Short-Range Direct and Diffuse Irradiance Forecasts for Solar Energy ApplicationsBased on Aerosol Chemical Transport and Numerical Weather Modeling

HANNE BREITKREUZ*

German Aerospace Center, German Remote Sensing Data Center, Oberpfaffenhofen, and Institute of Geography,

University of Wurzburg, Wurzburg, Germany

MARION SCHROEDTER-HOMSCHEIDT AND THOMAS HOLZER-POPP

German Aerospace Center, German Remote Sensing Data Center, Oberpfaffenhofen, Germany

STEFAN DECH

German Aerospace Center, German Remote Sensing Data Center, Oberpfaffenhofen, and Institute of Geography,

University of Wurzburg, Wurzburg, Germany

(Manuscript received 24 July 2008, in final form 30 January 2009)

ABSTRACT

This study examines 2–3-day solar irradiance forecasts with respect to their application in solar energy

industries, such as yield prediction for the integration of the strongly fluctuating solar energy into the elec-

tricity grid. During cloud-free situations, which are predominant in regions and time periods focused on by the

solar energy industry, aerosols are the main atmospheric parameter that determines ground-level direct and

global irradiances. Therefore, for an episode of 5 months in Europe the accuracy of forecasts of the aerosol

optical depth at 550 nm (AOD550) based on particle forecasts of a chemical transport model [the European

Air Pollution Dispersion (EURAD) CTM] are analyzed as a first step. It is shown that these aerosol forecasts

underestimate ground-based AOD550 measurements by a mean of 20.11 (RMSE 5 0.20). Using these aerosol

forecasts together with other remote sensing data (ground albedo, ozone) and numerical weather prediction

parameters (water vapor, clouds), a prototype for an irradiance forecasting system (Aerosol-based Forecasts

of Solar Irradiance for Energy Applications, AFSOL) is set up. Based on the 5-month aerosol dataset, the

results are then compared with forecasts of the ECMWF model and the fifth-generation Pennsylvania State

University–National Center for Atmospheric Research Mesoscale Model (MM5), with Meteosat-7 satellite

data, and with ground measurements. It is demonstrated that for clear-sky situations the AFSOL system

significantly improves global irradiance and especially direct irradiance forecasts relative to ECMWF fore-

casts (bias reduction from 226% to 111%; RMSE reduction from 31% to 19% for direct irradiance). On the

other hand, the study shows that for cloudy conditions the AFSOL forecasts can lead to significantly larger

forecast errors. This also justifies an increased research effort on cloud parameterization schemes, which is a

topic of ongoing research. One practical solution for solar energy power plant operators in the meanwhile is to

combine the different irradiance models depending on the forecast cloud cover, which leads to significant

reductions in bias for the overall period.

* Current affiliation: Stadtwerke Munchen GmbH, Munich, Germany.

Corresponding author address: Hanne Breitkreuz, German Aerospace Center, German Remote Sensing Data Center, Postfach 1116,

82234 Wessling, Germany.

E-mail: hanne.breitkreuz@gmx.de

1766 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 48

DOI: 10.1175/2009JAMC2090.1

� 2009 American Meteorological Society

1. Introduction

Because of the limitation of fossil fuel resources and

their impacts on climate change, our future energy sys-

tem will increasingly depend on utilizing growing shares

of renewable energy sources. This poses a major chal-

lenge to the development of future energy systems, since

energy production from most renewable resources is

highly variable in space and time. Because of this high

variability, an efficient integration of solar energy into

the existing energy supply system will only be possible if

reliable information on ground-level solar irradiance is

available. It should be noted that information on surface-

level global irradiance is needed for photovoltaic facili-

ties, whereas concentrating solar thermal power plants

can only process direct irradiance.

To calculate direct and global irradiance at the surface

level, exact information about clouds, aerosols, water

vapor, and ozone is needed. For overcast skies, knowl-

edge of cloud cover and type is most important in de-

termining irradiance values. Cloud cover information is

also important for distinguishing between overcast and

clear situations. But in the clear-sky case, precise aerosol

information is indispensable for providing accurate ir-

radiance forecasts, since up to 20%–30% of direct irra-

diance extinction has been reported for cases of high

particle occurrence (Henzing et al. 2004; Jacovides et al.

2000; Latha and Badarinath 2005). Despite the high

spatial and temporal variabilities of aerosol occurrence,

with typical scale lengths of a few hours or several tens

of kilometers (Anderson et al. 2003; Holzer-Popp et al.

2008), most irradiance calculation systems use simple

aerosol climatologies of fixed yearly or monthly values

with a spatial resolution of several degrees instead of

detailed aerosol information (e.g., Kinne et al. 2005;

Schmidt et al. 2006).

A focus of the solar energy industry lies in relatively

cloud-free regions, such as the Mediterranean area. This

explains why clear-sky calculations are of great rele-

vance for irradiance forecasts. The importance of ac-

curate aerosol information is enhanced by the fact that

these regions with comparably low cloud cover are often

situated near desert regions, such as the Sahara Desert,

which leads to frequent occurrences of dust transport and

therefore episodically high atmospheric aerosol loads

(Meloni et al. 2007).

So far, irradiance information has been primarily

available as retrospective long time series used by the

solar energy industry for site auditing and facility mon-

itoring. For example, data from Meteosat satellites

(Rigollier et al. 2004) is used to optimize solar power

plants with respect to local characteristics. In addition,

satellite-based data of the past 1–30 days are used to

monitor the performance of existing solar energy sites,

allowing for the near-real-time management of power

sites as well as for retroactive performance checks by

automatic failure detection routines (Drews et al. 2007).

However, near-real-time forecasts of direct and global

solar irradiance are also needed for forecasts of facility

yields of several hours to 2 days. Only with this information

can sensible and efficient control and maintenance of solar

power plants in combination with conventional plants be

facilitated, as well as the stability of local and regional

power grid systems. Additionally, besides air temperature,

the amount of available solar irradiance largely determines

customer consumption behavior. Therefore, irradiance

forecasts are also needed when calculating consumer de-

mands, an essential aspect in controlling both traditional

and solar energy power plants.

Satellite-based cloud motion vectors for cloud fields

and therefore surface irradiance are already used to

calculate nowcasting irradiance predictions (Hammer

et al. 1999). For example, the European Meteosat Second

Generation (MSG) satellite provides high-resolution

images of Europe and Africa every 15 min, where the

movement of the clouds is used to determine and ex-

trapolate motion vectors. Such an approach is of interest

since it allows for forecast updates during the day, such as

are needed for intraday adjustments of power plant

management (Rikos et al. 2008) or energy trading ap-

plications. The accuracy of this method can be increased

by the use of smoothing filter techniques (Lorenz 2004).

However, it should be noted that this approach is limited

to an approximate forecast horizon of up to 6 h.

One of the earliest approaches to predicting solar

irradiance dealt with 1–2-day forecasts using model

output statistics (MOS; Jensenius and Cotton 1981). The

MOS technique uses statistical correlations between

observed weather elements and climatological long-

term data, satellite retrievals, or modeled parameters to

obtain a highly localized statistical function. This allows,

for example, for the adaptation of low-resolution me-

soscale data to local conditions by considering local

effects or by correcting systematic deviations of a nu-

merical model, satellite retrievals, or climatological val-

ues. A disadvantage of this method is seen in the large

amount of measurement data needed to develop sta-

tistical correlations separately for each location. This

means that MOS-based forecasts are not available for

larger areas or for locations without a priori information

(Glahn and Lowry 1972).

For day-ahead production forecasts, global irradiance

forecasts from numerical weather predictions such as

from the European Centre for Medium-Range Weather

Forecasts (ECMWF) are already operationally available

for various grid sizes and on a global scale. However,

SEPTEMBER 2009 B R E I T K R E U Z E T A L . 1767

temporal resolutions of 3 h as well as the restriction to

global irradiance only instead of also direct irradiance

needed for concentrating solar power systems, leads to

problems when employing ECMWF irradiance fore-

casts for energy applications. Also, the forecasts are

calculated with an aerosol climatology containing annual

aerosol optical depth (AOD) cycles for four tropospheric

aerosol types (ECMWF 2008) instead of actual aerosol

forecasts, causing errors for clear-sky cases in particular.

For energy applications a combination of numerical

weather prediction and air quality modeling is more

suitable, where both meteorological and chemical fore-

casts rely on satellite- and ground-based data assimila-

tion systems. The approach discussed in this paper is

aimed at providing irradiance forecasts designed to meet

the needs of the solar energy industries. This is achieved

by improving the accuracy of clear-sky irradiance fore-

casts through the use of a chemistry transport model

(CTM) for aerosol information, in combination with

atmospheric input data from satellite and model data.

In section 2 the concept of an irradiance forecasting

model is presented, followed by a detailed description of

the atmospheric input parameters used. In section 3 the

validation data sources are presented. The performance

of the irradiance prediction system is detailed in section 4.

2. The AFSOL system

a. Concept

A modeling system of Aerosol-based Forecasts of

Solar Irradiance for Energy Applications (AFSOL) has

been developed to match the needs of the solar energy

community regarding irradiance forecasts (Breitkreuz

2008). From this system, not only global but also di-

rect irradiance information is available at high tempo-

ral resolution covering Europe and the Mediterranean

region. A focus is placed on irradiance forecasts in

clear-sky conditions since these include the most in-

teresting situations for the efficient operation of solar

energy plants.

All irradiance calculations of the AFSOL system

are performed with the library for radiative transfer

(libRadtran) program code (Mayer and Kylling 2005).

The systems main routine, uvspec, calculates direct and

global spectral irradiances at the surface level, taking

into account atmospheric multiple scattering and ab-

sorption as well as surface properties. For this study the

underlying standard atmosphere file is altered by vari-

ous additional input data sources described in the fol-

lowing sections: aerosol information, column-integrated

atmospheric water vapor, and cloud information is taken

from a numerical weather prediction model, whereas

atmospheric ozone content and ground albedo values

are provided by satellite measurements (see Fig. 1).

The model is capable of calculating spectrally resolved

irradiance values (Kato et al. 1999). However, due to the

lack of sufficient numbers of appropriate validation mea-

surements, most cases in this study consider only spectrally

integrated global and direct irradiance predictions.

Various algorithms for solving the radiative transfer

equations are available, which allows choosing accord-

ing to different applications or required accuracies, such

FIG. 1. AFSOL irradiance forecasting system scheme.

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as between very exact time-consuming calculations or

less exact faster routines applicable to operational ser-

vices. For this study, the disort solver (Stamnes et al.

1988) is used for solar zenith angles of up to 708, and the

sdisort algorithm (Dahlback and Stamnes 1991) for solar

zenith angles between 708 and 858.

The temporal resolution of the direct and global ir-

radiance forecasts is 1 h and the spatial resolution is a

½8 grid across Europe and the Mediterranean African

coastal regions. Each forecast is calculated for 72 h.

b. Atmospheric input data

Both cloud and aerosol information needed for the

irradiance predictions are taken from the European Air

Pollution Dispersion (EURAD) model (Ebel et al.

1997). This system has been developed for air quality

monitoring and forecast purposes by the Rhenish Insti-

tute for Environmental Research at the University of

Cologne, Cologne, Germany. It incorporates physical,

chemical, and dynamical processes related to the emis-

sion, transport, and deposition of atmospheric sub-

stances. The system consists of three main submodels

treating meteorological input from the fifth-generation

Pennsylvania State University–National Center for At-

mospheric Research Mesoscale Model (MM5; Grell

et al. 1995), emission data (Memmesheimer et al. 1995),

and a chemical transport model (Hass et al. 1995).

An additional subsystem, the Modal Aerosol Dynamic

Model (MADE) treats aerosol processes, which include

particle emission, coagulation and growth, transport,

and wet and dry deposition (Ackermann et al. 1998).

Secondary organic species are accounted for within the

Secondary Organic Aerosol Module (SORGAM) sub-

system (Schell 2000).

The EURAD forecasts provide hourly data, with each

forecast run covering 72 h. Depending on the target of

the analysis, various grid sizes are available, ranging

between a resolution of 1 km for local to regional studies

and 125 km for hemispherical coverage. For the case

study presented, a grid of approximately 54 km (½8 grid)

width was chosen, allowing coverage of Europe and

the Mediterranean region from 308 to 608N and from

2108W to 408E with a reasonable amount of calculation

resources.

1) CLOUDS AND WATER VAPOR

Information on cloud parameters (cloud-top and

-bottom height, cloud liquid water content, and cloud

fraction) and total atmospheric water vapor is obtained

from the meteorological part of the EURAD model. For

information on cloud liquid water, vertically averaged

values of all cloud-containing levels are calculated. This is

a simplification of the three-dimensional EURAD cloud

water output, which can be justified by the fact that a

54-km model grid is used: neglecting subgrid cloud struc-

tures masks any possible errors caused by the simplifi-

cation of the vertical distribution of cloud liquid water.

The determination of the effective radius of cloud

droplets is not part of the EURAD output. To calculate

irradiances, a simple linear parameterization of radius

growth with height is employed, consistent with the

algorithm used at the ECMWF for shortwave irradiance

calculations (ECMWF 2008).

2) AEROSOLS

The complete EURAD system yields mass concen-

trations of all treated species in three different size

modes (nucleation, accumulation, and coarse), differ-

entiated into 23 tropospheric height levels. Primary

organic material and elemental carbon, sulfate, ammo-

nium, nitrate, anthropogenic particulate matter, and

aerosol liquid water are considered in the accumulation

and nucleation modes. Anthropogenic aerosols are ad-

ditionally included as coarse-mode particles.

A method of integrating natural coarse-mode parti-

cles (sea salt, fire particles, and dust) is in preparation

and the effects of these additional modules will be sub-

ject to further investigations. This is a crucial point since,

especially in the Mediterranean area, Saharan-based

dust storms can occur frequently (Meloni et al. 2007),

leading to a large extinction of direct irradiance. Con-

sequently, the integration of external dust information

into the aerosol model has a high priority from a solar

energy point of view.

The particle mass concentrations of different aerosol

species are usually combined to produce single PM10

values (total mass concentration of particles smaller

than 10 mm at the surface, dedicated for air quality

users) as standard output. In the case study presented,

however, separate mass concentration values of all

substances and size distributions modeled are used in

order to calculate extinction coefficients for each grid

point using a fast Mie extinction parameterization

(Evans and Fournier 1990). Extinction coefficients sext

are then vertically integrated through all height layers to

produce aerosol optical depth values for each grid point.

In accordance with most studies regarding the influ-

ence of aerosols on solar radiation, AOD values pre-

sented in this paper are given at a wavelength of 550 nm

(AOD550).

3) GROUND ALBEDO

As a source of ground albedo information data from

the National Aeronautics and Space Administration’s

(NASA) Moderate Resolution Imaging Spectroradiom-

eter (MODIS) on board the Terra and Aqua satellites

SEPTEMBER 2009 B R E I T K R E U Z E T A L . 1769

(Schaaf et al. 2002) are used. Bimonthly composites of

1-km resolution are aggregated to match the ½8 grid of

EURAD aerosol output. To improve the retrieval ac-

curacy, only pixels that were recorded in cloud-free at-

mospheres over land and with solar zenith angles of up

to 608 are considered.

Satellite-based ground albedo data are difficult to

validate since the measurement angles have to be the

same in order to avoid anisotropy effects. However,

validation studies over homogeneous semidesert terrain

show that MODIS albedo values result in a mean RMSE

of 0.02–0.07, thus deviating only slightly from ground

measurements (Wang et al. 2004).

4) OZONE

For information on atmospheric ozone content, Total

Ozone Mapping Spectrometer (TOMS) measurements

from NASA’s Earth Probe Satellite are used (Bhartia

2004). The data (available online at http://wdc.dlr.de)

offer global coverage and have a resolution of 18 3 1.258.

Accuracy studies show a deviation of TOMS total ozone

columns against ground measurements of less than 2%,

which is within the accuracy to be expected between dif-

ferent Dobson ground measurement devices (Bramstedt

et al. 2002).

For this study daily mean values of ozone columns are

used since the interdaily variability of ozone is not very

high and because ozone’s influence is restricted to less than

1% when dealing with spectrally integrated irradiances

(Mueller et al. 2004). In a few cases of limited data cov-

erage, values from adjacent days are linearly interpolated.

c. Accuracy assessment of aerosol forecasts

The main purpose of this study is the evaluation of the

coupling of an air quality model with numerical weather

predictions to improve direct solar irradiance forecasts at

the ground. Therefore, a validation of the aerosol model

has to be conducted as a first step toward quantifying the

aerosol input dataset accuracy and statistics. In a second

step irradiance forecasts are validated against radiation

measurements (section 3), in order to show the overall

improvement in direct irradiance forecasting.

This validation study of the EURAD-based aerosol

optical depth values was performed using Aerosol Robotic

Network (AERONET) ground-based sun photometer

measurements (Holben et al. 1998). The AERONET

program is operated to gather aerosol information

and provide validation data for satellite retrievals of

aerosol optical properties. Datasets are available online

(at http://aeronet.gsfc.nasa.gov) and contain AOD

measurements at 16 different wavelengths at 1640, 1020,

870, 675, 667, 555, 551, 532, 531, 500, 490, 443, 440, 412,

380, and 340 nm, as well as solar zenith angles, total

water vapor column measurements, and several vari-

ability coefficients used for automatic cloud screening

procedures. The accuracy of AERONET AOD values is

60.01 for wavelengths up to 440 nm and 60.02 for

shorter wavelengths (Holben et al. 1998).

In this study, all ground stations in Europe providing

level 2 data (automatically cloud screened and manually

inspected measurements) during a 5-month study pe-

riod from July to November 2003 are considered. This

leads to the use of 32 ground stations with a total of

more than 70 000 measurements to be included in the

analysis. Table 1 presents a complete list of all stations,

including information about the number of measure-

ments available, mean station AOD550, and corre-

sponding variability information.

For the whole time period and all locations consid-

ered, a mean underestimation by the modeling system of

0.11 (1s 5 0.16) was found for measured AOD550. This

is not within the accuracy requirements aimed at for

TABLE 1. AERONET validation stations (in order of ascending

latitude) with mean AOD550, standard deviation of AOD, and

number of considered measurements.

Name

Lat

(8N)

Lon

[8E–W8(2)]

Mean

AOD550

Sigma

AOD550 No.

Crete 35.33 25.28 0.19 0.09 4225

Lampedusa 35.52 12.63 0.25 0.16 3922

Blida 36.51 2.88 0.10 0.04 139

El Arenosillo 37.11 26.73 0.16 0.11 3752

Etna 37.61 15.01 0.27 0.20 2401

Evora 38.57 27.91 0.14 0.12 4028

Oristano 39.91 8.50 0.26 0.22 3104

Lecce 40.33 18.1 0.21 0.13 3840

Rom 41.84 12.64 0.23 0.14 4540

Palencia 41.99 24.51 0.14 0.09 2046

Toulouse 43.58 1.37 0.17 0.11 2595

Avignon 43.93 4.88 0.18 0.10 4145

Venice_Adria 45.31 12.50 0.19 0.14 4857

Venice 45.44 12.33 0.22 0.15 3927

Ispra 45.80 8.63 0.22 0.16 3182

Kishinev 47.00 28.81 0.10 0.04 152

Laegeren 47.48 8.35 0.14 0.08 904

Munich 48.21 11.25 0.18 0.11 581

Fontainebleau 48.41 2.68 0.20 0.13 1918

Palaiseau 48.70 2.21 0.23 0.13 1877

Lille 50.61 3.14 0.19 0.14 1738

Dunkerque 51.04 2.37 0.18 0.14 1805

Oostende 51.23 2.93 0.21 0.14 1542

Leipzig 51.35 12.43 0.20 0.12 821

The Hague 52.11 4.33 0.10 0.02 9

Minsk 53.00 27.50 0.13 0.07 1269

Mace Head 53.33 29.90 0.13 0.02 10

Hamburg 53.57 9.97 0.16 0.11 1846

Helgoland 54.18 7.89 0.14 0.10 391

Moscow 55.70 37.51 0.19 0.11 1155

Gotland 57.92 18.95 0.12 0.07 1733

Toravere 58.26 26.46 0.14 0.08 590

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aerosol input data with regard to solar energy purposes,

such as an RMSE of 0.10 (EHF and Ecole de Mines/

Armines 2001). Thus, in the following several aspects of

the accuracy analysis will be presented in order to

identify crucial points for improvement.

The general underestimation is altered by a strong

regional gradient, leading to good forecast accuracies in

northern and middle Europe and significant underesti-

mations in the central and western Mediterranean re-

gion as is clearly visible in the bias values of individual

AERONET stations shown in Fig. 2. This pattern can be

partly explained by the occurrence of forest fires in

Portugal in August 2003, which is not considered in the

model version used for the dataset analyzed in this

paper. Also, some regions might be subject to misrep-

resentation in the emission database, leading to severe

underestimations of the AOD, such as in the strongly

industrialized Po Valley in northern Italy (Breitkreuz

et al. 2007). The consideration of higher spatial resolu-

tions is also expected to lead to reduced forecast errors

in some of these cases, for example, for ground stations

near large cities such as Palaiseau near Paris, France, or

Munich, Germany, but is an approach that is neglected

in this study for computational reasons, in order to allow

the treatment of a larger study area.

However, a main reason lies in the occurrence of

Saharan dust storms, which cannot be accounted for in

the model system, thus leading to underestimations in

AOD forecasts relative to ground measurements. The

transport of Saharan dust across the Atlantic and the

Mediterranean and toward middle Europe often lasts

for 2–3 days and is typically caused by cyclones south of

the Atlas Mountains, originating from the thermal

contrast of cold maritime and warm continental air

masses. In the central Mediterranean, most cases occur

in summer, with a total occurrence of 4–5 days each

month (Meloni et al. 2007). Consequently, the most se-

vere underestimations of AOD550 can be found on days

and in regions with Saharan dust outbreaks, as identified

by visual analysis of MODIS or Advanced Very High

Resolution Radiometer (AVHRR) satellite color com-

posites (Breitkreuz 2008).

The mean forecast accuracy of single locations and

also of all stations considered can be altered significantly

by these short dust events. As demonstrated in Fig. 3, the

daily mean values of absolute differences in AOD550 are

strongly negative in cases of dust events. Dust events,

such as on 21 July, were verified by visual analysis of

color composite images from the MSG satellite and

marked with black polygons in Fig. 3. As an example,

FIG. 2. Mean absolute bias (EURAD 2 AERONET) of AOD550 forecasts at the 32 ground measurement stations used; the sizes of the

circles correspond to the AOD forecast variability.

SEPTEMBER 2009 B R E I T K R E U Z E T A L . 1771

when ignoring all measurements of the station of Etna,

Sicily, during two 2-day dust episodes, where large

particles were dominating the atmospheric aerosol load,

the mean value of AOD difference for this location

decreased from 20.16 AOD550 to 20.09 and the stan-

dard deviation for the whole time period changed from

0.23 to 0.14. This means that the integration of data on

the atmospheric dust load has great potential to signifi-

cantly improve the accuracy of aerosol forecasts from

chemistry transport models. One approach, the assimi-

lation of satellite-based aerosol measurements into the

CTM, is currently being followed at the German Aero-

space Center/German Remote Sensing Data Centre

(DLR/DFD) together with the Rhenish Institute for

Environmental Research at the University of Cologne,

within the German Research Foundation’s (DFG)

project Boundary Layer Aerosol Characterization from

Space by Advanced Data Assimilation into a Tropo-

spheric Chemistry Transport Model (AERO-SAM;

Nieradzik and Elbern 2006; Martynenko et al. 2008).

Another significant pattern of forecast accuracy can be

attributed to seasonal variations. Figure 3 already dem-

onstrates that episodes with significant underestimation

of AOD do occur in the summer months. Histograms of

AOD error for separate months are shown in Fig. 4,

where it becomes evident that during the summer months

there is an absolute underestimation in AOD of 20.15 for

July and August, whereas the aerosol predictions are

much more accurate in September–November, with an

underestimation of only 20.05.

This fact can partly be explained by the Saharan dust

episodes in the western and central Mediterranean re-

gion, as mentioned above. Additionally, the summer of

2003 was exceptionally dry and dusty, with long periods

without wet deposition. This caused an increase in

atmospheric aerosol loads and therefore significantly

increased AOD values over large areas of Europe

(Hodzic et al. 2006). However, the model is not capable

of reproducing these uncommonly long aerosol accu-

mulation periods, which consequently led to underesti-

mations in AOD during these summer months.

There is no interdependency between forecast errors

and the time of day. A weak correlation between fore-

cast length and accuracy can be established: the mean

underestimation of AOD550 (EURAD model minus

ground measurements) decreases from 20.13 (hours

1–24) to 20.11 (hours 25–48) to 20.09 (hours 49–72),

while at the same time the standard deviation increases

from 0.15 to 0.18. As a result, there is an apparently

constant RMSE for all three forecast days, caused by the

combination of these two reversed error tendencies.

Although there are still deficiencies in the EURAD-

based AOD forecasting system, especially with regard to

the treatment of desert dust particles, the model system

is capable of reproducing the general features of the

atmospheric aerosol load over Europe. Consequently,

the approach of using aerosol information from a chem-

istry transport model for solar energy purposes seems

promising—if the enhancements described above, such as

the integration of dust information from near-real-time

satellite data, will be pursued. It is shown in section 4

that using the EURAD AOD forecasts that have been

assessed in section 2 yield a significant improvement in

the irradiance forecasts despite their deficiencies.

3. Validation data sources

To quantify the accuracy of AFSOL irradiance fore-

casts, its results are compared to ground- and satellite-

based measurements of global and direct irradiances for

FIG. 3. Daily mean values of absolute differences in AOD550

(EURAD 2 AERONET, all stations). Days with marks coincide

with dust events in the central Mediterranean as measured

from ground stations and visually identified from MODIS color

composites. FIG. 4. Histogram of the absolute differences in AOD550

(EURAD 2 AERONET), separated for each month of the anal-

ysis period; normalized.

1772 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 48

the period of July–November 2003. To enable compar-

isons with other available solar forecast data, the results

are also checked against the performance of routinely

available ECMWF and EURAD–MM5-based irradi-

ance forecasts.

a. Ground measurements

For validation purposes ground measurements of

global and (if applicable) direct irradiance from 121

locations in Europe and the Mediterranean area are

used. Figure 5 shows their distribution throughout Europe.

The accuracy of the pyranometers used at the various lo-

cations is given with a 2% deviation for the daily sum of

the global irradiance, corresponding to the ‘‘secondary

standard’’ of the World Meteorological Organization.

To improve compatibility, the sometimes hourly and

sometimes instantaneous measurements of various tem-

poral resolutions are all aggregated into hourly values, for

easier comparison with the hourly AFSOL model values.

However, it should be noted that the model data used are

set on a rather coarse 54-km grid. This means that due to

subgrid variabilities of clouds, and thus ground-level ra-

diation, there will always be a certain minimal variability

between hourly model values for an area (in this case,

2916 km2) and temporally averaged point measurements.

For 29 cases in which more than one ground measure-

ment station can be assigned to an AFSOL grid box, an

average RMSE of 16% for hourly global irradiance mea-

surements and the complete 5-month period can be de-

duced (Breitkreuz 2008). This intrinsic variability of solar

irradiance measurements, as the ground truth data, should

be kept in mind when evaluating forecast accuracies: de-

viations of this order of magnitude are not necessarily due

to incorrect forecasts, but might instead reflect natural

subgrid variabilities of ground-level solar irradiance.

b. Other forecast data

Data from the ECMWF are used as a second source of

irradiance forecasts. Solar global irradiance forecasts are

operationally archived whereas direct irradiance is not

available. To minimize the processing time, the compu-

tation of shortwave transmissivities is performed only

every 3 h, using the values of temperature, specific hu-

midity, liquid/ice water content, and cloud fraction at this

time step, and climatologies for aerosols, atmospheric

carbon dioxide, and ozone content (ECMWF 2008).

For this study atmospheric fields of the solar surface

radiation downward (SSRD) parameter were obtained

at a model grid of 0.58 3 0.58. An arithmetic mean value

of the four closest ECMWF grid points is attributed to

each AFSOL grid point in order to balance out the

somewhat incongruent grid systems. Each forecast starts

at midnight. The radiation values are spectrally inte-

grated from 200 to 4000 nm. All irradiation parameters

at ECMWF are accumulated from the start of the

forecast (in J m22). To produce instant 3-h mean values

for each time step given, irradiation values for each time

step have to be isolated and normalized to the time in-

terval. These 3-h values are then interpolated to obtain

hourly mean values of irradiance. As linear interpolation

FIG. 5. Locations of the 121 ground stations used for the validation of AFSOL

irradiance forecasts.

SEPTEMBER 2009 B R E I T K R E U Z E T A L . 1773

leads to underestimations for high sun elevations and to

overestimations for low sun, an interpolation method us-

ing the clear-sky index—the ratio of the forecasted global

irradiance against the modeled clear-sky irradiance for

the same situation (Girodo 2006)—is implemented here.

An additional model (Skartveit et al. 1998) is needed to

separate the direct and diffuse components since the

ECMWF only offers global irradiance values. Depending

on the solar elevation and the clear-sky index, this allows

for the determination of direct normal irradiance, which

is the parameter needed for managing concentrating solar

thermal plants. It has to be noted that there are slight

distortions in the diurnal cycle of ECMWF global irra-

diances, caused by the fact that irradiance data need to be

interpolated from 3-hourly values where no additional

information on subhourly variability is available. These

deviations tend to multiply when being transferred to the

direct irradiance, leading to a slightly compressed daily

curve of the ECMWF-derived direct irradiance values.

The accuracy of these temporally and spatially im-

proved ECMWF forecasts has been analyzed for the

summers 2003 and 2004 using data from 18 ground sta-

tions in Germany, where relative RMSEs of 14%–15%

for cloud-free hourly values and of 35%–42% for all

cloud situations are reported (Girodo 2006).

The meteorological part of the EURAD model also

produces irradiance forecasts that are used as another

source of comparison. These MM5-based data do not

use aerosol climatologies to produce the global irradi-

ance, like the operationally available ECMWF data.

Instead, these data parameterize the clear-sky irradi-

ance only as a function of solar zenith and climatological

water vapor values (Grell et al. 1995). The MM5 global

irradiance forecasts are employed in this study in order

to show the importance of using accurate aerosol in-

formation instead of no explicit aerosol input, as in the

EURAD–MM5 model.

c. Satellite-based measurements

Satellite-based irradiance measurements are used to

give an impression of the theoretically possible accuracy

when comparing spatially averaged data with point

measurements. Global and direct irradiances from the

European Meteosat-7 satellite, operated by the Euro-

pean Organisation for the Exploitation of Meteorological

Satellites (EUMETSAT), at a resolution of approxi-

mately 2.5 km 3 4.5 km over middle Europe, are used for

this study. For the retrieval of ground-level irradiance,

the Heliosat algorithm (Hammer et al. 2007) is em-

ployed, which is used for Meteosat-7 and MSG opera-

tional retrievals at the Department of Energy and

Semiconductor Physics of the Institute of Physics of the

University of Oldenburg, Oldenburg, Germany.

The Heliosat method relies on the assumption that the

radiance measured at the satellite, after ground and at-

mospheric reflection processes, is proportional to the at-

mospheric reflection (Cano et al. 1986) and thus mainly

due to the impact of cloud reflection. Ground-level irra-

diance is then calculated from atmospheric transmission

characteristics in combination with a model for clear-sky

irradiance values. For the data used in this study, atmo-

spheric aerosol input for the clear-sky model consists of a

simple turbidity climatology (Dumortier 1998).

Together with geometrical corrections for the effects

of cloud heights on the spatial distribution of ground-

level irradiance as well as improvements in the deter-

mination of global irradiance in totally cloudy and

cloud-free situations, this method has been attributed an

RMSE of 19% and a bias of 20.5% for hourly values of

global irradiance at 20 German locations for a period of

9 months during 2004 (Hammer et al. 2007).

4. Accuracy assessment of the AFSOL system

a. Clear-sky situations

For the period of July–November 2003, the perfor-

mance of the AFSOL system is validated against ground-

based measurements of 121 European sites described in

section 3a (see Fig. 5). To allow for comparison with

other available solar irradiance datasets, operationally

available ECMWF global irradiance forecasts, EURAD–

MM5-based global irradiance forecasts, and Meteosat-7

irradiance measurements are included.

Clear-sky cases are defined in this study by a low

variability (s , 0.02) of the clear-sky index based on the

hourly ground measurements and a maximum cloud

cover of 10% forecasted by both the EURAD–MM5

and the ECMWF model. This is a rather strict criterion

employed to exclude any differences resulting from dif-

ferent cloud physical parameterizations in the ECMWF

and MM5 models. Both models show different error sta-

tistics regarding clear-sky prediction, which shall not be

discussed here in detail (Breitkreuz 2008).

For these clear-sky situations, AFSOL irradiance

forecasts turn out to have a higher accuracy than oper-

ationally available ECMWF products. This is especially

true for direct irradiance forecasts where the influence

of aerosols is most relevant: the AFSOL system has a

bias of 111.2% and an RMSE of 18.8%, whereas the

ECMWF-derived direct irradiance forecasts underesti-

mate by a mean of 226.3% (bias), together with an

RMSE of 31.2%. Table 2 summarizes the statistical re-

sults of the intercomparison of the various irradiance

datasets against ground-based measurements, whereas

Fig. 6 shows histograms of their absolute deviations

in global irradiance for cloud-free situations. As a

1774 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 48

comparison, the accuracy of direct irradiance measure-

ments from Meteosat-7 leads to 15.6% (RMSE) and

21.7% (bias), thus giving the range of accuracy that

can approximately be reached when comparing point

measurements to 54-km-averaged model data.

This tendency is also valid for global irradiance fore-

casts: while the AFSOL forecasts can be characterized by

a bias of 15.1% and an RMSE of 7.2%, the operationally

available ECMWF forecasts lead to a bias of 29.8% and

an RMSE of 11.5% (see Fig. 6). The EURAD irradiance

forecasts have a bias of 15.0% and an RMSE of 19.4%.

On days with desert dust outbreaks, AFSOL direct

irradiance forecasts in the central Mediterranean area

have increased RMSE values relative to other regions.

For example, if two 3-day-periods of dust outbreaks are

eliminated from the whole 5-month period, the relative

RMSE of all clear-sky situations decreases from 18.4%

to 15.8% in the Mediterranean region, which then is

within the accuracy range of other European regions

such as southern Germany. This can be attributed to the

fact that these regional dust episodes could not be

modeled by the version of the EURAD system that is

used for input in the aerosol forecasting system. Addi-

tionally, direct irradiance values are much more strongly

influenced by extinction processes through dust particles

than global irradiance. Consequently, this problem does

not appear in global irradiance forecasts.

b. Cloudy situations

For cloudy situations the AFSOL system is less ac-

curate than the ECMWF forecasting system, as is shown

in Fig. 7, where the bias and RMSE of the global

irradiance values of the different datasets for cloudy

conditions are included. RMSE values for all model

systems are closely coupled to the maximum cloud cover

forecasts of the subset of situations included in the

analysis (i.e., clear sky, maximum of 10% cloud cover,

and maximum of 60% cloud cover; see Fig. 7). However,

the accuracy of the AFSOL global and direct irradiance

forecasts decreases more strongly when allowing cloud

cover predictions of up to 100%, as compared to

ECMWF data. This leads to relative RMSE values for

global irradiance of up to 60% (AFSOL) for all cloud

situations, or 37% (ECMWF) or 22% (Meteosat-7

measurements). It is pointed out, however, that the

RMSE values of the EURAD–MM5 global irradiance

forecasts are always higher than the RMSEs of the

AFSOL predictions, showing the value of additional

aerosol information.

While the mean underestimation of ECMWF global

irradiance forecasts is at approximately 210%, regard-

less of the subset of cloud situations included in the

analysis, the MM5-based irradiance systems (AFSOL

and EURAD models) tend to significantly underesti-

mate the results with increasing maximum cloud cover,

causing a bias of up to 225% if all cloud cover situations

are analyzed. This tendency can be explained by the

effect that the MM5-based systems predict more high

cloud-cover situations and less low cloud-cover situa-

tions relative to the ground-truth data.

For direct irradiance, similar tendencies are observed,

with higher RMSE values for all subsets of cloud situa-

tions. This is shown in Fig. 8, where direct irradiance

forecast biases and RMSEs of the different datasets are

depicted for different subsets of cloud situations. Be-

cause of the more significant influence of aerosols on

direct irradiance, the gain in accuracy in the clear-sky

case from using actual aerosol forecasts instead of sim-

ple climatologies (AFSOL versus ECMWF) is more

apparent here than for global irradiance (Figs. 7 and 8).

Forecast length has a significant impact on forecast

accuracy, as long as cloudy situations are included in the

analysis: for the AFSOL system, this can be quantified

by RMSEs of 49.7% for the first day, 62.4% for the

second day, and 67.7% for the third day. When consid-

ering only cloud-free cases, forecast length has no effect

on bias or RMSE for any of the model systems analyzed.

Thus, it can be deduced that this error tendency is

TABLE 2. Absolute and relative forecast accuracies (bias, RMSE) of

direct irradiance forecasts (model 2 ground measurements).

Modeling

system

Relative

bias

(%)

Relative

RMSE

(%)

Absolute

bias

(W m22)

Absolute

RMSE

(W m22)

AFSOL direct 11.2 18.8 57 96

ECMWF direct 226.3 31.2 2134 159

Meteosat-7 direct 21.7 15.6 29 80

FIG. 6. Histogram of the absolute deviations in global irradiance

for cloud-free situations in Europe: AFSOL, black; ECMWF, light

gray; EURAD–MM5, dark gray; and Meteosat-7 retrieval, black

dashed.

SEPTEMBER 2009 B R E I T K R E U Z E T A L . 1775

caused exclusively by difficulties in cloud forecasts that

increase with growing forecast duration.

The correlation of forecast accuracy with seasonal

variation is only true for cloudy situations. Both direct-

and global-irradiance forecasts of all model systems as

well as the satellite measurements have smaller errors in

the summer months (e.g., a relative RMSE of 55.9% for

AFSOL global irradiance forecasts for July and August)

than in the fall months (e.g., a relative RMSE of 68.9%).

If only clear-sky situations are analyzed, no seasonal

correlation at any location can be observed. This can be

explained by the seasonal cycles in average cloud cover,

leading to a higher percentage of cloudy situations and

therefore larger errors in middle European autumn.

The different European regions also show a distinct

pattern of forecast accuracy, when considering all cloud

situations. RMSE values are highly correlated with av-

erage cloud cover for all considered models or retrieval

systems. This leads to larger errors in the United King-

dom and around the Baltic Sea, whereas the Mediter-

ranean region has higher forecast accuracies for both

global- and direct-irradiance forecasts. Consistently, if

only cloud-free situations are examined, no consistent

regional tendency can be found.

c. Accuracy performance of combined forecastingsystems

The two irradiance forecasting systems analyzed in

this study have complementary strengths and weak-

nesses: while the AFSOL forecasts produce high errors

for cloudy cases and very good agreements with ground

measurements for clear-sky situations, the reverse is

true for the ECMWF-based irradiance forecasts (see

Figs. 7 and 8). This means that a possible practical way of

improving overall irradiance forecasts is the combina-

tion of both models according to their strengths and thus

eliminating the weaknesses of both approaches. This

combined product will then have high forecast accura-

cies for clear-sky situations due to the AFSOL system,

and acceptably high forecast accuracies for cloudy situ-

ations from the ECMWF data. Thus, it cannot only be

used for direct irradiance applications, such as for con-

centrating solar systems, but also for global irradiance

applications such as load forecasts.

It has to be clearly stated that the overall solution is an

integration of an aerosol modeling scheme into the

ECMWF model as currently prepared at ECMWF. But

in the meantime, a practical solution that is easily

implemented has to be found, as only this fulfills the

daily operational needs, for example, of power plants

that utilize concentrated solar energy.

If the AFSOL forecast is used for situations with

forecasted EURAD–MM5 cloud cover of up to 10%

and the ECMWF data for all other situations, then for

the whole analysis period and all locations the bias for

global irradiance can be reduced from 28% (ECMWF)

and 225% (AFSOL) to 21% (combined method) with

no changes in the RMSE (combined method and

ECMWF: 37%). For the ground locations chosen and

the period from July to November 2003, this is based on

a ratio of approximately 20% AFSOL data and 80%

ECMWF data. This means that combining both model

systems is a promising practical way of improving the

irradiance forecasts based on numerical weather pre-

diction without an extended aerosol modeling scheme.

However, the optimal cloud cover threshold value is

subject to further investigation, especially with respect

to the influence of different regions or seasons.

5. Discussion and conclusions

This study deals with short-term solar irradiance fore-

casts with respect to their application in solar energy

FIG. 7. Global irradiance forecast accuracy for different subsets

of cloud situations in Europe, relative bias (dashed) and RMSE

(solid): ECMWF, light gray; AFSOL, black; EURAD–MM5, black

squares; and Meteosat-7 retrieval, dark gray.

FIG. 8. Direct irradiance forecast accuracies for different subsets

of cloud situations in Europe, relative bias (dashed) and RMSE

(solid): ECMWF, light gray; AFSOL, black; EURAD–MM5, black

squares; and Meteosat-7 retrieval, dark gray.

1776 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 48

industries. The main atmospheric parameter responsible

for the extinction of solar irradiance is clouds. However,

the main focus and economic potential of the solar en-

ergy industry is found in regions and time periods that

have minimal cloud cover. During these ‘‘clear sky’’

cases, it is mainly aerosols—solid and liquid particles in

the atmosphere—that influence the direct and diffuse

irradiances at ground level. Aerosols are highly variable

in space and time, which leads to difficulties in calcu-

lating and forecasting their spatiotemporal patterns and

thus their influence on irradiance.

For an episode of 5 months (July–November 2003) in

Europe, forecasts of the aerosol optical depth at 550 nm

(AOD550) based on particle forecasts of the EURAD

chemistry transport model are analyzed. It is shown that

the aerosol forecasts underestimate ground-based mea-

surements by a mean of 20.11 (RMSE of 0.20), which is

not within the accuracy required by the solar energy

industry for input parameters of irradiance forecasts. In

particular, sporadic Saharan dust storm events in the

central Mediterranean region lead to large inaccuracies

that cannot be accounted for in the version of the

EURAD model system used. Improvements in forecast

accuracy can thus be expected from the integration of

dust and also fire particle modules into the model sys-

tem; these improvements are being prepared at the

moment. Furthermore, the consideration of higher

spatial resolutions is expected to lead to reduced fore-

cast errors—an approach that has been neglected in this

study for computational reasons, due to the spatial di-

mension of the study area. However, in other case

studies the EURAD system has also been successfully

applied on 15-, 5-, and 1-km scales.

Because of the high regional variability of aerosol

presence and type, large differences in the representation

accuracy for different European regions can be distin-

guished, such as severe underestimations of particle loads

in the highly industrialized Po Valley in northern Italy or

good results for remote continental areas in northern

Europe. However, in spite of these deficiencies in the

EURAD-based AOD forecasting system, especially with

regard to the treatment of desert dust particles, the model

system is capable of reproducing the general features of

the atmospheric aerosol load over Europe.

Using these aerosol forecasts and other remote sens-

ing data (ground albedo, ozone), as well as numerical

weather prediction parameters (water vapor, clouds), a

prototype for an irradiance forecasting system is set

up: the Aerosol-based Forecasts of Solar Irradiance

for Energy Applications system. Based on the 5-month

dataset, its results are compared to forecasts of ECMWF

and the MM5, satellite-based irradiance data from

Meteosat-7, and ground measurements. It is demon-

strated that for clear-sky situations the AFSOL system

significantly improves direct irradiance forecasts com-

pared to ECMWF forecasts, with a reduction in the

relative bias from 226% to 111% and a reduction in the

relative RMSE from 31% to 19%.

On days with desert dust outbreaks, AFSOL direct

irradiance forecasts in the central Mediterranean area

have increased RMSE values relative to other regions.

This means that the integration of dust information into

the model system, such as through the assimilation of

satellite-based data in the forecasting system, would

significantly improve the direct irradiance forecast ac-

curacy for clear-sky situations especially in the Medi-

terranean region, both of which are areas of interest for

the solar energy industries.

Global irradiance forecasts in the clear-sky case are

also shown to have higher accuracies in comparison to

the operationally available ECMWF forecasts, with a

reduction in the relative bias from 210% to 15% and a

reduction in the relative RMSE from 12% to 7%.

However, for cloudy situations, the AFSOL forecasts

can lead to significantly larger forecast errors due to

cloud modeling deficiencies in the underlying mesoscale

numerical weather model. Also, it should be pointed out

that for all cloud situations except completely cloud-free

cases the accuracy of MM5 irradiance forecasts is lower

than for the ECMWF predictions.

This means that both direct and global irradiance

forecasts of the AFSOL model show higher accuracies

than ECMWF global irradiance forecasts or the derived

direct irradiance products in clear-sky cases. Since in

cloud-free situations the aerosols are the dominant pa-

rameter for determining solar irradiance at ground level,

we suggest that the enhanced aerosols’ input into the

irradiance calculation schemes significantly contributes

to this accuracy improvement. This is valid even if there

are still deficiencies within the aerosol forecasting sys-

tem, especially regarding the representation of dust

episodes, because the enhanced aerosol information still

provides an improvement relative to the AOD clima-

tologies used operationally at ECMWF.

In conclusion, regarding the application of the AFSOL

system for solar energy purposes, it can be stated that for

cloudy situations the AFSOL model in its current state is

not well suited to forecasting irradiance and power

loads: a forecast of consumer behavior and thus power

consumption is only possible when cloudy and cloud-

free situations can be forecasted accurately enough.

However, the AFSOL systems produces good agree-

ment with ground measurements for cloud-free situa-

tions and especially for direct irradiance forecasts—both

of which determine the situations of greatest relevance

for the management of solar thermal and photovoltaic

SEPTEMBER 2009 B R E I T K R E U Z E T A L . 1777

power plants. Therefore, the need for an inclusion of a

more detailed aerosol scheme into the ECMWF oper-

ational model has been clearly shown in this study. In the

meantime, the combined use of ECMWF and AFSOL

irradiance forecasts, depending on the level of fore-

casted cloud cover, can be recommended to fulfill daily

operative needs at present-day concentrating solar power

plants. This approach has been tested with satisfying re-

sults: a reduction in bias from 225% (AFSOL) and 28%

(ECMWF) to 21% (combination method) is achieved.

Possible benefits from using the AFSOL model for

optimizing the management strategies of a concentrat-

ing solar thermal power plant in Spain are described in a

separate case study (Wittmann et al. 2008). Further test

cases and the use of larger datasets for the development

of power plant operation strategies are foreseen.

Acknowledgments. We thank the Rhenish Institute

for Environmental Research of the University of Co-

logne, especially Lars Nieradzik, for providing large

amounts of EURAD-CTM aerosol and EURAD/MM5

meteorological data. As well, we thank the AERONET

PIs and their staff for establishing and maintaining the

32 sites used in this investigation.

For the provision of ground-based irradiance mea-

surements at 121 sites, we thank the ECMWF, the Deut-

sche Wetterdienst (DWD), the Spanish Instituto Nacional

de Meteorologıa, the Met Office, the AERONET net-

work, the National Observatory Athens, the Global

Atmospheric Watch Programme, the International Day-

light Measurement Project, the Sveriges Meteorologiska

och Hydrologiska Institut, the CEOP project, the Baseline

Surface Radiation Network, the Institute of Construction

and Architecture of the Slovak Academy of Sciences, the

Solar Millennium AG, the Centre Universitaire d’Etude

des Problemes de l’Energie of the University of Geneva,

and the colleagues at the Plataforma Solar de Almerıa.

Our acknowledgment also goes to the developers of

libRadtran (information online at www.libradtran.org)

for their radiative transfer tools, which were used for all

irradiance calculations.

We thank Dr. Elke Lorenz from the Energy and

Semiconductor Research Department of the University of

Oldenburg for Meteosat-7 retrieval data and for routines

and help with the ECMWF data interpolation procedures.

This work was financed by the Virtual Institute of

Energy Meteorology (vIEM), supported by the ‘‘Impuls-

und Vernetzungsfond’’ of the Helmholtz Foundation.

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