7B-i 

APPENDIX 7B. TYPICAL METEOROLOGICAL YEAR ASSIGNMENTS

TABLE OF CONTENTS

7B.1 INTRODUCTION .........................................................................................................7B-1

7B.1.1 Purpose and Intent...........................................................................................7B-1

7B.1.2 Issues ...............................................................................................................7B-1

7B.2 APPROACH ..................................................................................................................7B-1

7B.2.1 Additional Data Sets .......................................................................................7B-1

7B.2.2 Mapping Approach .........................................................................................7B-3

7B.2.3 Calculating State-by-State Weighting Factors ................................................7B-7

LIST OF FIGURES

Figure 7B.2.1. Locations of TMY2 Stations Superimposed on Heating Degree-Day 

Contours .......................................................................................................7B-2

Figure 7B.2.2. Locations of 4,775 NOAA Stations ...................................................................7B-3

Figure 7B.2.3. Locations of Populated Places ...........................................................................7B-3

Figure 7B.2.4. PPL→NOAA Mappings for Iowa Showing a Predominance of NearestLocation Mappings ......................................................................................7B-5

Figure 7B.2.5. PPL→NOAA Mappings for Washington State Showing Some Distant 

Mappings in Mountainous Regions .............................................................7B-5

Figure 7B.2.6. NOAA→TMY2 Mappings ................................................................................7B-7
7B-1 

APPENDIX 7B. TYPICAL METEOROLOGICAL YEAR ASSIGNMENTS

7B.1 INTRODUCTION

7B.1.1 Purpose and Intent

This appendix describes the methodology by which weighting factors were developed to 

scale the influence of each typical meteorological year (TMY2) weather station on each state’s 

results. The intent is to ensure that the cost-effectiveness calculations properly reflect the 

geographic distribution of buildings in which the heating, ventilating, and air-conditioning 

(HVAC) systems under analysis will be used. State-level aggregate results need to be most 

heavily influenced by the climates having the most buildings.

7B.1.2 Issues

The 230+ TMY2 stations in the United States are distributed throughout the country to 

provide good coverage of the climatic variation in the United States. Unfortunately for this study, 

the TMY2 stations are not distributed so as to match the distribution of buildings. It is therefore 

necessary to identify a reasonable mapping between the climate-based (TMY2) simulation 

results and the geographical distribution of buildings in each state. Although TMY2 data give 

good climatic coverage, they do not give sufficient detailed geographical coverage.

Detailed data on the geographical/climatic distribution of buildings were not readily 

available for this task. However, data on the geographical distribution of persons (population 

data) can serve as a reasonable surrogate for buildings data, and are available from a number of 

sources. The challenge facing this study was matching up population data, which are available in 

great geographical detail but not matched to climate indicators, with TMY2 data, which 

characterize climate quite well but are not connected to any population indicators. 

7B.2 APPROACH

Figure 7B.2.1 shows the locations of the 239 TMY2 stations superimposed on a contour 

map showing heating degree-days. It is clear that the geographic distribution of the TMY2 

stations is too sparse to capture many of the climate variations that occur over relatively short 

distances, especially in mountainous regions. It is therefore not feasible to develop weighting 

factors based solely on the TMY2 stations within a state.

7B.2.1 Additional Data Sets 

To develop weighting factors, the U.S. Department of Energy (DOE) identified two data 

sets (in addition to the TMY2 data set) that provide a connection between detailed population 

distribution and TMY2 stations. These are as follows: 

National Oceanic and Atmospheric Administration (NOAA) Climate Stations—The 

National Climatic Data Center’s (NCDC’s) “CLIM81” database contains summary statistics 

from a large number of climate stations in the United States. DOE used the 1961–1990 period of 

record (POR), which corresponds to the POR used to define the TMY2 weather tapes, for which 
7B-2 

4,775 climate stations are represented (see 

http://lwf.ncdc.noaa.gov/oa/documentlibrary/clim81supp3/clim81.html). Each NOAA station is 

characterized by its location (latitude/longitude), annual heating and cooling degree-days, 

elevation, and various other metrics. 

Figure 7B.2.1. Locations of TMY2 Stations Superimposed on Heating Degree-Day Contours

• United States Geological Survey (USGS) Populated Places —The USGS, in its 

Geographic Names Information System, maintains a detailed database of cities, 

towns, and other important features. (For the latest version of this data set, see 

http://geonames.usgs.gov/domestic/download_data.htm.) DOE’s analysis used a 

version of this data set from the early 1990s, making it contemporaneous with the 

TMY2 and NOAA data sets.) Of interest to this task are the features known as 

“populated places” (PPL), which are generally cities and towns, but also include large 

housing subdivisions, trailer parks, and other places where people may live. For many 

of these populated places, the USGS has a population estimate. The PPL data give 

excellent geographical coverage. The version of the data set used here has over 

22,000 entries that include a population estimate out of more than 164,000 total.1

Each PPL location is characterized by its location (latitude/longitude), elevation, 

population, and various other metrics. 

The NOAA data are important because they contain climate summary information that 

can be mapped to the TMY2 stations, greatly increasing DOE’s geographical coverage. Figure 

 

1 Locations without USGS population estimates tend to be those with very little population. DOE has kept these 

smaller sites in its mapping analysis by assigning each a population estimate of one. This gives them negligible 

influence against the more populous locations but allows the mapping procedure to work even in very sparsely 

populated regions of some states.
7B-3 

7B.2.2 shows the locations of the 4,775 NOAA stations. The PPL database is used to make the 

final link between the climate information in the mapped NOAA/TMY2 stations and the 

geographical distribution of population. Figure 7B.2.3 shows the PPL locations. 

Figure 7B.2.2. Locations of 4,775 NOAA Stations 

Figure 7B.2.3. Locations of Populated Places 

7B.2.2 Mapping Approach 

DOE’s approach to generating TMY2 weighting factors involves three major steps: 
7B-4 

1. Map each PPL location in the United States to a best-representative NOAA station. 

This gives each location some summary climate metrics (chiefly, heating and cooling 

degree-days) that facilitate further mapping.

2. Map each NOAA station to a best-representative TMY2 station. This completes the 

link between the geographic population estimates (PPL data) and the TMY2 stations. 

3. For each state, compute the fraction of the total PPL population that “points” (via its 

PPL→NOAA→TMY2 mappings) to each TMY2 station. 

These are described in order below.

7B.2.2.1 Mapping Populated Places Locations to NOAA Stations 

Mapping each of the 164,000+ PPL locations to a best-representative NOAA station is a 

mostly straightforward process. Because there is no climate information in the PPL database, the 

only metrics available to associate each PPL location with a NOAA station are location 

(latitude/longitude) and elevation (although elevation is not known for all PPL locations). The 

mapping algorithm is as follows:

• For each PPL location, identify the nearest NOAA station. Distances between 

PPL/NOAA pairs are calculated using the latitudes and longitudes of the two 

locations and simple spherical geometry. 

• If the elevation of the nearest NOAA station is within 300 feet of the PPL location 

or if the elevation of the PPL location is unknown, then the nearest NOAA station 

is the final mapping.

• If the nearest NOAA station differs in elevation from the PPL location by more 

than 300 feet, 

o Identify the 20 closest NOAA stations to the PPL location.

o Choose, from among the 20, the NOAA station that is nearest in elevation 

to the PPL location. 

This algorithm is imperfect in many situations, but was designed by trial and error to give 

reasonable mappings in a large majority of cases. In locations with relatively flat terrain and 

fairly dense population, the algorithm almost always maps to the closest NOAA station. Figure 

7B.2.4 shows the mappings in the state of Iowa as an example. Each plotted point on the graphic 

is one NOAA station; the “hairs” are drawn outward to the various PPL locations mapped to that 

station. In mountainous terrain or in locations with large distances between PPL locations 

(e.g., Alaska), the second part of the algorithm does a reasonable job of identifying a 

representative NOAA station, even if that station is some distance from the PPL location. Figure 

7B.2.5 shows the mappings for Washington State as an example. Note that many of the PPL 

locations (the ends of the hairs) are mapped to distant NOAA stations. 
7B-5 

Figure 7B.2.4. PPL→NOAA Mappings for Iowa Showing a Predominance of NearestLocation Mappings 

Figure 7B.2.5. PPL→NOAA Mappings for Washington State Showing Some Distant 

Mappings in Mountainous Regions 
7B-6 

7B.2.2.2 Mapping NOAA Stations to TMY2 Stations 

Having mapped each PPL location to a best-representative NOAA station, the next step is 

to map each NOAA station to a best representative TMY2 station. Because the NOAA and 

TMY2 data sets provide less dense geographical coverage and because both data sets contain 

climate information, this mapping process relies less on spatial proximity and more on similarity 

of climate. Proximity remains important, though. In finding a best TMY2 mapping for a NOAA 

station, DOE hopes for similarity in a number of climate variables: temperature (heating and 

cooling degree-days), solar and wind characteristics, rainfall and humidity characteristics, 

snowfall, etc. The NOAA data set supports direct comparison only of heating and cooling 

degree-days, but DOE relies on spatial proximity to ensure similarity in the other variables.

The NOAA-to-TMY2 mapping algorithm is as follows: 

• For each NOAA station, identify the nearest TMY2 station. If the elevation of the 

nearest TMY2 station is within 300 feet of the NOAA station, then the nearest 

TMY2 station is the final mapping.

• If the nearest TMY2 station differs in elevation from the NOAA station by more 

than 300 feet, then select instead the TMY2 station that has the minimum 

“combined distance” from the NOAA station. The combined distance is defined 

as the sum of the literal distance (miles) between the two stations and an 

“equivalent latitude miles” value that accounts for known differences in heating 

and cooling degree-days and elevation. 

The equivalent latitude miles metric was developed as a means to characterize 

temperature (heating and cooling degree-days) and elevation in the same units as literal distance 

(i.e., miles). By casting heating and cooling degree-days and elevation effects into units of miles, 

DOE is able to simply sum the literal distance between two locations with the equivalent heating 

and cooling degree-days and elevation distance to give the two values equal weight in assigning 

a best-representative TMY2 station to each NOAA station.

The equivalent latitude miles values are based on several related observations. First, 

differences in both heating and cooling degree-days correlate with differences in latitude. 

Second, those same degree-day differences also correlate with differences in elevation. 

Combining these observations, DOE discovered that differences in north-south distances 

(i.e., latitude miles) can be characterized in terms of differences in heating degree-days, cooling 

degree-days, and elevation. That is, as one moves northward (increasing latitude) and upward 

(increasing elevation), heating degree-days tend to increase and cooling degree-days tend to 

decrease. A regression analysis of these correlations allows DOE to cast heating and cooling 

degree-days, and elevation differences into units of distance (miles). A linear regression on 

NOAA/TMY2 station pairs within 300 miles of each other gives the following result. 
7B-7 

Id HDD CDD Elev equiv α γβ ∆×+×+∆×+=

Eq. 7B.1

Where: 

dequiv = equivalent latitude distance (miles),

ΔHDD = difference in heating degree-days (base-65 °F), 

ΔCDD = difference in cooling degree-days (base-65 °F), 

ΔElev = difference in elevation (feet),

I = -6.8938, 

α = 0.1061, 

β = -0.0149, and 

γ = -0.0718. 

The best representative TMY2 station for each NOAA station was selected as the one 

with the minimum sum of actual distance and equivalent latitude distance. Figure 7B.2.6 shows 

the final NOAA→TMY2 mappings for the continental United States. Each plotted point is a 

TMY2 station, from which lines are drawn outward to the NOAA stations mapped to it. 

Figure 7B.2.6. NOAA→TMY2 Mappings 

7B.2.3 Calculating State-by-State Weighting Factors 

Weighting factors were developed for each U.S. state that express the fraction of that 

state’s population that is represented by each TMY2 station. These weighting factors are based 

directly on the PPL→NOAA→TMY2 mappings described above. The weighting factor for a 

TMY2 station is defined as the summed population of all PPL locations in the state that point to 

that TMY2 station divided by the summed population of all PPL locations in the state. Thus, the 

sum of all the TMY2 weighting factors for a state is 1.0.