d 1.3.1 future research objectives and priorities in the ... · d 1.3.1 future research objectives...

Management and Exploitation of Solar Resource Knowledge

CA – Contract No. 038665

D 1.3.1 Future research objectives and priorities in the field of solar resources

Edited by Marion Schroedter-Homscheidt, DLR

Date: August 16th, 2009

Version History Version Date Authors Partner Sent To Major Changes 1.0 4.6.2009 M.

Schroedter-Homscheidt

1.1 16.08.2009 C.Hoyer-Klick

DLR Commission Minor Edits

Contributing Authors H.-G. Beyer, FH Magdeburg-Stendal, D D. Dumortier, ENTPE, F E. Gaboardi, Icons, I D. Heinemann, Univ. Oldenburg, D C. Kurz, Meteocontrol GmbH, D E. Lorenz, Univ. Oldenburg, D J. Polo Martinez, CIEMAT, E J. Remund, Meteotest, CH M. Wittmann, DLR, D L. Wald, Ecole des Mines de Paris, F

Acknowledgement and Disclaimer The MESOR team acknowledges the financial support of the European Union under contract CA – Contract No. 038665. We would also like to thank all reviewers for their valuable comments. No member of the MESOR team or any person acting on their behalf (a) makes any warranty, express or implied, with respect to the use of any information or methods disclosed in this report or (b) assumes any liability with respect to the use of any information or methods disclosed in this report

MESOR – 038665 D 1.3.1 Future research objectives 5 of 49

Table of Contents 1 Executive Summary .......................................................................................7

1.1 Purpose and Scope................................................................................7 1.2 Major Findings........................................................................................7 1.3 Research Priorities...............................................................................12

2 Introduction ..................................................................................................13 2.1 Methodology.........................................................................................13 2.2 Document Structure .............................................................................14

3 Research Area 1 - Long-term Satellite Databases ......................................15 3.1 General Research Needs for Irradiance Databases ............................16

3.1.1 Re-sampling and Interpolation in Space and Time ......................16 3.1.2 Change in Altitude within a Pixel or a Cell....................................18 3.1.3 Spatial Disaggregation within a Pixel or a Cell.............................18 3.1.4 Long-term combination of data sources .......................................19

3.2 Improvement in Satellite-Based Cloud-Index Methods ........................20 3.3 Improvement in Satellite-Based Radiative Transfer Methods ..............23 3.4 Tilted Plane Irradiances .......................................................................26 3.5 Circum-solar radiation ..........................................................................26 3.6 Spectral Irradiances .............................................................................27 3.7 Sky Luminances and Illuminances.......................................................28

3.7.1 Sky Luminances...........................................................................28 3.7.2 Illuminance ...................................................................................29

4 Research Area 2 - Near-Real-Time Information ..........................................30 4.1 Global Irradiance Improvement for PV Plant Monitoring......................31 4.2 Improvement in Nowcasting for Electricity Grid Dispatching................32

4.2.1 Photovoltaics................................................................................32 4.2.2 Concentrating Solar Thermal Power ............................................33

4.3 Nowcasting for Optimizing Short-term Plant Operation .......................33 5 Research Area 3 - Forecasting Services .....................................................34

5.1 Current Status ......................................................................................35 5.2 Global Irradiance..................................................................................37 5.3 Direct Normal Irradiance ......................................................................38

6 Research Area 4 - Seasonal to Inter-annual Variability of Solar Radiation .40 7 Research Area 5 - Atmospheric Parameters for Radiation Retrieval...........41

7.1.1 Aerosols and Atmospheric Turbidity.............................................41 8 Research Area 6 - Auxiliary Information ......................................................41

8.1.1 Ground Reflectance .....................................................................41 8.1.2 Relative Humidity .........................................................................42 8.1.3 Snow Cover..................................................................................42 8.1.4 Water Temperature ......................................................................43 8.1.5 Wind Speed and Direction............................................................43

9 Research Area 7 - Interaction with other Renewables.................................43 10 Abbreviations ...........................................................................................45 11 References...............................................................................................47


1 Executive Summary

1.1 Purpose and Scope This report intends to develop an agenda for further R&D priorities in the field of solar resources. It provides guidance to decision makers in research as well as in energy related governmental bodies, space agencies, weather services, and related organisations for planning of future Earth Observation programmes. Knowledge of the solar energy resource has been generated over the past years within several European and national projects. Large steps forward have been made for the benefit of research, renewable energy industry, policy making and the environment. Nevertheless, these multiple efforts have led to a fragmentation and uncoordinated access: different sources of information and solar radiation products are now available, but uncertainty about their quality remains. At the same time, user communities lack common understanding on how to exploit the developed knowledge. The co-ordination action MESoR aims at removing the uncertainty and improving the management of the solar energy resource knowledge. The results of past and present large-scale initiatives in Europe, are integrated, standardised and disseminated in a harmonised way to facilitate their effective exploitation by stakeholders. This coordination action contributes to preparing the future roadmap for R&D and strengthening the European position in the international field. The project includes activities in user guidance (benchmarking of models and data sets; handbook; best practices), unification of access to information (use of advanced information technologies; offering one-stop-access to several databases), connecting to other initiatives (INSPIRE of the EU, POWER of the NASA, SHC and PVPS of the IEA, GMES/GEO) and to related scientific communities (energy, meteorology, geography, medicine, ecology), and dissemination (stakeholders involvement, future R&D, communication). Further, an overview on future objectives and priorities is developed, describing requirements for measurement systems, including Earth observation systems, services for effective management and deployment of solar resource knowledge and better fulfilment of the demands of the stakeholders. This report specifically focuses on research needs of the upcoming 10 years.

1.2 Major Findings The analysis reveals seven different research areas (Tab. 1) where focus is laid on by several research partners.

Research Area 1 Long-term Satellite Databases

Research Area 2 Near-Real-Time Information

Research Area 3 Forecasting Services

Research Area 4 Seasonal to Inter-annual Variability of Solar Radiation

Research Area 5 Atmospheric Parameters for Radiation Retrieval

Research Area 6 Auxiliary Information

Research Area 7 Interaction with other Renewables


Long-term satellite based databases

A precise representation of the probability distribution both of the global and direct irradiance is required. Additionally, information on the angular and spectral distribution of the in-plane irradiance is requested. Long-term data bases are available, but additional basic research is needed to fulfil the requirement of representative probability distributions.

The consistency property requiring that the average of a time series of instantaneous measurements should represent the average one could obtain from higher temporally resolved measurements is often not met. A standardized procedure to fill possible gaps or erroneous data in databases should be defined. Ground measurements of at least 10 minute temporal resolution are needed to study the variability within every hour. These fluctuations should be statistically characterized in order to be implemented in a typical meteorological year (TMY) of hourly radiation data for modelling purposes.

Processes providing short term variability as e.g. forest fires, sand storms, or small-scale cloud effects are currently not covered at all and need to be included in such approaches.

Changes in surface irradiance within a satellite pixel may be induced by changes in elevation, obstruction and shadowing by terrain, or by ground reflection.

Therefore, solutions are looked for that post-process the average surface irradiance and apply a corrective function or abacus depending on the actual site situation and taking these properties into account.

Methods for a systematic assessment of differences between ground measurements, model results and long term time series have to be developed or to be transferred from other fields of science to this application. The overall aim is to establish methods for long-term adjustment of local data sets.

The traditional method for deriving surface irradiance is based on the cloud-index approach. It generally provides accurate global irradiance measurements, but could be improved in certain situations. In order to separate clouds from snow, information from other spectral channels as e.g. infrared channels is necessary. Cloud top temperature products in combination with temperature profiles could help to avoid errors due to the parallax view between sun and satellite relative to the ground.

New aerosol optical depth data sets with high temporal resolution like the MATCH data set provided by DLR or the GEMS aerosol analysis data provided by the European Centre of Medium Range Weather Forecasts (ECMWF) need to be assessed and implemented to achieve a better representation of clear sky irradiances. In addition, also water vapour information should be considered from NWP analysis with high spatial and temporal resolution e.g. from ECMWF.

Cloud detection over bright surfaces (e.g. deserts) which have low contrast to clouds can be improved by using infra-red channels additionally to visible imagery.

A new paradigm is studied based on a direct modelling of the radiative transfer. This would permit to deliver knowledge on direct, diffuse components and spectral distribution, which is seldom offered by current methods. Explicit radiative transfer models exploit advanced products derived from recent satellite data such as aerosols from MODIS, ENVISAT and METOP and water vapour e.g. from the MSG satellite and are based on known radiative transfer models (RTM) already in use in the scientific community dealing with atmospheric optics.

It is known that the surface irradiance for a cloudy atmosphere can be considered as equal to the product of the irradiance obtained under a clear sky and a function of the cloud extinction and ground albedo contribution. This especially, allows retrieving new parameters as spectral or direct/diffuse irradiances


directly without loosing accuracy compared to existing CI methods. Therefore, this may replace empirical parameterisations for direct/diffuse or spectral separation which turn out to be mostly location dependent.

The consideration of bidirectional reflectance characteristics for different cloud types and for the combined reflectivity of ground and cloud free atmosphere based on radiative transfer calculations could improve the accuracy as well.

Table 1. Research Areas and Research Tasks.

MESOR Research Areas and Research Tasks

Research Area Research Task

RA 1

Long-term Satellite Databases

RT 1.1 Accurate distributions of global and direct irradiances

RT 1.2 Accurate angular and spectral irradiances

RT 1.3 Post-processing and long-term adaptation schemes for local effects as elevation, terrain, albedo, or climatic effects

RT1.4 Improved auxiliary information as snow cover, cloud height, and ground reflection

RT1.5 Irradiance retrieval methods based on radiative transfer

RT 1.6 Conversion of horizontal irradiances to tilted planes and illuminance values

RA 2

Near-Real-Time Information

RT 2.1 Forecasting standard for nowcasting of global and direct irradiances up to 3-6 hours

RT 2.2 Downscaling and statistical post-processing methods for highly resolved irradiance nowcasting

RA 3

Forecasting Services

RT 3.1 Forecasting standard for 48 hour forecasting of global and direct irradiances (day-ahead)

RT 3.2 Forecasting standard for intra-day forecasting based on merging and fusion technologies

RT 3.3 Probability information from ensemble forecasting

RT 3.4 Downscaling and statistical post-processing methods for highly resolved irradiance nowcasting

RA 4

Seasonal to Inter-annual Variability

RT 4.1 Long-term assessment of inter-annual variability over larger spatial areas

RT 4.2 Collaboration with the climate community

RA 5

Atmospheric Parameters for Radiation Retrieval

RT 5.1 Improved aerosol information for irradiance products

RT 5.2 Wind speed and gust modelling

RT 5.3 Cloud physical parameters as cloud height and cloud detection over bright surfaces

RA 6

Auxiliary Information

RT 6.1 Mapping of ground reflectance and standardized bi-directional reflectance functions

RT 6.2 Meteorological parameter accuracy

RA 7

Interaction with other Renewables

RT 7.1 Mapping of renewable energy resources together with balancing and averaging effects over large spatial scales

Once global, direct and spectral surface irradiance is known, it is needed to convert these parameters towards a tilted plane. It is recommended to develop services for irradiance on a tilted plane that propose an algorithm leading to the lowest uncertainty, depending on the location or situation.


Concentrating solar systems rely on accurate direct irradiance information. The circum solar radiation depends on typical cirrus and aerosol conditions at a certain location. Methods to derive climatologies or long-term time series of the circum solar radiation have to be developed based on radiative transfer modelling.

Spectral products or models aim at estimating the surface solar spectrum range from radiative transfer models with a higher precision compared to more simple models based on a parametric formulation of the atmospheric transmittance in specific wavelength intervals. In any case a specified set of atmospheric parameters has to be defined which is the decisive part for a solar spectrum. In that sense, the set-up of a data base for typical solar spectra for various atmospheric and geographical conditions is suggested.

The existing sky luminance models could be improved or even replaced by radiative transfer models which will better describe the physical phenomena behind the diffusion of solar radiation in the atmosphere. The quality of these models strongly depends on the righteous description of the atmospheric content: aerosol content, aerosol type, water vapour, and ozone. Therefore, this will only be possible, if the methods able to derive atmospheric characteristics from satellite images become operational, at least on a daily basis. Going from monthly averaged atmospheric input information as e.g. for aerosols in existing cloud index to daily information will be an improvement.

Increasing the spatial resolution (< 1 km) of the satellite images could be one way to improve the accuracy of the information needed for direct illuminance.

Near-Real-Time Information and Forecasting Services

A comprehensive forecasting scheme using the best practice for each time frame (now, upcoming hour, next 3 hours, intra-day up to 24h, day-ahead up to 48 h) is needed for plant monitoring and operations, grid integration and market participation in liberalized electricity markets. For all these forecast horizons the identification of most-promising models is needed.

The required resolution of few minutes and less than one kilometre is still too small for satellite nowcasting at the moment, but it might be possible to develop models for predicting these “high resolution disturbances”, when information of both satellite nowcasting and measurements on and near the site are combined.

In view of current and future solar energy applications major efforts have to be undertaken to improve the overall accuracy of solar irradiance forecasts, to increase its spatial and temporal resolution, and to provide information on the expected accuracy specific to the prevailing meteorological situation. The most relevant parameters to predict are surface solar irradiance (global irradiance) and its direct component (direct normal irradiance)

Improvement of models for solar radiation predictions can be expected by aerosol forecasting, ensemble forecasting, best member selection in a probabilistic approach, and through the assimilation by satellite data.

The introduction of aerosol information into NWP models has been recently shown and may lead to superior results. Improvements are expected in regions frequently showing strong events of atmospheric contamination by dust, desert sand, and biomass burning.

Dynamical downscaling uses a limited-area, high-resolution model (e. g. a meso-scale model) driven by boundary conditions from a large-scale model („Nesting“) to simulate finer-scale physical processes consistent with the large scale weather evolution.


Statistical methods for assessing uncertainty in NWP ensembles and statistical post processing via Bayesian Model Averaging have been introduced, but not applied for solar irradiance forecasting yet.

The spatial resolution of all existing forecasts is rather coarse. The use of meso-scale meteorological models may partly overcome these deficiencies as they inherently include a more detailed physical parameterisation of the relevant processes and show a spatial resolution down to 1 km and below. Currently, forecasts of the European Centre for Medium Range Weather Forecasts (ECMWF) are expected to provide best results when acting as a driving model. A systematic analysis of the quality of different combinations for the purpose of solar irradiance forecasting needs to be performed.

It has to be investigated whether commonly available forecasting information on cloud parameters may be beneficially used to infer values on solar irradiance.

To improve site-specific irradiance forecasts it has to be tested if modeling of the sub-pixel-sized cloud structures leads to higher accuracy for broken clouds.

Statistical post processing techniques, which may be applied to all classes of numerical models, eliminate systematic errors mainly introduced by a model bias or by the influence of local effects not covered by the model.

Satellite-based nowcasting of solar irradiance using cloud motion vector techniques and data assimilation of aerosols into air quality models may be combined with conventional forecasting techniques to provide intra-day global and direct irradiance forecasts.

Meteorological parameters as global and direct irradiance, temperature, relative humidity, wind speed and gust speed have to be provided in a time resolution of at least one hour and with a spatial resolution of less than 1 km². Research is needed to fulfil these user requirements. Current operational systems are far from these goals.

As the day-ahead schedule can be adopted during the day in the intra-day trading, updated solar irradiance forecasts according to the intra-day trading timelines promise a further reduction of scheduling uncertainty.

Furthermore, inter-relations between operation strategy, electricity stock prices, forecasts and actual weather situation need to be systematically investigated to provide optimal forecast-based operation strategies of solar power plants.

Besides high quality predictions for the next two days information of the probability of the forecasts is valuable for the operator.

Seasonal to Inter-annual Variability of Solar Radiation

Inter-annual variability with its temporal-spatial characteristics has to be assessed using long-term satellite data over larger spatial areas (e.g. NASA SSE or DLR SOLEMI databases) or meteorological long-term reanalysis data sets.

It is recommended to seek collaboration with the climate modelling science community to develop, assess, and disseminate model results.

Downscaling approaches for regional-level energy assessment activities responding to climate change impacts have to be developed.

Scenarios of global climate models should be analysed for their sensitivity to describe possible changes of the available solar resource due to global change and subsequent changes in regional cloud patterns or atmospheric aerosol load.

Atmospheric Parameters for Radiation Retrieval and Auxiliary Information

A strong influence of aerosols on surface irradiance is observed in regions frequently affected by dust, desert sand, and biomass burning events. Therefore, larger research effort to provide more accurate and better time-space resolved aerosol optical depth information is recommended.


This effort has to be defined by solar energy users to ensure suitable research covering not only the currently dominating climate and air quality communities and their information needs, but also the solar energy community needs.

A further scientific challenge is provided due to the temporal and spatial changes of ground reflectance. Ground reflectance depends upon land cover which may change dramatically within short distance, e.g., between the sea and the beach, as well within hours, e.g., snow fall on a dark surface. Work should be encouraged for the mapping of the ground reflectance over the world at say, 1-km scale on a daily basis.

The bi-directional reflectance function of natural surfaces should be expressed in a simple parameterisation for operational reasons.

For air temperature and relative humidity a further analysis of user requirements vs. the state of the art modelling accuracy is recommended.

An extended validation of existing snow cover data sets is required and a testing phase of integrating these data sets into operational photovoltaic plant monitoring schemes is recommended. Even if the detection of snow from a satellite pixel is improved, it is still difficult to decide if a PV system is covered by snow, even if the ground is covered by snow. Further research is needed to combine all the meteorological information and the alarm management software from the monitoring services.

Water temperature will get important for CSP plants operated close the coast using sea water for cooling or if they are combined with sea water desalination. For the desalination part the temperature of the incoming water is an important parameter.

For security warnings in large solar power plants, a now-casting approach for wind gusts needs to be developed.

1.3 Research Priorities Short-term (2010-2012) Long-term irradiance databases exist already and their frequent usage can be observed. An urgent need to provide improved auxiliary and atmospheric input data is observed. Partly, such data exists, but needs to be made available (e.g. RT 1.3 and 1.4). On the other hand, atmospheric research needs to be exploited in an optimum way to improve irradiance measurements (RT 5.1, 5.2, 5.3, RT 6.1 and RT 6.2) Major effort should be laid on the development of nowcasting and forecasting standards (RT 2.1, 2.2, 3.1, and 3.2) to allow electricity grid integration and market participation for solar power plants. Medium-term (2013-2016) Method development for irradiance measurements is recommended to take new or improved additional information into account (RT 1.1, 1.2, 1.5 and 1.6). Improved forecasting techniques known in atmospheric sciences as ensemble forecasting, downscaling methods and statistical post-processing (RT 3.3, and 3.4) are required if larger solar energy shares occur in the European electricity grid. Long-term assessments of inter-annual variability, the collaboration with the climate community and the assessment of balancing and averaging effects due to the use of several renewables in the electricity grid are further medium-term priorities.


2 Introduction

2.1 Methodology Results from the user surveys of MESOR WP 4 (D4.1 and D4.2) on requirements from various stakeholders are taken as the base for the work towards defining future research objectives. Additionally, the report on ‘New Solar Radiation Services’ (D1.3.2) prioritizes new services needed. MESOR WP1 reported on available data sources and their quality. From analysing strengths and weaknesses of each current data source and the inter-comparison to actual user requirements gaps of existing products and services are derived. Research needs to fill these gaps are analysed. If possible the report gives estimates on the achievable accuracy of new solar irradiance products by modelling. Emphasis is laid on capabilities available for European industries. A first effort was taken to harmonize terminology on timescales of data availability. Based on different traditions in the energy and the meteorological community the following compromise is used:

Historic long-term data: > 5 years duration of time series Near real time data: last 24 hours up to the upcoming 3 hours Nowcasting: Now and up to 3 hours from now Forecasting: Up to 2 days from now Seasonal forecasting: several months from now

Additionally, some definitions on physical radiation quantities are given in Tab. 2. Table 2. Parameter definitions Parameter name

Unit Explanation

aerosol optical depth

δae unit less Aerosol optical depth integrated over the whole solar spectrum

atmospheric absorption

α unit less or %

Atmospheric absorption, may be specified as below.

atmospheric transmission

τ unit less or %

Atmospheric transmittance, may be specified by indices for atmospheric constituents, e.g. ae for aerosol, cl for clouds or O3 for ozone. If not further indicated this refers to the perpendicular path of radiation through the atmosphere, otherwise sl indicates the slant optical depth.

clearness index k* unit less Ratio of the irradiance (irradiation) at ground level to the extraterrestrial irradiance (irradiation) for a given instant

cloud index n unit less A quantity used in the Heliosat-1, -2 family of algorithms. Denotes the total attenuation of the radiation by the atmosphere

cloud optical depth

δcl unit less Cloud optical depth integrated over the whole solar spectrum

diffuse irradiance

Edif W m-2 Irradiance reaching the ground after being scattered by the atmosphere and received on an horizontal surface

diffuse irradiation

Hdif J m-2 or kWh m-2

Irradiation reaching the ground after being scattered by the atmosphere and received on an horizontal surface

direct irradiance direct normal irradiance (or beam Irradiance)

ED

EDN W m-2 Irradiance received directly from the solar disk on an

horizontal surface Same as direct irradiance but for a surface normal to the sun beam

direct irradiation direct normal irradiation (or beam irradiation)

HD

HDN J m-2 or kWh m-2

Irradiation received directly from the solar disk on an horizontal surface Same as direct irradiation but for a surface normal to the sun beam

extraterrestrial irradiance

E0 W m-2 Extraterrestrial Irradiance at top of Earth’ atmosphere. Often called solar constant, which actually is relatively constant with an annual average of 1366 W m-2 +/-0.1% for the period 1979 to 1997 (Fröhlich and Lean, 1998).


global irradiance Eg W m-2 Sum of direct and diffuse irradiances global irradiation Hg J m-2 or

kWh m-2 Sum of direct and diffuse irradiations

illuminance Lux Irradiance integrated over wavelength from approx. 0.3 to 0.7 m following a spectral gauge describing the human eye sensitivity

irradiance E W m-2 Radiant flux of any origin incident onto an area element and integrated over the whole solar spectrum wavelength range from 0.3 to 3 m (International Lighting Vocabulary, CIE-no 45-05-160, 1970). The irradiance is a power. It is usually calculated from a temporal integration. Dividing by the time gives the average irradiance over this period. Consequently, one should formally say mean irradiance over an hour (hourly mean irradiance) etc. Note that the mean daily irradiance is calculated with days of 24 hours, whatever the daytime length.

irradiation H J m-2 or kWh m-2

Irradiance integrated over a certain period of time. For example, quarter-hourly irradiation is the integration of the irradiance over 15 minutes. Daily irradiation, also called daily sum of irradiation, is the energy received during a day (24 hours). Monthly irradiation values refer to the actual sum of all days in the indicated month. Due to variable length of months these values are harder to compare against each other than monthly averaged irradiances.

irradiance (irradiation) on slopes

Esl (Hsl)

W m-2 (J m-2)

Irradiance (irradiation) received on an inclined surface

radiance L W m-2 sr-1 Irradiance from a certain solid angle onto an area element and integrated over the whole solar spectrum wavelength range (International Lighting Vocabulary, CIE-no 45-05-150, 1970)

radiation General term denoting either irradiance or irradiation or any quantity relating to radiation depending upon the context.

reflectance ρ unit less or %

Reflectance of surfaces, e.g. indicated by indices cl for clouds, gr for ground (= albedo when integrated over all viewing angles)

spectral irradiance, spectral irradiation, spectral radiance

Eλ Hλ

Lλ

Denotes these quantities but integrated over a spectral window, e.g. a satellite channel or the reponse function of PV cell. May also denotes the spectral distribution of these quantities. If a wavelength is indicated this refers to the geometric center wavelength of a specific window.

All MESOR partners were invited to contribute to this document. Additionally, all participants of the International Energy Agency, Task 36 Solar Resource Knowledge Management and the GEOSS Energy Community of Practice Solar Energy Working Group were invited to review the document.

2.2 Document Structure Chapter 1 provides an executive summary providing an overview on all recommendations given together with the common understanding of the MESoR consortium about their priorities. Chapter 2 describes shortly the methodology used to set up this report and gives a basic glossary of physical quantities used. Chapter 3 describes research needs for long-term satellite based irradiance data bases (research area 1), while chapter 4 reviews near-real-time data provision and its research needs (research area 2). Chapter 5 focuses on forecasting of solar resources (research area 3). Chapter 6 is dedicated to discuss research needs for seasonal and inter-annual variability (research area 4). Chapter 7 and 8 deal with input parameters used in solar resource assessments as aerosols, clouds, turbidity, ground reflectance, relative humidity, snow, water temperatures and wind speed (research area 5 and 6). Research area 6 (chapter 8) deals with the research needs for modelling the interaction of different renewables.


3 Research Area 1 - Long-term Satellite Databases In view of the assessment of the economic outcome of solar energy projects their energy gain has to be predicted with high precision. Concerning the requirements on the respective meteorological input parameters this calls basically on the precise representation of the probability distribution both of the global and direct irradiance (Fig. 1) on the focusing plane or the PV module plane. Additionally, information on the angular and spectral distribution of the in-plane irradiance required. Knowledge of the angular distribution is necessary for the assessment of reflection losses. Information on the spectral distribution is needed for the modelling of the performance of PV-technologies showing a spectral response in a narrow wavelength band (e.g. amorphous silicon material, or a remarkably structured wavelength response (e.g. staged – tandem or triple – cells). The modelling of the performance of an ensemble of solar energy systems that are connected to the same electrical supply grid requires information on the irradiance field beyond the single point characteristics. Depending on the ratio of the total solar generation to grid load, the energy flow caused by the solar systems may affect both, the schedule for the dispatchable generation facilities and the general energy management, and the stability of the grid operation. To describe this energy flow, the space and time characteristics of the irradiance field must be known. This raises on one hand the question of the spatial and temporal resolution of irradiance data necessary to cope with the required detail of the modelling of the energy flows in the grid. If necessary, methods to interfere characteristics of higher spatial frequencies from low resolution sets have to be developed. On the other hand, the addition of the spatial component sets new requirements to the data quality, as multi-dimensional characteristics of the irradiance field must be properly reflected. To ease the analyses of the performance of distributed systems, new methods to classify the spatial-temporal characteristics of the irradiance field have to be developed together with methods that correlate these characteristics to indicators of the quality of supply. Fig. 1: Direct, diffuse and global radiation as needed in solar energy applications According to stakeholder interviews, the average satisfaction with global irradiance databases is higher (4.0 out of maximum of 5.0 points) than for all other required geophysical parameters.


According to stakeholder interviews, the average satisfaction with direct irradiance databases is only 3.0 of a maximum of 5.0 points. The same situation is found for direct and diffuse illuminance. For these parameters missing data accuracy was named as a blocking factor.

3.1 General Research Needs for Irradiance Databases Applied mathematics is used in every aspect of the solar radiation assessment. Here, we focus on the research area that deals with the environmental modelling and more exactly on the modelling of the sub-scale variability. The environment, including the surface solar irradiance (SSI), is perceived through instruments, imagers or not, or is simulated by numerical models. Both instruments and models have properties to support information both in space (equivalent to a pixel or cell) and time. Within their resolution, there is always a variability which is unresolved. This variability has an influence on several aspects of the further processing and exploitation of the SSI and derived quantities as it should be modelled in such processes. In several cases, the modelling of the variability is necessary for the assessment of the SSI. In the MESoR project, we have identified several specific areas of research pertaining to the modelling of the unresolved variability. They often share similar mathematical tools which originate from signal and image processing. Such researches are not new. The accumulation of experience shared in scientific literature evidences the need for further progresses as present solutions are challenged by the increasing request in accuracy. The identified specific areas are:

resampling and interpolation in space and time; change in altitude within a pixel or a cell; spatial disaggregation within a pixel.

A result of the MESoR project in this aspect is the emergence of the property of consistency. Assume a SSI computed over a day. Assume a processing that resamples this SSI value over 24 h. The summation of the hourly values should be equal to the original daily value. The consistency property expresses this concern in a formal way: “the average of a series of values obtained from an original value by taking into account the unresolved variability within the support of this original value, should be equal to the original value.” This property should be taken into account in the work to be accomplished.

3.1.1 Re-sampling and Interpolation in Space and Time Regarding irradiance data, a standardized procedure to fill possible gaps or erroneous data in databases should be defined. Interpolation is a mathematical operation for inferring a missing value from two or more available observations. Interpolation may be used to fill gaps in time-series of observations or in spatial fields. Methods for the assessment of the surface solar irradiance (SSI) from satellite data do not function when the sun is low above horizon (Cano et al. 1986; Deneke et al. 2008; Diabaté et al. 1988; Hammer et al. 2003; Lefèvre et al. 2007; Perez et al. 1997; Raschke et al. 1987). Interpolation is then called upon to provide a complete time-series for the day. Re-sampling is a mathematical operation for changing the representation of a variable with time or space starting from an available representation. For example, the method of Collares-Pereira and Rabl (1979) is a re-sampling operation: it infers hourly values of SSI from a daily value. Re-sampling may also


be used to convert data from one time system, e.g., Universal Time, to another, e.g., true Solar Time. Re-sampling and interpolation are routinely performed in meteorology, including the SSI. The large number of articles devoted to interpolation or re-sampling in literature in signal processing demonstrates that there is no single solution for each of them. In fact, regarding the SSI and other meteorological parameters, re-sampling and interpolation methods share a common environmental background: the behaviour of the SSI in time or space. Consequently, working on interpolation or re-sampling often leads to progress to the other operation. This is illustrated by a relevant example: In most cases, there are not enough solar radiation ground measurements available to develop a complete study of the energy production of a planned solar thermal power plant. In such cases solar radiation time series provided by satellite has to be used. Although satellite information is highly valuable it is normally delivered only on an hourly basis. However, cloudy transients can occur in a time scale of 10-minutes or less, and they can have a noticeable impact in the plant performance, so it would be desirable to make the modelling of a power plant with solar radiation time series of high frequency, al least 10-minute data. In absence of 10 minute data measured some kind of synthetic generation method could be used for downscaling the hourly values to 10 minute data. Ground measurements of 10 minute data are needed to study the fluctuations of 10-minute radiation data within every hour. These fluctuations could be statistically characterized in order to be implemented in a typical meteorological year (TMY) of hourly radiation data for developing a synthetic TMY of 10-minute radiation data suitable for modelling purposes. Processes besides clouds providing short term variability as e.g. forest fires, sand storms, or local cloud effects are currently not covered at all and need to be included in such approaches. Prior to selecting a mathematical method, e.g., bi-cubic interpolation, one must also contemplate the relevant parameter to interpolate. For example, one may use the SSI itself, or the clearness index (Cano et al. 1986, Deneke et al. 2008; Diabaté et al. 1988; Raschke et al. 1987) or the clear-sky index if a clear-sky model for SSI is available (Lefèvre et al. 2007). Each parameter has advantages and drawbacks and this should be analysed. One may question the practical interest of such studies. To illustrate the deviations due to re-sampling, we performed a simple case using minute values of the direct component of the SSI measured at Payerne (Switzerland) on a horizontal plane. These 1-min values are aggregated to 15-min values by averaging (equal weight). These 15-min values constitute the data to be re-sampled. The re-sampling to 1-min is made by a usual hypothesis of locally linear trend in clearness index. Table 1 (column labelled “1-min”) reports the discrepancies between the original 1-min values and the re-sampled ones, for the direct component of the SSI and on a plane always normal to the sun rays (DNI). One may note the large error – represented by the relative root-mean square of the differences (RMSE) – induced by the re-sampling; approximately 30 % of the values exhibit a relative error greater than 35 %. Further research could permit to increase the accuracy in re-sampling. In addition, we have tested the consistency property. The re-sampled 1-min values are aggregated to 15-min and compared to the original 15-min values. Would the process be perfect, no difference would appear, except for numerical


rounding: the consistency property should be respected. This is not the case. Table 3 shows a relative RMSE of respectively 12 % and 11 % for direct component of the SSI and DNI. Approximately 30 % of the values exhibit a relative error greater than 11 %. The Kolmogorov-Smirnov index KSI (not shown in the Table) reaches large values: 8 %, in both cases, showing the harm caused by the re-sampling to the statistical distribution of values. This example shows that the consistency property is not respected by this standard approach and illustrates the needs for improvement. Table 3. Difference (relative root mean square error) between original 1-min values and the re-sampled ones (column “1-min”) and between original 15-min values and those resulting from the aggregation of the 1-min re-sampled values (column 15-min”), source Mines ParisTech.

1-min 15-min

Direct component of SSI 35 % 12 %

DNI 37 % 11 %

3.1.2 Change in Altitude within a Pixel or a Cell The SSI depends on the altitude of the site. The SSI is assessed as an averaged value over a pixel or a cell, using the mean altitude of this pixel or cell. Within the pixel, the altitude may vary and the actual SSI may differ significantly from the SSI computed at mean altitude. Wahab et al. (2009) report that a difference in altitude of 300 m may induce a relative difference greater than 1 % on the monthly mean value of the SSI. It is not always easy to compute the SSI for each possible altitude within a pixel as the SSI results from a complex processing of satellite data or numerical model that requests large computer resources or time. Therefore, solutions are seeked for that post-process the averaged SSI and apply a corrective function or abacus depending on the actual altitude compared to the mean altitude. These models cannot integrate an explicit description of the radiative transfer in the atmosphere. There are several possible approaches e.g., an analytical function or an abacus to this problem which should be analysed to establish their advantages and disadvantages. Practical solutions should then be established that deal with the SSI and its components and its spectral distribution. The spatial variability of solar radiation is also influenced by the topography of the Earth’s surface, where shadowing by neighbouring terrain is the most important effect. Approaches of integrating a correction for topographic effects to retrieval methods have been proposed e.g. by Dürr and Zelenka (2007) for the Alps or by Ruiz et al. (2009). The adaptation of terrain correction methods to the different cloud index methods and respective evaluations are recommended.

3.1.3 Spatial Disaggregation within a Pixel or a Cell Databases of SSI covering large geographical areas contain values that are averaged over a pixel or a grid cell. The size of the pixel or cell may vary from typically 1 km to 300 km. An increase in accuracy of the SSI needs to take into account the spatial variability of the SSI within this pixel. Taking into account this variability may induce different gains in accuracy: a priori or a posteriori correction of the mean SSI by a better modelling of this unresolved variability, or a better assessment of the SSI for a given location within the pixel.


This section deals with the latter, i.e., the case where the SSI should be assessed at a given site starting from the knowledge of a SSI value averaged over a pixel. Of course, the solution to this problem includes aspects discussed before: re-sampling techniques and modelling the change in altitude. This problem can be seen as a spatial disaggregation of the averaged value. Fig. 2 represents the relief in the Southern part of the French Alps extracted from the Google Earth tool. On each figure, the red square denotes the limits of the Meteosat pixel on which an average value of the irradiance is provided. Both figures stand for the day 25 March 2009. The left figure exhibits the shadows induced by the terrain at 12:22 True Solar Time (TST), while the right figure is for 16:40 TST. The hourly mean of irradiance produced over the pixel by the database HelioClim-3 is respectively 533 W/m² and 222 W/m². These figures show that within the pixel, the actual irradiance depends strongly upon the position within the pixel and that there is a large variability due to the shadowing.

Fig. 2: Relief in the Southern French Alps together with a METEOSAT pixel (red). Both images represent 25 March 2009. The left image is taken at 12:22 True Solar Time (TST), while the right image shows the same area, but for 16:40 TST. More shadows can be observed (source Mines ParisTech). Changes in SSI within a pixel may be induced by changes in elevation, obstruction and shadowing by terrain, in ground reflection coefficients and in atmospheric constituents, including clouds. The spatial disaggregation must take into account these various sources. Depending on specific cases, the fractal dimension of the atmospheric phenomena may be used to predict changes within the pixel. Changes due to terrain may be modelled by the joint exploitation of detailed digital elevation models and modelling of the direct, diffuse and reflected components of the SSI. Changes in ground albedo, including bi-directional effects, are delicate to model especially because of the lack of knowledge of this parameter within the pixel and of its spectral variation.

3.1.4 Long-term combination of data sources For a potential power plant site ground measurements are typically available only for one or two years. On the other hand, both economic assessments and technical design of the plant requires long-term data information. Therefore, satellite time series lasting e.g. 10 years long are used. Ground measurements are assumed to be more accurate than satellite measurements assuming that they are cleaned daily and are well maintained. Therefore, the comparison of a 1 year ground data set versus the overlapping year of satellite measurements can reveal systematic deviations typical for this


certain location. These deviations can occur as a mean bias difference, a different statistical distribution of occurring values or as different scatter or bias in certain meteorological conditions (‘bias monitoring’). The same holds for the assessment of solar irradiance based on a reanalysis numerical weather prediction dataset. Methods for a systematic assessment of such differences between ground measurements, model results and long term time series have to be developed or to be transferred from other fields of science to this application. The overall aim is to establish methods for long-term adjustment of local data sets.

3.2 Improvement in Satellite-Based Cloud-Index Methods Cloud-index methods are operationally used to derive global and direct irradiance from geostationary satellites with high temporal and spatial resolution (e.g. (Hammer et al, 2003; Perez et al., 2002; Rigollier et al., 2004; Schillings et al., 2004; fig. 3 and 4). Also most of the high resolution databases investigated within MESoR (see MESoR D1.2.1, Handbook on Benchmarking) are based on cloud-index methods. Fig.3: Measurement principle as used in cloud-index methods (source Mines Paris Tech) To determine the solar irradiance at ground level information on clouds is necessary as well as information on atmospheric parameters as e.g. aerosol optical depth (AOD) or water vapour (WV). Cloud-index methods take advantage of the differences in reflectivity between clouds and ground in the visible channel to derive information on cloudiness, characterized by the dimensionless cloud index (CI). To calculate SSI the CI is combined with a clear sky model describing the irradiance for clear sky conditions in dependence on the atmospheric parameters using an empirical relation. Fig.4: Example of satellite data as used in cloud-index methods (source DLR)


Detailed accuracy assessment has been performed for different versions of cloud-index methods (e.g. Drews et al., 2007; Perez et al., 2002; Rigollier et al., 2004; Schillings et al., 2004). A comparison of different versions using the same validation data base is given in the MESoR deliverable D1.1.3 ‘Report on Benchmarking’. The evaluations show that cloud-index methods provide a good estimate of the global surface irradiance, especially long-term monthly mean values show high accuracy of 5% RMSE. The deviations between satellite derived and ground measured direct normal irradiance are larger than for global irradiance, but still a reasonable accuracy of 15% RMSE for monthly mean values is found. Fig. 5 illustrates the performance of cloud index methods for hourly values of global irradiance (left) and direct normal irradiance (right). Fig. 5: Example for satellite derived global (left) and direct normal irradiance (right) in comparison versus ground measured irradiance for a site in Southern Spain for four days in May 2005 (source Univ. Oldenburg). Despite the good performance of cloud index methods in general, there are certain situations, like the presence of snow cover, where large deviations between grounds measured and satellite derived irradiances may occur, and further research is required. In addition, the use of additional information, e.g. on cloud height or detailed topographic features, can further improve the accuracy of cloud index methods. In detail, the following approaches for improvement of cloud index methods are recommended: Better discrimination of clouds and snow In the presence of snow cover larger errors may occur, when using cloud-index methods. The satellite derived irradiance values indicate a cloudy sky, since the snow covered pixels appear bright as clouds in the visible channels of the satellite images, as illustrated in Fig. 6. In order to separate clouds from snow, information from other spectral channels, including different infrared channels is necessary. First approaches to better account for snow cover have been presented by (Dürr et Zelenka, 2007) for the Alps and by (Heinecke 2006). For further improvement the use of different snow cover data sets should be investigated. A first validation of different snow data sets performed by (Wirth et al., 2008) can contribute to the selection of suitable data sets.


Fig.6: Example for satellite derived global irradiance with (blue) and without (green) snow detection in comparison versus ground measured values for a German station in March 2005 (source Univ. Oldenburg). Parallax correction Most operational versions of the cloud index methods do not account for different viewing angels of sun and satellite (Fig. 7). Lorenz (2007) have shown that the accuracy of satellite derived irradiance values can be enhanced with a parallax correction using typical cloud heights depending on the latitude and season by Minnis and Young et al. (2004). A further improvement can be expected when evaluating information on cloud heights with high spatial and temporal resolution. Cloud top temperature products (e.g. APOLLO, Kriebel et al., 1989) in combination with typical temperature profiles or temperature profiles from NWP reanalysis data could deliver this enhanced information. Fig.7: Scheme of parallax correction principle (source Univ. Oldenburg) Angular dependence of reference values Cloud index methods compare the actual measured reflectivity value to reference values for cloud free sky and cloudy sky to derive the degree of cloudiness. Different cloud index methods apply different approaches to determine these reference values, including simple parameterisations, or empirical methods based on reflectivity histograms. The consideration of bidirectional reflectance characteristics for different cloud types and for the combined reflectivity of ground and cloud free atmosphere based on radiative transfer calculations could improve this situation. Broken clouds The influence of inhomogeneous clouds on the irradiance is in general not modelled explicitly by cloud index methods. Lorenz (2007) developed a simple parameterisation to consider for the effect of non-homogenous clouds in dependence on the variability in consecutive satellite images or in the spatial domain. Further investigations of broken cloud effects using three-dimensional radiative transfer modelling (e.g. (Mayer et Killing) can provide the basis for a better description of the irradiance conditions for inhomogeneous cloud situations


Atmospheric input parameters To derive the surface irradiance, the cloud index is combined with a clear sky model describing the irradiance for clear sky conditions in dependence on the atmospheric parameters. Most cloud-index methods consider atmospheric input parameters (Linke turbidity or alternatively aerosol optical depth, water vapour and ozone) in form of climatological values. There exist several data sets of atmospheric parameters that considerably differ from each other. The choice of the atmospheric input data has a strong influence on the irradiance calculation, especially when considering direct normal irradiance. Fig. 8 illustrates the large differences in annual DNI sums for different data sets of AOD. Two of these data sets consist of monthly climatology values. In addition, a first analysis of the use of daily values resulting from the Model of Atmospheric Transport and Chemistry (MATCH), developed at NCAR (Collins et al., 2001), and operated at DLR-DFD for solar energy purposes, is included. We recommend analysing the use of new AOD data sets with high temporal resolution like the MATCH data set or the GEMS aerosol analysis data (Benedetti et al, 2008) provided by the European Centre of Medium Range Weather Forecasts (ECMWF) for a better representation of clear sky irradiances. In addition, also WV information should be considered from NWP analysis data with high spatial and temporal resolution e.g. also from ECMWF. Fig. 8: RMSE and bias of satellite derived global irradiance in comparison to ground measured values for six Spanish, two Swiss, and one English station for 2005 (source Univ. Oldenburg). Comparison of three input data sets of AOD following Kinne et al. (2005), GACP, and MATCH (Collins et al., 2001).

Summarizing it can be stated that there is potential to further improve cloud-index methods by integrating additional or enhanced information on e.g. snow cover, cloud height, cloud type, and atmospheric input parameters. In a first step, the use of different state of the art data sets should be investigated. In long term, cloud-index methods will take advantage of further upgrades of these input data sets. As stated in section 4, precise ground measurements are required as reference values for further model development and to investigate model performance in different climatic regions.

3.3 Improvement in Satellite-Based Radiative Transfer Methods Satellite data are routinely used for the assessment of SSI. Most current methods are inverse, i.e. the inputs are satellite images whose digital counts result from the ensemble of interactions of radiation with the atmosphere and the ground during the downward and upward paths of the radiation. Experience has been gained in such methods at an international scale which is shared through publications and collaboration, especially within the project MESoR and its counterpart IEA SHC-36.


Very good results are presently attained by the most powerful inverse cloud index methods, as shown by the benchmarking activities in MESoR and IEA SHC-36. However, it is felt that limits of such methods are presently reached in terms of accuracy, except for those are specialized to a specific region using empirical fittings (Polo et al., 2006). Consequently, a new paradigm is studied based on a direct modelling of the radiative transfer (fig. 9). This would permit to deliver knowledge on direct, diffuse components and spectral distribution, which is seldom offered by current methods. Fig.9: Retrieval principle as used in explicit radiative transfer methods to derive irradiance information (source Mines Paris Tech) Explicit radiative transfer models exploit advanced products derived from recent satellite data such as aerosols (Holzer-Popp et al., 2002) and water vapour (Schroedter-Homscheidt et al., 2008), and may be based on known radiative transfer models (RTM) already in use in the scientific community dealing with atmospheric optics, such as libRadtran (Mayer and Kylling, 2005). This would ensure a strong collaboration between the two communities and the “solar energy” community will benefit from the progress made in atmospheric optics either in the assessment of optical components or in modelling the radiative transfer. Another point is cloud detection of bright surfaces (e.g. deserts) which have low contrast to clouds. In these regions, a cloud detection using infra-red channels additionally to visible imagery needs to be included. There are many inputs to RTMs. Oumbe et al. (2008) performed an inventory of the variables (e.g., cloud) and their attributes (e.g., optical depth) available in an operational mode and assessed to which degree the uncertainty on an attribute of a variable –including the absence of value– leads to a variation on the SSI. They found a number of significant inputs:

solar zenith angle and number of the day in the year, cloud optical depth, cloud type, water vapor amount, aerosol optical depth and its spectral variation, aerosol type, ground albedo and its spectral variation, atmospheric profile, ground altitude.


Oumbe et al. (2008) underline that these influence parameters are currently available on different spatial resolutions and temporal frequencies. For example, water vapour may be estimated every 50 km once a day, while cloud properties may be assessed every 3 km and 15 min using Meteosat data. This heterogeneity in inputs has a strong impact on the design and realisation of an operational system for providing direct and diffuse components and spectral distribution of the SSI for the Meteosat image pixels. Knowledge gained in activities relating to the use of applied mathematics (see section 5.1) will be therefore used in such an operational system as “resampling”, “change in altitude” and “disaggregation” are necessary to accommodate or fuse data offering such an heterogeneity. Another result of this sensitivity analysis (Oumbe et al., 2009) is that the influences of vertical position and the geometrical thickness of clouds in the atmosphere are negligible. Thus, the SSI for a cloudy atmosphere can be considered as equal to the product of the irradiance obtained under a clear sky (Iclearsky) and a function of the cloud extinction and ground albedo contribution (Tcloud+albedo):

I = Iclearsky Tcloud+albedo

This result is important in the view of an operational system as it permits to separate the whole processing into two distinct models, whose inputs are different. This is also a mean to further cope with the heterogeneity of the inputs as discussed above. Besides the scientific aspects of the modelling of the radiative transfer, the operational difficulty in computing time exists. Radiative transfer models are time-consuming and this is often contradictory with real-time execution on images of 9 millions of pixels acquired every 15 min. Degraded versions of models are available which may satisfy time-constraints but usually do not satisfy the request for increasing accuracy in SSI retrieval. The equation above may be an element of solution, especially coupled with analytical modelling of the SSI under clear-sky as proposed by Mueller et al. (2004) and two-stream approximation for cloud layer (Paris and Justus, 1988). Other elements of solution lie in the design of the method. For example, one may adopt look-up tables, or pre-calculated abacus, that are lengthy to compute but then allow rapid calculations. Other solutions exist that may e.g., analyse the frequency of occurrence of sets of inputs and provide less accurate values as this frequency decreases, i.e., for extreme cases; these solutions are not further discussed here. Nevertheless, it should be emphasized that the selection of a solution has an impact on the accuracy of the retrieval of the SSI and conversely, one should exploit physics to design the most appropriate solution. The Heliosat-4 method is under joint development by the DLR (German Aerospace Center) and MINES ParisTech. The libRadtran RTM has been selected for this development. A prototype is available and Oumbe et al. (2009) report on preliminary results on the comparison between several stations used in the benchmarking made within MESoR and the hourly means of SSI retrieved by Heliosat-4 (Tab. 4 and 5). The authors found an overestimation while the standard deviation is within the typical range of CI methods. The bias can be large and may be explained by the poor quality of several inputs to the clear-sky model. The correlation coefficient is large which shows that the rapid variations of the SSI are well-reproduced. The standard-deviation of the differences is fairly small with respect to what is usually


observed with the current methods. This means that the scattering of the SSI around the mean value is small, i.e., that the retrieval is currently very reliable, though inaccurate (because the bias is large). This is very encouraging and this preliminary work shows that there is space for progress in this new type of method. This especially, would allow retrieving new parameters as spectral or direct/diffuse irradiances directly and without loosing accuracy compared to existing CI methods. Therefore, this may replace empirical parameterisations for direct/diffuse or spectral separation which turn out to be mostly location dependent. Table 4. Stations used for Heliosat-4 validation (source Mines ParisTech) Site Station Country Latitude /

Longitude(°) Altitude (m)

1 Carpentras France 44.05 / 5.05 110 2 Dresden Germany 51.13 / 13.78 214 3 Freiburg Germany 48.04 / 7.87 241 4 Geneva Switzerland 46.25 / 6.13 407 5 Ispra Italia 45.82 / 8.60 191 6 Payerne Switzerland 46.81 / 6.94 492 7 Tamanrasset Algeria 22.78 / 5.51 1368 8 Vaulx-en-Velin France 45.78 / 4.93 173

Table 5. Performance of Heliosat-4 for global SSI (source Mines ParisTech). Site Mean (W m-2) Bias (W m-2) Standard-deviation (W

m-2) Correlation coefficient

1 566.5 38 (7 %) 79 (14 %) 0.948 2 451.0 58 (13 %) 95 (21 %) 0.901 3 528.7 82 (15 %) 89 (17 %) 0.932 4 491.8 23 (5 %) 104 (21 %) 0.917 5 499.9 60 (12 %) 82 (16 %) 0.942 6 556.0 62 (11 %) 91 (16 %) 0.918 7 714.9 16 (2 %) 68 (9 %) 0.966 8 542.4 87 (16 %) 100 (18 %) 0.917

3.4 Tilted Plane Irradiances Another source of uncertainties using satellite derived irradiation is the conversion into tilted planes. A large variety of empirical algorithms exist to convert the horizontal insolation derived from satellite data into the plane of the PV modules. The accuracy of these algorithms depends, for example, on the fraction of direct radiation. Even if customers were confident on the direct/diffuse fraction, there is a large uncertainty on what model to use for calculating irradiance on tilted planes. In practice, and based on experience, the Perez model (Perez et al., 1987) is used for locations in Germany, while the same companies prefer Klucher’s algorithm (Klucher, 1979) for systems in Spain. There is the strong need to harmonise these approaches. It is recommended to develop services for the conversion of irradiance on a tilted plane that propose an algorithm leading to the lowest uncertainty, depending on the current location or situation.

3.5 Circum-solar radiation There is a need to describe the circum solar ratio (CSR) on a site-specific level. Circum solar radiation is mainly caused by thin cirrus clouds and aerosols. Depending on typical cirrus and aerosol conditions the CSR is location-specific. Methods to derive climatologies or long-term time series of CSR have to be developed based on radiative transfer modelling.


3.6 Spectral Irradiances The energy yield of most solar energy technologies not only depends on the available broad-band radiant fluxes but also shows a significant influence of the site-specific spectral irradiance distribution (Fig. 10). This importance increases with the introduction of thin-film and multiple-junction photovoltaic technologies which show an even stronger spectral dependency.

Fig.10: Spectral global irradiance for different air mass factors (source Univ. Oldenburg)

Two major applications are the reason for increased research efforts in solar spectral irradiance. A proper yield estimate for photovoltaic power plants using these new technologies needs detailed information on the spectrally resolved irradiance as well as appropriate simulation tools to simulate the system yield based on this spectral information. In addition, multi-junction solar cells may be designed to optimise its spectral response with respect to (site-specific) power generation. This makes the availability of realistic spectral data and simulation tools necessary. Spectral products or models aim at estimating the surface solar spectrum range from radiative transfer models with a higher precision compared to more simple models based on a parametric formulation of the atmospheric transmittance in specific wavelength intervals. In any case a specified set of atmospheric parameters has to be defined which is the decisive part for a solar spectrum. For that reason, commonly used reference spectra (e.g., the ASTM spectra for global and direct normal irradiances) are not necessarily representative for a given site as they are developed to be applicable in a very large amount of cases. In that sense, the set-up of a data base for typical solar spectra for various atmospheric (and geographical) conditions is suggested. A more systematic analysis of these specific spectral conditions on the power output of solar systems needs to be conducted. Especially for regions with high or special aerosol concentrations this information is of very high importance. As an example, thin-film solar cells show an improved performance in low light conditions compared to crystalline silicon cells. This is because of the high


contribution from the short-wave part of the spectrum under diffuse irradiance. These cells therefore are adequate technologies for northern latitudes. Another promising option to provide solar spectral irradiance data with high spatial and temporal resolution is again the use of satellite-derived data. In case the method is based on an integrated use of radiative transfer calculations it inherently operates on a spectral separation of the total radiant flux. This approach - whilst showing a very high potential - depends on the availability of atmospheric information especially on aerosols and water vapour. Studies on the quality of this approach have not yet been presented. This has to be accompanied by measurements for validation, both with an appropriate spectroradiometry and with simultaneous PV characteristic measurements.

3.7 Sky Luminances and Illuminances

3.7.1 Sky Luminances Daylighting a building is a complex task which requires defining (1) the size and the position of the windows which will provide the best distribution of daylight in the space, (2) the shades and their control which will protect users against glare and overheat and (3) the controls which for energy savings will adjust the power of the artificial lighting system according to the amount of daylight available. Daylighting requires describing how much light is available inside the space. This is only possible if the spatial distribution of the luminance (or radiance) of the sky vault is known. This is the main input parameter for daylight simulation program. Since, the luminance distribution of the sky vault is difficult to measure; models have been developed to produce this information from more widely measured parameters such as the global and diffuse horizontal irradiances and illuminances. The existing sky luminance models could be improved or even replaced by radiative transfer models which will better describe the physical phenomena behind the diffusion of solar radiation in the atmosphere. The quality of these models strongly depends on the righteous description of the atmospheric content: aerosol content, aerosol type, water vapor, ozone. Therefore, this will only be possible, if the methods able to derive atmospheric characteristics from satellite images become operational, at least on a daily basis. All these models have the inconvenience to describe the sky as being homogeneous; in other words, the luminance distribution that they predict is symmetrical with respect to the solar meridian; this rarely occurs under sparsely cloudy or cloudy conditions. In previous European research projects (SATEL-LIGHT-1996, HELIOSAT-3-2001), P. Ineichen from the University of Geneva has tested whether pixels of the satellite images could be used to derive useful information on the luminance distribution of the sky vault (Fig. 11). Unfortunately, this is not the case with the current generation of Meteosat satellites; results from the Heliosat-3 programme (see an example below) showed that satellite images could describe the luminance distribution of the sky vault with only four values (4 zones) which is far from sufficient and not enough to improve the performance of the existing models. However, it is expected that a better knowledge of the atmospheric state (aerosol content, water vapour, cloud opacity) combined with increased spatial (< 1 km) and temporal (< 5 mn) resolutions should allow the


processing of satellite images to produce non homogeneous sky luminance distributions much closer to reality than the existing models. Fig. 11: The existing generation of METEOSAT HRV channel cannot provide information on sky luminance distribution better than existing models based on simplified ground measurements (source P. Ineichen).

3.7.2 Illuminance For the time being, the methods which produce illuminances from satellite images apply a luminous efficacy model on the irradiances derived from the satellite. Luminous efficacy is the ratio between illuminance (fig. 12) and irradiance. The luminous efficacy of the diffuse radiation (sky vault) varies around 140 lm/W. The luminous efficacy of the direct radiation (sun) varies around 100 lm/W. The luminous efficacy depends on the content of the atmosphere, therefore the models would certainly benefit from the better knowledge of the atmosphere acquired from satellites. Furthermore, the recent use of radiative transfer models in the methods used to derive irradiances from satellite images (HELIOSAT-3 on the new generation of METEOSAT satellites) could also benefit the computation of illuminances by removing the use of luminous efficacy models. Radiative transfer models can produce spectral irradiances for the whole solar spectrum. The spectral irradiances can be integrated to produce the irradiance. They can also be combined with the spectral sensitivity of the eye to produce through integration the illuminance. This method has the advantage to offer a perfect coherence between irradiances and illuminances and a better accuracy on illuminances, since the processes of diffusion and attenuation of solar radiation through the atmosphere are better taken into account by RTMs than empirical luminous efficacy models.


Figure 12: Principle of illuminance (source P. Ineichen).

The method was tested within the framework of the HELIOSAT-3 project. It showed improved results in two of the four sites used in the test. It also showed that additional research was needed to perform a complete validation of the spectral irradiances provided by the RTMs knowing that they depend on the availability of daily information on the atmosphere. For daylighting, there is more importance in being able to compute the absolute value of the diffuse illuminance with accuracy than the absolute value of the direct illuminance. The direct illuminance is related to glare, so whenever the sun is on the façade and exceeds a given luminance threshold, shades are drawn or moved, to protect users against visual discomfort. The critical information for direct illuminance is therefore related to the frequency at which this threshold is exceeded which indeed gives an idea on how often the shades will be used. Satellite methods do not have problems at predicting whether the sun shines or not, under cloudless or fully cloudy conditions. They do have problems under intermediate conditions where blue sky and clouds are mixed into the same pixel. Increasing the spatial resolution (< 1 km) of the satellite images could be one way to improve the accuracy of the information needed for direct illuminance.

The accuracy of predicted diffuse illuminance depends on aerosol and water vapour content (cloudless sky situations) and on cloud opacity (cloudy situations). Going from monthly averaged atmospheric input information as e.g. for aerosols (existing methods) to daily information will be an improvement. When the satellite methods will be able to produce sky luminance distributions, the diffuse horizontal illuminance will be obtained by integrating luminances over the sky vault.

4 Research Area 2 - Near-Real-Time Information Research needs described in the long-term database section to improve data accuracy are generally applicable also for nowcasting services based on satellite measurements. This section therefore concentrates on additional requirements in nowcasting services.


It is recommended to compare existing now-casting based on different principles based on satellite and ground measurements. These are based on the improvement of models for solar radiation predictions by aerosol forecasting, ensemble forecasting, best member selection in a probabilistic approach, and through the assimilation by satellite data. A comprehensive forecasting scheme using the best practice for each time frame (now, upcoming hour, next 3 hours, intra-day up to 24h, day-ahead up to 48 h) is needed for plant monitoring and operations, grid integration and market participation in liberalized electricity markets. For all these forecast horizons the identification of most-promising models is needed.

4.1 Global Irradiance Improvement for PV Plant Monitoring PV plant monitoring (fig. 13) is still an evolving market. The use of satellite derived irradiance is important especially for small systems where no irradiation sensor is attached due to economical reasons. Also operators of multi megawatt plants are interested in using satellite data as also ground measurements at the PV plant site can show data gaps, e.g. due to short time loss of electricity of the data loggers. In these cases, plant operators ask for satellite irradiance to fill the gaps. Fig. 13: The principle of photovoltaic plant monitoring based on satellites (source Univ. Oldenburg). Additionally, the spatial integration inherent in satellite data measurements is valuable as auxiliary information for PV power plants with a large horizontal extent. The approach for simulating PV systems is well understood. The largest uncertainty comes from the uncertainty of satellite derived irradiances. For PV plant monitoring, irradiation data have to be given at least on an hourly basis, better in a 15 minutes interval. Large efforts have been made within the MESoR project to compare hourly global satellite data based on different retrieval methods (deliverable 1.1.3 ‘Report on Benchmarking’). Most customers still expect errors of 5% for hourly irradiation values, while the MESoR benchmarking results show errors of about 30%. Though there is an ongoing effort to further improve the retrieval of satellite irradiation (see chapter 3), it is unlikely to achieve error values of 5% within the next years. So, it is an important task to communicate the MESoR benchmarking results on global irradiance near-


real-time services to the stakeholder community to bring in mind the remaining uncertainties in satellite data.

4.2 Improvement in Nowcasting for Electricity Grid Dispatching Nowcasting of solar power production is expected to become more important in the future. There are, however, already now examples where short term forecasts are asked for by electrical grid operators. One example is the dissolving of fog, another one is solar power forecasting for the Spanish market both for PV and CSP technologies. There are two reasons why improved nowcasting is important in fog situations. On the one hand, of course, the power output of PV systems is very low during foggy situations, while at the same time there is enhanced demand for electrical power e.g. for lighting. As soon as the fog clears up, there is a strong and sudden increase of PV power coming into the electrical grid, while the demand for electrical power drops rapidly. This can lead to a grid overload, at least in smaller distribution grids. To some extent, but in the opposite direction, this also holds for thunderstorm situations, where a rapid decrease of PV power production occurs, while at the same time there may be increased need for lighting. In both cases, the problem can be overcome by improved short term forecasts. For the Spanish market, there already is the need for short term forecasts. By law, solar plant operators in Spain are forced to provide forecasts of their system’s power output for the day ahead. Penalties have to be paid if real power output deviates from these forecasts. However, plant operators have the possibility to provide updates of their forecasts during the day. Certainly, nowcasting has the potential to improve the results compared to forecasts that are based on numerical weather prediction models only. Fig. 14: The principle of satellite based nowcasting using cloud motion vectors (source Univ. Oldenburg).

4.2.1 Photovoltaics Approaches for modelling the energy yield of a PV system are pretty straight forward. Well documented models exist for simulating the maximum power point performance of PV modules or the characteristics of inverters. Also loss factors, occurring in real world systems, like losses due to cabling or shading, are implemented in actual simulations. The power output of a PV system also depends on the module temperature. However, this effect is rather small, and temperature measurements are believed to have reached a high level of confidence. Only few customers see a real need to further improve temperature measurements or weather model analysis data. Remaining uncertainties in PV


energy yield simulations are to a major part dominated by uncertainties in irradiation data. In practise, only global irradiation is used as a basis for PV yield simulations. From this, the fractions of direct and diffuse radiation are computed internally in the PV system models or their pre-processing tools, respectively (Maier et al., 2002). Many customers feel the need for further improvements of the algorithms to calculate this partitioning. For example, an important input parameter for these models is the aerosol load in the atmosphere. At the moment, almost all operational models are based on aerosol climatology only. There is strong need to implement more realistic aerosol data into surface irradiance calculation models, e.g. by using chemical transport models (CTM).

4.2.2 Concentrating Solar Thermal Power Nowcasting aspects of grid dispatching for concentrating solar power (CSP, e.g. fig. 15) plants are similar to those with PV plants and those described in section 4.2.1, but with the relevant input quantity being DNI instead of global irradiance. Fig. 15: Example for a concentrating parabolic solar trough system (sourceSolar Millennium AG)

4.3 Nowcasting for Optimizing Short-term Plant Operation There is an additional item for large scale CSP plants (Fig. 16). Since these plants have a high thermal inertia, start-up and shut-down processes need some time. Having a nowcasting on the available solar irradiance available, these processes can be optimized in terms of component stresses and life times. Also, thermal storage management can be based on such information. Beside start-up/shut-down processes a second aspect can be distinguished for short-term plant operations: the “very” short-term control of the solar field. A sudden decent of DNI can cause transients in the solar field that are challenging to control. At the moment, the appropriate control depends predominantly on the experience of the plant operator. If nowcasting of these descents was possible, control strategies could make use of that. The required resolution of few minutes and less than one kilometre is still too small for satellite nowcasting (at the moment), but it might be possible to develop models for predicting these “high resolution disturbances”, when information of both satellite nowcasting and measurements on and near the site are combined. A method to develop such a nowcasting model could be a long-term research topic, but would be very valuable for plant operators.


Fig. 16: Andasol-1 as an example for a large concentrating solar thermal power plant (source Solar Millennium AG)

5 Research Area 3 - Forecasting Services One of the largest problems of the fluctuating renewable energy sources solar and wind power, as compared to conventionally generated electricity, is its dependence on the partially non-deterministic weather patterns. This characteristic behaviour is most relevant (a) on a short time scale from seconds to a few hours in which the control of the respective system (e.g., a wind turbine or a large building) is done (see research area 2) and (b) on a larger time scale of typically one to three days, in which the integration of power in the electrical grid takes place. Photovoltaic energy yield prediction is an evolving market. Though, e.g. in Germany, the fraction of PV power is rather small compared to wind power, transmission grid operators are already concerned about grid load, especially in summer. In other countries, like Spain, the need for yield predictions is manifested by law, as PV plant operators have to publish the expected yield of their systems one or more days ahead. Penalties have to be paid if real power generation differs from their forecasts. Operators of Concentrating Solar Power (CSP) systems are encouraged by the Spanish laws to act like conventional power producers and sell their electricity on the wholesale market. This force the plant operator to define the electricity output one day ahead of its production, thus the operator has to base its yield on meteorological predictions for the next day (Wittmann et al., 2008). The main meteorological parameters which influence the output are direct normal irradiance, temperature, relative humidity and wind speed including the wind speed in gusts. Constantly increasing contributions of solar energy technologies to grid-connected electricity generation will therefore require high-quality information on all aspects of solar power generation as it is already the case for wind power generation, where forecasting systems show a very high economic value on the energy market. Typical areas requiring forecast information are: Solar photovoltaic power generation

The strong fluctuations of solar photovoltaic power generation both in time and space make a high-resolution day-ahead forecast of solar irradiance evident

uctuations of solar photovoltaic power generation both in time and space make a high-resolution day-ahead forecast of solar irradiance evident


for an efficient integration of solar energy into the distribution grid. High-quality solar irradiance forecasts combined with information of the expected accuracy specific to the prevailing weather situation have to be provided to maximise the value of the solar-generated electricity. Time scales are typically one to three days.

Solar thermal power plants

Forecasting the power generated by solar thermal power plants (STPP) mainly needs information on the direct solar irradiance resource. Grid integration issues require information for the same time scales as for PV.

Load management

Forecasting of electricity demand becomes more important as electricity grid structures become more and more complex and a larger variety of decentralised units contribute to generating, conversion and even storage of the electricity. Given this new structure, an energy flow optimisation between the interacting components for use and generation of electricity is necessary. Therefore, more sophisticated techniques making use of all available information in higher resolution are required aiming especially at higher accuracy. Ambient temperature and cloud cover or solar irradiance are the most important meteorological parameters.

Thermal control of buildings

Heating and cooling loads of buildings depend considerably on meteorological parameters as temperature, wind, humidity and irradiance. An efficient control therefore needs information regarding these variables. This is primarily the case for buildings with high thermal inertia and/or thermal storage (e. g., thermo-active building systems). Results from previous research on building control show an insufficient quality of forecast information of solar irradiance.

5.1 Current Status Solar irradiance is - contrary to wind speed - not available as a direct prognostic variable in numerical weather prediction (NWP) schemes. It is diagnostically calculated and most NWP models even do not provide irradiance information as forecasting product. Improvements in its parameterisation within these models have been introduced usually regarding their impact on overall model accuracy and computing efficiency. This results in an overall poor quality of surface solar irradiance forecasts provided by these models. A common way to handle these deficiencies is the introduction of Model Output Statistics (MOS). MOS is an entirely statistical method based on simple regressions between NWP model outputs and given meteorological variables not provided by these models. Also, gridded low-resolution NWP output can be adapted to the special local situation (Fig. 17). This method is based on regression analyses using long-term time series of NWP data and observations.


Fig. 17: Principle of model output statistics use (source Univ. Oldenburg) Forecasts of solar irradiance using MOS are widely used because of its simple implementation. However, maintenance of such a system is expensive due to its need of continuous adaptation to the NWP model and its potential for improvements is limited as it adds no additional (physical) information to the NWP results. In case of a surface solar irradiance product provided by NWP models as a diagnostic variable, MOS can be replaced by an intelligent interpolation scheme (e.g. taking solar height into account) to handle the typically low resolution of NWP models. A comparison of this method using interpolated ECMWF forecasts with a MOS-based forecast already showed similar accuracies. Especially for a cloud-free atmosphere this method significantly improves solar irradiance forecasting. A strong benefit of a NWP-integrated approach is the potential to directly include further atmospheric influences on surface solar irradiance. The introduction of aerosol information into NWP models has been recently shown and may lead to superior results. Improvements are expected in regions frequently showing strong events of atmospheric contamination by dust, desert sand, and biomass burning. As these are usually regions with high irradiance, the impact on the accuracy of solar power forecasts is evident. Another path to a finer resolution of irradiance forecast products is the intro-duction of downscaling techniques. Downscaling is a method for obtaining high-resolution information from more coarsely resolved models, e.g. NWP models. Dynamical downscaling uses a limited-area, high-resolution model (e. g. a meso-scale model) driven by boundary conditions from a large-scale model („Nesting“) to simulate finer-scale physical processes consistent with the large scale weather evolution. In addition to reduce the resolution the use of embedded meso-scale models allows for the direct simulation of irradiance. However, currently no results of meso-scale forecasts superior to the other techniques have been presented. Any information from weather forecasts is inherently uncertain. A proper assessment of this uncertainty provides very valuable information for any forecast user when appropriate methods for the integration of this knowledge in the applications are available. Uncertainty of weather forecasts results to a large extend from errors in the estimate of the current atmospheric state. By performing a number of simulations (an ensemble) made by making small changes to the estimate of the current state used to initialise the simulation this uncertainty can be addressed. It can be expressed by probabilistic forecasts, which provide probability distributions of future weather quantities or events instead of deterministic (i.e. fixed) values. Statistical methods for assessing uncertainty in NWP ensembles and statistical post processing via Bayesian Model


Averaging have been introduced, but not yet applied for solar irradiance forecasting. Forecast uncertainty is frequently related to the prevailing weather situation. By statistical techniques of weather classification the dependency of forecast uncertainty on quantities describing a certain weather pattern as cloud amount and variability, aerosol distribution, solar position etc. can be described and used. Generally, clear-sky situations with high irradiance typically show smaller relative errors than with overcast or broken cloudiness.

5.2 Global Irradiance As the general aims of research on solar irradiance forecasting are similar for the relevant parameters they are discussed under this subsection. Deficiencies of state-of-the-art weather prediction models with respect to fore-casting solar irradiance mainly arise from the insufficient representation of smaller scale physical processes - especially of local effects leading to a change in cloudiness. In addition to an inadequate physical modelling necessary informa-tion about the small-scale surface characteristics is lacking. The use of meso-scale meteorological models may partly overcome these deficiencies as they inherently include a more detailed physical parameterisation of the relevant processes and show a spatial resolution down to 1 km and below. Furthermore they can provide prognostic results for variables (e.g., solar irradiance) which are only diagnostically available in most conventional forecasting models. The potential of meso-scale models for solar irradiance forecasting therefore needs to be investigated in greater detail. Any meso-scale model has to be embedded (‘nested‘) in a larger scale model which provides the boundary conditions throughout the calculation. Various combinations of large- and meso-scale models typically show systematically dif-ferent results - often depending on the prevailing weather pattern. Currently, forecasts of the European Centre for Medium Range Weather Forecasts (ECMWF) are expected to provide best results when acting as a driving model. A systematic analysis of the quality of different combinations for the purpose of so-lar irradiance forecasting needs to be performed. In a detailed analysis it has to be investigated whether commonly available forecasting information on cloud parameters may be beneficially used to infer values on solar irradiance. These results have to be compared to those from the direct path, i.e., calculating solar irradiance within the forecasting model. When using predicted cloud information, the parameterisation may be optimised for this task. Measurements should be used to validate these steps. The quality of the predicted cloud information may be assessed by performing case studies with radiative transfer models using the specified cloud parameter. The conversion of the derived cloud parameter into measures of atmospheric transmission which are widely used in solar energy meteorology (e.g., clear sky index) and which finally result in the solar irradiance values.


Fig. 18: Principle of probability density functions derived from an ensemble prediction approach (after ECMWF) When relying completely on numerical models, the use of now established ensemble prediction systems (EPS, fig. 18) needs to be investigated. This provides additional probabilistic information by a statistical evaluation of several forecast runs with slightly changing start conditions. Methods of best member selection then will lead to an optimal selection depending on the actual situation. In both cases the integration of an advanced model for aerosol content will in-crease the accuracy especially in clear sky situations with high irradiance. Here special adaptations to meso-scale models are available as well as an integration of aerosol forecasts in the ECMWF model. To improve site-specific irradiance forecasts it has to be tested whether modeling of the sub-pixel-sized cloud structures leads to a higher accuracy in broken cloud situations. Whatever model will be used, a significant error will remain after all modelling ef-forts. These errors are partly of a stochastic nature but also show some systema-tic - and thus deterministic - behaviour. Statistical post processing techniques, which may be applied to all classes of numerical models, eliminate systematic errors mainly introduced by a model bias or by the influence of local effects not covered by the model. This may be introduced by a variety of statistical techniques, such as simple Model Output Statistics (MOS) or Neural Network based methods.

5.3 Direct Normal Irradiance Short term (some hour), and day ahead forecast as well as knowledge of reliable historic data of direct normal irradiances are required for solar thermal power plants for project development (historical data) solar field control (short term forecast) and storage management or electricity trading (short-term, day-ahead forecast and two days-ahead forecast). For solar field control a high spatial (less than 1 km²) and temporal resolution (less than one hour) is required. The forecast horizon for solar field control could be 1-2 hours. For storage management the forecast horizon should be 1-2 days with a time resolution of 1 hour. The spatial resolution should be the size of the solar field (approx. 1km²). The value of direct irradiance forecasts for determining optimal operation strategies of solar thermal power plants (STPP) is expected to increase with future applications of this technology. Satellite-based nowcasting of solar irradiance using cloud motion vector techniques and data assimilation of aerosols into air quality models may be combined with conventional forecasting techniques to provide day-ahead direct irradiance forecasts. This has to be combined with optimised operation strategies for the STPPs.


Meteorological parameters as DNI, temperature, relative humidity, wind speed and gust speed have to be provided in a time resolution of at least one hour, since this is the resolution demanded by the market. A higher resolution of up to 10 minutes is appreciated in order to get higher model output quality of the CSP system, thus less uncertainty in the prediction forecast. For a meaningful yield prediction for a single CSP plant a spatial resolution of less than 1 km² is needed. Research is needed to fulfil these user requirements. Currently operational systems are far from these goals. As the day-ahead schedule can be adopted during the day in the intra-day trading, updated direct solar irradiance forecasts according to the intra-day trading timelines promise a further reduction of scheduling and therefore economic uncertainty. Furthermore, the apparent inter-relations between operation strategy, electricity stock prices, forecasts and actual weather situation need to be systematically investigated to provide optimal forecast-based operation strategies of solar thermal power plants. The integration of satellite and ground based measurements into a forecasting scheme, data assimilation of near real time observations, considering real storage characteristics and price forecast models are further options for improvement. Research on the optimum combination of these approaches is necessary together with a demonstration phase. For CSP with integrated storage the economical benefit can be further improved by longer forecast horizons. Extending the forecast horizon to the next two days may lead to higher yearly revenues for the CSP plant – if sufficient forecast accuracy is provided. A further increase in forecast horizon show only minor economical advantages by a significantly higher prediction precision. The integrated storage allows balancing forecast deviations up to a certain level. It is important to define the needed level of forecast accuracy for the yield predictions of CSP. The needs will depend on different system configurations, foremost on the size of the storage, and the applied operation strategy. Here more research activities are needed. The operator of CSP systems may optimize its storage management when acting on the market, i.e. storing solar energy during low demand periods and producing electricity during high demand. The dispatchability of CSP systems with storage allows a higher economical result at the end of the year. Beside the high quality predictions for the next two days as stated above, information of the probability of the forecasts is valuable for the operator. There are mainly two useful probability information types: First, the probability of different irradiance levels for a certain point in time is needed. Secondly, the probability of the point in time and the time shift during periods characterized by the high fluctuating solar irradiance are needed. The information about probabilities will allow the operator to further minimize the deviations of predicted and actually fed-in electricity by adapted storage management, thus providing a better integration of renewable solar energy into the grid. A definition of one or more standard CSP filter functions that offer the possibility to evaluate the uncertainty of the meteorological inputs (DNI, temperature, wind) on the electricity yield would be helpful to concentrate research activities to those meteorological conditions dominating the technical needs. Several CSP-configurations especially with a different storage size might be defined in order to cover typical systems.


The yield prediction is used also during project development for the site assessment. Thus it would be interesting to investigate in depth the quality of DNI predictions achievable for specific regions (arid, semi arid, coastal regions, distance to coast…). The result could be an uncertainty band for DNI predictions for specific regions.

6 Research Area 4 - Seasonal to Inter-annual Variability of Solar Radiation For all solar applications, the inter-annual variations are needed to explain by how much the annual production of the solar system could vary from the results presented in the design phase. The inter-annual variability with its temporal-spatial characteristics have to be assessed first using long-term satellite data bases over larger spatial areas (e.g. NASA SSE or DLR SOLEMI databases) or meteorological long-term reanalysis data sets. Therefore, systematic deviations between long-term data sets have to be assessed (Fig. 19). Dependencies on NAO/ENSO structures or the long-term influence of volcanic eruptions on direct solar irradiance should be assessed. Fig. 19: Long-term variability of yearly mean global irradiance at the meteorological station at Potsdam, Germany, based on 1937 – 1999 period (source Quaschning, 2001) Up to now there are only very few results available on long-term affects due to climate change. It is recommended to seek collaboration with the climate modelling science community to develop, assess, and disseminate model results. Downscaling approaches for regional-level energy assessment activities responding to climate change impacts have to be developed. This includes e.g. supply and load forecasting, renewable energy resource assessments, urban heat island impacts, and population growth. Scenarios of global climate models should be analysed for their sensitivity to describe possible changes of the available solar resource due to global change and subsequent changes in regional cloud patterns or atmospheric aerosol load. Recommendations to solar energy industry and decision makers for future market development based on expected regional climate changes are highly welcome.


7 Research Area 5 - Atmospheric Parameters for Radiation Retrieval

7.1.1 Aerosols and Atmospheric Turbidity Lowest satisfaction by stakeholders was expressed for parameters like dust concentration and atmospheric turbidity. Both parameters are representing the atmospheric aerosol load, which originates e.g. from dust outbreaks, wild fires, industrial and transport emissions, heating systems, erosion, or sea salt crystallisation. Improved aerosol data either from remote sensing or chemical transport models is necessary especially to improve the modelling of direct irradiance. Databases of aerosol load in the atmosphere are not sufficient in their temporal and spatial resolution and their accuracy. This is partly caused by missing knowledge on aerosol sources in the atmospheric community. Therefore, larger research effort to provide more accurate and better time-space resolved aerosol optical depth information is recommended. This includes especially improvements in emission databases, modelling of aerosols and data assimilation of satellite or ground based observations into models. Fig. 20: Available global aerosol data sets provide very different results showing the large uncertainty of the aerosol content. All figures show the values to be used in July, plotted in the same color scale from 0 to 1.5

ource DLR).

ealth effects.

(s This effort has to be defined by solar energy users to ensure suitable research covering not only the currently dominating climate and air quality communities and their information needs, but also the solar energy community needs. Distinguishing requirements are mainly found in the temporal and spatial resolution and in the request for deriving spectrally resolved aerosol optical depth. An accurate modelling of dust outbreak situations is of major importance for the solar energy community using concentrating technologies, while the air quality community concentrates their research efforts on smaller particles which cause negative h

8 Research Area 6 - Auxiliary Information

8.1.1 Ground Reflectance Ground reflectance impacts the SSI because of its influence in the multiple reflections between the ground and the atmosphere and the clouds layer. These reflections contribute to the diffuse component of the SSI. If the surface is not


horizontal, it also influences the reflected component of the SSI that impinges on the surface. The influence of the ground reflectance is more important in cloudy skies than in clear-sky. Simulations made by libRadtran show that for a cloud optical thickness of 30, i.e., an overcast sky with thick clouds, changing the albedo from 0.2 to 0.8 almost doubles the SSI. Consequently, there is a need to know the ground reflectance at every pixel for SSI assessment. The scientific challenge is here due to the temporal and spatial changes of the ground reflectance. This reflectance depends upon the land cover which may change dramatically within short distance, e.g., between the sea and the beach, as well within hours, e.g., snow fall on a dark surface. Work should be encouraged for the mapping on a daily basis of the ground reflectance over the world at say, 1-km scale. In addition, the flux reflected by the ground is usually a function of the illuminating angles. This function, called bi-directional reflectance function, should be expressed in simple form, analytical or abacus, for operational reasons. Several functions have been proposed, but further work is needed on this subject.

8.1.2 Relative Humidity Some stakeholders express their need for atmospheric relative humidity. Relative humidity is generally provided by meteorological modelling, but it is not clear if up-to-date atmospheric modelling provides a sufficient accuracy. A further analysis of user requirements vs. state of the art modelling accuracy of relative humidity is recommended. Humidity has large impact on the power output of steam turbine plants (with wet cooling tower) since it defines together with the ambient temperature the condensation pressure. Therefore information on this is one of the key input parameters for CSP-plant modelling. It should be analyzed which impact this parameter has and which accuracy is favourable.

8.1.3 Snow Cover Snow cover is monitored from satellites (e.g. MODIS, AVHRR or SEVIRI instruments) or using a combination of meteorological analysis and satellite based information (e.g. NOAA-NESDIS). A first validation of different data sets specifically for the need of photovoltaic plant monitoring was performed for the period Jan – April 2006 (Wirth et al., 2008). An extension to larger data sets is required and a testing phase of integrating these data sets into operational plant monitoring schemes. So, even if the detection of snow from a satellite pixel is improved, it is still difficult to decide if a PV system is covered by snow, even if the ground is covered by snow. Further research is needed to combine all the meteorological information and the alarm management software from the monitoring services. It is unlikely that false alarms can be reduced by the improvement of snow detection alone, as is will be hardly possible to distinguish snow covered system from string faults only on the basis of error pattern algorithms.


8.1.4 Water Temperature In CSP technology there is a need of condensing the exhaust steam of the turbine. The condensing temperature at the cold end has a strong impact on the overall efficiency. There are different technologies for the cold end of CSP systems, and therefore, different meteorological parameters are influencing the condensation temperature. Currently most projects are using a cooling tower, where the two main parameters are ambient temperature and relative humidity while water temperature plays an ancillary role. Open circuit water cooling is the direct cooling with river or well water, where the water temperature is the only influence on the performance of the cold end. Water temperature will get important for CSP plants operated close the coast using sea water for cooling or if they are combined with sea water desalination. For the desalination part the temperature of the incoming water is an important parameter.

8.1.5 Wind Speed and Direction Some stakeholders express their need for wind speed and direction. These parameters are generally provided by meteorological, but it is not clear if up-to-date atmospheric modelling provides a sufficient accuracy especially in gusty conditions. A further analysis of user requirements vs. state of the art modelling accuracy is recommended. Wind speed has some impact on CSP plant performance since optical efficiency of the concentrator decreases under wind loads or the concentrator has to be de-focussed in case of too strong wind gusts. Therefore, information on wind speed and its variability is one input parameters for CSP-plant modelling. It should be analyzed which impact this parameter has and which accuracy is favourable. A nowcasting approach for wind gusts needs to be developed. Wind direction has an impact on the preferred direction of the fume over the collector field. The information about wind direction is only of minor use during operation (and thus short term forecasts), but the knowledge of its distribution is important during the design phase of the projects.

9 Research Area 7 - Interaction with other Renewables Large-scale integration of renewable energies is increasingly demanding in terms of planning, resource scheduling and overall system control. To improve trans-port characteristics of future electricity grids their structure and topology are criti-cal points. Different renewable energy sources (especially solar and wind) may have complementary patterns in availability. E.g more solar energy in summer and more wind energy in winter, or solar and wind in sunny and cloud weather conditions. These patterns will get important if electricity systems reach a high penetration of renewable energy sources. This makes a detailed knowledge of the production capacities and the spatial and temporal distribution of their contributions to the overall power generation necessary. Any assessment of future electricity generation with large contributions from wind and solar therefore needs a precise analysis of this fluctuating distributed generation. Data bases of such data need to be set up in order to be able to


model e.g. the hourly or quarter hourly load flow in such high penetration systems. High quality data sets for analyses of meteorological fields with high spatial and temporal resolution are available through recent re-analysis projects. They pro-vide long-term (>40y) homogeneous data with typical resolution of 1°x1°. For wind power, the combination of these re-analysis data with meso-scale meteorological models provides an excellent basis for a detailed analysis of the spatial and temporal structure of wind power production. By adding dynamical downscaling procedures both, the long term dynamic behaviour as well as regional processes can be described. For the solar resource satellite data provide a data source of high quality as well. All relevant data can be derived from the techniques described in this report. Special research needs are in the development of statistical techniques to de-scribe coherence structures of the generation fields. Also statistical analyses of extreme events are necessary as well as probability distributions for exceeding given thresholds for power production in given (grid) areas.


10 Abbreviations AOD aerosol optical depth APOLLO AVHRR Processing scheme Over cLouds, Land and Ocean ASTM American Society for Testing and Materials AVHRR Advanced Very High Resolution Radiometer CI cloud index CS clear sky CSP concentrating solar power CSR circum solar radiation CTM chemical transport model CTT cloud top temperature DLR Deutsches Zentrum für Luft- und Raumfahrt e.V. DNI direct normal irradiance ECMWF European Centre for Medium-Range Weather Forecasts ENSO El Niño / Southern Oscillation EPS ensemble prediction scheme IEA Internal Energy Agency GACP Global Aerosol Climatology Project GEMS Global and regional Earth-system (Atmosphere) Monitoring using

Satellite and in-situ data GHI global horizontal irradiance MATCH Model of Atmospheric Transport and Chemistry MESoR Management and Exploitation of Solar Resource Knowledge METEOSAT meteorological satellite series MODIS Moderate Resolution Imaging Spectroradiometer MOS model output statistics MPI Max Planck Institut für Meteorologie NAO Northern-Atlantic oscillation NASA National Aeronautics and Space Administration NCAR National Center of Atmospheric Research (US) NESDIS National Environmental Satellite, Data, & Information Service NOAA National Oceanic and Atmospheric Administration NWP numerical weather prediction OMEL Operador del Mercado Iberico de Energia OMIP Operador do Mercado Ibérico derivative market PV photovoltaics RMSE root mean square error RTM radiative transfer modeling SEVIRI Spinning Enhanced Visible and Infrared Imager SHC Solar Heating and Cooling Programme of the IEA SOLEMI solar energy mining, long-term solar irradiance database SSE surface meteorology and solar energy dataset (NASA) SSI surface solar irradiance STP solar thermal power STPP solar thermal power plant TMY typical meteorological year WV water vapour


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