The Weather Almanac E-Book

Steven L. Horstmeyer

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 429

Veröffentlichungsjahr: 2011

Beschreibung

The Weather Almanac, 12th Edition is a resource for a variety of climate and meteorological data including both domestic and international weather trends, historical weather patterns dating back 1000 years, natural disasters, and a 20 page glossary of weather terminology. The book is complete with detailed maps, pictures, and tables compiling climate data from a variety of sources, including the National Weather Service and the US Geological Survey.

Separate sections in The Weather Almanac are devoted to tornadoes, hurricanes, thunderstorms, and lightening, flash floods, and winter storms, and they have been edited from official reports by governmental agencies. The new edition has been updated to include recent disasters such as the 2004 Indian Ocean Tsunami that devastated Indonesia as well as 2005’s Hurricane Katrina. These chapters serve as a basic reference for severe weather and extreme conditions, which can assist in preparing for a weather emergency.

Leseprobe

Contents

Cover

Title Page

Copyright

Preface

Chapter 1: Climate Maps of the United States

WHAT IS CLIMATE?

THE CLIMATE MAPS

Chapter 2: Renewable Energy

INTRODUCTION

RENEWABLE ENERGY

SOME HISTORY OF WIND MEASUREMENT

WIND ENERGY POTENTIAL IN THE UNITED STATES

SOLAR ENERGY POTENTIAL IN THE UNITED STATES

Chapter 3: Extreme Weather—Disasters, Records, Floods, Heat, Cold, and Blizzards

THE COST OF US WEATHER DISASTERS

BILLION DOLLAR DISASTERS IN THE UNITED STATES

TABLES OF RECORD-SETTING WEATHER: TABLES 3.5–3.9

MISCELLANEOUS WEATHER RECORDS AND EXTREMES

FLOODS

HEAT WAVES

COLD WAVES

DROUGHT

WINTER STORMS

THE COST OF EXTREME WEATHER TO THE UNITED STATES

Chapter 4: Thunderstorms, Lightning, Hail, and Tornadoes

INTRODUCTION

THUNDERSTORM BASICS

ISOLATED CONVECTIVE CELLS

MESOSCALE CONVECTIVE SYSTEMS

SQUALL LINES

BOW ECHOES

LINE ECHO WAVE PATTERN

THE MESOSCALE CONVECTIVE COMPLEX

SUPERCELL THUNDERSTORMS

TORNADO FORMATION

THE FUJITA SCALE

THE ENHANCED FUJITA SCALE

SOME TORNADO HISTORY

LIGHTNING AND THUNDER

HAIL

REFERENCE TABLES AND MAPS

Chapter 5: Tropical Cyclones

INTRODUCTION

GEOGRAPHY AND FORMATION

TROPICAL CYCLONE STRUCTURE

HURRICANE CLASSIFICATION

THE DVORAK SATELLITE TECHNIQUE

REFERENCE MATERIALS

Chapter 6: El Niño, La Niña, and the Southern Oscillation

INTRODUCTION

SOME EL NIÑO/LA NIÑA HISTORY

SIR GILBERT WALKER—THE INDIAN MONSOON AND ENSO

THE MECHANICS OF THE SOUTHERN OSCILLATION AND EL NIÑO/LA NIÑA

THE SOUTHERN OSCILLATION INDEX AND SSTS

MONITORING THE EQUATORIAL PACIFIC

ENERGY AND EL NIÑO/LA NIÑA

ENSO AND HURRICANES

ENSO AND MONSOON RAINFALL

FOUR HISTORICALLY IMPORTANT EL NIÑOS OF THE 20TH CENTURY

EL NIÑO–LA NIÑA OCCURRENCES

OTHER OSCILLATIONS AND TELECONNECTION PATTERNS

Chapter 7: Global Warming and Climate Change

INTRODUCTION

INTRODUCTION TO CLIMATE CHANGE

THE GREENHOUSE EFFECT

FORCING MECHANISMS AND FEEDBACK LOOPS

CLIMATE RESEARCH

PREDICTING FUTURE CLIMATES

Chapter 8: Air Pollution

INTRODUCTION

SOME AIR POLLUTION HISTORY

NATIONAL AMBIENT AIR QUALITY STANDARDS AND PROGRESS IN CLEANING AMERICA'S AIR

INFORMING THE PUBLIC—AIR QUALITY INDEX

Chapter 9: Climate Data from Around the World

INTRODUCTION

CLIMATE CONTROLS

INTERNATIONAL WEATHER DATA

USING WEATHER AND CLIMATE DATA

DATA KEY

Chapter 10: Local Climatological Data Annual Summaries 2009

INTRODUCTION

READING LCD REPORTS FOR 128 US CITIES

OTHER DATA PAGES

Chapter 11: A Time Line of Meteorology: 9000 b.c.–2000 a.d.

INTRODUCTION

1850–PRESENT: SIX TECHNOLOGICAL INNOVATIONS ON THE ROAD TO MODERN METEOROLOGY

1830–PRESENT: NINE CONCEPTUAL ADVANCES ON THE ROAD TO MODERN METEOROLOGY

Index of Acronyms and Abbreviations

General Index

Index

Index of Select Storms and Events

Copyright © 2011 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada.

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Library of Congress Cataloging-in-Publication Data

Horstmeyer, Steven L. The weather almanac: a reference guide to weather, climate, and related issues in the United States and its key cities. Twelfth Edition / Steven L. Horstmeyer p. cm. Includes bibliographical references and index. ISBN 978-0-470-41325-8 (cloth)

oBook ISBN: 978-1-118-01521-6 ePDF ISBN: 978-1-118-01519-3 ePub ISBN: 978-1-118-01520-9

Preface

Whenever an established work like The Weather Almanac is updated, the primary concern of the editor is to remain true to the traditions established in earlier editions. The editor is faced with retaining some material, updating other material, and saying goodbye to material that has seen better days.

The 12th edition of The Weather Almanac is my best effort at staying true to the hard work of my predecessors, but at the same time move the book in the direction that my 33 years as a broadcast meteorologist, public speaker, and educator tell me is most useful to the user of the book seeking answers to weather questions.

If you are looking at material that contains numbers or maps or graphs, admittedly most of the book, then you are reaping the rewards of the hard work of James Patterson. During the course of this rewrite, James was working full- and part-time jobs, finishing his meteorology degree, buying a house, and starting a family. He always found time to get the job done. He has a great ability to find problem numbers and fix them. Thanks James. James, did I say thanks?

Chapter 1 contains 42 climate maps of the United States and communicate what maps can best the spatial patterns that determine what we call climate. Chapter 2 is all new and a response to a heightened interest in renewable energy. In Chapter 2, you will find detailed information about the solar radiation and wind climates of the United States and many wind roses. They are hard to come by, but there are many here.

I have lumped the many varieties of extreme and record-setting weather into Chapter 3 and dug long and hard to assemble a chapter with some very difficult to find but important weather information. I have stayed with the customary chapters for severe and tropical weather (Chapters 4 and 5, respectively). Chapter 6 covers El Niño/La Niña, perhaps the most important climate discovery of the last half of the 20th century. Chapter 6 is updated with all new easy-to-follow diagrams.

The second entirely new chapter is Chapter 7: Global Warming and Climate Change. As a professional meteorologist deeply concerned with human impact on our environment, I am continually dismayed by the amount of bad information out there about global warming. I have intentionally steered clear of forecasts and hope this chapter clarifies physical principles, research methods, the role of proxy data, and what climate models are and what they are not.

Air Pollution is the topic of Chapter 8 and contains the latest available data that comes primarily from the US Environmental Protection Agency (EPA).

Chapter 9 contains international climate data. I have included more locations (321) and each location has more data. While this does not give a complete look at Earth's climate, most places you can easily go are covered.

In Chapter 10, you will find climate data for 128 US cities in the form of the 2009 Local Climatological Data Annual Summary. There is a great deal of information here on each station, and there are 20 more stations than the last edition. We also graphed the daily high temperatures, low temperatures, and precipitation for 2009 so you can see at a glance what 2009 was like in each city.

Chapter 11 is an annotated time line of human interaction with and knowledge of the atmosphere. As of this writing, there is no other popular reference work with such a listing. History buffs should find this interesting.

I can now answer the question my wife put to me about a week ago. Yes honey, you now get your husband back.

Steven L. HorstmeyerAugust 23, 2010Cincinnati, OH

1

Climate Maps of the United States

WHAT IS CLIMATE?

Weather is the day-to-day, sometimes minute-to-minute, changes in the atmosphere. Everyone has an intuitive idea what weather is. But when the time period is extended to months, seasons, years, decades, and longer, we talk about climate. Climate is the long-term state of the atmosphere. It is how you expect the atmosphere to behave.

The change of seasons is part of what climate is. In the Midwestern United States, residents expect hot, humid summer weather to gradually yield to autumn, characterized by cool mornings and toasty warm afternoons dominated by blue sky. In southern and central California, residents know that the brown hillsides that dominate the landscape from late spring into late autumn will begin to green as seasonal rains replenish soil moisture and plants begin to grow.

In Hawaii there is hardly any seasonal temperature change at all, but there are subtle differences from summer into winter in wind and rain events.

Climate is much more than seasonal change. It has been called the “average” of all weather, but it is still more. It can also be the daily, weekly, monthly, or annual range of a weather variable. Climate can be the frequency of occurrence of any weather event such as lightning. In addition there are more complex statistical measures, such as standard deviation that measures the variation about the average, that can help define climate.

Climate can be the average relative humidity at a specific hour of the day or the number of days the relative humidity drops below a certain value. The number of days snow fall exceeds a given amount gives you an idea of the frequency of traffic snarls, while the number of hours the average wind exceeds a given value during a year may help decide about the placement of a wind-powered turbine.

Climate can be defined however you need it to be. You decide what weather variables affect your project and develop a climatology that describes what to expect. The average afternoon temperature for a given place may give you an idea of how comfortable the location is but including a humidity variable and wind speed will give you a better idea of the “comfort climate.”

If you are projecting the heating cost of locating a new office facility, you would want detailed information about lowest temperatures, how long the temperature is colder than a particular value, how sunny the location is, and how windy it is. Each weather variable is part of the “natural gas for heating” climatology, and each affects the demand for natural gas for heating.

In summer a “residential cooling” climatology would include the same variables as for heating along with a humidity variable to account for electrical power demand.

Think of it this way: weather is a rainy day, while climate is a rainy place. All US cities have rainy days, but Seattle has a rainy climate. Portland, ME, has occasional hot days, but Orlando, FL, has a hot climate.

THE CLIMATE MAPS

The 42 maps in this chapter represent a detailed picture, a climatology, of what you can expect over the long term in the lower 48 states.

The data were prepared and quality-controlled by the National Climatic Data Center (NCDC) of National Oceanic and Atmospheric Administration (NOAA) for the Climate Maps of the United States (CLIMAPS) database. The maps were redesigned and replotted for grayscale reproduction in this volume.

If you have experience with using or creating contour maps, you may be accustomed to having a fixed data interval between contour lines. That almost never works when creating climate maps because the distribution of climate is not regular and there are many factors that complicate how quickly values change. The contour intervals used here are the intervals chosen by the NCDC.

Figure 1.1 Mean annual minimum dew point temperature.

Elevation is probably the most difficult complicating factor to deal with. When you examine the maps in this chapter, you will see how small some of the areas can be because of dramatic changes in climate over a short distance in mountainous terrain. For that reason it was decided not to use fill patterns because they can be very confusing when small areas are involved.

For most of the climate maps we opted to use a symmetrical shading scale ranging from white at the minimum value through medium–dark gray back to white at the maximum value. To the unaccustomed this may seem confusing, but it is standard practice in many science publications. There are a few geographical areas where maximum white and minimum white come close together, such as from the Central Valley of California into the Sierra Nevada Mountains. In areas such as this you will need to be careful to interpret the date correctly.

To help guide you in using the climate maps, many have specific values plotted for a contour or an area. Doing this is always problematic because map data can be obscured by the numbers. We carefully considered the placement of each and every number so as to minimize covering fine detail.

Not all the climate maps we prepared are in this chapter; many fit better in chapters on specific topics.

Climate maps covering sunshine, solar radiation, cloud cover, and wind variables are in Chapter 2, covering solar and wind renewable energy-generating technologies.

The climatology of US severe weather is in Chapter 4, with maps of tornado tracks, hail, and lightning occurrence, and the climatic information about Atlantic and eastern Pacific hurricanes is included in Chapter 5. There are maps dealing with specific aspects of past climate (paleoclimate) in Chapter 7. Extensive numerical information about 128 US cities is found in Chapter 10, while weather data for 321 locations outside the United States are found in Chapter 9.

Figure 1.2 Mean annual maximum dew point temperature.

Figure 1.3 Mean January minimum dew point temperature.

Figure 1.4 Mean January maximum dew point temperature.

Figure 1.5 Mean July minimum dew point temperature.

Figure 1.6 Mean July maximum dew point temperature.

Dew Point Temperature

Notes: Dew point temperature is one of the many measures of humidity. It is defined as the temperature to which a mass of air must be cooled for condensation to begin or equivalently the temperature at which a relative humidity of 100% occurs when the air is cooled. It is a measure often used by forecasters, and the smaller the difference between dew point temperature and air temperature, the higher the relative humidity. When the dew point temperature equals the ambient air temperature, the relative humidity is 100%.

When the dew point temperature reaches 60°F, nearly everyone feels the humidity, and when the dew point temperature reaches 70°F, nearly everyone says the weather is sticky. Because of that, dew point temperature along with temperature is a primary predictor of energy usage for cooling.

Energy Use for Heating and Cooling

Figure 1.7 Mean annual number of cooling degree days.

Figure 1.8 Mean annual number of heating degree days.

Degree Days Maps

Notes: The terms “cooling degree days” and “heating degree days” are confusing and it is best to think of them as cooling units and heating units.

Using a base temperature of 65°F as the dividing point between the need for heating and the need for cooling, both heating and cooling degree days are the difference between the average daily temperature and the base temperature of 65°F. If the average temperature is warmer than 65°F, the difference in °F is the number of cooling degree days. If the average temperature is cooler than 65°F, the difference in °F is equal to the number of heating degree days.

Figure 1.9 Mean length of the freeze-free period.

If the high temperature for a day is 80°F and the low is 66°F, the average for the day is 73°F, which is 8°F warmer than the base temperature of 65°F, so the day adds 8 cooling degree days to the running seasonal total.

The way degree days are calculated can lead to a significant error. For example, if at midnight it is 60°F and the temperature drops to 45°F at 1:00 A.M., then for the next 22 hours, the temperature hovers at 40°F; the average temperature for the day using only the high and low is 50°F, but using the 24-hourly temperatures yields an average of 41°F, an 18% difference. Whenever there is a large temperature change early or late in a day, large errors can occur when using only the daily high and daily low to calculate average temperature. For this reason many private utilities use hourly data in forecasting and tracking electrical loads and natural gas demands.

Figure 1.10 Earliest date of the first autumn freezing temperature.

Figure 1.11 Mean date of the first autumn freezing temperature.

Figure 1.12 Latest date of the last spring freezing temperature.

Figure 1.13 Mean date of the last spring freezing temperature.

Growing Season/Freeze–Thaw Maps

Notes: Traditionally, the growing season ends with the first-observed frost and begins again after the last frost. Frost can occur when the air temperature at thermometer height is as warm as 35°F–37°F, but because colder air is denser and settles to the ground, the temperature where the frost occurs is freezing or colder.

Figure 1.14 Mean annual number of days with measurable precipitation.

Instead of the term “growing season,” “freeze-free period” is used, and it is defined as the number of days between the last spring freezing temperature and the first autumn freezing temperature. This use prevents confusion between the traditional definition and the one currently in use.

Just because a location reaches 32°F does not mean plant growth has stopped and plant damage has occurred. The length of time the temperature stays at or below a certain temperature threshold is also important. The freeze-free period gives a general indication of the length of the growing season, but crop-specific and site-specific information is required for practical application in agriculture.

Precipitation Maps

Notes: In the United States, measurable precipitation is defined as an amount of 0.01” or more.

Figure 1.15 Mean annual number of days with freezing rain or freezing drizzle.

Figure 1.16 Mean annual total precipitation.

Figure 1.17 Record annual total precipitation.

Atmospheric Pressure (Sea Level)

Notes: Atmospheric pressure is always mathematically adjusted to what it would be at sea level the standard reference level in meteorology. Unadjusted pressure values are referred to as “station pressure.”

Figure 1.18 Mean annual minimum pressure.

Figure 1.19 Extreme lowest pressure.

Figure 1.20 Mean annual maximum pressure.

Figure 1.21 Extreme highest pressure.

Figure 1.22 Mean annual total snowfall.

Snowfall Maps

Notes: In the United States, measurable snowfall is defined as a depth of 0.1” or more.

Figure 1.23 Record annual total snowfall.

Figure 1.24 Mean annual number of days with snowfall of 0.1” or more.

Figure 1.25 Mean annual number of days with snowfall of 1” or more.

Figure 1.26 Mean annual number of days with snowfall of 5” or more.

Figure 1.27 Mean annual number of days with snowfall of 10” or more.

Figure 1.28 Median date of first measurable (≥0.1”) snowfall.

Figure 1.29 Extreme first date of first measurable snowfall.

Figure 1.30 Median date of last measurable (0.1”) snowfall.

Figure 1.31 Extreme last date of last measurable snowfall.

Figure 1.32 Probability of a white Christmas.

Temperature

Figure 1.33 Mean annual number of days ≥90°.

Figure 1.34 Mean annual number of days ≤32°.

Figure 1.35 Mean annual minimum temperature.

Figure 1.36 Mean annual maximum temperature.

Figure 1.37 Extreme minimum temperature.

Figure 1.38 Extreme maximum temperature.

Figure 1.39 Mean January minimum temperature.

Figure 1.40 Mean January maximum temperature.

Figure 1.41 Mean July minimum temperature.

Figure 1.42 Mean July maximum temperature.

2

Renewable Energy

INTRODUCTION

The goal of this chapter is to summarize the “wind power” climate and the “solar energy” climate of the 48 contiguous states and provide the user with data regarding the feasibility of both solar power energy generation and wind power energy generation.

The material in this chapter could easily have been included in Chapter 1, but because of the specific nature of the information I have treated the wind and solar energy climates of the United States separately.

There are 97 figures, a combination of maps, and 132 graphs, in this chapter covering 44 cities. You will find a combination of annual maps covering the lower 48 states, and detailed wind roses, wind speed histograms, and solar energy graphs for specific locations.

RENEWABLE ENERGY

Of the renewable energy sources available the most well known are solar energy and wind energy. In addition, the US Department of Energy's National Renewable Energy Laboratory (NREL) lists geothermal, hydrogen (including fuel cells), and biomass as sources of renewable energy. Not listed by the NREL is tidal energy as a potential source in the future.

Weather and climate play a part in the utilization of any energy source for heating and cooling because of varying demand. Only wind and solar renewable energies are directly dependent on weather and climate for supply.

Definitions

Renewable energy is produced from natural processes that are replenished constantly. In most cases energy is derived directly from the sun, such as wind, solar, and biomass energy sources. Heat generated deep within the earth is also considered renewable, but it is not a product of solar radiation. Geothermal energy is largely the result of radioactive decay deep within the earth. Hydrogen as a fuel source is earth-based, also because water is its source.

Modern biomass energy production, also called bioenergy, involves processing plant materials to a more readily usable form such as a gas or liquid like ethanol. Directly burning wood continues to be the most common form of bioenergy worldwide.

Geothermal energy has been in use for thousands of years. Rome was famous for hot baths and heating small structures using geothermal heat. The modern approach to geothermal energy production involves generating electricity with turbines turned by steam. Geothermal energy production is growing slowly at 3% per year primarily because under present technologies, generation must take place near the sources that are tectonically active regions of earth. The largest geothermal power plant in the world, The Geysers, is located north of San Francisco in Sonoma and Lake Counties and has a generating capacity of 750 MW.

Tidal energy uses the power of incoming and outgoing tidal water to power generators. This source has also been used since Roman times to power mills. Tidal energy falls into two broad categories: the capture of the kinetic energy of water moving horizontally under the influence of tides and the capture of potential energy or water lifted in the tidal bulge. Tidal energy is the only energy source relying on the relative motions of the Earth and moon. Tidal energy production is rare because of infrastructure costs, potential environmental damage, and the lack of suitable sites.

Wind energy is the fastest growing of the renewable sources, with capacity in the United States growing at 30% per year. Worldwide generating capacity in 2008 totaled 121,000 MW. The modern approach to wind energy uses air flow to turn a wind turbine, also called an “aerogenerator.” If the mechanical energy is used directly by machinery for grinding grain or pumping water, for example, the term “windmill” is used.

Solar energy is the use of sunlight for power. The NREL lists five categories of solar energy production:

1.Concentrating solar power (CSP) technologies use parabolic reflectors to focus sunlight and concentrate the heat to boil water and drive a turbine. CSP technologies are realized as expensive projects that require large investment and therefore designed as large-scale electrical distribution systems in much the same way as traditional coal- or oil-based electrical power generation. Because of the expense CSP facilities are limited to the sunniest parts of the country.

2.Photovoltaic (PV) technology is the most well-known solar energy technology. Sunlight generates electricity using the PV effect, which was discovered in 1954. First-generation solar cells used silicon flat plates and are what most people think of when referring to solar energy production. Second-generation cells involve thin-film technology that can be used to make roof shingles as collectors and thus overcome the esthetic objections to solar panels. Third-generation solar technology uses materials other than silicon alone to more efficiently capture solar energy. Because PV technology is modular and individual modules are relatively inexpensive when compared to CSP technologies, PV technology is well suited for individual residences.

3.Passive solar technology involves building designs that allow or promote daytime heating of walls and spaces through exposure to sunlight.

4.Solar water heating is simple, water exposed directly or within a collector to sunshine is heated and stored for future use in an insulated tank.

5.Solar process heating refers to the use of a variety of technologies in industrial buildings. Some of the technologies are too expensive for residential uses, but solar process heating encompasses the use of PV technology, the primary residential solar energy technology.

SOME HISTORY OF WIND MEASUREMENT

In 1805, before anemometers were invented to measure the wind speed, British Admiral Sir Francis Beaufort created a scale named after him. The first version gave a qualitative description of the effect of the wind on the sails on a man of war. There were 13 classes ranging from “just sufficient to give steerage” to “that which no canvas sails could withstand.”

In the 1830s, reporting the Beaufort Scale became standard practice in the British Navy. In the 1850s, the scale was adapted for land use, with scale numbers corresponding to anemometer cup rotations. At this time anemometers did not display wind speed.

As steam power supplanted sails the Beaufort Scale was changed in 1906 to reflect the character of the sea, not the state of the sails. In 1923, anemometer cup rotations were standardized and land-based descriptions were added. In 1946, forces 13–17 were added for special cases such as tropical cyclones. Today the Beaufort Scale has been nearly abandoned for measured units of meters per second, kilometers per hour, nautical miles per hour (knots), and miles per hour. The modern Beaufort Scale is found in Table 2.1.

WIND ENERGY POTENTIAL IN THE UNITED STATES

Wind energy potential is highly variable from place to place and also highly variable in any one place from time to time. The latter is termed the “intermittency problem” because wind-generated power, even in the most reliable locations, will at times be unavailable. In general, areas with the greatest wind potential are where some factor, often terrain, influences the speed of the wind.

But wind speed is not the only variable that makes a place good or bad for producing wind energy. A place with a reliable, more constant wind may produce more energy than one with higher speeds and less consistency. The suitability of any location for generating wind energy is the product of the complex interaction of many factors that vary at a very small scale. A good location for a wind turbine may not be obvious from casual inspection.

Because wind speed generally increases with height above the surface, wind energy potential is calculated above the surface. Common values used to estimate wind power density are 10 m (33 feet), 30 m (98 feet), and 50 m (164 feet) above ground level.

Because most wind measurements are made near the surface, wind power density estimates apply a rule of thumb called the “wind profile power law” or the “one-seventh power law.” This relationship works well over unobstructed ground in stable atmospheric conditions, but over open water/very rough ground or over areas with numerous obstructions to low-level air flow, the error can be substantial.

Better estimates can be made using the log wind profile equation that includes input for surface roughness and stability. When these are missing, which is quite often, the wind profile power law is used.

Table 2.2 shows the wind power density in watts per square meter at 10, 30, and 50 m above ground level. The associated wind speeds in both meters per second and miles per hour calculated (estimated) from the wind profile power law are listed. They are classified using the US Department of Energy's wind potential classification.

Superb potential for wind power generation (800–1600 W/m−2) at 50 m above ground level is hard to find. Superb potential is most often found in isolated areas of the Great Plains, coastal areas, and high mountain passes. Electricity from wind can be generated at other areas, but it may not be economically feasible on a large scale. Most of the Great Plains and foothills of the Rocky Mountains are rated as having good or better potential.

The maps and tables in this chapter present a general wind climatology of the contiguous 48 states. Wind roses and wind speed frequency histograms are included for 33 cities along with annual national maps of average wind speed, the occurrence of peak gusts at three limits, and the fastest mile of wind.

The wind speed climatology of a place is also important as input in building and structure design, including towers for capturing wind energy. Modern standards are moving away from the “fastest mile” as a building standard and toward the “3-s wind gust” with a 2% annual occurrence, the same as a 50-year recurrence interval.

Table 2.1 The Modern Beaufort Wind Scale

Table 2.2 Estimates of Power Density and wind Speed at Three Altitudes Above Ground Level Using the Wind Profile Power Law.

The fastest mile of wind is measured in miles per hour and is the fastest average wind speed during the period of 1 min that is observed during the time period required for the air to travel 1 mile past the anemometer. The fastest mile is always slower than the 3-s wind gust.

Table 2.3 provides the approximate conversion from fastest mile to 3-s wind gust according to the International Building Code.

Figure 2.1 shows the wind energy potential at 50 m above ground level of the United States as estimated by the US Department of Energy. Notice how large parts of the country, especially the east and south, have little potential as sites for generation of wind energy.

Figures 2.2–2.6 are maps that show the wind climate of the United States using traditional measures. Figure 2.2 shows the average wind speed in miles per hour; Figures 2.3–2.5 show the occurrences of wind gusts exceeding 30, 40, and 50 mph, respectively; and Figure 2.6 shows the fastest mile of wind for the 48 lower states. Notice the similarities between Figure 2.1 and Figures 2.2–2.6.

Included in this chapter are wind roses and wind speed frequency bar graphs (histograms) for 44 locations in the lower 48 states (Figures 2.7–2.50). It is important to realize two factors when using this information: (1) most of these are near-surface velocities, so wind speed at turbine level will be faster, and (2) local variations in exposure and topography can drastically alter the availability of wind power.

Table 2.3 Conversion from Fastest-Mile Wind Data to 3-Second Gust Wind Data

Figure 2.1 National wind parameter map.

Figure 2.2 National wind parameter map.

Figure 2.3 National wind parameter map.

Figure 2.4 National wind parameter map.

Figure 2.5 National wind parameter map.

Figure 2.6 National wind parameter map.

Figure 2.7 City wind roses and wind speed frequency histogram.

Figure 2.8 City wind roses and wind speed frequency histogram.

Figure 2.9 City wind roses and wind speed frequency histogram.

Figure 2.10 City wind roses and wind speed frequency histogram.

Figure 2.11 City wind roses and wind speed frequency histogram.

Figure 2.12 City wind roses and wind speed frequency histogram.

Figure 2.13 City wind roses and wind speed frequency histogram.

Figure 2.14 City wind roses and wind speed frequency histogram.

Figure 2.15 City wind roses and wind speed frequency histogram.

Figure 2.16 City wind roses and wind speed frequency histogram.

Figure 2.17 City wind roses and wind speed frequency histogram.

Figure 2.18 City wind roses and wind speed frequency histogram.

Figure 2.19 City wind roses and wind speed frequency histogram.

Figure 2.20 City wind roses and wind speed frequency histogram.

Figure 2.21 City wind roses and wind speed frequency histogram.

Figure 2.22 City wind roses and wind speed frequency histogram.

Figure 2.23 City wind roses and wind speed frequency histogram.

Figure 2.24 City wind roses and wind speed frequency histogram.

Figure 2.25 City wind roses and wind speed frequency histogram.

Figure 2.26 City wind roses and wind speed frequency histogram.

Figure 2.27 City wind roses and wind speed frequency histogram.

Figure 2.28 City wind roses and wind speed frequency histogram.

Figure 2.29 City wind roses and wind speed frequency histogram.

Figure 2.30 City wind roses and wind speed frequency histogram.

Figure 2.31 City wind roses and wind speed frequency histogram.

Figure 2.32 City wind roses and wind speed frequency histogram.

Figure 2.33 City wind roses and wind speed frequency histogram.

Figure 2.34 City wind roses and wind speed frequency histogram.

Figure 2.35 City wind roses and wind speed frequency histogram.

Figure 2.36 City wind roses and wind speed frequency histogram.

Figure 2.37 City wind roses and wind speed frequency histogram.

Figure 2.38 City wind roses and wind speed frequency histogram.

Figure 2.39 City wind roses and wind speed frequency histogram.

Figure 2.40 City wind roses and wind speed frequency histogram.

Figure 2.41 City wind roses and wind speed frequency histogram.

Figure 2.42 City wind roses and wind speed frequency histogram.

Figure 2.43 City wind roses and wind speed frequency histogram.

Figure 2.44 City wind roses and wind speed frequency histogram.

Figure 2.45 City wind roses and wind speed frequency histogram.

Figure 2.46 City wind roses and wind speed frequency histogram.

Figure 2.47 City wind roses and wind speed frequency histogram.

Figure 2.48 City wind roses and wind speed frequency histogram.

Figure 2.49 City wind roses and wind speed frequency histogram.

Figure 2.50 City wind roses and wind speed frequency histogram.

Proper estimation of wind power generating potential requires careful application of the wind profile power law. It is also important to remember that detailed field studies are needed to assess the wind power generating potential of a particular location.

Wind Roses and Wind Speed Frequency Histograms

A wind rose is a circular histogram representing the percentage of winds from all compass directions. Winds are referenced by the direction from which they blow, so when examining a wind rose, the wedge-shaped bars indicate wind blowing toward the center of the circle.

A wind rose with approximately equal-length bars (or petals) from all directions represents a wind environment with great variation of wind direction from day to day. The same goes for the wind speed frequency histogram; the more the bars are close to being equal, the more variable the wind speed.

A wind rose with one or a few long dominant bars represents a location with lower variability, which is usually good for generating wind power if the wind velocity is great enough. Likewise if the wind speed histogram has one or a few dominant bars, the wind speed variation is low. Another way of saying this is that the wind is more constant or reliable.

Each of the 44 wind roses and speed frequency histograms has been calculated from thousands of hourly observations. In most cases, more than 80,000 hourly observations make up the wind roses and wind speed frequency histograms. The data were obtained form NOAA's National Climatic Data Center (NCDC) Solar and Meteorological Surface Observation Network (SAMSON) archive. In this chapter, values from 1981–1990 were used.

Although not a comprehensive atlas of wind direction and wind speed frequency, the 44 sets of wind graphs are widely distributed enough to give a general indication of wind power potential.

Using Wind Roses and Wind Speed Frequency Histograms

At the center of each wind rose, the percentage represents the frequency of calm winds. For the purpose of this chapter, calm is defined as a reported velocity of less than or equal to 2 mph. The histogram represents the frequency of occurrence of wind speeds in the indicated range and is reported as a decimal value above the bar. Two bars may be of slightly different length and have the same decimal value because of rounding.

Above the histogram are four lines of data. Average wind speed is the numerical average of all velocities, including calms. Maximum wind speed is the maximum hourly observation reported in the data set. This is not necessarily the highest wind speed to have occurred at the location; it is merely the highest wind speed observed at the time the hourly observations were made.

Resultant wind direction is the vectorial average of all wind directions and wind speeds reported in the 1981–1990 time period. It was calculated using WRPLOT View, © 1998–2008, Lakes Environmental Software. In almost all cases it is different from the prevailing wind direction, which is the most frequently occurring wind category. Neither the resultant wind nor the prevailing wind includes the wind speed only wind direction.

Following both the prevailing wind and the resultant wind is the percentage of time the wind blows from that direction. In the case of the prevailing wind, the percentage represents the percent of time the wind blows from a 10° range centered on the value of the prevailing wind. A prevailing wind entry of 240° 11% means the wind blows between 235° and 245°, 11% of the time.

There are situations when calculating a resultant wind gives almost nonsensical results, for example, if the wind blows with nearly equal frequency from due west and due east. The resultant wind could be from either the north or the south and be of little use. This is caused because the circular nature of the data and the discontinuity as due north is crossed.

Both the prevailing wind direction and the resultant wind direction are less reliable as indicators for wind power as the variability of the wind increases. Prevailing wind may not be a majority wind in highly variable wind direction climates. Resultant wind is a measure of total air movement over a location, and because it is an average, it does not represent an actual wind. Because it is an average, the resultant wind may underrepresent the windiness of an area as winds from opposite directions cancel each other.

An example of this problem is the wind rose for Boise, ID. The wind rose shows a bimodal distribution with two predominant wind directions nearly 180° apart. The resultant wind is from 57°, which is so infrequent that it does not show up on the wind rose. The resultant wind in this situation contains no usable information. The prevailing wind is however obvious from the wind rose, and for wind power generating estimates, prevailing wind direction along with wind speed data stratified by wind direction contains valuable information.

Omaha, NE, is another example of this problem, though not as extreme as Boise.

Los Angeles, CA, is an example where the resultant wind and prevailing wind are close to being the same. The two would be even closer if the wind data were stratified into smaller ranges; in that case the difference could be accounted for by the influence of wind speed in the resultant calculation. Notice that the prevailing wind and resultant wind are in close agreement if there is a single dominant wind direction, in this case from the west-southwest.

Another example like Los Angeles is Dallas/Fort Worth, TX.

A wind rose with a nearly equal distribution of wind directions is not common. New Orleans, LA, comes close. Notice that the resultant wind and prevailing wind differ by 12°. For a wind rose with nearly equal representation of all wind directions prevailing and resultant winds should be nearly the same.

When used together, the prevailing wind and resultant wind can be fairly reliable. The closer the directions and the greater the individual percentages of occurrence, the more reliable the indication of most frequent wind direction.

A very good example of high reliability of the two measures together is the wind rose and histogram of Great Falls, MT. An excellent example of both together being a poor indicator is Fargo, ND. Note how the winds in Great Falls are predominately from the southwest being forced by the terrain to the west. In Fargo, there are two nearly equal dominant directions and the opposites cancel each other to a high degree.

SOLAR ENERGY POTENTIAL IN THE UNITED STATES

Solar energy has the potential for producing 20% of the electricity demand in the United States primarily through CSP and PV technologies. Unlike wind power generating potential that is highly dependent on very specific site characteristics, solar energy generating potential relies primarily on exposure. Like wind power generation, solar energy suffers from the intermittency problem. Solar energy is of course not available at night and is severely limited on cloudy days. Haze and other obstructions to visibility can reduce the amount of solar energy available for generating electricity.

CSP technology is almost exclusively a commercial-scale effort. Through the use of concentrating reflectors steam is generated and used to turn a turbine and produce electric current. The cost of such projects puts them well beyond the reach of individual residencies.

PV technology, typically solar panels, is what most people think of when discussing solar energy. It continues to be costly and marginally affordable, but it is expected to benefit from the economies of scale and the modular nature as mass production of solar panels lowers the cost.

In even cloudy northern climates, solar radiation can be a source of electricity using PV technology. The potential for generating electricity is similar over wide geographic areas, being primarily dependent on latitude and cloud cover. Atmospheric transparency is a secondary factor and is affected by anthropogenic pollutants and natural sources of dust. Unlike the potential for generating electricity using wind, local site characteristics do not affect the use of PV technology as drastically.

Maximum solar energy is captured when a PV panel is perpendicular to the sun's incoming rays, which for a specific location varies with time of day and day of the year. Most solar panels are mounted in a fixed position that is calculated to maximize the receipt of solar radiation. This means early and late in the day and at various times of the year the panel will receive less than the maximum possible for that time period.

Solar panel tilt can vary when panels are placed on single- and double-axis mounts using a solar tracker. Single-axis mounts follow the sun through the day to keep the angle at which the solar radiation strikes the panel as near to 90° as possible. Most single-axis mounts have another axis that is adjusted periodically manually to compensate for time of year. Solar panels on double-axis mounts also move, adjusting for time of day and day of year. Single-axis mounts generally output 30% more energy than fixed mounts. Adding a second axis yields an additional 6% beyond a single-axis mount.

Figures 2.51–2.94 are graphs for 44 cities in the contiguous 48 states in this chapter plotted from data provided by the US Department of Energy's NREL. They are a good starting point for evaluating the solar energy potential for most of the lower 48 states.

These graphs are the average daily direct normal solar radiation from 1998 to 2005 in kilowatt-hours (kWh) per square meter per day. One kilowatt-hour is approximately equal to 3412 British thermal units and 860 kilocalories.

Direct solar radiation excludes scattered radiation and is only that which arrives directly from the sun. The word “normal” in the title means that it is measured for a flat plate oriented at 90°, or normal, to the sun's rays. A fixed solar panel will receive less than indicated on the graph because the sun angle varies continuously through the day. A solar-receiving system that tracks the sun will remain perpendicular to the sun's rays through the day and can be expected to receive the indicated amount.

The graphs then represent the maximum energy that can be expected on a given day of the year. Because these are based on 8 years of measured solar energy and not calculated maximum amounts they represent a realistic estimate.

The heavy line is the average daily direct solar radiation, and the upper and lower lines give the values for +1 and −1 standard deviations. Approximately 68% of the time the solar radiation for a day will fall between the upper and lower lines.

In addition to the average normal solar radiation graphs, there are three maps (Figures 2.95–2.97) the 48 contiguous states that summarize the annual solar radiation climate and annual cloudiness.

Figure 2.51 Solar energy graph.

Figure 2.52 Solar energy graph.

Figure 2.53 Solar energy graph.

Figure 2.54 Solar energy graph.

Figure 2.55 Solar energy graph.

Figure 2.56 Solar energy graph.

Figure 2.57 Solar energy graph.

Figure 2.58 Solar energy graph.

Figure 2.59 Solar energy graph.

Figure 2.60 Solar energy graph.

Figure 2.61 Solar energy graph.

Figure 2.62 Solar energy graph.

Figure 2.63 Solar energy graph.

Figure 2.64 Solar energy graph.

Figure 2.65 Solar energy graph.

Figure 2.66 Solar energy graph.

Figure 2.67 Solar energy graph.

Figure 2.68 Solar energy graph.

Figure 2.69 Solar energy graph.

Figure 2.70 Solar energy graph.

Figure 2.71 Solar energy graph.

Figure 2.72 Solar energy graph.

Figure 2.73 Solar energy graph.

Figure 2.74 Solar energy graph.

Figure 2.75 Solar energy graph.

Figure 2.76 Solar energy graph.

Figure 2.77 Solar energy graph.

Figure 2.78 Solar energy graph.

Figure 2.79 Solar energy graph.

Figure 2.80 Solar energy graph.

Figure 2.81 Solar energy graph.

Figure 2.82 Solar energy graph.

Figure 2.83 Solar energy graph.

Figure 2.84 Solar energy graph.

Figure 2.85 Solar energy graph.

Figure 2.86 Solar energy graph.

Figure 2.87 Solar energy graph.

Figure 2.88 Solar energy graph.

Figure 2.89 Solar energy graph.

Figure 2.90 Solar energy graph.

Figure 2.91 Solar energy graph.

Figure 2.92 Solar energy graph.

Figure 2.93 Solar energy graph.

Figure 2.94 Solar energy graph.

Figure 2.95 National sunshine/sky cover map.

Figure 2.96 National sunshine/sky cover map.

Figure 2.97 National sunshine/sky cover map.

3

Extreme Weather—Disasters, Records, Floods, Heat, Cold, and Blizzards

In this chapter you will find information about extreme weather. Included are lists of the hottest, coldest, wettest, driest, and snowiest places in the United States and around the world. Record-setting floods, heat waves, cold waves, drought, and blizzards are events that seemingly defy the physics of our atmosphere, but all have a logical explanation and provide a path to a deeper understanding of how weather works.

THE COST OF US WEATHER DISASTERS

Between 1955 and 2007, according to the Extreme Weather Sourcebook, Societal Impacts Program, National Center for Atmospheric Research, the United States incurred losses totaling more than $620 billion (2007 dollars) that, when adjusted for total national wealth, are in excess of $921 billion. Natural disaster loss figures are estimates, and the estimates of different studies do not necessarily include the same weather elements; therefore, they do not necessarily agree.

The cost estimates from the Extreme Weather Sourcebook include only flood, tornado, and hurricane losses.

Table 3.1 ranks the states by total cost of weather disasters from 1955 through 2007 with dollar figures adjusted for inflation to 2007 dollars and adjusted for total national wealth that varies with the economic health of the nation.

BILLION DOLLAR DISASTERS IN THE UNITED STATES

In the 29 years from 1980 and 2008, NOAA's National Climatic Data Center (NCDC) has compiled a list of 90 events costing $1 billion or more in dollars adjusted to the year 2007. The total cost of these events, in 2007 dollars, was more than $700 billion.

Table 3.1 Total Losses from Weather Disasters, Tornado, Hurricane, and Floods, by State Ranked from Costliest

Source: Extreme Weather Sourcebook.

The list compiled by NOAA's NCDC places the 90 events in 9 categories: nor’easters, ice storms, blizzards, freezes, fires, nontropical floods, heat waves/droughts, severe storms, and tropical storms and hurricanes. This list is more comprehensive than the disasters included for the estimates of Table 3.1. Figure 3.1 is a map summarizing the geographical distribution of the billion dollar disasters and Figure 3.2 is a graph that shows the annual frequency and costs. Both Figures 3.1 and 3.2 were produced by NCDC.

Table 3.2 summarizes the 90 events in chronological order. The numbers are not final because periodically new information becomes available and NCDC updates its files.

Thirty percent of the events (27 events) are tropical in nature and account for more than $367 billion in damages, nearly 52% of the total. This reflects the nature of tropical storms and hurricanes covering wide areas and affecting thousands of square miles per event.

The average tropical billion dollar disaster costs more than $13 billion. With the continued growth of coastal communities this will only continue to increase.

Severe weather that includes tornadoes and severe thunderstorms, along with the wind, hail, and lightning that accompany them, account for nearly 18% of the events and 4.7% of the damage costs. The average billion dollar severe weather disaster costs $2.1 billion.

Of all the disasters to occur from 1980 to, and including, 2008, the most expensive was Hurricane Katrina with damage estimates totaling $133.8 billion, followed by the 1988 heat wave/drought ($71.2 billion) and the 1980 heat wave/ drought ($55.4 billion). Table 3.3 lists the 90 events in order of cost from highest to lowest.

The deadliest of these expensive disasters was the 1980 heat wave/drought killing an estimated 10,000 people, with the 1988 heat wave/drought (7500 deaths) and Hurricane Katrina (1833 deaths) coming in second and third places. Table 3.4 lists the 90 events in order of death toll from greatest to smallest.

The annotated list in reverse chronological order, along with a brief account of each of the 90 billion dollar plus disaster events follows. For dollar figures, the actual dollars at the time of the disaster is the first figure and the second figure in parentheses is the cost adjusted to 2007 dollars.

The United States’ Billion Dollar Weather Disasters: 1980–2008

2008

Widespread drought.

Entire year, 2008. Severe drought and heat caused agricultural losses in areas of the south and west. Record low lake levels also occurred in areas of the southeast. Includes states of CA, TX, NC, SC, GA, and TN. Estimate of over $2.0 billion in damages/costs.