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 2.1.1 What are prediction models and how are they made?
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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.
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Tomàs Molina
[MUSIC]
In today's lesson we will try to understand the process of how we
make the weather maps and for this,
we go back to the 60s, at that time was when they defined what is called
the primitive prognostic equations.
We are not going to put them here, as they contain very long equations
with values of the pressure, the temperature, the humidity.
We know that these meteorological variables are
related, so the resolution of the forecast equations is not simple,
because since something is not linearly called linear,
the thing is not as easy as it might seem, but we'll get there.
But, there is a problem related to the equations of weather,
that Lorentz raised, this man might be one of the people,
one of the scientists who in the year 1963
threw a jug of cold water over the predictors,
over the forecasters of the weather, because he was the one who said
that we could never exactly predict, the weather of the future.
And that was a mathematical problem, it was an insurmountable difficulty
which gave us a horizon of predictability.
Why?
Surely you have heard, of the butterfly effect.
Surely you have heard of it.
What is the idea of the butterfly effect?
Well, regarding an initial state,
if we, we have an initial state, the beginning of the atmosphere,
what we see at first, that differs very little.
Notice that, in this point here, it seems that there is only one, but in reality,
there are two little dots that are very close, but not exactly the same.
If we calculate the forecast equations we would see that they make a
evolution, and the end point of this first calculation of the prognostic equations,
we would get a little bit of difference, it is a bit different.
Note that if this is the first hour, and we want to forecast
now the second hour, starting from those two initial states, which would be
the end of first hour of forecast and we will calculate the second hour.
We would calculate again, and note that the second hour would come at this level.
If we went to the third, or fourth, or successive ones, to notice that a
and the other are one on one side and the other on the same side of this surface.
Therefore, we will not go into much detail,
but there are different regions of sensitivity,
which is a product of mathematics associated with prognostic equations.
The reality is that everyone, in the end, all cases are possible,
with a small difference from the initial state and, therefore,
seems to be unpredictable, it seems impossible to predict the weather.
This is the idea of the butterfly, do you remember the idea?
That is, if you do not keep in mind the small error of the initial state,
you can change the forecast you make for a certain period.
This will make it impossible to do the weather forecast.
Lorentz seems to be right, in fact, he was right, but forecasters are
quite clever, and we have this phrase here, and that's it.
We are talking about chaos, meteorology, air is a chaotic system,
but being chaotic does not mean it is random.
What is the meaning of that?
For chaos can be something that is impossible to predict,
to measure, but it is not a thing that can happen anything,
but in reality there are a few ways that can be followed,
and those are the paths that will really happen.
Therefore, the rest of the screen,
the rest of these areas of sensitivity do not always have to matter.
Let's try to understand a little what I'm saying.
Look, we can have a butterfly that fills everything, but in reality,
we have a few fixed elements in the atmosphere.
It means, for example, that we have a solar force,
means that there is a part of day, part of night, part of summer,
a part winter, and that is going to govern a certain periodicity
in the meteorological systems that are going to happen.
Nothing will happen, but it will only happen
that which is forced by the presence of solar radiation.
We have a fixed geometry of the earth, can not pass anything, it has to
be placed within the atmosphere and in the parameters that the atmosphere allows.
We have a position of the lands, the masses of air and the
sea, we now have stations, that is, we have a very important series of
fixed phenomena that will dramatically reduce the number of
of possibilities that we have in this butterfly.
Therefore, it is true that meteorology and is true
which is very sensitive to the initial state, but air is not a theoretical element,
but is a physical element, is something concrete that tends to repeat itself.
And in that concrete thing that tends to repeat itself,
there we are able to find a possibility of forecasting, that of some
way be able to overcome the mathematical problems raised by Lorentz.
This has been news because June 3, 1944 was D-Day,
the famous D-Day, the Normandy landing.
These were the weather stations that were available that day,
this is what marks the initial state.
When we want to forecast the weather,
we have prognostic equations that are deterministic,
means that from an initial state we will calculate the state of the future,
therefore, it is very important to know the initial state.
That's what we knew on D-Day, from World War II,
you see that neither was it a great knowledge of the initial state
that day of the beginning of the World War.
We are going to divide the world in squares,
all of it, and we're going to start it in layers.
Each of these squares, in each part of the square,
there we will take a data, and in each of the layers on top,
and we are going to do all the calculations of those forecast equations
with the problems associated that I have mentioned, of the butterfly
that we said with Lorentz, we are going to do the calculation for the whole world.
Even though you see that here is a very important number of data to calculate,
that if we expand any of these boxes,
it is not enough to separate to know the weather that will do.
Because you notice that, in a square 100 square kilometers, if you want,
or in this case would be 300 kilometers or almost 1,000 kilometers in this case,
for 1,000 kilometers, would be 100,000 square kilometers.
Notice that inside can have different weather,
can have rain, snow, sea, land, different types of radiation.
It is that, we have the limitation of the weather by Lorentz,
but at the same time the limitation of space is also very important.
Because even though we have so much data to calculate,
you see, we still have a lot of time inside those boxes.
but there we will see how we solve it.
In meteorology we have, the problem of weather, the problem of scales,
What is the story about scales?
Look, we have the planetary scale,
we perhaps want to know the weather it will do all over the world.
Because we will see later when we talk about the forecast horizon
what needs we have.
We have the most classical, which is called the synoptic scale,
which would be these little squares that we have put here.
The synoptic scale, typically,
is watching the world with little squares of about 100 kilometers of mesh.
It means that each square is separate,
each data is separated 100 kilometers of the following data.
The mesoscale is when we want to see a smaller world.
Note that, with 100 kilometers of mesh,
here we have too many different weathers.
If we want to see what this would be like here,
we would need a smaller mesh pitch.
Typically the mesoscale scale or limited scale, we would be with steps of
mesh of ten, twenty, thirty kilometers to see smaller things.
But notice that if I put a mesh step of ten kilometers,
I can still have very different types of weather inside.
If I want to know smaller things I still need a microscale idea,
which could be, from meters to a kilometer.
Note that, if I want to do a race and I have a race course of a pair of
miles, there would be a very, very tiny microscale.
Therefore it would have to do from the whole world, go down, down, down,
going down to get my half-mile mesh step,
Mm-hm, very small things.
And that will certainly complicate our need for calculations.
But let's go back to the synoptic scale.
Let's think of the world as such.
Look, this is the network
of meteorological observations of the World Meteorological Organization.
This would be data coming from different parts of the world.
And this is the percentage of data received, whether it works or does not work.
You can see that in Europe they work quite well,
these blue colors are between 90 and 100%, the greens are between 45 and 90%
so in South America, in some places in Africa, in some places
weather stations in Indonesia do not always work.
Therefore, what we said of the initial state will be important because if
we don't know well the initial state, the future,
the butterfly will complicate the possibility of knowing the future.
But we have many data from the whole world, we have the observation network
the Meteosat, there are the Goes that are in America, they are
those of India, those of Japan, many satellites of geostationary orbit.
But then there are also all those of polar orbit,
that also a multitude of data associated to satellites of polar orbit.
Therefore, ground stations, radiosondes,
meteorological stations associated with meteorological satellites of all kinds.
What generates this?
Because in the end that generates, generates a huge data network, which has
to go around the world, that has to reach the places of forecast and with that,
put them into the forecast equations to know the weather they will do.
And that's called the Global Telecommunications System,
a global telecommunications of the World Meteorological Organization,
and that tries to integrate all that enormous multitude of data
which is going to be the initial state that will allow us to later forecast the weather.
This you see, you are going to have a data assimilation problem.
Why? Because we have different satellites,
of different organizations, of different metric systems,
of a multitude of different computer systems, of communications.
Even the matter of ingesting the data is going to be a problem,
because we are going to need a huge database,
with the capacity to absorb everything and integrate it.
Then we will have to decode,
Because each observation will be made in different ways.
Therefore, we have to do as a kind of language,
universal that does not exist.
So we have to do a series of conversions between the data.
And then, among so many data, there are going to be many who are going to be bad.
There will be many errors: connection errors, measurement errors,
calibration errors.
There are going to be so many mistakes out there that,
as we have seen that the forecast-to-error equation is so sensitive,
that if there is data, do not say they are not especially good.
If there are to be bad, bad in the strict sense of the word,
that can be catastrophic when we make a forecast.
So this part of here is going to be crucial.
When we want to run for forecast models.
Then how do you do this?
So basically what is done,
now I will show you what we call a data-deletion system wizard.
Let's try, the idea is, compare this data that we do not know if they are good or
bad, with which we would expect in some of the fields of short models
reach and eliminate them or try to interpolate in those positions.
Look, I'm going to show you one of the
wizards, these "magicians", used to delete data.
We have, imagine that this is a field of observations and
these would be the observed data.
Notice that here is one you see that can be bad,
because it is like a different direction with a different force.
What can you do?
This, this is an invention.
This the world's meteorologists in different meteorological services
literally invent a system to filter,
and make it more feasible to make the forecast.
Look at what they do.
They compare it with what is expected and, if that is not the
expected, recalculate it.
They make it, for example, an integration with the next ones and give it a new value.
And then they put it back
in the analysis so that the forecast is then made.
This could be one of these wizards to clean up potential erroneous data.
But, at the same time, it might be the right one.
And this, in fact, has happened once.
This happened in France.
It happened on 26, 27 December 1999.
There was a very important wind storm that crossed the north of France,
which caused huge damages and fatalities.
And that happened just by one of these filters,
and was not accurately predicted by just one of these filtrates.
there was some data in the Atlantic that gave
a situation of very strong winds and of forecast of violent weather.
But the filtering cleaned them, because they thought they were wrong and eliminated them.
So, the day after Christmas, of course Christmas is also true that
we are in Europe many are low guard, because people are partying,
it's not very much at work, sometimes.
I'm not saying the French meteorologists were not keeping watch,
but it is a day in which the guard is lowered.
In this case the forecast was not the best.
The filtering erased the prognosis, and there really was a lot of damage.
It is necessary to filter because otherwise, in general,
it would not work, but sometimes the filtering can generate us forecast errors.
There are always good things and bad things in any method.
The method has to exist, because this would be the flowchart
between generation between incoming data and generation of forecast.
We can not go into detail,
But really for you to understand there are many data,
we have to enter this data, we have to put the dynamics, the physics, the part
this saying of assimilation to get them all in the database.
Put it in, make the model out, see if this model is working, check it,
do it again and,
that is that to make meteorological forecast by numerical models is not simple,
and it is not within reach, at least the great synoptic models, of anyone.
Basically, what the large meteorological services do,
those are who have a sufficiently strong database,
a sufficiently important calculation capacity and a facility of reanalysis
sufficiently noticeable, so that the whole system can function.
Where is that?
In the United States, France, the European Forecast Center
in Japan, but not in all parts of the world.
Later we will see that there are other models,
which are the mesoscale models and other limited scale models.
But the large, these models of global scale or synoptic scale,
these only have the most powerful meteorological services in the world.
So what do they look like? We'll see.
They need all three things, in meteorology,
the real meteorology, which makes numerical models.
Needs science.
This science is quite old, I told you before that it was 63,
or even of the First World War,
meteorology as a science is also not very old, you have a great tour.
what has changed a lot and changes systematically,
is the capacity of observation, these satellites that we have,
these automatic weather stations all over the world
that every time we get more data, even more data, at this time.
And then we'll see what we do with them and the computing capacity,
weather in database to access memory
such as computing to run the models.
For that, you need the meteorological services on grand scale.
But then there will be a part where we can participate.
Which is,
once we have the numerical models we will have to elaborate the forecast.
What is the prognosis?
The forecast is, from the weather maps, which are data,
we have to make a decision, that decision is the forecast.
The forecast means, how will affect those data that we have predicted,
which the maps have provided.
How will a meteorology that is related to people or
the places where something is going to happen.
And that, the most important thing is to deliver it to the user in an efficient,
that is reliable, that it is accurate and that it is in weather.
Any of us when we have a meteorologist function,
predictor, we have to put this in the head.
We have to be a service or a system that is reliable,
that we are always there when it is necessary, that it be successful.
In other words, in general our forecast is correct, it is similar
at the same time that it will do, and that we arrive on time, that we are not thinking much
and so much to think to do better, and in the end when we deliver it is late.
But the most important thing we have to keep in mind,
the people that we dedicate ourselves to the forecast, is this.
That we are here to save lives and goods,
this we must always keep in mind.
We the meteorologists, we see in the future, because we have some capacity
to see the future, we are here to save lives and goods, and then,
to make people's lives easier.
So they can navigate faster,
have more fun, so they can go out with the family.
This will be most of the time, this is the truth, huh?
Make people's lives better
simple will be our most of the time, will be this.
But when this is needed from above, to save lives and goods,
is when we have to be, we always have it in the head,
and be more attentive to our users.
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