APPENDIX D. TYPICAL METEORO LOGICAL 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 Atmos pheric Administration Stations to Typical Meteorological Year Stations

................................................................ D-6 D.2.4 Calculating State-by-Stat e Weighting Factors

.................................................... D-7

LIST OF TABLES

Figure D.2.1 Locations of Typical Meteorol ogical Year Stations Superimposed on Heating Degree-Day Contours

............................................................................ D-2 Figure D.2.2 Locations of 4,775 National O ceanic 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 Atmo spheric Administration to Typical Meteorological Year Mappings

.......................................................................... D-7

D-i BVM_FINAL_FR_TSD_AppendixD_08-14-09.docAPPENDIX D. TYPICAL METEOROLOGICAL YEAR ASSIGNMENTS

D.1 INTRODUCTION

D.1.1 Purpose and Intent
This appendix describes the methodology by wh ich weighting factors were developed to scale the influence of each typi cal 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 th e 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 Unit ed States that provide good coverage of the Nation’s climatic variation. Unfo rtunately 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) simula tion results and the ge ographical 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 build ings 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 conn ected 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 s howing heating degree-days (HDD). It is clear that the geographic distribution of the TMY2 stati ons is too sparse to capture many of the climate variations that occur ove r relatively short distances, especi ally in mountainous regions. It is therefore not feasible to devel op weighting factors based solely on the TMY2 stations within a state. Therefore, two additional data sets are us ed to map TMY2 stations to detailed population distribution as discussed below.

D.2.1 Additional Data Sets
To develop weighting factors, we identified tw o data sets (in addition to the TMY2 data set) that provide a connection between detail ed population distributi on and TMY2 stations. These are:

 National Oceanic and Atmospheric Admin istration (NOAA) Climate Stations – The National Climatic Data Center’s (NCDC’s) “CLI M81” 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.doctapes, for which 4,775 climate stations are represented ( http://lwf.ncdc.noaa.gov/oa/docum entlibrary/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 databa se of cities, towns, and other important features. (For the latest vers ion 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, tr ailer 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 te nd to have little population. DOE kept these smaller sites in the mapping analysis by assi gning each a population estimate of one. This gives them negligible influe nce 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), elevati on, population, and various other metrics.

D-2 BVM_FINAL_FR_TSD_AppendixD_08-14-09.docThe NOAA data are important because they contain climate summary information that can be mapped to the TMY2 stations, great ly 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 geographi cal 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.docFigure Error! No text of specified style in document. .3 Locations of Populated Places

D.2.2 Mapping Approach

The approach to generating TMY2 weight ing 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 me trics (chiefly, heating and cooling degreedays) that facilitate further mapping.

2. Map each NOAA station to a best-representat ive 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 Nation al Oceanic and Atmospheric Administration Stations

Mapping each of the 164,000+ PPL locations to a best-representat ive NOAA station is a straightforward process. Because there is no c limate information in the PPL database, the only metrics available to associat e each PPL with a NOAA station ar e location (la titude/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 near est NOAA station. Distances between PPL/NOAA pairs are calculated using the lati tudes and longitudes of the two locations

D-4 BVM_FINAL_FR_TSD_AppendixD_08-14-09.docand simple spherical geometry. If the eleva tion of the nearest NOAA station is within 300 feet of the PPL location or if the eleva tion of the PPL location is unknown, then the nearest NOAA station is the final mapping.

2. If the nearest NOAA station differs in elev ation from the PPL location by more than 300 feet: a) Identify the 20 closest NOAA sta tions 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 tr ial 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 th e graphic is one NOAA station; the “hairs” are drawn outward to the various PPL locations mapped to that station. In m ountainous terrain or in locations with large distances between PPL locations (e.g., Alas ka), 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 spec ified style in document. .5 shows the mappings for Washington as an example. Note that many of th e 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 Ma p of Iowa Showing a Predominance of Nearest-Location Mappings

D-5 BVM_FINAL_FR_TSD_AppendixD_08-14-09.docFigure 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 Atmospheri c Administration Stations to Typical Meteorological Year Stations

Having mapped each PPL location to a best re presentative NOAA station, the next step is to map each NOAA station to a best repres entative TMY2 station. Because the NOAA and TMY2 data sets provide less dense geographical coverage and because both data sets contain climate information, this mapping pr ocess relies less on spatial proxi mity 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 va riables: temperature (HDD and CDD), solar and wind characteristics, rainfall and humidity char acteristics, snowfall, etc. The NOAA data set supports direct comparison only of HDD and CD D, 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 elev ation 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 distan ce 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.docaccounts for known differences in HDD, CDD, a nd elevation. This la tter metric requires some explanation (see below).

The equivalent latitude metric (in miles) was developed as a means to characterize temperature (HDD and CDD) and elev ation 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 loca tions with the equivalent HDD/C DD/elevation distance to give the two values equal weight in assigning a best-representativ e TMY2 station to each NOAA station.

The equivalent latitude miles values are ba sed on several related observations. First, differences in HDD and CDD correlate with diffe rences in latitude. Second, those same degreeday differences also correlate with differen ces 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 northwar d 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 diffe rences into units of distance (miles). A linear regr ession on NOAA/TMY2 station pair s within 300 miles gives the following result.

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

Where:

d

equiv = equivalent latitude distance (miles), Δ HDD = difference in heating degree-days (base-65 degrees Fahrenheit (° F)), Δ CDD = difference in cooling degree-days (base-65 ° F), Δ Elev = difference in elevation (feet), I = -6.8938 (latitude distance in miles),

α = 0.1061 (latitude distance per Δ HDD)

, β = -0.0149 (latitude distance per Δ CDD) , and
γ = -0.0718 (latitude distance per diffe rence 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 a nd 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.docFigure 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 TM Y2 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