Transcript Chapter 09

Chapter 9: Weather
Forecasting
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Acquisition of weather information
Weather forecasting tools
Weather forecasting methods
Weather forecasting using surface charts
Acquisition of Weather
Information
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World Meteorological Organization
National Centers for Environmental Prediction
Watches: favorable condition for potential hazardous weather
Warnings: hazardous weather is imminent or actually occurring
Advisories: like warning but for less hazardous weather
• The internet plays a crucial role in the global
communication of weather information.
• Go to:
http://www.weather.gov
Weather Forecasting Tools
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AWIPS: Advanced Weather
Interactive Processing System
used by forecasters and can
process satellite, radar, surface
observation, radiosonde data and
weather forecasting model output
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meteogram
sounding
Near Orlando, FL
Hail size > 3 inch
100% probability
Fig. 9-1, p. 238
Satellites and Weather
Forecasting
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geostationary satellites
polar orbiting satellites
Geostationary Operational Environmental
Satellite
infrared images: estimate height; 3-D image
visible images: not useful at night
water vapor images: particularly useful for clear sky
http://www.goes.noaa.gov
Cannot see polar
areas
850km
altitude
Fig. 9-5, p. 240
Fig. 9-7, p. 241
Visible
Infrared
Enhanced Infrared
Fig. 9-9a, p. 242
Fig. 9-11, p. 243
The Computer and Weather
Forecasting: Numerical
Weather Prediction
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Analysis: final chart using available data
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numerical weather prediction (NWP): based on
models
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atmospheric models: fluid dynamics and atmospheric
physics
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Progs: prosnostic chart for weather forecast
• One of the world’s first computers was built for the
specific purpose of performing weather forecasts.
48-hr
NWP
forecast
of 500 mb
Height
12km vs 60km
Resolution refers to
grid spacing
Fig. 9-12a, p. 245
Why NWS Forecasts go Awry
and Steps to Improve Them
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grid spacing
incomplete data coverage
model deficiencies due to
subgrid processes
chaos
ensemble forecasting:
spaghetti plot to indicate the
robustness of forecast
• Chaos theory tells us that weather forecasts are highly sensitive
to our ability to observe the weather. Since it is impossible
to observe the weather at all places at all times, weather
forecasts will never be perfect.
Other Forecasting Methods
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persistence forecast: not bad for Tucson in June
trend forecast: assuming constant change rate
analogue method: search for similar chart in history
statistical forecast: routinely used; Model Output
Statistics (MOS)
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probability forecast: particularly for precipitation
climatological forecast: good for Tucson rainfall in
June
What does it mean by
`chance of rain is
50% for one area’?
Choose the right answer
a. It will rain over 50%
of the area
b. 50% chance that
rainfall will not occur
in the whole area
c. 50% change that any
random location in
the area will receive
measurable rainfall
in the given period
Table 9-1, p. 249
Probability for a `white Christmas’ – 1 inch or more of snow
Fig. 9-15, p. 250
Types of Forecasts
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very short range forecast or nowcast: 0-6 hr
short range forecast: 6 hr – 3 days
Mediate-range forecast: 3-8.5 days
long range forecast: 8.5 days – 2 weeks
Monthly and seasonal outlooks
• Long-range forecasts are less specific than short range
forecasts.
Fig. 9-17, p. 251
Accuracy and Skill in
Forecasting
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persistence
climatology
• Both persistence and climatology are surprisingly
accurate forecasting methods.
Predicting the Weather from
Local Signs
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Halo: `a halo around the
moon portends rain’
(foldlore)
• To see a halo: block out the sun
with your hand and
look at the cirrostratus clouds.
Wear polarized sunglasses if
possible.
Determining the Movement of
Weather Systems
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forecasting rules of thumb:
surface pressure systems tend to move in the same
direction as the 500 mb wind; the speed at which surface
systems move is about half the wind speed at 500 mb
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using the surface chart
• Internet now provides much of the weather information
http://www.weather.gov
http://www.atmo.arizona.edu
http://www.wrh.noaa.gov/twc
Current front and front 6 hr ago
Estimate for the next 24 hr
500 mb height
observation for 24 hr later
Fig. 9-20, p. 256