Introduction
Renewable energy such as wind and solar power is gaining importance for the energy supply of many areas of the world. For example, in
Germany renewable energy currently contributed up to 32.3 % in 2016 to the gross electricity consumption, of which the
contribution of solar energy is 6.5 % . Instantaneously, solar power can even cover up to 50 % of Germany's
electricity demand . Weather-dependent renewable energy, such as photovoltaic (PV) power, poses a particular challenge
to transmissions system operators (TSOs) and forecast providers because power production forecasts are afflicted with errors. A summary
of the diverse state-of-the-art methods and related challenges in PV-power forecasting is given in . For time horizons
of hours to days, numerical weather prediction (NWP) models deliver the basis for PV-power predictions. Forecast errors arise during
weather situations or phenomena that are insufficiently represented in NWP models for PV power see. Large errors
in the power forecast for weather-dependent renewables even endanger the stability of the electricity grid. To avoid power outages, the
German TSOs need to take costly redispatch measures, which are short-term changes to the operating schedule of power plants. From 2014
to 2015, the gross electricity generation from wind and solar energy increased by 15 % ; the redispatch
volume, however, tripled . Ultimately, the related costs are added to the household electricity prices. To guarantee
a reliable and economic integration of increasing shares of renewables, there is a strong demand in the energy market to provide the
most accurate PV-power forecasts possible e.g.,. Driven by the growth of renewable energy
shares in electricity production, the accuracy of solar NWP and power forecasts has to meet increasing standards. Additionally, the
literature in this field of application is growing, although it is still a new area of research.
Solar radiation is modified by the clouds and aerosols in the
atmosphere before reaching the solar panels. Aerosol particles
interact with radiation by scattering and absorption (direct
effect). They also change the physical properties of clouds, such
as effective radii and droplet number concentration, which also
modifies the radiation reaching the ground (indirect effect).
Mineral dust is a prominent aerosol species in the atmosphere
, with global estimates for the mineral dust
emissions ranging from 1000 to 5000 Tg a-1
. Mineral dust can be transported large distances
from the source area, for example from the Sahara to
Europe .
Current operational NWP models are unable to account for the
effect of mineral dust during such episodes because they rely
on aerosol climatologies. investigated the
spatial and temporal variability of aerosols over Europe and
stressed the necessity for near-real-time forecasts of aerosol
loads instead of climatological values. In areas of high
desert-dust intrusions or intense anthropogenic activities, the
reduction of direct normal irradiance (DNI) was found to reach
values of up to 35 and 45 %, corresponding to 4 and
6 kWhm-2 day-1. Recently,
showed that considering prognostic dust
aerosol considerably improves DNI forecasts in Spain and the
Canary Islands, which is of great importance for concentrating
solar power. These findings are supported by the study of
, who apply an interactive aerosol
scheme for clear-sky cases. highlight the
importance of using accurate aerosol concentration, optical
properties and an accurate vertical distribution of aerosols in
NWP forecasts of shortwave radiative fluxes in case of a
wildfire. Concerning mineral dust, have shown
a significant potential to improve the surface temperature
forecasts during a strong Saharan dust event over southern
Germany when dust as well as direct and indirect effects are
considered in the NWP system COSMO-ART (COnsortium for
Small-scale MOdeling – Aerosol and Reactive Trace gases). For
solar energy applications, however, it is of great importance to
consider the influence on ground level radiation by dust aerosols
.
Since recent research has shown the importance of
meteorology and aerosol or chemistry feedback in many research areas,
many online-coupled mesoscale meteorology atmospheric chemistry models
have been developed. give an extensive overview of
such models in Europe. Considering prognostic aerosols and the
interactions with the atmosphere in NWP models is costly in terms of
computing time. However, thanks to the increase in computing
power, there are several modeling systems worldwide providing daily
forecasts of mineral dust distributions. Table provides an
overview on current operational mineral dust forecasting models. A more detailed
description of available daily mineral dust
forecasts and activities in this research area can be
found at the .
Overview on operational mineral dust forecasting models.
Model name
Institution
References
BSC-DREAM8b
Barcelona Supercomputing Center
,
NMMB/BSC-Dust
Barcelona Supercomputing Center
,
DREAM8-NMME-MACC
South East European Virtual Climate Change Center
,
LOTOS-EUROS
The Netherlands Organisation for Applied Scientific Research
,
SKIRON
University of Athens
CAMS
European Centre for Medium-Range Weather Forecasts
,
Met Office UM
UK Met Office
,
NGAC
National Centers for Environmental Prediction
GEOS-5
National Aeronautics and Space Administration
A quantitative example of solar energy reduction due to mineral dust
is given by . For five dust episodes in Romania, a
reduction in collectable PV power of 6.5 to 17.5 % was
reported. investigated a controlled fire burn in
Canberra, Australia, and indicated an overall PV-power reduction of
7 % during the study period and a peak reduction of 27 %. Besides
a more accurate radiation forecast, operational dust forecasts also
provide the possibility to account for the deposition of dust on PV
panels and for better maintenance planning. For a review on energy
yield losses by dust deposition see .
Within this paper, the focus is on the beginning of April 2014, when
central Europe was influenced by an intensive Saharan dust
outbreak. At the same time, large errors in the day-ahead forecasts of
PV-power production challenged Germany's electrical grid management.
On 4 April 2014, the day-ahead PV-power forecast overestimated the
actual power production for Germany by up to 5.3 GW
. The transport of mineral
dust and its influence on atmospheric composition and radiation are
not explicitly considered within conventional numerical weather
prediction. Although it is speculated that these events have a large
impact on PV production over Europe, a quantitative assessment is
currently not available. In this study we use ICON-ART
ICOsahedral Nonhydrostatic – Aerosol and Reactive Trace
gases; to assess and quantify the effect of the
Saharan dust outbreak in April 2014 on PV-power production. We will address the following questions in detail:
What is the quantitative impact of mineral dust on simulated surface radiation
and PV power?
What is the contribution of the direct aerosol effect on radiation in comparison
to effects caused by modifications of clouds?
Section provides an overview of the modeling system ICON-ART. In particular, the new emission scheme
for mineral dust, aerosol–radiation, aerosol–cloud and cloud–radiation interactions are explained.
The method to calculate PV power is outlined as well. Section summarizes the synoptic situation
during the Saharan dust outbreak and Sect. describes the model setup.
In Sect. , the results are presented, followed by the conclusions.
Model description
For this study, we use the online-coupled modeling system ICON-ART. The host model ICON is jointly
developed by the German Weather Service (DWD) and the Max Planck Institute for
Meteorology (MPI-M). ICON features a nonhydrostatic dynamical core and physical
parameterization packages for numerical weather prediction, global climate modeling
and for large eddy simulations .
The governing equations of ICON are discretized on a triangular mesh offering an
intuitive way of grid refinement by performing bisections of the triangles.
This enables the possibility of incorporating further nests with two-way interactions
within one simulation. In the NWP configuration, ICON is used as a global model for
numerical weather prediction by DWD since January 2015 on a so-called R3B07 grid,
i.e., with 13 km effective horizontal grid spacing. In July 2015, an R3B08 nest
was added over Europe with 7 km effective horizontal grid spacing. Hence,
ICON in its NWP configuration is continuously validated by the DWD. A further
highlight of ICON is the high scalability and, therefore, high efficiency of modern computer
architectures. Very important for the extended modeling system ICON-ART is the local mass
conservation and the mass-consistent tracer transport featured by ICON.
Parameters for the log-normally distributed mineral dust. d‾0,l,E
(d‾3,l,E) is the median diameter of the specific number (mass)
emission of mode l. The SD of mode l, σl, is held
constant for the whole simulation.
Dust mode A
Dust mode B
Dust mode C
d‾0,l,E (µm)
0.6445
3.454
8.672
d‾3,l,E (µm)
1.5
6.7
14.2
σl
1.7
1.6
1.5
The ART extension is developed at the Karlsruhe Institute for Technology (KIT) with
the goal to describe the spatiotemporal evolution of atmospheric trace substances and
their associated atmospheric feedback processes. A detailed overview of the
governing equations and the coupling concept of ICON-ART is given by .
Mineral dust aerosol is described by three log-normally distributed modes with prognostic specific numbers and mass mixing ratios. The standard deviations (SDs) are kept constant
during the simulation, making the median diameters diagnostic variables which can change
during transport. The initial median diameters and the SDs are listed in
Table . Sedimentation, dry deposition and washout of mineral dust
are
parameterized as described in , while coagulation and chemical aging
are
neglected.
The emission of mineral dust and the interactions with radiation and clouds are explained in the following.
Emission of mineral dust
We have implemented the mineral dust emission scheme of improved by the
following benefits.
(1) It is based on a global dataset of soil properties (size distribution, residual
soil moisture).
(2) It accounts for the soil dispersion state.
(3) A tile approach was introduced to account for soil type heterogeneity at coarse
resolutions.
The scheme combines the parameterization of for the
saltation flux with a parameterization of for the threshold friction
velocity. When the friction velocity is above this threshold, soil erosion by wind is initiated.
The resulting saltation flux is then used to
calculate a dust emission flux using the parameterization of .
The size distribution of the emitted mineral dust varies according to soil type and
meteorological situation.
The size distributions of soil particles are crucial input parameters for the mineral dust
emission scheme. In order to obtain a global coverage,
HWSD data Harmonized World Soil Database; containing the
global distribution of soil types are used. For each of these soil types, two
limiting particle size distributions, each consisting of up to four log-normal
distributions,
are used see Table B1 in. The following approach to calculate dust
emission fluxes applies for one single soil type.
For a grid box containing different soil types, a tile approach is used
and
explained afterwards.
For the size distribution of soil particles ns(dp) we take the soil dispersion
state into account. This can be described with the help of two limiting size
distributions for weak erosion ns, m (minimally dispersed) and strong erosion ns, f
(fully dispersed), in which the dispersion factor γd (between 0 and 1) determines
the actual size distribution between the two limits depending on the friction velocity
u* :
ns(dp)=γd⋅ns, m(dp)+(1-γd)⋅ns, f(dp),γd=e-0.5⋅(u*-u*t, m)3.
u*t, m is the global minimum (in a mathematical
sense) of the threshold friction velocity u*t(dp). The threshold friction
velocity is defined as the value of the friction velocity at an equilibrium of
aerodynamic, cohesive and gravitational forces e.g.,. For values higher than the
threshold friction velocity, emission of soil particles takes place.
derive an expression for the threshold friction velocity from the equilibrium of
moments of forces acting on the particle:
u*t(dp)=fr⋅fη⋅An⋅ρsρ⋅g⋅dp+γnρ⋅dp,
with An=0.0123 and γn=3⋅10-4 kgs-2.
ρ is the air density, ρs=2650 kgm-3 is the density of the soil and
g is
the gravitational acceleration. For application in a global or regional model, we include
correction factors to account for the effects of roughness elements (fr) and
soil moisture (fη) on u*t e.g.,. Applying
Eq. (), we can analytically derive
the global minimum of the threshold friction velocity, which is needed in
Eq. ().
Independent of the size distribution, no emission is possible below u*t, m.
The factor to account for soil roughness elements is calculated using the expression of
. This correction term is based on the percentage of plant coverage
pp, a variable which is typically available in atmospheric modeling systems:
fr=1-0.5⋅λ⋅1+0.5⋅90⋅λ,withλ=-0.35⋅ln(1-pp).
Higher soil moisture leads to increased adhesive forces between the soil particles.
Therefore the threshold friction velocity increases for higher soil moisture.
To consider this behavior, the correction term by
is used, which is based on gravimetric soil moisture in % η and
the percentage clay content pc of the soil:
fη=1+1.21⋅(η-η′)0.68,withη′=az⋅(0.0014⋅pc2+0.17⋅pc),
where η′ is a minimum value of η. The factor az=5 is introduced to account
for too-high soil moisture content in the model and to increase the
performance of the emission scheme within ICON-ART. The derivation of pc
is explained in more detail at the end of this section. The saltation flux is
calculated following :
Fh(dp)=Cwhite⋅ρgu*3⋅1+u*t(dp)u*⋅1-u*t2(dp)u*2,
where Cwhite=0.7 is a linear scaling parameter to adapt the dust emission flux to
measurements.
The total saltation flux Fth is the result of an integration over all
saltation particle diameters weighted by the product of the cross-sectional area and the number of particles
. This weighting represents the contribution of particle surface area at a certain diameter
to the total soil surface area:
Fth=fre⋅∑s=14∫-∞∞Fh(dp)⋅π4dp2⋅ns(dp)∫-∞∞π4dp2⋅ns(dp)dlndpdlndp.
In contrast to ,
ns(dp) takes into account the soil dispersion state by using
Eq. (). The summation is performed for the four log-normal
size distributions of soil particles as described above.
fre is the fraction of erodible soil calculated based on GlobCover2009
land use data . The fractions frb,i of the land use
classes –
bare areas, sparse vegetation, closed to open grassland, closed to open shrubland
and mosaic grassland/forest-shrubland – sum up to the erodible fraction
fre=∑i=15frb,i.
By impaction of the saltating particles, the saltation flux leads to the release
of small particles creating a dust emission flux. use the kinetic
energy of the impaction, which has to exceed the binding energies of particles in the soil
to describe this process. Larger particles are less tightly bound to the soil, and hence their
binding energy is smaller than that of smaller particles. This leads to the following
equation that connects the saltation flux with the dust emission flux of
aerosol mode l :
Fv,l(dp)=π6⋅ρp⋅d3,l3⋅pl(dp)⋅βkin⋅Fh(dp)el,
with βkin=163ms-2. The product
βkin⋅Fh(dp) is the kinetic energy of the saltation particles and el
the binding energy of particles of mode l. pl is the percentage of kinetic energy that
is spent to release particles of mode l and is calculated based on the binding energies
as summarized in Table 2 of . These percentages of kinetic energy
are chosen such that particles in the largest mode are emitted first when the threshold
friction velocity is exceeded. With increasing friction
velocity, the share of smaller particles that are released increases. An integration of
Eq. () over all saltation particle diameters weighted by their cross-sectional areas yields the total dust emission flux of mode
l:
Ftv,l=fre⋅∑s=14∫-∞∞Fv,l(dp)⋅π4⋅dp2⋅ns(dp)∫-∞∞π4⋅dp2⋅ns(dp)dlndpdlndp.
Similar to the saltation flux, ns(dp) takes the soil dispersion state
into account (Eq. ). Equation () was derived
for one specific soil type. As ICON-ART is typically used with grid spacings ranging from
100 to 102 km, a grid box may contain a mixture of
different soil types. To account for this subgrid-scale heterogeneity, a tile approach
is introduced.
Clay content assumed for the USDA soil types.
USDA name
Abbreviation
pc,i
Heavy clay
HCLA
100 %
Light clay
LCLA
80 %
Silty clay
SILC
50 %
Sandy clay
SCLA
45 %
Silty clay loam
SICL
30 %
Clay loam
CLOA
30 %
Sandy clay loam
SCLO
30 %
Loam
LOAM
15 %
Silt loam
SILO
10 %
Sandy loam
SLOA
10 %
Silt
SILT
5 %
Loamy sand
LSAN
5 %
Sand
SAND
5 %
The size distributions of soil particles from are available for soil
types as defined by the US Department of Agriculture (USDA) except for silt, where
silty loam is used instead. Table provides an
overview of the soil types and the percentaged clay content pc,i of soil type
i. To determine the soil type in an ICON-ART grid element, information from the high-resolution (30 arc seconds) global database HWSD is aggregated
to the target grid. By this, the fraction fs,i covered by soil type i with
∑i=114fs,i=1 is available to the model. There are 14 fractions as,
in addition to the 13 soil types, 1 fraction accounting for water, rock and urban
surfaces is considered. As stated previously, a tile approach is used to calculate the
dust emission flux. For clarity, Eq. () was derived for
one single soil type i. To consider the subgrid-scale heterogeneity of soil properties,
the dust emission fluxes Ftv,l,i are calculated for each soil type separately and the
result is then weighted with the corresponding fraction of the soil to get the final dust
emission flux:
Ftv,l=∑i=113fs,i⋅Ftv,l,i.
The soil clay content used in Eq. () to calculate the residual
soil moisture content in a grid element can be calculated with
pc=∑i=113fs,i⋅pc,i.
The calculation of dust emission fluxes Ftv,l,i for individual soil types within one grid element
differs by the use of the individual soil particle size distributions ns,i(dp).
However, it differs neither in the clay content of the soil within one grid element nor in
the residual soil moisture η′. The simple reason for this is that the gravimetric
soil moisture η used from the surface scheme of ICON-ART is a grid-scale variable
and hence is the average value for one grid element. As Eq. ()
includes a difference between η and η′, grid-scale values for both variables
are used.
Radiation
ICON-ART includes online mineral dust–radiation interaction utilizing the Rapid
Radiative Transfer Model (RRTM) as described in more detail by
. To account for the influence of mineral dust on radiation, the optical properties
of mineral dust are calculated offline once. For this, Mie calculations are applied, which require
the complex refractive index of mineral dust as input. For the Mie calculations,
a code developed by was used. This code in turn utilizes a subset developed
by for the calculation of the scattering coefficients and
truncation of the series. The code was adapted to allow for processing of multiple
wavelengths and averaging to the RRTM wavebands in a post-processing step.
A new polynomial parameterization was introduced to account for the change in median
diameter during transport.
The local radiative transfer parameters (extinction coefficient, single scattering
albedo and asymmetry parameter) are then calculated online within ICON-ART through
multiplication of the mass-specific mineral dust optical properties derived from Mie calculations with
the current mineral dust mass
concentration from ICON-ART. The actual radiative transfer parameters are then used by the RRTM
radiation scheme, thereby accounting for the local mineral dust effect on radiation.
Thus, changed radiative fluxes from the RRTM scheme feedback on the meteorological
conditions, which themselves can influence the mineral dust processes again.
Overview of studies determining mineral dust refractive indices.
Publication
Characterization
Source
Collection
Waveband
Acronym
SAMUM
Sahara
Aircraft
SW
SAM
Compilation
Various
–
SW, LW
HEL
ECLATS
Sahara
Niamey, Niger
LW
FOU
GOADS Comp.
Various
–
SW, LW
KOE
Sahara
Barbados
LW
SVO
Rain-out Dust
Various
USA
SW, LW
DVO
AERONET
Various
Worldwide
SW
DUB
Real (a) and imaginary (b) part of refractive indices according to studies listed in Table and used in ICON-ART
(BUSE). The borders of the RRTM radiation scheme wavebands are adumbrated as light grey lines in the background. The filled grey band
represents the waveband present both in the longwave and shortwave part of the RRTM.
The values of the refractive index of mineral dust used for the Mie calculations are of great importance
for the radiative properties extinction coefficient, single scattering albedo and asymmetry parameter.
give a summary
of the various influences.
For example, peaks in the real part show as maxima of the extinction coefficient, whereas
the single scattering albedo and thereby absorption is determined by the imaginary part of
the refractive index.
In order to assess the uncertainties related to the refractive indices we have conducted a
sensitivity study. The data sources used for the refractive indices are shown in
Table and the values for the real part and the
imaginary part of the refractive index are presented in Fig. .
The refractive indices used in ICON-ART (BUSE) are the same as those used by
for COSMO-ART.
For the longwave part of the spectrum down to 4 µm we use values as published
by .
For wavelengths smaller than 4 µm the shape of the curve is
still replicated, however, with a fit through smaller values for the imaginary part which are
obtained from .
This part of the spectrum is especially important as solar radiation intensity is highest
at these wavelengths.
The smaller values correspond to weaker absorptive and enhanced scattering properties of
mineral dust in ICON-ART for this part of the spectrum.
These smaller values are in better agreement with , who by inversion of AERONET
(AErosol RObotic NETwork)
retrievals determined values similar to .
This is further supported by several studies: report inconsistencies between popular dust models and remote
measurements due to uncertainties in the imaginary part of the refractive index.
show a better representation of observed mineral dust radiative forcing
in models using these refractive indices obtained from AERONET, confirming that mineral dust is
less absorptive than previously thought. point out that in situ measurements reporting
higher absorption values possibly measured a mixture of dust and absorbing aerosol.
obtained imaginary part refractive index values similar
to those used in ICON-ART.
Influence of different refractive indices on the specific extinction coefficient (EXT) in m2g-1,
single scattering albedo (SSA) and asymmetry parameter (ASY) for mode A (a). Influence of varying count median
diameter (CMD) on EXT, SSA and ASY for mode A (b).
The data presented in Fig. exhibit a large scatter of the
refractive indices. This has to be considered alongside other sources of uncertainty, such as the
size distribution of the dust particles. To account for these uncertainties, we conducted Mie
calculations for the refractive
indices differing most from the ones we are using (Fig. a), namely that of
and , as well as for varying median diameters of the
log-normal distribution of the dust particles (Fig. b). The results show
that changes in the size distribution lead to a stronger signal than changes in the refractive index.
This is in agreement with findings of .
A recent study by provides the first regionally detailed values of
refractive indices for the longwave part of the spectrum. Although for the direct effect of mineral dust
on the PV-power forecast the shortwave part of the spectrum is of greater importance, a usage of
this dataset can lead to changes in the mineral dust radiative effect on meteorological conditions and
to a further improvement of the forecast.
Aerosol–cloud interactions
In the operational version of ICON used at DWD, a bulk scheme is applied to treat the
cloud microphysical processes.
For this study, we are using the two-moment microphysics scheme of .
This scheme solves prognostic budget equations for number and mass concentrations of six
hydrometeor classes (cloud, rain, ice, snow, graupel, hail). For the size distribution of
hydrometeors, generalized gamma distributions with constant shape parameters are used.
It considers the microphysical processes of autoconversion, accretion,
self-collection and breakup in the warm phase. For cold clouds, diffusional growth,
freezing, aggregation, self-collection, riming and melting are taken into account.
For the nucleation of ice particles, a competition between heterogeneous and homogeneous
freezing occurs. Homogeneous freezing describes the formation of an ice particle without
the involvement of a solid ice nucleus (IN). It takes place at temperatures below 235 K
at high supersaturations with respect to ice on the order of 40 to 80 %. As this
process does not depend on aerosol characteristics and there are
always sufficient liquid droplets available e.g.,, a large number of
ice particles form nearly instantly as soon as these ambient conditions are met.
In accordance with these assumptions, we set the number of liquid droplets available
for homogeneous freezing to 1000 cm-3.
This leads to a strong increase in water vapor depletion and therefore a fast decline
of supersaturation, which in turn leads to small ice particles.
Heterogeneous freezing occurs at surfaces of IN that grant favorable
conditions for freezing. Depending on IN characteristics, heterogeneous freezing
can even occur at temperatures close to 0 ∘C at ice saturation
for a review see. Due to comparatively low concentrations of IN
at heights where freezing occurs, the nucleation rate of pure heterogeneous freezing is
typically 1 to 2 orders of magnitude lower than that of pure homogeneous freezing.
Hence, the depletion of supersaturation takes longer and larger ice particles
are formed. For ICON-ART, the empirical parameterization of is used
to describe heterogeneous formation of ice particles.
In an ascending air parcel, heterogeneous freezing occurs earlier (i.e., at
higher temperatures and lower supersaturation) than homogeneous freezing. Hence, a
sufficient number of IN can suppress homogeneous freezing due to depletion of water
vapor. To account for these competing mechanisms, ICON-ART uses the parameterization
of .
The resulting discrepancy in number concentration and size distribution of ice particles
between the two freezing mechanisms leads to differences in the radiative properties of
the clouds.
Mineral dust is one of the most ubiquitous types of aerosol and acts as IN at temperatures
as high as -10 ∘C.
Measurements show that other IN can usually be neglected for modeling studies
.
The coupling of microphysics and the parameterization to account for competing effects
of the freezing mechanisms was performed in a similar way to
and with two exceptions. The SD
of the assumed subgrid-scale Gaussian distribution of the vertical velocity was reduced by
a factor of 0.3 to σw=0.3TKE, where TKE is the (prognostic) turbulent
kinetic energy. The value of 0.3 was derived by tuning based on ML-CIRRUS measurements
. Additionally, a budget variable for mineral dust acting as IN
was introduced to prevent double counting. We followed the approach of
by using a characteristic relaxation timescale of 4 h in which only part of the
mineral dust is available for further heterogeneous freezing.
Clouds and radiation
As mineral dust serves as ice nuclei in ICON-ART it modifies the physical properties
of the simulated clouds. Consequently, the optical properties of the ice clouds are
modified when mineral dust is present. The optical properties of the ice crystals are
calculated according to based on the cloud ice effective radius.
Since in the operational setup a one-moment scheme for the microphysical processes is
used, the effective radii are calculated as a function of the ice mass concentration
only. Instead, for this study, the ice particle size distribution obtained from the
two-moment microphysics scheme of is used.
For the calculation of the ice particle effective radii, we apply the formula of
.
PV-power calculation
To calculate PV power we apply the open-source PV modeling environment
PV_LIB for python . It converts direct and diffuse radiation, temperature and wind speed
into normalized PV power. Here the normalization is done with respect to the nominal
capacity. Therefore, a specific PV module and PV inverter combination, as well as the
module's tilt and orientation, need to be specified. Furthermore, the surface albedo and
station height are necessary input parameters.
All evaluations concerning PV power presented in the following assume a
south-oriented PV module with a nominal power of 220 W and a size of 1.7 m2.
The chosen configuration represents a typical system for applications on residential
or industrial rooftops in Germany. In more detail, a
“Canadian Solar CS5P-220M” PV module and the micro-inverter
“ABB: MICRO-0.25-I-OUTD-US-208” are selected. The software PV_LIB retrieves
the corresponding module and inverter properties automatically from an online
database provided by NREL (National Renewable Energy Laboratory of the US
Department of Energy).
Model setup
For our simulations, we are using a global R2B06 grid with an effective horizontal grid spacing
of 40 km. A nested R2B07 grid (20 km) is added covering source and target region,
i.e., north Africa and central Europe. Further successive R2B08 and R2B09 nests cover
central Europe with effective horizontal grid spacings of 10 and 5 km. Our analysis is
performed for the highest resolved nest with 5 km effective grid spacing.
We are using ICON-ART in its NWP configuration with
the corresponding package of physical parameterizations with the exceptions described above.
Schematic overview of the simulations that were performed as a preparation for the
simulation of the analysis period.
Simulations performed for this study and the mineral dust concentration used to
calculate radiation and cloud microphysics.
Case name
Radiation
Cloud formation
TT
Original concentration
Original concentration
FF
0.1⋅ concentration
0.1⋅ concentration
TF
Original concentration
0.1⋅ concentration
FT
0.1⋅ concentration
Original concentration
Figure sketches the simulation procedure applied to provide the most
realistic spatiotemporal distribution of mineral dust aerosol for 4 April 2014.
A spinup simulation is started from ECMWF's (European Centre for Medium-Range
Weather Forecasts) Integrated Forecast System (IFS) analysis on 15 March 2014, 00:00 UTC,
in order to generate background concentrations of mineral dust. This simulation runs free for 14 days.
On 29 March, 00:00 UTC, a reinitialization is performed using the corresponding IFS analysis
in combination with the mineral dust concentrations calculated by the
spinup simulation. From 29 March until 3 April this procedure is repeated daily
in order to accurately capture the emission and transport processes of the mineral dust in this
important period. From 3 April 00:00 UTC onwards, the simulation is running
free again with mineral dust feedback processes activated; i.e., no reinitialization is
performed on 4 April 00:00 UTC, giving the
clouds 1 day to adjust before an aerosol effect on clouds is analyzed.
As stated before, we are interested in the improvement of PV-power forecast due to a
better representation of mineral dust concentrations and its impact on radiation in the model. A classical
approach to quantify these differences would be to carry out two simulations, A and B. In case A,
a climatological mineral dust distribution would be applied as it is done in operational
weather forecast. In case B, the online-calculated mineral dust concentrations would be used instead.
Subtracting the results of both simulations the effect of mineral dust on temperature
and radiation could be quantified. However, this method suffers from several shortcomings.
The spatial distribution of the simulated and the climatological concentrations may
differ considerably. At some places the online-calculated concentrations might be
higher than the simulated ones and vice versa. Thus, the effects of mineral dust on
radiation and PV could not be quantified.
For this reason, we decided to choose a different approach for the reference
simulation. We are using in all cases prognostically derived mineral dust
concentrations. In the false case (F), however, we assume a reduced impact on
radiation and/or cloud formation, which is realized by a reduction of the mineral
dust concentrations by a factor of 0.1 when used to calculate these processes. This results
in the 22 simulations summarized in Table that we need for our
analysis. Although the mineral dust horizontal, vertical and size distributions
of the individual simulations differ slightly due to the feedback of radiation
and cloud microphysics on the mineral dust distributions, the impact of this is
negligible. Consequently, compared to the classical approach, this method has the
advantages that (1) the location of extreme values agrees between the individual
simulations and (2) there is no impact of different size distributions on the results.
Mineral dust optical depth at 500 nm over Germany on 4 April 2014 at 00:00 UTC, 06:00 UTC, 12:00 UTC and 18:00 UTC (TT case). Note the saw-tooth
shape in the northern part which marks the margin of the high-resolution (5 km) domain and must not be confused with mineral dust-free conditions.
Mineral dust optical depth at 500 nm over Europe on 4 April 2014
at 09:00 UTC, 12:00 UTC, 15:00 UTC and 18:00 UTC (TT case) at 20 km grid spacing. The
filled circles represent observations from AERONET stations. Note the saw-tooth
shape next to the borders which marks the margin of nest R2B07 and must not be confused with mineral dust-free conditions.
Results
In the following, results of the simulations as well as thereof calculated PV-power forecasts
are presented and evaluated.
Simulated mineral dust distribution
Mineral dust emitted from the Sahara during 1 and 2 April is transported towards central Europe along the forward flank of a
trough. After being transported across France, it reaches Germany on the 4 April 2014. This can be seen in Fig. , which
shows the spatial distribution of mineral dust optical depth at 500 nm (in the following abbreviated with AOD) at different dates.
Already during the night of 4 April 2014, the southern part of Germany is covered by a mineral dust plume, which leads to an AOD
between 0.25 and 1. Over France, higher values between 1 and 1.5 are simulated. During the day, the mineral dust is transported to
the north so that in the evening all of Germany is affected by the mineral dust, with the highest AOD values of about 1 in the
northwest. In the remaining parts the AOD values are between 0.25 and 0.5.
A qualitative comparison to satellite, ceilometer and lidar observations shows that the spatial distribution and temporal evolution
of mineral dust as simulated over Europe is in good agreement with the available measurements. Unfortunately, these observations of
mineral dust are hampered by the presence of clouds. The areas with high mineral dust loads coincide also with cloudy conditions and
only a few observation time steps within the period of interest are available for a quantitative comparisons. In
Fig. , the mineral dust aerosol optical depth as forecasted at 20 km grid spacing for case TT is
shown for 4 April 2014. On top of that, filled circles provide the corresponding AERONET measurements. The observations are averaged
within a time interval of 1 h before target time and represent level 2 coarse-mode AOD at 500 nm derived with Direct Sun
Algorithm version 2 and Spectral Deconvolution Algorithm version 4.1; for a description see. The arrival of the dust cloud in eastern Germany
is observed by the station Lindenberg. There is only a small spatial discrepancy with the forecasted location of the dust
cloud. Note that the region with rapid increase of mineral dust concentration also visualizes the weak frontal zone spanning from
the North Sea over eastern Germany to the southeast of Europe (see Sect. ).
SIS of TT (a), FF (b) and SARAH-2 dataset
by CM SAF (c) on 4 April 2014 at 12:30 UTC in Germany.
Dots: SIS at SYNOP (thin circle lines) and
pyranometer stations (thicker circle lines).
Radiation
(a) Histogram of difference in SIS on 4 April 2014 (05:30–16:30 UTC) in Germany for FF and TT
simulation relative to SYNOP station measurements
(see Fig. ). (b) Joint histogram of SIS difference on 4 April 2014
for simulations FF and TT relative to SYNOP station measurements (see
Fig. ). Green lines indicate linear regressions for the sector
with negative ΔSIS FF (slope: 0.959; intersection: -12.086) and positive
ΔSIS FF (slope: 0.638; intersection: -7.659).
Statistical quantities of the distributions of ΔSIS shown in Fig. a.
Mean
SD
5% Percentile
95% Percentile
Min
Max
TT
-4.45
125.00
-245.80
188.33
-449.90
444.98
FF
27.19
145.10
-234.42
273.59
-441.01
523.33
The surface incoming shortwave irradiance (SIS, or global radiation) is the
key parameter for adequate PV-power forecasts. In order to evaluate the
numerical simulations we use in situ and remote sensing observations.
Surface measurements of global radiation are available from SYNOP stations hourly
and, with a temporal resolution of 1 min, from the pyranometer network of the DWD.
Additionally, the surface shortwave radiation as retrieved from the SEVIRI
(Spinning Enhanced Visible and InfraRed Imager) sensor
on board the geostationary METEOSAT (Meteorological satellite) second-generation (MSG) satellite number 10 is available for comparison
with model results. In particular, hourly SARAH-2 data Surface Solar Radiation Data records
– Heliosat; are used, which are provided by the EUMETSAT
(European Organisation for the Exploitation of Meteorological Satellites)
Satellite Application Facility
on Climate Monitoring (CM SAF). It should be noted that the retrieval
algorithm for the SARAH-2 data employs a modified MACC (Monitoring Atmospheric
Composition and Climate) aerosol climatology , which deviates from the actual
concentrations especially during mineral dust episodes. Therefore, the satellite-derived
SIS can be expected to overestimate the real SIS in the considered
time period.
Figure shows the horizontal distribution of SIS for the simulations TT
and FF and for the satellite product SARAH-2 on 4 April 2014, 12:30 UTC.
In addition to the simulation results, the SIS measured at SYNOP and pyranometer
stations are depicted in circles. Pyranometer stations are indicated by bold circles.
There are two main synoptic features that were not correctly captured by both
numerical simulations. On the one hand, the cloud band along the Alps is
missing
(see Fig. ). On the other hand, the activity of the frontal system
in the northern part of Germany and the related clouds are represented in a
different way (see Fig. ). The cloud cover and corresponding
precipitation is overestimated in eastern Germany, whereas the rainfall in northern
Germany in the afternoon is underestimated (not shown). Such fine structures are
challenging for day-ahead numerical weather predictions. Inherently, deterministic
NWP forecasts are afflicted with errors. These arise from inaccurate initial
conditions as well as from deficiencies in the NWP model, whereas small errors in
the finer structure, such as the position of individual clouds, tend to grow more
rapidly . Ensemble forecasts could provide an estimate of the
reliability of the forecast and of individual synoptic patterns such as the
discussed frontal system. However, this is not within the scope of
this study,
and we concentrate on the differences between the simulations TT and FF.
In the northern part of Germany both simulations show very low SIS values, which
were also observed by the satellite and by the ground-based stations. In
the western part, where the AOD reaches the highest values, simulation TT
gives noticeably lower SIS than in case of simulation FF, which is in better agreement
with the observations. These improvements can be attributed to the
consideration of the interactions between mineral dust and meteorology.
Time series of hourly averaged observed photovoltaic power production (green) and
corresponding day-ahead forecast (red) for Germany on 4 April 2014. The red
shading marks a large overestimation of up to 5.3 GW. At the bottom, the
day-ahead forecast error for the four German control areas is depicted. Data
source: .
The improvements in TT are confirmed by Fig. a), which shows a histogram of
the differences (simulation – ground observation) for 4 April 2014 using hourly
data at SYNOP stations (see Fig. ). In case of underestimation
(negative ΔSIS), the errors for TT are
larger than for FF. The opposite happens in the range of overestimation
(positive ΔSIS), where the error is remarkably lower for TT. Furthermore, the
difference between TT and FF is larger in the overestimation sector than in the
underestimation sector.
This becomes even more clear in the joint histogram (Fig. b).
When the difference is negative in the FF case, the green regression line is almost
identical to the one-to-one line indicating no systematic differences between
TT and FF in the underestimation sector. In the overestimation sector, however, the
overestimation is strongly reduced by the TT simulation as indicated by the regression
line. This finding is confirmed by Table . The mean error and
the SD is substantially reduced in case of TT. While the 5th
percentile slightly increases in case of TT, the 95th percentile indicates that the
overestimation of SIS is drastically reduced. The same holds for minimum and
maximum values. In summary, this means that in the TT case, overestimations are
substantially reduced compared to the FF case whereas the results
remain similar for underestimations.
PV production
Comparison of observed (black, by pyranometer) and simulated (TT
in green, FF in red) surface incoming shortwave irradiance (SIS, a–d) and the
resulting computed normalized PV power (e–h) for the stations Mannheim,
Meiningen, Trier and Weihenstephan on 4 April 2014. The normalization is done
with respect to peak power.
Beside the technical characteristics of the solar panels, PV-power output depends mainly on SIS, temperature and
panel geometry such as orientation with respect to the sun. From that, it becomes
clear that reliable day-ahead forecasts of PV power depend on accurate
weather forecasts. The following example demonstrates that
the PV-power forecasts for Germany failed tremendously for 4 April 2014.
Figure shows the day-ahead PV-power forecast for 4 April 2014.
The illustrated day-ahead PV-power forecast is the so-called meta-forecast of
the German TSOs. It is a multi-model and multi-method product, which combines
many different NWP and power forecast models as well as many different post-processing methods.
The day-ahead PV-power forecast for 4 April 2014 overestimated the actual power
production for Germany by up to 5.3 GW. The forecast error is also divided into
the control areas of the four TSOs. In the areas of TenneT and Amprion, which
cover west, central and southeast Germany see Fig. 1 of,
the largest forecast errors occurred on 4 April 2014. These are not only the regions where large contributions
of PV capacity are installed see Fig. 1b of but also the
forecast of incoming solar radiation was challenged by the presence of clouds and
aerosols as already discussed in Sect. .
These discrepancies between energy demand and day-ahead forecast of supply need
to be compensated, for example on the intra-day market of the European power exchange EPEX SPOT, where electricity is
traded within the Austrian, French, German or Swiss transmission systems.
Corresponding market data are openly published on the EEX Transparency Platform
. Wrong forecasts may cause economic costs on the order
of tens of millions of euros per day.
The transport of Saharan dust and the interactions of mineral dust particles
with the atmosphere are not explicitly considered within conventional NWP
forecasts. Most likely, this has contributed to the large PV-power forecast errors on 4 April 2014
(Fig. ). We will quantify the effect of the mineral dust outbreak on PV-power forecast
for our own model results.
PV_LIB (see Sect. ) is used to transfer the observed and simulated meteorological variables
into normalized PV power. Computed PV power based on the observed radiation by the 26
pyranometer stations throughout Germany (see Appendix ) is taken as reference.
To convert meteorological variables from the NWP simulations into PV power, the closest grid point to each pyranometer station is considered.
Figure shows the observed and simulated SIS as well as normalized
PV power for the stations Mannheim, Meiningen, Trier and Weihenstephan.
A reduction in positive forecast error (overestimation) can be observed for the stations Mannheim,
Trier and Weihenstephan. For stations with considerable cloud cover, for example
Meiningen, smaller differences between the TT and FF simulations are observed.
To quantify the improvement of PV-power simulations of scenario TT,
Table summarizes error quantities for all 26 pyranometer
stations and for different lead times. For 4 April 2014, the root mean square
error (RMSE) for example is reduced by about 17 % from 0.124 to 0.103. Bold values in
Table indicate better results and confirm that the TT simulation showed better performance with
respect to the observations.
Statistical measures describing the quality of the simulated PV-power values
using data of all 26 pyranometer stations for different lead times
(3 April 2014: 0–23 h; 4 April 2014: 24–47 h; 5 April 2014: 48–72 h;
3–5 April 2014: 0–72 h).
Values are given for the root mean square error (RMSE), the mean absolute error
(MAE), the bias (BIAS), the SD of errors (SD) and
the minimum and maximum error (Emin, Emax) in W m-2.
Bold values mark the better simulation.
RMSE
MAE
BIAS
SD
Emin
Emax
Time
FF
TT
FF
TT
FF
TT
FF
TT
FF
TT
FF
TT
3 Apr 2014
0.099
0.092
0.049
0.044
0.013
0.006
0.099
0 092
-0.534
-0.542
0.554
0.563
4 Apr 2014
0.124
0.103
0.059
0.048
0.009
-0.004
0.123
0.103
-0.660
-0.672
0.546
0.442
5 Apr 2014
0.110
0.079
0.054
0.040
0.029
0.003
0.106
0.079
-0.405
-0.418
0.577
0.418
3–5 Apr 2014
0.111
0.092
0.054
0.044
0.017
0.001
0.110
0.092
-0.660
-0.672
0.577
0.563
Temporal evolution of the difference in normalized PV power between TT
and measurements (green line, right ordinate) and the corresponding difference
between FF and measurements (red line, right ordinate) on 4 April 2014 at
Mannheim (a) and Meiningen (b). Contours: percentaged contribution
of direct radiative, indirect radiative and synergistic interaction effects
to the change between TT and FF based on the FM calculation (left ordinate).
Same as Fig. for Trier (a) and Weihenstephan (b).
Visualization of the different scores described in Appendix for
all pyranometer stations. The stations are sorted by their ID score
with the station with the highest score on top.
Radiation vs. cloud microphysics
As mentioned before, a substantial number of clouds were present on 4 April 2014.
From that, the question arises of which portion of the changes we found in PV power
are due to the direct aerosol effect and which are due to the indirect effect.
In our case we are using a factorial method (FM) to separate the direct radiative
effects from the indirect radiative effects on PV-power generation.
For this purpose, an unreplicated 2k factorial design with k=2 is used
. A short description of this FM is
given in Appendix . This method was also used for other similar
problems in atmospheric science e.g.,.
Figures and show the results of the FM calculation
for four selected SYNOP stations (Mannheim, Meiningen, Trier and
Weihenstephan-Duernast). The geographical positions of the stations are given in
Table . The figures show the temporal evolution of the
difference in normalized PV power between TT and the measurements (green line,
right ordinate) and the corresponding difference between FF and the measurements
(red line, right ordinate). In contours, the percentage contribution of direct
radiative (beige), indirect radiative (blue) and synergistic interaction (orange) effects to the change
between TT and FF based on the FM calculation is given (left ordinate).
The more westerly stations (Mannheim, Trier) show a systematic overestimation
of PV-power generation in the FF case. At both stations, the PV-power forecast
is improved significantly in the TT simulation. For Mannheim, the contributions
of the different effects is alternating, whereas in Trier the direct radiative
effect dominates the result, nearly always accounting for more than 50 %.
In Meiningen, the contributions of the different effects vary strongly,
accompanied by several intersections of the FF and TT curves. However,
the green TT curve looks like a damped version of the red FF curve. For
overestimations of the PV power in the FF case, the TT result is lower and for
underestimations the TT result is higher, leading to a better agreement with
measurements and therefore an improvement in the forecast. One could argue
that the overestimations are damped mostly by direct radiative effects
(e.g., at 10:00 UTC and 14:00 UTC) whereas the underestimations are damped mostly
by indirect effects (e.g., at 08:00 UTC and 13:00 UTC). This becomes more obvious at
Weihenstephan. Between 06:00 UTC and 11:00 UTC, the overestimation of PV power is
nearly exclusively caused by direct radiative effects. Starting from 12:00 UTC,
the subsequent underestimation is nearly completely related to indirect radiative
and synergistic interaction effects. From 14:00 UTC on, FF shows an overestimation,
whereas in the TT case the forecast is improved mostly by direct radiative effects.
The results for all SYNOP stations with high-quality radiation measurements
in Germany are shown in Fig. . For the classification of the results
at these stations, five different characteristic measures are calculated.
A mathematical description of these characteristic measures is given in
Appendix . The integrated difference ID is a measure for the magnitude of
the difference between TT and FF without any information about the sign of the
difference. The mean improvement ratio IR‾ is a measure for whether the TT result is
better or worse than the FF result, where IR‾=0 characterizes
a perfect FF result, IR‾=1 a perfect TT result and
IR‾=0.5 an indifferent result. Hence,
IR‾>0.5 shows an improvement of the results due to the impact
of mineral dust on cloud formation and radiation. CR‾,
CC‾ and CI‾ state the mean percentage
contribution of direct radiative, indirect radiative and synergistic interaction
effects on the difference between FF and TT. The synergistic interaction represents
the nonlinear feedbacks, which are acting between the two factors
direct radiative effect and indirect radiative effect, when both effects are active
at the same time.
The contributions of the different effects are derived by the FM
formulae B1–B7 given in Appendix B.
Of the 26 stations in total, 17 show an improvement of the forecast in the TT case
compared to the FF case (i.e., IR‾>0.5). However, this includes also stations with small differences
between the results of TT and FF where the significance of the measure
IR‾ is low as it does not contain any information about the
magnitude of the change. Focussing on stations with a high difference
(here defined as ID>1), 14 out of 16 stations show an improvement.
For very high differences (here defined as ID>2), nearly every station
(8 out of 9) shows improvements in the TT forecast. The only exception is
Nuernberg,
where there is a strong underestimation
of the PV power in the early morning which is not compensated by the improved
forecast afterwards (not shown).
In total, the indirect effect contributes for 45.41 % to this underestimation in Nuernberg.
Averaging over all stations, 63.86 % of the
differences are caused by the direct radiative effect, whereas 20.22 and
15.92 % are caused by the indirect and synergistic interaction effects,
respectively. For stations with very high differences, this shifts to higher
contributions of the direct effect (68.08, 16.21 and 15.71 %).
Taking a look at outlying stations, Hohenpeissenberg, Saarbruecken, Weihenstephan
and Hamburg show comparatively high contributions of the direct effect of more
than 80 %. They have in common that, in the FF case, the PV power was nearly
always overestimated. The direct effect in the TT case leads to a decrease
in the PV-power forecast, which in turn leads to an improvement. At Luedenscheid
and Nuernberg, indirect effects contribute more than direct effects
to the changes in PV-power forecast. These are the only two stations that
show a worsening of the PV-power forecast out of the 16 stations with high
differences. For Luedenscheid, strong contributions of indirect and synergistic
interaction effects in the afternoon lead to an overestimation of the PV power
in the afternoon while the FF forecast already shows a good result (not shown).
The forecast for Nuernberg in the FF case already shows a systematic
underestimation of the PV power. A mixture of direct and indirect radiative
effects in the morning leads to a strong further underestimation, dominating
the IR‾ score. The improvements mostly due to indirect
and synergistic interaction effects afterwards are smaller (not shown).
Conclusions
Reliable PV-power forecast is gaining importance especially in those countries
with increasing use of renewable energy as is the case in Germany. Aerosol
particles have a major impact on the radiation reaching the solar panels at the
ground. The aerosol concentration differs in space and time, nevertheless, most current
numerical weather prediction models use climatological maps of the aerosol distribution.
Thus, they are not able to account for the actual impact of
aerosol particles on PV-power production. Within this study we extended the
operational weather forecast model ICON-ART by including the treatment of direct and
indirect effects of prognostic mineral dust. Through this, we are able to quantify PV-power
forecast improvements when considering mineral dust radiative effects
for a Saharan dust episode on 4 April 2014.
Compared to observations at 26 pyranometer stations, the forecast including mineral
dust feedback processes strongly reduces overestimations of incoming solar radiation that exist in the forecast
without mineral dust feedback. For underestimations, the results are indifferent.
For 65 % of the pyranometer stations, the simulated PV is in much better
agreement with observations when the feedback between mineral dust, radiation and clouds
is accounted for. For the period from 3 to 5 April 2014, as well as for each
day individually, RMSE, mean absolute error (MAE), bias
and SD are reduced in the simulation that accounts for mineral
dust feedback compared to the reference simulation.
For 4 April 2014, this results in a reduction in RMSE of 17 %, MAE by 19 %,
SD by 16 % and the bias from 0.09 to -0.04 Wm-2.
We quantify the individual contributions of the direct and indirect effect of
mineral dust on PV-power forecast and find that the direct effect is most
important. Eight out of nine stations with very high differences between
the simulation with mineral dust feedback and the reference simulation show an
improvement due to the consideration of mineral dust. For stations with high
differences, we find an improvement at 14 out of 16 stations.
The direct radiative effect dominates these improvements, accounting for
64 % of the differences at all stations, whereas indirect effects account
for 20 % and synergistic interaction effects account for 16 %. At the
stations with very high differences, even higher contributions can be attributed
to the direct effect (68, 16 and 16 %, respectively).
We also find that for our simulations, the improvement also depends on the
dominating effect. This means that for stations with direct radiative effect
dominating the differences, the improvement is higher than for stations with
indirect effects dominating the differences. We assume that indirect effects
may be superimposed by the challenge of representing complex cloud structures,
independent of mineral dust availability.
Our study shows the importance of considering mineral dust in numerical weather
prediction systems. Understanding and assessing the role of mineral dust in the
atmosphere and in particular during special weather situations such as Saharan
dust outbreaks will not only help to improve numerical weather predictions but
also contribute to reliable PV-power forecasts and a safe electricity supply.