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Case description

In this exercise we will use Metview to explore the various ways ensemble forecast forecasts can be processed and visualised. The case we will investigate is related to a low pressure system crossing the UK on 10 August 2014: it caused high winds and heavy rain especially in the South-Western part of the country.

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The plots we want to produce with Metview are as follows:

The first three plots show the precipitation forecast for the period of August 24 00UTC - 25 00UTC from various model runs preceding the event by 1, 3 and 5 days, respectively. The last plot shows the observed rainfall for the same period.

We will use both Macro and icons to  ....

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Verify that the data are as expected.

Remember to give your icons useful names!

Setting the map View

With a new Geographical View icon, set up a cylindrical projection with its area defined as

South/West/North/East: 43/-20/60/10

Set up a new Coastlines icon with the following:

• the land coloured in cream
• the coastlines thick black
• use grey grid lines at every 5 degrees.

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Evaluating the forecasts

In this part of the exercise we will create the plot shown below:

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In this plot each map contains the various forecasts for the maximum 10 m wind gust in a 6-hour period from various model runs:

• top left: the closest latest forecast to before the event
• top right: the forecast run 4 days before the event
• bottom left: the ensemble mean for the ENS (Ensemble Prediction System)  ENS  forecast run 4 days before the event
• bottom right: the ensemble spread for the ENS forecasts run 4 days before the event

Setting the View

With a new Geographical View icon, set up a cylindrical projection with its area defined as

South/West/North/East: 43/-20/60/10

Set up a new Coastlines icon with the following:

• the land coloured in cream
• the coastlines thick black
• use grey grid lines at every 5 degrees.

Defining the layout

With a new Display Window icon define a 2x2 layout so that each plot should contain your view. 

Visualising the latest forecast

The GRIB file fc_latest_oper.grib contains the latest forecast run preceding the event. Drag it into the top left map and customise it with the wgust_shade Contouring icon and the title_oper Text Plotting icon. Animate through the fields to see the location of the areas heavily hit by the storm.

Visualising the operational forecast

The GRIB file fc_oper.grib contains the operational forecast run at 7 August 00 TC UTC (four 4 days before the event). Drag it into the top right map and customise it in the same way as the previous plot. You will see that the wind storm was not present in the forecast.

Visualising the ensemble mean

The GRIB file fc_ens.grib contains the control forecast member and the 50 perturbed and 1 control forecast member members of the ENS run at 7 August 00 TC UTC (four 4 days before the event). We will compute and visualise the mean of the ensemble members using a Macro.

The computations will be done in Macro, so create Create a new Macro and edit it. First, read the GRIB file in:

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Our GRIB contains three time steps (78, 84 , and 90 and 96 hours, respectively) and we would like to compute the ensemble mean for each one separately. To achieve this goal we will write a loop going through the time steps. First, define the fieldset that will contain the results:

e_mean=nil

Next, add this piece of code to define the loop (we store the time steps in a list).:

tsLst=[78,84,90,96]

loop step in tsLst

    ...your code will go here ...

end loop

Within the loop, first, read all the 51 ENS members for the given time step:

f=read(data: g,
		step: step
	)	

Next, compute their mean with the mean() macro function:

f = mean(f)

and Last, add it this field to the resulting fieldset:

e_mean = e_mean & f

By doing so the loop's body is completed. We finish the macro by returning the resulting fieldset:

return e_mean

Drag it your Macro into the bottom left map and customise it with the wgust_shade Contouring icon. You would also need a custom Text Plotting icon for the title. Take a copy of the one used for the previous plots (called title_oper) and tailor it to your needs. When you analyse the plot you will notice that the ensemble mean hints for higher wind gusts in the our area of interest.

Visualising the ensemble spread

The ensemble spread is the standard deviation of the ENS members. You We can compute it in a very similar way to the ensemble mean. The only difference is that this time you we need to use the stdev() function instead of mean(). Now it is your task to write a Macro for it. Once you finished your Macro drag it into the bottom right map and customise it with the wgust_spread_shade Contouring icon and with a custom Text Plotting icon. You will see that the ensemble spread is fairly high in the investigated area indicating that ...

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Checking the probabilities

In this part we will estimate the risk of the wind gust being higher than a certain thresholdsthreshold. We will compute the probability of the wind gust exceeding 22 m/s (that is really stormy weatherabout 80 km/h) and generate the plot shown below:

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You can We will compute the probabilities with a Macro in a very similar to the ones you wrote way as we did for the ensemble mean or standard deviation. Basically you need to loop through time steps and for each time steps you need to derive a new field(and standard deviation). The difference is that this time you we need to compute a probability . Supposing that all the ENS members are stored in fieldset f and the threshold value in variable threshold the computation should go like thisfor each time step.

Now duplicate the ensemble mean Macro and edit it. Find the code line computing the mean and replace it with this code block:

f=f > threshold22	
f=100*mean(f)

Here the The first line performed in the code above, performs a logical operation on the fieldset and resulted results in a new fieldset having 1s . In this new fieldset we have only 1s and 0s:

• the value is 1 in each gridpoint where the condition

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• meets (i.e. the value is larger than the threshold)

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• the value is 0 in all other gridpoints.

The probability is simply second line simply derives the probability as the mean of these fields. We multiplied multiply the result by 100 to scale it into the 0-100 range for an easier interpretation.

Now it is your task to write a Macro and compute the probabilities. Once you finished your Macro drag it into the bottom right map and customise , visualise your Geographical View icon and drag the Macro into the  plot. Customise it with the wgust_spread prob_shade Contouring icon and with . Also use a custom Text Plotting icon . You will see that the ensemble spread is fairly high in the investigated area indicating that ...

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to define the title. As for the probabilities, you should see that there is some probability of high wind speeds.

Creating a stamp plot

In this part we will look into investigate the individual ENS members and create a plot showing them all the ENS members for a given time step on the same page like this:

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These plotsThis plot, for an obvious reason, are is called a stamp plotsplot. This is a complex plot so we will work in Macro againwrite a Macro to generate it.

Create a new Macro and edit it. Drop your Geographical View and the Coastlines icons into the Macro editor. Once you've tidied up the code is generated and tidied up , define a 6x9 layout so that each plot should contain your view:

dw=plot_super_pagesuperpage(pages: mxn_layout(my_view,9,6))

Next, drop your wgust_shade Contouring icon into the Macro editor and tidy up the generated code, . We will apply this icon to all the fields in the stamp - plot.

Continue with reading in the GRIB file with of the ENS forecasts in:

g=read("fc_ens.grib")

Define a variable to hold the time step we want to plot:

step = 90

The first stamp plot will contain the control forecast. We need to filter it out with following read() command: 

f=read(
	data: g,
	step: step,
	type: "cf"	
)

The available space for the title in the plot will be confined so we try to use a very short titlet:

title = mtext(
	text_line_1	: "control"
)

be generated by plotting each perturbed forecast member into a separate map, so we need to write a loop like this:

for i=1 to 50 do 

    ...your code will go here ...

end 

...

for

Within the loop, simply read all the members current perturbed forecast member for the given timesteptime step:

f=read(data: g,
		number: i,
		steptype: step"pf",
		numberstep: istep
	  )	

then compute their mean with the mean() macro functionNext, define a title. The available space for the title in the plot is confined (we need to squeeze more than 50 maps into a page!) so the title should be short:

title = mtext(
		text_line_1	 : "memberPF: " &i,
		text_font_size: 0.2 
	) i)

Last, plot the field into the right map in our layoutand add it to the resulting fieldset:

plot(dw[i],title,f,wgust_shade)	

We finish the macro by returning the resulting fieldset:

return e_mean

By using the return statement our Macro behaves as if it were a fieldset (GRIB file). Drag it into the bottom left map and customise it with the wgust_shade Contouring icon and the title_mean Text Plotting icon. You will see that the ensemble mean hints that high wind speed can happen.

Having done so we have finished the code inside the loop. Now visualise your Macro (this will take a minute or so) and try to identify the ENS members predicting high wind speeds in our area.

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Creating a spaghetti plot

We finish the exercise case study by looking into the predictability of the large scale flow pattern by generating spaghetti plots for 500 hPa geopotential from the same ENS run as we investigated before.   A In a spaghetti plot is composed by plotting each ENS member is rendered into the same map using a single contouring isoline value. The plot we want to generate is shown below (it contains the spaghetti plot for 500 hPa geopotential using the 560 gpm isoline value):

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This is a fairly complex plot and we will write a Macro to produce itThese plots, for an obvious reason, are called stamp-plots. This a complex plot so we will work in Macro again.

Create a new Macro and edit it. Drop your Geographical View and the Coastlines icons into the Macro editor . Once the code is generated and tidied up define a 6x9 layout so that each plot should contain your view:

dw=plot_super_page(pages: mxn_layout(my_view,9,6))

Next, drop your wgust_shade Contouring icon into the Macro editor and tidy up the generated code, We will apply this icon to all the fields in the stamp-plot.

Continue with reading the GRIB file with the ENS forecasts in:

and change the map area to

[40,-40,70,20]

so that our map could show a larger (North Atlantic) area.

Next, define the contouring used for the "spaghetti" by dropping the cont_spag Contouring icon into the Macro. A code like this should be generated for you:

cont_spag = mcont(
	contour_label: "off",
	contour_level_selection_type	:	"level_list",
	contour_level_list	:	560,
	contour_line_colour: "blue",
	contour_highlight: "off"
 )

In this mcont() we turned contour labels off to keep the plot uncluttered and defined only a single contour value (for 560 gpm). 

Continue with reading in the GRIB file of the ENS forecasts used for the "spaghetti":

 g = read("spag

g=read("fc_ens.grib")

Define a variable to hold the time step we want to plotThe "spaghetti" will be generated by plotting each perturbed forecasts member as a separate layer into the same map. To achieve this goal we need to write a loop like this:

stepfor i= 901 to 50 do 

    ...your code will go here ...

end for

Within the loop, read all the perturbed forecast members for the all the time steps:The first plot will contain the control forecast. We need to filter it out with following read() command: 

f=read(
	data: g,
	steptype: step"pf", 
	typenumber: "cf"	i
 )	

By default, if no title definition is specified, Metview adds a title line for each field in the plot. Since we are about to plot 50 fields into the same map this would result in 50 titles in the plot! To avoid having too many titles we use a custom Text Plotting iconThe available space for the title in the plot will be confined so we try to use a very short titlet:

title = mtext(
	text_line_1	: "control"
)

Now we willfor i=1 to 50 do ...your code will go here ... end for

Within the loop, simply read all the members for the given timestep

...

Value: 560 gpm T+<grib_info key='step' where='number=50' /> h" )

Here we used the where statement inside the grib_info tag (as described here)  to make the title appear for one member (the 50th member) only.

Last, plot the field with our contour settings and title:

plot(your_view,f,cont_spag,title) 

Having done so we have finished the code inside the loop. Now visualise your Macro (it will take half a minute or so) and animate through the steps to see how the spaghetti is spreading out over time.

Extra Work if You Have Time

Add more fields to the stamp plot

The stamp plot only shows the perturbed ENS members but there is still space left to display additional fields, as well. Try to add the control forecast (from ENS) and the operational forecast to it. Some hints:

• plot the control forecast into the 51st map (dw[51]). The control forecast is stored in the same file as the perturbed forecast members: fc_ens.grib. Read it in with this code:

  Code Block
  f = read(data: g,

...

• type: "cf", step: step)

• plot the operational forecast into the 52nd map (dw[52]). The operational forecast is stored in fc_oper.grib. Read it in with this code:

  Code Block
  f =read(source: "fc_oper.grib", step: step)

NOTE: While setting up these extra plots it is a good idea to temporarily comment out the loop processing the perturbed forecast members.

Add more fields to the spaghetti plot

The spaghetti plot only shows the perturbed ENS members. Try to add the control forecast (from ENS) and the operational forecast to it as well. You should use different isoline colours for them. Some hints:

• use a thick red contour line. The control forecast is stored in the same file as the perturbed forecast members: spag_ens.grib. Read it in with this code:

  Code Block
  f = read(data: g, type: "cf")

• use thick green contour line. The operational forecast is stored in spag_oper.grib. Read it in with this code:

  Code Block
  f = read("spag_oper.grib")

NOTE: While setting up these extra plots it is a good idea to temporarily comment out the loop processing the perturbed forecast members.

then compute their mean with the mean() macro function

title = mtext(
		text_line_1	: "member: " &i,
		text_font_size: 0.2 
	)

and add it to the resulting fieldset:

plot(dw[i],title,f,wgust_shade)	

We finish the macro by returning the resulting fieldset:

return e_mean

By using the return statement our Macro behaves as if it were a fieldset (GRIB file). Drag it into the bottom left map and customise it with the wgust_shade Contouring icon and the title_mean Text Plotting icon. You will see that the ensemble mean hints that high wind speed can happen.