So what I tried to do on Friday, I
tried to explain what cloud modeling is,
meaning what are the underlying equations, what
are the physical parametrization that we are using,
and what do we do with initial and natural boundary condition.
And then I went on trying to explain
which grid spacing we should be using when we try to explicitly
resolve the cloud.
And the main upshot was that it really
depends on the cloud that we are interested at.
And as a kind of a summary, before I start today lecture,
we can try to infer which grid spacing we might want to use,
depending on the convective processes
that we are interested in simulating.
So let's say I want to simulate shallow convection
over the ocean.
And mostly kind of random shallow convection
developing over the ocean.
And in that case, I would probably use a very high, very
fine grid spacing.
Maybe that is something like 20 meter,
because everything is just controlled
by a small-scale processor.
Let's say I want to simulate shallow convection.
And I'm more interested at the organization.
And I want to do this over heterogeneous land,
for instance.
And in that case, I would probably use,
oh, this is not so good now.
I might use a grid spacing like 100 meter.
And I have two reasons why I would use 100 meter.
First, because here I'm more interested at the organization.
I probably want to have a bigger domain.
And bigger domain comes with a penalty
that I cannot use such a high grid spacing,
because I cannot simulate 1,000 and 1,000 of grid points.
And the other reason why I would be
confident in using a core circuit spacing
is that I'm doing a simulation over an heterogeneous land
surface.
The heterogeneous land will create
a mesoscale circulation, which will strongly
control the development of my shallow cloud.
So the small-scale processor would
be less important in that case than in that case.
So I probably find I'm using a core circuit spacing.
So I could say, OK, let's say now I
want to simulate deep convection.
And maybe I'm most interested in other transitions,
so how I go from shallow to deep.
And I want to do this over the ocean.
I would probably use something like 100 meter,
because I'm now interested in that deep convection.
So I need a large enough domain so that I have not just
one cloud in my domain, but a couple of clouds
so that I can get some statistics.
And since I'm interested in other transition,
I also probably want to be able to represent
the shallow convection phase without distorting it too much.
So you can just go on with this type of example.
I can say, OK, I want to do deep convection.
I'm most interested in the organization of deep convection
over the ocean.
And in that case, I would use a rather coarse grid spacing,
maybe something like 500 meter or even 1 kilometer.
Because if I really want to look
at the organization of the deep convection on large scale,
I really need a big domain.
And I probably will assume that in that case,
what the shallow convection is doing
is not so important for my organization.
So I kind of use rather the coarse grid spacing
so that I really have the big domain
if it is what I would like to investigate.
And the last example was the question
that I asked, which was, what do you
do if you want to look at deep convection over a mountain?
And so many people said, well, I will
use something like 300 meter grid spacing.
And I could use 300 meter grid spacing.
It's probably a very safe bet.
I would say it's probably actually even too
fine for the type of problem, because you also
have a strong forcing, which is given by the mountain.
So basically, you could say, well,
I would probably maybe use a little bit of coarse grid
spacing and go for the larger, bigger domain
so that I can study small organization types.
So I would probably use something like 1 to 2 kilometer.
If I use 200 meter, I would do explicit deep convection,
explicit shallow convection.
And I won't use a binary layer scheme.
If I would do 2 kilometer, I would
use explicit deep convection, explicit shallow convection.
But I would use a binary layer scheme.
So this is more or less a summary
of what I've been trying to say on Friday.
Yes?
So how did you explain the difference
between the shallow convection over the ocean and over the land?
Yeah, so the point here is the heterogeneous land.
So if you do a simulation of a kind
of heterogeneous surface, it's like this example, actually.
Like a land sea breeze, like a heterogeneous surface.
So because you have heterogeneous surface condition,
you will get a circulation.
So if this is developing from one patch to the other one,
and the circulation is basically given
by the size of the heterogeneity.
And the circulation will strongly
constrain where the cloud can develop.
So instead of having like here, you
will get probably like a random cloud developing
a little bit everywhere.
And it's all controlled by the small scale processor.
Here, the cloud will be strongly triggered and organized
by the circulation.
So they will probably just end up kind of lightly aligned.
So the small scale processor are probably not
as important in that case because you have a strong forcing.
So that's why I would probably, I mean,
you could still do 20 meter.
But I would feel OK doing 100 meter.
I mean, the best is probably just, I mean, ideally what
you would do would be like an ensemble of simulation
with 100 meter, 50 meter, 20 meter,
and see why you get the convergence.
Of course, computing time is expensive.
Yes?
Is the vertical resolution scale very good about the accuracy?
Yeah, that's a very good question.
I haven't really seen anything about the vertical resolution.
Generally, you should also tend to use higher vertical
resolution if you tend to use higher grid spacing.
And it will even cost you more computing time.
So that's an issue.
What we will tend to do is very high resolution,
especially if you do shallow cumulative
because you want to have a lot of layer, especially
in the boundary layer and at the top.
If you do the deep cloud, what we
tend to do because we don't want to have
too many vertical levels, we will do the stretching.
So we will use a lot of layer also in the boundary layer
and at the top.
And then in the higher atmosphere, we'll just stretch.
But that's another issue.
Yes?
So this vertical scale depends on the numerical scheme.
Do you use the spectral type of numerical scheme?
All the type of LES cloud-ordered dyno
tend to use a grid point scheme.
So I think spectral data doesn't work so well
with a very high, very fine resolution.
But I mean, there is certainly a dependency on this type
of scheme that you are using, also
what you do for a super-scale turbulence.
OK.
So what I would like to do today is first,
so here is to try to show some example of application
where we use cloud model either for parameterization
development or for process understanding.
And I mean, there have been many studies,
so one way to do it.
I could just kind of bombard you with all the study
that have been done and all the great things
that people have found.
But then I thought that might get a little bit boring.
So I will just try to show a few examples.
We'll try to illustrate how we go about it
and what are the different steps that we
need to do if we want to use a cloud model for parameterization
development.
So the basic idea is that the LAS domain
or the domain of the cloud resolving model
will correspond to one GCM grid box.
And we will derive the statistic from the LAS domain
and use the statistic to derive a parameterization
or to derive the parameter that are needed
by the parameterization.
And the first example that I would like to give
is an example where we use a cloud resolving model
to try to improve the representation of deep convection.
And the example that I will show is from what we did.
But that doesn't mean that this is the best example.
I just showed that one because I know it best.
So it's a little bit easier for me.
And I can show the intermediate step.
But there are other examples.
So the first thing that we need to do, of course,
is to run the model.
So here we were interested in that deep convection.
So we use still kind of coarse grid spacing, 400 meter.
We had a domain of 200 by 200 kilometers.
So this will mostly represent a big GCM grid box.
We had Adobe periodic domain.
And for the forcing, we used large scale forcing
from the quadratic experiment.
So the quadratic experiment was an experiment in the Pacific.
So it's really like tropical convection about the Pacifics.
And this is what we are using for the cloud resolving model.
This is the output from the cloud resolving model.
This is just precipitation as function of time.
And actually, it kind of matched observation.
And you see each time we get deep convection,
you cannot get a strong, very strong precipitation.
So first, OK, you do the cloud resolving simulation.
And then from this simulation, we
have to derive the parameter that we
need from the convection scheme.
And here we were trying to improve
entrainment and detrainment using a bulk mass plug scheme.
So the key point is really to compute the quantity exactly
as they are needed by the convection scheme.
So there are various ways to compute
entrainment and detrainment, which you should really
do with exactly the same way as what
the convective parameterization is doing.
So this is the equation that were already
shown by Catrin for a bulk mass plug scheme.
So this is the change of the mass flux
with height, which is given by entrainment,
Reynolds detrainment.
And this is a change of properties
of the updraft with height, which
is given by the entrainment.
This is the mean property over the grid box.
We use the property in the updraft,
plus you have a source term.
So this is the two equations that the convection scheme
is using.
So now, if you take as a property, a property which
is conserved, this term kind of drops out.
And you can solve these two equations
for entrainment and detrainment.
So here, this is basically very simple math.
You just solve for the entrainment rate,
which is given by this term divided by those two terms.
And here, you can solve for the detrainment rate, which
is given by the entrainment rate minus this term.
So what we want to do now is basically
we want to use the LAS to compute the detrainment
and the entrainment.
And for that, what we need to know,
we need to know the mass flux.
And I mean, we know the mass flux.
The mass flux is the density times the updraft fraction
times the velocity in the updraft.
So we need to know the vertical profile of the mass flux.
And here, we need to know the vertical profile
of an updraft property, which is conserved and the mean.
And here, we use the MSC.
So phi, we use a more static energy,
which is defined as gravitation times the height
plus CPT plus LQV.
So we know all this term from the LAS.
The only thing that we need to do
is to sample those two quantity over the updraft fraction.
And then, we can compute entrainment, detrainment.
We need to know the vertical velocity in the updraft.
And here, the more static energy in the updraft
and in the other element.
The wire signal is the updraft fraction.
So what we need here is to conditionally sample
the updraft so that we can compute
entrainment and detrainment rate.
So what is an updraft?
And there are a couple of definitions lying around.
So if the current state is larger than 0, this is a cloud.
And if the current state is larger than 0
and the point is buoyant, this is the core updraft.
So this is the definition that we
use to sample only the point where we have an updraft.
Because this is what the correction scheme is predicted.
It's predicting the correction scheme predict
with the core updraft and not the convective cloud.
So we do this sampling.
We know everything.
And we get entrainment and detrainment rate.
And this is what we get then.
So the left plot shows the entrainment rate
as function of height that we got from the simulation.
The right plot showed the detrainment rate
as function of height that we got from the simulation.
And they are kind of tree-lined because then I
subsample the cloud by the adapt.
So those are clouds which go below 4 kilometers,
something like that.
This is up to 5, and this is up to kind of 6 kilometers.
But it's not so important.
So we get entrainment and detrainment rate.
And this is basically the value that the correction scheme
should be using if we would like to reproduce what we got
from the cloud-resolving simulation.
So we can say, well, let's compare
what is in the convection scheme.
So if you take ICON, we have a convection scheme,
and you can see what it is in the convection scheme.
So this part here, it is what we would call the turbulent
entrainment rate.
So it's really only the mixing between the edge of the cloud
with the other arrangement.
It is a constant in the ICON model,
and the constant is open one.
So this is actually quite good.
I mean, it's practically what the cloud-resolving model
are saying.
For the detrainment rate, again, this part here
is mostly controlled by the turbulent detrainment, which
is, again, just the mixing at the edge of the cloud.
And ICON uses a value which is also open one.
And you could say, well, maybe it is a little bit small
compared to what the cloud-resolving model is
basically suggested.
You also have here larger entrainment at cloud base
and larger detrainment rate at cloud top.
This is what people tend to call organized entrainment
or dynamic entrainment.
And this is organized detrainment
or dynamic detrainment.
And those will be varied with the large-scale conditions,
so with the temperature and the humidity that you have.
So another point that I would like to make
is if you compare the two, the entrainment rate and the
detrainment rate, actually, and you also
see it with the black line, the detrainment rate
tend to be larger than the entrainment rate.
And actually, it's something that people haven't really
fully realized.
But there was a nice study by Buang et al in 2012.
So what they show here is they did similar type of experiment
with the LAS as they compute entrainment and detrainment
rate for a range of cases, so for cases
with different humidity and different stability.
And what you can see is that the entrainment rate
doesn't vary that much.
And the detrainment rate vary a lot
between those different cases.
So this is the same scale.
And so basically, the detrainment rate
is much more uncertain than the entrainment rate.
And basically, we should be mostly
concerned with getting the detrainment rate right
in a convection scheme.
But what most people try to do is
to get to the right entrainment rate.
So anyway, so now we have your entrainment,
your detrainment from your cloud-resolving model.
And the only thing that you have to do
is to find a relationship to predict
entrainment and detrainment that you can then
use in the convective parameterization.
So this is why it gets complicated.
And I don't want to go into the details.
But basically, you have to make up a relationship
to get those type of curve and those type of curve.
And this relationship should be valid over ocean, over land,
for any type of condition.
So let's say you find up a relationship.
And here, we find kind of a complicated relationship.
So I don't want to go into details.
So you find that relationship.
So then you can test, basically, the model.
And what I show here is the mass flux as functional of height.
So this is height.
This is the mass flux.
The black line is the mass flux as predicted
by the cloud-resolving model, so by our simulation.
The blue line, this is when we use an old version of the GCM
come, of the climate GCM come.
This was the mass flux profile from the convection scheme.
So if you just output the mass flux, which is computed
by the convection scheme.
And you see, it doesn't look at all
like what you would expect from the cloud-resolving simulation.
And the reason is that most of the time,
we don't really look at the mass flux
when we try to validate the GCM.
We look at precipitation, or maybe mean temperature,
or maybe humidity profile, but not as a mass flux.
And the red one is when we basically
change entrainment and detrainment rate
and a couple of other things.
So this is still not perfect, but it's actually
looking a bit more like what the cloud-resolving model is doing.
So this is basically your first example of how you will go
and what you will do to try to improve your convection scheme.
Why did you just get the cloud-resolving mass flux
correct, and then could you try to shift it?
Yeah, I mean, so we were trying.
So basically, it's because the formula
that we use for the detrainment rate is not,
and entrainment rate is not exactly correct.
So we basically, so it's kind of a trade-off.
We didn't get a perfect, I mean, you
could try to make a very complicated formula
to get exactly the correct entrainment and detrainment
for that case.
But then you are still not sure, will it
work for another case?
And when we derive it, since we didn't
want it to make a too complicated formula,
we kind of make just kind of mean profile, which basically
then had the consequence that we had too much detrainment
higher up here, so that we kind of losing too much mass flux.
So this was the first example on how
we use cloud model to kind of try
to improve a parameterization.
This is a second example, again, how
to use a cloud model to improve a parameterization.
And I took the example from Anne Christine.
So if you have a question, you can just ask Anne Christine.
Here, what we wanted to do was to improve the parameterization
of cloud cover.
But for model, which are using a resolution,
maybe between 10 and 50 kilometers.
And again, the first step is to run the LAS.
And here, since we were interested at shallow
convection, we use a very high kind of a high grid spacing
between 20 and 50 meters.
We have a high grid spacing.
We have a small domain from 10 to 51.
So this mostly targeted at kind of model,
which we'll use grid spacing of like 10 to 50 kilometer.
We have, again, kind of this doubly periodic condition.
And we use different cases to simulate the cloud.
We use IRM, which was a shallow convection over land.
Ricoh, which is shallow convection over the ocean.
Dicom, which is protocuminous.
And Aztecs, which is also protocuminous.
And this is just a plot showing kind of how
the simulation looks like.
This is the cloud albedo.
And you see you have kind of shallow convection
developing into the domain.
So again, you have the simulation.
It looks like you would expect from observation.
Then we can use the simulation to derive the parameterization.
And again, the first step is to compute the quantity exactly
as they are needed here in that case by the cloud cover scheme.
And the scheme that we are targeting at
is the double Gaussian PDF.
So the double Gaussian probability density function.
This is the equation for a double Gaussian PDF.
It's a very nice equation because there
are a lot of unknown.
So if the parameterization want to use this type of scheme,
you need to know all the different parameter.
And you need to know what a is, which
is basically giving the weight between the two PDF.
We need to know what are the two standard deviations, the sigma
1 and the sigma 2.
And we need to know what is the S1 and the S2, which
is the mean extended liquid water for the two PDF.
The S here is the mean extended liquid water,
but this is no in the grid box.
So basically, we have five unknown
that the cloud cover scheme will need
to know if you want to use this parameterization.
And what you can show is that you can reduce those.
What you can show is that if you know the mean, the variability,
and the skewness in the full grid box,
you can get three equations.
But I have five unknown, so I still need two more equations.
And basically, what we did here is
we used the LAS to come up with a relationship
for these two more equations that you need
if you want to use that scheme.
And what the two equations are used to predict here,
what is sigma 1 and what is sigma 2.
So the first thing is, OK, we need
to derive sigma 1 and sigma 2 from the LAS.
This is an example for the Ricoh case,
and this is an example for the Dicom case.
So we use the LAS, and we just plot the PDF of this quantity
S. So the black line is the output from the LAS.
And here, again, the black line is output from the LAS.
You see the difference between shallow convection and strato
cumulus.
And the gray line is if you do a double Gaussian fit
through the LAS data.
So this is what you will get.
So from this fit, we get a value for sigma 1 and sigma 2.
So now you can do a scatter plot.
So we can plot sigma 1, which we divided here
by the standard deviation of the grid box versus the skewness.
And this is the sigma 2.
And basically, what we want to have
is a relationship relating sigma 1 to this variable.
And it is what the cloud cover scheme will be using.
And those are all the points for the different experiments.
And I put two relationships into each.
So the first one, which is the dash line,
was a relationship which was proposed by Larson et al.
in 2001.
And the second one is the new one by Anne Christine.
And I create the main difference between the two issues
that Larson didn't have the Ricoh and the RMKs.
So basically, just assume that then the relationship
will look like that here, because it didn't have
all this point here.
Whereas if you get more cases, then you
see that actually you should do a parameterization, which
look more like this one.
So these are also a little bit the issue
when we try to develop parameterization.
We should really have enough cases
because it is kind of easy then to put a wrong relationship
through the data.
So now we have the relationship between the sigma 1
and the skewness.
And actually, the next step will just
be then to implement these everything in a model
and then test if this works.
And there is nothing because we haven't done it.
But basically, then these basically
allow us to compute what is the sigma 1, what is the sigma 2.
And we know all the parameter for the double Gaussian PDF.
And you can use these in the model.
And this should give basically a better representation
of the clock cover than if you were
using these parameterizations, especially when
you have shallow convection.
Because that one was just basically underestimated it.
So these are basically two examples
of how we use Cloud Model to kind of improve parameterization.
And there are many more examples in the literature.
But I saw that I would just stop after two
because I can just go on and say, again,
you have to use the Cloud Model.
And then you just derive the quantities.
You try to make something up.
And then you test in a GCM.
So this is?
When you're proceeding this way, I
don't see how you can get the parameterization
for that to work in different cases.
Yes.
It's too local to represent something big.
So basically, it's the assumption.
So the assumption, well, you basically
hope that even if you tested it only for those cases,
those cases are representative enough for any situation
that you might get in your GCM so that the parameterization
will still be valid.
Of course, most of the time, it's not true.
But this is the best what we can do.
Could you, for instance, use a, if it's in the perspective
of climate building, could you use the output of the climate
model to force your LES?
You could use the output of the climate model
to force the LES.
The problem is that the biases that you
have in the climate model that you
would like to get rid of will strongly
affect the solution of your LES.
And then you're kind of, yeah, it doesn't really.
So yeah.
So the two examples that I showed work very well
if you basically know which parameterization you want
to improve and where are the problem.
So for instance, we want you to improve on treatment
and the treatment rate.
But quite often, you basically know that there are biases.
But you don't really know what is
the cause of the biases, so which
part of the connection scheme or one of your parameterization
should be improved.
So ideally, what we should be trying to do
is basically do the cloud modeling first, where
we assume connection is explicitly resolved,
and try to understand one specific aspect of convection.
And then we can compare what we got here
with when we do simulation at coarser resolution, which
we use convective parameterization.
We can try to understand the biases based on what we learn
from the cloud modeling.
And then as a first step, then we
can use what we have understood from where the biases come
from and how the process should work to really develop
the parameterization.
And we can use here the output of the cloud model
to improve the parameterization.
And we can then just kind of go into a circle
until we get the perfect parameterization.
So it's kind of a tedious way, but in theory, it can work.
And I want to show last example where we really try.
We started here, and then we compare with what's
happened here, and then we try to improve the parameterization.
So this is my last example of using cloud model
to kind of better understand what's happening
and to improve parameterization.
And here we were interested at the interaction
between convection and thermally induced
circulation, like convection and the sea breeze.
And the question for that we were just asking,
what is the effect of convection on the thermally
induced circulation?
So first, we try to understand how convection
interact with the sea breeze.
Then we try to see how this is represented
if we use a convective parameterization.
And finally, you try to improve the convective parameterization
as a last step.
So for this case, we use, again, LAS.
We use a good spacing of 100 meters, which is actually
too fine for that case.
But anyway, the domain size was kind of a channel,
so very long in one direction so that we
get a circulation developing.
So 820 kilometer by 50 by 21 in the vertical.
It is, again, doubly periodic.
And in that case, we don't use any large scale forcing
because we are only interested at the interaction
between convection and thermally induced circulation.
So we need convection that we get by specifying
an initial profile.
And we need a sea breeze that we get
by specifying two different surface type.
This is just an overview of the simulation.
This will be the land part.
This will be the sea part.
And you see after some time, you get a circulation developing.
This will be the sea breeze coming in.
And you also get convection developing
on top of the sea breeze with strong precipitation.
And you also see the outflow at the convective top.
So this will be retrainment from the convection.
So the first step is to try to understand, OK,
what is the effect of the cloud on the propagation
of the circulation?
So at GCM's scale, the thermal in this operation
is so great, or a light scale?
So if you use the, it's mostly not represented
because the grid box is just too large.
So the only location where you could get such circulation
would be at the coastline.
But generally, you won't really see the circulation
because if you have a grid box which is like 300 kilometers,
I mean, the circulation would never propagate too much
in land so that you would never see it.
But if you would use a model with a resolution
like from 10 to 50 kilometers, then you
will see the sea breeze.
And then you should be able to correctly interact
with your convection on top of it.
So this is just the propagation.
So the location of the front of the circulation
as function of time from the simulation that we did.
And you see that what happened is you
get a circulation which kind of begin to appear.
The circulation slowly go into the land.
And then you get a first acceleration, which
is a few hours after the cloud appears.
And then you get a second acceleration
of the circulation, which is a few hours
after precipitation start.
So here, the first idea was, OK, the formation of cloud
and precipitation led to an acceleration
of the propagation of the breeze front.
Of course, one could argue, well, maybe this
is just coincidence that the formation of cloud
and precipitation accelerate the breeze front.
So we did some tests to test whether this was really
the case.
And this is also what people like to do with cloud model,
is just to do sensitivity experiment.
So we did two sensitivity experiments.
The green one is an experiment where
we turn off all conversation processing.
So there is no cloud formation.
And you can see that in that case,
we get the same propagation as first,
but then it gets much more slower than in the black case.
And we also did a simulation where
we didn't remove precipitation, but we
removed the effect of precipitation
on the boundary layer, which is the evaporation
of precipitation.
And you see again that here, at first,
they are kind of both on top of each other.
But then when you get precipitation
and strong evaporation, the control
case go much faster than that case.
So the conclusion was, from the cloud resolving simulation,
we kind of expect that the formation of cloud
and precipitation will accelerate the breeze front.
And we understand this as due to the formation
of cloud and precipitation.
So now you can say, OK, no, I use my model.
I have a collective parameterization.
And ideally, I should hopefully see something
very similar to the black curve.
So we compare the result from the cloud resolving simulation.
Here, we use a kind of smaller grid spacing
to a model where we had parameterized convection
with a grid spacing of 11 kilometers,
so that we still get a kind of sea breeze developing.
And this is basically what then we get.
This is again the location of the front as function of time.
So the black line is what the alias is basically
saying.
And oh, this is wrong.
This should be found in meta.
It doesn't matter.
And the blue line is what the model
with the collective parameterization is doing.
So what the collective parameterization is doing
is that you get suddenly the breeze front coming in
and rapidly propagating it to in long.
Then the breeze front go back towards the coast
and propagate again into the land.
So this is very different from what you would expect
from the cloud resolving simulation.
And again, you can now try to use the result
of the cloud resolving simulation
to improve here the representation when you
use the collective parameterization.
And as I said before, there are two things which
accelerate the breeze front that we learn from the LAS
is that the formation of cloud and of precipitation
led to the acceleration of the breeze front.
And if you plot the precipitation as function
of time, you see that in the blue case,
or in the one with the collective parameterization,
you start to trigger a correction so early.
So you begin to accelerate the breeze front much more
too early compared to the LAS.
So this is basically exactly what you see here.
The precipitation starts way too early, is way too strong.
So you get this huge acceleration
of the breeze front, which is different to what
you see in the LAS.
So you can try to prevent the correction scheme
to fire too quickly.
And then this is what basically you get.
You see then you more or less get something very similar
to what you see from the LAS.
You still have here a too rapid propagation, which
is due to too strong evaporation.
So these are kind of trying just to kind of give an overview
of how we try to use cloud model to on.
I have one more question.
Yes.
If you have now the second parameterization,
is the first parameterization then also effective?
What do you mean if I have the which second parameterization?
This first parameterization of shadow convection.
Now if you use the second parameterization, or the M.
So in that case, so the parameterization
is doing both shallow and deep convection.
And in that case, mostly the parameterization mostly
do deep convection right from the beginning.
So there is not really a shallow convection phase.
And what we did here, we kind of prevent the convection scheme
to fire too quickly.
So to fire to trigger the convection too quickly.
And if you prevent the convection scheme
to trigger the convection too quickly,
then the circulation, which is really
triggered by the learned heterogeneity,
has time to kind of slowly develop.
And then only when the precipitation begins
to develop, you get the acceleration.
So there are a couple of ways how you can do that.
So if you are lucky enough and you
can use the scheme from the ECM-Lupel-UF convection
scheme, so they have a new version
where you get a kind of better dyno cycle of convection.
So convection will be triggered kind of later during the day.
So that if you are lucky enough, if you are not lucky enough
to have such kind of scheme that two-way or you can do it,
you can change the trigger function.
So you can artificially just change the trigger function
to see if by changing the trigger,
you will get the effects that you would expect.
And then you could go back and say, well, no, I
know that I have to change the trigger function.
I do so that then I can use it in my model.
Or what you can do is you can change the atmospheric profile
to try to make it very stable to really not let
the convection develop too quickly.
But this will, again, just test the hypothesis
that this is the triggering, which is kind of causing you
a problem.
But then at some point, you have to find
a new parameterization for your triggering.
But then it's up to you again to try to find a new way
how you will do the triggering.
So this basically just helps you
to understand which part of the convective parameterization
you would like to improve.
And from here, you would say, well, I would like to improve.
I would like to improve the triggering or the closure,
for instance, and I would like to improve the evaporation
of precipitation.
I would like to come to the last point of cloud model.
Even if cloud model are great and you
can learn a lot of things with them, they have limitation.
And there are three limitations that I
would like to briefly talk about from this type of simulation.
So the first one, this is the one
that we already spoke a little bit.
On Friday, it is really the issue
of the large-scale forcing.
So if you only do the cloud model on a domain,
you have to provide the large-scale information,
as I explained on Friday.
So you have to provide this term, which basically
you provide what is the vertical velocity,
and the vertical velocities strongly
confirm what precipitation you will get.
So one of the critics is that, of course,
if you provide the vertical velocity,
you will always get the correct solution.
But what you would like is to be able to basically predict
what is the vertical velocity.
And so I understand what controls the vertical velocity.
So there are two ways to go around this issue.
One way is to use a very large domain,
and just assume that then the domain is large enough
so that convection can develop and you can get
the large-scale effect in it.
Or you can use something which is called the weak temperature
gradient approach.
And the idea here is, instead of prescribing
the vertical velocity, if you look in the tropics,
the temperature is pretty much homogeneous.
So the idea is that you basically
prescribe the temperature.
And from the temperature, you can actually
compute, indirectly, what should this term be,
so that you get the observed structure.
I mean, this is not a perfect approach.
There are kind of issues, what happens in the boundary layer
and how you implement it.
But it's a little bit the idea here.
So you prescribe the temperature.
So you relax your temperature to a profile.
And then you kind of compute what the vertical velocity is
interacting with the LAS.
I guess the weak temperature gradient approach
can work if you have a lot of gradients, particularly
in mid-latitudes.
Yeah, so it works very well over the tropics, I agree,
and over the mid-latitude.
There are issues the same way like in the boundary layer,
because then you cannot really assume
that the temperature is kind of so homogeneous.
So if you have a gradient, it's an issue.
So there are issues.
Yes, it has mostly been used for more kind
of tropical type situation.
The second issue from Cloud Model,
this is something that I think Pierre also briefly showed,
is that not all processes are explicit,
and we still need microphysics.
And so the issue, I mean, we are strongly
interested at convection and precipitation, which is then
strongly controlled in the Cloud Model by the microphysics.
And this is plot showing precipitation
as function of height.
And those are kind of precipitation frequency,
and this is precipitation normalized by the amount
of rain.
And those of all this simulation are Cloud Model simulation.
So they're all high resolution simulation,
but they use different microphysics.
Some of them use bin models.
Some of them use one-moment model.
Some of them use two-moment model,
which would be the more complicated over the one
moment.
And you see, actually, these were all shallow convection, which
was precipitating.
And you see, they do very different precipitation.
And I mean, there is nothing much we can do about it.
Of course, it's a little bit an issue if no.
I use, for instance, let's say I use meso-in-harsh.
So I will believe this curve, and I'll
try to develop my convective parameterization.
But maybe we would say, well, you should rain a lot.
And maybe if I would have used, let's say, Dallas,
here's the blue one, I would maybe get this.
I should believe this curve, so I should not rain so much.
So that's a limitation.
And the third one, which is often happening,
is that if you use a simulation at convection permitting
resolution, so in the order of 1, 2 kilometer,
what people often have observed is
that we trigger convection too early.
So this is an example.
This is precipitation as function of time.
The green is from radar observation.
This is over Germany.
The black one is from a controlled simulation,
which was run with 2.8 kilometer explicit convection.
The red one is an ensemble of simulation
where the boundary conditions have been modified,
and the blue line is an ensemble of simulation
where both boundary conditions and initial conditions
have been modified.
And you see that in all the cases,
you tend to trigger convection too early compared
to the observation.
And this has been observed quite frequently
with various models.
And this is really because the grid spacing
is written with two cores.
This is really an issue of the grid spacing.
So this is one issue when we use this type of resolution.
So this is the smallest summary of what
I've been trying to say.
So when we do cloud modeling, we try
to use high resolution to explicitly resolve
small-scale phenomena.
But the grid spacing really depends on the phenomena
that we are interested in simulating.
The cloud modeling studies are useful both for process
understanding and parameterization development.
And we learn quite a lot of things.
I mean, two things that we really learn
were, for instance, that the on-trend one for shallow
cumulus was too high in previous convective parameterization,
or that the on-trend one should depend on relative humidity.
Those are things that we really learned
from cloud modeling studies.
I mean, there are two possible advantages over observation.
One is that you may not be restricted
to a measurement complaint.
And you can also get quantities that are hard to observe.
So like on-trend, de-trend, or other stuff which
are really hard to observe.
But of course, we should not forget the observation.
As I said, the microphysics may strongly
impact the solution that we get.
And that's all.
Thank you.