Weather data around the world for design of field hospital HVAC

WEATHER DATA AROUND THE WORLD FOR DESIGN OF FIELD HOSPITALHVACLubos Forejt 1 , Jan Hensen 2 , Frantisek Drkal 1 a Petra Barankova 1www.cvut.cz, lubos.forejt@fs.cvut.cz1The Czech Technical University in Prague, 166 07 Prague, The Czech Republic2Technische Universiteit Eindhoven, P.O. Box 513, 5600 MB Eindhoven, The NetherlandsABSTRACTField hospital (FH) is a military mobile complex tobe deployed in almost any climate around the world.Heating, ventilation and air-conditioning (HVAC)system for the Czech Republic FH units is being redesigned.Computer simulation software will be usedfor the design of HVAC under variety of specificoutdoor conditions.Simulation software requires weather data tocalculate energy balance of buildings and HVACsystems. Currently there are several weather data setsavailable for this purpose. All contain weather databut they may differ significantly. Therefore theyshould be carefully selected prior to their use. Eventhough a lot of databases are available, there is pooraccess to proper data outside of U.S., WesternEurope and Japan, and in non-typical regions interms of simulation exercises (i.e. developingcountries).This paper reviews accessible weather data files andsuggests which weather data should be used fordesign and long-term performance analysis of FHHVAC system in various non-typical geographicallocations around the world.INTRODUCTIONThe Czech military FH is a mobile complex thatprovides most of the services found in commonhospitals, i.e. operating room, laboratories, computertomography, X-ray, etc. In the Czech FH these unitsare separated into individual containers which aretypically 2.1 m high with floor area from 18 to36 m 2 . A central tent corridor connects all thesesingle units. Unlike buildings usually simulated,containers are much smaller, have negligible thermalstorage of walls and often have no windows. Fieldhospitals are to be deployed in almost any placearound the world, i.e. climates ranging from cold (-19°C) to hot (45°C) including warm and humidtropics (32°C, 70% RH) regions.Weather conditions are different every year. Meanopencast temperature on the north hemispherefluctuates interannually by up to 0,4 K and thecurrent tendency shows that increased by 1 K withinthe last century (Glick 2004). Therefore, weatherdata essential in the design of HVAC systems shouldbe applied prudently.The manual load calculation using simplified weatherdata overestimates cooling load compared to thesimulation (Bowman et al. 2000). Moreover hospitalsare dynamic buildings in terms of cooling load andoccupancy which make the manual calculations morecomplicated. Hence energy simulations will beapplied for energy calculations of the FH.Accurate estimation of the building energyperformance requires knowledge of weather to whicha building is subjected. Modern design methods (i.e.building and system energy demand analysis) bycomputer simulations require one year of properhourly meteorological data. These data files normallycomprise of air temperature, solar radiation,humidity, wind velocity and wind direction. In somebuilding simulations there is a need for additionalweather data e.g. sky luminance, sky radianttemperature, snow cover, the concentration of gases(NO x , CO, CO 2 , O 3 ), pollen, etc. (Hensen 1999).Meteorological data are usually measured byclimatological agencies or other subjects at particulargeographical locations with certain time-step ofrecording (e.g. hourly, daily). Extreme, near-extreme,average or typical values might be constituted anddeduced from over 10 to 30 years of record. Howthese data are compiled what do they contain andhow they should be used is reviewed in this paper.Weather data sets in terms of simulation exercisessuitable for the FH buildings and HVAC systems arerecommended.WEATHER DATA SETSAvailable weather data files differ in the process theywere compiled, amount and type of offered data,accessibility of data from desired location, accuracy,availability, and applicability.Long-term average data setsA set of long-term average monthly values is thesimplest type of a database. Usually 10 to 30-yearperiod of record from measured data is employed.

These values are often taken as reference when newdata sets for energy simulations are validated.Near-Extreme data setsAnother way of processing and using measured datais to create near-extreme data sets. They are moreappropriate to assist the design of a plant andbuilding by simulation. Near-extreme data (i.e. drybulb and dew point temperature) in period of 1, 2, 3,5, and 7 days are available for North America(Colliver et al. 1998) and Europe (CIBSE 2002).However, the selection is often based just on nearextremetemperatures without accounting for solarand wind data (Levermore and Doylend 2002).Typical yearsNorth American locationsOnly the most common weather data sets for theNorth America are summarized. The intention is toshow different approach to creating typical yearsrather than complete detailed analysis.One of the earliest hourly weather data setsspecifically designed for use in building energysimulations is the Test Reference Year (TRY)(NCDC 1976). TRY’s for 60 locations in the UnitedStates are available. However, basic data of TRY didnot include any solar radiation. Another weaknesswas the data selection method. The TRY data are anactual historic year of weather from period of record(~1948-1975). The selection method excluded yearswith months with extremely high or low meantemperatures until one mild year remained.Typical Meteorological Year (TMY) (NCDC 1981)data set was developed to deal with limitations ofTRY. At 26 locations solar data were measured andfor the other 208 locations solar data were calculatedfrom cloud cover and type. The period of recordconsisted of 23 years (~1952-1975). TMY data filescontain months from a number of different years.Months were selected based on monthly compositeweighting of basic data. The months that were closestto the weighted long-term distribution were selected.This data set was later updated to TMY2 based onthe new period of record (1961-1990) (NREL 1995).To represent more typical weather patterns thaneither a single representative year or a grouping ofmonths, ASHRAE commissioned a research projectfinished with the weather data set Weather Year forEnergy Calculations (WYEC) (ASHRAE 1985). Thebasic method used to select data for WYEC was todetermine for each month of the year, the single, realmonth of hourly data whose mean dry-bulb airtemperature was closest to the average dry-bulbtemperature for that month in the 30-year period ofrecord. Then individual days from other months weresubstituted when those days helped bring the meanfor the month closer to the 30-year mean. Thedatabase was competed in 1983 and consists of 51sites in the North America.WYEC data set was upgraded to WYEC2 by TMYformat, adding hourly luminance data and dataquality assessment flags (indicating the process ofadjustment). An updated model for calculation ofsolar radiation from cloud cover data was used forsolar radiation components and illuminance data. TheNational Renewable Energy Laboratory (NREL)working with ASHRAE processed the existingWYEC and TMY data to crate 77 WYEC2 formatfiles (Stoffel and Rymes 1998).Non-North American locationsENISO15927-4 is a new international standard thatshould serve as a guideline to production of typicalyears from available meteorological data.“European” Test Reference Year (TRY) and DesignReference Year (DRY) were tested and the latterproposed to form a basis of this new standard.The “European” TRY is similar to TMY (NCDC1981). The “European” TRY selection process isspecified to try to obtain (a) the mean, (b) thefrequency distribution of individual variables, and (c)the implied correlations between the differentvariables within each month of the long-term dataset. Selection method is based on dry-bulbtemperature, solar radiation, and humidity but notwind speed. The month with the lowest deviation isselected as the month to be included.DRY is an attempt to modify the TRY to be evenmore like year average months by adjusting theselected months. The parameters such as dry bulbtemperature, solar radiation, and humidity, but notwind are adjusted by replacing certain days with thedays from other years, but in the same month.Although this is referred to as a design reference yearit is not a near-extreme year that could be used forHVAC design (Levermore and Doylend 2002).SHASE developed new weather data file that is anexpansion from the original AutomatedMeteorological Data Acquisition System (AMeDAS)for building energy calculations in Japan (Akasaka etal. 2000). Standard expanded AMeDAS is similar toTRY (European), DRY, and TMY. The data werecompiled from 15-year record of weather data.Typical Meteorological Year data set was developedfor 69 Chinese locations (Zhang et al. 2002) using amethodology similar to that used for the TMY data.These TMYs were produced from 16 years weatherhistory (1982-1997) reported from Chinese airportsand obtained from the U.S. NCDC.

Few more typical years are currently being createdfrom measured data sets e.g. in Czech Republic,Greece, Turkey, Egypt, Syria. They are Mostly basedon the above mentioned principles.Locations throughout the world227 locations outside the U.S. are available in theInternational Weather for Energy Calculations(IWEC) weather files that were developed under theASHRAE research project RP-1015 (Thevenard andBrunger 2002a). TMY procedure was chosen forselecting representative years from up to 18-yearsequences of weather data. The 12 typicalmeteorological months were chosen from the set ofavailable candidate months by comparing statisticsfor candidate months against corresponding longtermmonthly statistics based on daily total globalradiation, dry-bulb temperature, dew-pointtemperature, and wind speed. The selection wasbased on the weights for particular meteorologicalvalue. Emphasis was put on the daily solar radiationand the mean dry-bulb temperature (40% and 30 %respectively). Depending on how the solar radiationmodel performed compared with long-term data theIWEC files were divided into three Categories.Category 1 corresponded well, 2 poorly, and forCategory 3 were no comparable data. Category 2applies particularly to the sites around tropics in thesense that the model fails to predict the wide range ofradiation conditions experienced at the site. A plot ofthe sites can be found in Figure 1 (Thevenard andBrunger 2002b).Figure 1. IWEC stations worldwide (Adopted fromThevenard and Brunger 2002b)Databases of long-term measured dataFor many locations outside the U.S., Canada,Western Europe, Japan, there are no weather datareadily available for building simulation (see e.g.IWEC stations in Figure 1). However, for mostlocations the long-term (monthly) averages and otherstatistics of the major weather variables could befound registers or meteorological publications.Weatherbank is one of the companies offering paidhourly and daily climatic data. Hourly data areavailable since 1994. In some cases daily valuesreach back to the middle of the 19 th century. Some ofthe incomplete data are compensated based onclimate knowledge from particular region. Offeredhistorical database consist of about 20 parameters.National Oceanographic and AtmosphericAdministration (NOAA) offers paid integratedhourly data deduced from the hourly data of NCDC,Navy surface hourly data, etc. This database calledInternational Surface Weather Observations(INSWO) contains hourly synoptic data from theperiod of 1982-1997 from approximately 1500worldwide locations.An example of commercial software which generatesweather data is METEONORM. The latest version(5.1) includes the climatological data from the years1961-90 from about 7400 stations around the world,where 1000 measured also solar radiation. These dataobtained also from the Global Energy BalanceArchive (GEBA) and from the World MeteorologicalOrganization (WMO) mainly contain averagemonthly values of air temperature, humidity, rain,sunshine duration, and days with rain. For remotelocations (more that 300 km from the station) thesolar radiation is interpolated with data from satelliteobservations.World Wide Web sources of weather dataAnother source of abundant weather data is freelyavailable on the World Wide Web. Ku (1999) gives asummary of information sources dividing them intofour categories: Weather - specific sites;Meteorological offices and weather/climate researchinstitutes; News and travel sites; Internet directoriesand Internet service providers. Internet sitesoutnumber the paid databases however they offer noguarantee of accuracy, reliability, completeness,updates or wide range of locations, etc.ASHRAE research project RP-1170 developed a newsystem, which provides its members with access toinformation on sources of international weather dataon the Web. A Global Weather Data Source(GWDS) database enables users to select a country,choose a type of data (e.g. hourly, monthly) andclimatic parameter of interest, and it then gives a listof data sources with information how to obtain thesedata (Plantico et al. 2002).Even historical data with all required valuesincluding solar radiation could be found free on theWeb for some U.S. locations. Other regions of theworld have the data much less complete, e.g. onlyaverage month temperatures, precipitation, butfurther information is missing. Some of the sources,where the data from a large number of stations orfrom longer period could be found are listed below:

EnergyPlus – contains measured data from the past 5years, often with missing data that additionalprogram may substitute.GCOS Surface Network (GSN) – daily, monthly, andannually averages, often from the past 40 years.Weather data from 439 stations in 52 countriesexcluding Western Africa and South Asia areavailable.NOAA „Climates of the world“ is a documentcontaining average and extreme daily temperaturesfor over 800 locations around the world. Averagetemperatures are presented for January, April, July,and October. Average monthly precipitation valuesare shown for each location.Weatherbase provides climatic data from most of thecountries around the world. Generally, severalmeasured locations within one country are available.Data consist of annual and monthly averagetemperatures and precipitation deduced from about30-year period.DISCUSSIONHarriman et al. (1996) published a concise review ofcommonly used weather data types including theirsource and common application for various regionsaround the world (Table 2). Important are the firstand the third column, where the type and use ofparticular weather data are shown. Table 2 furtherrecommends using long-term extreme data for sizingof equipment and typical year of weather data forlong-term behavior and energy consumption. Fornon-typical locations e.g. tropics in South America,locations in Africa generally, and south Asia (i.e.range of climatic zones where the deployment of FHis assumed) only long-term means or historical data(Meteonorm, INSWO) are available. These could beprocessed and used for the FH, i.e. near-extremes forsizing HVAC and long-term means for estimatinglong-term behavior and energy consumption.Table 2. Common types and sources of weather data. Adapted from Harriman et al. (1999)Sizing EquipmentUse Source Data type Coverage PublisherMonitoring andTroubleshootingInstalled EquipmentEstimating Long- termBehavior and EnergyConsumption1997 ASHRAE Handbook –FundamentalsSequences of extreme temperatureand humidityHourly weather data archiveTypical meteorological years – 2(TMY-2)Weather years for EnergyCalculations – 2 (WYEC-2)Canadian Weather Year for EnergyCalculations (CWEC)Example Weather Year (EWY)Test Reference Year (TRY ) andDesign Reference Year (DRY)Solar and Meteorological SurfaceObservational Network (SAMSON)Long-termextremesCurrent hourlydry bulb temp.and humidityTypical hourlyobservations1459 U.S. andint’l locations320 U.S. andCAN locations240 U.S. andCanadianlocations239 U.S.locations withPuerto Rico76 U.S.locations145 Canadianlocations15 locations inGreat Britain156 locations inEurope, Russiaand Turkey237 U.S.locationsASHRAE,GRIASHRAEGRIGRIASHRAEAESCIBSECECNOAASimulating EquipmentBehavior for a SpecificYearCanadian Weather for Energy andEngineering (CWEEDS)MeteonormInternational Surface WeatherObservations (INSWO)Actual hourlyobservations forspecific years145 Canadianlocations7405 worldwidelocations1500 worldwidelocationsAESGEBA,WMONOAA

He concludes that, users of energy simulationprograms should avoid using single-year, TRY-typeweather data (i.e. NCDC 1976). No single-year canrepresent the typical long-term weather patterns(Crawley 1998, Huang 1998). TMY2 and WYEC2were recommended since they better correspond tolong-term averages.In terms of weather data reference years vs. historicclimatological data, there is strong evidence that careis needed when using reference years. The majorproblem is that each reference year is designed with acertain purpose in mind, i.e. to accurately predict theannual energy consumption of an "average" building.For example, for a building simulation of particulartype (i.e. a building without windows and withoutnatural ventilation openings i.e. container of the FH)only the temperature is relevant, so a specific TRYshould be developed using a high weighting factorfor the temperature. In case of HVAC plantsimulation, temperature and relative humidity areimportant. For another building (solar collector like)it might be the radiation which is much moredominant, and for yet another building (whichcompletely depends on wind driven naturalventilation) the wind speed and wind direction mightbe the dominant variables. The European TRY (orTMY in U.S.) will somehow assume ‘an averagebuilding’; whereas in reality there are many nonaverage buildings, which, ideally, should have theirown TRY. For a recent overview of the various TRYgeneration methodologies, see Argiriou et al. (1999).Another approach would be to create a typicalweather file that has three years: typical (average),cold/cloudy, and hot/sunny. This would capture morethan the average or typical conditions and providesimulation results that identify some of theuncertainty and variability inherent in weather(Crawley 1998).CONCLUSIONSObjective of computer simulations and type andlocation of investigated building should be carefullyconsidered prior to weather data selection.Based on presented review the near-extrememeasured data sets from INSWO or Meteonormdatabase are applicable for the field hospital HVACsizing simulations.Production of DRY from available meteorologicaldata especially for units of FH seems to be the bestbut also the most difficult approach towards theenergy simulations.No worldwide database offering typical years in mostof the non-typical regions was found. Moreover,there is poor access to national databases in nontypicalregions in terms of simulation exercises.Therefore, long-term averages will be used forperformance analysis of HAVC plant in theseparticular locations.ACKNOWLEDGEMENTSThis study was supported by the Czech Ministry ofDefense Gant 2805.REFERENCESAkasaka H., Hideyo N., Soga. K. et al.,“Development of Expanded AMeDAS weather datafor building calculation in Japan“, ASHRAETransactions: Symposia, 2000, Vol. 106, pp. 455-465Argiriou A., Lykoudis S., Kontoyiannidis S., et al.,“Comparison of methodologies for TMY generationusing 20 years data for Athens, Greece”, SolarEnergy, 1999, Vol. 66, no. 1, pp. 33-45.ASHRAE, “Weather Year for Energy Calculations”,1985, Atlanta: American Society of Heating,Refrigerating, and Air-Conditioning Engineers, Inc.Bowman G., Holmes M. J., Levermore G. J.,“Comparison of manual load calculation usingsimplified weather data with simulation and hourlyweather data.” ASHRAE Transactions: Symposia2000, Vol. 106, pp. 475-481.CIBSE, “CIBSE Guide J Weather Solar andIlluminance Data”, 2002, London: The CharteredInstitution of Building Services Engineers.Colliver, D. G., Gates R. S., Zhang HY., Priddy K.T., “Sequences of extreme temperature and humidityfor design conditions (RP828)”, ASHRAETransactions, 1998, Vol. 104(1), pp. 133-144.Crawley, D. B., “Which weather data should you usefor energy simulations of commercial buildings?”,ASHRAE Transactions: Symposia. 1998, vol. 104,pp. 498-515.EN ISO 15927-4:2005, Hygrothermal performanceof buildings - Calculation and presentation ofclimatic data - Part 4: Hourly data for assessing theannual energy use for heating and cooling,International Organization for Standardization,SwitzerlandEnergyPlus, www.eren.doe.gov/buildings/energy_toolsGlick D., “Oteplení a geologie”, NationalGeographic Journal-Czech Issue, 2004, September,pp. 44-63.GSN, www.ncdc.noaa.gov/oa/climate/gsn/gsnmap.html

Harriman L.G., Colliver D.G., and Quinn Hart K.1999. “New weather data for energy calculations”,ASHRAE Journal, vol. 41, no. 3, pp. 31-38.Hensen J.L.M, “Simulation of building energy andindoor environmental quality - some weather dataissues“, in Proc. Int. Workshop on Climate data andtheir applications in engineering, 1999, CzechHydrometeorological Institute in Prague.Huang J., “The impact of different weather data onsimulated residential heating and cooling loads”,ASHRAE Transactions: Symposia, 1998, vol. 104,pp. 516-527.Ku A., “Weather Sources on the Web“, in Free Pint.ISSN 1460-7239, 1999, http://www.freepint.co.uk/Levermore, G. J., Doylend, N. O., “North Americanand European hourly based weather data andmethods for HVAC Building Energy Analyses anddesign by simulation”, ASHRAE Transactions:Symposia, 2002, vol. 108, pp. 1053-1062.METEONORM, http://www.meteotest.chNCDC, “Test Reference Year (TRY), tape referencemanual, TD-9706”, 1976, Asheville, North Carolina:National Climatic Data Center, U.S. Department ofCommerce.NCDC, “Typical meteorological year use’s manual.TD-9734, hourly solar radiation – Surfacemeteorological observations”, 1981, Asheville, NorthCarolina: National Climatic Data Center, U.S.Department of Commerce.NOAA, International Surface Weather Observations,http://ols.nndc.noaa.gov/plolstore/plsql/olstore.prodspecific?prodnum=C00442-CDR-A0001NREL, “User’s manual for TMY2s (TypicalMeteorological Years), NREL/SP-463-7668, andTMY2s, Typical Meteorological Years derived fromthe 1961-1990 national solar radiation database”,1995, CD-ROM, Golden, Colorado: NationalRenewable Energy Laboratory.Plantico M. S., Whitehurst T., Creech T. G., “Webaccess to information on international weather datasources”, ASHRAE Transactions: Research, 2002,Vol. 108, pp. 487-496.Stoffel T. L., and Rymes M. D., “Production of theweather year for energy calculations version 2(WYEC2), data files”, 1998, ASHRAE Transactions:Symposia. 1998, vol. 104, pp. 487-497.Thevenard, D. J., Brunger A. P., “The developmentof typical weather years for international locations:Part I, Algorithms”, ASHRAE Transactions:Research. 2002a, vol. 108, pp. 376-383.Thevenard, D. J., Brunger A. P., “The developmentof typical weather years for international locations:Part II, Production”, ASHRAE Transactions:Research. 2002b, vol. 108, pp. 480-486.WEATHERBASE, http://www.weatherbase.comZhang Q., Huang J., Lang S., “Development oftypical year weather data for Chinese locations”,ASHRAE Transactions: Symposia, 2002, Vol. 108,pp. 1063-1075.NOMENCLATUREAES - Atmospheric Environment Service (CA)AMeDAS – Automated Meteorological DataAcquisition System (Jap)ASHRAE - American Society of Heating,Refrigerating and Air-Conditioning EngineersCEC - Commission of the European CommunityCIBSE - Chartered Institution of Building ServicesEngineers (UK)CWEC - Canadian Weather for Energy CalculationsDOE – Department of EnergyDRY - Design Reference YearEWY - Example Weather Year (UK)FH – field hospitalGEBA – Global Energy Balance ArchiveGRI - Gas Research Institute (USA)GSN – Global Surface NetworkGWDS – Global Weather Data SourceHVAC - Heating, Ventilation and Air-ConditioningINSWO - International Surface WeatherObservationsIWEC – International Weather for EnergyCalculationsNCDC - National Climatic Data CenterNOAA - National Oceanographic and AtmosphericAdministration (USA)SHASE - Society of Heating, Air-conditioning andSanitary Engineers of JapanTMY - Typical Meteorological Year (USA, EU)TRY - Test Reference Year (EU, USA)WMO – World Meteorological OrganizationWRDC – World Radiation Data CentreWYEC - Weather Year for Energy Calculations