Types of heat generation in Ukraine in 2016 and their cost
Январь 31st, 2016
. _ ms ga а gtnvi (2)
_ з tк* V 4 sin^Vt (S. ffiLGS 9&a ri tT V 4 GJGGCaca?
This equation approaches the simple expression 1/sin V asymptotically for angles greater than 250. (The reader may wish to try using the angle V = 900 in the KastenYoung equation. Note that L = 1 with the sun directly overhead.
2.4 Enhanced global irradiance model
One would expect the diffuse contribution IF to the global irradiance to increase with increasing atmospheric turbidity (increasing values of a) and that these two quantities are related to one another and to the solar elevation angle. This connection has been examined in earlier work, and the graphical result is shown in Figure 3. The Linke turbidity factor TL enters the analysis in the term for the direct irradiance and is equal to unity for a pure Rayleigh atmosphere (no aerosols — only molecular scattering). The term aL is in this formulation replaced by:
a1 = «ф [0.B662 ■ Тя ■ L ■ (3)
where Dr(L) is the Rayleigh optical depth as a function of the air mass L. A very useful empirical equation for 1/Dr has been developed by Louche, Peri and Iqbal and modified by Fritz Kasten [8]:
7T = 6J&296+ 1.7 515 ■ L — 0.12Є2 ■ — (ШВ65 ■ £a — 0JD0Q13 ■ L4 <4)
The data of Figure 3 has been used to find an expression for the diffuse irradiance on a horizontal surface IF as a function of the elevation angle V and the turbidity factor TL:
£r = (4ВД4 ■ Г, — Ш2) (1ехр[аі — Г, — 0.P905 Y]’) (5)
2.5 Equation for finding TL from observations
In view of the foregoing remarks it is now possible to write an algorithm for the determination of the Linke turbidity factor from observations of the global solar irradiance on a horizontal surface and with knowledge of the solar elevation angle. The elevation angle is readily computed when the latitude, longitude and time of day are known.
tc = 2» ■ Щ — sinF ■ дСазьи*!. — lвд + (4^4,^ _ 4^33) ■ (1 — (6)
IG is the global irradiance measured, and V can be found from the time and position data. Knowledge of V yields the air mass L which in turn permits the optical depth DR to be determined. The only unknown parameter in the equation is the Linke turbidity factor TL which can then be calculated. This program has been carried out for clear days at a wide range of locations during the voyage from the Arctic to the Antarctic.
For all of the good, clear days during the eight month expedition the Linke turbidity factor was computed using the algorithm described above. For these same days the mid day temperature and relative humidity were obtained by examination of the Galathea III database. From the temperature and humidity data it is straightforward to compute the amount of water present in a cubic meter of surface air. These calculations were performed with the data shown in Figure 4 as the result. The regression shows that a moisture content close to zero should yield a Linke turbidity factor near unity as expected. The value 1,19 may reflect the fact that some aerosols will typically be present in the maritime environments from which data is available in addition to water vapor.
This analysis permits the estimation of the Linke turbidity factor in maritime environments based upon knowledge of the temperature and relative humidity. Compute the water content of a cubic meter of surface air, and apply the equation shown in Figure 4 to find TL. With TL in hand a good prediction of the global irradiance on the horizontal on a clear day, including the distribution of direct and diffuse irradiance, can be made using Equation 6. As local temperature and relative humidity are standard meteorological parameters, no special equipment or data is needed to do the calculations. Visibility conditions are also derivable from knowledge of the turbidity factor as discussed elsewhere [7]. This method does not take account of other aerosol which may be present, but it can be applied in typical maritime conditions without a modest amount of dry atmospheric aerosol particles.
[1] Galathea 3 Expedition portal in English: http://galathea3.emu. dk/eng/index. html
[2] Frank Bason; Solar Irradiance Measurements from the Danish Galathea 3 Expedition, ISES Solar World Conference Beijing 2007, Proceedings.
[3] Pierre Ineichen, Richard Perez; A new airmass independent formulation for the Linke turbidity coef
ficient, Solar Energy 73, no. 3, pp 151157, 2002.
[4] Linke, F.; TransmissionsKoeffizient und Trubungsfaktor, Beitr. Phys. fr. Atmos. 10, 91103 (1922).
[5] Frank Bason; Solar Radiation Measurements from Greenland to Antarctica — Optics Table Data from the Danish Galathea III Expedition (20062007) , North Sun Conference 2007, Riga,
Latvia, May 2007
[6] John Duffie, William Beckman, Solar Engineering of Thermal Processes, Wiley & Sons, New York, 1991.
[7] Frank Bason; Diffuse Solar Irradiance and Atmospheric Turbidity, EuroSun 2004 Conference
Proceedings, Freiburg, Germany, June 2004.
[8] F. Kasten, A. T. Young; Revised optical air mass tables and approximation formula, Applied Optics,
28, no. 22, pp 47354738, 15 Nov 1989.
The meteorological and climatic features of Galicia are monitored by a network made up by 93 weather stations, located all over the region covering the usual meteorological parameters. Ten — minute averaged data were collected for both monitoring and climatic studies. The network is managed by MeteoGalicia, the Galician weather service (www. meteogalicia. es). Currently, 25 stations provide the solar radiation measurements with Ph Schenk first class pyranometers (Philipp Schenk GmbH Wien & Co KG, Wien, Austria) and Kipp & Zonen sunshine duration sensors (Kipp & Zonen, Delft, The Netherlands) located in the most representative sites of Galicia. Moreover, 40 silicon cell sensor pyranometres (Skye Instruments LTD, UK), are installed in as many stations. These sensors are periodically cleaned and calibrated by specialized crew, following the manufacturer’s recommendations. Data are collected by dataloggers, sent to a central data server and stored in a database specifically designed for environmental data sets. Here, several filters are applied to ensure the data quality [10, 11].
Fig. 1: Names and locations of the meteorological stations considered in this work, and location of the two pyranometers (at A Coruna and Vigo) used by Vazquez et al. [7]. 
As a part of the Solar Radiation subject’s programme, each student was required to choose one station between the 65 equipped with pyranometers and to analyse data between September 2006 and August 2007. The main features of these stations are listed on Table 1.
Data proceeding from every selected meteorological station have been processed to obtain monthlyaveraged daily values of global irradiation, sunshine duration and clearness index. Measurements of precipitation, temperature and relative humidity have also been processed in order to complete the dataset for analysis.
Table 1: Main features of the meteorological stations applied in the analysis.

Finally, monthly and yearly values of global solar irradiation measured in the meteorological stations have been compared with the results obtained by Vazquez et al. [7].
The clearness index, KT, has been evaluated by the ratio G/G0, where G is the ground measured irradiation and G0 is the extraterrestrial irradiation. For this purpose, a solar constant value of
1376 Wm2 was adopted, as recommended by Davies [12]. Astronomical relationships were obtained following Iqbal [13].
Fig. 2: Annual mean values of daily global solar irradiation (kJm2) distribution over the region. Squares show the locations of the stations selected by the students and applied in this analysis.
Fig. 3: Annual values of (a) precipitation (mm) and (b) mean relative humidity (%) over the region.
Data for every station were collected, joined together and interpolated using Kriging technique, in order to get appropriate maps for analysis. Software package SURFER (http://www. ssg surfer. com/) provided graphical representation of these results.
As shown in figures 3 to 5 the presented MOS model improves considerably the output of the ARPS model simulation. Small amount of performance improvement may be obtained by the single convolution of each data block, instead of chaining, to avoid the boundary uncertainties at the borders of each of the data block on the training set (se section 4). However, to evaluate the operation of the statistical correction, the uncertainties under continuous boundary conditions have to be evaluated (see sections 3.2 and 5).
Furthermore, as the presented results are only based on simulated reanalysis data, they have to be still compared with the statistical corrections of ARPS simulations based on data of analysis, and forecast global simulations. The former is important to verify the performance loss for the analysis data. The latter is important to verify the statistical correction of both the analysis and the forecast uncertainties, since they appear in a combined form within the forecast results. Furthermore, actualizations of the NWP model may lead to additional uncertainties in the analysis and forecast corrections. With a forecast based on the reanalysis data, also named reforecast, these actualizations are avoided [12]. Additional performance improvement may be obtained by the inclusion of time series of other variables forecasted by the NWP [36]. Comparable to the MOS in [12], the presented MOS method DWTANN is only able to improve the forecasts at sites where measurements of the simulated variable are available. A solution for this problem is proposed in [36] with a site unspecific time series MOS, based on a wavelet model. This model may be applied with low uncertainties for the NWP output corrections of a limited region, as e. g. a city, where CSDHWS installations can be find in different locations.
The authors are indebted to the CAPES — Coordenagao de Aperfeigoamento de Pessoal de Nivel Superior for support to the present work and to Dr. Reinaldo Haas to place at disposal the simulated data using the ARPS model.
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Validation dataset results showed, overall, that the ordinary kriging is able to provide fair estimates. Error values present a seasonal pattern: the summer months shows the lowest RMSE values (in percentage) and the winter months the highest ones. For instance, in October RMSE is 1.44 MJ m2day1 (11.21%) and for June RMSE is 1.63 MJ m2day1 (6.20%). The MAE values ranges from 0.26 MJ m2day1 (2.6%) in February to 0.78 MJ m2day1 (6%) in October. Additionally, the ordinary kriging estimates shows scarce bias, being the ME values almost negligible for almost all the months. Only for November, a slight overestimation is found. Values of R2 range from 0.9 in December to 0.96 in June. Figure 3 shows the estimated H maps of June and October based on the ordinary kriging procedure.
The cloud reflectivity pc also depends on the sunsatellite geometry. Therefore, different values of pc are calculated using histograms of reflectivity values for classes with similar geometric configurations. Fig. 2 illustrates the approach to assign the cloud reflectivity to each class: The measured histogram is fitted by a superposition of two functions, the first is representing the ground reflectivity distribution fitground and the second the cloud reflectivity distribution fitcioud. As cloud reflectivity we chose a value close to the reflection point (Pdoud , a*max(fitcioud)).
Firstly, one calculates Ig0 which is the SSI for a clear atmosphere containing none of the six gases nor aerosol. Attenuation in this case is due to the rest of molecules in the atmosphere, which is called background Mbg. Secondly, one computes the clearness index KT0 which is the ratio of Ig0 to the extraterrestrial irradiance I0. Then, one varies the amount of a molecule M in the atmosphere and maintains to zero the other quantities. M will be successively H2O, O3, CO2, O2, CH4, and N2O, thus leading to SSI IgM. The ratio of IgM to I0 gives the clearness index KTM+ for the molecule Mplus Mbg. Since the transmittance of several gases is obtained by multiplying the transmittances of each gas, the clearness index due to the single molecule M is given by:
KTM = KTM+ / KT0
We observe an important variation of the transmittance with molecule and wavelength. Changes in quantities of O2, CO2, CH4, and N2O create a variation of transmittance of the atmospheric column less than 1 / 10000 for all wavelengths. We can thus conclude that changes in these quantities have a negligible effect on radiation. The transmittance of O3 is almost zero for wavelengths less than 0.3 pm. Its change is large in the region [0.31 pm, 0.33 pm] (more than 0.2) and is about 0.02 in the region [0.52 pm, 0.68 pm]. Regarding water vapour, the variation of transmittance which change in content is important in the region [0.57 pm, 4 pm] (Fig. 1 left). In addition, the influence of the atmosphere profile on the range of variation is significant ; the errors committed on the SSI if one does not take the right atmospheric profile are shown in Fig. 1 right.
Figure 1. Spectral transmittance of H2O (on left) for different water content (in kg m2) and relative error due to
atmosphere profile (on right); the reference model is afglus. Calculations for Kato bands.
For assessing the deviation on the SSI induced by deviation of the properties of aerosols, one computes the SSI of reference Iaref with the aerosol optical thickness at 550 nm Taer 550 set to 0.1, a to 1.5 and the aerosol types to haze 1 and vulcan 1 and season 1. Then these parameters are changed and one computes the absolute deviation of the SSI to the reference case.
Fig. 2 (left) shows the influence of aerosol optical thickness on the SSI. The deviation due to a is large (up to 20% of deviation on the SSI for a = 2) and decreases as the wavelength increases. As a increases, the SSI decreases. The influence of aerosol optical thickness at 550 nm is similar. Then, aerosol type is changed from haze 1 to haze 4, haze 5 and haze 6, vulcan 1 to vulcan 2, vulcan 3 and vulcan 4 and season 1 to season 2. The influences of the models vulcan and season are very low: the absolute deviations on the SSI are below 0.5%. Fig. 2 (right) shows the influence of the aerosol types on the SSI. The deviation due to model haze (on right) is less important than that due to the aerosol optical thickness and reaches 3 %.
Figure 2. Absolute deviation on spectral irradiance in comparison to the reference case. Solar zenith angle
(30°), water content (15 kg m2), ozone amount (300 DU) and ground albedo (0). haze 1, 4, 5 and 6 respectively
means rural, marine, urban and tropospheric aerosol type from 0 km to 2 km altitude.
We compute the spectral transmittance of clouds in the same way than the gas transmittances (Fig. 3). An increase in tc leads to a large decrease in cloud transmittance and consequently in the SSI. This decrease is wavelengthdependent. For tc greater than 15, cloud transmittance is very small or null for wavelengths greater than 2 pm. For the same tc, attenuation of radiation is stronger for water cloud than for ice cloud. The decrease in direct SSI is more marked than that in diffuse SSI; the direct SSI normal to sun rays reaches zero for tc around 7.
1st International Congress on Heating, Cooling, and Buildings — 7th to 10th October, Lisbon — Portugal /
Figure 3. Change in spectral transmittance of clouds with tc. Water cloud, ztop = 5 km, zbot = 2 km, ref = 10 pm.
To assess the influence of ztop and zbot on the SSI, we use the typical values given by Liou (1976) for different types of clouds. Fig. 4 shows the variation of cloud transmittance with ztop and zbot as the function of tc. All curves are superimposed: the maximum difference in transmittance is equal to 0.01 for albedo 0. The influence of ztop and zbot on the SSI is negligible; this is true for other albedos. Similar results are obtained with the ice cloud. Our results are similar to those of Kuhleman and Betcke (1995).
zbot=0, ztop=1 zbot=2, ztop=3 zbot=3, ztop=8 zbot=4, ztop=7 zbot=1, ztop=4 zbot=2, ztop=6 zbot=1, ztop=7 zbot=2, ztop=10