scholarly journals Historical and Future Typical Meteorological Years for 33 locations in Greece: a handy tool for various applications

2021 ◽  
Author(s):  
Basil Psiloglou ◽  
Harry D. Kambezidis ◽  
Konstantinos V. Varotsos ◽  
Dimitris G. Kaskaoutis ◽  
Dimiitris Karagiannis ◽  
...  

<p>It is generally accepted that a climatic data set of meteorological measurements with true sequences and real interdependencies between meteorological variables is needed for a representative climate simulation. In the late 1970s the Typical Meteorological Year (TMY) concept was introduced in USA as a design tool for approximating expected climate conditions at specific locations, at a time when computers were much slower and had less memory than today. A TMY is a collation of selected weather data for a specific location, listing usually hourly values of meteorological and solar radiation elements for one-year period. The values are generated from a data bank much longer than a year in duration, at least 10 years. It is specially selected so that it presents the range of weather phenomena for the location in question, while still giving annual averages that are consistent with the long-term averages for the specific location. Each TMY data file consists of 12 months chosen as most “typical“ among the years present in the long-term data set. Although TMYs do not provide information about extreme events and do not necessarily represent actual conditions at any given time, they still reflect all the climatic information of the location. TMY sets remain in popular use until today providing a relatively concise data set from which system performance estimates can be developed, without the need of incorporating large amounts of data into simulation models. </p><p>TMY sets for 33 locations in Greece distributed all over the country were developed, covering for the first time all climatic zones, for both historical and future periods. Historical TMY sets generation was based on meteorological data collected from the Hellenic National Meteorological Service (HNMS) network in Greece in the period 1985-2014, while the corresponding total solar radiation values have been derived through the Meteorological Radiation Model (MRM).</p><p>Moreover, the generation of future TMY sets for Greece was also performed, for all 33 locations. To this aim, bias adjusted daily data for the closest grid point to the HNMS station’s location were employed from the RCA4 Regional Climate Model of the Swedish Meteorological and Hydrological Institute (SMHI) driven by the Earth system model of the Max Planck Institute for Meteorology (MPI-M). Simulations were carried out in the framework of the EURO-CORDEX modeling experiment, with a horizontal RCA4 model resolution of 0.11<sup>o</sup> (~12 x 12 km). We used daily data for four periods: the 1985-2014 used as reference period and the 2021-2050, 2046-2070 and 2071-2100 future periods under RCP4.5 and RCP8.5 scenarios. </p><p>This work was carried out in the framework of the “Development of synergistic and integrated methods and tools for monitoring, management and forecasting of environmental parameters and pressures” (KRIPIS-THESPIA-II) Greek national funded project.</p>

Author(s):  
Mustapha Chaker ◽  
Cyrus B. Meher-Homji

There is a widespread interest in the application of gas turbine power augmentation technologies such as evaporative cooling or mechanical chilling in the mechanical drive and power generation markets. Very often, the selection of the design point is based on the use of ASHRAE data or a design point that is in the basis of design for the project. This approach can be detrimental and can result in a non optimal solution. In order to evaluate the benefits of power augmentation, users can use locally collected weather data, or recorded hourly bin data set from databases such as TMY, EWD, and IWS. This paper will cover a suggested approach for the analysis of climatic data for power augmentation applications and show how the selection of the design point can impact performance and economics of the installation. The final selection of the design point depends on the specific application, the revenues generated and installation costs. To the authors’ knowledge, this is the first attempt to treat this topic in a structured analytical manner by comparing available database information with actual climatic conditions.


2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Haixiang Zang ◽  
Qingshan Xu ◽  
Pengwei Du ◽  
Katsuhiro Ichiyanagi

A modified typical meteorological year (TMY) method is proposed for generating TMY from practical measured weather data. A total of eleven weather indices and novel assigned weighting factors are applied in the processing of forming the TMY database. TMYs of 35 cities in China are generated based on the latest and accurate measured weather data (dry bulb temperature, relative humidity, wind velocity, atmospheric pressure, and daily global solar radiation) in the period of 1994–2010. The TMY data and typical solar radiation data are also investigated and analyzed in this paper, which are important in the utilizations of solar energy systems.


Author(s):  
Manuel Ibañez ◽  
William A. Beckman ◽  
Sanford A. Klein

Abstract The clearness index for hourly and daily radiation is an important parameter in describing solar radiation. Liu and Jordan demonstrated that the monthly average daily clearness index could be used to predict the long-term distribution of daily solar radiation in a month. This paper reviews recent literature on the prediction of hourly and daily frequency distributions and cumulative frequency distributions of clearness indices. Ten years of measured weather data for six cities in the US are used to investigate the nature of the hourly and daily frequency distributions. A second set of ten years of data for six cities is used to verify the predictions. A bi-exponential probability density function is proposed that fits the observed bimodal nature of the data better than existing models. A case is made for the function being universal.


2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
M. S. Okundamiya ◽  
A. N. Nzeako

This study proposes a temperature-based model of monthly mean daily global solar radiation on horizontal surfaces for selected cities, representing the six geopolitical zones in Nigeria. The modelling was based on linear regression theory and was computed using monthly mean daily data set for minimum and maximum ambient temperatures. The results of three statistical indicators: Mean Bias Error (MBE), Root Mean Square Error (RMSE), andt-statistic (TS), performed on the model along with practical comparison of the estimated and observed data, validate the excellent performance accuracy of the proposed model.


2001 ◽  
Vol 124 (1) ◽  
pp. 28-33 ◽  
Author(s):  
Manuel Iban˜ez ◽  
William A. Beckman ◽  
Sanford A. Klein

The clearness index for hourly and daily radiation is an important parameter in describing solar radiation. Liu and Jordan demonstrated that the monthly average daily clearness index could be used to predict the long-term distribution of daily solar radiation in a month. This paper reviews recent literature on the prediction of hourly and daily frequency distributions and cumulative frequency distributions of clearness indices. Ten years of measured weather data for six cities in the U.S. are used to investigate the nature of the hourly and daily frequency distributions. A second set of ten years of data for six cities is used to verify the predictions. A bi-exponential probability density function is proposed that fits the observed bimodal nature of the data better than existing models. A case is made for the function being universal.


Data in Brief ◽  
2020 ◽  
Vol 33 ◽  
pp. 106397
Author(s):  
Marley Vanegas Chamorro ◽  
Edwin Espinel Blanco ◽  
Jhan Piero Rojas

2017 ◽  
Vol 17 (24) ◽  
pp. 15069-15093 ◽  
Author(s):  
Elizabeth C. Weatherhead ◽  
Jerald Harder ◽  
Eduardo A. Araujo-Pradere ◽  
Greg Bodeker ◽  
Jason M. English ◽  
...  

Abstract. Sensors on satellites provide unprecedented understanding of the Earth's climate system by measuring incoming solar radiation, as well as both passive and active observations of the entire Earth with outstanding spatial and temporal coverage. A common challenge with satellite observations is to quantify their ability to provide well-calibrated, long-term, stable records of the parameters they measure. Ground-based intercomparisons offer some insight, while reference observations and internal calibrations give further assistance for understanding long-term stability. A valuable tool for evaluating and developing long-term records from satellites is the examination of data from overlapping satellite missions. This paper addresses how the length of overlap affects the ability to identify an offset or a drift in the overlap of data between two sensors. Ozone and temperature data sets are used as examples showing that overlap data can differ by latitude and can change over time. New results are presented for the general case of sensor overlap by using Solar Radiation and Climate Experiment (SORCE) Spectral Irradiance Monitor (SIM) and Solar Stellar Irradiance Comparison Experiment (SOLSTICE) solar irradiance data as an example. To achieve a 1 % uncertainty in estimating the offset for these two instruments' measurement of the Mg II core (280 nm) requires approximately 5 months of overlap. For relative drift to be identified within 0.1 % yr−1 uncertainty (0.00008 W m−2 nm−1 yr−1), the overlap for these two satellites would need to be 2.5 years. Additional overlap of satellite measurements is needed if, as is the case for solar monitoring, unexpected jumps occur adding uncertainty to both offsets and drifts; the additional length of time needed to account for a single jump in the overlap data may be as large as 50 % of the original overlap period in order to achieve the same desired confidence in the stability of the merged data set. Results presented here are directly applicable to satellite Earth observations. Approaches for Earth observations offer additional challenges due to the complexity of the observations, but Earth observations may also benefit from ancillary observations taken from ground-based and in situ sources. Difficult choices need to be made when monitoring approaches are considered; we outline some attempts at optimizing networks based on economic principles. The careful evaluation of monitoring overlap is important to the appropriate application of observational resources and to the usefulness of current and future observations.


2021 ◽  
Author(s):  
Karel Jedlička ◽  
Pavel Hájek ◽  
Tomáš Andrš ◽  
Otakar Čerba ◽  
Jiří Valeš ◽  
...  

<p><span>Our contribution presents a prototype of Agroclimatic atlas - a web map application, presenting agroclimatic factors: </span><span>Frost-free period, </span>Water balance, Total precipitation, Total solar radiation, Last date with soil temperature above 10 °C for nitrogen application, Number of days with growing temperatures for a crop, Number of days with optimal growing temperatures for a crop HSU - Heat stress units for a crop, <span>The factors are calculated based on algorithms described in </span><em><span>Calculation of Agro-Climatic Factors from Global Climatic Data</span></em><span> (Jedlička et al. 2021, doi:  </span><span>10.3390/app11031245</span><span>).</span></p><p><span>The agroclimatic atlas application aims to provide a comprehensive overview of agriculture-related climatic characteristics of an area of interest in a time retrospective.  The application can be used by both an individual farmer or a precision farming expert exploring a wider area.</span></p><p><span>The principal source of climatic variables (such as temperature, rainfall, evaporation, runoff, and solar radiation) used in the atlas is the </span><span>ERA5-Land dataset</span><span> (available as the </span><span>Copernicus Climate Change Service (C3S) at its Climate Date Store</span><span>). </span></p><p><span>The contemporary version of the Agroclimatic Atlas application is accessible from here https://www.mdpi.com/2076-3417/11/3/1245#</span><span>. This version is in Czech only and portrays data from Czechia 10 years backward. However, the application is under ongoing development driven by the H2020 projects </span><span>Stargate</span><span>, </span><span>Sieusoil</span><span>, and </span><span>Smartagrihubs</span><span>. Therefore a newer version will be presented at the conference. The first design concepts can be seen in the figure below.</span></p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.475eafd0808065334309161/sdaolpUECMynit/1202SME&app=m&a=0&c=31dbfa2ddfd3719b82491d259ccc4117&ct=x&pn=gnp.elif&d=1" alt=""></p><p>Figure 1. - Mockup of Agroclimatic atlas application, accessible from https://xd.adobe.com/view/65199b72-db2f-420a-aee2-bc90dc83aaea-304a/</p>


1993 ◽  
Vol 44 (4) ◽  
pp. 713 ◽  
Author(s):  
PS Carberry ◽  
PS Carberry ◽  
RC Muchow ◽  
RC Muchow ◽  
RL McCown ◽  
...  

The establishment of a pulp and paper industry based on kenaf (Hibiscus cannabinus L.) in semi-arid northern Australia requires clear demonstration of the long-term production potential of kenaf in this region. Owing to the high rainfall variability both within and among seasons, it would be difficult to assess the potential of a new dryland industry from traditional experimentation. Accordingly, this study was undertaken to assess the climatic risks to dryland kenaf production in the Northern Territory (NT) using the kenaf simulation model NTKENAF, which has been developed and validated for this climatic zone. The kenaf model was run, using long-term historical weather data, to determine optimal sowing strategies and expected yields at four representative sites in the NT. In the NT, a codict existed between sowing early, with resulting long duration and high yield potential, but high probability of plant mortality, and sowing later, with more reliable plant population, but shorter duration and lower yields. A general recommendation over all sites was for a sowing window extending from the start of November to mid-December each year; lower yields were simulated for earlier sowing dates due to problems with crop establishment, and for later sowing dates due to crop growth extending past the end of the wet season in most years. However, in circumstances of high rainfall prior to November, there was a yield advantage at several sites from sowing early. Over the 100 years of climatic data for Katherine (14� 28'S.) and sowing when 30 mm rainfall occurred in a 5-day period after 1 November, simulated stem yields for kenaf ranged from 800 to 17200 kg ha-1, with a mean stem yield of 8673 kg ha-1 and coefficient of variation of 42%. At the higher rainfall site of Douglas Daly (13� 48'S.), over 21 seasons and using the same sowing criterion, stem yields ranged from 4490 to 19 200 kg ha-1, with a mean stem yield of 12 509 kg ha-1 and coefficient of variation of 27%. Simulated stem yields were higher at the wettest site of Adelaide River (13� 06'S.) and lowest at the driest site of Larrimah (15� 36'S.). In the planning of a potential kenaf industry in the northern Australia, this research study has provided the essential information of yield probability distributions for kenaf crops grown at selected sites in the NT.


Author(s):  
Mustapha Chaker ◽  
Cyrus B. Meher-Homji

There is a widespread interest in the application of gas turbine power augmentation technologies such as evaporative cooling and mechanical chilling in the mechanical drive and power generation markets. Very often, the selection of the design point is based on the use of American Society of Heating and Refrigeration Engineers (ASHRAE) data or a design point that is in the basis of design for the project. This approach can be detrimental and can result in a non optimal solution. In order to evaluate the benefits of power augmentation, users can use locally collected weather data, or recorded hourly bin data set from databases such as typical meteorological year (TMY), engineering weather data (EWD), and integrated weather surface (IWS). This paper will cover a suggested approach for the analysis of climatic data for power augmentation applications and show how the selection of the design point can impact performance. The final selection of the design point depends on the specific application, the revenues generated and installation costs. To the authors’ knowledge, this is the first attempt to treat this topic in a structured analytical manner by comparing available database information with actual climatic conditions.


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