APPENDIX D. TYPICAL METEOROLOGICAL YEAR ASSIGNMENTS 

TABLE OF CONTENTS
 

D.1 INTRODUCTION .......................................................................................................... D-1
 

D.1.1 Purpose and Intent............................................................................................... D-1 

D.1.2 Issues................................................................................................................... D-1 

D.2 APPROACH ................................................................................................................... D-1 

D.2.1 Additional Data Sets ........................................................................................... D-1 

D.2.2 Mapping Approach ............................................................................................. D-4 

D.2.3 Mapping National Oceanic and Atmospheric Administration Stations to 

Typical Meteorological Year Stations ................................................................ D-6 

D.2.4 Calculating State-by-State Weighting Factors .................................................... D-7 

LIST OF TABLES 

Figure D.2.1 Locations of Typical Meteorological Year Stations Superimposed on 

Heating Degree-Day Contours............................................................................ D-2 

Figure D.2.2 Locations of 4,775 National Oceanic and Atmospheric Administration 

Stations................................................................................................................ D-3 

Figure D.2.3 Locations of Populated Places ............................................................................ D-3 

Figure D.2.4 Populated Places to National Oceanic and Atmospheric Administration 

Map of Iowa Showing a Predominance of Nearest-Location Mappings ............ D-5 

Figure D.2.5 Populated Places to National Oceanic and Atmospheric Administration 

Map of Washington Showing Some Distant Mappings in Mountainous 

Regions ............................................................................................................... D-5 

Figure D.2.6 National Oceanic and Atmospheric Administration to Typical 

Meteorological Year Mappings .......................................................................... D-7 

D-i 

BVM_FINAL_FR_TSD_AppendixD_08-14-09.doc 

EERE-2006-STD-0125 COMMENT 74
APPENDIX D. TYPICAL METEOROLOGICAL YEAR ASSIGNMENTS 

D.1 INTRODUCTION 

D.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 beverage vending machines will be used. Statelevel aggregate results need to be most heavily influenced by the climates having the most 

buildings. 

D.1.2 Issues 

There are 239 TMY2 stations distributed throughout the United States that provide good 

coverage of the Nation’s climatic variation. Unfortunately for this study, TMY2 stations are not 

distributed to match 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 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 problem facing this study was to match up population data available in great 

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

climate well but are not connected to population indicators. 

D.2 APPROACH 

Figure Error! No text of specified style in document..1 shows the locations of the 239 

TMY2 stations superimposed on a contour map showing heating degree-days (HDD). 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. Therefore, two additional data sets are used to map TMY2 stations to detailed population 

distribution as discussed below. 

D.2.1 Additional Data Sets 

To develop weighting factors, we identified two data sets (in addition to the TMY2 data 

set) that provide a connection between detailed population distribution and TMY2 stations. 

These are: 

 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 

 D-1 

BVM_FINAL_FR_TSD_AppendixD_08-14-09.doc 

EERE-2006-STD-0125 COMMENT 74
tapes, for which 4,775 climate stations are represented 

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

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

elevation, and various other metrics. 

Figure Error! No text of specified style in document..1 Locations of Typical Meteorological 

Year Stations Superimposed on Heating Degree-Day Contours 

	 U.S. Geological Survey (USGS) Populated Places – In its Geographic Names Information 

System, the USGS maintains a detailed database of cities, towns, and other important 

features. (For the latest version of this data set, see 

http://geonames.usgs.gov/stategaz/00README.html. DOE 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 “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 populated 

places 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. 

Locations without USGS population estimates tend to have little population. DOE kept these 

smaller sites in the 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 sparsely populated regions. Each PPL location is characterized by 

its location (latitude/longitude), elevation, population, and various other metrics. 

 D-2 

BVM_FINAL_FR_TSD_AppendixD_08-14-09.doc 

EERE-2006-STD-0125 COMMENT 74
The NOAA data are important because they contain climate summary information that 

can be mapped to the TMY2 stations, greatly increasing geographical coverage. Figure Error! 

No text of specified style in document..2 shows the 4,775 NOAA station locations. 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 Error! No text of 

specified style in document..3 shows the PPL locations. 

Figure Error! No text of specified style in document..2 Locations of 4,775 National Oceanic 

and Atmospheric Administration Stations 

 D-3 

BVM_FINAL_FR_TSD_AppendixD_08-14-09.doc 

EERE-2006-STD-0125 COMMENT 74
Figure Error! No text of specified style in document..3 Locations of Populated Places 

D.2.2 Mapping Approach 

The approach to generating TMY2 weighting factors involves three major steps: 

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 degreedays) 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 three steps are described in order below. 

Mapping Populated Places Locations to National Oceanic and Atmospheric Administration 

Stations 

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

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

metrics available to associate each PPL 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. 

1. 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 

 D-4 

BVM_FINAL_FR_TSD_AppendixD_08-14-09.doc 

EERE-2006-STD-0125 COMMENT 74
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. 

2. If the nearest NOAA station differs in elevation from the PPL location by more than 

300 feet: 

a) Identify the 20 closest NOAA stations to the PPL location. 

b) Among the 20, choose the NOAA station closest 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 Error! No text of specified style in document..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 Error! No text of specified style in document..5 

shows the mappings for Washington as an example. Note that many of the PPL locations (the 

ends of the hairs) are mapped to distant NOAA stations. 

Figure Error! No text of specified style in document..4 Populated Places to National 

Oceanic and Atmospheric Administration Map of Iowa Showing a Predominance of 

Nearest-Location Mappings 

 D-5 

BVM_FINAL_FR_TSD_AppendixD_08-14-09.doc 

EERE-2006-STD-0125 COMMENT 74
Figure Error! No text of specified style in document..5 Populated Places to National 

Oceanic and Atmospheric Administration Map of Washington Showing Some Distant 

Mappings in Mountainous Regions 

D.2.3 Mapping National Oceanic and Atmospheric Administration Stations to Typical 

Meteorological Year 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. In finding a best TMY2 mapping for a NOAA station, 

there may be a similar number of climate variables: temperature (HDD and CDD), solar and 

wind characteristics, rainfall and humidity characteristics, snowfall, etc. The NOAA data set 

supports direct comparison only of HDD and CDD, but DOE relies on spatial proximity to 

ensure similarity in the other variables. 

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

1.	 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. 

2.	 If the nearest TMY2 station differs in elevation from the NOAA station by more than 

300 feet, 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 

 D-6 

BVM_FINAL_FR_TSD_AppendixD_08-14-09.doc 

EERE-2006-STD-0125 COMMENT 74
accounts for known differences in HDD, CDD, and elevation. This latter metric requires 

some explanation (see below). 

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

temperature (HDD and CDD) and elevation in the same units as literal distance (i.e., miles). By 

casting HDD, CDD, and elevation effects into units of miles, we are able to simply sum the 

literal distance between two locations with the equivalent HDD/CDD/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 HDD and CDD correlate with differences in latitude. Second, those same degreeday differences also correlate with differences in elevation. Combining these observations, 

differences in north-south distances (i.e., latitude miles) can be characterized in terms of 

differences in HDD, CDD, and elevation (i.e., as one moves northward increasing latitude and 

upward increasing elevation, HDD tends to increase and CDD tends to decrease). A regression 

analysis of these correlations allows casting HDD, CDD, and elevation differences into units of 

distance (miles). A linear regression on NOAA/TMY2 station pairs within 300 miles gives the 

following result. 

dequiv  I   HDD   CDD   Elev Eq. D.1 

Where: 

dequiv = equivalent latitude distance (miles), 

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

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

ΔElev = difference in elevation (feet),
 

I = -6.8938 (latitude distance in miles), 

α = 0.1061 (latitude distance per ΔHDD), 

β = -0.0149 (latitude distance per ΔCDD), and 

γ = -0.0718 (latitude distance per difference in elevation (miles/feet). 

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 Error! No text 

of specified style in document..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. 

 D-7 

BVM_FINAL_FR_TSD_AppendixD_08-14-09.doc 

EERE-2006-STD-0125 COMMENT 74
Figure Error! No text of specified style in document..6 National Oceanic and Atmospheric 

Administration to Typical Meteorological Year Mappings 

D.2.4 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. 

 D-8 

BVM_FINAL_FR_TSD_AppendixD_08-14-09.doc 

EERE-2006-STD-0125 COMMENT 74