Processing scheme Over cLouds, Land and Ocean AROME Applications of Research to Operations at Mesoscale AROME/HARMONIE Hirlam Aladin Regional

Keywords:

This report has been prepared by DLR in collaboration with CENER within WP4 'Weather forecasting and measurement for renewable energy predictability'. It describes meteorological conditions in Évora as the test site of PreFlexMS. Évora characteristics are compared to other stations in Portugal/Spain and in other regions being of interest als potential Concentrating Solar Power (CSP) markets. We want to avoid a development which would be too much driven by the Évora test site as such plant optimizations may not be applicable in other locations. Especially as other locations may not have the same wealth of numerical weather prediction available as on the Iberian Peninsula.

Chapter 1 provide a site assessment of Évora, Badajoz and Plataforma Solar de Almeria with respect to direct normal irradiation (DNI) as observed from ground and satellite-based platforms. Additionally, satellite-based cloud physical parameters and DNI variability classes are taken into account. Aerosols have been assessed with the help of AERONET ground observations of aerosol optical depth (AOD).

In Chapter 2 we review the ground-based DNI observation availability in South Africa, India, Morocco, Chile, and Saudi Arabia. The observations were quality controlled and frequency histograms with annual and monthly resolution are given. The same is done for a 10 year satellite-based time series. By that we can compare the conditions at the available ground stations to the reference locations Upington in South Africa and Noor in Morocco, which have no own publicly available ground observations. This helps to identify the most relevant ground observation station.

In chapter 3 the rationale of defining the optimum ground stations acting as 'virtual demo sites' in PreFlexMS is summarized. We focus on South Africa, Morocco, and Chile.

Research questions to be further addressed by both the dispatch optimizer developers and the meteorological forecast assessment team are named. The available numerical weather prediction (NWP) models for each of the research questions are named. Answering these research questions will quantify the value of aerosol forecasts, of storage, of higher temporal and spatial resolution, of higher forecast update frequencies, of post-processing efforts, and the use of probabilistic forecasts. This will serve as input to the business development task within WP4.

Finally, we suggest using station VAN (Vanrhynsdorp) in South Africa, Erfoud in Morocco, and either San Pedro de Atacama or Crucero in Chile as virtual demo sites for further work in PreFlexMS.

The decision for the station in Chile will be done in the upcoming weeks, when the recently found ground observations have been made available by the Chile government. Acronyms 3D-Var Three-Dimensional Variational data assimilation 4D-Var Four-Dimensional Variational data assimilation AEMET Agencia Estatal de Meteorología AEMET-SREPS AEMET Short Range Ensemble Prediction System AERONET Aerosol Robotic Network AIREP Air

1. Site assessment -How special is Évora?

In this section we want to understand the meteorological conditions in Évora in general. This study relies on existing, historic observations from ground and satellites. It allows a characterisation of Évora and to compare this to other stations in Portugal/Spain, but also in other regions of the world. Fig. 1-1 illustrates the location of Évora, Badajoz and the Plataforma Solar de Almeria (PSA) being the three stations in the focus of this first chapter.

Station details -Évora

Mitra´s station is situated in the countryside of Évora (about 8km South from Évora), in the Renewable Energies Chair experimental site (University of Évora). The station geographical coordinates are 38°31'50.28"N and 8° 0'40.33"W and the altitude is 220m.

The current station was installed in December of 2014 and has the following instrumentation:

-1 Kipp-Zonen CHP-1 pyrheliometer measuring DNI (direct normal irradiation) -2 Kipp-Zonen CM-11 pyranometers measuring GHI (global horizontal irradiation) and DHI (diffuse hemispherical irradiation) -1 Thies Combined Sensor, measuring air temperature and relative Humidity -The Sun's apparent motion is tracked by a Soly 2 Suntracker. This station has been measuring since mid-December of 2014. There are some missing measurements due to technical problems and maintenance operations.

Station details -Badajoz

Badajoz station (Fig. 123456) is situated on the flat roof of the local AEMET building (Fig. 12345), in the campus of Extremadura University, 4 km west of the city (Fig. 1234). Its geographical coordinates are 38º 53' N, 07º 01' W, and it is at 190m altitude. Its distance to Évora station is approx. 85 km. The data are recorded with first class pyrheliometers and secondary standard pyranometers as defined in ISO 9060. A pyrgeometer was installed in 2010. Data gaps are reduced to a minimum due the completive use of other meteorological stations with sensors of the same classes situated within 500 m of the main station. Until 2011 gaps were filled with co-located Rotating Shadowband Irradiometers (RSIs), with a posteriori spectral, temperature and air-mass correction. Several other radiometric stations exist at PSA. Intensive maintenance, including cleaning of the sensors on every working day, is provided. In addition to the radiometric parameters just described, air temperature and relative humidity, wind speed, wind direction and air pressure are measured. In 2010, an AERONET sun photometer has been installed, as well as an all-sky imager that documents the state of the sky during measurements. A sun and aureole radiance measurement system, aerosol particle-counters, a 3D wind sensor and visibility measurement systems are operated since 2012. Since 2013 also ceilometer data are available. Correlation coefficients between 0.91 and 0.97 were found.

Satellite-based irradiance information

The bias for the direct irradiance at normal incidence was comprised between 163 and +50 W/m². The RMSE ranged from 160 W m 2 (29% of the mean observed irradiance) to 288 W/m² (63%). The correlation coefficient ranged between 0.67 and 0.87.

For this project, we have obtained 10 years of CAMS data (2006 to 2015) for each station where available.

Irradiance statistics -Évora

In the following we present the irradiances themselves, both from ground and satellite observations.

Ground observations

Satellite/model based observations (CAMS)

Long-term satellite-based observations allow generating information on typical seasonal patterns. In order to ensure that CAMS data is sufficiently accurate in Évora, we compare the histograms for all months with available ground observations. We do not compare the annual histogram as the ground observations have missing data in January and June. 1.1.6 Irradiance statistics -Évora vs. Badajoz

In the following section we repeat the same statistics, but compare Évora with Badajoz. Évora is the PreFlexMS demo site, but Badajoz has a much longer record of ground observations. Therefore, forecast assessments will be based for Badajoz if longer time series are requested to gain significance and reliability in statistical results. Consequently, we want to understand how Badajoz and Évora stations differ.

Having only a short time overlap of ground observations, the direct comparison is only of restricted information content. We therefore use satellite observations at both locations to compare. First we have to understand if Badajoz conditions are met in the CAMS dataset before we compare long-term CAMS time series at the two locations. Nevertheless, for our purpose of validating forecasts it is more relevant, that the monthly distribution of values is similar (Fig 1.16). We see that the variation from month to month is sufficiently met. This is an indication that the weather pattern is well met by the satellite observations. Therefore, CAMS observations may be used for forecast evaluations on a longer multi-annual forecast dataset.

Ground observations

Having discussed the difference between ground and satellite-based observations for the time series with available ground observations, we now continue to look at the satellite observations with their 10 year time series. Both stations have similar 10-year mean histograms, and also, the band of annual variability is very similar (Fig 1.17). Likewise, the monthly 10-year mean histograms are very similar (Fig 1.18). Small differences occur e.g. in January, May, or November, but the general seasonality is similar in both datasets. Therefore, Badajoz observations are well suited to be taken to evaluate numerical weather prediction (NWP) forecasts with respect to Évora. This statement is valid both for the ground observations being available from different periods only, but also for the long-term satellite observations.

Ground observations

Thanks to the multi-annual data record based on ground observations at PSA, we can now assess the multi-annual variability in the period from 2006 to 2013 also based on ground observations (Fig 1 .19 and 1.20). This had not been possible for Évora and Badajoz stations.

Satellite/model based observations (CAMS)

In order to ensure that CAMS data is sufficiently accurate in PSA, we compare the mean histograms for the years and months with available ground observations ( In the annual mean histogram an underestimation of smaller DNI values and an overestimation of values between 800 and 900 W/m2 is seen in CAMS data. This difference originates from the months October to February, where also the monthly mean histograms show a similar shift. Especially in October, the shift in the 800 to 900 W/m2 range can be seen in several individual years.

Having discussed the difference between ground and satellite-based observations, we now continue to look at the satellite observations with their long-term time series. Comparing CAMS at Évora and PSA shows a larger occurrence of 200 to 550 W/m2 values in Évora, while values between 600 and 850 W/m2 occur less in Évora (Fig. 1.23). For DNI above 850 W/m2, the two locations show a similar long-term behaviour. Despite their rather similar multi-annual yearly histograms, the view on monthly histograms (Fig. 1.24) reveals significant differences. In July and August, Évora has much more high DNI values than PSA, while in October to April the PSA shows more high DNI values above 800 W/m2. This could be caused by a higher Saharan dust frequency in July and August (see chapter 2.3.2). Only in the transition months May, June and September both histograms are similar with respect to high DNI values.

Satellite-based cloud physical parameter statistics 1.2.1 Method

The APOLLO methodology (AVHRR Processing scheme Over cLouds, Land and Ocean; [START_REF] Saunders | An improved method for detecting clear sky and cloudy radiances from AVHRR data[END_REF][START_REF] Saunders | Cloud top temperature/height: A high resolution imagery product from AVHRR data[END_REF][START_REF] Kriebel | Optical Properties of Clouds Derived from Fully Cloudy AVHRR Pixels[END_REF][START_REF] Kriebel | Optical Properties of Clouds Derived from Fully Cloudy AVHRR Pixels[END_REF][START_REF] Kriebel | The cloud analysis tool APOLLO: Improvements and Validation[END_REF] uses multiple spectral channels of the MSG to discriminate between different cloud types and to provide cloud properties. For some, the ground can be seen, for others, the ground is not visible due to the cloud optical thickness. The same applies for DNI -for some clouds the solar power plant will see the sun (which is related to DNI > 0), in other situations, the DNI is close to zero. Also, please note the spatial structures of clouds and gaps in cloud cover. This is evaluated quantitatively by the APOLLO satellite retrieval and a post-processing of APOLLO results for cloud statistics. Cloud top temperature Additionally to the single pixel results, the surrounding 29x29 pixel window is evaluated in order to understand the medium scale cloud situation. In this window, several values are computed, mostly the number of cloud elements, the number of gradients in a binary cloud mask changing from cloud to noncloud, the window cloud fraction, and the cloud shape complexity from the fractal box counting dimension. The main aspect highlighted by the fractal box dimension is its ability to identify different structures according to their level of aggregation, the circular patterns, and the isotropy of their pixel distribution [START_REF] Carvalho | An application of fractal box dimension to the recognition of mesoscale cloud patterns in infrared satellite images[END_REF]. This fractal box counting dimension represents the complexity of the clouds shape and evolves from zero (a point) through one (a line) to two (an area). In most cases, it lies between one and two if the cloudy pixels clusters in the window are several pixels wide. Additionally, we discriminate between overcast/broken, scattered and isolated cloud fields and call this classification 'cloud area type'. If there are more than 10 individual cloud elements in the surroundings, the situation is classified as 'scattered' unless the total cloud fraction in the surroundings is above 80%, which classifies the case as 'broken/overcast'. If there are less than 10 cloud elements, the situation is classified as 'broken/overcast' unless there is a high number of more than 175 cloud/no cloud changes from pixel to pixel in any direction, which again results in a 'scattered' case. Also, any cloud/no cloud change from the central pixel to the direct neighbors always results in a 'scattered' classification.

Évora station

First of all, we quantify the occurrence of various clouds. Fig. 1.29 discriminates cloud-free from cloudy cases. Cloud-free, but snow on the ground cases are also given, as this evaluation tool is also used for more photovoltaic (PV) related studies in more snowy regions. The Évora station has about 35% of cloudy cases during daytime hours. The cloudy cases are further split in optically thick cases with low, medium and high cloud top levels (named 'low water', 'medium wat.', and 'high wat./mix.') and optically thin cirrus cases ('high thin ice'). With respect to DNI, the separation in optically thick and thin cloud types is most relevant, for meteorologists the further split into vertical levels is of further interest to assess the situation at a site. Évora has a majority of low level optically thick water clouds (approx. 15%), followed by the amount of thin ice clouds (approx. 10%), which allow parts of the DNI to reach the ground. The number of broken/overcast cases is reduced in May to September and also the number of thin cirrus cases is smaller from June to September. The pattern over the months is similar with exception of July and August, when there are more cloudy situations at the PSA then at the other stations. Also, the pattern over time of the day is similar. PSA has more clouds in the late afternoon, while at other times PSA is less cloudy. Each of these variability classes can be also described by a distribution of occurring APOLLO cloud parameters. Please note that despite the maximum in mean AOD per month in the summer (June-August), the number of strong events during the summer months does not show a similar pattern. The large events with AOD > 1.0 do not occur at all in these months and for the events with AOD between 0.5 and 1.0 are also more equally distributed over the months. These patterns are not unexpected due to the typical weather patterns. Atmospheric low pressure systems bringing dust aerosols from Northern Africa to the Iberian Peninsula are more frequent in the non-summer months.

Typical conditions in other regions of interest

2.1 South Africa

Available ground observations

The BSRN station in De Aar follows rules and measurement practises as described in (Ohmura et al., 1998). BSRN is a project of the World Climate Research Program (WCRP) that aims to measure surface radiative fluxes at the highest possible accuracy with well-calibrated state-of-the-art instrumentation at selected sites in the major climate zones (Ohmura et al., 1998). BSRN data underwent rigorous quality checks (Ohmura et al., 1998;[START_REF] Gilgen | The global energy balance archive[END_REF], to assure high accuracy as well as homogeneity in the data. The quality control procedures are described in a report of [START_REF] Long | BSRN Global Network recommended QC tests, V2.0[END_REF]. The BSRN radiation variables are sampled at 1 Hz with a one-minute averaging time. Unfortunately, the data is only available until January 2005.

The Southern African Universities Radiometric Network (SAURAN, http://www.sauran.net/, [START_REF] Brooks | SAURAN: A new resource for solar radiometric data in Southern Africa[END_REF] provides DNI, global and diffuse horizontal irradiances in 1 minute resolution for South Africa, Namibia, Botswana and Reunion Island. Figure 2.1 shows Location of South African stations, which details can be found in Table 5.

- - - - 0 0 0 2 2 0 0 0 - 2015 0 0 0 8 0 1 1 0 2 0 0 0 12 2016 0 0 0 0 - - - - - - - - -
In STA station there are no DNI measurements in the whole period.

- - - - - - - - - - - 0 - 2015 0 0 0 0 0 1 1 2 2 0 0 0 6 2016 0 0 0 0 - - - - - - - - -
Measured data are available in UFS station from October 13 th 2013. Few invalid DNI days found are caused by missing data or tracking errors.

- - - - - - - - - - 5 0 - 2014 1 0 0 0 0 0 0 0 0 0 0 5 6 2015 0 0 0 0 2 0 0 0 0 0 0 0 2 2016 0 0 0 0 - - - - - - - - -
Measured data are available in VRY station from October 25 th 2013. Few invalid DNI days found are caused mainly by tracking errors.

- - - - - - - - - - 5 1 - 2014 1 0 3 0 0 0 0 0 0 0 0 0 4 2015 0 1 2 1 0 0 0 1 0 - - - -
Measured data are available in UPR station from September 20 th 2013. Few invalid in 2014 are due to missing data, while in 2015 they are found also tracking and shadow errors. It is also appreciated a slight asymmetry to the left in measured DNI from some clear days (a deeper investigation is required for evaluating in this asymmetry is due to natural reasons or to tracking errors).

- - - - - - - - 19 0 1 0 - 2014 1 0 0 0 1 0 0 0 0 0 0 0 2 2015 0 0 0 0 2 1 0 0 0 0 0 0 3 2016 0 0 0 0 9 - - - - - - - -

Irradiances

We concentrate on stations RVD, VAN, SUT, GRT, UFS, and UPR. We exclude UNV and UNZ as they are having only short ground observation data records. And we exclude stations VRY, KZW, KZH, STA, NMU, and SUN as being in regions where CSP is not expected to be built.

Ground observations

First we plot the histograms of existing ground observations (Fig. 2.2 for all observations, Fig 2 .3 split in months). Please note that the number of years of the plots is different. Therefore, a direct comparison with respect to the y-axis is not suitable.

Satellite/model based observations (CAMS)

In order to ensure that CAMS data is sufficiently accurate at SAURAN stations, we compare the mean histograms for the years and months with available ground observations. We omit the stations SUT and GRT (as discussed below). Comparing CAMS satellite based DNI with the ground based DNI, a similar annual histogram is found for RVD and VAN stations. UPR is strongly overestimated in CAMS and also for UFS, CAMS is showing an overestimation of DNI.

The monthly histograms show a good agreement at station RVD with exception of January and February.

But also these two months are not extremely off. The same applies for station VAN, where all months are sufficiently well met in the CAMS data. For UFS, an overestimation is visible in February, March, May, September, November, and December. For UPR, the same applies for all months.

Having discussed the difference between ground and satellite-based observations, we now continue to look at the long-term time series of satellite observations. They are available for a 10 years duration and at any point of interest in South Africa.

We Based on this comparison, the stations SUT and GRT are excluded from the view on the monthly conditions as seen by the satellite as already the mean conditions differ strongly.

For the station RVD, the monthly structure is similar with respect to the DNI values occurring most frequently and the width of the histogram peak. Only small shifts are visible in February and March.

Nevertheless, the number of the DNI cases in the histogram peak is higher than in Upington for January to April. For station VAN, the width of the peak is smaller in May to August, and the peak is higher in November to April, while it is lower in May to July. For station UFS the position of the histogram peak is shifted toward higher DNI values in October to July, while the peak height is similar in all months. Only during August and September both histograms look different. For station UPR the location of the peak is similar for all months, but the height of the peak value is much smaller for October to April, while there are strong differences in the small DNI value part of the histogram. On the other hand, for July and August, the peak height is higher than in Upington and in August, the frequency of small DNI values is much smaller than in Upington. Based on this comparison, stations RVD and VAN have more similarity to Upington than UPR and UFS.

Cloud conditions

First of all, we compare the stations with respect to the occurrence of clouds. We take the location of Upington as reference and compare the others against it -this selection is driven by the existence of several CSP power plant developments in this area. Fig. 2.17 discriminates cloud-free from cloudy cases. Cloud-free, but snow on the ground cases are also given, as this evaluation tool is also used for more PV related studies in more snowy regions. Upington has about 18% of cloudy cases during daytime hours, while all SAURAN stations show larger occurrence of clouds with increasing values for RVD, VAN, UFS/SUT (being equal), UPR and GRT with values up to 35%. In Fig. 2.18 the cloudy cases are further split in optically thick cases with low, medium and high cloud top levels (named 'low water', 'medium wat.', and 'high wat./mix.') and optically thin cirrus cases ('high thin ice'). With respect to DNI the separation in optically thick and thin cloud types is most relevant, for meteorologists the further split into vertical levels is of further interest to assess the situation at a site.

The station RVD has the largest part of low level optically thick water clouds compared to other stations, while it has the lowest amount of thin ice clouds, which allow parts of the DNI to reach the ground. If adding all optically thick clouds to one group and comparing the thick and thin cloud groups, the station VAN is closest to Upington. The station RVD has too few thin cirrus cloud cases, while GRT and UFS show too many such cases.

The split of all cases in scattered, broken/overcast, thin ice clouds, clear and snow conditions is done first for each station separately, but split into time of the day and month of the year (Fig. 2.19 to 2.25) and normalized on the number of daytime cases in each bin. Several seasonal and daily cycles can be seensome of them we discuss as examples before we compare the stations. Please note that the first and last hours of the day are typically populated with less cases due to shorter days in winter time. But we prefer to give relative values here. If needed, absolute histograms can be provided as well.

Upington has a strong dependency of clouds as a function of hour of the day during summer months October to April, while this pattern is not available during winter months. Overall, the relative frequency of clouds is less in the winter months than in summer months. RVD shows a clear seasonal cycle of having more clouds in winter months than in summer months. In all months, cloudy conditions dominate the morning hours, but the number of broken/overcast cases is more dominant in August to December, while in May to September also a strong contribution of scattered cases is found. Thin ice clouds during the whole day occur mostly in April to July and in the afternoon hours in September to October. VAN shows a strong cycle over time of the day for many months, most pronounced in August to January. The months May to October are more cloudy, but in May to August the dependence of the time of the day is less pronounced. SUT also has a very distinct daily cycle of cloud occurrence from April to October. In several months (e.g. October to May) a secondary maximum of cloud cover in afternoon hours is found. Such an afternoon peak in cloud cover is strongly found as well at GRT. Here also a large number of thin cirrus cases over the whole day in most months is found. GRT is strongly affected by thin cirrus clouds from April to November. They will result in volatile DNI with frequent changes between approx. 50% DNI and 100% DNI values (percentages given in relation to the clear sky potential DNI value).

UFS is also strongly affected by both scattered and broken/overcast conditions in afternoon hours for October to April, while it has frequently cloud conditions in the morning in winter months. And finally, UPR also shows the afternoon peak in cloud conditions for October to May, while in June to September only a morning peak in cloud cover exists in the first daytime hour of the day but not the rest of the day the cloud conditions are more flat. Note the increased number of scattered cloud cases in the afternoon from May to November -they will most likely produce a volatile DNI in the afternoon hours with frequent switches between zero and large DNI. Also, the occurrence of cirrus clouds from October to April will result in volatile DNI, but more between 50% of DNI and full DNI and not going down to 0% DNI frequently. Having discussed some seasonal and daily cycles which are different at the 6 stations, we now combine the monthly and hourly plots of all stations. And finally, the duration of cloud periods inside a day is evaluated. We separate in optically thick (DNI < 200 W/m2) and optically thin (DNI reduced) cases. Based on these numbers none of the stations is very close to Upington in all characteristics. The values being closest to the Upington site are marked in green, but none of the sites is close in their characteristics with respect to all metrics. So, none of the SAURAN stations is similar with respect to the distribution of variability classes. For all locations we expect higher RMSE in the forecast verification due to their higher natural variability of DNI.

From that perspective it is recommended to use the station RVD, VAN or SUT as they show the smallest amount of variable conditions. Even if the ratio of very low vs. very high DNI cases (class 1 vs 8) is very different from the Upington conditions at these stations, the number of variable situations is not too high. Otherwise, we would end with RMSE estimates which too pessimistic for the Upington site.

Aerosol conditions

There is an AERONET station available at the Upington reference station. Unfortunately, it went online only in the last few days of January 2016. We have obtained the standard data visualization as provided by AERONET (http://aeronet.gsfc.nasa.gov/cgi-bin/type_one_station_opera_v2_new?site=Upington& nachal=0&year=24&month=5&aero_water=0&level=2&if_day=0&if_err=0&place_code=10&year_or_mo nth=0). Any further evaluation like done for the Iberian stations is not meaningful with only 4 months of data. Nevertheless, one gets a first impression of generally very low AOD around 0.1 to 0.2 (given as AOT_500 in green in the plots). Only late January and early March, there is a larger event reaching values up to 0.3 and 0.4, respectively.

Available meteo forecasts

AEMET does not provide any forecasts for South Africa routinely. Therefore, only ECMWF (European Centre for Medium-Range Weather Forecasts) based forecasts are available. Please note that from 2004 onwards ECMWF has applied a new aerosol climatology which changed the overall biases of GHI and DNI strongly (see section 2.5.1 for a more detailed discussion). Therefore, only the use of ECMWF forecasts after 2004 is recommended.

India

Available ground observations

For India only BSRN candidate stations exist as public available data. They are not providing data so far due to their candidate status.

Ground based irradiances

Such data does not exist for these stations.

Cloud conditions

This country is outside the MSG field of view and cannot be analysed.

DNI variability conditions

This country is outside the MSG field of view and cannot be analysed.

Available meteo forecasts

Aerosol conditions

As we have no ground observations we do not investigate this further. Quality control has been performed as described in Annex B. High quality measurements have been found in these stations. Practically all invalid DNI days are due to missing data. The following tables show, in each station, the number of days of each month for which no suitable DNI measurements are available.

Morocco

Available ground observations

Measured data are available in Oujda station from February 28 th 2012. Main problems are due to missing data and (but in a lesser measure) tracking errors.

Irradiances Ground observations

Satellite/model based observations (CAMS)

In order to ensure that CAMS data is sufficiently accurate at EnerMENA stations, we compare the mean histograms for the years and months with available ground observations. In the annual histograms, Missour, Erfoud, and to a lesser extent also Zagora, are underestimated in the CAMS satellite observations. Please note, that from other projects we know about an underestimation of DNI especially in the morning hours in winter months at these stations. This is due to a cloud retrieval error over cold, bright desert surfaces already being identified. Nevertheless, the current database still has this effect. This effect can clearly been seen in November to January in Missour, to a lesser extent in Erfoud, and from September to January in Zagora.

Second main differences are found in June to August. Please note that June to August are the months with the most intense dust loads in Morocco (Fig. 2.44 a and 2.44 b). CAMS is known to overestimate Saharan dust in Tamanrasset, Algeria, and one can assume that this also applies to Morocco. This results in an underestimation of DNI in the months June to August if being close to the Sahara. This can be seen in June to August in Missour, July and August in Erfoud, and July to September in Zagora. Oujda is not affected as it is in the North of Morocco, not affected so heavily by Saharan dust.

Concerning the location of Noor used below, we do not know definitely, but assume that both effects also exist in CAMS data for Noor. So, any relative comparisons of CAMS at e.g. Erfoud vs. CAMS at Noor are still meaningful despite the known difficulties of the current version of CAMS in this region. Both effects are under investigation in CAMS, but will only be improved in the next CAMS version by better cloud retrievals (APOLLO_NG -Next Generation is not showing this effect anymore) and more extensive data assimilation of aerosol observations (MODIS deep blue with new data being available now also over deserts) in the CAMS model.

Tan-Tan on the other hand is always overestimated by CAMS and shows very low DNI in general. Having discussed the difference between ground and satellite-based observations, we now continue to look at long-term satellite observations. They are available for a 10-years period and at any point of interest in Morocco.

In the following histograms we compare results versus the location of Noor (30.994°N, -6.863°E) in the vicinity of various CSP power plant projects. Hourly resolved, satellite-based CAMS DNI is used from 2006 to 2015. With respect to the monthly histograms, Zagora shows for all months a similar shift towards smaller DNI values, but the overall seasonal pattern is met. The shift is more pronounced in May to September than in the winter months. Erfoud has a DNI peak shifted to smaller DNI values especially in the months April to November. Without discussing the other stations in detail, we still would like to mention, that this consistent shift over the months cannot be found at Oujda, Missour, Erfoud, and Tan-Tan. For the other EnerMENA stations there are always some months which are very similar to Noor and others which differ very much. Having all months in mind, Zagora seems to be the best choice.

Cloud conditions

First of all, we compare the stations by the occurrence of clouds. We take the station Noor as reference and compare the others against it. In Fig. 2.51 we discriminate cloud-free from cloudy cases. Cloud-free, but snow on the ground cases are also given, as this evaluation tool is also used for more PV related studies in more snowy regions. Zagora has 80% of cloud-free cases, while Noor has about 75% and is closer to Erfoud in this parameter. Oujda, Tan-Tan and Missour have only about 60% cloud-free cases. In Fig. 2.52 the cloudy cases are further split in optically thick cases with low, medium and high cloud top levels (named 'low water', 'medium wat.', and 'high wat./mix.') and optically thin cirrus cases ('high thin ice'). With respect to DNI the separation in optically thick and thin cloud types is most relevant, for meteorologists the further split into vertical levels is of further interest to assess the situation at a site.

With respect to thin ice clouds, all stations with exception of Tan-Tan are close to the conditions at the Noor site. For optically thick clouds, Tan-Tan has very often low level clouds, while all other clouds are less than at Noor. Low level clouds are also more frequently found for Oujda and Missour, while Erfoud and Zagora have similar conditions as the Noor site. The split of all cases in scattered, broken/overcast, thin ice clouds, clear and snow conditions is done first for each station separately, but split into time of the day and month of the year (Fig. 2.53 to 2.58) and normalized on the number of daytime cases in each bin. Several seasonal and daily cycles can be seensome of them we discuss as examples before we compare the stations.

Please note that the first and last hours of the day are typically populated with less cases due to shorter days in winter time. But we prefer to give relative values here. If needed, absolute histograms can be provided as well.

Please note: During winter months, the underlying cloud database is detecting cloud situations during morning hours too frequently. The next version of the database will not have this data anymore, but it is not available for statistical evaluation so far. Therefore, the shown maximum cloud cover during morning hours from October to January is not reliable.

Noor has a dedicated seasonal cycle. Months April to September show much more cloudy cases during the afternoon than in the morning. During October to March this feature is not existing in a similar way.

For Oujda, the same behavior of afternoon clouds is found for July to September, but the effect is much smaller. Cloud cover is much higher in February to May compared to June to August, and during November to January the number of cloudy cases around noon is higher than in the afternoon. Please, take the note given above into account and do not worry about the early morning in winter peak.

Missour on the other hand has the afternoon peak in all months and not only during summer. Again, do not take the morning peak in winter months seriously. Erfoud has a more similar pattern over the months to Noor. During April to October an increase of cloud cases in the afternoon is observed, while in winter and spring months the dependence over time of the day is not visible. Especially the increase of cirrus cases in September to November is similar to the Noor situation.

The same applies for Zagora, which has an afternoon increase of cloud situations during April to October. Nevertheless, the dependency of the time of the day in June to September is not as strong as in Noor.

Finally, Tan-Tan is an example of a complete different seasonal pattern. Here we see a decrease of cloud situations over the time of the day. Thin cirrus clouds occur mostly only in winter months while there are frequent in Noor during August to May. And finally, the duration of cloud periods inside a day is evaluated. We separate in optically thick (DNI < 200 W/m2) and optically thin (DNI reduced) cases. Based on these numbers none of the stations is very close to Noor in all characteristics. The values being closest to the Noor site are marked in green, the station with the best agreement is Erfoud. Zagora shows good agreement with respect to the 90 and 98% percentiles, but not with respect to the short-term cloud duration.

Aerosol conditions

For Morocco, only AERONET stations Saada (31.626N, 8.156 W) and Oujda have sufficient long data records to provide an assessment. As seen on the Iberian Peninsula, the monthly distribution of strong AOD events between 0.5 and 1.0 and above 1.0 does not follow the general mean AOD seasonality. The Ministry of Energy of Chile together with the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) is operating a network of wind and solar radiation measuring stations in northern Chile. The objective of this measurement campaign is to better understand the characteristics of these resources in northern Chile. It is not intended in this measurement campaign to obtain quality data that meets the highest standards, but reliable, consistent and comparable data. In this network there are 10 stations, detailed in the following table and shown in the following map:

Available meteo forecasts

Measured solar irradiance data by PUC-FONDEF (not available online)

A research project financed through FONDEF (El Fondo de Fomento al Desarrollo Científico y Tecnológico, run within the Ministerio de educación, Chile) has deployed a network of 13 ground stations starting in January 2010. 4 of these stations are designed and operated under BSRN standards, being the remaining 9 of three different rotating shadow band radiometer configurations. This network has the objective of supplying data that satisfies international standards and criteria for design, operation and maintenance. The name, type, and start date of operation for the stations are shown in the following table (the map shows the approximate location of the ground stations of this network). Finally, it is interesting to note that even as the maximum distance between Crucero and Santiago is slightly less than 1600 km, in clear days the DNI has very similar profiles and maximum levels that are only slightly reduced by the effects of latitude and altitude. Thus, yearly total in Patache for DNI is closer to Santiago than to Crucero. This, even as Crucero and Patache are closely located, they show how the coastal and desert climates of northern Chile have very different solar resource availability.

Ground based irradiances

The FONDEF network has only recently be found by the PreFlexMS team after realizing the difficulties with the network operated by the Ministry of Energy of Chile. The complete ground data need to be ordered in the FONDEF network and will be analysed only later.

Cloud conditions

Due to missing satellite capabilities we have no information about typical cloud conditions in Chile as available for the other countries.

DNI variability conditions

Due to missing satellite capabilities we have no information about typical DNI variability conditions in Chile as available for the other countries.

Aerosol conditions

For Chile, only AERONET stations Arica (18.472S, 70.313 W) and Santiago_Beauchef (33.457S, 70.662 W) have sufficient long data records to provide an assessment. Different to the Iberian Peninsula, the monthly distribution of AOD in Chile shows less seasonality. Nevertheless, the distribution of strong AOD events between 0.5 and 1.0 and above 1.0 does not follow the general mean AOD pattern.

Available meteo forecasts

Irradiances

As we already have discussed that there are no representative forecasts available for the observation period, we do not perform any further evaluation of these locations.

Cloud conditions

As we already have discussed that there are no representative forecasts available for the observation period, we do not perform any further evaluation of these locations.

DNI variability conditions

As we already have discussed that there are no representative forecasts available for the observation period, we do not perform any further evaluation of these locations.

Aerosol conditions

As we already have discussed that there are no representative forecasts available for the observation period, we do not perform any further evaluation of these locations.

Available meteo forecasts during ground observation period

NWP availability

In the following table we summarize the available NWP forecasts for all countries. Which NWP approaches should be finally used?

• For tests in the demonstration phase (WP7) only one or two NWP approaches per station will be used. The dispatch optimization (WP3) and meteorological forecast assessment (WP4) need to make this recommendation as prerequisite for the virtual demo site testing in WP7.

Virtual demo site recommendation

Finally, we have to select a station in South Africa, Morocco, and Chile based on the analysis made in chapter 2.

South Africa

• Based on ground data quality control all stations are suitable.

• Overall, we suggest to use Erfoud, despite its larger deviation in the annual DNI histograms than Zagora. It is the 2 nd best choice with respect to DNI and has more similar cloud patterns. Also, it is part of the AROME/HARMONIE domain providing a 2 nd evaluation of this model suite.

Chile

• Based on ground data quality control we cannot yet decide which station is suitable.

• Based on GE priorities, a station close to the Atacama desert is of interest.

San Pedro de Atacama or Crucero are possibilities, but the ground data needs to be checked first.

Annex A-NWP forecasts

This table originates from D3.1. We add it here for an easier reading.

Annex B -Ground observation quality control

The final objective of this task is to dispose of high quality ground measured DNI series. Quality analysis procedures established by the BSRN for measured solar radiation data have been applied (Ohmura et al., 1998). Quality control methodology includes the following procedures: Control of the data recording time. For each of the days with available data, there is be a check of the correct timestamp corresponding to the measured data.

Visual inspection of solar radiation components (GHI, DNI and DHI). Before a comprehensive numerical check, the recorded values are visually analyzed day by day to classify them as valid or invalid. This checking allows detecting possible problems that are not detected by means of a numerical method.

Quality control tests of the BSRN. In order to evaluate the quality of solar irradiation data measured at station, these data will be checked according with the following recommendations of BSRN:

o "Physically Possible" test, intended to detect extremely large errors in the measurements and the large random errors introduced during data handling. This check is to ensure that solar irradiances measured are below the physical limits. Table 23 shows the physical limits pointed out in the BSRN recommendations.

Figure

  Figure 1.1 Location of Évora Badajoz, and PSA (source GoogleMaps)

  .1-2 shows the map location in relation to EMSP (Évora Molten Salt Platform) and Fig.1-3 is a picture of the actual station.

Figure 1 . 2

 12 Figure 1.2 Mitra's station location at the Évora site in the west of the EMSP site (image: Google)

Figure 1 . 4 Figure 1 . 5

 1415 Figure 1.4 Location of Badajoz station approx. 85 km north-east of Évora (GoogleMaps)

Figure 1 . 7

 17 Figure 1.7 View of the PSA DLR radiometric station (source Google Maps, DLR)

Figure 1 . 8 :

 18 Figure 1.8: Frequency histogram of hourly DNI for daytime ground measurements in 2015 at Évora station.

Figure 1 . 9

 19 Figure 1.9 Ground observation based daytime hourly DNI values from 2015 and 2016 at Évora station and split in monthly frequency histograms.

Figure 1 .

 1 Figure 1.10 Ground based (blue) and CAMS-based (orange) daytime hourly DNI values from 2015 at Évora station and split in monthly frequency histograms.

Figure 1 .

 1 Figure 1.11 Frequency histogram of hourly DNI for daytime satellite measurements from 2006 to 2015 at Évora station.The mean is given in red and all individual years in grey.

Fig 1 .

 1 Fig 1.13 and 1.14 present Badajoz ground observations as annual and monthly histograms derived from observations in 2015. Also in Badajoz a clear pattern over the months can be observed. We do not show multi-annual variability due to lack of observations.

Figure 1 .

 1 Figure 1.13 Frequency histogram of hourly DNI for daytime measurements in 2015 at Badajoz station.

Figure 1 .

 1 Figure 1.15 Ground (red) and CAMS-based (orange) hourly daytime DNI values from 2015 at Badajoz station.

Figure 1 .

 1 Figure 1.16 Ground (red) and CAMS-based (orange) daytime hourly DNI values from 2015 at Badajoz station (split in monthly frequency histograms)

Figure 1 .

 1 Figure 1.17 CAMS hourly daytime DNI values from 2006 to 2015 at Évora (red) and Badajoz (green) stations

Figure

  Figure 1.18 CAMS hourly daytime DNI values from 2006 to 2015 at Évora (red) and Badajoz (green) stations and split in monthly frequency histograms

Figure 1 .

 1 Figure 1.19 Ground observation daytime based hourly DNI values from 2006 to 2013 at PSA station.The mean is given in red and all individual years in grey.

Figure 1 .

 1 Figure 1.20 Ground observation based daytime hourly DNI values from 2006 to 2013 at PSA station and split in monthly frequency histograms. The mean is given in bold blue and all individual years in light blue.

  Fig 1.21 and 1.22).

Figure 1 .

 1 Figure 1.21 Ground based (red) and CAMS-based (orange) hourly daytime DNI values from 2006 to 2013 at PSA station. Individual years are given in thin lines (grey for ground observations, yellow for CAMS), while bold lines show the multiannual average.

Figure 1 .

 1 Figure 1.22 Ground based (dark blue) and CAMS-based (orange) hourly daytime DNI values from 2006 to 2013 at PSA station and split in monthly frequency histograms. The mean is given in bold and all individual years in thin lines.

Figure 1 .

 1 Figure 1.23 CAMS hourly daytime DNI values from 2006 to 2015 at Évora (red) and PSA (blue) stations. Multi-annual mean histograms are given in bold, while individual years are given in light colours.

Figure 1 .

 1 Figure 1.24 CAMS hourly daytime DNI values from 2006 to 2015 at Évora (red) and PSA (blue) stations and split in monthly frequency histograms.

Fig. 1 .

 1 25 provides an example image of MSG giving a visual impression how different cloud types look from space. Please note various colors -indicating low or high level clouds -and the thickness of various clouds.

Figure 1 .

 1 Figure 1.25 MSG example image (copyright EUMETSAT/DLR)

Figure 1 .

 1 Figure 1.26 Clouds as seen from the ground -overcast (left) and thin cirrus (right; copyright Karlsruher Wolkenatlas, B. Mühr)

  Fig. 1.28 shows typical examples of these situations.

Figure1. 28 :

 28 Figure1.28: Typical box dimensions of different cloud masks, source: S. Glas

Figure 1 .

 1 Figure 1.29 Cloud statistics at Évora stations: cloud type; frequency of occurrence in percentage of all daytime satellite images; split into cloud-free and cloudy cases -cloudy cases are separated in low, medium, high level cloud top height clouds (being optically thick) and optically thin ice cloud cases; based on 2005-2015.

Fig 1 .

 1 Fig 1.30 provides the cloud type statistics as function of the month. We discriminate between scattered, broken/overcast, thin ice and cloud-free cases. During July and August a minimum of cloud cases is observed, while in other months the number of scattered cloud cases is quite constant over the year.The number of broken/overcast cases is reduced in May to September and also the number of thin cirrus cases is smaller from June to September.

Fig 1 .

 1 Fig 1.31 provides the cloud type statistics as function of the time of the day. For scattered clouds there is no distinct diurnal cycle, but for the broken/overcast situations there is a dependency of the time of the day.

Figure 1 .

 1 Figure 1.30 Cloud statistics at Évora station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month; based on 2005-2015.

Figure 1 .

 1 Figure 1.31 Cloud statistics at Évora station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into hour of the day; based on 2005-2015.

Fig 1 .

 1 Fig 1.32 provides a split of Fig. 1.30 and 1.31 into months. The patterns over time of the day are highly variable. Some months show a dedicated diurnal cycle either in one of the cloud area type class or the overall cloud cases.

Figure 1 .

 1 Figure 1.32 Cloud statistics at Évora station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hour of the day; based on 2005-2015.

Figure 1 .

 1 Figure 1.33 Cloud statistics at Évora stations vs. Badajoz and PSA: cloud type; frequency of occurrence in percentage of all daytime satellite images; split into cloud-free and cloudy cases -cloudy cases are separated in low, medium, high cloud top height clouds being optically thick and optically thin ice cloud cases; based on 2005-2015.

Figure 1 .

 1 Figure 1.34 Cloud statistics at Évora station vs. Badajoz and PSA: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month; based on 2005-2015.

Figure1. 36 :

 36 Figure1.36: Arbitrarily chosen examples of the variability cloud classes 1 to 8. Hours being classified in one of the classes are marked by a red box. For some classes, the red box extends the range of a single hour and illustrates several hours being included in the reference database. Minute values (yellow), 10 min moving averages (black) and CAMS clear sky values (thin) are given.

  Fig.1.37 gives e.g. the distribution of satellite-based variability parameters as being typical for variability class 3. Performing the same analysis for all variability classes defines typical APOLLO parameter patterns for each class. Applying a distance criterion, the best fitting variability class is selected for each individual satellite pixel in a time series. The sum of all distances between the actual cloud parameter value for a satellite image and the median of the n-th class is calculated. For all 8 classes, the minimum distance is chosen and the respective class is allocated to the satellite image time instant. Finally, we obtain every 15 minutes, so for every satellite pixel, an estimate of the variability class. The variability class is a characteristic of a 1-min resolved time series of 1-hour duration around the time of the satellite observation. The update of this classification can be obtained every 15 minutes.

Figure1. 37 :

 37 Figure1.37: Box-Whisker plot for the distribution of normalized satellite-based cloud parameters as found for class 3 related cases in the reference data base

Figure1. 38 :

 38 Figure1.38: DNI variability class statistics of Évora, Badajoz and PSA based on 2005-2015. The best fitting class is shown.

Figure 1 .

 1 Figure 1.39: AERONET based mean aerosol optical depth (AOD) as found at stations Évora, Badajoz and PSA. Average over 2013-2015 (upper left) and the individual years.

Figure 1 .

 1 Figure 1.40: Extinction of direct and global irradiances due to dust aerosols for various aerosol optical depths (from Schroedter-Homscheidt et al., 2010)

Figure 1 .

 1 Figure 1.41: AERONET based mean aerosol optical depth (AOD) as found at stations Évora, Badajoz and PSA. Only AOD values between 0.5 and 1.0 (left) and AOD greater 1.0 (right) are evaluated.

Figure 2 . 1

 21 Figure 2.1 Location of South African stations (yellow = SAURAN network, blue = BSRN) Based on GoogleMaps as background.

  Measured data are available in SUN station from May 25 th 2010. Also, there are not available data from December 9 th to February 16 th 2012. Most invalid data are due to missing data and (but in a lesser measure) tracking errors.

  Measured data are available in UNV station from April 22 th 2015. Few invalid DNI days found are caused by missing data.

Figure 2 . 2 :

 22 Figure 2.2: Ground-based frequency distributions of hourly daytime DNI at SAURAN stations RVD, VAN, SUT, GRT, UFS, UPR -data periods are as available, years vary.

Figure 2 . 3 :

 23 Figure 2.3: Ground-based frequency distributions of hourly daytime DNI at SAURAN station RVD (blue) for all calendar months.

Figure 2 . 4 :

 24 Figure 2.4: Ground-based frequency distributions of hourly daytime DNI at SAURAN station VAN (blue) for all calendar months.

Figure 2 . 5 :

 25 Figure 2.5: Ground-based frequency distributions of hourly daytime DNI at SAURAN station UFS (blue) for all calendar months.

Figure 2

 2 Figure 2.6: Ground-based DNI frequency distributions of hourly daytime DNI at SAURAN station UPR (blue) for all calendar months.

Figure 2 . 7

 27 Figure 2.7 Ground based (red) and CAMS-based (orange) hourly daytime DNI values at SAURAN stations.

Figure 2 . 8

 28 Figure 2.8 Ground based (blue) and CAMS-based (orange) hourly daytime DNI values at SAURAN station RVD and split in monthly plots. In case of several years of available data, all years are plotted separately.

Figure 2 . 9

 29 Figure 2.9 Ground based (blue) and CAMS-based (orange) hourly daytime DNI values at SAURAN station VAN and split in monthly plots. In case of several years of available data, all years are plotted separately.

Figure 2 .

 2 Figure 2.10 Ground based (blue) and CAMS-based (orange) hourly daytime DNI values at SAURAN station UFS and split in monthly plots. In case of several years of available data, all years are plotted separately.

Figure 2 .

 2 Figure 2.11 Ground based (blue) and CAMS-based (orange) hourly daytime DNI values at SAURAN station UPR and split in monthly plots. In case of several years of available data, all years are plotted separately.

  compare results versus a location near of Upington (-28.157°N, 21.354°E) in the vicinity of various CSP power plant projects. Hourly resolved, satellite-based CAMS DNI is used from 2006 to 2015.

Figure 2 .

 2 Figure 2.12: CAMS based DNI, frequency distributions of hourly daytime DNI at SAURAN stations RVD, VAN, SUT, GRT, UFS, UPR together with the Upington location; 2006-2015.

Figure 2 .

 2 Figure 2.13: CAMS based DNI, frequency distributions of hourly daytime DNI at SAURAN station RVD (blue) and Upington (red) for all calendar months, based on data from 2006-2015.

Figure 2 .

 2 Figure 2.14: CAMS based DNI, frequency distributions of hourly daytime DNI at SAURAN station VAN (blue) and Upington (red) for all calendar months, based on data from 2006-2015.

Figure 2 .

 2 Figure 2.17 Cloud statistics of Upington and 5 SAURAN stations: cloud mask; frequency of occurrence in percentage of all daytime satellite images; split into cloud-free, cloud-free but snow on surface, and cloudy cases; based on 2004-2015.

Figure 2 .

 2 Figure 2.18 Cloud statistics of Upington and 5 SAURAN stations: cloud type; frequency of occurrence in percentage of all daytime satellite images; split into cloud-free and cloudy cases -cloudy cases are separated in low, medium, high cloud top height clouds being optically thick and optically thin ice cloud cases; based on 2004-2015.

Figure 2 .

 2 Figure 2.19 Cloud statistics Upington station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015.

Figure 2 .

 2 Figure 2.20 Cloud statistics SA-RVD station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015.

Figure 2 .

 2 Figure 2.21 Cloud statistics SA-VAN station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015.

Figure 2 .

 2 Figure 2.22 Cloud statistics SA-SUT station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015.

Figure 2 .

 2 Figure 2.23 Cloud statistics SA-GRT station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015.

Figure 2 .

 2 Figure 2.24 Cloud statistics SA-UFS station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015.

Figure 2 .

 2 Figure 2.25 Cloud statistics SA-UPR station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015.

Figure 2 .

 2 Figure 2.26 Cloud statistics of Upington and 6 SAURAN stations: cloud area type; relative frequency of occurrence over months; split into scattered, broken/overcast, thin ice, clear and snow cases; based on 2004-2015.

Figure 2 .

 2 Figure 2.27 Cloud statistics of Upington and 6 SAURAN stations: cloud area type; relative frequency of occurrence over hours of the day; split into scattered, broken/overcast, thin ice, clear and snow cases; based on 2004-2015.

Figure 2 .

 2 Figure 2.28 DNI variability class statistics of Upington and 6 SAURAN stations based on 2004-2015.The best fitting class is shown.

Figure

  Figure 2.29 DNI variability class statistics of Upington and 6 SAURAN stations based on 2004-2015. The 2 nd best fitting class is shown.

Figure 2 .

 2 Figure 2.30 AERONET observations at Upington (source AERONET website)

Figure 2 .

 2 Figure 2.31 Location of Indian stations (light blue = BSRN candidate stations, no data available yet) Based on GoogleMaps as background.

Figure 2 .

 2 Figure 2.32 Location of stations in Morocco (red = EnerMENA stations) Based on GoogleMaps as background.

Fig. 2 .

 2 Fig. 2.33 provides the annual histograms of hourly daytime DNI at the EnerMENA stations. If more than a single complete year is available, the red line represents the mean and the other years are given as grey lines. Fig. 2.34 to Fig 2.37 present the monthly histograms of hourly daytime DNI observations at the stations Oujda, Missour, Erfoud, Tan-Tan, and Zagora.

Figure 2 .

 2 Figure 2.33: Ground-based DNI, frequency distributions of hourly DNI at EnerMENA stations Oujda, Missour, Erfoud, Tan-Tan and Zagora

Figure 2 .

 2 Figure 2.35: Ground-based DNI, frequency distributions of hourly DNI at EnerMENA station Missour (blue) for all calendar months.

Figure 2 .

 2 Figure 2.36: Ground-based DNI, frequency distributions of hourly DNI at EnerMENA station Erfoud (blue) for all calendar months.

Figure 2 .

 2 Figure 2.37: Ground-based DNI, frequency distributions of hourly DNI at EnerMENA station Tan-Tan (blue) for all calendar months.

Figure 2 .

 2 Figure 2.39 Ground based (red) and CAMS-based (orange) hourly DNI values at EnerMENA stations Oujda, Missour, Erfoud, Tan-Tan and Zagora. The placement of the plots reflects the geographical position relative to each other.

Figure 2 .

 2 Figure 2.40 Ground based (blue) and CAMS-based (orange) hourly DNI values at EnerMENA station Oujda and split in monthly frequency histograms.

Figure 2 .

 2 Figure 2.41 Ground based (blue) and CAMS-based (orange) hourly DNI values at EnerMENA station Missour and split in monthly frequency histograms.

Figure 2 .

 2 Figure 2.42 Ground based (blue) and CAMS-based (orange)hourly DNI values at EnerMENA station Erfoud and split in monthly frequency histograms.

Figure 2 .

 2 Figure 2.43 Ground based (blue) and CAMS-based (orange) hourly DNI values at EnerMENA station Tan-Tan and split in monthly frequency histograms.

Figure 2 .

 2 Figure 2.44 Ground based (blue) and CAMS-based (orange) hourly DNI values at EnerMENA station Zagora and split in monthly frequency histograms.

Figure 2 .

 2 Figure 2.44a: Spatial distribution of more than 30% DNI extinction due to dust AOD (Schroedter-Homscheidt et al., 2016b)

Figure 2 .

 2 Figure 2.45: CAMS based DNI, frequency distributions of hourly DNI at EnerMENA stations Oujda, Missour, Erfoud, Tan-Tan and Zagora together with the Noor location for 2006-2015. The placement of the plots reflects the geographical position relative to each other.

Figure 2 .

 2 Figure 2.46: CAMS based DNI, frequency distributions of hourly DNI at EnerMENA station Oujda (blue) and Noor (red) for all calendar months, based on data from 2006-2015

Figure 2 .

 2 Figure 2.51 Cloud statistics of Noor and 5 EnerMENA stations: cloud mask; frequency of occurrence in percentage of all daytime satellite images; split into cloud-free, cloud-free but snow on surface, and cloudy cases; based on 2004-2015.

Figure 2 .

 2 Figure 2.52 Cloud statistics of Noor and 5 EnerMENA stations: cloud type; frequency of occurrence in percentage of all daytime satellite images; split into cloud-free and cloudy cases -cloudy cases are separated in low, medium, high cloud top height clouds being optically thick and optically thin ice cloud cases; based on 2004-2015.

Figure 2 .

 2 Figure 2.53 Cloud statistics Noor station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015. PLEASE NOTE COMMENT ABOUT MORNING HOURS IN WINTER MONTHS MADE ABOVE.

Figure 2 .

 2 Figure 2.54 Cloud statistics EnerMENA Oujda station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015. PLEASE NOTE COMMENT ABOUT MORNING HOURS IN WINTER MONTHS MADE ABOVE.

Figure 2 .

 2 Figure 2.55 Cloud statistics EnerMENA Missour station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015. PLEASE NOTE COMMENT ABOUT MORNING HOURS IN WINTER MONTHS MADE ABOVE.

Figure 2 .

 2 Figure 2.56 Cloud statistics EnerMENA Erfoud station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015. PLEASE NOTE COMMENT ABOUT MORNING HOURS IN WINTER MONTHS MADE ABOVE.

Figure 2 .

 2 Figure 2.57 Cloud statistics EnerMENA Zagora station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015. PLEASE NOTE COMMENT ABOUT MORNING HOURS IN WINTER MONTHS MADE ABOVE.

Figure 2 .

 2 Figure 2.58 Cloud statistics EnerMENA Tan Tan station: cloud area type (red = scattered water/mixed phase; blue = broken/overcast water/mixed phase; yellow = thin ice phase; green = clear; dark green = snow); frequency of occurrence in percentage of all daytime satellite images; split into calendar month and hours of the day; based on 2004-2015. PLEASE NOTE COMMENT ABOUT MORNING HOURS IN WINTER MONTHS MADE ABOVE.

Figure 2 .

 2 Figure 2.59 Cloud statistics of Noor and 5 EnerMENA stations: cloud area type; relative frequency of occurrence over months; split into scattered, broken/overcast, thin ice, clear and snow cases; based on 2004-2015.

Figure 2 .

 2 Figure 2.60 Cloud statistics of Noor and 5 EnerMENA stations: cloud area type; relative frequency of occurrence over hours of the day; split into scattered, broken/overcast, thin ice, clear and snow cases; based on 2004-2015.

Figure 2 .

 2 Figure 2.61 DNI variability class statistics of Noor and 5 EnerMENA stations based on 2004-2015.

Figure 2 .

 2 Figure 2.62 DNI variability class statistics of Noor and 5 EnerMENA stations based on 2004-2015. The 2 nd best fitting class is shown.

Fig 2 .

 2 Fig 2.63: AERONET based mean aerosol optical depth (AOD) as found at stations Saada and Oujda. Average over 2013-2015 (upper left) and the individual years. In the lowest row, only AOD values between 0.5 and 1.0 (left) and AOD greater 1.0 (right) are evaluated.

Figure 2 .

 2 Figure 2.65 Location of GIZ stations in Chile. Based on GoogleMaps as background.

Figure 2 .

 2 Figure 2.66 Location of GIZ stations in Chile. Based on GoogleMaps as background.

  Some remarks on the solar resource of Chile are drawn below (Fig.2.68):• Crucero, with a yearly DNI measured in 2012 of 3389 kW h/m2 is one of the top solar resource worldwide sites. Several regions of northern Chile share these high DNI values. In Crucero, there are a large number of days with high DNI during most of the day, with only a few low-DNI days occurring during altiplanic winter and seasonal winter. • Alto Patache, is located on the edge of the region that rises from sea level up to 1000 m in less than 2 km from the coastal line (note that even if it is close to Crucero, is not located in the intermediate depression of Chile). Its yearly DNI is 1908 kW h/m2. Its climate is very similar to that shared by the major cities in northern Chile and thus useful to gain insight on the potential for solar energy supply to the residential and commercial sectors that concentrate most of the population. Northern coastal climate is characterized by clear skies from December to April, with presence of cloud cover which led to variability with decreased DNI the rest of the year. • The solar resource variability in Santiago and Curico is higher than in Crucero, as they belong to a Mediterranean climate (characterized by occurrences of cloudy and clear days throughout the year with strong summer/winter seasonality). In winter, cloud covers are more common, days are shorter, and radiation levels are greatly reduced. The yearly totals are 1911 kW h/m2 for DNI in Santiago, and 1952 kW h/m2 for DNI in Curico.

.

Figure 2 .

 2 Figure 2.68 DNI daily totals from January to December 2012 at four different locations (Escobar et al., 2015)

Fig 2 .

 2 Fig 2.76: AERONET based mean aerosol optical depth (AOD) as found at stations Arica and Santiago Beauchef. Average over 2013-2015 (upper left) and the individual years. In the lowest row, only AOD values between 0.5 and 1.0 (left) and AOD greater 1.0 (right) are evaluated.

Figure 2 .

 2 Figure 2.77. Day ahead hourly DNI forecast verification -annual absolute and relative bias of ECMWF/IFS (line) together with two-day persistence (dotted). This is Fig. 4 from Schroedter-Homscheidt et al. (2016a).

Figure 2 .

 2 Figure 2.78. Day ahead hourly DNI forecast verification -annual absolute and relative RMSE of ECMWF/IFS (line) together with two-day persistence (dotted). This is Fig. 5 from Schroedter-Homscheidt et al. (2016a).

  Stations RVD, VAN, SUT, GRT, UFS, and UPR have sufficiently long data records and are located in regions of interest for CSP. • Comparing long-term satellite observations versus Upington, SUT and GRT are excluded and RVD and VAN have the highest similarity in monthly patterns. • Stations RVD and VAN show a good agreement of CAMS satellite-based with the ground-based DNI observations. • With respect to cloud/cloud-free statistics, RVD and VAN are most similar to Upington. • With respect to the occurrence of optically thin cirrus clouds with highly variable and non-zero DNI values, VAN is similar to Upington. • Both RVD and VAN show a very different cloud pattern over time of the year and over time of the day than Upington. With respect to these cloud patterns, the station UFS is more similar, but UFS shows generally higher DNI values than Upington and at UFS CAMS strongly overestimates DNI compared to the ground observations. • With respect to the duration of optically thick clouds, the station RVD and UFS are more similar to Upington than the others. • With respect to the duration of optically thin clouds, the station VAN and UFS are more similar to Upington than the others. • With respect to DNI variability in the 1-min range, the stations RVD, VAN and SUT show the smallest amount of variable conditions, but still more such conditions than in Upington. • We do not have any spatial observations of aerosol conditions. Finally, we reduced the number of stations under evaluation to two: RVD and VAN. Setting priority on the duration and availability of ground observations and the availability of consistent CAMS long-term observations we suggest using station VAN (Vanrhynsdorp) further. This allows validating forecasts for 2013-2016 against ground observations and for 2004 to 2016 against satellite observations. Morocco • Based on ground data quality control all stations are suitable. • Comparing satellite-based CAMS to ground based DNI, the best fitting station is Oujda. Missour, Erfoud and Zagora are affected by both missing aerosols and a winter morning cloud retrieval error in the current CAMS version. • Annual DNI histograms are all different from Noor, but Zagora is closest compared to other EnerMENA stations. Erfoud is second closest, but has less DNI values above 800 W/m² and more between 600 and 800 W/m². We know that the closer to the Saharan desert, the more dust aerosols are overestimated in CAMS and the less DNI values above 800 W/m² will be in the CAMS dataset. • Monthly DNI histograms from Zagora are shifted to smaller DNI values, but the overall pattern is similar while the other EnerMENA stations have a larger variability in the differences to the Noor histograms. • With respect to cloud/cloud-free ratio, Erfoud is more similar and Zagora is next. • With respect to thin cirrus clouds, all stations besides Tan-Tan are suitable. • With respect to optically thick clouds, Erfoud and Zagora have similar conditions. • With respect to seasonal and diurnal cycles, Erfoud and Zagora are similar. • With respect to cloud duration, Erfoud is closest to Noor and Zagora the 2 nd best choice. • DNI variability in Erfoud is closest to Noor. • Zagora is not included in the AROME/HARMONIE region.

. References 5. Annex A-NWP forecasts 6. Annex B -Ground observation quality control

	2.3.5 Aerosol conditions 2.3.6 Available meteo forecasts 2.4 Chile 2.4.1 Available ground observations 2.4.2 Ground based irradiances 2.4.3 Cloud conditions 2.4.4 DNI variability conditions 2.4.5 Aerosol conditions 2.4.6 Available meteo forecasts 2.5 Saudi Arabia 2.5.1 Available ground observations 2.5.2 Irradiances 2.5.3 Cloud conditions 2.5.4 DNI variability conditions 2.5.5 Aerosol conditions 2.5.6 Available meteo forecasts during ground observation period 3. Suggested validation sites 3.1 Rationale 3.2 Availability of observations 3.3 NWP availability 3.4 Research questions for WP3 and WP4 3.5 Virtual demo site recommendation ensemble mean EMPS Evora Molten Salt Platform EnerMENA project acronym ETKF Ensemble Transform Kalman Filter FONDEF El Fondo de Fomento al Desarrollo Científico y Tecnológico, Chile) GHI global horizontal irradiation GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit GRT Graaff-Reinet (SAURAN station) gSREPS AEMET New Mesoscale Short Range Ensemble Prediction System Heliosat-4 surface downwelling solar irradiance model by MINES/DLR HIRLAM HIgh Resolution Limited Area Model HITRAN High Resolution Transmission Molecular Absorption Database HRES high-resolution forecast IC initial condition IDEO Inca de Oro (Station in Chile) IR infrared JMA-GSM LAF lagged average forecast LAM limited-area models PILOT observations from pilot sounding PPA Power purchase agreements PSA Plataforma Solar de Almeria QS quantile score R&D research and development REFIT Renewable Energy Feed-in Tariff REIPPP Renewable Energy Independent Power Producer Procurement programme relMAD relative mean absolute difference relRMSD relative root mean square difference relSD relative standard deviation of the residuals REST2 a radiative transfer model RMSD root mean square difference RMSE root mean square error ROC relative operating characteristic curve RPS Ranked Probability Score RPSS ranked probability skill score RR Ramp Rate RRTM Rapid Radiation Transfer Model RSI rotating shadowband irradiometers RUC relative user characteristic RUC1 hourly rapid updated circle RVD GIZ Richtersveld (SAURAN station) SAURAN Southern African Universities Radiometric Network SHIP automatic ship observations VAN GIZ Vanrhynsdorp (SAURAN station) VRY GIZ Vryheid (SAURAN station) WCRP World Climate Research Program 4em EPS ensemble prediction system QC quality control WMO World Meteorological Organisation
	LBC SLAF	lateral boundary conditions scaled algged averaged forecast technique
	LR SPPT	logistic regression Stochastically Perturbed Parameterization Tendencies
	LTEKF STA	Local Transform Ensemble Kalman Filter Mangosuthu Univ. of Technology STARlab (SAURAN station)
	LW STDV	long wave standard deviation
	MAD SUN	mean absolute difference Stellenbosch University (SAURAN station)
	MAE SUT	mean absolute error Eskom Sutherland (SAURAN station)
	McRad SW	radiative transfer model used in ECMWF/IFS shortwave
	MENA SYNOP	Mediterranean Europe and Northern Africa regions synoptical observations
	Met Office SZA	United Kingdom Meteorological Office solar zenith angle
	MSG TEMP	Meteosat Second Generation Temp measurement observations
	NCEP TKE	National Center for Environmental Prediction turbulent kinetic energy
	NCEP-GFS U95%	the Global Forecast Model from NCEP uncertainty at 95%
	NEMS UFS	NOAA Enviromental Modeling System GIZ University of Free State (SAURAN station)
	NMMB UNV	Non-hydrostatic Multi-Scale Model USAid Venda (SAURAN station)
	NMU UNZ	Nelson Mandela Metropolitan University (SAURAN station) University of Zululand (SAURAN station)
	NOAA UPR	National Oceanic and Atmospheric Adminstration GIZ University of Pretoria (SAURAN station)
	NWP UV	numerical weather prediction ultraviolet
	PDF VAN	Probavility Density Function Vanrhynsdorp (Station in South Africa)

Global Spectral Model from the Japan Meteorological Agency JNNSM Jawaharlal Nehru National Solar Mission kcB clear sky beam index KSI Kolgorov-Smirnov test integral KZH University of KwaZulu-Natal Howard College (SAURAN station) KZW University of KwaZulu-Natal Westville (SAURAN station)

Table 1 :

 1 Station information Évora

	Station	Latitude	Longitude	Elevation	Pyrheliometer	Period	Temporal
				(m)			resolution
	Évora	38.531 N	8.011 W	220	KIPP-ZONEN
					CHP1

Table 2 :

 2 Station information BadajozThe following table show the number of invalid DNI days in Badajoz both caused by missing data and incorrectly measured data (see Annex B for QC procedures). Few missing days are found, as well as punctual tracking errors. Table2a: Station information Badajoz -data availability

	Station		Latitude		Longitude	Elevation		Pyrheliometer		Period	Temporal
							(m)
													resolution
	Badajoz		38.883 N		7.017 W		190		KIPP-ZONEN		From	1-min
									CH-1			2008 to
												date
							Badajoz (AEMET)
	Year	Jan	Feb	Mar	Apr	May Jun Jul Aug	Sep	Oct	Nov	Dec Annual
	2015	2	1	1	1	1	0	1	0	0	1	0	0	8

There is a Cimel sun photometer nearby (approximately 300m away), managed by Extremadura University. AERONET observations (Level 1.5, automatically cloud-screened) are available from July 2012 to May 2016. It is part of the AERONET network. AOD (aerosol optical depth) measurements from this instrument will be used in the project as well.

1.

1.3 Station details -PSA

DLR's radiometric station (Fig 1.7) at Plataforma Solar de Almería (PSA) collects data since February 2001.

Table 3 :

 3 Station information PSA

	Station	Latitude	Longitude Elevation	Pyrheliometer	Period	Temporal
				(m)			resolution
	PSA_DLR 37.09081 N 2.35808 W	486	Kipp&Zonen	Feb 2001	1-min
						onwards

The following table show the number of invalid DNI days at the PSA_DLR station both caused by missing data and incorrectly measured data (see Annex B for QC procedures). The following issues were found: 2005: There are no data on January, February and December 14 th -31 st 2006: July: 7 days without data. 2007: No data on December 19 th -31 st 2013: Tracking system failures 2014: There are no data from April 3 rd There is also an AERONET station available (named 'Tabernas_PSA-DLR'), operated by DLR. Level 1.5 (automatically cloud-screened) observations are available from March 2011 to April 2014.

Table 4 :

 4 Ground observation based variability class characterisation by k cDNI, the number of direction changes in DNI within the hour, and the number of cases in the reference database.

	Class	name	mean k cDNI	no. of DCH (#DCH)	no. of cases
				in DNI
	1	very high DNI, low #DCH	0.99	0-2	61
	2	high DNI, low #DCH	0.95	0-7, mean 1	44
	3	high DNI, medium #DCH	0.92	0-18, mean 8	27
	4	high DNI, high #DCH	0.71	6-33, mean 15	37
	5	medium DNI and #DCH	0.66	0-13, mean 6	66
	6	medium DNI, high #DCH	0.41	6-22, mean 14	41
	7	low DNI, medium #DCH	0.18	0-20, mean 7	64
	8	low DNI, low #DCH	0.0	0-2	67

Table 5 :

 5 Station information South AfricaMeasured data are available in KZH station from April 9 th 2013. In the period between 22 and 29 June 2013 there is an error in time scale.Measured data are available in KZW station from April 10 th 2013. Most invalid data are missing days.

	Name	Latitude	Longitude	Elevat-	Period	Pyrhelio-		Temporal	Net-	Comments
		(+N)	(+E)	ion (m)		meter		resolution	work
	De Aar	-30,67	23,99	1287	2000-07-01	Kipp	&	1-min	BSRN	Data available in
					to 2005-01-	Zonen CH1			BSRN website
					31
	GRT	-32,49	24,59	660	2013-11-01	Kipp	&	1-min	SAURAN Data available in
					to 2016-01-	Zonen				SAURAN website
					31	CHP1
	KZH	-29,87	30,98	150	2013-02-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP1
	KZW	-29,82	30,94	200	2013-04-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP1
	NMU	-34,01	25,67	35	2014-02-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP1
	RVD	-28,56	16,76	141	2014-04-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP1
	STA	-29,97	30,91	95	2014-08-01	Eppley NIP	1-min	SAURAN Data available in
					to date					SAURAN website
	SUN	-33,93	18,87	119	2010-03-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP1
	SUT	-32,39	20,66	1450	2014-11-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP1
	UFS	-29,11	26,19	1491	2013-10-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP2
	UNV	-23,13	30,42	628	2015-04-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP3
	UNZ	-28,85	31,85	90	2016-02-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP4
	UPR	-25,75	28,23	1410	2013-09-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP5
	VAN	-31,62	18,74	130	2013-10-01	Kipp	&	1-min	SAURAN Data available in
					to date	Zonen				SAURAN website
						CHP6
	VRY	-27,83	30,50	1277	2013-09-01	Kipp	&	1-min	SAURAN Data available in
					to 2015-10-	Zonen				SAURAN website
					31	CHP7

Quality control has been performed as described in Annex B. In general, quality measurements have been found in SAURAN stations. The main problems found are due to missing data and (but in a lesser measure) tracking errors. The following tables show, in each station, the number of days of each month for which no suitable DNI measurements are available. Table 5b: Station information South Africa -availability KZH University of KwaZulu-Natal Howard College -KZH (SAURAN)

Table 5c :

 5c Station information South Africa -availability KZW

				University of KwaZulu-Natal Westville -KZW (SAURAN)
	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2013	-	-	-	9	0	7	0	0	3	0	5	0	-
	2014	0	0	0	0	1	2	5	7	2	5	9	14	45
	2015	0	0	1	0	12	30	31	31	30	23	3	0	161
	2016	4	0	7	3	21	-	-	-	-	-	-	-	-
	Measured data are available in NMU station from February 22 th 2014. Main problems are due to missing
	data and (but in a lesser measure) tracking errors. From October to December 2014 they are found at

noon DNI records slightly low (notwithstanding, these data have been considered as valid if it is not pronounced and if no tracking errors are appreciated).

Table 5d :

 5d Station information South Africa -availability NMU

Nelson Mandela Metropolitan University -NMU (SAURAN)

  Measured data are available in RVD station from April 24 th 2014. Most invalid data are tracking errors.

	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2014	-	-	8	0	0	0	1	0	0	14	0	10	-
	2015	6	8	2	3	0	0	0	11	0	0	16	31	77
	2016 19	3	8	5	-	-	-	-	-	-	-	-	-

Table 5e: Station information South Africa -availability RVD GIZ Richtersveld -RVD (SAURAN) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual 2014

Table 5f

 5f

: Station information South Africa -availability STA Mangosuthu Univ. of Technology STARlab -STA (SAURAN) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Table 5g :

 5g Station information South Africa -availability SUN Measured data are available in SUT station from November 26 th 2014. Few invalid DNI days found are caused by missing data or tracking errors.

					Stellenbosch University -SUN (SAURAN)
	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2010	-	-	-	-	-	22	0	0	0	0	0	22	-
	2011	-	-	-	-	-	-	-	-	-	-	-	-	-
	2012 31	15	0	0	0	12	31	17	6	5	1	31	149
	2013 10	0	0	11	0	5	0	0	3	2	0	2	33
	2014	0	5	2	1	0	8	0	0	2	0	0	28	46
	2015	9	9	0	8	1	2	0	0	3	1	2	0	35
	2016	0	0	0	0	-	-	-	-	-	-	-	-	-

Table 5h :

 5h Station information South Africa -availability SUT

	Eskom Sutherland -SUT (SAURAN)
	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2014

Table 5i :

 5i Station information South Africa -availability UFS

GIZ University of Free State -UFS (SAURAN)

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual

Table 5j :

 5j Station information South Africa -availability UNV

USAid Venda -UNV (SAURAN)

  Measured data are available in UNZ station only from February 11 st 2016 to May 22 th 2016. In few days at the beginning of this period there are tracking errors.Measured data are available in VAN station from November 6 th 2013. Few invalid DNI days found are caused mainly by missing data.

	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2015	-	-	-	-	0	2	0	8	1	1	0	0	-
	2016	0	0	0	0	-	-	-	-	-	-	-	-	-
	Table 5k: Station information South Africa -availability UNZ
						University of Zululand -UNZ (SAURAN)
	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2016	-	17	0	0	-	-	-	-	-	-	-	-	-

Table 5l :

 5l Station information South Africa -availabilityVAN

	GIZ Vanrhynsdorp -VAN (SAURAN)
	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2013

Table 5m :

 5m Station information South Africa -availability VRY

GIZ Vryheid -VRY (SAURAN)

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual 2013

Table 5n :

 5n Station information South Africa -availability UPR

GIZ University of Pretoria -UPR (SAURAN)

	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2013

Table 6 :

 6 Length of cloud periods occurring in a day -optically thick clouds with DNI close to zero

	Station	%	of	cloud<	%	of	cloudy	90% of cloud	98% of cloud
		periods <= 15 min	periods <=1 hour	periods are <= X	periods are <= X
		duration		duration		hours	hours
	Upington	34.92			66.80			4.0	9.5
	RVD	31.97			65.42			3.5	8.0
	VAN	29.47			58.51			4.75	9.5
	SUT	27.48			55.70			4.25	9.0
	GRT	29.73			57.97			5.0	10.5
	UFS	32.11			63.43			4.5	10.75
	UPR	29.99			58.56			5.75	11.75

Table 7 :

 7 Length of cloud periods occurring in a day -optically thin clouds with variable DNI

	Station	%	of	cloud<	%	of	cloudy	90% of cloud	98% of cloud
		periods <= 15 min	periods <=1 hour	periods are <= X	periods are <= X
		duration		duration		hours	hours
	Upington	37.22			74.60			2.25	5.5
	RVD	28.54			67.37			3.0	6.5
	VAN	33.58			71.77			2.75	6.25
	SUT	33.74			70.82			3.0	7.0
	GRT	32.35			66.93			3.5	7.75
	UFS	36.83			71.67			2.75	7.25
	UPR	34.88			69.64			3.0	7.25
		2.1.4 DNI variability conditions

DNI variability classes are derived based on MSG cloud parameters as described in section 1.3. Histograms of their frequency in 2004-2015 cases are given

(Fig 2.28)

. Upington is characterized by either no or small variation and cloud-free sky (class 1) or by no variation and cloudy sky (class 8). Class 2 is represented but significantly less than class 1 and 8. Class 2 is characterized by high DNI and a small amplitude variability. Very variable conditions (classes 4 and 6) and medium variable conditions (classes 3, 5, and 7) are also very seldom. This is confirmed by the 2 nd best fitting class (Fig.

2

.29) where classes 2 and 7 are most frequent. None of the SAURAN stations has similar conditions. They all have a significant higher amount of variable cases in classes 2 to 7. Stations VAN and SUT have nearly the same frequency of class 1, but show only half of the occurrence of class 8, while variable classes occur more frequently. Station RVD on the other hand has a very high (doubled compared to Upington) occurrence of class 8, but only approx. 20% of the class 1 cases compared to Upington.

Table 8 :

 8 Available NWP datasets

	Available	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
	deterministic	IFS	HARMONIE	IFS + post-	HARMONIE	HARMONIE
	models		(RUC3	processing	+ post-	RUC1
			from 2016		processing
			onwards)
	Available	ECMWF EPS	gSREPS	ECMWF EPS	gSREPS
	probabilistic models			+ post-processing	+ post-processing

Legend: Green = available, red = not available, orange = available if effort or upcoming verification results allows

Table 9 :

 9 Station information India

	Name	Lati-	Longi-	Elevatio	Period Pyrhelio-	Net-	Comments
		tude	tude	n (m)		meter	work
		(+N)	(+E)
	Gandhi-	23,11 72,63	65	19-05-	Not	BSRN	BSRN	Candidate
	nagar				2014	available		stations.	Data	not
					to			available yet from BSRN
					date			website
	Gurgaon	28,42 77,16	259	21-04-	Not	BSRN	BSRN	Candidate
					2014	available		stations.	Data	not
					to			available yet from BSRN
					date			website
	Howrah	22,55 88,31	51	15-06-	Not	BSRN	BSRN	Candidate
					2014	available		stations.	Data	not
					to			available yet from BSRN
					date			website

Table 10 :

 10 Available NWP datasets

	Available	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
	deterministic	IFS	HARMONIE	IFS + post-	HARMONIE	HARMONIE
	models		(RUC3	processing	+ post-	RUC1
			from 2016		processing
			onwards)
	Available	ECMWF EPS	gSREPS	ECMWF EPS	gSREPS
	probabilistic models			+ post-processing	+ post-processing
	Legend: Green = available, red = not available, orange = available if effort or upcoming verification results
	allows

Table 11 :

 11 Station information Morocco

	Name	Latitude	Longitude	Eleva-	Period	Pyrheliometer Temporal	Network
		(+N)	(+E)	tion (m)			resolution
	Oujda	34,65	-1,9	617	2011-	Kipp & Zonen	1-min	enerMENA
					08-18	CHP1
					to date
	Missour 32,86	-4,107	1107	2013-	Kipp & Zonen	1-min	enerMENA
					05-27	CHP1
					to date
	Tan-	28,498	-11,322	75	2013-	Kipp & Zonen	1-min	enerMENA
	Tan				05-05	CHP1
					to date
	Erfoud	31,491	-4,218	859	2013-	Rotating	1-min	enerMENA
					05-30	Shadowband
					to date	Irradiometer
	Zagora 30,272	-5,852	783	2013-	Rotating	1-min	enerMENA
					05-31	Shadowband
					to date	Irradiometer

EnerMENA is a project being fully named 'Towards a Sustainable Implementation of Solar Thermal Power in the MENA Region'. The MENA acronym represents the Mediterranean Europe and Northern Africa regions. EnerMENA includes a dedicated ground measurement program 'enerMENA meteo network' applying ventilated CMP21 Secondary Standard Kipp & Zonen pyranometers and Kipp & Zonen CHP1 First Class pyrheliometer instruments at various locations in Algeria, Egypt, Jordan, Morocco, and Tunisia.

Table 11a :

 11a Station information Morocco -data availability Oujda Measured data are available in Missour station from June 1 st 2013. Main problems are due to missing data.

							Oujda (EnerMENA)
	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2012	-	-	0	0	0	0	2	4	0	4	0	0	10
	2013	0	1	0	0	1	0	0	0	0	0	7	0	9
	2014	2	0	1	1	6	16	5	-	-	-	-	-	31

Table 11b :

 11b Station information Morocco -data availability Missour

							Missour (EnerMENA)
	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2013	-	-	-	-	-	0	0	0	0	0	0	0	0
	2014	0	0	0	7	5	0	0	0	0	0	0	1	13
	Measured data are available in Erfoud station from June 3 rd 2013. Main problems are due to missing
	data.

Table 11c :

 11c Station information Morocco -data availability Erfoud Measured data are available in Zagora station from June 3 rd 2013. No problems are detected in this station.

							Erfoud (EnerMENA)
	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2013	-	-	-	-	-	2	0	0	0	0	0	0	2
	2014	0	0	0	0	11	0	0	0	0	0	0	0	11

Table 11d :

 11d Station information Morocco -data availability Zagora

							Zagora (EnerMENA)
	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2013	-	-	-	-	-	2	0	0	0	0	0	0	27
	2014	0	0	0	0	0	0	0	0	0	0	0	0	0
	Measured data are available in Tan-Tan station from June 6 th 2013. Invalid DNI days in 2014 are due to
	missing data.

Table 11e :

 11e Station information Morocco -data availability Tan-Tan

							Tan-Tan (EnerMENA)
	Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual
	2013	-	-	-	-	-	5	0	0	0	0	0	0	0
	2014	0	5	2	15	1	0	0	0	0	0	0	0	23

Table 12 :

 12 Length of cloud periods occurring in a day -optically thick clouds

	Station	%	of	cloud<	%	of	cloudy	90% of cloud	98% of cloud
		periods <= 15 min	periods <=1 hour	periods are <= X	periods are <= X
		duration		duration		hours	hours
	Noor	28.55			58.44			4.0	8.5
	Oujda	27.92			57.11			6.0	10.25
	Missour	24.94			52.42			4.75	9.0
	Erfoud	30.5			60.8			3.5	8.75
	Zagora	32.60			64.92			3.0	8.0
	Tan Tan	31.31			61.33			4.75	9.75

Table 13 :

 13 Length of cloud periods occurring in a day -optically thin cloudsPlease note: Due to the cloud overestimation in winter time in morning hours, the numbers of 90 and 98% percentiles are too large, but the relative agreement to each other is still valid information. DNI variability classes are derived based on MSG cloud parameters as described in section 1.3. Histograms of their frequency in 2005-2015 cases are given(Fig 2.61). Noor is characterized by a large number of no or small variation and cloud-free sky (class 1 and 2) or by no variation and cloudy sky (class 8). Class 2 is represented but significantly less than class 1 and as often as class 8. Class 2 is characterized by high DNI and small amplitude variability.

	Station	%	of	cloud<	%	of	cloudy	90% of cloud	98% of cloud
		periods <= 15 min	periods <=1 hour	periods are <= X	periods are <= X
		duration		duration		hours	hours
	Noor	31.58			66.73			3.25	7.75
	Oujda	28.91			60.51			4.25	9.0
	Missour	27.65			61.66			4.0	9.0
	Erfoud	31.14			67.7			3.5	8.75
	Zagora	34.79			71.70			2.75	7.0
	Tan Tan	27.91			63.78			3.75	8.0
	2.3.4 DNI variability conditions
					Very variable conditions (classes 4 and 6) and medium
	variable conditions (classes 3, 5, and 7) are less frequent. This is confirmed by the 2 nd best fitting class
	(Fig. 2.29) where classes 2 and 7 are most frequent.
	With respect to the variability classes Zagora is not very similar. It has many more class 1 and less
	variable class 2 to 7 conditions. Also, the low DNI and low variability class 8 is less frequent.
	Erfoud has very similar occurrence of classes 5 and 8 and is among the most closest 3 for classes 1, 3, 4,
	6,7. Oujda and Missour are also more similar than Zagora and finally, Tan-Tan shows completely different
	characteristics.

Table 14 :

 14 Available NWP datasets Green = available, red = not available, orange = available if effort or upcoming verification results allows blue = available in EnerMena stations (with exception

	Available	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
	deterministic	IFS	HARMONIE	IFS + post-	HARMONIE	HARMONIE
	models		(RUC3 from 2016 no Zagora) onwards;	processing	+ post-no Zagora processing;	RUC1 no Zagora
	Available	ECMWF EPS	gSREPS	ECMWF EPS	gSREPS +
	probabilistic			+ post-	Post
	models			processing	processing
	Legend:

of Zagora) Figure 2.64: AEMET AROME/HARMONIE forecast region 2.4 Chile 2.4.1 Available ground observations

  Measured solar irradiance data by the Ministry of Energy of Chile (available online)

Table 15 :

 15 Station information Chile These stations measures GHI, and tracked global and diffuse solar irradiances. The calculation of DNI by these quantities has led to acceptable DNI daily profiles in summer months (figure 2.66, left) but unrealistic DNI daily profiles in winter months (figure 2.66, right). Consequently, quality tests applied (detailed in Annex B) are reduced to • Control of the data recording time • Visual inspection of solar radiation components (in measured GHI and calculated DNI). • Quality control tests of the BSRN: Physically Possible and Extremely Rare tests (in measured GHI and calculated DNI).

	Station (code)	Start	end	latitude	longitude	Elevation (m)

  , data are available from 2010-01-27 to 2013-04-08. Daily evaluation of QC tests can be made available on request. GHI measurements show a low number of QC errors, and also good visually performance (except for spare days in 2012, where a non-realistic GHI dynamics are found at the beginning of the day). DNI calculated values show also a low number of QC errors, but a good visually performance only in summer months. The rest of the stations show a similar behavior to the observed in these stations. Therefore, we do not investigate this instrument network any further.

	This station measures GHI and both tracked global and diffuse solar irradiance with the following
	instruments:
	Instrument	Model	Variables
	Pyranometer	Kipp & Zonen (CMP11)	Tracked diffuse irradiance
	Pyranometer	Kipp & Zonen (CMP11)	Tracked global irradiance
	Pyranometer	Kipp & Zonen (CMP11)	GHI
	In CAMA station

Table 16 :

 16 Available NWP datasets

	Available	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
	deterministic	IFS	HARMONIE	IFS + post-	HARMONIE	HARMONIE
	models		(RUC3	processing	+ post-	RUC1
			from 2016		processing
			onwards)
	Available	ECMWF EPS	gSREPS	ECMWF EPS	gSREPS
	probabilistic models			+ post-processing	+ post-processing
	Legend: Green = available, red = not available, orange = available if effort or upcomings verification
	results allows
	2.5 Saudi Arabia
		2.5.1 Available ground observations

For Saudi-Arabia a number of stations is available, but they only provide data until 2002 or 2003.

Table 17 :

 17 Station information Saudi ArabiaDue to the station data availability before 2003, the ECMWF forecasts are only available in a model version which is not valid anymore.Schroedter-Homscheidt et al. (2016a) has shown, that the results in biases have dramatically changed in 2003. Therefore, making an assessment of pre-2003 forecasts is not representative anymore. The following figures 2.77 to 2.79 are taken from this paper. Before October 2003, the ECMWF had strong negative biases and RMSE was also much larger than for more recent years. Station names used in these plots are given in Tab. 18.

	Name	Latitu	Longitu	Elevati	Period		Pyrheliome	Tempor	Netwo	Comments
		de	de (+E)	on (m)			ter	al	rk
		(+N)						resoluti
								on
	Solar	24.91	46.41	650	1998-08-	Eppley NIP		BSRN	Data available
	Village				01	to				in BSRN website
					2002-12-
					12
	Abha	18.23	42.66	2039	1998-01-	Eppley NIP		NREL	Data available
					01	to			&	in	rredc.nrel
					2003-01-			KACST	website
					31
	Al-Ahsa 25.30	49.48	178	1998-01-	Eppley NIP		NREL	Data available
					01	to			&	in	rredc.nrel
					2003-01-			KACST	website
					31
	Gizan	16.90	42.58	7	1998-01-	Eppley NIP		NREL	Data available
					01	to			&	in	rredc.nrel
					2003-01-			KACST	website
					31
	Qassim 26.31	43.77	647	1998-01-	Eppley NIP		NREL	Data available
					01	to			&	in	rredc.nrel
					2003-01-			KACST	website

Table 19 :

 19 Available NWP datasets

	Available	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
	deterministic	IFS	HARMONIE	IFS + post-	HARMONIE	HARMONIE
	models		(RUC3	processing	+ post-	RUC1
			from 2016		processing
			onwards)
	Available	ECMWF EPS	gSREPS	ECMWF EPS	gSREPS
	probabilistic models			+ post-processing	+ post-processing
	Legend: Green = available, red = not available, orange = available if effort or upcoming verification results
	allows

Table 21 :

 21 Available NWP datasets Does ECMWF/EPS perform better than ECMWF/IFS? (applicable in Évora, ZA, MA, CL) • Does gSREPS perform better than AROME/HARMONIE? (applicable in Évora)

	Iberian	Deter-	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
	Peninsula	ministic	IFS	HARMONIE (RUC3	IFS +PP	HARMONIE	HARMONIE
				since 2016)		+ PP	RUC1
		Proba-	ECMWF	gSREPS	ECMWF	gSREPS +
		bilistic	EPS		EPS + PP	PP
	South	Deter-	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
	Africa	ministic	IFS	HARMONIE (RUC3	IFS + PP	HARMONIE	HARMONIE
				since 2016)		+ PP	RUC1
		Proba-	ECMWF	gSREPS	ECMWF	gSREPS +
		bilistic	EPS		EPS + PP	PP
	India	Deter-	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
		ministic	IFS	HARMONIE (RUC3	IFS +PP	HARMONIE	HARMONIE
				since 2016)		+ PP	RUC1
		Proba-	ECMWF	gSREPS	ECMWF	gSREPS +
		bilistic	EPS		EPS + PP	PP
	Morocco Deter-	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
		ministic	IFS	HARMONIE (RUC3	IFS + PP	HARMONIE	HARMONIE
				since 2016, no		+ PP; no	RUC1; no
				Zagora)		Zagora	Zagora
		Proba-	ECMWF	gSREPS	ECMWF	gSREPS +
		bilistic	EPS		EPS + PP	PP
	Chile	Deter-	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
		ministic	IFS	HARMONIE (RUC3	IFS + PP	HARMONIE	HARMONIE
				since 2016)		+ PP	RUC1
		Proba-	ECMWF	gSREPS	ECMWF	gSREPS +
		bilistic	EPS		EPS + PP	PP
	Saudi	Deter-	ECMWF/	AROME/	ECMWF/	AROME/	AROME/
	Arabia	ministic	IFS	HARMONIE (RUC3	IFS + PP	HARMONIE	HARMONIE
				since 2016)		+ PP	RUC1
		Proba-	ECMWF	gSREPS	ECMWF	gSREPS +
		bilistic	EPS		EPS + PP	PP

Legend: Green = available, red = not available, orange = available if effort or upcoming verification results allows; blue = available in EnerMena stations (with exception of Zagora), PP = post-processing

Is a probabilistic forecast of value?

•

Table 22 :

 22 Available NWP datasets

	Task	Product		Horizon [h] Temp.-Resolution	Spat. Res.	Run started at ..
					[min]		[UTC]
	4.3.1	AROME/HARMONIE*	48	>=15	2.5 km	0,6,12,18
	Det	(will get an update for 3-				0,3,6,…21
		hourly runs)
	4.3.1	ECMWF		240	>=60	0.16°	0,12
	Det				(available 180)
	4.3.1	AROME/HARMONIE/Stat.
	Det	Post-Proc
	4.3.1	ECMWF/Stat. Post-Proc
	Det
	4.3.1	AROME/HARMONIE RUC	48	>=15	2.5 km	0,1,2,3,4,..23
	Det	1*
	4.3.2	ECMWF, EPS		360	180	0.25°	0,12
	Prob
	4.3.2	gSREPS		36/48	60	2.5km	0,6,12,18
	Prob
	4.3.2	ECMWF,	EPS/Stat.
	Prob	PostProc
	4.3.2	gSREPS/Stat. Post Proc
	Prob

Table 23 .

 23 Physical limits for the components of solar radiation I SC Solar Constant (1367 Wm -2 ), ε eccentricity, Θ z solar zenith angle o "Extremely Rare" test, used to evaluate whether the measurements are within the limits known as extremely rare. Table24shows the extremely rare limits proposed in the BSRN recommendations.

	Component Minimum						Maximum
	GHI	0	I	SC	ε	1.2 1.5(cos ) z θ	+	100 / W m	2
	DHI	0	I	SC	ε	1.2 0.95(cos ) z θ	+	50 / W m	2
	DNI	0					SC I ε

Table 24 .

 24 Extremely rare limits for the components of solar radiation "Across Quantities" test, based on empirical relations of the different quantities measured. The fail in this check could indicate that any component has not been measured properly or that the solar tracker has not worked correctly indicating misalignment of the pyrheliometer. Table25shows the limits for checking the fulfillment of this test.

	Component Minimum	Maximum
	GHI	-2 W/m 2	𝐼𝐼 𝑆𝑆𝑆𝑆 𝜀𝜀1.2(𝑐𝑐𝑐𝑐𝑐𝑐𝜃𝜃 𝑧𝑧 ) 1.2 + 50𝑊𝑊/𝑚𝑚 2
	DHI	-2 W/m 2	𝐼𝐼 𝑆𝑆𝑆𝑆 𝜀𝜀0.75(𝑐𝑐𝑐𝑐𝑐𝑐𝜃𝜃 𝑧𝑧 ) 1.2 + 30𝑊𝑊/𝑚𝑚 2
	DNI	-2 W/m2	𝐼𝐼 𝑆𝑆𝑆𝑆 𝜀𝜀0.95(𝑐𝑐𝑐𝑐𝑐𝑐𝜃𝜃 𝑧𝑧 ) 0.2 + 10𝑊𝑊/𝑚𝑚 2

o

Table 25 .

 25 Conditions for the Across Quantities test

	Parameter	Condition	Limit

Figure 2.15: CAMS based DNI, frequency distributions of hourly daytime DNI at SAURAN station UFS (blue) and Upington (red) for all calendar months, based on data from 2006-2015.

Figure 2.38: Ground-based DNI, frequency distributions of hourly DNI at EnerMENA station Zagora (blue) for all calendar months.

Figure 2.47: CAMS based DNI, frequency distributions of hourly DNI at EnerMENA station Missour (blue) and Noor (red) for all calendar months, based on data from 2006-2015

Figure 2.48: CAMS based DNI, frequency distributions of hourly DNI at EnerMENA station Erfoud (blue) and Noor (red) for all calendar months, based on data from 2006-2015

Figure 2.49: CAMS based DNI, frequency distributions of hourly DNI at EnerMENA station Tan-Tan (blue) and Noor (red) for all calendar months, based on data from 2006-2015

APOLLO AVHRR DCH direction changes in DNI DHI diffuse hemispherical irradiation DLR German Aerospace Center DMO Direct Model Output DNI direct normal irradiation DNIcast Project acronym www.dnicast-project.net DRIBU buoys observations DRIFTER observations from drifting buoys ECMWF European Centre for Medium-Range Weather Forecast ECMWF/EPS ECMWF/Ensemble Prediction System ECMWF/IFS ECMWF/Integrated Forecast System EDA Ensemble Data Assimilation ELR extended logistic regression India no no no Morocco yes yes yes Chile yes, but to be checked further yes no Saudi Arabia yes no yes

The Type of stations is detailed below:

• Sun Tracker stations Although these stations are not part of BSRN, their operation follows its main guidelines. They are composed of sun trackers, sun sensors for accurate positioning, pyranometers, heating and ventilation units, pyrgeometers, pyrheliometers, UV meters, and also temperature, atmospheric pressure, relative humidity, wind speed and direction sensors, all connected to data loggers, with power supply from the grid.

• RSBR stations

Their basic configuration includes photoelectric radiometers, a motor controller and rotating shadow band, temperature, atmospheric pressure, relative humidity, wind speed and direction sensors, all connected to data loggers, with power supply from a small-scale PV system. A second configuration lacks all meteorological sensors, for use in locations that already have a meteorological. A third configuration is similar to the first one, with the addition of a thermopile pyranometer for a redundant measurement of global horizontal radiation, which is used at sites where radiation conditions are particularly interesting and where local personnel is readily available for maintenance and cleaning of the thermopile pyranometer. H2020-PreFlexMS/ Grant no. 654984 110

Suggested validation sites

Rationale

The site Évora is pre-selected in PreFlexMS as the demonstration site. A detailed description of Évora versus other stations (Badajoz and PSA) on the Iberian Peninsula has been given in chapter 1.

In order to widen the geographical scope of knowledge on DNI forecasting, a number of stations is selected for the further validation work within WP4 (Task 4.4 Comparing forecasts) and in the interaction task between WP3 (dispatch optimizer) and WP4. The stations selected in this chapter will serve as 'virtual demo sites' in PreFlexMS.

The countries have been selected based on the PreFlexMS market analysis outcomes and GE priorities. We want to find 3 stations separate to Évora as additional validation sites in • Different climate conditions • Different meteo forecast availability conditions

Please note: This is not only a question on different meteorological conditions. The selection process also should reflect the reality of different forecast options being available for real power plants. Therefore, having not all PreFlexMS forecasts available is not a show-stopper -it is even a good argument for a site, as in reality the user of the PreFlexMS technology wants to know how this performs in conditions with less advanced forecast capabilities in real life.

Availability of observations

Suitable stations are found based on:

1 st criterion: They should have ground observations. This allows both the development of postprocessing for forecasts as also the forecast verification. 2 nd criterion: Ground observations should be available 2004 or later to cover the nowadays ECMWF forecast capabilities.

3 rd criterion: They should provide extended site auditing information based on DNI, cloud and aerosol conditions. Such information is only available in the Meteosat Second Generation field of view, so, Europe, Africa, Middle East and parts of Brazil.

Research questions

Based on the NWP data availability the following questions can and should be addressed by the dispatch optimizer developers and the meteorological forecast assessment team for the different countries:

Influence of the storage on error cancelling?

• In case of a power plant with storage, a low MAE is helpful, but over a number of hours the storage allows absolute errors to cancel out over a certain period. Does this occur in practice? If yes, the assessment of meteorological forecasts should take this into account into their metrics.

(applicable in Évora, MA, ZA, CL)

Influence of aerosol forecasts?

• Having the dependency of CSP from only strong AOD events in mind, the evaluation of aerosol affected NWP forecast should be done for all cases as well as being concentrated on the strong AOD event cases only. An evaluation with respect to aerosols to all situations only may result in misleading outcomes.

Based on the available AERONET observations, WP4 developers can perform evaluations for all cases, but also should restrict their evaluation with respect to aerosols to a number of relevant cases as being observed by AERONET. Results should be given for 'all AOD', 'AOD in 0.5 to 1.0' and 'AOD > 1.0' cases separately. (applicable in Évora)

Is higher spatial and temporal resolution of value?

• Does AROME/HARMONIE perform better than ECMWF/IFS? (applicable in Évora, MA)

• Does gSREPS perform better than ECMWF/EPS? (applicable in Évora)

Is a higher update frequency of value?

• Does AROME/HARMONIE RUC1 perform better than AROME/HARMONIE? (applicable in Évora, MA)

Is post-processing worth the effort?

• Does ECMWF/IFS + Post-Processing perform better than ECMWF/IFS only? (applicable in Évora, ZA, MA, CL) • Does AROME/HARMONIE + Post-Processing perform better than AROME/HARMONIE only?

(applicable in Évora, MA) • Does ECMWF/EPS + Post-Processing perform better than ECMWF/EPS? (applicable in Évora, ZA, MA, CL) • Does gSREPS + Post-Processing perform better than gSREPS? (applicable in Évora)

Is higher spatial and temporal resolution + post-processing of value?

• Does AROME/HARMONIE + Post-Processing perform better than ECMWF/IFS? (applicable in Évora, MA)