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The simulation itself is defined by the 'tr_run' FLEXPART Run icon and the 'rel_ncastle' FLEXPART Release icon, respectively. Both these are encompassed in a single macro called 'tr_run.mv'. For simplicity will use this macro to examine the settings in detail. 

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If we run this macro (or alternatively right-click execute the FLEXPART Run icon) the resulting CSV file, 'tr_r001.csv', will appear (after a minute or so) in folder 'result_tr'. For details about the FLEXPART trajectory outputs click here.

Step 1 - Plotting the mean track

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The plotting of the track is the same as in Step1:

#Read columns from table
mLat=tolist(values(tbl,"meanLat"))
mLon=tolist(values(tbl,"meanLon"))

#visualiser
iv_curve = input_visualiser(
	   input_plot_type	:	"geo_points",
	   input_longitude_variable	:	mLon,
	   input_latitude_variable	:	mLat	  	  
	)

#line attributes
graph_curve=mgraph(graph_line_colour: "red",
         graph_line_thickness: "3",
         graph_symbol: "on",
         graph_symbol_marker_index: 15,
         graph_symbol_height: 0.5,
         graph_symbol_colour: "white",
         graph_symbol_outline: "on"
        ) 

Then we need to add a new plotting layer for the date labels. Here we use a loop to define construct  and plot the date labels and their plotting instructions one by one with Input Visualiser and Symbol Plotting:

#Read waypoint times from table
#These are seconds elapsed since the middle of the release interval
tt=values(tbl,"time")

#Build and define the visualiser for the date strings
#The plot definitions are collected into a list
pltDateLst=nil
for i=1 to count(tt) do

    d=releaseMidDate + second(tt[i])
    label="  " & string(d,"dd") & "/" & string(d,"HH")
    
    #visualiser
    iv_date = input_visualiser(
	   input_plot_type	:	"geo_points",
	   input_longitude_variable	:	mLon[i],
	   input_latitude_variable	:	mLat[i]	   	  	  
	)
    
    #text attributes
    sym_date=msymb(symbol_type: "text",
         symbol_text_list: label,
         symbol_text_font_size: 0.3,
         symbol_text_font_colour: "navy"
        ) 
    
    #collect the plot definitions into a list  
    pltDateLst= pltDateLst & [iv_date,sym_date]          

end for    

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Finally we define the title and mapview in the same way as in Step 1 and generate the plot:

Finally we define the title and mapview in the same way as in Step 1 and generate the plot:

plot(view,iv_curve,graph_curve,pltDateLst,title)

Having run the macro we will get a plot like this:

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Step 3 - Plotting the cluster centres

We will further improve the trajectory plot by indicating  the particle distribution along the mean track. 

The macro to use is 'plot_tr_step3.mv' and is basically the same as the one in Step 2 but contains an additional plot layer. In this plot layer we draw circles around the mean trajectory waypoints using the RMS (root mean square) of the horizontal distances of the particles to this waypoint. The code goes like this:

#Get rms of the horizontal distances (in km) to the mean particle positions (i.e. waypoints)
mRms=values(tbl,"rmsHBefore")

#Draw an rms circle around every second waypoint
iStart=1
if mod(count(mRms),2)= 0 then
    iStart=2
end if   

pltRmsLst=nil
for i=iStart to count(mRms) by 2 do

   if mRms[i] > 0 then
        
        #input visualiser defining the circle
        iv_rms=mvl_geocircle(mLat[i],mLon[i],mRms[i],100)

        #circle line attributes
        graph_rms=mgraph(           
            graph_line_colour: "magenta",
            graph_line_thickness: "2",
            graph_line_style: "dot",
            graph_symbol: "off"
            ) 

        #collect the plot definitions into a list  
        pltRmsLst=pltRmsLst & [iv_rms,graph_rms]

    end if
end for

Please note that we use mvl_geocircle() to construct the circle and plotted the circle around every second waypoint to avoid cluttering. The only other change with respect to Step 2 is that we need to extend the plot command with the new data layer (pltRmsLst):

plot(view,iv_curvetrack,graph_curvetrack,pltRmsLst,pltDateLst,title)

Having run the macro

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you will get a plot like this:

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Step

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4 - Plotting the cluster centres

The trajectory output file also contains the coordinates of the cluster centres. In this step we will show a possible way to plot this extra bit of information together with the mean trajectory. Our approach is as follows:

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The macro to use is 'plot_tr_step3step4.mv'. This is a fairly long and advanced macro so we will not examine it here but try to encourage you to open it and study how it works.

Having run the macro you will get a plot like this:

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