If we have so much knowledge of the forces that affect planet earth,
as well as the winds, currents, polar magnetic pull... why is it that
meterologists are so often incorrect in their predictions?

barrys,

You may have heard a saying about a butterfly flapping its wings in
Asia causing a hurricane in Mexico.  This is an outgrowth of chaos
theory, the study of the cause and effect of immensely complex systems
such as the weather throughout Earth.

Quite simply, there are SO many variables to the weather that even our
current technology yields no better results than an educated guess,
especially five days and beyond in advance, when a full analysis of
the entire world's weather patterns is necessary to make an accurate
local forecast.  We know a lot about the large forces that effect our
planet, but the cumulative effect of all of the small forces we
haven't catalogued still have a great impact, and even if we could
account for all of them we don't yet have the computing horsepower to
take everything into account.  It's entirely possible that a butterfly
somewhere could flap its wings in exactly the right place and exactly
the right time in such a manner that would set off a chain of events
that, over a very long period of time, would cause a hurricane that
could not have been started in any other way.  Also, even if we could
acquire ever bit of information needed to predict the weather in a
fraction of a second, quantum physics states that the mere act of
observing an action changes the quality of that action in some way,
albeit in an extremely miniscule way.  So obtaining information
actually changes information, and for the extremely large quantity of
data that is required for accurate weather prediction, the cumulative
effect of this would significantly reduce the accuracy of the data
gathered.

The result of all this is that, while science can come increasingly
closer to 100% accuracy in weather prediction, they will never have
the ability to absolutely guarantee that you can leave the house
without your umbrella.

Sites cited:
------------
What is Chaos Theory?
http://www.fractalfoundation.org/whatchaos.html

Earth & Sky: Science Facts About Weather Forecasting
http://www.earthsky.org/scienceqs/browsefaq.php?f=50

A Simple explanation of quantum mechanics and the essentials of quantum physics
http://members.aol.com/jimb3d/quantum.html

Google search tems used:
------------------------
why can't we predict the weather?
simple explanation of quantum physics
butterfly flapping wings hurricane

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Request for Answer Clarification by barrys-ga on 02 Feb 2004 10:22 PST

I was looking for specific examples, causes and reasons. Details. Real
cases, factual reasons behind. Meaning, I am looking to use this as an
example, because of the  the many and varied causes, ie... it is so
difficult to predict.

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Request for Answer Clarification by barrys-ga on 04 Feb 2004 01:59 PST

Sorry to press this point, but the lecture is this evening, and I
really could use some material to fill in this gap...

What would help is some examples of a miscalcualation that is highly
noticeable. for instance, if the national weather service predicted a
major hurricane and it didn't happen. Or the oppisite. Or even
statistics, ie, meterologists are correct x percentage of times....

To expand upon the lack of data used in weather prediction -- the
atmosphere occupies three dimenions in space, not just two.  That
means we need accurate data from the ground up THROUGH the atmosphere
to the 'top' of the atmosphere.  Unfortunately, we only have upper
atmosphere data taken mostly be weather balloons, and they are only
sent up every 12 hours and EVEN THEN at points that can be 100 miles
apart or more, and that's in the 'data-rich' continental United
States!  Worse, most of our air blows in from the Pacific Ocean, the
Gulf of Mexico, or Canada, and there is much more limited data coming
in from those regions.  It is VERY difficult to get high-density data,
even during specially-designed weather research projects that are
extremely expensive (in the million$).

So, your researcher discussed how a butterflies wings can
theoretically affect the development of a hurricane.  Well, forget the
stupid butterfly.  How about weather systems 150 miles across, such as
massive thunderstorm complexes or compact polar low pressure systems
(with circular wind-flow and snow, etc.)?  Those can OBVIOUSLY affect
the weather, yet they slip through the grid of our weather data
collection services.  You can probably now imagine that any weather
prediction of a non-linear (chaotic) system is actually quite amazing.
 You might say we're lucky to have the accuracy that we do.

By the way, I find a good analogy of weather prediction is stock
market prediction.  Imagine if you bought Martha Stewart's company
stock when it was doing quite well.  You might not have been aware of
one 'small' piece of data.  She engaged in the illegal trade of a
relatively small event of stock.  However, that trade occurred in a
non-linear environment.  It exploded into a big problem that brought
down Martha and hurt her company's stock.  Well, the same can happen
in weather.  For example, during November a small cold 'pocket' of air
could move over the warm Great Lakes, and suddenly explode into snow
showers reaching as far as New York City.

I hope I've clarified what is almost certainly the biggest problem in
weather prediction.

-anonymous, w/Master of Science degree in Meterology from Penn State University.

One other comment from me -- you had wanted an example of such a case.
 Well then, how about the wreck of the Edmund Fitzgerald, the ship
immortalized in the famous song by Gordon Lightfoot (during the
1970's, I think).  That ship sunk during weather that was definitely
not predicted by the NWS or the Canadian weather service.  The problem
in that case was the 'limited' computing power of weather prediction
computer systems.  Let me explain how limited computing powers is
dealt with and how a nasty storm that sank a ship was not even
predicted...

As you probably know, weather forecasts are primarily generated from
computer programs that process large amounts of data through countless
iterations of *incredibly* complex weather prediction programs.  In
order to get these forecasts out in a somewhat timely manner, these
programs absolutely must take short-cuts.  That is, they don't
necessarily compute every single factor affecting the weather.  For
example, we know that ind will not blow as easily across a rough ocean
as it will a smooth one.  However, you can bet that factor is not
built into our major weather-prediction programs because it would
probably barely affect the accuracy of forecasts.  That whole concept
is very key to understanding the some of the design limits of
computational prediction.

Now, in the case of the Edmund Fitzgerald, a fundamentally
well-understood phenomenon affected the weather, but it was not
included in the weather prediction programs because it is usually
insignificant.  Get this -- we know that air pressure differentials
accelerate air from high pressure to low pressure, right?  That's how
a fan works, every idiot knows that.  Well, maybe, but during the
1970's weather prediction programs didn't include this 'obvious'
effect (the 'isallobaric' wind effect).

In the '70s, computer programs assumed that wind flowed roughly at a
constant speed in a circular pattern around a low-pressure system (the
'geostrophic' wind).  It turns out this is a good assumption in nearly
all situations.  That is because when wind starts to accelerate
towards low pressure, the earth's rotation (and the winds momentum)
cause the wind to turn (*relative* to the moving surface).  After the
wind turns 90 degress to the pressure gradient, it then **stops
accelerating** and continues to flow at a constant speed in a balanced
'geostrophic' flow.  At that point, the apparent force of the earth's
rotation has negated the force of the pressure differential.  The
Coriolis effect is forcing the wind to the right, but the low-pressure
gradient is forcing the wind to the left.  Because this type of wind
flow occurs during the vast majority of time, weather programs don't
calculate the acceleration, they only calculate what the final
velocity would be after the wind reached this equilibrium of circular
flow around the low pressure.

If this confuses you, then perhaps an analogy misht help.  The turning
wind is equivalent to a skier being accelerated by gravity down a ski
slope.  If he uses his skis to turn 90 degrees to the right, he'll
stop accelerating and instead he will continue across the slope at a
constant speed.  Imagine that skiers normally did this after skiing
downhill for 10 seconds, and by then the earth had rotated so they
were skiing across the slope (and no longer accelerating).  That
analogy is like your normal wind.  Now suppose someone skied down some
extremely steep terrain for 10 seconds without turning (an extreme
'differential').  They would really be screaming downhill!  That is
kind of what happened with the wind before the Edmund Fitzgerald sank
from huge waves.  The wind accelerated towards the low very rapidly
within only a few hours.  During this brief time, the earth had barely
rotated and was not altering the wind's direction by much.  It
certainly had not turned the wind enough to make the wind go around
the low and stop accelerating.  To use the skiing example, the skier
had reached a very high speed very quickly before he turned.

It was this phenomena that brought about some very strong winds that
heralded the demise of the Edmund Fitzgerald (and a popular ballad by
Gordon Lightfoot).

In some ways, it's incredible to think that our computer programs
didn't predict air would accelerate into a low pressure system!  After
all, the longer the air accelerates due to pressure, the faster it
will go.  Unfortunately, those weather programs disregarded the time
spent accelerating before turning.  They only calculated the wind
speed based on fixed formula related to earth's rotation and the
pressure differential.  That calculation allowed for a timely
forecast, but a useless one for the crew of the Edmund Fitzgerald. 
:-(