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

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 the 1-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 performances of the MSG-GHI estimate and RAMS-GHI 1-day forecast are evaluated for 1 year (1 June 2013–31 May 2014) against data of 12 ground-based pyranometers over Italy spanning a range of climatic conditions, i.e. from maritime Mediterranean to Alpine climate.Statistics for 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 analysisResults for hourly data show an evident dependence on the sky conditions, with the root mean square error (RMSE) increasing from clear to cloudy conditions. The RMSE is substantially higher 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 computed from hourly data for the RAMS model, the RMSE ranges from 152 W m−2 (31 %) obtained for Cozzo Spadaro, a maritime station, to 287 W m−2 (82 %) for Aosta, an Alpine site. Considering the yearly statistics computed from hourly data for MSG-GHI, the minimum RMSE is for Cozzo Spadaro (71 W m−2, 14 %), while the maximum is for Aosta (181 W m−2, 51 %). The mean bias error (MBE) shows the tendency of RAMS to over-forecast the GHI, while no specific behaviour is found for MSG-GHI.Results for daily integrated GHI show a lower RMSE compared to hourly GHI evaluation for both RAMS-GHI 1-day forecast and MSG-GHI estimate. Considering the yearly evaluation, the RMSE of daily integrated GHI is at least 9 % lower (in percentage units, from 31 to 22 % for RAMS in Cozzo Spadaro) than the RMSE computed for hourly data for each station. 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 improve the GHI forecast at hourly and daily temporal scales. The application of MOS shows an improvement of RAMS-GHI forecast, which depends on the site considered, while the impact of MOS on MSG-GHI RMSE is small.

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

Remote Sensing ◽

10.3390/rs12060920 ◽

2020 ◽

Vol 12 (6) ◽

pp. 920 ◽

Cited By ~ 2

Author(s):

Sabrina Gentile ◽

Francesco Di Paola ◽

Domenico Cimini ◽

Donatello Gallucci ◽

Edoardo Geraldi ◽

...

Keyword(s):

Data Assimilation ◽

Solar Irradiance ◽

Cloud Cover ◽

Weather Prediction ◽

National Research Council ◽

Mean Bias Error ◽

Bias Error ◽

Clear Sky ◽

Sky Conditions ◽

The Impact

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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Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

Natural Hazards and Earth System Science ◽

10.5194/nhess-11-487-2011 ◽

2011 ◽

Vol 11 (2) ◽

pp. 487-500 ◽

Cited By ~ 11

Author(s):

S. Federico

Keyword(s):

Forecast Error ◽

Background Field ◽

Horizontal Resolution ◽

Atmospheric Modeling ◽

Forecast Skill ◽

Maximum Temperature ◽

Model Forecast ◽

Maximum Temperatures ◽

Analysis Error ◽

The Impact

Abstract. Since 2005, one-hour temperature forecasts for the Calabria region (southern Italy), modelled by the Regional Atmospheric Modeling System (RAMS), have been issued by CRATI/ISAC-CNR (Consortium for Research and Application of Innovative Technologies/Institute for Atmospheric and Climate Sciences of the National Research Council) and are available online at http://meteo.crati.it/previsioni.html (every six hours). Beginning in June 2008, the horizontal resolution was enhanced to 2.5 km. In the present paper, forecast skill and accuracy are evaluated out to four days for the 2008 summer season (from 6 June to 30 September, 112 runs). For this purpose, gridded high horizontal resolution forecasts of minimum, mean, and maximum temperatures are evaluated against gridded analyses at the same horizontal resolution (2.5 km). Gridded analysis is based on Optimal Interpolation (OI) and uses the RAMS first-day temperature forecast as the background field. Observations from 87 thermometers are used in the analysis system. The analysis error is introduced to quantify the effect of using the RAMS first-day forecast as the background field in the OI analyses and to define the forecast error unambiguously, while spatial interpolation (SI) analysis is considered to quantify the statistics' sensitivity to the verifying analysis and to show the quality of the OI analyses for different background fields. Two case studies, the first one with a low (less than the 10th percentile) root mean square error (RMSE) in the OI analysis, the second with the largest RMSE of the whole period in the OI analysis, are discussed to show the forecast performance under two different conditions. Cumulative statistics are used to quantify forecast errors out to four days. Results show that maximum temperature has the largest RMSE, while minimum and mean temperature errors are similar. For the period considered, the OI analysis RMSEs for minimum, mean, and maximum temperatures vary from 1.8, 1.6, and 2.0 °C, respectively, for the first-day forecast, to 2.0, 1.9, and 2.6 °C, respectively, for the fourth-day forecast. Cumulative statistics are computed using both SI and OI analysis as reference. Although SI statistics likely overestimate the forecast error because they ignore the observational error, the study shows that the difference between OI and SI statistics is less than the analysis error. The forecast skill is compared with that of the persistence forecast. The Anomaly Correlation Coefficient (ACC) shows that the model forecast is useful for all days and parameters considered here, and it is able to capture day-to-day weather variability. The model forecast issued for the fourth day is still better than the first-day forecast of a 24-h persistence forecast, at least for mean and maximum temperature. The impact of using the RAMS first-day forecast as the background field in the OI analysis is quantified by comparing statistics computed with OI and SI analyses. Minimum temperature is more sensitive to the change in the analysis dataset as a consequence of its larger representative error.

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Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

Advances in Science and Research ◽

10.5194/asr-14-187-2017 ◽

2017 ◽

Vol 14 ◽

pp. 187-194 ◽

Cited By ~ 5

Author(s):

Stefano Federico ◽

Marco Petracca ◽

Giulia Panegrossi ◽

Claudio Transerici ◽

Stefano Dietrich

Keyword(s):

Data Assimilation ◽

Mesoscale Model ◽

Weather Prediction ◽

Horizontal Resolution ◽

Probability Of Detection ◽

Precipitation Forecast ◽

Time Range ◽

False Alarms ◽

Forecast Time ◽

The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

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Ensemble forecasting with a stochastic convective parametrization based on equilibrium statistics

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-11-30457-2011 ◽

2011 ◽

Vol 11 (11) ◽

pp. 30457-30485 ◽

Cited By ~ 3

Author(s):

P. Groenemeijer ◽

G. C. Craig

Keyword(s):

Large Scale ◽

Mesoscale Model ◽

Initial Conditions ◽

Deep Convection ◽

Horizontal Resolution ◽

Ensemble Forecasting ◽

Total Variance ◽

Ensemble Prediction ◽

Ensemble Prediction System ◽

The Impact

Abstract. The stochastic Plant-Craig scheme for deep convection was implemented in the COSMO mesoscale model and used for ensemble forecasting. Ensembles consisting of 100 48 h forecasts at 7 km horizontal resolution were generated for a 2000 × 2000 km domain covering central Europe. Forecasts were made for seven case studies and characterized by different large-scale meteorological environments. Each 100 member ensemble consisted of 10 groups of 10 members, with each group driven by boundary and initial conditions from a selected member from the global ECMWF Ensemble Prediction System. The precipitation variability within and among these groups of members was computed, and it was found that the relative contribution to the ensemble variance introduced by the stochastic convection scheme was substantial, amounting to as much as 76% of the total variance in the ensemble in one of the studied cases. The impact of the scheme was not confined to the grid scale, and typically contributed 25–50% of the total variance even after the precipitation fields had been smoothed to a resolution of 35 km. The variability of precipitation introduced by the scheme was approximately proportional to the total amount of convection that occurred, while the variability due to large-scale conditions changed from case to case, being highest in cases exhibiting strong mid-tropospheric flow and pronounced meso- to synoptic scale vorticity extrema. The stochastic scheme was thus found to be an important source of variability in precipitation cases of weak large-scale flow lacking strong vorticity extrema, but high convective activity.

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Adaptation of an empirical model for erythemal ultraviolet irradiance

Annales Geophysicae ◽

10.5194/angeo-25-1499-2007 ◽

2007 ◽

Vol 25 (7) ◽

pp. 1499-1508 ◽

Cited By ~ 17

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.

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Mapping and evaluation of reference evapotranspiration methods under arid conditions

10.35688/2413-8452-2018-01-007 ◽

2018 ◽

Vol 1 ◽

pp. 39-45

Author(s):

Mahmoud M.A. ◽

Ali G.M.

Keyword(s):

Reference Evapotranspiration ◽

Climatic Conditions ◽

Estimation Methods ◽

Mean Bias Error ◽

Percentage Error ◽

Bias Error ◽

Distribution Maps ◽

Climatic Regions ◽

Arid Conditions ◽

Vital Factor

Reference evapotranspiration ( ETo ) is a vital factor in water resources managing and planning. Various estimation methods have been developed for different climatic regions and according to the available data. Therefore, the reliability of such methods depends upon climatic conditions. The present investigation evaluates four temperature based methods: FAO Blaney-Criddle (BC), Turc, Jensen-Haise (JH) and Hargreaves (HG), and two radiation based methods: FAO-radiation (FAO-rad) and Priestley-Taylor (PT) in comparison with the FAO-PM method under arid conditions of Libya. In order to select the best ETo method, the percentage error of estimate ( PE ), the root mean square error ( RMSE ), and mean bias error ( MBE ) were calculated. The obtained ETo values (FAO-PM and the average of best-estimated monthly ETo ) were utilized to generate spatial distribution maps of ETo with the aid of Kriging technique. Statistical analysis of the obtained results revealed that, Turc equation fitted well for the northern part of the study area, which include Nalut, Zuara, Mosrata, Sirt, Shahat, Derna, Tubruk, Hon, Galo and Gagbub. While for southern zone, HG equation performed better for Opari and Tazirbu, BC equation for Kufra and Ghadames, FAO-Rad equation for Sebha; and JH equation for Ghat.

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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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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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The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin: An Evaluation of 15 Year WRF Simulations

Applied Sciences ◽

10.3390/app10051765 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1765 ◽

Cited By ~ 2

Author(s):

Ghulam Hussain Dars ◽

Courtenay Strong ◽

Adam K. Kochanski ◽

Kamran Ansari ◽

Syed Hammad Ali

Keyword(s):

Mean Squared Error ◽

Pearson Correlation ◽

Wrf Model ◽

Horizontal Resolution ◽

Indus Basin ◽

Spatiotemporal Variability ◽

Mean Bias Error ◽

Bias Error ◽

Upper Indus Basin ◽

Precipitation And Temperature

Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01–d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.

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