Revolutions in Remote Sensing
Greatly Enhanced Weather
Prediction from the 1950s
Through Today
Satellite and Weather Radars Give Us a More
Comprehensive View of the Atmosphere
Before Satellites We Had a VERY Poor
Knowledge of What Was Happening
Over the Oceans!
• As a result, forecasts were often very poor,
particularly in coastal locations.
The 1938
Hurricane was
basically
unforecast
Weather Satellites Give Us Much More
than Pretty Pictures
• We start with imagery in several wavelengths:
– Visible
– Infrared
– Water vapor (wavelengths where we see the
water vapor distribution)
• Plus many new capabilities
Better than Star
Trek!
Water Vapor Imagery
Looks at wavelengths where water vapor absorbs and emits infrared radiation
Each wavelength
gives us information
Cloud and
Water Vapor
Track Winds
Based on
Geostationary
Weather Satellites
GOES sounder unit
Satellite Temperature and Humidity Soundings
QuickScat Satellite
Bounces microwaves off the ocean surface
Capillary waves dependent on wind speed and directon
Weather Radar Has
Revolutionized Local
Forecasting
Weather Radar
Camano
Island
Weather
Radar
Radar was first used operationally in WWII by the
British to track German planes
• But they found some interference by heavy
precipitation!
After WWII Meteorologists
Experimented with Military Radars
Hurricane Radar Image
In the late 1950’s a meteorological
radar network was established.
In the late
1980s,
the NWS put in
a network of
Doppler
Weather
Radars
NEXRAD
WSR88D
Sound of train passing:
http://www.fourmilab.ch/cship/sounds/doppler.au
Now
With Two New Radars
But even with all this improving
technology, some forecasts fail.
Why?
Weather Radars
• Lets us track thunderstorms and hurricanes
• Can see where it is precipitating and where it
is moving.
• Many other uses.
Problems with the Models
Some forecasts fail
due to inadequacies
in model physics….
How the model
handles
precipitation,
friction, and other
processes.
Example: too much
precipitation on
mountain slopes
Some forecasts fail due to poor
initialization, i.e., a poor starting
description of the atmosphere.
This is particularly a problem for the Pacific
Northwest, because we are downstream of
a relatively data poor region…the Pacific
Ocean.
Pacific Analysis
At 4 PM
18 November
2003
Bad Observation
3 March 1999: Forecast a snowstorm … got a
windstorm instead
Eta 48 hr SLP Forecast valid 00 UTC 3
March 1999
The problem of initialization should
lessen as new observation
technologies come on line and
mature.
New ways of using or assimilating
weather data are also being
developed.
A More Fundamental Problem
• In a real sense, the way we have
been forecasting has been
essentially flawed.
• The atmosphere is a chaotic
system, in which small differences
in the initialization…well within
observational error… can have
large impacts on the forecasts,
particularly for longer forecasts.
• Not unlike a pinball game….
A More Fundamental Problem
• Similarly, uncertainty in our model physics
also produces uncertainty in the forecasts.
• Thus, all forecasts have some uncertainty.
This is Ridiculous!
Or this…
Forecast Probabilistically
• We should be using probabilities for all our
forecasts or at least providing the range of
possibilities.
• There is an approach to handling this issue
that is being explored by the forecasting
community…ensemble forecasts
Ensemble Prediction
• Instead of making one forecast…make
many…each with a slightly different
initialization
• Possible to do now with the vastly greater
computation resources that are available.
The Thanksgiving Forecast 2001
42h forecast (valid Thu 10AM)
Verification
SLP and winds
1: cent
- Reveals high uncertainty in storm track and intensity
- Indicates low probability of Puget Sound wind event
2: eta
5: ngps
8: eta*
11: ngps*
3: ukmo
6: cmcg
9: ukmo*
12: cmcg*
4: tcwb
7: avn
10: tcwb*
13: avn*
Ensemble Prediction
•Can use ensembles to give the
probabilities that some weather
feature will occur.
•Can also predict forecast skill!
•It appears that when forecasts are
similar, forecast skill is higher.
•When forecasts differ greatly,
forecast skill is less.
Probabilistic Prediction
• So instead of saying the temperature in two
days will be 67F. We might tell you:
50% probability it will be between 64 and 69F
90% probability it will be between 62 and 72F.
ENSEMBLE SYSTEMS
http://www.atmos.washington.edu/~ens/uwme.cgi
http://www.esrl.noaa.gov/psd/map/images/ens/ens.html#us
The Future
• As computer get faster and faster and our
understanding of atmospheric processes
improve, there will be a transition to higher
resolution, more specific, forecasts and more
probabilistic information.
• Too much information for TV…so the web and
other online media will dominate.
The Limit
• Forecast skill will be pushed out in time—
more skill at longer projections.
• But there are theoretical limits and we will
probably never be able to forecast specific
weather features out past about 2 weeks.
• But we do have some skill out months for
average conditions.