Annette Hammer

Solar energy assessment using remote sensing technologies

About 20% of the final energy consumed in Europe is used in buildings. The active and passive use of solar energy is an approach to reduce the fossil energy consumption and the greenhouse gas emissions originated by buildings. Consideration of solar energy technologies in urban planning demands accurate information of the available solar resources. This can be achieved by the use of remote sensing data from geostationary satellites which show a very high spatial and a sufficient temporal resolution compared to ground station data. This paper gives a brief introduction to the HELIOSAT method applied to derive surface solar irradiance from satellite images and shows examples of applications: The use of daylight in buildings, the generation of correlated time series of solar irradiance and temperature as input data for simulations of solar energy systems and a short-term forecast of solar irradiance which can be used in intelligent building control techniques. Finally an outlook is given on potential improvements expected from the next generation of European meteorological satellites Meteosat Second Generation (MSG).

Short-term forecasting of solar radiation: a statistical approach using satellite data

Short-term forecasting of solar irradiance is an important issue for many fields of solar energy applications. As the solar surface irradiance can be inferred from satellite measurements with a high temporal and spatial resolution, we use satellite images as a data source for forecasting. The satellite data provide information on cloudiness, the most important atmospheric parameter for surface irradiance. This paper describes the application of a statistical method to detect the motion of cloud structures from satellite images. Extrapolating the temporal development of the cloud situation, solar radiation can be predicted for time scales from 30 min up to 2 h. The forecasts are evaluated with respect to accuracy and an example for the application of the forecast algorithm to predict PV power output is presented.

A New Algorithm for the Satellite-Based Retrieval of Solar Surface Irradiance in Spectral Bands

Accurate solar surface irradiance data is a prerequisite for an efficient planning and operation of solar energy systems. Further, it is essential for climate monitoring and analysis. Recently, the demand on information about spectrally resolved solar surface irradiance has grown. As surface measurements are rare, satellite derived information with high accuracy might fill this gap. This paper describes a new approach for the retrieval of spectrally resolved solar surface irradiance from satellite data. The method combines a eigenvector-hybrid look-up table approach for the clear sky case with satellite derived cloud transmission (Heliosat method). The eigenvector LUT approach is already used to retrieve the broadband solar surface irradiance of data sets provided by the Climate Monitoring Satellite Application Facility (CM-SAF). This paper describes the extension of this approach to wavelength bands and the combination with spectrally resolved cloud transmission values derived with radiative transfer corrections of the broadband cloud transmission. Thus, the new approach is based on radiative transfer modeling and enables the use of extended information about the atmospheric state, among others, to resolve the effect of water vapor and ozone absorption bands. The method is validated with spectrally resolved measurements from two sites in Europe and by comparison with radiative transfer calculations. The validation results demonstrate the ability of the method to retrieve accurate spectrally resolved irradiance from satellites. The accuracy is in the range of the uncertainty of surface measurements, with exception of the UV and NIR ( ≥ 1200 nm) part of the spectrum, where higher deviations occur.

Analysis and synthesis of cloud pattern for radiation field studies

Abstract A method for the analysis of small scale, short term characteristics of radiations fields under intermediate sky conditions (clouds of cumulus type) is described. It is based on the extraction of cloud field strutures from sky photographs. The cloud structures are described as fractal objects. Based on this representation a procedure for the generation of synthetic cloud pattern and related radiation fields is given. Statistical characteristics of synthetic irradiance data sets are compared with empirical data from an array of ground sensors.

Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index

The cloud index is a key parameter of the Heliosat method. This method is widely used to calculate solar irradiance on the Earth’s surface from Meteosat visible channel images. Moreover, cloud index images are the basis of short-term forecasting of solar irradiance and photovoltaic power production. For this purpose, cloud motion vectors are derived from consecutive images, and the motion of clouds is extrapolated to obtain forecasted cloud index images. The cloud index calculation is restricted to the daylight hours, as long as SEVIRI HR-VIS images are used. Hence, this forecast method cannot be used before sunrise. In this paper, a method is introduced that can be utilized a few hours before sunrise. The cloud information is gained from the brightness temperature difference (BTD) of the 10.8 µm and 3.9 µm SEVIRI infrared channels. A statistical relation is developed to assign a cloud index value to either the BTD or the brightness temperature T10:8, depending on the cloud class to which the pixel belongs (fog and low stratus, clouds with temperatures less than 232 K, other clouds). Images are composed of regular HR-VIS cloud index values that are used to the east of the terminator and of nighttime BTD-derived cloud index values used to the west of the terminator, where the Sun has not yet risen. The motion vector algorithm is applied to the images and delivers a forecast of irradiance at sunrise and in the morning. The forecasted irradiance is validated with ground measurements of global horizontal irradiance, and the advantage of the new approach is shown. The RMSE of forecasted irradiance based on the presented nighttime cloud index for the morning hours is between 3 and 70 W/m2, depending on the time of day. This is an improvement against the previous precision range of the forecast based on the daytime cloud index between 70 and 85 W/m2.

Satellite-derived irradiance statistics for Africa

Abstract Hourly images registered by the Meteosat satellite are used to derive the global irradiance at ground level. Keeping almost the full spatial information of the original images, maps of irradiance characteristics for Africa are derived with a resolution of approx. 10 × 10 km 2 . Monthly irradiance conditions are characterized by means and standard deviations of daily sums and the irradiance of specific daytime hours. The data also allow for the extraction of irradiance time series for specific sites. In addition, the analysis of the spatial structure of the satellite-derived irradiance fields is used to identify regions in which the radiation characteristics call for special attention.

Correction: Hammer, J., et al. Short-Term Forecasting of Surface Solar Irradiance Based on Meteosat-SEVIRI Data Using a Nighttime Cloud Index. Remote Sens. 2015, 7, 9070–9090

Due to an oversight by the authors, the following correction is necessary in this publication [1].[...]