A History of Modern Weather Forecasting

A History of Modern Weather
The Beginning: Weather Sayings
"Red Sky at night, sailor's delight. Red sky in the morning, sailor take
"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
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
• 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”
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
Professor Cleveland Abbe, who issued the first public
“Weather Synopsis and Probabilities” on February 19,
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,
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
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 fourtenths 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
Norwegian Cyclone Model
• Provided a coherent consistent picture of
airflows, clouds, and precipitation of
cyclones and fronts
• Provided a model for frontal and cyclone
evolution, aiding future prediction.
1940s: Upper Air Charts Became
• Gave a 3D picture of what was happening
• Upper flow steered storms, and thus
provided a tool for forecasting cyclone
Summary I
• Prior to approximately 1955, forecasting was
basically a subjective art, and not very skillful.
• The technology of forecasting was basically
subjective extrapolation of weather systems, in the
latter years using the upper level flow (the jet
• Local weather details—which really weren’t
understood-- were added subjectively.
The Development of NWP
• Vilhelm Bjerknes in his
landmark paper of 1904
suggested that NWP was
– A closed set of equations
existed that could predict
the future atmosphere
(primitive equations)
– But NWP wasn’t practical
then because there was no
reasonable way to do the
computations and sufficient
data for initialization did
not exist.
L. F. Richardson: An Insightful
But Unsuccessful Attempt
• In 1922 Richardson
published a book
Weather Prediction by
Numerical Process that
described an approach to
solving the primitive
equations: solving the
equations on a grid using
finite differences.
L. F. Richardson
• He attempted to make a numerical forecast using a
mechanical calculator
• Unfortunately, the results were not good, probably
because of problems with his initial conditions.
• He imagined a giant theater filled with human
• So NWP had to wait until a way of doing the
computations quickly was developed and more
data…especially aloft… became available.
NWP Becomes Possible
• By the mid to late 1940’s there was an
extensive upper air network, plus many
more surface observations. Thus, a
reasonable 3-D description of the
atmosphere was possible.
• Also during this period digital
programmable computers were becoming
available…the first..the ENIAC
The Eniac
The Last Piece of the Puzzle
• Meteorologists realized that useful
numerical weather predictions were
possible using a simplified equation set that
were easier to solve.
• The Barotropic Vorticity Equation
(conservation of absolute vorticity) was
suggested as a first step
First NWP
• The first successful numerical prediction of
weather was made in April 1950, using the
ENIAC computer at Maryland's Aberdeen
Proving Ground
• The prediction was for 500 mb height,
covered North America, used a twodimensional grid with 270 points about 700
km apart.
• The results showed that even primitive
NWP was superior to human subjective
prediction. The NWP era had begun.
Evolving NWS
• Early 50s: one-level barotropic model
• Late 50s: Two-level baroclinic QG model (just
like Holton!)
• 1960s: Primitive equation models of increasing
resolution and number of levels.
• Resolution increases (distance between grid points
decrease): 1958: 380 km, 1985: 80 km, 1995: 40
km, 2000: 22 km, 2002: 12 km
NWP Improvements in the Later
20th Century
• Better resolution
• Rapidly increasing data for initialization
from weather satellites, radars, more surface
observations, and other sources.
• Better models: better numerics and physics
Forecast Skill Improvement
NCEP operational S1 scores at 36 and 72 hr
over North America (500 hPa)
National Weather Service
S1 score
"useless forecast"
36 hr forecast
72 hr forecast
Error 35
10-20 years
"perfect forecast"
The Advent Of Statistical PostProcessing
• In the 1960s and 1970s, the NWS developed and
began using statistical post-processing of model
output…known to most as Model Output
• The idea: models have systematic biases..why not
remove them based on past performance?
• Also, might be able to statistically add the effects
of local features not resolved by the model.
• Based on linear regression: Y=a0 + a1X1 +
a2X2+ a3X3 + …
• MOS is available for many parameters and
time and greatly improves the quality of
most model predictions.
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.
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
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 predicton, 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 even the ensemble approach
was not tractable at this time due to limited
computer resources.
The Thanksgiving Forecast 2001
42h forecast (valid Thu 10AM)
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 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 Prediction
• To create a collection of ensembles one can
used slightly different initializations or different
• By the early 1990s, faster computers allowed
the initiation of global ensemble prediction at
NCEP and ECMWF (European Centre for
Medium Range Weather Forecasts).
Ensemble Prediction
• During the past decade 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, shortrange ensemble system (SREF) that uses breeding
to create initial condition variations.
Ensemble-Based Probabilistic Products
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
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 ten years high resolution (or
mesoscale) NWP has begun to make inroads in
taking over mesoscale prediction.
• The ability to produce probabilistic forecasts using
ensembles is improving quickly and should
become a central element in NWP during the next
The National Weather Service
Forecaster at the Seattle National Weather Service Office