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
• Imagery in several wavelengths:
– Visible
– Infrared
– Water vapor (wavelengths where we see the
water vapor distribution)
• Vertical soundings, water vapor and cloud
track winds
Each
wavelength
gives us
information
Cloud and
Water Vapor
Track Winds
Based on
Geostationary
Weather
Satellites
GOES sounder unit
Satellite Temperature and Humidity Soundings
The Effects of Satellites on Weather Forecasting Has
Been Profound—particularly starting in the mid-1990s
Weather Radar Has
Revolutionized Local
Forecasting
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
A More Fundamental Issue
• The work of Lorenz (1963, 965,
1968) demonstrated that 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.
• Lorenz is a series of experiments
demonstrated how small errors in initial
conditions can grow so that all deterministic
forecast skill is lost at about two weeks.
• Talked about the butterfly effect…
Probabilistic NWP
• One approach to probabilistic prediction, ensemble
prediction, was proposed by Leith (1974), who
suggested that prediction centers run a collection
(ensemble) of forecasts, each starting from a
different initial state. The variations in the resulting
forecasts could be used to estimate the uncertainty
of the prediction.
• But the ensemble approach was not tractable at this
time due to limited computer resources.
Ensemble Prediction
•Can use ensembles to provide for the generation
of products that give the probabilities that some
weather event will occur.
•Can also predict forecast skill!
•It appears that when ensemble forecasts are similar,
forecast skill is higher.
•When forecasts differ greatly, forecast skill is less.
Ensemble Prediction
• During the two decades the size and sophistication
of the NCEP and ECMWF ensemble systems have
grown considerably, with the medium-range,
global ensemble system becoming an integral tool
for many forecasters.
• Also during this period, NCEP has constructed a
higher resolution, short-range ensemble system
(SREF).
The Thanksgiving Forecast 2001
42h forecast (valid Thu 10AM)
SLP and winds
1: cent
Verification
- 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*
The Evolving Forecasting Problem
• Prior to ~1955, humans did everything-subjectively forecast at all scales.
• Between 1955 and 1980 numerical weather
prediction essentially took over synoptic
forecasting. Humans were left with deciding
between models or modifying (often
unsuccessfully) the computer guidance.
• Humans retained the role of translating synoptic
guidance to the mesoscale/microscale.
• Model Output Statistics (MOS) became
competitive with human forecasters during the
1980s.
Summary II: Evolving Forecasting
• By around 1990, large scale forecasts had gotten
very good for scales >~ 1000 km for 0 to 48h
• Starting to consistently get the big storms
• But humans still crucial for local forecasting,
interpreting imagery, and providing guidance on the
reliability of forecasts.
• During the past 10-15 years high resolution (or
mesoscale) NWP has begun to make inroads in
mesoscale prediction.
• The ability to produce probabilistic forecasts using
ensembles is improving quickly and should become
a central element in NWP during the next decades.
One day?
The National Weather Service
Forecaster at the Seattle National Weather Service Office