Annual climatic design conditions are contained in the first three sections following the top part of the table. They contain information as follows:

Monthly design conditions are divided into subsections as follows:

Monthly Design Dry-Bulb, Wet-Bulb, and Mean Coincident Temperatures.

These values are derived from the same analysis that results in the annual design conditions. The monthly summaries are useful when seasonal variations in solar geometry and intensity, building or facility occupancy, or building use patterns require consideration. In particular, these values can be used when determining air-conditioning loads during periods of maximum solar radiation. The values listed in the tables include

For a 30-day month, the 0.4, 2.0, 5.0 and 10.0% values of occurrence represent the value that occurs or is exceeded for a total of 3, 14, 36, or 72 h, respectively, per month on average over the period of record. Monthly percentile values of dry- or wet-bulb temperature may be higher or lower than the annual design conditions corresponding to the same nominal percentile, depending on the month and the seasonal distribution of the parameter at that location. Generally, for the hottest or most humid months of the year, the monthly percentile value exceeds the design condition for the same element corresponding to the same nominal percentile. For example, Table 1 shows that the annual 0.4% design dry-bulb temperature at Atlanta, GA, is 94.0°F; the 0.4% monthly dry-bulb temperature exceeds 94.0°F for June, July, and August, with values of 94.5, 97.6, and 97.4°F, respectively. Fifth and tenth percentiles are also provided to give a greater range in the frequency of occurrence, in particular providing less extreme options to select for design calculations.

A general, very approximate rule of thumb is that the n% annual cooling design condition is roughly equivalent to the 5n% monthly cooling condition for the hottest month; that is, the 0.4% annual design dry-bulb temperature is roughly equivalent to the 2% monthly design dry-bulb temperature for the hottest month; the 1% annual value is roughly equivalent to the 5% monthly value for the hottest month, and the 2% annual value is roughly equivalent to the 10% monthly value for the hottest month.

 Mean Daily Temperature Range. These values are useful in calculating daily dry- and wet-bulb temperature profiles, as explained in the section on Generating Design-Day Data. Three kinds of profile are defined:

 Clear-Sky Solar Irradiance. Clear-sky irradiance parameters are useful in calculating solar-related air conditioning loads for any time of any day of the year. Parameters are provided for the 21st day of each month. The 21st of the month is usually a convenient day for solar calculations because June 21 and December 21 represent the solstices (longest and shortest days) and March 21 and September 21 are close to the equinox (days and nights have the same length). Parameters listed in the tables are

 All-Sky Solar Radiation. All-sky solar radiation parameters are useful for evaluating the potential of solar technologies (e.g., solar heating, photovoltaics), which are valuable in the design of net-zero energy buildings. Parameters listed in the tables are

The following primary sources of observational data sets were used in calculating design values:

In most cases, the period of record used in the calculations spanned 25 years (1990 to 2014). This choice of period is a compromise between trying to derive design conditions from the longest possible period of record, and using the most recent data to capture climatic or land-use trends from the past two decades. The actual number of years used in the calculations for a given station depends on the amount of missing data, and, as discussed in the next section, may be as little as 8 years. The first and last years of the period of record used to calculate design conditions are listed in the top section of the tables of climatic design conditions, as shown in Table 1 for Atlanta. For a limited number of stations, years as far back as 1982 or as recent as 2015 were used instead of 1990 to 2014 because that time frame lacked the necessary data.

Precipitation data were derived from a number of sources, including station data from the Global Historical Climatology Network, version 2 (GHCN 2015) and the United Nations Food and Agriculture Organization (FAO 2011), as well as gridded data from the Global Precipitation Climatology Centre, version 7 (GPCC 2015), and the Global Precipitation Climate Project (GPCP).

Clear-sky solar irradiance parameters listed in the tables constitute a simple parameterization of the more sophisticated REST2 broadband clear-sky radiation model (Gueymard 2008; Gueymard and Thevenard 2009; Thevenard 2009). The REST2 model requires detailed knowledge of various atmospheric constituents, such as aerosols, water vapor, or ozone. To extend applicability of the model to the whole world, multiple data sets, mainly derived from space observations and reanalysis models, were used to obtain these inputs. These sources of data have changed or have been updated since the 2013 edition, which explains the coincident changes in site-specific coefficients. Water vapor, ozone, and ground albedo data are now derived from the National Aeronatucis and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis dataset (Molod et al. 2015), corrected for elevation in the case of water vapor (Gueymard and Thevenard 2009). The period of data is now uniform and longer, from 2000 to 2014. An exception is nitrogen dioxide, for which a database from Ozone Monitoring Instrument (OMI) satellite observations (aura.gsfc.nasa.gov/omi.aspx) is used over the period 2005 to 2014. Pressure is estimated from station’s elevation.

Aerosol turbidity data (in the form of separate evaluations of aerosol optical depth and Ångström exponent) received special attention, because they are the primary inputs that affect the accuracy of direct and diffuse irradiance predictions under clear skies. Spaceborne retrievals of aerosol optical depth at various wavelengths from NASA’s Multi-angle Imaging SpectroRadiometer (MISR; www-misr.jpl.nasa.gov) and two Moderate Resolution Imaging Spectroradiometer (MODIS; modis-atmos.gsfc.nasa.gov) instruments were used between 2000 and 2014 and compared to reference data from a large number of ground-based sites, mostly from the Aerosol Robotic Network (AERONET; aeronet.gsfc.nasa.gov), after appropriate scale-height corrections to remove artifacts from the effect of elevation (Gueymard and Thevenard 2009). Regional corrections of the satellite data were devised to remove as much bias as possible, compared to the reference ground-based data. To fill missing data or correct biased satellite observations, modeled aerosol datasets were used, including 10 years (2003 to 2012) of simulated monthly-average aerosol optical depth from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis model (Eskes et al. 2015; Inness et al. 2013) and 13 years (2002 to 2014) of MERRA-2 reanalysis data (Molod et al. 2015). Results from the REST2 model (Gueymard 2008) were then fitted to the simple two-parameter model described in this chapter. The fits enable a concise formulation requiring tabulation, on a monthly basis, of only two parameters per station, referred to here as the clear-sky beam and diffuse optical depths. Details about the fitting procedure can be found in Thevenard and Gueymard (2013).

Global horizontal irradiance at the surface, and its standard deviation, were calculated from the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) dataset (ceres.larc.nasa.gov/products.php?product=EBAF-Surface). From the available 1°×1° dataset, a bilinear interpolation, without altitude adjustment, was made given the station latitude and longitude for the period 2000 to 2014.

Values of ambient dry-bulb, dew-point, and wet-bulb temperature and wind speed corresponding to the various annual percentiles represent the value that is exceeded on average by the indicated percentage of the total number of hours in a year (8760). The 0.4, 1.0, 2.0, and 5.0% values are exceeded on average 35, 88, 175, and 438 h per year, respectively, for the period of record. The design values occur more frequently than the corresponding nominal percentile in some years and less frequently in others. The 99.0 and 99.6% (cold-season) values are defined in the same way but are usually viewed as the values for which the corresponding weather element is less than the design condition for 88 and 35 h, respectively.

Simple design conditions were obtained by binning hourly data into frequency tables, then deriving from the binned data the design condition having the probability of being exceeded a certain percentage of the time. Mean coincident values were obtained by double-binning the hourly data into joint frequency matrices, then calculating the mean coincident value corresponding to the simple design condition.

Coincident temperature ranges were also obtained by double-binning daily temperature ranges (daily maximum minus minimum) versus maximum daily temperature. The mean coincident daily range was then calculated by averaging all bins above the simple design condition of interest.

The weather data sets used for the calculations often contain missing values (either isolated records, or because some stations report data only every third hour). Gaps up to 6 h were filled by linear interpolation to provide as complete a time series as possible. Dry-bulb temperature, dew-point temperature, station pressure, and humidity ratio were interpolated. However, wind speed and direction were not interpolated because of their more stochastic and unpredictable nature.

Some stations in the ISD data set also provide data that were not recorded at the beginning of the hour. When data at the exact hour were missing, they were replaced by data up to 0.5 h before or after, when available.

Finally, psychrometric quantities such as wet-bulb temperature or enthalpy are not contained in the weather data sets. They were calculated from dry-bulb temperature, dew-point temperature, and station pressure using the psychrometric equations in Chapter 1.

Measures were taken to ensure that the number and distribution of missing data, both by month and by hour of the day, did not introduce significant biases into the analysis. Annual cumulative frequency distributions were constructed from the relative frequency distributions compiled for each month. Each individual month’s data were included if they met the following screening criteria for completeness and unbiased distribution of missing data after data filling:

Although the nominal period of record selected for this analysis was 25 years (1990 to 2014 for most stations), some variation and gaps in observed data meant that some months’ data were unusable because of incompleteness. Some months were also eliminated during additional quality control checks. A station’s dry-bulb temperature design conditions were calculated only if there were data from at least 8 months that met the quality control and screening criteria from the period of record for each month of the year. For example, there had to be 8 months each of January, February, March, etc. for which data met the completeness screening criteria. These criteria were ascertained from results of RP-1171 (Hubbard et al. 2004) and were the same as used in calculating the design conditions in the 2001 to 2013 editions of the ASHRAE Handbook—Fundamentals.

Dew-point temperature, wet-bulb temperature, and enthalpy design conditions were calculated for a given month only if the number of dew-point, wet-bulb, or enthalpy values was greater than 85% of the minimum number of dry-bulb temperature values defined previously; wind speed and direction conditions were calculated for a given month only if the number of values was greater than 28.3% (i.e., one-third of 85%) the minimum number of dry-bulb temperature values. For example, a month of January was included in calculations if the number of dry-bulb temperature values exceeded 85% of 744 h, or 633 h. The month was included in calculation of dew-point temperature design conditions only if dew-point temperature was present for at least 85% of 633 h, or 538 h. The month was included in calculation of wind speed design conditions only if wind speed was present for at least 28.3% of 633 h, or 179 h.

Annual dry-bulb temperature extremes were calculated only for years that were 85% complete. At least 8 annual extremes were required to calculate the mean and standard deviation of extreme annual dry-bulb temperatures.

Daily minimum and maximum temperatures were calculated only for complete days; so were daily temperature ranges and mean coincident temperature ranges.

Details about quality checks and other steps taken during data processing to ensure results as free from error as possible are detailed in Roth (2017).

Climatic design values in this chapter represent different psychrometric conditions. Design data based on dry-bulb temperature represent peak occurrences of the sensible component of ambient outdoor conditions. Design values based on wet-bulb temperature are related to the enthalpy of the outdoor air. Conditions based on dew point relate to the peaks of the humidity ratio. The designer, engineer, or other user must decide which set(s) of conditions and probability of occurrence apply to the design situation under consideration. Additional sources of information on frequency and duration of extremes of temperature and humidity are provided in the section on Other Sources of Climatic Information. Further information is available from Harriman et al. (1999). This section discusses the intended use of design conditions in the order they appear in Table 1.

Annual Heating and Humidification Design Conditions. The month with the lowest mean dry-bulb temperature is used, for example, to determine the time of year where the maximum heating load occurs.

The 99.6 and 99.0% design conditions are often used in sizing heating equipment.

The humidification dew-point and mean coincident dry-bulb temperatures and humidity ratio provide information for cold-season humidification applications.

Wind design data provide information for estimating peak loads accounting for infiltration: extreme wind speeds for the coldest month, with the mean coincident dry-bulb temperature; and mean wind speed and direction coincident to the 99.6% design dry-bulb temperature.

Annual Cooling, Dehumidification, and Enthalpy Design Conditions. The month with the highest mean dry-bulb temperature is used, for example, to determine the time of year where the maximum sensible cooling load occurs, not taking into account solar loads.

The mean daily dry-bulb temperature range for the hottest month is the mean difference between the daily maximum and minimum temperatures during the hottest month and is calculated from the extremes of the hourly temperature observations. The true maximum and minimum temperatures for any day generally occur between hourly readings. Thus, the mean maximum and minimum temperatures calculated in this way are about 1°F less extreme than the mean daily extreme temperatures observed with maximum and minimum thermometers. This results in the true daily temperature range generally about 2°F greater than that calculated from hourly data. The mean daily dry-bulb temperature range is used in cooling load calculations.

The 0.4, 1.0, and 2.0% dry-bulb temperatures and mean coincident wet-bulb temperatures often represent conditions on hot, mostly sunny days. These are often used in sizing cooling equipment such as chillers or air-conditioning units.

Design conditions based on wet-bulb temperature represent extremes of the total sensible plus latent heat of outdoor air. This information is useful for design of cooling towers, evaporative coolers, and outdoor-air ventilation systems.

The mean wind speed and direction coincident with the 0.4% design dry-bulb temperature is used for estimating peak loads accounting for infiltration.

Design conditions based on dew-point temperatures are directly related to extremes of humidity ratio, which represent peak moisture loads from the weather. Extreme dew-point conditions may occur on days with moderate dry-bulb temperatures, resulting in high relative humidity. These values are especially useful for humidity control applications, such as desiccant cooling and dehumidification, cooling-based dehumidification, and outdoor-air ventilation systems. The values are also used as a check point when analyzing the behavior of cooling systems at part-load conditions, particularly when such systems are used for humidity control as a secondary function. Humidity ratio values are calculated from the corresponding dew-point temperature and the standard pressure at the location’s elevation.

Annual enthalpy design conditions give the annual enthalpy for the cooling season; this is used for calculating cooling loads caused by infiltration and/or ventilation into buildings. Enthalpy represents the total heat content of air (the sum of its sensible and latent energies). Cooling loads can be calculated knowing the conditions of both the outdoor ambient and the building’s interior air.

The extreme maximum wet-bulb temperature provides the highest wet-bulb temperature observed over the entire period of record and is the most extreme condition observed during the data record for evaporative processes such as cooling towers. For most locations, the extreme maximum wet-bulb value is significantly higher than the 0.4% wet-bulb (discussed previously) and should be used only for design of critical applications where an occasional short-duration capacity shortfall is not acceptable.

Extreme Annual Design Conditions. Extreme annual design wind speeds are used in designing smoke management systems.

The mean and standard deviation of the extreme annual maximum and minimum dry-bulb temperatures are used to calculate the probability of occurrence of very extreme conditions. These can be required for design of equipment to ensure continuous operation and serviceability regardless of whether the heating or cooling loads are being met. These values were calculated from extremes of hourly temperature observations. The true maximum and minimum temperatures for any day generally occur between hourly readings. Thus, the mean maximum and minimum temperatures calculated in this way are about 1°F less extreme than the mean daily extreme temperatures observed with maximum and minimum thermometers.

The 5-, 10-, 20- and 50-year return periods for maximum and minimum extreme dry-bulb temperature are also listed in the table. Return period (or recurrence interval) is defined as the reciprocal of the annual probability of occurrence. For instance, the 50-year return period maximum dry-bulb temperature has a probability of occurring or being exceeded of 2.0% (i.e., 1/50) each year. This statistic does not indicate how often the condition will occur in terms of the number of hours each year (as in the design conditions based on percentiles) but describes the probability of the condition occurring at all in any year. The following method can be used to estimate the return period (recurrence interval) of extreme temperatures:

where

Tn	=	n-year return period value of extreme dry-bulb temperature to be estimated, years
M	=	mean of annual extreme maximum or minimum dry-bulb temperatures, °F
s	=	standard deviation of annual extreme maximum or minimum dry-bulb temperatures, °F
I	=	1 if maximum dry-bulb temperatures are being considered
	=	–1 if minimum dry-bulb temperatures are being considered
F	=

For example, the 50-year return period extreme maximum dry-bulb temperature estimated for Atlanta, GA, is 106.2°F (according to Table 1, M = 96.6°F, s = 3.7, and n = 50; I = 1). Similarly, the 50-year return period extreme minimum dry-bulb temperature for Atlanta, GA, is 3.0°F [M = 15.0°F, s = 4.6, and n = 50; I = −1]. The n-year return periods can be obtained for most stations using ASHRAE’s Weather Data Viewer 6.0 (ASHRAE 2017), which is discussed in the section on Other Sources of Climatic Information.

New in 2017 are the parameters required to calculate the 5-, 10-, 20- and 50-year return periods for maximum and minimum extreme wet-bulb temperature. The maximum conditions in particular may be useful in determining very extreme wet-bulb temperatures during which evaporative systems may have to operate.

Calculation of the n-year return period is based on assumptions that annual maxima and minima are distributed according to the Gumbel (Type 1 Extreme Value) distribution and are fitted with the method of moments (Lowery and Nash 1970). The uncertainty or standard error using this method increases with standard deviation, value of return period, and decreasing length of the period of record. It can be significant. For instance, the standard error in the 50-year return period maximum dry-bulb temperature estimated at a location with a 12-year period of record can be 5°F or more. Thus, the uncertainties of return period values estimated in this way are greater for stations with fewer years of data than for stations with the complete period of record from 1990 to 2014.

Temperatures, Degree-Days, and Degree-Hours. Monthly average temperatures and standard deviation of daily average temperatures are calculated using the averages of the minimum and maximum temperatures for each complete day within the period analyzed. They are used to estimate heating and cooling degree-days to any base, as explained in the section on Estimation of Degree-Days.

Heating and cooling degree-days (base 50 or 65°F) are calculated as the sum of the differences between daily average temperatures and the base temperature. For example the number of heating degree-days (HDD) in the month is calculated as

where N is the number of days in the month, T_base is the reference temperature to which the degree-days are calculated, and T̄_i is the mean daily temperature calculated by adding the maximum and minimum temperatures for the day, then dividing by 2. The + superscript indicates that only positive values of the bracketed quantity are taken into account in the sum. Similarly, monthly cooling degree-days (CDD) are calculated as

Degree-days are used in energy estimating methods, and to classify stations into climate zones for ASHRAE Standard 169.

Monthly Design Dry-Bulb and Mean Coincident Wet-Bulb Temperatures. These values provide design conditions for processes driven by dry-bulb air temperature. In particular, air-conditioning cooling loads are generally based on dry-bulb design conditions (plus clear-sky solar irradiance).

Monthly Design Wet-Bulb and Mean Coincident Dry-Bulb Temperatures. Wet-bulb design conditions are of use in analysis of evaporative coolers, cooling towers, and other equipment involving evaporative transfer. Note also that air wet-bulb temperature and enthalpy are closely related, so applications with large ventilation flow rates may have maximum cooling requirements under high wet-bulb conditions.

Mean Daily Temperature Range. Mean daily range values are computed using all days of the month, as opposed to coincident values that derive from design days. Mean daily range values have been published in previous Handbook editions and are included for completeness. Coincident daily range values should be used for generating design-day profiles.

Clear-Sky Solar Irradiance. Clear-sky solar irradiance data are used in load calculation methods. Beam normal irradiance refers to solar radiation emanating directly from the solar disk and measured perpendicularly to the rays of the sun. Diffuse horizontal irradiance refers to solar radiation emanating from the sky dome, sun excluded, and measured on a horizontal surface. Because the beam and diffuse irradiances vary during the course of the day, current load calculation methods require their estimation at various times, which can be done with the method described in the section on Calculating Clear-Sky Solar Radiation. The method uses the clear-sky optical depths τ_b and τ_d, listed in Table 1 as taub and taud, respectively, as inputs. Clear-sky beam normal and diffuse horizontal irradiances at solar noon are also listed in Table 1 for convenience.

All-Sky Solar Radiation. All-sky solar radiation data are used in the design of solar energy systems (either thermal or photovoltaic). Monthly average daily radiation on the horizontal refers to average amount of solar radiation received on a horizontal surface during the course of a day, for the month under consideration. The standard deviation of monthly average daily radiation on the horizontal is the standard deviation of the previous monthly quantity, calculated over the period of record used for the Handbook, and is an indicator of the year-to-year variability of solar radiation.

The solar constant E_sc is defined as the intensity of solar radiation on a surface normal to the sun’s rays, just beyond the earth’s atmosphere, at the average earth-sun distance. One frequently used value is that proposed by the World Meteorological Organization in 1981, E_sc = 433.3 Btu/h · ft² (Iqbal 1983).

Because the earth’s orbit is slightly elliptical, the extraterrestrial radiant flux E_o varies throughout the year, reaching a maximum of 447.6 Btu/h · ft² near the beginning of January, when the earth is closest to the sun (aphelion) and a minimum of 419.1 Btu/h · ft² near the beginning of July, when the earth is farthest from the sun (perihelion). Extraterrestrial solar irradiance incident on a surface normal to the sun’s ray can be approximated with the following equation:

where n is the day of year (1 for January 1, 32 for February 1, etc.) and the argument inside the cosine is in degrees. Table 2 tabulates values of E_o for the 21st day of each month.

The earth’s orbital velocity also varies throughout the year, so apparent solar time (AST), as determined by a solar time sundial, varies somewhat from the mean time kept by a clock running at a uniform rate. This variation is called the equation of time (ET) and is approximated by the following formula (Iqbal 1983):

with ET expressed in minutes and

Table 2 tabulates the values of ET for the 21st day of each month.

The conversion between local standard time and solar time involves two steps: the equation of time is added to the local standard time, and then a longitude correction is added. This longitude correction is four minutes of time per degree difference between the local (site) longitude and the longitude of the local standard meridian (LSM) for that time zone; hence, AST is related to the local standard time (LST) as follows:

where

AST	=	apparent solar time, decimal hours
LST	=	local standard time, decimal hours
ET	=	equation of time in minutes, from Table 2 or Equation (5)
LSM	=	longitude of local standard time meridian, °E of Greenwich (negative in western hemisphere)
LON	=	longitude of site, °E of Greenwich

Most standard meridians are found every 15° from 0° at Greenwich, U.K., with a few exceptions, such as the province of Newfoundland in Canada. Standard meridian longitude is related to time zone as follows:

where TZ is the time zone, expressed in hours ahead or behind coordinated universal time (UTC). TZ is listed for each station on the CD-ROM accompanying this book. Table 3 lists time zones and standard time meridians for the United States and Canada.

If daylight saving time (DST) is to be used, rather than local standard time, an additional correction has to be performed. In most locales, local standard time can be obtained from daylight savings time by subtracting one hour:

where DST is in decimal hours.

Because the earth’s equatorial plane is tilted at an angle of 23.45° to the orbital plane, the solar declination δ (the angle between the earth/sun line and the equatorial plane) varies throughout the year, as shown in Figure 2. This variation causes the changing seasons with their unequal periods of daylight and darkness. Declination can be obtained from astronomical or nautical almanacs; however, for most engineering applications, the following equation provides sufficient accuracy:

where δ is in degrees and the argument inside the sine is also in degrees. Table 2 provides δ for the 21st day of each month.

The sun’s position in the sky is conveniently expressed in terms of the solar altitude above the horizontal and the solar azimuth measured from the south (Figure 3). The solar altitude angle β is defined as the angle between the horizontal plane and a line emanating from the sun. Its value ranges from 0° when the sun is on the horizon, to 90° if the sun is directly overhead. Negative values correspond to night times. The solar azimuth angle ϕ is defined as angular displacement from south of the projection, on the horizontal plane, of the earth/sun line. By convention, it is counted positive for afternoon hours and negative for morning hours.

Solar altitude and azimuth angles, in turn, depend on the local latitude L (°N, negative in the southern hemisphere); the solar declination δ, which is a function of the date [see Table 2 or Equation (10)]; and the hour angle H, defined as the angular displacement of the sun east or west of the local meridian caused by the rotation of the earth, and expressed in degrees as

where AST is the apparent solar time [Equation (7)]. H is zero at solar noon, positive in the afternoon, and negative in the morning.

Equation (12) relates the solar altitude angle β to L, δ, and H:

Note that at solar noon, H = 0 and the sun reaches its maximum altitude in the sky:

The azimuth angle ϕ is uniquely determined by its sine and cosine, given in Equations (14) and (15):

The relative air mass m is the ratio of the mass of atmosphere in the actual earth/sun path to the mass that would exist if the sun were directly overhead. Air mass is solely a function of solar altitude β and is obtained from (Kasten and Young 1989)

where β is expressed in degrees.

Solar radiation on a clear day is defined by its beam (direct) and diffuse components. The direct component represents the part of solar radiation emanating directly from the solar disc, whereas the diffuse component accounts for radiation emanating from the rest of the sky. These two components are calculated as

where

Eb	=	beam normal irradiance (measured perpendicularly to rays of the sun)
Ed	=	diffuse horizontal irradiance (measured on horizontal surface)
Eo	=	extraterrestrial normal irradiance [Equation (4) or Table 2]
m	=	air mass [Equation (16)]
τb and τd	=	beam and diffuse optical depths (τb and τd are more correctly termed pseudo-optical depths, because optical depth refers to an air mass coefficient without exponentiation; “optical depth” is used here for convenience.)
ab and ad	=	beam and diffuse air mass exponents

Values of τ_b and τ_d are location-specific, and vary during the year. They embody the dependence of clear-sky solar radiation on local conditions, such as elevation, precipitable water, aerosols, ozone, and surface reflectance. In previous editions, their average values were determined through ASHRAE research projects RP-1453 (Thevenard 2009) and RP-1613 (Thevenard and Gueymard 2013). For this edition, the results are from RP-1699 (Roth 2017), and are tabulated for the 21st day of each month for all the locations in the tables of climatic design conditions. Values for other days of the year should be found by interpolation.

Air mass exponents ab and ad are correlated to τ_b and τ_d through the following empirical relationships:

Equations (17) to (20) describe a simple parameterization of a sophisticated broadband radiation model and provide accurate predictions of E_b and E_d, even at sites where the atmosphere is very hazy or humid most of the time.

The orientation of a receiving surface is best characterized by its tilt angle and its azimuth, shown in Figure 3. The tilt angle Σ (also called slope) is the angle between the surface and the horizontal plane. Its value lies between 0 and 180°. Most often, slopes are between 0° (horizontal) and 90° (vertical). Values above 90° correspond to surfaces facing the ground. The surface azimuth ψ is defined as the displacement from south of the projection, on the horizontal plane, of the normal to the surface. Surfaces that face west have a positive surface azimuth; those that face east have a negative surface azimuth. Surface azimuths for common orientations are summarized in Table 4. Note that, in this chapter, surface azimuth is defined as relative to south in both the northern and southern hemispheres. Other presentations and software use relative-to-north or relative-to-equator; care is required.

The surface-solar azimuth angle γ is defined as the angular difference between the solar azimuth ϕ and the surface azimuth ψ:

Values of γ greater than 90° or less than −90° indicate that the surface is in the shade.

Finally, the angle between the line normal to the irradiated surface and the earth-sun line is called the angle of incidence θ. It is important in fenestration, load calculations, and solar technology because it affects the intensity of the direct component of solar radiation striking the surface and the surface’s ability to absorb, transmit, or reflect the sun’s rays. Its value is given by

Note that for vertical surfaces (Σ = 90°) Equation (22) simplifies to

whereas for horizontal surfaces (Σ = 0°) it simplifies to

Total clear-sky irradiance E_t reaching the receiving surface is the sum of three components: the beam component E_t,b originating from the solar disc; the diffuse component E_t,d, originating from the sky dome; and the ground-reflected component E_t,r originating from the ground in front of the receiving surface. Thus,

Only a simple method for computing all the factors on the right side of Equation (25) is presented here. More elaborate methods, particularly with regard to the calculating the diffuse component, can be found in Gueymard (1987) and Perez et al. (1990).

Beam Component. The beam component is obtained from a straightforward geometric relationship:

where θ is the angle of incidence. This relationship is valid only when cos θ > 0; otherwise, E_t,b = 0.

Diffuse Component. The diffuse component is more difficult to estimate because of the anisotropic nature of diffuse radiation: some parts of the sky, such as the circumsolar disc or the horizon, tend to be brighter than the rest of the sky, which makes the development of a simplified model challenging. For vertical surfaces, Stephenson (1965) and Threlkeld (1963) showed that the ratio Y of clear-sky diffuse irradiance on a vertical surface to clear-sky diffuse irradiance on the horizontal is a simple function of the angle of incidence θ:

with

For a nonvertical surface with slope Σ, the following simplified relationships are sufficient for most applications described in this volume:

where Y is calculated for a vertical surface having the same azimuth as the receiving surface considered.

Note that Equations (27) to (30) are appropriate for clear-sky conditions, but should not be used for cloudy skies.

Ground-Reflected Component. Ground-reflected irradiance for surfaces of all orientations is given by

where ρ_g is ground reflectance, often taken to be 0.2 for a typical mixture of ground surfaces. Table 5 provides estimates of ρ_g for other surfaces, including in the presence of snow.

The tables of climatic design conditions in this chapter list heating and cooling degree-days (bases 50 and 65°F). Although 50 and 65°F represent the most commonly used bases for the calculation of degree-days, calculation to other bases may be necessary. With that goal in mind, the tables also provide two parameters (monthly average temperature T, and standard deviation of daily average temperature s_d) that enable estimation of degree-days to any base with reasonable accuracy.

The calculation method was established by Schoenau and Kehrig (1990). Heating degree days HDD_b to base T_b are expressed as

where N is the number of days in the month and Z_b is the difference between monthly average temperature T̄ and base temperature T_b, normalized by the standard deviation of the daily average temperature s_d:

Function f is the normal (Gaussian) probability density function with mean 0 and standard deviation 1, and function F is the equivalent cumulative normal probability function:

Both f and F are readily available as built-in functions in many scientific calculators or spreadsheet programs, so their manual calculation is rarely warranted.

Cooling degree days CDD_b to base T_b are calculated by the same equation:

except that Z_b is now expressed as

Alternative Equations. The following formulas from ISO Standard 15927-6 give results very similar to Equations (32) and (36) but are somewhat simpler:

When T̄ = T_b, the right-hand side of these equations become .

Annual degree-days are simply the sum of monthly degree days over the twelve months of the year.

The climatic design information in this chapter was obtained by direct analysis of observations from the indicated locations. Design values reflect an estimate of the cumulative frequency of occurrence of the weather conditions at the recording station, either for single or jointly occurring elements, for several years into the future. Several sources of uncertainty affect the accuracy of using the design conditions to represent other locations or periods.

The most important of these factors is spatial representativeness. Most of the observed data for which design conditions were calculated were collected from airport observing sites, the majority of which are flat, grassy, open areas, away from buildings and trees or other local influences. Temperatures recorded in these areas may be significantly different from built-up areas where the design conditions are being applied. For example, the maximum urban heat island intensity may be 18°F or more (Oke 1987), although intraurban variability is typically quite large. Urban microclimate is affected by the three-dimensional density of building construction, usually represented by the ratio of building height to street width(H/W); by type and extent of plant cover; and by anthropogenic heat emissions from buildings and vehicles. Significant variations can also occur with changes in local elevation, even if elevations differ by a few hundred feet, or in the vicinity of large bodies of water. It should be emphasized that such variations are not constant in time: intraurban differences in temperature and humidity fluctuate not only in predictable diurnal patterns, but also in response to changes in synoptic conditions and wind direction. Urban heat islands, for example, are typically prominent on clear nights with little or no wind, and are weaker or nonexistent in windy conditions and during daytime. Therefore, judgment must always be used in assessing the representativeness of the design conditions. Consult an applied climatologist regarding estimating design conditions for locations not listed in this chapter. For online references to applied climatologists in the United States, see wcdirectory.ametsoc.org/certified-consulting-meteorologists; in Canada, consult cmos.ca/client/roster/clientRosterView.aspx?clientRosterId=190. Also, GIS-compatible files (KML format) are provided as a special feature in ASHRAE Handbook Online. This allows use of the data in a GIS environment such as Google Earth or ArcGIS, which provides capabilities to overlay various layers of information such as elevation, land use, and bodies of water. This type of information can greatly assist in determining the most representative location to use for an application.

Depending on a site’s specific geographic location and setting (e.g., proximity to large body of water or hills), the data in this chapter for the nearest weather station may not be representative of the actual climate experienced at the project site. In these instances, it may be beneficial to obtain climate data using procedures developed by ASHRAE research project RP-1561 (Qiu et al. 2016). The methodologies provide a protocol for using state-of-the-art mesoscale modeling techniques to derive meteorological conditions specific to the study area. The research project included the methodology based on the Weather Research and Forecasting (WRF) model designed to develop site-specific climate data where standard weather stations are unavailable or not representative of site conditions. The methodology was evaluated by using observations in various geographic regions, including coastal, mountain valley, mountain plateau, and major cities. A simplified procedure was developed; it is freely available at klimaat.github.io/emspy/.

The underlying data also depend on the method of observation. During the 1990s, most data gathering in the United States and Canada was converted to automated systems designated either an automated surface observation system (ASOS) or an automated weather observing system (AWOS). This change improved completeness and consistency of available data. However, changes have resulted from the inherent differences in type of instrumentation, instrumentation location, and processing procedures between the prior manual systems and ASOS. These effects were investigated in ASHRAE research project RP-1226 (Belcher and DeGaetano 2004). Comparison of one-year ASOS and manual records revealed some biases in dry-bulb temperature, dew-point temperature, and wind speed. These biases are judged to be negligible for HVAC engineering purposes; the tabulated design conditions in this chapter were derived from mixed automated and manual data as available. Changes in the location of the observing instruments often have a larger effect than changes in instrumentation. On the other hand, ASOS measurements of sky coverage and ceiling height differ markedly from manual observations and are incompatible with solar radiation models used in energy simulation software. An updated solar model, compatible with ASOS data, was developed as part of RP-1226. The ASOS-based model was found less accurate than models based on manually observed data when compared to measured solar radiation.

Weather conditions vary from year to year and, to some extent, from decade to decade because of the inherent variability of climate. Similarly, values representing design conditions vary depending on the period of record used in the analysis. Thus, because of short-term climatic variability, there is always some uncertainty in using design conditions from one period to represent another period. Typically, values of design dry-bulb temperature vary less than 2°F from decade to decade, but larger variations can occur. Differing periods used in the analysis can lead to differences in design conditions between nearby locations at similar elevations. Design conditions may show trends in areas of increasing urbanization or other regions experiencing extensive changes to land use. Longer-term climatic change brought by human or natural causes may also introduce trends into design conditions. This is discussed further in the section on Effects of Climate Change.

Wind speed and direction are very sensitive to local exposure features such as terrain and surface cover. The original wind data used to calculate the wind speed and direction design conditions in Table 1 are often representative of a flat, open exposure, such as at airports. Wind engineering methods, as described in Chapter 24, can be used to account for exposure differences between airport and building sites. This is a complex procedure, best undertaken by an experienced applied climatologist or wind engineer with knowledge of the exposure of the observing and building sites and surrounding regions.

ASHRAE research project RP-1171 (Hubbard et al. 2004) investigated the uncertainty associated with the climatic design conditions in the 2001 ASHRAE Handbook— . The main objectives were to determine how many years are needed to calculate reliable design values and to look at the frequency and duration of episodes exceeding the design values.

Design temperatures in the 1997 and 2001 editions were calculated for locations for which there were at least 8 years of sufficient data; the criterion for using 8 years was based on unpublished work by TC 4.2. RP-1171 analyzed data records from 14 U.S. locations (Table 9) representing four different climate types. The dry-bulb temperatures corresponding to the five annual percentile design temperatures (99.6, 99, 0.4, 1, and 2%) from the 33-year period 1961–1993 (period used for the 2001 edition’s U.S. stations) were calculated for each location. The temperatures corresponding to the same percentiles for each contiguous subperiod ranging from 1 to 33 years in length was calculated, and the standard deviation of the differences between the resulting design temperature from each subperiod and the entire 33-year period was calculated. For instance, for a 10-year period, the dry-bulb values corresponding to each of the 23 subperiods 1961–1970, 1962–1971, … 1984–1993 were calculated and the standard deviation of differences with the dry-bulb value for the same percentile from the 33-year period calculated. The standard deviation values represent a measure of uncertainty of the design temperatures relative to the design temperature for the entire period of record.

The results for the five annual percentiles are summarized in Figures 4A to 4E, each of which shows how the uncertainty (the average standard deviation for each of the locations in each climate type) varies with length of period.

To the degree that the differences used to calculate the standard deviations are distributed normally, the short-period design temperatures can be expected to lie within one standard deviation of the long-term design temperature 68% of the time. For example, from Figure 4A, the uncertainty for the cold snow forest for a 1-year period is 6.5°F. This can be interpreted that the probability is 68% that the difference in a 99.6% dry-bulb in any given year will be within 6.5°F of the long-term 99.6% dry-bulb. Similarly, there is a 68% probability that the 99.6% dry-bulb from any 10-year period will be within 1.8°F of the long-term value for a location of the cold snow forest climate type.

The uncertainty for the cold season is higher than for the warm season. For example, the uncertainty for the 99.6% dry-bulb for a 10-year period ranges from 1.1 to 1.8°F for the five climate types, whereas the uncertainty for the 0.4% dry-bulb for a 10-year period ranges from 0.7 to 1.1°F.

A variety of other general characteristics of uncertainty are evident from an inspection of Figure 4. For example, the highest uncertainty of any climate type for a 10-year period is 2.0°F for the cold snow forest 99% dry-bulb case. The smallest uncertainty is 0.4°F for the tropical rainy 1% and 2% dry-bulb cases.

Based on these results, it was concluded that using a minimum of 8 years of data would provide reliable (within ±1.8°F) climatic design calculations for most stations.

The evidence is unequivocal that the climate system is warming globally (IPCC 2007). The most frequently observed effects relate to increases in average, and to some degree, extreme temperatures.

This is partly shown by the results of an analysis of design conditions conducted as part of calculating the values for the 2009 edition of this chapter (Thevenard 2009). For 1274 observing sites worldwide with suitably complete data from 1977 to 2006, selected design conditions were compared between the period 1977–1986 and 1997–2006. The results, averaged over all locations, are as follows:

Although these results are consistent with general warming of the world climate system, there are other effects that undoubtedly contribute, such as increased urbanization around many of the observing sites (airports, typically). There was no attempt in the analysis to determine the reasons for the changes.

A more recent study by Thevenard and Shephard (2014), using stations used in the 2013 edition of this chapter, looked at trends for yearly average dry-bulb temperature and other quantities over the 1986 to 2010 period using statistical methods. The study showed that statistically significant increases in average dry-bulb temperature can be detected in only 19% of stations on an individual basis. However, trends become more apparent when stations are evaluated in groups. Stations were grouped in 5°×5° cells covering the globe. Of these cells, 44% showed an increase in average dry-bulb temperature, and 2% showed a decrease; 26% showed an increase in average dew-point temperature and 10% a decrease; finally, 34% showed an increase in average wet-bulb temperature, and 5% showed a decrease. Geographically, increases in average dry-bulb temperature were most visible throughout Europe, in China and southeast Asia, the eastern United States, and southern Australia, and are typically in the range of 0.36 to 1.08°F per decade. Northern locations exhibited higher positive trends (above 1.8°F per decade). Dew-point temperature increases were most visible in eastern Europe, whereas decreases were experienced in the southern United States and South America.

Regardless of the reasons for increases, the general approach of developing design conditions based on analysis of the recent record (25 years, in this case) was specifically adopted for updating the values in this chapter as a balance between accounting for long-term trends and the sampling variation caused by year-to-year variation. Although this does not necessarily provide the optimum predictive value for representing conditions over the next one or two decades, it at least has the effect of incorporating changes in climate and local conditions as they occur, as updates are conducted regularly using recent data. Meteorological services worldwide are considering the many aspects of this complex issue in the calculation of climate “normals” (averages, extremes, and other statistical summary information of climate elements typically calculated for a 30-year period at the end of each decade). Livezey et al. (2007) and WMO (2007) provide detailed analyses and recommendations in this regard.

Extrapolating design conditions to the next few decades based on observed trends should only be done with attention to the particular climate element and the regional and temporal characteristics of observed trends (Livezey et al. 2007).

Design temperatures based on annual percentiles indicate how many hours each year on average the specific conditions will be exceeded, but do not provide any information on the length or frequency of such episodes. As reported by Hubbard et al. (2004), each episode and its duration for the locations in Table 9 during which the 2001 design conditions represented by the 99.6, 99, 0.4, 1, and 2% dry-bulb temperatures were exceeded (i.e., were more extreme) was tabulated and their frequency of occurrence analyzed. The measure of frequency is the average number of episodes per year or its reciprocal, the average period between episodes.

Cold- and warm-season results are presented in Figures 5A and 5B, respectively, for Indianapolis, IN, as a representative example. The duration for the 10-year period between episodes more extreme than the 99.6% design dry bulb is 37 h, and 62 h for the 99% design dry bulb. For the warm season, the 10-year period durations corresponding to the 0.4, 1, and 2% design dry bulb, are about 10, 12, and 15 h, respectively.

Although the results in Hubbard et al. (2004) varied somewhat among the locations analyzed, generally the longest cold-season episodes last days, whereas the longest warm-season episodes were always shorter than 24 h. These results were seen at almost all locations, and are general for the continental United States. The only exception was Phoenix, where the longest cold-season episodes were less than 24 h. This is likely the result of the southern latitude and dry climate, which produces a large daily temperature range, even in the cold season.

Design values in this chapter were developed by ASHRAE research project RP-1699 (Roth 2017). The frequency tables used to calculate the simple design conditions, and the joint frequency matrices used to calculate the coincident design conditions, are available in ASHRAE’s Weather Data Viewer 6.0 (WDView 6.0) (ASHRAE 2017). WDView 6.0 gives users full access to the frequency tables and joint frequency matrices for all 8118 stations in the 2017 ASHRAE Handbook—Fundamentals via a spreadsheet, and provides the following capabilities:

The Engineering Weather Data CD (NCDC 1999), an update of Air Force Manual 88-29, was compiled by the U.S. Air Force 14th Weather Squadron. This CD contains several tabular and graphical summaries of temperature, humidity, and wind speed information for hundreds of locations in the United States and around the world. In particular, it contains detailed joint frequency tables of temperature and humidity for each month, binned at 1°F and 3 h local time-of-day intervals. This CD is available from NCDC: www.ncdc.noaa.gov/nespls/olstore.prodspecific?prodnum=5005.

The International Station Meteorological Climate Summary (ISMCS) is a CD-ROM containing climatic summary information for over 7000 locations around the world (NCDC 1996). A table providing the joint frequency of dry-bulb temperature and wet-bulb temperature depression is provided for the locations with hourly observations. It can be used as an aid in estimating design conditions for locations for which no other information is available. The CD is available at gcmd.nasa.gov/records/GCMD_gov.noaa.ncdc.C00268.aspx. A web version of this product is now available free of charge from NCDC at www7.ncdc.noaa.gov/CDO/cdoselect.cmd?datasetabbv=SUMMARIES. This service is also available via gis.ncdc.noaa.gov/map/viewer.

The monthly frequency distribution of dry-bulb temperatures and mean coincident wet-bulb temperatures for 134 Canadian locations is available from Environment Canada (1983–1987).

The 1981 to 2010 climate normals for over 6000 United States locations are available online (free of charge) from the National Climatic Data Center: gis.ncdc.noaa.gov/map/viewer/.

The Canadian Climate Normals (updated every 10 years; the most recent values are for the 1981–2010 period) can be found at climate.weather.gc.ca/climate_normals/index_e.aspx.

The Climatography of the United States No. 20 (CLIM20), monthly station climate summaries for 1971 to 2000 are climatic station summaries of particular interest to engineering, energy, industry, and agricultural applications (NCDC 2004). These summaries contain a variety of statistics for temperature, precipitation, snow, freeze dates, and degree-day elements for 4273 stations. The statistics include means, medians (precipitation and snow elements), extremes, mean number of days exceeding threshold values, and heating, cooling, and growing degree-days for various temperature bases. Also included are probabilities for monthly precipitation and freeze data. Information on this product can be found at www.ncdc.noaa.gov/oa/documentlibrary/pdf/eis/clim20eis.pdf. Note that this is for 1971 to 2000 and not for the 1981 to 2010 period (latest normals) noted previously.

Heating and cooling degree-day and degree-hour data for 3677 locations from 115 countries were developed by Crawley (1994) from the Global Daily Summary (GDS) version 1.0 and the International Station Meteorological Climate Summary (ISMCS) version 4.0 data.

Software is available to simulate the annual energy performance of buildings requiring a 1-year data set (8760 h) of weather conditions. Many data sets in different record formats have been developed to meet this requirement. The data represent a typical year with respect to weather-induced energy loads on a building. No explicit effort was made to represent extreme conditions, so these files do not represent design conditions.

The National Renewable Energy Laboratory’s (NREL) TMY3 data set (Wilcox and Marion 2008) contains data for 1020 U.S. locations. TMY3, along with the 1991–2010 National Solar Radiation Data Base (NSRDB) (NREL 2011), contains hourly solar radiation [global, beam (direct), and diffuse] and meteorological data for 1454 stations; TMY3 is available at rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/, and the NSRDB at www.ncdc.noaa.gov/land-based-station-data/solar-radiation/. These were produced using an objective statistical algorithm to select the most typical month from the long-term record. A more recent source of gridded weather, solar radiation, and environmental TMY data with a visual and dynamic interface is available from maps.nrel.gov/nsrdb-viewer. The solar radiation data are derived from satellite data, and the environmental data are downscaled from the MERRA reanalysis data set derived from a large climate forecasting model (Rienecker et al. 2011). The grid spacing for this source of data is about 2.5 miles, and currently covers North America up to 50° N, as well as a part of South America down to 10° S. Various types of TMY data are available there, currently for the period 1998 to 2014, as well as each historical year within that time period, with anticipated annual updates.

Canadian Weather Year for Energy Calculation (CWEC) files for 47 Canadian locations were developed for use with the Canadian National Energy Code, using the TMY algorithm and software (Environment Canada 1993). Files for 75 locations are now available.

ASHRAE’s International Weather for Energy Calculations (IWEC2) data set (Huang et al. 2014) contains typical-year weather data for 3012 international locations outdoor of the United States and Canada. The IWEC2s were developed through ASHRAE RP-1477, which used the same source of raw weather data (ISD; Lott et al. 2001) as used for the design condition tables in this chapter, but for a slightly earlier time period of 12 to 25 years ending in 2009. The IWEC2 data set is available on a DVD from the ASHRAE Climate Data Center at www.ashrae.org/resources--publications/bookstore/climate-data-center#iwec; individual files and country sets are also available online from commercial resellers.

Colliver (1997) and Colliver et al. (1998) compiled extreme sequences of 1-, 3-, 5-, and 7-day duration for 239 U.S. and 144 Canadian locations based independently on the following five criteria: high dry-bulb temperature, high dew-point temperature, high enthalpy, low dry-bulb temperature, and low wet-bulb depression. For the criteria associated with high values, the sequences are selected according to annual percentiles of 0.4, 1.0, and 2.0. For the criteria corresponding to low values, annual percentiles of 99.6, 99.0, and 98.0 are reported. Although these percentiles are identical to those used to select annual heating and cooling design temperatures, the maximum or minimum temperatures within each sequence are significantly more extreme than the corresponding design temperatures. The data included for each hour of a sequence are solar radiation, dry-bulb and dew-point temperature, atmospheric pressure, and wind speed and direction. Accompanying information allows the user to go back to the source data and obtain sequences with different characteristics (e.g., different probability of occurrence, windy conditions, low or high solar radiation). These extreme sequences are available on CD (ASHRAE 1997).

These sequences were developed primarily to assist the design of heating or cooling systems having a finite capacity before regeneration is required or of systems that rely on thermal mass to limit loads. The information is also useful where information on the hourly weather sequence during extreme episodes is required for design.

Because of growing demand for more comprehensive global coverage of weather data for HVAC applications around the world, ASHRAE sponsored research project RP-1170 (Plantico 2001) to construct a Global Weather Data Sources (GWDS) web page. Many national climate services and other climate data sources are making more information available over the Internet. The purpose of RP-1170 was to provide ASHRAE membership with easy access to major sources of international weather data through one consolidated online system. This web page was later updated to better use the resources of the World Meteorological Organization (WMO) and NCDC. The GWDS web page is accessible at www.ncdc.noaa.gov/oa/ashrae/gwds-title.aspx.

For detailed designs, custom analysis of the most appropriate long-term weather record is best. National weather services are generally the best source of long-term observational data. The National Climatic Data Center (NCDC), in conjunction with U.S. Air Force and Navy partners in Asheville’s Federal Climate Complex (FCC), developed the global Integrated Surface Data (Lott 2004; Lott et al. 2001) to address a pressing need for an integrated global database of hourly land surface climatological data. The database of over 20,000 stations contains hourly and some daily summary data from as early as 1900 (many stations beginning in the 1948–1973 timeframe), is operationally updated each day with the latest available data, and is now being further integrated with various data sets from the United States and other countries to further expand the spatial and temporal coverage of the data. For access to ISD, go to www.ncdc.noaa.gov/isd or, for a GIS interface, gis.ncdc.noaa.gov/map/viewer/. For a complete review of ISD and all of its products, go to www.ncdc.noaa.gov/isd.

The National Solar Radiation Database (NSRDB) (www.ncdc.noaa.gov/land-based-station-data/solar-radiation/; maps.nrel.gov/nsrdb-viewer) and Canadian Weather Energy and Engineering Data Sets (CWEEDS) (Environment Canada 1993) provide long-term hourly data, including solar radiation values for the United States and Canada. A previous version of the NSRDB required a modified solar radiation model because of the implementation of automated observing systems that do not report traditional cloud elements. The current NSRDB Data Viewer (maps.nrel.gov/nsrdb-viewer), which is mentioned earlier in the section on Typical Year Data Sets, also contains both solar radiation and environmental data for every year from 1998 through 2014 covering North America up to 50° North, and South America down to 10° South. The solar radiation data are derived from GOES satellites and the environmental data are downscaled from MERRA reanalysis data set.

Considerable information about weather and climate services and data sets is available elsewhere online. Information supplementary to this chapter may also be posted on the ASHRAE Technical Committee 4.2 website, the link to which is available from the ASHRAE website (www.ashrae.org).