In this course you can learn about the mechanics of global weather, the foundations of ocean meteorology, predictive modeling and how sailors receive data via satellite and use high-performance navigation software. This course looks at oceanic meteorology and climatology through the lens of the sport of sailing. You will gain a basic knowledge of meteorology needed by sailors to take part in a regatta such as the Barcelona World Race, the only double-handed, round the world regatta with no stops. You will learn about the strategies employed during a round the world regatta and how these are put to use on board the latest ocean racing yachts.
Video 2.1.2 Reliability and accuracy of models. Predictability
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Del curso dictado por Universitat de Barcelona
Strategies for winning. Meteorology in a round the world regatta
33 calificaciones
Universitat de Barcelona
33 calificaciones
De la lección
Meteorological predictions
In this module you will learn:
How meteorological predictions are made. What numerical weather models and equations look like. This will be explained by Tomàs Molina in Module 2.1
You will look at the forecasting structures that help us to interpret the information given by the models:
Advection, troughs, ridges, barometric swamps and low pressure systems. These will be explained by Tomàs Molina in Module 2.2
What the basic prediction models used by sailors are. This will be explained by Marcel van Triest in Module 2.3
Remember you can access to the help forum
. If you are experiencing difficulty learning or accessing course content, or if you simply want more information.
Conoce a los instructores
Tomàs Molina
[MUSIC]
Is the weather forecast accurate?
It could be the question we should ask ourselves and, in fact,
this part of verification has to be always in our minds.
We need to be able to learn from our mistakes.
A good meteorologist is not the one who says what the weather will be tomorrow,
but the one who knows why he has not done the weather he had said he would do tomorrow.
We must always learn from our mistakes,
a bit like the goalkeepers, because is the weather forecast accurate?
Over the years it has changed a lot.
Look, the 1960s, when the numerical models only included the atmosphere,
therefore, the atmosphere the air,
but Earth is much more complicated than air.
In the 80's you included the part of the orography, and we have seen in some of the
classes that the orography is determinant regarding the weather that is going to do.
But it is that the oceans were not yet there, the oceans
end of the 80, until the 90, in the 90 was included in the numerical models.
The presence of ice in the world and, from then on,
with the arrival of you in computing and supercomputing, there has grown
exponential and has been placed after the sulfides, carbons, atmospheric chemistry.
So the system, the forecast equation, that we said at the beginning,
has been lengthening in a remarkable way.
The ability to calculate has gone, the need is increasing, you can not
do any, because you need a lot of calculation ability to calculate everything.
And really the quality of the forecast has increased in an abysmal way.
This is a map of weather, which has these stripes like this, this is when I started,
almost 30 years ago, that I began to make man of the weather,
the weather maps were like that, it was stripes, isobars, isotherms.
And you had to interpret the ways to know how long it would take.
Nowadays you do not have weather maps,
But we have what are called numerical forecasting models, which is a
concept maybe a little more elaborate that gives us weather variables.
What he gives us are, for example in this case, would be forecast isohyets,
precipitation expected from the model, and, not only that, but also gives us
the places where it will be convective, where there will be storms or squalls.
But it can also give us other types of systems, like this one here,
which would be Lifted Index, that means where they will occur, for example,
possibility of storms.
In what places, in these more negative areas,
here, there are more likely to be storms than in this other place.
More and more complex systems are forecasting these,
they would be of current lines, that is, that there are more and more,
And more access, because this is on the Internet,
this can be consulted practically anywhere in the world.
And then what generates is a bit of confusion,
as everything seems, everything is colored maps,
we do not know very well what we are looking at and sometimes we lose.
Always, when we look at a weather map we have to see who has done it,
what is what we are seeing, who has done, in this case see the GFS,
it's the Americans, these are the Japanese, these are the Europeans.
We need to know what meteorological center the map has done
and what kind of map we are seeing, if it is synoptic, mesoscale,
of microscale, because if not, we do not know exactly what is happening.
I told you before that Lorenz told us that we could not predict the future
because there were limitations because of the butterfly effect.
This was the first trap invented by meteorologists to pass the
limitations that we put Lorenz, which is called the poor man ensemble,
what does this mean?
Well someone realized the first thing, that if you took maps they had
made various weather services and put them together, and did the average,
was more accurate than each of them separately.
Here what you see are maps made by different meteorological centers,
if they had put them in average, that would have been better,
This would be the poor man ensemble.
But then, with the increase in the calculation capacity, what is done
nowadays, is done in maps that we would call probabilistic determinist,
deterministic.
What does this mean? The forecasting equation, he told them, is deterministic.
From an initial state we calculate the future state,
and that we have seen that by Lorenz can give us many possibilities for the future.
Fix it, this is to make the same forecast
many times different with the prognostic equations,
to notice you giving us each of the lines is once we have done it.
To notice to you that it is a mess, and that is a single line,
in this case would be a contour of 500 hectopascales.
What is done?
Do thousands of forecasts and take the average or the most probable,
and that is, only, that is drawn on the map of weather.
Map 500 is a simple map, but look at a surface map,
the imbroglio that supposes the map of surface.
I do not know what happens here...
This is the surface map, fix the striped mountain here,
The concoction, for this, the black, for example,
would be the average and that we would put on the weather map.
So what have we thought now?
The idea is that if we do it many times, there will be some that will be the most
likely, that is likely to be the future.
So what we are doing now is, we make deterministic maps, because
they use the prognosis equation, but probabilistic because they do a lot
times and see what will be the most likely evolution facing the next few days.
and that is allowing us to reach beyond the forecast line,
not only three days, but to arrive until eight,
nine days at this time, and more that will be seen in the future.
This is a typical map of what we call spaghetti.
What is a spaghetti chart?
Well, look, this is the map of 500, in this case note that the lines
are similar, therefore, here we would believe the prognosis.
Here is a little more separated, here a little more, more, more, more.
Notice how, as the days progress,
the possible lines, the same forecast, becomes very different,
that of the butterfly that filled the map.
Here we would believe the forecast, no, because, well,
I do not know which way the line goes, the isohips, we would not know, right?
Here we would believe it.
And in this place from here too, but perhaps in this place not here.
You see that in every place,
for each situation we will have a different forecasting ability.
And in general we, to notice, this would be a very long-term forecast,
you notice that here the stripes are quite disparate,
here we would not believe anything.
And the problem is that they will always ask us
the weather you are going to do, for example, here in Barcelona where I am.
Look at this is the northeast of Spain, Catalonia, and here is Barcelona.
Can I be able to predict how much time I will spend on
Barcelona with these absolutely disparate stripes?
The answer is no.
A meteorologist needs to know when and when he can not.
Forecast the weather,
even though we have a weather map that looks like we have to know.
what lies behind the maps, to know from when,
our forecast you will not be reliable, you will not make sense,
because the weather maps you are not telling us the truth.
There are some concepts that we have to remember and in particular is this,
which is that of predictability.
What is predictability?
Predictability is the ability we have to forecast, this situation,
with the conditions in which I am, it is predictable by me that I am the
meteorologist, in your case would be the same idea, under the conditions in the
that you are here, can I predict what I want to predict here?
Is it within my predictability?
What will this mean?
Let's try to understand.
We remember Lorenz, we said that,
if we calculate once the forecast line would make us a ray,
if we do it again, by the mere fact of mathematics
associated with the forecast equations, we would see that we are different.
This is the same thing done twice, because of the mathematics he has.
From a certain step we would say, from, this is good, good, good,
well, from here it starts to be a different thing, for such, from here
we would say that our predictability would be out, because we are not able to
determine whether or not our forecast will be true.
Therefore, this will give us a forecast horizon, how far we can go.
Typically the forecast horizon would be three days.
When making a deterministic prognosis, the deterministic prognosis,
it could take us to the fifth, sixth or ninth day depending on whether it is
A situation more or less stable, of those spaghetti that I have taught you.
But, if we want to know the weather that is going to do in a specific place,
this is a very old graphic, we have known this for a long time.
If I want to forecast the weather that will be done within three days,
of four days, something that is going to happen in Barcelona.
He's in New York now.
The weather that,
in four or five days is going to happen in Barcelona, is now in New York.
So if I want to forecast three to five days, I need
to know the weather you are doing, look at you, this is meant to be latitude,
and third 90 degrees of latitude, that is, to the other side of the world, 90 degrees,
and what height of the atmosphere and what height of the oceans I need to know.
If I want to know the next three, three, four,
five days, I need to know the weather on the other side of the Earth.
If I want to know the one for four or ten days,
I need practically the whole world, because What will happen in ten
days is now on the other side of the world, literally.
If I want to predict, I need to know, but, at the same time, if I want
to make a very fine forecast of something and do a mesoscale situation,
that's to say, I am looking for a little window in which to look at my race course.
Everything that is not in my race course, whatever is outside, I can not know
what is going to come to me, therefore, will be out of my predictability.
If I want to make a forecast, if I make a numerical model that only measures me
our racing incident, my forecast horizon is going to be very short.
For the things that are outside I can not know, until they come,
I will not know.
Therefore, notice that we always have a limitation,
we have a limitation between the predictability that will give us the
length of the forecast weather and the space in which I want to forecast.
The smaller the forecast I want to make
the less things I get from outside, therefore, the shorter it can be.
And the longer the forecast,
I will need more knowlegde of a larger space of the Earth.
Bye.
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