Case description

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Preparation

In this exercise we will apply use Metview to produce the plots shown above:

• top plot:the plot at the top shows a map with mean sea level pressure forecast fields overlayed with the track of Hurricane Sandy.
• bottom plot: the plot at the bottom contains a graph chart showing the evolution of the minimum of the mean sea level pressure forecast along the storm track.

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We will also attach a marker to show a specific location on the map (New York City) and customise the legend and title.

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Verify that the data are as expected.

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will work both interactively with icons and write Macro code. First, we will create the two plots independently then align them together in the same page.

Creating the map plot

Setting the View

With a new Geographical View icon, set up a cylindrical projection with its area defined by its lower-left corner [20^oN, 110^oW] and its upper-right corner [70^oN, 30^oW].as

South/West/North/East: 17/-97/51/-45

Set up a new Coastlines icon with the following:

• the land coloured in creamthe coastlines in grey
• the grid as a grey dashed linesea coloured as #dcf0ff (or your favourite shade of blue)
  

Plotting the Mean Sea Level Pressure field

Plot the GRIB file sandy_msl.grib into this your view using a new Contouring icon. Plot black isolines with an interval of 5 hPa between them. Since this will be plotted on top of another field, it would also be a good idea to increase the thickness of the isolines. Activate the legend in the Contouring icon and set the legend text for this icon to "MSLP".

Plotting the Precipitation Field

The GRIB file precip.grib contains a pre-processed field of precipitation accumulated over 3 hours before and after the time of the MSLP field (a total of 6 hours of precipitation).

Plot the precipitation data using new Contouring icon. Do this in isolation from the MSLP field until you are happy with the result.

• use the following list of levels for contouring: 0.5, 2, 4, 10, 25, 50, 100, 250  (but remember that in the Contouring icon editor to use a forward slash to separate the items)
• use the following list of colours:  cyan, greenish_blue, blue, bluish_purple, magenta, orange, red, charcoal (use the Contour Shade Colour List helper tool)
• deactivate the contour highlight
• activate the legend

Overlaying Both Fields

Visualise your Geographical View icon and drop the following icons into the Display Window:

• your Coastlines icon
• the msl.grib icon with your MSLP Contouring icon
• the precip.grib icon with your precipitation Contouring icon

Improving the Legend

Create a new Legend icon and change Legend Display Type to Disjoint. Play with the font size and colour and set the legend title to say something about the precipitation field. Drop it into the Display Window to see the results.

Adding the Position of New York City

One easy way to add a place-mark is to use the Input Visualiser icon and combine it with a Symbol Plotting icon.

Edit a new Input Visualiser icon and set the following:

Create a new Symbol Plotting icon to plot this as a red marker (the filled circle is marker index 28) with some text for its legend entry.

Adding a Custom Title

Using a Text Plotting icon, add a custom title as shown in the plot.

Generating a Macro to Reproduce the Plot

Generate a macro which will reproduce your plot with a single click. This can be done either by clicking the Generate Macro button from the Display Window or by editing a new Macro icon, dropping your data and visdef icons into it and adding a plot() command.

Extra Work

Try the following if you have time.

Ensuring the title has the correct date and time

There are various ways we can ensure that the title has the date and time according to the actual data. The default title in fact contains the date and time, but in this exercise we want more control over it.

Construct the second line of the title by extracting the date and time from the MSLP field and converting into an appropriate string - do this in the Macro code.

Hints:

• if you have a fieldset variable called msl_grib, the following line will extract the date at which the field is valid:
  • msl_date = grib_get_long(msl_grib, 'validityDate')
• we can do something similar for the validity time
• these are extracted as integer numbers, but can be combined into a proper date variable in Macro:

  • full_date = date(msl_date) + hour(msl_time)

• use the string() function to construct a date string similar to the one used in the current title
  • see String Functions for details of how to use it
• insert this into the mtext() function instead of the current title
• it is now more robust - if you use data from a different date or time, the title will still be correct
• note that this method will not work directly if you want to generate an animation from different time steps of data

Experiment with different backgrounds

Modify the Coastlines icon, for example:

• plot the US state boundaries
• try different land or sea shading colours
• change the frequency of the grid lines

Animate through the fields to see how the forecast is evolving.

Plotting the storm track

The storm track data is stored in the CSV file called sandy_track.txt. If you open this file you will see that it contains the date, time and geographical coordinates of the track points.

Create a new Table Visualiser icon and set it to visualise your CSV file:

• set the plot type to geo points
• select the columns storing the latitude and longitude by their index (column indexing start at 1)
  
• carefully specify the table delimiter and header information by setting
  • table_delimiter to whitespace (enter a space character here)
  • table_combine_delimiters to on (it means we allow multiple whitespaces as separators)
    
  • table header_row to 0 (it means do not have a header in the file)
    

Now drag your Table Visualiser icon the plot to overlay the pressure forecast with the track.

Customising the storm track

The storm track in its current form needs some customisation. Use a Graph Plotting icon for it by setting the

• the track line to black and thick
• the track points to be white filled circles (their marker index is 15) with red outline.

Plotting date/time labels onto the track

To finalise the track plot we will add the date/time labels to the track points. This could be done with a Symbol Plotting icon by specifying the list of labels we want to plot into the map. However, to make it reusable for other datasets as well, we will do it programmatically by using Metview Macro.

Create a new Macro and edit it. First, read the CSV file in with the Table Reader:

tbl = read_table(
	table_delimiter	:	" ",
	table_combine_delimiters	:	"on",
	table_header_row	:	0,
	table_filename	:	"sandy_track.txt"
	)

In the code above, the object referenced by variable tbl contains all the columns from the CSV file. Now read the date and time (from the first two columns) into separate vectors:

val_date=values(tbl,1)
val_time=values(tbl,2)

Next, we build the list of labels. Each label is made up from a day and an hour part separated by a slash. We convert the date into a string and then take the last two characters to get the day. Use this loop to construct the list of labels:

labels=nil

for i=1 to count(val_date) do
    dPart = substring(string(val_date[i]),7,8)
    tPart = val_time[i]
    label =  "   " & dPart & "/" & tPart
    labels = labels & [label]
end for

Next, define a Symbol Plotting visual definition using the text mode.

The code we need to add is like this:

sym = msymb(
	symbol_type	:	"text",
	symbol_text_font_colour : "black",
	symbol_text_font_size: "0.3",
	symbol_text_font_style: "bold",
	symbol_text_list	:	labels	
	)

We finish the macro by returning our Visual Definition.

return sym

Save the Macro and drag it into the plot to see the labels appearing along the track.

Creating the graph plot

Setting the View

With a new Cartesian View icon, set up a view to cater for the graph showing the mean sea level pressure values in hPa units by setting

• the x-axis type to date
• the x axis minimum to 2012-10-27 and its maximum to 2012-10-31
• the y-axis label to hPa
• the y-axis minimum value to 940 and its maximum to 1000

Computing the minimum pressure along the track

Since this task is fairly complex we will use a Macro for it. The idea goes like this:

• we read the track points from the CSV file
• we define a lat-lon box around each point
• we read the forecast mean sea level data for the box for the corresponding time
• we compute the minimum of the pressure in the box
• from these minimum values we can build the curve data to plot.

Create new Macro and edit it. First, read the CSV file in the very same way as before. However, this time, on top of date and time, we also need to read latitude and longitude into vectors:

val_lon=values(tbl,3)
val_lat=values(tbl,4)

Next, read in the GRIB file containing the mean sea level forecast:

g=read("sandy_mslp.grib")

The curve data requires two lists: one for the dates and one for the values. First we initialise these lists:

trVal = nil
trDate = nil

Now the main part of the macro follows: we will loop through the track points and build the curve dataset. We will use a loop like this:

for i=1 to count(val_date) do

      ... your code will go here ...

end for

Within the loop first construct an area of e.g. 10 degrees wide centred on the current track point.

Next, read the forecast data for the current forecast step and the area you defined (supposing your area is called wbox):

p=read(
		data: g,
		step: (i-1)*12,
		area : wbox
		)

Here we used the fact the forecasts steps are stored in hours units in the GRIB file.

Next, compute the minimum of the field in the subarea using the minval() macro function:

pmin=minvalue(p)

Finally, build the list for the values (scaling Pa units stored in the GRIB to hPa units):

trVal= trVal & [pmin/100]

And also build the list of dates:

dt = date(val_date[i]) + hour(val_time[i])
trDate = trDate & [dt]

Having finished the body of the loop the last step in our Macro is to define an Input Visualiser and return it. The code we need to add is like this:

vis = input_visualiser (
    input_x_type        : "date",
    input_date_x_values : trDate,
    input_y_values      : trVal
)   

return vis 

Now visualise your Cartesian View icon and drag your Macro into it.

Customising the graph

Customise the graph with a Graph Plotting icon by setting the

• the line thicker
• the points to be blue filled circles (their marker index is 15) with a reasonable size.

Creating a title

Define a custom title as shown in the example plot with a new Text Plotting icon.

Putting it all together

With a new Display Window icon design an A4 portrait layout with two views: your Geographical View icon should go top and your Cartesian View icon into the bottom. Now visualise this icon and populate the views with the data.

Extra Work

Adding new curves to the x-y plot

On top of the minimum pressure try to add the maximum and average pressure to the graph plot.  Use a different colour to each curve and add a custom legend as well.

Hints:

• first, just try to add your Graph Plotting definition to the Macro. In the end return both the Input Visualiser and the Graph Plotting as a list like this

return [vis,graph]

If you visualise the Macro your Graph Plotting settings will be directly applied to the resulting curve.

• next, compute the maximum of the pressure (with the maxvalue() function)  in the loop and store its values in another list. Build an input visualiser out of it (e.g. call it vis_max). Add a Graph Plotting for it (e.g. call it graph_max) using a different colour. In the end you need to return a longer list like this:

return [vis,graph,vis_max,graph_max]

• the average pressure curve (with the average() function) can be derived in a very similar manner

• add a Legend with disjoint mode. Set legend_text_composition to user_text_only and carefully set the legend_user_lines to provide a textual description to each curve in the legend. Add your legend to the back of the list you return from the Macro.

Doing the whole task in Macro

Try to write a single Macro that is doing all the tasks in one go and directly produces the composite plot with the map and graph in the end