#### Types of heat generation in Ukraine in 2016 and their cost

Январь 31st, 2016

The satellite measures the sunlight in the visible spectral region reflected by the earth. Hence the measurement signal depends on the irradiation hitting the reflecting layers. After subtraction of the radiometer offset Co the remaining Count behaves proportionally to the irradiance I. Under utilization of these proportionalities the relative reflectivity p can be defined as standardized backscattering value:

p := C-Co /1. (1)

In the following only relative differences not absolute values of the reflecting properties are important therefore it is sufficient to divide by the cosine of the sun zenith.

The standardized backscattering values of clouds usually exceed those of the Earth’s surface, excluding the case of snow. Thus it is possible to identify the occurrence of clouds. If the reflectivity for a completely cloudy pixel pc and the reflectivity of the unclouded ground (and ocean respectively) pg is known, the cloud index n as a degree of cloudiness can be defined as

n = (P — Pg)/(Pc — Pg). (2)

If the maximum and minimum of the standardized backscattering values of a pixel is selected as reference values, the Cloud index takes values in the range of 0<=n<=1.

Armel Oumbe1*, Lucien Wald1, Philippe Blanc1, Marion Schroedter-Homscheidt2

1Ecole des Mines de Paris, BP 207, 06904 Sophia Antipolis, France

2German Aerospace Center — German Remote Sensing Data Center, Postfach 1116, D-82234 Wessling, Germany

Corresponding Author, armel. oumbe@ensmp. fr

Solar radiation is modified in its way downwards by the content of the atmosphere. Quantifying the influences of the parameters describing the optical state of the atmosphere is necessary for development of a method for the assessment of the solar surface irradiance (SSI). This paper performs an inventory of the variables (e. g., cloud) and their attributes (e. g., optical depth) available in an operational mode and then assesses to which degree the uncertainty on an attribute of a variable -including the absence of value — leads to a departure from the perfect result, i. e., when assuming that all attributes are known with a perfect accuracy. Clouds are the most important variable for the SSI. Aerosol loading and type, water vapour amount and atmospheric profile have a great influence. Ground albedo has an important influence on diffuse part and spectral distribution of SSI. The influences of vertical position and geometrical thickness of clouds in the atmosphere are negligible. Thus, the solution of the RTM for a cloudy atmosphere is equivalent to the product of the irradiance obtained under a clear sky and the extinction coefficient due to the cloud. The results are combined with the data availability for design of the new method Heliosat-4 for assessing the SSI.

Keywords: solar radiation, atmospheric optics, satellite images, Heliosat method

A wealth of methods has been developed in the past years to assess solar surface irradiance (SSI) from images taken by satellites (Cano et al. 1986; Pinker et al. 1995; Hammer 2000; Rigollier et al. 2004). Current methods are inverse, i. e., the inputs are satellite images whose digital counts result from the ensemble of interactions of radiation with the atmosphere and the ground and the method deduces the radiation from the inputs. On the opposite, solar radiation may be assessed by a direct method, i. e., the various processes occurring during the path of the light from the outer space towards the ground can be modelled by the means of a radiative transfer model (RTM) in 2D or 3D. RTMs take into account a large number of inputs: optical properties including spectral aspects of gases, aerosols, clouds and ground reflectance, types of interactions, mathematical solving methods (Kato et al. 1999; Liou 1976, 1980; Mayer, Kylling 2005; Perrin de Brichambaut, Vauge 1982; Vermote et al. 1997). The quality of the results depend strongly on the quality of the inputs.

Nowadays, the exploitation of recent sensors and satellite data such as MSG, Envisat and MetOp combined with recent data assimilation techniques into atmospheric modelling offers a favourable context for the design and exploitation of a method based on direct modelling. Despite noticeable

advances in the operational assessment of optical properties of the atmosphere at any location, we do not have enough information for 3D RTM. The available atmospheric information is typically 2D.

Even so, many of the inputs are unknown. Some are known every % h (clouds), others every day (ozone, water vapour) and others only from times to times (aerosols). The ground albedo and its spectral distribution is known only if the sky is clear. Furthermore, if available, these quantities are known at different spatial resolutions. Hence, the set of inputs to the RTM is heterogeneous with respect to spatial coverage, spatial sampling step, spatial support of information, temporal sampling frequency, temporal support of information, and accuracy.

The goal of the work presented here is to perform an inventory of the variables (e. g., cloud) and their attributes (e. g., optical depth) available in an operational mode and then to assess to which degree the uncertainty on an attribute of a variable — including the absence of value — leads to a departure from the perfect result, i. e., when assuming that all attributes are known with a perfect accuracy.

The spectral region of interest is [0.3 pm, 4 pm]. Energy-related applications require spectrally — integrated or spectrally-resolved SSI. For this sensitivity study, we use: the code libRadtran (Mayer, Kylling 2005) because it is accurate, versatile and well exploited in atmospheric optics (Bernhard et al. 2002 ; Mueller et al. 2004 ; Ineichen, 2006); the correlated-k approach of Kato et al. (1999) for spectral resolution; the radiative transfer solver DISORT (Stamnes et al. 1988).

User guidance is one of the main objectives of the MESoR project. This will be realized by the development of a guide of best practices in the application of solar resource data. The above described benchmarking will be one chapter in this guide. The results will give the users a better indication of the uncertainty of the available data sources and which data bases are suitable for different applications. Best practices in the application of solar resource information will be demonstrated in use cases. The applications taken into account by the guide will cover photovoltaics, solar thermal, solar concentrating and daylighting systems. As a basis it will cover requirements and examples for the design of these systems. Further it will cover solar forecasting applications.

Based on the feedback from the stakeholders and the benchmarking results road map documents are foreseen within the MESoR project. They will cover future research objectives in the field of solar resources, new solar radiation services to faster deploy the market for solar energy applications and optimize grid integration and recommendations for an improved Earth Observation system to better support solar energy.

ERA-40 is a reanalysis of the global atmosphere and surface conditions for 45-years, over the period from September 1957 through August 2002 by ECMWF. Many sources of the meteorological observations were used. This data was run through the ECMWF computer model at a 40km resolution. As the ECMWF’s computer model is one of the more highly-regarded in the field of forecasting, many scientists take its reanalysis to have similar merit. The data is stored in GRIB format. The reanalysis was done in an effort to improve the accuracy of historical weather maps and aid in a more detailed analysis of various weather systems through a period that was severely lacking in computerized data. With the data from reanalyses such as this, many of the more modern computerized tools for analyzing

storm systems can be utilized, at least in part, because of this access to a computerized simulation of the atmospheric state.

The period of data studied goes from 1994 to 2004 and the variable studied is downward global solar radiation. The resolution of the dataset is 1° or 100km.

Accuracy of each one of the models presented is studied in terms of Root Mean Squared Deviation (RMSD):

being N the population size, O the variable observed and O* predicted variable.

The validation is done comparing with ground measurements from 40 stations from AEMet. Results from validation can be show in Figs. 2-5. . The graphic shows all the stations ordered from lower latitude to higher latitude. Overall data is underestimated for high solar radiation values and Normalized RMSD goes from 20% to 45% and the error gets increased with the latitude.

We have shown in earlier work how a simplified model can be used to achieve a good fit to the global irrad — iance data [2]. The following expression shows that the global irradiance on a horizontal surface IG as the sum of two terms: the first term expresses the direct solar beam irradiance, and the second term expressed the diffuse irradiance due to Rayleigh and Mie scattering from molecules and aerosols in the sky and from clouds.

Ic = U0 F, a1 slnV — I,

I0 = 1367 W/m2 is the solar constant. FJ takes account of the yearly variation of the solar irradiance due to the elliptical orbit of the earth around the sun. A practical equation for FJ is available in reference [6]. The factor aL accounts for the attenuation of direct beam irradiance due to absorption and scattering, where L is the Rayleigh air mass. Finally, the factor sin V takes the geometry of the situation into account for a solar elevation angle V. The solar elevation angle can be computed with knowledge of the latitude, the solar declination angle and the local time. The equation required is widely available in the literature of solar energy design [6].

The air mass L through which the direct rays of the sun must pass depends of course on the angle V between the horizontal and a line from the observer to the center of the sun. For angles V > 250 a simple drawing will show that the air mass L = 1/sin V, for in this case it is reasonable to assume that the earth is a flat surface with a thin layer of atmosphere. However, for angles less than 250 with the sun low on the horizon it is essential to take the curvature of the earth and temperature gradients into account. Fritz Kasten and Andrew

Alberto Pettazzi, Jose Antonio Souto Gonzalez*

Department of Chemical Engineering, University of Santiago de Compostela, Lope Gomez de Marzoa St.,

Campus Sur. 15782 Santiago de Compostela, Spain

Corresponding author, ja. souto@usc. es

Abstract

A basic target of the Renewable Energies and Energetic Sustainability MSc Degree organized by the University of Santiago de Compostela is to provide to the students an adequate knowledge about the estimation of solar resource. Students of this MSc in 2007-08 academic year were involved in analysing and processing solar radiation measurements provided by the Galician weather stations network (www. meteogalicia. es). By the combination of their results, a solar resource map may be obtained and, in addition, it will be compared to other solar resource estimations at Galicia.

Apart from the good results of this work in terms of self-learning teaching, comparison of solar irradiation distribution to other results obtained processing satellite data shows the necessity to consider local effects in the estimation of the solar resource at this region. Keywords: self-learning, solar resource, ground-satellite comparison

The development of technologies for renewable energies is a priority target in the policy from different fields. To reach this goal, the education on these issues is of primary importance.

A basic target of the Renewable Energies and Energetic Sustainability MSc Degree organized by the University of Santiago de Compostela is to provide the students an adequate knowledge about the estimation of solar resource.

Several works dealing with solar climatology may be found; solar radiation atlases are obtained by different techniques, as satellite measurements ([1], [2], [3]), ground measurements ([4], [5]) or combining ground data with radiative transfer models [6]. In Galicia (NW Spain), Vazquez et al. [7] elaborated a solar radiation atlas using Meteosat-6 satellite measurements [8] over the years 2002-2004. In this work, atlas data were compared against two pyranometers located in Vigo and A Coruna (see Fig. 1).

Following the methodology used by Pettazzi et al. [9], 34 students of the Solar Radiation subject of the MSc above mentioned during 2007-08 academic year were involved in analysing and processing solar radiation measurements provided by the Galician weather stations network [10], from September 2006 to August 2007. The task committed to the students included annual and seasonal analysis of the following parameters: global irradiation, sunshine hours and clearness index, KT. Additional analysis of other climatologically relevant parameters — temperature, relative humidity and precipitations — was also undertaken.

By the combination of their results, solar resource maps have been obtained and, in addition, were compared to results achieved by Vazquez et al. [7].

The performance of the NWP model ARPS and the proposed MOS procedure is verified for the site Florianopolis, localized in the south of Brazil with 48° 3ri5’’W longitude and 27° 36’ 76’’S latitude. The measured global horizontal radiation within the period from January 2000 to June 2006 was used to calculate the daily mean values. For daily mean values, the utilized pyranometer CM11 has a measurement uncertainty of 1 % for 95 % confidence as stated in [31]. For the quality control [32], [33] the measured radiation vales have to appear in the measurement range of (0 to 1367) W/m2. If this criterion was not fulfilled for a time interval larger than 10 min, the daily mean value was rejected as recommended in [33]. From the training-validation of 6.5 years, 119 days were excluded by the quality criterion, leading to the remaining data, which appears in 53 consistent time series. To facilitate the implementation, the data blocks of the training data set were chained, rather than is accomplished a specific DWT of each block, obtaining three equal length vectors with synchronized day numbers. The vector of residuals {sA} is obtained by subtraction of the measured {H} from the forecasted daily solar radiation means {HA}. The obtained predictors sAsi_1 to sAsi-k and predictands sAsi (eqn. 3), selected from the partially reconstructed sub-signals (eqn. 2, {sAS}), have to consider the limitations of each of the data blocks to avoid uncharacteristic modification of the ANN input pattern. As to see in eqn. 3, the data of the first k days of each block are used exclusively as predictors, thus a time series with the length nj provide (n-k) training samples for the ANN. Each sample has an input vector with the pattern length k, the predictors, and one output variable, the observed predictand, of the forecast at the considered time scale. The total number of training-validation samples ntv is obtained with expression (5).

ntv = YU (nj — k ) (5)

Where j = 1…nb defines the number of data blocks obtained by the data qualification, with block individual number of training samples nj. The resulting data set is subdivided in two subsets, the training and the validation set. As recommended in Kaastra [34], the validation set, which is independent from the training set has to represent (10 … 30) % of the data. This set may be selected randomly from the data or it follows immediately the training set [34]. From the data the last year, representing a validation set of 18 % was separated. Due to hardware improvements of the measurement system [35], the validation set was not exposed to system outages, which leads to its consistence.

The average of the selected daily mean values of the measured solar radiation is 182.36 W/m2. For ARPS model simulations, based on the reanalysis dada, was obtained a RMSE of 70 W/m2 that corresponds to 38.4% of the measured average value. The maximal error of the daily mean solar radiation simulation is with 264.3 W/m2 higher than the measured average value (compare figure 3 — third chart and figure 4). If the correction is build up with data of twelve subsequent previous days (k = 12), a set of 1356 predictor vectors (sAs, (i-1) … sAs, (i-k) ) were selected. With the proposed MOS method the RMSE of the ARPS model reduces to 18.92 W/m2 for the training data set, which corresponds to 10.37 % of the measured average value of 182.36 W/m2. For the independent validation data set was obtained 9.06 % (see figure 3, fourth chart and figure 5). Worst performances were observed for the sub-signal di which contains the details of the higher frequency band (RMSE = 9.08 W/m2) and for the approximation sub-signal a1 (RMSE = 6.82 W/m2). The generalization performance of the ANN was verified with the validation data set. By arbitrary configured number of neurons in each layer with (k = 12), the best performance of the d1 sub-signal correction was observed, for 22 neurons at the first, and 12 neurons at the second hidden layer. This configuration of the neurons was used also for the other three ANN, whereby the one used for the approximation signal was configured as RNN, due to slight improvement in its performance. To access the probably higher boundary uncertainties under operation of the prediction model, it is necessary to accomplish ntv times (equation 5) the DWT for the data set having ntv predictor/predictand samples (see discussion in section 3.2). Avoiding numerical effort, the present article release only the results based on a single DWT of the data set as accomplished in

[9] .

Figure 3 — Daily mean values of the solar radiation — charts from the top to the bottom: (1) measured solar radiation H; (2) forecasted solar radiation with the ARPS model HA; (3) (H — HA); (4) (H — HA, corr), where HA, con — is the corrected ARPS forecast. The validation set appears from 2000 to 2500 days.

The ordinary and residual kriging models were obtained based on the training dataset. After that, the validation dataset were used to evaluate these models. Particularly, model estimates were evaluates in terms of the ME, MAE, RMSE and the correlation coefficient.

In order to calculate the ground reflectivity pg, in an iterative procedure values larger than the mean value of the distribution plus ag are filtered out, until convergence is achieved. The mean value of the new distribution is assigned to pg.

In the original Heliosat method a constant value ag was used, ag = 27 is a suitable value for MSG. But an approach that accounts for the dependency of ag on the sun-satellite geometry leads to even better results. The bias of the clear sky irradiance is used as a measure to quantify the influence of ag. For clear sky situations, the quality of the ground reflectivity can be evaluated directly and there is little super imposition with other effects. If ag is chosen too small, the corresponding values of the ground reflectivity will be too small as well. This results in an underestimation of the irradiance. On the other hand, if CTg is too high, an overestimation of the irradiance is the consequence. Best results were found for 15<=CTg<= 50 for different classes of solar and satellite zenith angles and the azimuth angle between sun and satellite.

Clouds regularly cover about 50 % of the earth and represent the most important modulators of radiation in the earth-atmosphere system (Liou 1976). A cloud in libRadtran is characterized by its optical thickness (tc), type (cloud water or ice cloud), height of cloud top (ztop) and bottom (zbot) and the effective radius (ref) particles.

Many radiative transfer codes contain the same six atmospheric profiles corresponding to geographical and seasonal averages (Mayer, Kylling 2005; Vermote et al. 1997): Midlatitude Summer (afglms), Midlatitude Winter (afglmw), Subarctic Summer (afglss), Subarctic Winter (afglsw), Tropical (afglt) and U. S. Standard (afglus). “afgl” means Air Force Geophysics Laboratory.

The gas whose variation have a major influence on the SSI are water vapour (H2O), ozone (O3), carbon dioxide (CO2), oxygen (O2), methane (CH4), and nitrous oxide (N2O) (Vermote et al. 1997).

The attenuation of radiation by aerosols varies with its nature, density and size distribution. Following Shettle (1989), required parameters in libRadtran are: aerosol type from 0 km to 2 km altitude (haze), aerosol type above 2 km altitude (vulcan), season, and visibility (vis). The visibility is closely linked to the aerosol optical thickness (iaer) (Vermote et al. 1997). Taer represents the total extinction induced by aerosols of the medium for a given wavelength. It is sensitive to micro-physical properties of aerosols. Because these properties are difficult to assess accurately, the spectral variation of the aerosol optical thickness is usually calculated using a simplified method:

Taer A = в (Я/ Ям/" (1)

where AM = 1000 nm, в is the aerosol optical thickness at the wavelength 1000 nm and a is the Angstrom coefficient (Perrin de Brichambaut and Vauge, 1982).

When radiation reaches the earth’s surface, it can be absorbed or reflected. The intensity of the reflected radiation varies with the value of the incident radiation and the reflectance of the receiving surface. The ground reflectance is the function of the illuminating and emitting angles; the albedo is its hemispherical average. Both change with soil type and wavelength. A portion of this reflected radiation is then backscattered by the atmosphere and increases the value of the diffuse component of the SSI.