a presentation to find how to predict the weather and other stuff useful

1. Introduction to Weather
Forecasting
Cliff Mass
Department of
Atmospheric
Sciences
University of
Washington

2. The Stone Age
• Prior to approximately 1955, weather
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 stream).
• Local weather details—which really weren’t
understood-- were added subjectively.

3. Upper
Level
Chart

4. The Development of Numerical
Weather Prediction (NWP)
Vilhelm Bjerknes in his landmark
paper of 1904 suggested that NWP--
objective weather prediction-- was
possible.
– A closed set of equations existed
that could predict the future
atmosphere
– But it wasn’t practical then
because there was no reasonable
way to do the computations and a
sufficient 3-D description of the
atmosphere did not exist.

5. 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)
Using observations we can determine the
mass and forces, and thus can calculate the
acceleration--giving the future

6. NWP Becomes Possible
• By the 1940’s an extensive upper air network
was in place, plus many more surface
observations. Thus, a reasonable 3-D
description of the atmosphere was possible.
• By the mid to late 1940’s, digital
programmable computers were becoming
available…the first..the ENIAC

7. The Eniac

8. 1955-1965:
The Advent of Modern Forecasting
• Numerical weather prediction became the
cornerstone.
• New observing technologies also had a
huge impact:
– Weather satellites
– Weather radar

9. Satellite and Weather Radars Provides a More
Comprehensive View of the Atmosphere

10. Camano
Island
Weather
Radar

11. Weather Prediction Steps
• Data collection and quality control
• Data assimilation: creating a physically realistic
3-D description of the atmosphere called the
initialization.
• Model integration. Solving the equations to
produce future 3D descriptions of the atmosphere
• Model output post-processing using statistical
methods
• Dissemination and communication

12. Using a wide range of weather observations we
can create a three-dimensional description of the
atmosphere…
Initialization

13. Numerical Weather Prediction
• The observations are interpolated to a 3-D grid where they are
integrated into the future using a computer model--the
collection of equations and a method for solving them.
• 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.

14. Model Postprocessing in the U.S.:
Model Output Statistics (MOS)
• Main post-processing approach used by the
National Weather Service
• 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.

15. Prob. Of Precip.– Cool Season
(0000/1200 UTC Cycles Combined)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002
Year
Brier
Score
Improvement
over
Climate
Guid POPS 24 hr Local POPS 24 hr
Guid POPS 48 hr Local POPS 48 hr

17. Major Improvement
Weather forecasting skill has substantially
improved over the last 50 years. Really.

18. Forecast Skill Improvement
15
25
35
45
55
65
75
1950 1960 1970 1980 1990 2000
NCEP operational S1 scores at 36 and 72 hr
over North America (500 hPa)
P
Year
"useless forecast"
"perfect forecast"
S1
score
72 hr forecast
36 hr forecast
10-20 years
Forecast
Error
Year
Better
National Weather Service

19. Why Large Improvement in Weather
Forecast Skill?
•As computers became faster, were able to
solve the equations at higher resolution
•Improved physics
•New observational assets allowed a better
initialization

24. A More Basic Problem
• There is fundamental uncertainty in
weather forecasts that can not be ignored.
• This uncertainty has a number of causes:
– Uncertainty in initialization
– Uncertainty in model physics
– Uncertainties in how we solve the equations
– Insufficient resolution to properly model
atmospheric features.

25. The Atmospheric is Chaotic
• The work of Lorenz (1963, 1965,
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….

27. Probabilistic Prediction
• Thus, forecasts must be
provided in a probabilistic
framework, not the
deterministic single answer
approach that has dominated
weather prediction during the
last century.
• Interestingly…the first public
forecasts were probabilistic

28. “Ol Probs”
Professor Cleveland Abbe, who issued the first public
“Weather Synopsis and Probabilities” on February 19,
1871
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
in 1869.

29. Ensemble Prediction
• The most prevalent approach for producing
probabilistic forecasts and uncertainty
information…ensemble prediction.
• Instead of making one forecast…make
many…each with a slightly different initialization
or varied model physics.
• Possible to do now with the vastly greater
computation resources that are now available.

30. The Thanksgiving Forecast 2001
42h forecast (valid Thu 10AM)
13: avn*
11: ngps*
12: cmcg*
10: tcwb*
9: ukmo*
8: eta*
Verification
1: cent
7: avn
5: ngps
6: cmcg
4: tcwb
3: ukmo
2: eta
- Reveals high uncertainty in storm track and intensity
- Indicates low probability of Puget Sound wind event
SLP and winds

31. 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.

32. Ensemble-Based Probabilistic Products

33. Ensemble Post-Processing
• To get the maximum benefits from
ensembles, post-processing is needed, such
as:
– Correction for systematic bias
– Optimal weighting of the various ensemble
members--e.g., Bayesian Model Averaging

34. The UW-MURI Project
• Possibility the most advanced
ensemble/postprocessing system in the world has
been developed at the UW
• Includes UW Atmospheric Sciences, Statistics,
Psychology, and Applied Physics Lab
• Remaining talks will describe some of the
research and development completed by this
effort.

36. But you can have too much of a good thing…
Providing forecast uncertainty information is good….

37. The END