, clear sky conditions were selected for reflectivity values lower than 7.5%, while for measurements inside the vortex, a relaxed threshold was determined from statistically similar UV values as a function of reflectivity. UV changes and ozone differences from the climatology were analyzed in the 40° S–50° S and 50° S–60° S latitude bands during the spring period (September to November). The largest UV increases and ozone decreases, reaching 200% and 65%, respectively, were found in the 50° S–60° S latitude band in September and October. The heterogeneous ozone loss during vortex occurrences was estimated using a chemical transport model. The largest impact of vortex occurrences was found in October with mean UV increase, total ozone decrease and accumulated ozone loss in the 350 K–650 K range of respectively 47%, 32% and 63%. The region close to South America is the most affected by the Antarctic ozone depletion due to the combined effect of large number of vortex occurrences, lower cloud cover and large ozone decrease. This region would be the most vulnerable in case of cloud cover decrease linked to climate change, due to more frequent occurrence of ozone poor air masses during austral spring.

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Evaluation of Global Solar Irradiance Estimates from GL1.2 Satellite-Based Model over Brazil Using an Extended Radiometric Network

Remote Sensing ◽

10.3390/rs12081331 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1331 ◽

Cited By ~ 1

Author(s):

Anthony C. S. Porfirio ◽

Juan C. Ceballos ◽

José M. S. Britto ◽

Simone M. S. Costa

Keyword(s):

Solar Irradiance ◽

Global Radiation ◽

Weather Forecast ◽

Ground Level ◽

Daily Basis ◽

Mean Bias Error ◽

Bias Error ◽

Large Network ◽

Clear Sky ◽

The North

The GL (GLobal radiation) physical model was developed to compute global solar irradiance at ground level from (VIS) visible channel imagery of geostationary satellites. Currently, its version 1.2 (GL1.2) runs at Brazilian Center for Weather Forecast and Climate Studies/National Institute for Space Research (CPTEC/INPE) based on GOES-East VIS imagery. This study presents an extensive validation of GL1.2 global solar irradiance estimates using ground-based measurements from 409 stations belonging to the Brazilian National Institute of Meteorology (INMET) over Brazil for the year 2016. The INMET reasonably dense network allows characterizing the spatial distribution of GL1.2 data uncertainties. It is found that the GL1.2 estimates have a tendency to overestimate the ground data, but the magnitude varies according to region. On a daily basis, the best performances are observed for the Northeast, Southeast, and South regions, with a mean bias error (MBE) between 2.5 and 4.9 W m−2 (1.2% and 2.1%) and a root mean square error (RMSE) between 21.1 and 26.7 W m−2 (10.8% and 11.8%). However, larger differences occur in the North and Midwest regions, with MBE between 12.7 and 23.5 W m−2 (5.9% and 11.7%) and RMSE between 27 and 33.4 W m−2 (12.7% and 16.7%). These errors are most likely due to the simplified assumptions adopted by the GL1.2 algorithm for clear sky reflectance (Rmin) and aerosols as well as the uncertainty of the water vapor data. Further improvements in determining these parameters are needed. Additionally, the results also indicate that the GL1.2 operational product can help to improve the quality control of radiometric data from a large network, such as INMET's. Overall, the GL1.2 data are suitable for use in various regional applications.

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Increased UV radiation due to polar ozone chemical depletion and vortex occurrences at Southern Sub-polar Latitudes in the period [1997–2005]

Atmospheric Chemistry and Physics ◽

10.5194/acp-8-5339-2008 ◽

2008 ◽

Vol 8 (17) ◽

pp. 5339-5352 ◽

Cited By ~ 4

Author(s):

A. F. Pazmiño ◽

S. Godin-Beekmann ◽

E. A. Luccini ◽

R. D. Piacentini ◽

E. J. Quel ◽

...

Keyword(s):

Uv Radiation ◽

Total Ozone ◽

Cloud Cover ◽

Transport Model ◽

Austral Spring ◽

Latitude Band ◽

Ozone Loss ◽

Clear Sky ◽

Sky Conditions ◽

The Impact

Abstract. The variability of total ozone and UV radiation from Total Ozone Mapping Spectrometer (TOMS) measurements is analyzed as a function of polar vortex occurrences over the southern subpolar regions during the 1997–2005 period. The analysis of vortex occurrences showed high interannual variability in the 40° S–60° S latitude band with a longitudinal asymmetry showing the largest frequencies over the 90° W–90° E region. The impact of vortex occurrences on UV radiation and ozone in clear sky conditions was determined from the comparison between the measurements inside the vortex and a climatology obtained from data outside the vortex over the studied period. Clear sky conditions were determined from TOMS reflectivity data. For measurements outside the vortex, clear sky conditions were selected for reflectivity values lower than 7.5%, while for measurements inside the vortex, a relaxed threshold was determined from statistically similar UV values as a function of reflectivity. UV changes and ozone differences from the climatology were analyzed in the 40° S–50° S and 50° S–60° S latitude bands during the spring period (September to November). The largest UV increases and ozone decreases, reaching ~200% and ~65%, respectively, were found in the 50° S–60° S latitude band in September and October. The heterogeneous ozone loss during vortex occurrences was estimated using a chemical transport model. The largest impact of vortex occurrences was found in October with mean UV increase, total ozone decrease and accumulated ozone loss in the 350–650 K range of, respectively, 47%, 30% and 57%. The region close to South America is the most affected by the Antarctic ozone depletion due to the combined effect of large number of vortex occurrences, lower cloud cover and large ozone decrease. This region would be the most vulnerable in case of cloud cover decrease, due to more frequent occurrence of ozone poor air masses during austral spring.

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A High-Resolution Topographic Correction Method for Clear-Sky Solar Irradiance Derived with a Numerical Weather Prediction Model

Journal of Applied Meteorology and Climatology ◽

10.1175/2011jamc2571.1 ◽

2011 ◽

Vol 50 (12) ◽

pp. 2460-2472 ◽

Cited By ~ 30

Author(s):

José A. Ruiz-Arias ◽

David Pozo-Vázquez ◽

Vicente Lara-Fanego ◽

Francisco J. Santos-Alamillos ◽

J. Tovar-Pescador

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Numerical Weather Prediction ◽

Solar Irradiance ◽

Weather Prediction ◽

Mean Bias Error ◽

Bias Error ◽

Mean Square ◽

Numerical Weather

AbstractRugged terrain is a source of variability in the incoming solar radiation field, but the influence of terrain is still not properly included by most current numerical weather prediction (NWP) models. In this work, a downscaling postprocessing method for NWP-model solar irradiance through terrain effects is presented. It allows one to decrease the estimation bias caused by terrain shading and sky-view reduction, and to account for elevation variability, surface orientation, and surface albedo. The method has been applied to a case study in southern Spain using the Weather Research and Forecasting (WRF) mesoscale model with a spatial resolution of 30 arc s, resulting in disaggregated maps of 3 arc s. The validation was based on a radiometric network made of eight stations located in the Natural Park of Sierra Mágina over an area of roughly 30 × 35 km2 and 12 carefully selected cloudless days during a year. Three of the stations were equipped with tilted pyranometers. Their inclination and aspect were visually adjusted to the inclination and aspect of the local terrain and then carefully measured. For horizontal surface, the downscaled irradiance has proven to reduce the root-mean-square error of the WRF model by 20% to about 25 W m−2 in winter and autumn and 60 W m−2 in spring and summer. For tilted surface, downscaling to different spatial resolutions resulted in the best performance for 9 arc s, with root-mean-square error of 45% (57 W m−2) and a mean bias error close to zero.

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