Transcript 101Forecasting.ppt

A Brief History of Weather
Forecasting
The Stone Age
• Prior to approximately 1955, forecasting was
basically a subjective art, and not very skillful.
• Observations were sparse, with only a few
scattered ship reports over the oceans.
• The technology of forecasting was basically
subjective extrapolation of weather systems
using the upper level flow (the jet stream).
• Local weather details—which really weren’t
understood-- were added subjectively.
Upper
Level
Chart
1955-1965:
The Advent of Modern Forecasting
• During this period, numerical weather
prediction—forecasting future weather with digital
computers-- became the key tool in the
meteorologists tool bag.
• The launch of the first weather satellite (1960)
gave meteorologists a view of the entire planet.
• Weather radars were placed around the U.S.
explicitly showing areas of precipitation.
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.
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
•This gives 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
Lack of Coastal Weather Information
•There is a lack of detailed
weather information
immediately off the Northwest
Coast.
•Major issue… lack of a
coastal weather radar.
•The Northwest has the worst
coastal weather radar coverage
in the nation.
•Often can’t see the details of
weather features before they
make landfall. Seriously
impacts short-term forecasts.
NWS Doppler Radar
Now
With Two New Radars
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….
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
Forecast Dissemination:
The Achilles Heal
• Although the technology of weather prediction is
rapidly improving, our ability to communicate what
we know to the public is inadequate.
• Although the Internet and wireless communication
provides—for the first time—the potential to
distribute large amounts of weather information, we
have not yet found an effective way to do so.
• The amount of information is massive, how do we
distill and filter it for a wide variety of users?
• We are failing to communicate our degree of
confidence in the forecasts.