Improvement of Hourly Surface Solar Irradiance Estimation Using MSG Rapid Scanning Service

Abstract

The purpose of this work is to explore the effect of temporal sampling on the accuracy of the hourly mean Surface Solar Irradiance (SSI) estimation. An upgraded version of the Advanced Model for the Estimation of Surface Solar Irradiance from Satellite (AMESIS), exploiting data from the Meteosat Second Generation Rapid Scanning Service (MSG-RSS), has been used to evaluate the SSI. The assessment of the new version of AMESIS has been carried out against data from two pyranometers located in Southern (Tito) and Northern (Ispra) Italy at an altitude of 760 m and 220 m, respectively. The statistical analysis of the comparison between hourly mean SSI estimates based on temporal sampling every five minutes shows a quantitative improvement compared to those based on 15-minute sampling. In particular, for the whole dataset in Tito, the correlation increases from 0.979 to 0.998, the Root Mean Square Error (RMSE) decreases from 45.16 W/m2 to 13.19 W/m2 and the Mean Bias Error (MBE) is reduced from −0.67 W/m2 to −0.02 W/m2. For the whole dataset in Ispra, the correlation increases from 0.995 to 0.998, the RMSE decreases from 24.85 W/m2 to 15.59 W/m2, whereas the MBE increases from 3.84 W/m2 to 4.58 W/m2. This preliminary assessment shows that higher temporal sampling can improve SSI monitoring over areas featuring frequent and rapid solar irradiance variation.

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

“…The validation of this preliminary test in the two selected periods against the ground-based and satellite products is performed through the evaluation of statistical skills. Four commonly-used statistical indexes are considered for the analysis of the GHI observed by the ARPA stations and for the MSG SSI [56]: the Mean Absolute Error (MAE), the Mean Bias Error (MBE), the Root Mean Square Error (RMSE) and the correlation (CORR), defined as follows: For cloudy conditions, 01-02 April 2017 is selected, as during these days the southward regression of the high pressure centered over Italy allows the descent of a deep trough in the north-central Europe (Figure 4a). This synoptic structure encourages the rising from south-west of warm and wet air (Figure 4a) fostering the development of scattered clouds in Northern Italy (Figure 4b).…”

“…The validation of this preliminary test in the two selected periods against the ground-based and satellite products is performed through the evaluation of statistical skills. Four commonly-used statistical indexes are considered for the analysis of the GHI observed by the ARPA stations and for the MSG SSI [56]: the Mean Absolute Error (MAE), the Mean Bias Error (MBE), the Root Mean Square Error (RMSE) and the correlation (CORR), defined as follows:…”

“…Four commonly-used statistical indexes are considered for the analysis of the GHI observed by the ARPA stations and for the MSG SSI [56]: the Mean Absolute Error (MAE), the Mean Bias Error (MBE), the Root Mean Square Error (RMSE) and the correlation (CORR), defined as follows:…”

Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging

“…This shows that the shorter the prediction step size of the data, that is, the higher the time sampling frequency, the higher the prediction accuracy of the model will be [44]. This may be because a higher sampling frequency and time resolution can obtain a more accurate and representative average value, as the changes of solar irradiance caused by clouds are more likely to be captured [45]. Moreover, the shorter the forecast horizon, the greater the performance improvement will be.…”

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

“…As a result, many methods are only used for specific conditions. Therefore, a method to obtain the cloud index, developed for the FY-4A geostationary meteorological satellite, has not been found in the current literature [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Zhai et al [22] developed a cloud/shadow detection method for remote sensing images that could detect the clouds with a relatively high accuracy and low computational complexity.…”