J3.4 DESIGN TEMPERATURES FOR HEATING AND COOLING APPLICATIONS IN NORTHERN COLORADO -- AN UPDATE



Similar documents
ASHRAE Climatic Data Activities. Dru Crawley Didier Thevenard

Temperature and Humidity

2. The map below shows high-pressure and low-pressure weather systems in the United States.

UNIT 6a TEST REVIEW. 1. A weather instrument is shown below.

ENGINEERING WEATHER DATA

Cooling Load Calculation

Climate and Weather. This document explains where we obtain weather and climate data and how we incorporate it into metrics:

How do I measure the amount of water vapor in the air?

Page 1. Weather Unit Exam Pre-Test Questions

ES 106 Laboratory # 6 MOISTURE IN THE ATMOSPHERE

Akton Psychrometric Chart Tutorial and Examples

NordFoU: External Influences on Spray Patterns (EPAS) Report 16: Wind exposure on the test road at Bygholm

Total Heat Versus Sensible Heat Evaporator Selection Methods & Application

Environment-Laboratory Ambient Conditions

Wet Bulb Temperature and Its Impact on Building Performance

CASE STUDY: COOLERADO CORPORATION

Components HVAC General Standards HVAC Guidelines HVAC System Selection Life Cycle Cost Analysis

Geography affects climate.

GETTING TO THE CORE: THE LINK BETWEEN TEMPERATURE AND CARBON DIOXIDE

UNIFIED FACILITIES CRITERIA (UFC) DESIGN: ENGINEERING WEATHER DATA

Name: Date: LAB: Dew Point and Cloud Formation Adapted from Exploration in Earth Science, The Physical Setting, United Publishing Company, Inc.

Energy Recovery Ventilation Equipment

What Causes Climate? Use Target Reading Skills

Maximize energy savings Increase life of cooling equipment More reliable for extreme cold weather conditions There are disadvantages

THE PSYCHROMETRIC CHART: Theory and Application. Perry Peralta NC State University

Purpose: To determine the dew and point and relative humidity in the classroom, and find the current relative humidity outside.

Fire Weather Index: from high resolution climatology to Climate Change impact study

Examining the Recent Pause in Global Warming

THE HUMIDITY/MOISTURE HANDBOOK

Climate Extremes Research: Recent Findings and New Direc8ons

AIR TEMPERATURE IN THE CANADIAN ARCTIC IN THE MID NINETEENTH CENTURY BASED ON DATA FROM EXPEDITIONS

Absolute and relative humidity Precise and comfort air-conditioning

KILN SCHEDULES HARDWOOD KILN SCHEDULES KILN SCHEDULES KILN SCHEDULES KILN SCHEDULES KILN SCHEDULES

HAY MOISTURE & WEATHER:

EXPLANATION OF WEATHER ELEMENTS AND VARIABLES FOR THE DAVIS VANTAGE PRO 2 MIDSTREAM WEATHER STATION

Evaporative Cooling System for Aquacultural Production 1

Visualizing of Berkeley Earth, NASA GISS, and Hadley CRU averaging techniques

Managing Extreme Weather at Transport for London. ARCC Assembly - 12 June 2014 Helen Woolston, Transport for London Sustainability Coordinator

Seasonal Temperature Variations

*At the AHRI Standard flow rate of 3 gpm/ton, using today s efficient chillers, the ΔT is 9.3 F rather than the 10 F often assumed.

Economizer Fundamentals: Smart Approaches to Energy-Efficient Free-Cooling for Data Centers

Natatorium Building Enclosure Deterioration Due to Moisture Migration

M O N T E R E Y B A Y A Q U A R I U M

SIXTH GRADE WEATHER 1 WEEK LESSON PLANS AND ACTIVITIES

Scholar: Elaina R. Barta. NOAA Mission Goal: Climate Adaptation and Mitigation

CLIDATA In Ostrava 18/06/2013

CHAPTER 5 Lectures 10 & 11 Air Temperature and Air Temperature Cycles

Cost Estimation for Materials and Installation of Hot Water Piping Insulation

Microsoft Business Intelligence Visualization Comparisons by Tool

Optimization of Water - Cooled Chiller Cooling Tower Combinations

Super Heated Steam Drying of Wood on Industrial Scale

DATA VALIDATION, PROCESSING, AND REPORTING

DESIGN OF NATURAL VENTILATION WITH CFD CHAPTER SEVEN. Qingyan Chen. difficult to understand and model, even for simple

Mixing Heights & Smoke Dispersion. Casey Sullivan Meteorologist/Forecaster National Weather Service Chicago

Residential HVAC System Sizing

Data Quality Assurance and Control Methods for Weather Observing Networks. Cindy Luttrell University of Oklahoma Oklahoma Mesonet

Osmosis. Evaluation copy

CHAPTER 3. The sun and the seasons. Locating the position of the sun

Cool Spaces. earth-wise guide to. green strategies: light strategies: Keep Austin Cool

Kinetic Theory & Ideal Gas

Determination of Moisture Content

Chapter 14. At temperatures below the critical temperature, the gas GAS VAPOR MIXTURES AND AIR-CONDITIONING. Objectives

Green Roofs are the Way to Grow By Mark Gaulin

Green Building Handbook for South Africa Chapter: Heating, Ventilation and Cooling Luke Osburn CSIR Built Environment

Comparing Air Cooler Ratings Part 1: Not All Rating Methods are Created Equal

THE BODY TEMPERATURE OF THE FROG


CHAPTER 3. BUILDING THERMAL LOAD ESTIMATION

Activity 8 Drawing Isobars Level 2

Wind Resource Assessment for BETHEL, ALASKA Date last modified: 2/21/2006 Compiled by: Mia Devine

THE GEORGIA AUTOMATED ENVIRONMENTAL MONITORING NETWORK: TEN YEARS OF WEATHER INFORMATION FOR WATER RESOURCES MANAGEMENT

Indicator 2: Use a variety of algebraic concepts and methods to solve equations and inequalities.

Monsoon Variability and Extreme Weather Events

Optimum fin spacing for fan-cooled heat sinks

Moisture Control. It s The Dew Point. Stupid! Its not the humidity.

Infrastructure & Cities Sector

LECTURE N 2. - Meteorological Quantities and Climate Parameters - IDES-EDU

Chapter 3: Weather Map. Weather Maps. The Station Model. Weather Map on 7/7/2005 4/29/2011

Name: OBJECTIVES Correctly define: WEATHER BASICS: STATION MODELS: MOISTURE: PRESSURE AND WIND: Weather

CHAPTER 3 Window design for day lighting, ventilation and to

Quarterly Economics Briefing

Strategy Guideline: HVAC Equipment Sizing

Everyday Mathematics GOALS

6. Base your answer to the following question on the graph below, which shows the average monthly temperature of two cities A and B.

MOLECULAR WEIGHT BY BOILING POINT ELEVATION

Humid Air. Water vapor in air. Trace Glasses 1% Argon (A) Water vapor (H 2

CALIBRATION OF A THERMISTOR THERMOMETER (version = fall 2001)

Understanding Heating Seasonal Performance Factors for Heat Pumps

SKEW-T, LOG-P DIAGRAM ANALYSIS PROCEDURES

EFFECT OF TUNNEL VENTILATION AND EVAPORATIVE COOLING ON THE BARN ENVIRONMENT AND COW COMFORT IN MIDWEST DAIRY FACILITIES

National Hazard and Risk Model (No-HARM) Wildfire

By Tom Brooke PE, CEM

Optimum Solar Orientation: Miami, Florida

Physical Properties of a Pure Substance, Water

Transcription:

J3.4 DESIGN TEMPERATURES FOR HEATING AND COOLING APPLICATIONS IN NORTHERN COLORADO -- AN UPDATE Wendy A. Ryan 1a, Nolan J. Doesken a and Doug Swartz b a Colorado State University, Fort Collins, CO b Fort Collins Utilities 1. INTRODUCTION 1 Several values for design temperatures for heating and cooling applications for the Fort Collins area have been published in recent years. Each one was based on different data and each yielded, not surprisingly, somewhat different results. Accurate documentation of the data sources and data-handling methodologies has not been available. Most recently, two sets of values were published by ASHRAE (ASHRAE, 25). One is designated as AWOS data, WMO station #724769 for the period 1992 to 21. This is believed to be from the Fort Collins-Loveland Airport weather station. The authors do not believe this location provides a good representation of Fort Collins climate due to the station location, the short data set, and the quality of data from this station. The second ASHRAE site is designated as SAWRS data, WMO station #724697 with coordinates 4.58N, 15.8W, elevation 1,524 meters, for the period 1982 to 1994. In addition to a short data period, there is ambiguity regarding the source of these data. Digital data have not been easily available from any weather station in the Fort Collins city limits during that period. Also, this WMO station number does not uniquely define a station. City of Fort Collins asked the Colorado Climate Center to analyze a significantly longer temperature data set from a representative and well-documented source, in order to compute more reliable design temperatures for use by local engineers and contractors designing heating and cooling systems. The City was particularly interested in including recent temperature data because there have been several very warm years since 2. 2. METHODS 2.1 Data Source In this study, the heating and cooling design drybulb temperatures and Mean Coincident Wet Bulb (MCWB) temperatures were recalculated using the Fort Collins, CO campus weather station (NOAA Cooperative Station Number 5-35-4) data for the 39-year period 1968 to 26. The Fort Collins, CO weather station is located on the campus of Colorado State University, northwest of the Lory Student Center. It is considered to be representative of the urbanized (buildings, streets, etc.) but vegetated landscape that characterizes much of Fort Collins. The station has a rich history, with more than 117 years of climatological data. Since the 195s, the weather station has experienced urbanization, with encroachment by buildings and pavement and growth of trees. The environment near the station has been fairly stable since Lory Student Center was completed in the early 196s, but in 22 the CSU Transit Center was built within 9 meters of the station. No significant changes in the local temperatures were noted at the time associated with this change, but this situation deserves continued monitoring. The weather station is currently situated on a vegetated/landscaped island in an effort to preserve an acceptable and representative climatological exposure. Long term data show a gradual warming trend which is most noticeable in winter. The data from the Fort Collins National Weather Station Cooperative (NWS COOP) weather station are quality controlled before being digitized. Redundant and independent temperature measurement systems have been in use for many years. In addition to two pairs of traditional liquid-inglass thermometers (wet bulb/dry bulb and maximum/minimum), the weather station utilizes a hygrothermograph, an electronic MMTS (Maximum/Minimum Temperature System) system, and electronic 1-minute temperature readings. This redundancy of measurements allows data to be cross-checked on a nearly continuous basis and assures a high degree of data integrity. 3.2 Data Processing A summary of the current dataset is given in Table 1. In order to calculate heating and cooling design temperatures, the data needed to be compiled into usable forms. The ideal goal was to assemble complete 876-hourly temperature data records for every year in the dataset, as the basis for frequency distribution analysis. In practice, subsets of different types of data were assembled into a proxy record that allowed accurate calculations. This was done as follows: 1 Corresponding Author Address: 1371 General Delivery, Colorado State University, Fort Collins, CO 8523-1371. wendy@atmos.colostate.edu (97) 491-856

Table 1: Description of temperature data compiled from the Fort Collins, CO campus weather station (Cooperative Station #5-35-4) for this analysis. Date Range 1/1968-12/1977 1/1978-1/1986 11/1986-8/1994 9/1994-1/1996 2/1996 1/26 Data Source Bi-hourly observations of drybulb/wet-bulb temperatures Bi-hourly extreme temperatures only (<- 12ºC/1ºF; 29ºC/85ºF) and wet bulb temperatures, digitized from hard copy files Bi-hourly observations of drybulb/dew point temperatures Bi-hourly extreme temperatures only (<- 12ºC/1ºF; 29ºC/85ºF) and relative humidity, digitized from hygrothermograph charts Hourly electronic temperature data and relative humidity 1968-1977 and 1986-1984: Bi-hourly (once every two hours) manual observations during these periods had previously been digitized from the weather station data records. Missing data for these two periods were estimated by linear interpolation but only if the surrounding observations were greater than 29ºC /85ºF or less than -12ºC/1ºF. This assured that data were complete for both the warm and cold ends of the complete hourly temperature frequency distribution. 1/1978-1/1986: Bi-hourly manual data had not previously been digitized. To save time while still considering these data for the calculation of design temperatures, only the extreme temperatures (<-12ºC/1ºF; 29ºC/85ºF) were digitized from the hard copy surface observations sheets. Missing observations during extreme temperature periods were filled using linear interpolation. 9/1994-1/1996: Data during this period were recorded on hygrothermograph charts. Again, only extreme temperatures (<- 12ºC/1ºF; 29ºC/85ºF) were digitized. 2/1996 1/26. During this period, data were recorded by an electronic temperature/humidity sensor. The data were reported every 1 minutes to the weather stations web site (http://ccc.atmos.colostate.edu/~autowx). These data were converted to hourly data by taking the last report from each hour (i.e. at 5 minutes past the hour), which is consistent with manual observation practices. Both dry-bulb and wet-bulb temperatures were coincidentally available from direct weather station observations only during 1968-1977 and the extreme temperature data segments ( 29ºC/85ºF) that were digitized for the 1/1978-1/1986 period. During other portions of the data set, two other measures of moisture were observed: dew-point temperature or relative humidity. For the periods for which dew-point and relative humidity were available, the wet-bulb temperatures were derived using a software package called EZair Properties (Parks, 22), using the equations in the ASHRAE Handbook of Fundamentals, 1993 edition. The calculations were spot-verified using a psychometric chart. Additional quality control and processing steps were taken. Temperature values were graphed and visually inspected for suspicious data. Those data were investigated and removed if necessary (e.g. keying errors as data was digitized). If no cause could be found for erroneous data, one of two paths was followed. For observations during extreme temperature periods (<-12ºC/1ºF; 29ºC/85ºF), readings were linearly interpolated. For other observation times, the erroneous data were removed from the record. In a few instances it was found that data were mistakenly entered twice; redundant observations were removed. 3.3 Dry-Bulb Design Conditions The calculation of the design temperatures was done following the methods of ASHRAE, reported by Thevenard and Humphries (25). The data, observed to the nearest one-tenth degree Fahrenheit, were rounded to nearest whole degree in order to be comparable with the ASHRAE methods. Bi-hourly observations were double counted in order to create hourly data comparable to the hourly dataset from 1996-25. The data were binned into one degree intervals and arranged into a cumulative distribution function (CDF), shown in figures 1 and 2 for the heating and cooling ends of the distribution respectively. As illustrated in the figures, the design temperatures can easily be derived from the CDF. The x% design condition is the condition that is exceeded, on average, x% of the time. In simpler terms, it relates to the number of hours per year, on average, that a temperature is exceeded. For example, the 1% annual design dry-bulb temperature is the temperature that is exceeded on average 1% of the year, or 87.6 hours per year (Thevenard and Humphries, 25). This dataset has many missing data points from the years where only extreme temperatures were digitized. However, the dataset is complete on the tails of the CDF (i.e. <-12C; 29C). Therefore, instead of working uni-directionally along the CDF, design temperatures were found by working in from the ends of the CDF. This method disregards the middle of the CDF where data is missing.

.15.985 Heating Design Temperature CDF.99.1-15.5ºC/4.1ºF CDF.5.995-19.2ºC/-2.6ºF 1. -26-24 -22-2 -18-16 -14-12 -1-8 -6-4 -2 2 4 6 8 1 Temperature F Figure 1: Low temperature end of the Cumulative Distribution Function using the entire 39-year dataset. The method for calculating the heating design temperatures is illustrated..25.2 CDF.15 29.9ºC/85.8ºF.1.5 31.4ºC/88.6ºF 33.ºC/91.4ºF 85 86 87 88 89 9 91 92 93 94 95 Temperature F Figure 2: High temperature end of the Cumulative Distribution Function using the entire 39-year dataset. The method for calculating cooling design temperatures is illustrated.

3.4 Mean Coincident Wet-Bulb Temperature Another important parameter published by ASHRAE for cooling system design is the Mean Coincident Wet-Bulb (MCWB) temperature. MCWB temperatures were calculated following the methods of ASHRAE, reported by Thevenard and Humphries (25). All of the wet-bulb temperatures coincident with each one degree dry-bulb temperature bin are averaged. A MCWB is calculated for each temperature and graphed in Figure 3. From this graph the MCWB is found by finding the design temperature on the x-axis and reading the MCWB from the y-axis. 3. RESULTS In order to calculate representative design conditions, long-term data sets are required. ASHRAE prefers to use up to 3 years of data to smooth out year to year variations. The years 1972-21 were used for most stations in a recent ASHRAE summary (Thevenard and Humphries, 25). Fort Collins design conditions, based on the current analysis, are listed in Table 2. They were calculated for a variety of time periods. The first period was 29 years in length, including the three most complete temperature data subsets. The second period was the entire dataset of 39 years, including those years for which only the extreme temperatures were digitized. The other two time periods were simply the first and second halves of the 39-year dataset, to see how much design temperatures varied in successive time periods. The ASHRAE (25) data for the Fort Collins SAWRS weather station is included for reference. each of the cooling design temperature levels (See Figure 3). Of the various periods analyzed in this study, the authors recommend that results for the full 39-year dataset be used as the most representative characterization of the long-term Fort Collins climate. 4. ACKNOWLEDGEMENTS This project was funded by the City of Fort Collins. The Colorado Climate Center is funded by the Agricultural Experiment Station. 5. REFERENCES ASHRAE. 25. 25 ASHRAE Handbook- Fundamentals. Atlanta: American Society of Heating, Refrigerating, and Air-Conditioning Engineers, Inc. Thevenard, D.J. and Humphries, R.G. 25. The Calculation of Climatic Design Conditions in the 25 ASHRAE Handbook- Fundamentals. ASHRAE Transactions (111), Career and Technical Education, pp. 457. Parks, R.M. 22. EZAir Properites Version 1.3.2 www.parkssoft.com Copyright 22. The calculated cooling design temperatures in this analysis show little variation with time period. In contrast, the heating design temperatures show slightly larger variations. The difference between the first and second halves of the 39-year dataset suggests that the recent winters are warmer (or at least have fewer persisting extreme cold periods) than previous winters. In order to better illustrate what is occurring, figures 4 and 5 show the number of hours each year greater than or equal to 32ºC/9ºF and the number of hours less than or equal to -18ºC / F, respectively. As can be seen, in recent years the number of hours greater than or equal to 32ºC/9ºF has increased, while the number of hours less than -- 18ºC / F has considerably decreased since the beginning of the dataset. This helps explain the relatively large differences in heating design temperatures from the first half of the record to the last. While we do not know if this trend will continue, it is a definite trait of the recent past. The MCWB temperatures are also presented in Table 2 for each of the time periods. The MCWB stays nearly steady at approximately 16ºC/61ºF for

62 61.8 61.6 MCWB Temperature F 61.4 61.2 61 6.8 6.6 6.4 6.2 6 84 85 86 87 88 89 9 91 Dry-Bulb Temperature F Figure 3: Mean Coincident Wet-Bulb Temperature. Table 2: Design Temperatures for Fort Collins, CO (COOP 5-35-4). Results from the current analysis are listed along with ASHRAE (25) values for comparison. Design Temperatures (degrees Fahrenheit) Heating Cooling Design Criteria (%) --> 99.6% 99.% 2.% 1.%.4% Design Criteria Exceedance (hours/year) --> 8725 8672.4 175.2 87.6 35 Source and Weather Station Years Included in Data Set Parameter ºC/ºF ºC/ºF ºC/ºF ºC/ºF ºC/ºF ASHRAE (25) "Fort Collins" (specific location ambiguous) 13 years: 1982-1994 Dry-bulb temperatures -2.4/-4.8-16.2/2.9 29.2/84.6 3.7/87.2 32.1/89.8 SAWRS, WMO station # 724697 Mean Coincident Wet Bulb temperature 15.9/6.7 16./6.8 16.2/61.1 Colorado Climate Center (26) 27 years: 1968-77, 1986-93, 1996-26 Dry-bulb temperatures -18.8/-1.9-15.1/4.9 29.9/85.8 31.4/88.6 33./91.4 Colorado State University Campus Mean Coincident Wet Bulb temperature 16.3/61.3 16.4/61.5 16.3/61.4 NOAA Cooperative Station Number 5-35-4 38 years: 1968-26 Dry-bulb temperatures -19.2/-2.6-15.5/4.1 29.9/85.8 31.4/88.6 33./91.4 Mean Coincident Wet Bulb temperature 16.3/61.3 16.4/61.5 16.3/61.4 19 years: 1968-1986 Dry-bulb temperatures -19.8/-3.6-16.4/2.4 29.6/85.3 31.1/88. 32.6/9.6 19 years: 1987-26 Dry-bulb temperatures -18.4/-1.1-14.3/6.2 3.1/86.1 31.7/89. 33.2/91.8

2 18 16 14 Hours per Year 12 1 25 8 6 4 2 1967 1968 1969 197 1971 1972 1973 1974 1975 1976 1977 1978 1979 198 1981 1982 1983 1984 1985 1986 Figure 4: Number of hours greater than or equal to 32ºC/9ºF. 1987 Year 1988 1989 199 1991 1992 1993 1994 1995 1996 1997 1998 1999 2 21 22 23 24 25 26 2 Hours per Year 15 1 5 1967 1968 1969 197 1971 1972 1973 1974 1975 1976 1977 1978 1979 198 1981 1982 1983 1984 1985 1986 1987 Year Figure 5: Number of hours less than or equal to -18ºC/ºF. 1988 1989 199 1991 1992 1993 1994 1995 1996 1997 1998 1999 2 21 22 23 24 25 26