A Brief History of Weather
Forecasting
The Beginning: Weather Sayings
•
"Red Sky at night, sailor's delight. Red sky in the morning, sailor take
warning."
•
"Mare's tails and mackerel scales make tall ships take in their sails."
•
•
•
"Clear moon, frost soon."
.
"Halo around the sun or moon, rain or snow soon."
•
"Rainbow in the morning gives you fair warning."
•
"When the stars begin to huddle, the earth will soon become a puddle."
By the late 1700s, reasonable
weather instruments became
available
More and more people took
observations….and even some
early networks were started
The First Weather Forecaster?
The problem: no way to rapidly
communicate weather
observations
• This changed around 1845 with the
invention of the telegraph
First Real-Time Weather Maps
Weather Prediction Began
• The key approach…simple extrapolation
“Ol Probs”
Cleveland
Abbe
(“Ol’
Probabilities”), who led the
establishment of a weather
forecasting division within the
U.S. Army Signal Corps,
Produced the first known
communication of a weather
probability to users and the
public.
Professor Cleveland Abbe, who issued the first public
“Weather Synopsis and Probabilities” on February 19,
1871
On May 7, 1869, Abbe proposed to the Cincinnati Chamber of
Commerce "to inaugurate such a system, by publishing in the
daily papers, a weather bulletin, which shall give the probable
state of the weather and river for Cincinnati and vicinity one or
two days in advance”.
Cleveland Abbe released the first public weather forecast on
September 1, 1869.
Following the signing by President Ulysses S. Grant of an
authorization to establish a system of weather observations and
warnings of approaching storms, on February 19, 1871, Abbe
issued the first “official” public Weather Synopsis and
Probabilities based on observations taken at 7:35 a.m.
An early example of a report:
"Synopsis for past twenty-four hours; the barometric pressure
had diminished in the southern and Gulf states this morning; it
has remained nearly stationary on the Lakes. A decided
diminution has appeared unannounced in Missouri
accompanied with a rapid rise in the thermometer which is felt
as far east as Cincinnati; the barometer in Missouri is about
four-tenths of an inch lower than on Erie and on the Gulf.
Fresh north and west winds are prevailing in the north;
southerly winds in the south. Probabilities [emphasis added];
it is probable that the low pressure in Missouri will make itself
felt decidedly tomorrow with northerly winds and clouds on
the Lakes, and brisk southerly winds on the Gulf."
The Next Major Advance
• The Norwegian Cyclone Model around
1920
1940s: The Upper Air Chart
• Gave a 3D picture of what was happening
• Upper flow steered storms
Upper
Level
Chart
The Development of NWP
• Vilhelm Bjerknes in his landmark
paper of 1904 suggested that NWP
was possible.
– A closed set of equations
existed that could predict the
future atmosphere (primitive
equations)
– But it wasn’t practical then
because there was no
reasonable way to do the
computations and sufficient
data for initialization did not
exist.
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Numerical Weather Prediction
• The advent of digital computers in the late
1940s and early 1950’s made possible the
simulation of atmospheric evolution
numerically.
• The basic idea is if you understand the current
state of the atmosphere, you can predict the
future using the basic physical equations that
describe the atmosphere.
The Eniac
Numerical Weather Prediction
One such equation is Newton’s Second Law:
F = ma
Force = mass x acceleration
Mass is the amount of matter
Acceleration is how velocity changes with time
Force is a push or pull on some object (e.g.,
gravitational force, pressure forces, friction)
This equation is a time machine!
Numerical Weather Prediction
Using a wide range of weather observations we
can create a three-dimensional description of the
atmosphere… known as the initialization
Numerical Weather Prediction
•Observations give the distribution of mass
and allows us to calculate the various forces.
•Then… we can solve for the acceleration
using F=ma
•But this gives us the future…. With the
acceleration we can calculate the velocities in
the future.
•Similar idea with temperature and humidity.
Numerical Weather Prediction
• These equations can be solved on a threedimensional grid.
• As computer speed increased, the number of grid
points could be increased.
• More (and thus) closer grid points means we can
simulate (forecast) smaller and smaller scale features.
We call this improved resolution.
A Steady Improvement
• Faster computers and better understanding of
the atmosphere, allowed a better representation
of important physical processes in the models
• More and more data became available for
initialization
• As a result there has been a steady increase in
forecast skill from 1960 to now.
P
Forecast Skill Improvement
NCEP operational S1 scores at 36 and 72 hr
over North America (500 hPa)
National Weather Service
75
S1 score
65
"useless forecast"
55
36 hr forecast
72 hr forecast
45
Forecast
Error 35
10-20 years
Better
"perfect forecast"
25
15
1950
1960
1970
Year
1980
Year
1990
2000
Satellite and Weather Radars Give Us a More
Comprehensive View of the Atmosphere
Camano
Island
Weather
Radar
1995-2003+
The computers models become
capable of simulating/forecasting
local weather.
As the grid spacing decreased to 15 km and
below… it became apparent that many of
the local weather features could often be
simulated and forecast by the models.
The National Weather Service
Forecaster at the Seattle National Weather Service Office
But even with all this improving
technology, some forecasts fail or
are inadequate. Why?
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
• Intensive work at the UW to address this
problems.
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.
3 March 1999: Forecast a snowstorm
… got a windstorm instead
Eta 48 hr SLP Forecast valid 00 UTC 3
March 1999
Eta Model Sea Level Pressure: 12 UTC 2 March 99
Major
Initialization
Errors
Pacific Analysis
At 4 PM
18 November
2003
Bad Observation
The problem of initialization
should lessen as new observation
technologies come on line and
mature.
New ways of using or
assimilating the data are also
being developed.
Seascan Unmanned Aircraft
A More Fundamental Problem
• In a real sense, the way we have
been forecasting is 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….
The Lorenz Diagram…chaos
Is not necessarily random
A More Fundamental Problem
• Thus, there is fundamental uncertainty in
weather forecasts that can not be ignored.
• Similarly, uncertainty in our model physics
also produces uncertainty in the forecasts.
• 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)
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*
Ensemble Prediction
•Can use ensembles to provide a new generation
of products that 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.
Ensemble-Based Probabilistic Products