Introduction

The increasing penetration in the distribution grid of power generated by the solar electricity leads to the necessity of taking it into account in the operations and planning of IPPs, ISOs, and Balancing Authorities [START_REF] Mills | Integrating Solar PV in Utility System Operations[END_REF]. It is worth to highlight that the accurate knowledge of the load ahead of time (forecasting) is essential for the technically feasible (and also economically optimal) dispatch of generation sources, as the performance of the bulk grid is dependent on the balancing of a continuously varying load with equal amounts of generation.

The forecasting of solar power technologies has been in the focus of academic and industrial research and development for as long as significant amounts of them have been connected to the electric grid. The progress of forecasting capabilities has largely followed the penetration of the respective technologies. A substantial and material progress in the state-of-the-art of solar forecasting has taken place in the last few years. Within this period, Numerical Weather Prediction (NWP) models have become more sophisticated in assessing cloud cover forecasts; advanced data post-processing methods such as machine learning became increasingly accessible and probabilistic forecasts began replacing deterministic ones.

Accurate direct normal solar irradiance (DNI) forecast is of utmost importance for the accurate management of energy markets, for the management and operation of Concentrating Solar Thermal Power (CSTP) plants and for the power generation control by means of thermal energy storage (if available). This allows maintaining the grid stability in CSTP power management and ultimately facilitates the widespread implementation of CSTP technologies [START_REF] Mathiesen | Evaluation of numerical weather prediction for intra-day solar forecasting in the continental United States[END_REF].

The temporal autocorrelation of solar radiation time series can decrease rapidly, as solar radiation is critically affected by small-scale dynamics and cloud microphysics (evaporation, condensation, etc.). For DNI forecasts at prediction horizons beyond 6 hours, NWP models are the most appropriate and accurate procedure [START_REF] Jimenez | WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction[END_REF]. Some recent results suggest that forecasts from NWP models are also becoming competitive with statistical and satellite-based methods at shorter time scales [START_REF] Diagne | Review of solar irradiance forecasting methods and a proposition for small-scale insular grids[END_REF][START_REF] Inman | Solar forecasting methods for renewable energy integration[END_REF]. Notwithstanding, several issues related to these models (complex atmospheric modeling on which they rely, underlying systems for data capture and assimilation) as well as the complexity in atmospheric attenuating components and their radiative properties, make accurate localized and high-frequency forecasting a very difficult task.

NWP models provide at best an overall understanding of solar forecasting, but they are unable to cover a local statistical representativeness of the DNI. In particular, between ~1000 m and ~100 m the physics changes sufficiently that blending through those scales has been shown to be inappropriate [START_REF] Wyngaard | Toward Numerical Modeling in the "Terra Incognita[END_REF]. Moreover, the solar irradiance forecast in NWP models has not been treated traditionally as a priority, as it has been mainly used to force the average surface energy balance. This situation, influenced by the traditional lack of major stakeholders, is changing in recent years in which substantial investments are now being made in this field due to the increasing demand for operational and improved solar forecasts.

A precise knowledge of cloud cover and type is a key aspect in determining DNI. In the absence of clouds (clear-sky conditions), an accurate information of atmospheric aerosols is crucial for DNI forecasts, since up to 20%-30% of DNI attenuation has been reported for cases of high particle occurrence [START_REF] Henzing | Effect of aerosols on the downward shortwave irradiances at the surface: Measurements versus calculations with MODTRAN4.1[END_REF][START_REF] Jacovides | Spectral Solar Irradiance And Some Optical Properties For Various Polluted Atmospheres[END_REF]. It is worth to mention that clear-sky conditions are of particular relevance in CSTP plants, as they are predominant in regions prone to these technologies (DNI is 3~5 times more sensitive to AOD than GHI). Consequently, clear-sky DNI calculations are mandatory for DNI forecasts.

Based on aerosol optical data from diverse regions, Anderson [START_REF] Anderson | Mesoscale Variations of Tropospheric Aerosols[END_REF] demonstrated a mesoscale variability of lower-tropospheric aerosol DNI extinction (specifically, for horizontal scales of 40-400 km and temporal scales of 2-48 h). Despite of this, the use of simple aerosol databases of fixed yearly or monthly values with a spatial resolution of several degrees has been traditionally used in irradiance calculations [START_REF] Schmidt | Present-Day Atmospheric Simulations Using GISS ModelE: Comparison to In Situ, Satellite, and Reanalysis Data[END_REF][START_REF] Kinne | An AeroCom initial assessment -optical properties in aerosol component modules of global models[END_REF], instead the use of detailed aerosol information. It is worth to highlight that locations prone to CSTP technologies are often situated near desert regions, which leads to frequent occurrences of dust transport and therefore episodically high atmospheric aerosol loads [START_REF] Meloni | Seasonal behavior of Saharan dust events at the Mediterranean island of Lampedusa in the period 1999-2005[END_REF]. Consequently, there is a need in flexibility accounting for location specificity in DNI day-ahead forecasts. One possible way to overcome the weaknesses of DNI derived from NWP models is to integrate specific information of air quality models providing forecasts of aerosol variability [START_REF] Schroedter-Homscheidt | Aerosols for Concentrating Solar Electricity Production Forecasts: Requirement Quantification and ECMWF/MACC Aerosol Forecast Assessment[END_REF][START_REF] Wittmann | Case Studies on the Use of Solar Irradiance Forecast for Optimized Operation Strategies of Solar Thermal Power Plants[END_REF], where improvements of WRF forecasts have been found in GHI, DNI and DHI, respectively, compared to its standard version [START_REF] Jimenez | WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction[END_REF].

Aerosols forecasts are included neither in HARMONIE nor in the ECMWF IFS model. Currently, the most successful programs that provide atmospheric aerosol forecasts are the Copernicus Atmosphere Monitoring Service (CAMS) and the Sand and Dust Storm Warning Assessment and Advisory System (SDS-WAS). This report describes the integration of aerosol forecasts in a post-processing scheme of NWP-based DNI forecast. The rest of the report is organized as follows: Section 2 reviews the solar irradiance attenuation under clear sky conditions; Section 3 describes the forecast of aerosols; Section 4 proposes a solar radiation forecasting system including aerosol forecasts; Section 5 summarizes the conclusions; Section 6 enlists references.

Clear-sky solar radiation

In the absence of clouds, the solar radiation undergoes absorption and diffusion processes in its path through the atmosphere, caused by the different components of the atmosphere (water vapor, carbon dioxide, ozone…) and aerosols formed by solid and liquid particles in suspension, by means of scattering and absorption processes.

In this context, scattering is the process whereby the solar radiation beam is dispersed by collisions or similar interactions in the atmosphere: In the atmospheric absorption process, contrary to that happens in the scattering, an energy transfer occurs from the incoming solar radiation to the atmospheric constituents. From this point of view, the most effective solar radiation absorbents are water vapor, carbon dioxide and ozone. The selective absorption of certain wavelengths by these molecules causes that the atmosphere constitutes are an opaque medium for some spectral ranges and free absorption windows for other ranges. So, selective absorption exists practically only in bands at certain wavelengths, and within these bands it varies very rapidly. In each water vapor band, the absorption increases rapidly with increasing wavelengths to a maximum, and then decays more slowly.

Clear sky represents the nominal operating conditions of CSTP technologies, which operate when DNI is above a certain threshold level. Thus, they are of particular relevance in solar resource assessment and forecast for these technologies. The models that estimate the solar irradiance reaching the Earth's surface under clear sky conditions are known as clear-sky models, which make use of different mathematical approaches and a variety of inputs (solar elevation, site altitude, aerosol concentration, water vapor, and other atmospheric Rayleigh scattering Mie scattering Clear sky models typically rely in Radiative Transfer Models (RTMs) requiring local meteorological inputs such as atmospheric aerosols, ozone and water vapor in combination with solar geometry. The amounts and types of atmospheric aerosol as well as the amounts of total precipitable water vapor, ozone, and other constituents produce complicated interactions with solar radiation that influence the total amount and spectral distribution of solar irradiance available to a solar collector. Actual composition and concentration of clear sky atmospheres vary with geographic location, elevation and season.

Taking into account both permanent (N2, O2, and trace gases) and variable (O3) atmospheric gases, we can define a "clean and dry atmosphere" (Fig. 2, left). DNI calculated under this atmosphere using a clear sky model [START_REF] Ineichen | A broadband simplified version of the Solis clear sky model[END_REF] in a typical Mediterranean "clean and dry atmosphere" is shown in Fig. 2 (right, red curve). The inclusion of the water vapor atmospheric content in this ideal case ("clean and wet atmosphere") leads to a significant decrease in DNI (Fig. 2 right, blue curves). Finally, considering the atmospheric aerosols ("clear-sky" conditions), the decrease in DNI is substantial as shown in Fig. 2 (right, green curves). In the absence of clouds, aerosols and water vapor are the main atmospheric constituents that determine solar radiation extinction. Water vapor can be a key factor over tropical and some coastal areas, but in general aerosols are the dominating factor, particularly over arid regions. As seen in Fig. 2 (right), aerosols and water vapor are the main atmospheric attenuating constituents in the absence of clouds. Water vapor can be a key factor over tropical and some coastal areas, but in general aerosols are the dominating factor, particularly over arid regions.

It is also worth to mention that, under cloudless-sky conditions, the forward scattering of solar radiation by atmospheric aerosols decreases DNI and increases DHI. This redistribution of radiation near the solar disk is called circumsolar radiation. The amount of circumsolar radiation is important for any concentrating solar-energy conversion technology. Atmospheric conditions creating large amounts of circumsolar radiation affect sun shape, or the amount of DNI available to a concentrating collector.

Aerosols

Fundamentals

Aerosols are small particles (solid or liquid) that remain suspended in the atmosphere, originated from various sources as ground dust, sand storms, sea spray, volcanic plumes, biomass burning, industrial and urban pollution and other human activities. They follow the motion of the air (within certain limits), and present a high spatial and temporal variability.

In contrast to the permanent atmospheric gases, aerosols present a wide variety of volume, size distribution, form, and material composition. Aerosols strongly affect the propagation of solar radiation through the atmosphere. Aerosols scatter the radiation in a wavelengthdependent manner that is a function of their size distribution. Natural aerosol particles sizes present in the atmosphere are shown in Fig. 3: The amount of aerosols in the atmosphere can be specified by the number of particles per unit volume (typically cm 3 ). With respect to its radiative effect, different quantities have been traditionally used to characterize the effect of aerosols on atmospheric extinction. Aerosol optical depth (AOD) is a direct measure of the attenuation of the incoming direct beam radiation by aerosols at any wavelength, which can be defined also in a broadband scheme [START_REF] Gueymard | Turbidity Determination from Broadband Irradiance Measurements: A Detailed Multicoefficient Approach[END_REF]. Specifically, AOD is the extinction per unit path length due to aerosols alone. AOD is dependent on the size, type, and chemical composition of aerosols and varies as a function of the wavelength of incoming radiation.

AOD typically ranges between values of 0.05 and 0.20 in low atmospheric turbidity conditions, and up to occasional and very extreme values of 0.8 and higher in Central and West Africa, Southwest and Central Asia, Northern India, and several regions in China. Global distribution of atmospheric aerosols represented as AOD is shown in Fig. 4, obtained by merging data from several satellite sensors with ground-based sun photometer measurements [START_REF] Mo | Atmospheric aerosols versus greenhouse gases in the twenty-first century[END_REF]. AOD is typically higher in summer than in winter (which produces considerable impact on DNI, especially in desert regions as Sahara), with large inter-annual and daily variability. It is worth to highlight that daily and monthly AOD variabilities are proportional to their mean annual AOD value. Two measures of variability have been proposed [START_REF] Gueymard | Temporal variability in direct and global irradiance at various time scales as affected by aerosols[END_REF]:

 Aerosol Variability Index (AVI). Characterizes the magnitude of the variability in AOD over specific periods, from daily to yearly.

 Aerosol Sensitivity Index (ASI). Relates the magnitude of relative variations in irradiance to absolute variations in AOD.

Effect of aerosols in DNI

Aerosols strongly affect the propagation of solar radiation through the atmosphere in a wavelength dependent manner, through scattering and/or absorbing processes. Aerosol type determines the proportion of scattering and absorption (i.e., while black carbon is a highly absorbing aerosol, mineral dust is mostly scattering).

AOD measurement requires sophisticated instrumentation and complex satellite models. AOD is measured at different wavelengths by a multi-wavelength sun-photometer, which provides absolute accuracies < 0.002 units of AOD and relative accuracies of ~0.5% under low-tomoderate aerosol loading [START_REF] Dutton | Features and effects of aerosol optical depth observed at Mauna Loa, Hawaii: 1982-1992[END_REF][START_REF] Shiobara | Optical Depth Measurements of Aerosol Cloud, and Water Vapor Using Sun Photometers during FIRE Cirrus IFO II[END_REF]. Other proposal for quantifying AOD consists in a multi-filter rotating shadow-band radiometer (MFRSR) that uses an appropriate reduction method [START_REF] Harrison | Objective algorithms for the retrieval of optical depths from ground-based measurements[END_REF].

Worldwide available AOD datasets represent typically averaged values for a few years, which do not address their high-frequency changes. Notwithstanding, it is worth to mention the following sources which provide more detailed information:

 AERONET. Remote sensing aerosol monitoring network initiated by NASA, developed to support NASA, CNES, and NASDA's Earth satellite systems under the name AERONET and expanded by national and international collaboration [START_REF] Holben | AERONET-A Federated Instrument Network and Data Archive for Aerosol Characterization[END_REF].

 MATCH. Hourly/daily resolved modeled data validated against AERONET ground measurements in Europe, Africa and the Middle East [START_REF] Schroedter-Homscheidt | Validation of an hourly resolved global aerosol model in answer to solar electricity generation information needs[END_REF].

 Euroskyrad (or ESR). Established in 2010 as a free collaborative platform among scientists involved with studies of aerosol remote sensing by means of sun-sky radiometers, this is a volunteer European observational network of sun sky radiometers coordinated with SKYNET in Asia and established and maintained by several universities and research institutes [START_REF]EuroSkyRad -Sites[END_REF].

Complete spectral information of the aerosol optical thickness would be required for detailed DNI modeling, but that information is scarce and usually broad climatological information is available (or estimations of atmospheric transmittance based on ground DNI measurements).

The general lack of detailed aerosol data, as well as its effect on DNI, justifies the use of a simplified methodology. Turbidity is defined as any condition of the atmosphere which reduces its transparency to radiation, especially visible radiation [START_REF] Doja | International Encyclopaedia of Engineering and Technology[END_REF]. Ordinarily, this is applied to a cloud-free portion of the atmosphere that owes its turbidity to air molecules and suspensions such as smoke, dust, and haze, and to scintillation effects. The earliest appearance of a turbidity factor was defined as the equivalent number of ideal clean-dry (or 'Rayleigh') atmospheres to the actual atmosphere from the radiative point of view [START_REF] Linke | Transmissions-koeffizient und Trübungsfaktor[END_REF].

It is therefore necessarily expressed as a number, and can be defined either at a specific wavelength or for the integrated spectrum [START_REF] Kondratyev | Radiation in the atmosphere[END_REF]. The turbidity 'coefficient' was later defined as the AOD at 1 mm [START_REF] Ångström | On the Atmospheric Transmission of Sun Radiation and on Dust in the Air[END_REF]. An ideally clean-dry atmosphere would thus be characterized by a turbidity factor of 1 and a turbidity coefficient of 0. This historical background is necessary here because 'turbidity' and 'aerosol optical depth' are often used interchangeably.

Unfortunately, it appears that wording in the ASTM standards adds to the confusion of these terms by referring to the aerosol effect to 'turbidity at 500 nm' rather than to 'AOD at 500 nm'. The modified Angstrom approach takes into account only two different spectral regions, below and above 0=0.5 μm [START_REF] Gueymard | Parameterized transmittance model for direct beam and circumsolar spectral irradiance[END_REF]. In this scheme, the aerosol optical thickness, 𝜏 𝑎

𝜏 𝑎 =  𝑖 (   1 ) -𝛼 𝑖
provides the aerosol transmittance (𝑇 𝑎 ):

𝑇 𝑎 = exp (-𝑚 𝑎 𝜏 𝑎 )
where  1 = 1𝜇𝑚 ; 𝑚 𝑎 is the aerosol optical mass;

𝛼 𝑖 = 𝛼 1 if  <  0 and 𝛼 2 otherwise;  𝑖 =  1 = 2 𝛼 2 -𝛼 1  if  <  0 and  𝑖 =  1 =  otherwise.
 is related to the size of the aerosol particles, ranging in practice from about 0 (very large particles) to 2.6 (very small particles), usually called the Angstrom turbidity exponent, Angstrom exponent, or wavelength exponent.

The dependence of DNI on AOD at different air masses calculated with REST2 clear-sky model [START_REF] Gueymard | REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation -Validation with a benchmark dataset[END_REF] is shown in Fig. 5.

Water vapor

Water vapor plays a major role in climate as a dominant feedback variable in association with radiative effects and moist dynamics. Water is exchanged between the atmosphere, the oceans, and the land surface, driven by solar radiation. The Earth's atmosphere contains only 0.001% of the available water of our planet, but it has a particular significance in solar irradiance reaching the Earth's ground.

Water is present in the atmosphere in ice, liquid and gas states. Phase change bounds or releases considerable amounts of energy, thus influencing atmospheric dynamics and playing an important role in the energy budget (latent heat flux amounts to about 80 W/m 2 on global average for a mean insolation of 342 W/m 2 ). Water in gaseous state is called water vapor, and it will be the focus of this section.

Fundamentals

Water vapor is a highly variable constituent in the atmosphere, decreasing its concentration drastically with the height above the Earth's surface. The amount of water vapor in the Earth's atmosphere can be defined through different metrics. The solar resource community usually uses the precipitable water (w), which is the vertically integrated total mass of water vapor per unit area for a column of atmosphere. This quantity represents an upper limit to the total amount of precipitation that may be realizable from the given atmospheric column. It is usually expressed in units of volume of liquid water per unit area or equivalent depth of liquid water, and it can be indirectly measured by different experimental methods, or (most commonly) estimated by using empirical relationships between w and the surface temperature and humidity [START_REF] Leckner | The spectral distribution of solar radiation at the earth's surface-elements of a model[END_REF][START_REF] Gueymard | Critical evaluation of precipitable water and atmospheric turbidity in Canada using measured hourly solar irradiance[END_REF].

Precipitable water is defined usually as the thickness of the liquid water that would be formed by unit area if all the water vapor available in the zenith direction were condensed at the surface. The total atmospheric water vapor constitutes a column with a height of about 25 mm if it condensate and distributed homogeneously at the Earth's surface, whereas dry atmospheres contains less than 1 mm of precipitable water vapor, and humid atmospheres can contain more than 40 mm.

In general, the large scale distribution pattern of water vapor principally follows that of the temperature: the equilibrium vapor pressure strongly increases with temperature (Clausius-Clapeyron-equation), thus warm air masses can contain many more water molecules compared to colder ones before saturation (equilibrium vapor pressure) is reached. Notwithstanding, there are some remarkable exceptions to the temperature-related distribution of water vapor: the water vapor concentration in atmosphere is extremely low over the larger scale deserts despite their high temperatures (mainly due to large scale sinking motions over these parts of the continents). 5-years averaged water vapor calculated by the NASA water vapor project (NVAP) [START_REF] Vonder Haar | Weather and climate analyses using improved global water vapor observations: WEATHER AND CLIMATE ANALYSES FROM NVAP-M[END_REF] are shown in Fig. 6, where one can appreciate the greatest values over warm, equatorial regions and a decrease from the equator to the poles more or less continuously with increasing latitude down to very low values over the cold, Polar Regions. The transport of water vapor above the Earth's boundary layer (1-2 km) is related to the general circulation of the atmosphere. On the regional and local scale, the most effective transport of water vapor takes place via convection, whereas its upward vertical transport in the equatorial regions is coupled to the ascending branches of the Hadley cells and in mid and high latitudes is connected to extratropical cyclones. In a temporal sense, the water vapor distribution changes with seasonal changes in temperature, and is closely correlated to special events in the atmospheric circulation, such as monsoons and El Niño Southern Oscillation (ENSO): whereas the variability within a year is mainly driven by monsoon events, inter-annual variabilities are attributed to ENSO.

Regarding water vapor intra-annual dynamics, it is worth to mention that in the tropics a band of extremely humid air (part of the Intertropical Convergence Zone) follows seasonal changes moving around north and south of the equator, in such a way that water vapor concentrations are high in the summer hemisphere, and low in the winter hemisphere.

In Fig. 7 it is shown a worldwide water vapor distribution based on data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Aqua satellite in 30 January 2005. It can be appreciated a higher water vapor content in the southern hemisphere (the one experiencing winter in this day), and a gradually decrease of water vapor in colder regions.

Effect of water vapor in DNI

Precipitable water must be accurately quantified in order to obtain correct attenuation of DNI in the atmosphere. Water vapor absorbs in the near infrared region of solar spectrum, accounting for the 60% of the natural greenhouse effect in the atmosphere, whereas its contribution to the molecular scattering is negligible.

Molecular absorption is a function of local pressure and temperature, and is a highly oscillatory function of wavelength: the most critical water vapor absorption bands are 0.72, 0.82, 0.94, 1.1, 1.38, 1.87, 2.7, 3.2 and 6.3 μm (bands at higher wavelengths can be ignored in this context). It is necessary, for a correct evaluation of the spectral transmittance of the molecular absorption, to characterize the frequencies, intensities, shapes and widths of spectral lines.

For modeling the water vapor transmittance, Gueymard proposed a functional form which slightly modified previous models [START_REF] Gueymard | REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation -Validation with a benchmark dataset[END_REF]:

𝑇 𝑤 = exp {-[(𝑚 𝑤 𝑤) 1.05 𝑗 𝑤 𝑛 𝐵 𝑤 𝐴 𝑤 ] 𝑐 }
Where mw is the water vapor optical mass; w is the total precipitable water; c and n are exponents (wavelength dependent); Bw is a correction factor introduced for improving the parameterization away from the absorption band centers in varying humidity conditions; fw is a pressure-scaling factor that takes into account the inhomogeneities in the water vapor pathlength. The value of Aw was calculated from MODTRAN simulations.

The water vapor transmittance in a U.S. standard atmosphere (air mass = 1.5) at a precipitable water of 1.419 cm calculated by MODTRAN2, SMARTS2 and SPECTRAL2 models [START_REF]SMARTS -SMARTS Documentation[END_REF] is shown in Fig. 8, where special absorbing bands are highlighted. Copernicus is constituted by a complex set of systems collecting data from multiple sources: earth observation satellites and in situ sensors such as ground stations, airborne and sea-borne sensors. It processes these data and provides users with reliable and up-to-date information through a set of services related to environmental and security issues in the following thematic areas: land, marine, atmosphere, climate change, emergency management and security.

Forecasting of aerosols

It also delivers elaborated products, useful for the solar radiation and climate change communities. CAMS (and MACC before) uses the IFS-LMD aerosol scheme to model aerosols.

Five types of tropospheric aerosols are considered: sea salt, dust, organic and black carbon, and sulphate aerosols. Aerosols of natural origin (mineral dust and sea salt) are described using three size bins, and have their sources linked to some prognostic and diagnostic model variables. On the other hand, the anthropogenic aerosols (organic matter, black carbon and sulphate) are climatological values read from external emission datasets [START_REF] Benedetti | Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation[END_REF][START_REF] Morcrette | Prognostic aerosols in the ECMWF IFS : MACC vs GEMS aerosols[END_REF]. Several types of removal processes are considered: dry deposition, gravitational settling, and wet deposition. Hygroscopic effects are also considered for organic matter and black carbon aerosols. MODIS AOD data is also assimilated into the system.

Finally, it is worth to mention that the Copernicus programme is coordinated and managed by the European Commission. The development of the observation infrastructure for the space component is performed under the aegis of the European Space Agency, and for the in situ component is performed under the European Environment Agency and the Member States. This programme covers a wide range of applications (environment protection, management of urban areas, regional and local planning, agriculture, forestry, fisheries, health, transport, climate change, sustainable development, civil protection and tourism), and their services, provided free of charge to users, have reached different degrees of maturity.

Forecasted variables

The Copernicus Atmosphere Monitoring Service uses a comprehensive global monitoring and forecasting system that provides a 5-day forecast of the state of the atmosphere on a daily basis, combining information from models and observations. Also, 4-day forecasts of air quality are provided on the European domain through an ensemble of detailed regional models using as boundary conditions those provided by the global modelling system [37]. CAMS aerosol forecasts (Fig. 9) will be used in PreFlexMS to take into account the effect of aerosols in DNI. Up to 49 parameters are predicted by CAMS, including optical aerosol parameters, pollutant gases, cloud-related magnitudes and more. The parameter selected to account for the aerosol radiative effects is the total aerosol optical depth (AOD) at 550nm (Table 1). Total AOD forecasts are the sum of the contributions of the five major aerosol components mentioned before: sea salt, sulphate, dust, organic matter, and black carbon. The output is provided using grib files (version 1), covering an area over the Iberian Peninsula from 37.5° to 40° of latitude and from -9° to -6° of longitude, using a latitude-longitude (lat-lon) projection with a resolution of 0.25°. There are two daily runs, at 0 and 12 UTC, with outputs at 3-hour intervals from 0 to 120 h. The SDS-WAS Regional Centre for NA-ME-E consists of a network of research and operational partners implementing SDS-WAS objectives in the region organized as a federated system (Fig. 10). This Regional Centre, hosted by Spain, is managed by a consortium of the Meteorological State Agency of Spain (AEMET) and the Barcelona Supercomputing Center (BSC-CNS).

SDS-WAS

Forecasted variables

The aerosol-related parameters provided by the NMMB/BSC-Dust model are the dust optical depth at 550 nm and the dust concentration at 10m (Fig. 11).

Fig. 11. Dust optical depth provided by SDS-WAS ([42]).

The former is relevant for radiative studies, and it will be used in this project as input to take into account the effect of dust on the solar radiation (Table 2). The output is provided using grib files (version 2), covering an area over the Iberian Peninsula from 37.5° to 40° of latitude and from -9° to -6° of longitude, using a latitude-longitude (latlon) projection with a spatial resolution of 0.1°. There is one daily run at 12 UTC, with outputs at 3-hour intervals from 0 to 60 h.

Solar radiation forecasting system including aerosols

Despite the potential for NWP models to produce accurate shortwave forecasts across time scales, current NWP models often do not provide the most appropriate quantitative forecasts for the CSTP industry. Because weather predictions internally only require the global horizontal irradiance (GHI) for the model's energy budget, the DNI is not a key variable in these models. GHI is much less sensitive to aerosol optical properties than DNI, and sometimes NWP models do not account for atmospheric aerosols in the radiative transfer equation, which can be a limitation of NWP-derived DNI forecasts in CSTP technologies. In this section we describe the inclusion of forecasted atmospheric aerosols in a Machine Learning based post-processing scheme of NWP model forecasted DNI, intended to enhance local information in the adaptation of the NWP information to the site.

General post-processing scheme

The DNI forecast post-processing system is summarized in Fig. 12, where a general system data flow is shown highlighting the following phases: phase 1 is constituted of the training of the Machine learning modules; while in phase2 these modules are applied on the most recently available weather forecast. The available history of both validated ground measurements and simultaneous weather forecasts (Fig. 12, blue round shapes) constitute the inputs of the Machine Learning Modules (Fig. 12, orange rectangle).

The goal of the Machine Learning module is to find a relationship among the weather forecasts and the ground data. It eliminates systematic deviations of the meteorological predictions and represents other local characteristics not being reflected in the initial forecast. Once these relationships are established 3 , new weather forecasts provided by the NWP model (Fig. 12, green round shape) feeds the trained Machine Learning modules (Fig. 12, red rectangle) and provide post-processed DNI Forecasts. It is worth to note that the new weather forecasts are used both as input of the phase 2 and phase 1, playing different roles in each one. The design of the post-processing system is based in the extended strategy of problem solving that consists of the identification of minor problems that integrate the whole one. Following this starting point, the first step of the post-process is focused in data treatment. As solar radiation is reflected and scattered primarily by clouds, it follows that the treatment of clear and cloudy situations should be different. Therefore, the first module of the system has the goal of deciding if the time instant to be predicted will correspond to clear or cloudy sky conditions:

 Clear-sky conditions: In this case, the clear sky module acts to generate the DNI forecasts. This module consists on a clear sky model that could vary depending of the level of available information.

 Cloudy-sky conditions: A Machine Learning module is used to predict the DNI available in these scenarios. Roughly speaking, it consists in a two steps model that selects the similar situations in the historic and fits a nonlinear model that generates the DNI prediction from the meteorological information.

The whole system accomplishes a set of mandatory features that ensures both a correctly operative running and the future development and inclusion of improvements of each of the different tools included in them. For details of the whole system, the reader is referred to the Section 3.2 of the Deliverable 4.4 (Post-processing for deterministic and probabilistic weather forecasting, November 2016) of PreFlexMS project.

Integration of aerosols

Because of the coarse-graining of the physical atmospheric state, NWP forecasts often have errors dependent on the current weather situation category [START_REF] Cheung | Ensemble Solar Forecasting Statistical Quantification and Sensitivity Analysis[END_REF]. To improve the accuracy of DNI forecasts, the post-processing scheme shown in Section 4.1 is enhanced by the integration of aerosols forecasts.

Accurate aerosols forecasts are complicated due to several factors, including the chemical and physical heterogeneity of aerosols, the complex temporal and geographic variation in their sources, and their interaction with the hydrologic cycle among other factors.

A method of integration of natural coarse-mode particles (sea salt, fire particles, and dust) in the DNI forecasting system is currently under preparation, and the effects of these additional modules will be subject to further investigations in different climate zones and under different sky conditions. This is a crucial point since, especially in the Mediterranean area, Saharanbased dust storms can occur frequently, leading to a drastic attenuation of DNI.

Consequently, the integration of forecasted aerosol information into the post-processing scheme can significantly increase the accuracy of solar resource assessment and forecasting. So, the integration of these forecasts plays the role of a new input of the general scheme added to the NWP models and the ground measurements (Fig. 13), where the blue-sky round shapes represent them. For a detailed description of the whole system, the reader is referred to the Section 3.2.2 of the Deliverable 4.4 (Post-processing for deterministic and probabilistic weather forecasting, November 2016) of PreFlexMS project. Now, in this section, modules 1 and 2, which are those related to aerosol integration, are briefly described. The DNI forecast post-processing scheme is depicted in Fig. 14 where the new source of information is included as input of the different modules.

It is worth to mention that the post-processing scheme accounts for categorization parameters (ambient temperature, relative humidity and surface level pressure), which generate specific atmospheric-state categories exhibiting the particular local weather conditions. DNI forecasts post-processing are analyzed separately in each of these atmospheric states.

The integration of aerosols products in this scheme enhances the post-processing Machine Learning-based approach, as this information can improve both the performance of clear sky models and the accuracy of the atmospheric description which will lead to a better DNI estimation under cloudy-sky conditions.

Aerosol forecasts management

Following the philosophy of the whole system that tries to ensure easy access to data and its use in the post-process, aerosol forecast are treated in the same way that the NWP forecasts. Thus, the aerosol forecasts are stored in netCDF format and included in the system database, along with ground data and weather predictions. Once this information is available, it is able to play different roles in the system as explicative variables in the Machine Learning modules to classify or in the regression algorithms.

Integration of aerosols forecasts in Module 1

The goal of this module is to determine the typology of data to be predicted, taking into account the sky clearness. To achieve this, historic data are classified into two categories: clear or cloudy sky conditions. This classification will determine the model used for providing the DNI forecast: under clear-sky conditions, a clear sky model will be used; meanwhile a nonlinear approach based on Machine Learning will be applied in other case.

Cloud-sky conditions are assigned to those data whose absolute difference with respect to the corresponding theoretical DNI under clear-sky conditions is higher than 10%. The available and validated ground measurements at the site are therefore classified into clear and cloudy data, and each of them is associated to a set of variables provided by NWP forecasts.

Unfortunately, the presence of high AOD values can lead to DNI decreases even more than that percentage with respect to low AOD conditions (Fig. 15). Consequently, besides the meteorological variables, the aerosol forecast over the nearest grid point to the emplacement is explicitly added as a classificatory variable. The first four variables are commonly delivery by the NWP models meanwhile the last ones are provided by the different options of aerosol models or should be calculated by physical models. In any case, this approach could suffer variations depending on the NWP model included on the system, the classification algorithm, and others factors.

Through these inputs, this module carries out a supervised classification problem, where the historic validated ground data are assigned to clear or cloudy sky conditions (at the temporal resolution that meteorological data is available). Thus, we propose a data driven classification, through the comparison between the actual DNI and the expected DNI under clear-sky conditions (as estimated by using the locally adjusted ESRA model and the Turbidity Index as parameter for characterizing the atmospheric transparency in the historic measurements).

The classification algorithm implemented in this module is based on support vector machines (SVM) as depicted below (Fig. 16): SVM is a kernel based Machine Learning technique used in classification tasks and regression problems [START_REF] Vapnik | The Nature of Statistical Learning Theory[END_REF], which is playing an ever more important role in classification and regression analysis, and also has gained importance in environmental related applications [START_REF] Ornella | Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data[END_REF][START_REF] Jain | Supervised machine learning algorithms for protein structure classification[END_REF].

SVM searches for the optimal separating hyperplane between the classes through the maximization of the margin between the classes' closest points. Learning algorithms used in SVM use high-dimensional features, and its performance drastically depends on the definition of its model parameters and alignment [START_REF] Bao | A PSO and pattern search based memetic algorithm for SVMs parameters optimization[END_REF][START_REF] Friedrichs | Evolutionary tuning of multiple SVM parameters[END_REF][START_REF] Lorena | Evolutionary tuning of SVM parameter values in multiclass problems[END_REF]. Fig. 17 shows this optimal separation, through the separating hyperplane (green) defined by the middle of the margin defined by the support vectors (points lying on the boundaries). This is the theoretical fundamental of the problem. It is improved by different version of the algorithm [START_REF] Smola | A tutorial on support vector regression[END_REF], to be able of solve realistic situations. So, the approach for the non-linear cases includes kernel methods where, roughly speaking, data points are projected via kernel functions into a higher-dimensional space where they are linearly separable. On the other hand, overlapping classes are data points located on the "wrong side" of the discriminant margin, in this case they are weighted down to reduce their influence by mean of soft margins [START_REF] Cortes | Support-vector networks[END_REF].

Integration of aerosols forecasts in Module 2

Under cloud-sky conditions, DNI is attenuated by the presence of clouds in its path, as well as the various elements present in the atmosphere (gaseous molecules, dust, particulates, etc.). The depletion of the DNI by clouds depends on the type of clouds, their thickness and their number of layers. The calculation of solar radiation components under cloudy-sky conditions is quite difficult, as it requires detailed information on the amount, type and optical properties of clouds, their thickness, position and layers. Such data are rarely collected, and their forecasts are not accurate.

Consequently, even if aerosols are not dominant attenuating components under cloudy-sky conditions, they are considered in this module for a more detailed atmospheric characterization and thus a specific fitting according to the most similar atmospheric conditions. As it is detailed in Section 3.2.1 of deliverable D4. [START_REF] Diagne | Review of solar irradiance forecasting methods and a proposition for small-scale insular grids[END_REF] Post-processing for deterministic and probabilistic weather forecasting in PreFlexMS, the first step of this module consists of the selection of data from the available historic data with similar atmospheric conditions according with the current weather forecast. The subset selected will play the role of training the dataset constituted by local and validated ground measurements along with coincident NWP forecasts (including aerosols) to fit models for the particular atmospheric situations. In a second step, a nonlinear prediction model is fitted with this subset in order to post-process the DNI forecast (including the aerosols as explicative variables).

It is important to remark that the Module 2 (cloudy-sky conditions), can generate forecasts for near clear-sky conditions (or even for cloud-free situations when the Module 1 makes a badclassification). Consequently, the integration of aerosols forecasts in this module will enhance its performance.

Integration of aerosols forecasts in Module 3

Under clear-sky conditions, the atmosphere absorbs and scatters radiation as the DNI penetrates to the ground. Parts of the DNI radiation are absorbed (removed) and reflected (scattered) in many other directions away from the path of this beam radiation. The performance of a clear-sky model mainly depends on the algorithm and input it uses to describe the aerosol transmittance, as aerosols are normally the major source of extinction under clear skies [START_REF] Gueymard | REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation -Validation with a benchmark dataset[END_REF]. It is worth to highlight the wide variety of complexity levels present in clear-sky models:

 Simplest clear sky models use only geometric calculations and they are only a function of the solar zenith angle. These very simple clear sky models are essentially empirical correlations based on measurements for a site location and the astronomical parameters [START_REF] Haurwitz | Insolation in relation to cloudiness and cloud density[END_REF][START_REF] Kasten | Solar and terrestrial radiation dependent on the amount and type of cloud[END_REF][START_REF]Analyse statistique des processus meteorologiques appliquee a l'energie solaire[END_REF]  Simple clear-sky models include, in addition to zenith angle, some basic parameters of the atmospheric state such as air pressure, temperature, relative humidity, aerosol content, and Rayleigh scattering [START_REF] Bird | Simplified Clear Sky Model for Direct and Diffuse Insolation on Horizontal Surfaces[END_REF]  Complex clear-sky models take into account a number of atmospheric parameters such us ozone, aerosols, and perceptible water. These are the most accurate clear sky models if they are well calibrated, but they also require many inputs, which may not be readily available. Satellite data can help to estimate many of the parameters. A number of models can be found in the literature, they are well validated and have an accuracy of the order of 30 W/m 2 for the three components of solar radiation. Table 3 shows the inputs required by several clear-sky models [START_REF] Ineichen | A broadband simplified version of the Solis clear sky model[END_REF][START_REF] Gueymard | REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation -Validation with a benchmark dataset[END_REF].

A general finding common to these studies is that models that show the best performance are those whose aerosol algorithm is sufficiently detailed, and are based on spectral AOD data. Simplified SOLIS or REST2 models are proposed in this project for calculating DNI in clear sky conditions in the post-processing forecast scheme. These models allow a fast and easy integration of aerosol forecasts and both they have shown an accurate performance (under precise input meteorological information). The choice of one of these models for a particular site will be determined through the comparison with validated ground measurements at the location.

 The Simplified SOLIS model [START_REF] Ineichen | A broadband simplified version of the Solis clear sky model[END_REF] is a broadband version of the Solis model [START_REF] Mueller | Rethinking satellite-based solar irradiance modelling: The SOLIS clear-sky module[END_REF], which is a spectrally resolved physical model based on radiative transfer model (RTM) calculations requiring atmospheric water vapor column and aerosol content as main input parameters. It was developed within the frame of the European project Heliosat-3. The broadband simplified version of this model allows for a fast and accurate solar irradiance under clear sky conditions and can therefore be used in real time processes, as it avoids the time consuming RTM calculations. This simplified model was derived by a broadband fitting on LibRadTran [START_REF] Mayer | Technical note: The libRadtran software package for radiative transfer calculations -description and examples of use[END_REF] calculations in a 10-dimensions space including aerosol optical depths, water vapor column and ozone amount, in a wide variety of atmospheric conditions: altitudes in the range 0-7000 m above the sea level; AOD (at 700 nm) in the range 0-0.45; water vapor atmospheric content in the range 0.2-10 cm.

The basis of this simplified model is the Lambert-Beer relation:

𝐷𝑁𝐼 = 𝐼 0 ′ 𝑒𝑥𝑝 (- 𝜏 𝑏 sin (𝛼) 𝑏 ⁄ )
Where 𝐼 0 ′ is the enhanced extraterrestrial solar irradiance, 𝛼 the solar elevation angle, 𝜏 𝑏 is the beam total optical depth (a parameter dependent on AOD and water vapor) and 𝑏 the fitting parameter obtained from RTM calculations.

𝐼 0 ′ = 𝐼 0 (𝐼 02 𝐴𝑂𝐷 2 + 𝐼 01 𝐴𝑂𝐷 + 𝐼 0𝑜 + 0.070𝑙𝑛 ( 𝑃 𝑃 0 ⁄ ))

Where I0i are water vapor dependent parameters, P is the atmospheric pressure at the site altitude, P0 is the corresponding atmospheric pressure at sea level, and AOD is the aerosol optical depth at a given wavelength.

The DNI obtained with the SOLIS simplified version are within a 2% compared to the original RTM Solis model, with a negligible bias.

 REST2 (Reference Evaluation of Solar Transmittance, 2 bands) [START_REF] Gueymard | REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation -Validation with a benchmark dataset[END_REF] is a state-of-the-art, high-performance radiative model to predict cloudless-sky broadband irradiance, illuminance and photosynthetically active radiation (PAR) from atmospheric data developed using transmittance parameterizations. Its derivation uses the same twoband scheme as in the previous CPCR2 model [START_REF] Gueymard | A two-band model for the calculation of clear sky solar irradiance, illuminance, and photosynthetically active radiation at the earth's surface[END_REF], but with numerous improvements. In particular, in REST2 models has focused great attention to precisely account for the effect of aerosols. Basically, the model divides the shortwave spectrum in two broad bands (0.29 to 0.7 μm, about 46.5% of the total energy, and 0.7 to 4 μm, about 52% of the total energy). The model simplifies the retrieval of the illuminance whose spectral range corresponds almost perfectly to band 1. To calculate transmittances due to the different atmospheric components, the model requires the following input data: atmospheric water vapor, Angstrom turbidity coefficients (α, β), atmospheric NO2, atmospheric O3 and site atmospheric pressure, as well as solar zenith angle and the ground reflectivity of the site. Extinction due to aerosols affects, above all, to band 1 (290, 700nm) and so, the calculation of illuminance is strongly conditioned by the aerosols input data.

The formalism of REST2 model is, for both bands defined:

𝐷𝑁𝐼 𝑖 = 𝑇 𝑅𝑖 𝑇 𝑔𝑖 𝑇 𝑜𝑖 𝑇 𝑛𝑖 𝑇 𝑤𝑖 𝑇 𝑎𝑖 𝐸 0𝑛𝑖
Where TRi, Tgi, Toi, Tni, Twi, and Tai are the band transmittances for Rayleigh scattering, uniformly mixed gases absorption, ozone absorption, nitrogen dioxide absorption, water vapor absorption and aerosol extinction, respectively, which have been obtained by fitting a large number of parametric runs of the SMARTS code [START_REF]SMARTS -SMARTS Documentation[END_REF] to computationally efficient polynomial ratios. The source of input data used in this work is described following o Turbidity. The Angstrom turbidity coefficient β is a function of the aerosols content in the atmosphere, while the exponent coefficient α is a function of the aerosol size. In this work, forecasted aerosol data will be used as input to calculate these coefficients.

o Atmospheric water vapor and atmospheric pressure. In this work, forecasted water vapor and atmospheric pressure will be used as input for the REST2 model.

o Total columnar ozone amount. The O3 atmospheric content is taken in this work from climatic data.

o Ground albedo. Values estimated by MACC, or other databases, will be used for setting this parameter.

o Total columnar nitrogen dioxide amount. Given its small influence in the calculations, this parameter will be set to a default value of 0.0002 atm-cm.

o Aerosol single-scattering albedo. Defaulted values will be used.

A detailed validation analysis showed that the predictions of REST2 are within instrumental uncertainty (about 5% or better) when the inputs are known with good accuracy.

DNI values obtained by these models will be post-processed with validated ground-based coincident solar data measured by well-maintained, high accuracy measuring equipment available for local adaptation, using either Machine Learning techniques or classical regression techniques.

Integration of aerosols forecasts in probabilistic prediction

Related to the integration of aerosols into a probabilistic forecast we make difference between multi-model scheme and EPS situation.

In the case of a multi-model scheme, that is, when different deterministic approaches but with different inputs are available, the aerosols forecast can be integrated like has been described in this report. The probabilistic forecasts will come from the multi model scheme being the prediction aerosols one of the deterministic options described.

The methodology to integrate aerosols forecast when EPS is used is being analyzed. In this case, 50 different forecasts are delivered but provided by indistinguishable models. So, it is analyzed the option of integrating aerosols forecasts in the physical models to predict DNI and to include them when statistics as median forecast or percentiles are used.

Conclusions

In general, Machine Learning based post-processing approaches can easily incorporate a wide variety of useful information for improving the forecasts than radiation transfer models. This integration mostly depends on the flexibility and performance of the post-processing tool and also the availability of historical data for the training stages. These approaches for solar irradiance forecasting may make use different input variables, whose selection is crucial for the final system performance.

Even if clouds are the primary radiation attenuating component of the atmosphere, CSTP industry is focused in regions and time periods with minimal cloud cover. In the absence of clouds, the main attenuating components are aerosols, which are highly variable in space and time leading to difficulties in calculating and forecasting their spatiotemporal patterns and thus their influence on DNI. Thus, aerosols can provide useful information for the forecasting model [START_REF] Breitkreuz | Short-Range Direct and Diffuse Irradiance Forecasts for Solar Energy Applications Based on Aerosol Chemical Transport and Numerical Weather Modeling[END_REF]. This report deals with the integration of aerosols in NWP based DNI forecasts in a post-processing scheme based on Machine Learning.

The solar resource community requires high resolution aerosol information. Complete spectral information of the aerosol optical thickness would be required for detailed DNI modeling, but that information is scarce and usually broad climatological information is available (or estimations of atmospheric transmittance based on ground DNI measurements).The use of climatologic aerosol information usually leads to an overestimation of DNI in areas of dust storms, biomass burning or urban air pollution, where the use of climatology results smooth out high aerosol load episodes. Optical characteristics of tropospheric and stratospheric aerosols may change rapidly, with varying meteorological conditions. The general lack of detailed aerosol data, as well as its effect on DNI, justifies the use of NWP model based aerosols forecasts.

Typically, NWP DNI forecasts make use of aerosol climatic databases instead of actual aerosol forecasts, causing errors in clear-sky conditions (which constitute the highest solar irradiance periods and the basis for CSTP production). The disposal of operational and quasi-operational global aerosol models coupled with NWP post-processing techniques allows the possibility of increasing the accuracy of DNI NWP-derived forecasts.

The approach discussed in this report is intended at providing DNI forecasts designed to meet the needs of the CSTP plants. This is achieved by improving the accuracy DNI forecasts under clear-sky conditions through the integration of aerosol information with NWP based DNI forecasts. Preliminary results in the frame of this project suggest that the integration of aerosols forecast can provide a significant increase of the DNI forecasts accuracy. Notwithstanding, because of the high temporal and spatial variability of aerosols load and composition, dissimilar performances can be achieved in different locations and thus it is required to test the procedure in a wide variety of climatic scenarios and meteorological conditions, in order to accurately evaluate its performance.

Fig. 1 .

 1 Fig. 1. Different types of scattering.

Fig. 2 .

 2 Fig. 2. Left, different atmospheric conditions according to their constituting components; right, DNI calculated in different atmospheres.

Fig. 3 .

 3 Fig. 3. Sizes of different atmospheric aerosol types.

Fig. 4 .

 4 Fig. 4. Global distribution of AOD derived from AERONET.

Fig. 5 .

 5 Fig. 5. DNI attenuation as a function of air mass and AOD calculated with REST2 model (adapted from [31]).

Fig. 6 .

 6 Fig. 6. Atmospheric water vapor (5-years averaged) calculated by the NASA water vapor project (NVAP).

Fig. 7 .

 7 Fig. 7. Worldwide water vapor distribution calculated with Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's Aqua satellite in 30 January 2005.

Fig. 8 .

 8 Fig. 8. Water vapor transmittance in a U.S. standard atmosphere (air mass = 1.5) at a precipitable water of 1.419 cm calculated by MODTRAN2, SMARTS2 and SPECTRAL2 models (adapted from [29]).

Fig. 9 .

 9 Fig. 9. AOD forecasts from CAMS.

Fig. 10 .

 10 Fig. 10. SDS-WAS Regional Node for Northern Africa, Middle East and Europe.

Fig. 12 .

 12 Fig. 12. Post-processing phases.

Fig. 13 .

 13 Fig. 13. Post-processing phases including aerosol forecasts.

Fig. 14 .

 14 Fig. 14. DNI post-processing scheme.

Fig. 15 .

 15 Fig. 15. Ratio of ground measured DNI (BSRN Carpentras station) to extraterrestrial DNI ratio, along with coincident AOD measurements (AERONET) [31].

Fig. 16 .

 16 Fig. 16. Classification algorithm implemented in this Module 1.

Fig. 17 .

 17 Fig. 17. Optimal separation through SVM, highlighting separating hyperplane, margin and support vectors.

3.1 CAMS 3.1.1 System description MACC

  (Monitoring Atmospheric Composition and Climate) projects[START_REF]MACC Project -Home[END_REF] intend to establish an atmospheric environmental service within the European Copernicus programme to record, monitor and forecast the air quality and atmospheric composition, among others. MACC projects were funded by the European Commission first under the 6 th and 7 th European Framework Programme for Research and then under the EU-Horizon 2020 Programme, and coordinated by the ECMWF.Currently the project has become the Copernicus Atmosphere Monitoring Service (CAMS) [37], which objective is to put into operation the research carried out by MACC, providing users with near real-time analyses and forecasts of global atmospheric composition and air quality.

Table 1 . Parameter from MACC

 1

		Parameter Identifier
	Parameter definition	(indicatorOfParameter grib	Level	Inst/Acc	Units
		key)
	Total Aerosol Optical Depth at 550nm	207	0	Instantaneous Unitless

  BSC) in collaboration with NCEP, NASA Goddard Institute for Space Studies and the International Research Institute for Climate and Society[START_REF] Pérez | Atmospheric dust modeling from meso to global scales with the online NMMB/BSC-Dust model – Part 1: Model description, annual simulations and evaluation[END_REF]. The dust model was developed as a component of the Non-hydrostatic Multiscale Model on the B grib (NMMB) to provide short to medium-range dust forecasts for both regional and global domains. Currently, the NMMB/BSC-Dust model provides operational dust forecasting over Northern Africa, Middle East and Europe (NA-ME-E) and global regions. It is part of the ensemble used by the World Meteorological Organization (WMO) Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) framework to provide dust forecasts for the NA-ME-E area[START_REF]Northern Africa-Middle East-Europe (NA-ME-E) Regional Center -WMO SDS-WAS[END_REF].

	3.2.1 System description
	NMMB/BSC-Dust [40] is a multi-scale atmospheric dust model designed and developed at the
	Barcelona Supercomputing Center (

Table 2 . Parameter from NMMB-BSC/Dust.

 2

	Parameter definition (long_name)	Parameter Identifier Parameter number Parameter category	Level	Inst/Acc	Units
	Dust optical depth at 550 nm	253	13	0 Instantaneous Unitless

Table 3 . Parameters required for several clear-sky models [15,30,56-59].

 3

	Clear sky Model	*	 	Ozone	Precip. water	AOD at 700 nm	TL2 	Surface pressure	Surface albedo
	Bird & Hulstrom
	MMAC
	ESRA
	SunFlux
	Ineichen
	REST2
	*Ångström's turbidity;

 Ångström's exponent;  Turbidity Linke at air mass 2