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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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Here we defined both the input and output path paths and specified the simulation period and the output grid as well. We also told FLEXPART to only generate plume trajectories on output.

If we run this macro (or alternatively right-click execute the FLEXPART Run icon) the resulting CSV file, 'tr_r1r001.csv', will be available 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 input file
dIn="result_tr"
inFile=dIn  & "/tr_r1r001.csv"

#Read table (CSV) data
tbl=read_table(table_filename: inFile,
    table_header_row: "2",
    table_meta_data_rows: "1")

Next, we determine the trajectory (i.e. the release) start date and time from the table header (we will use them to construct the title):

#Read runDate from table header
runDate=date(metadata_value(tbl,"runDate"))
runTime=number(metadata_value(tbl,"runTime"))
runDate=runDate + hour(runTime/10000)

#Read release start  date from table header
startSec=number(metadata_value(tbl,"start"))
releaseDate=runDate + second(startSec)

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

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Step 2 - Plotting the dates along the mean track

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The macro to use is 'plot_tr_step2.mv'. This macro is basically the same as the one in Step 1, but we have to modify and extend it a bit.

We start with loading the CSV file and determining the start date and time as before:

#The input file
dIn="result_tr"
inFile=dIn  & "/tr_r001.csv"

#Read table (CSV) data
tbl=read_table(table_filename: inFile,
    table_header_row: "2",
    table_meta_data_rows: "1")

#Read runDate from table header
runDate=date(metadata_value(tbl,"runDate"))
runTime=number(metadata_value(tbl,"runTime"))
runDate=runDate + hour(runTime/10000)

Next The first difference is that we need to determine the middle of the release interval since the trajectory waypoint times are given in seconds elapsed since this date. The release date reading part needs to be modified like this:

#Read release dates from table header
startSec=number(metadata_value(tbl,"start"))
endSec=number(metadata_value(tbl,"end"))
releaseDate=runDate + second(startSec)
releaseMidDate=runDate + second((endSec+startSec)/2)

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. Using the information above we can build the list of strings we want to show along the track and use the Here we use a loop to construct  and plot the date labels one by one with Input Visualiser and Symbol Plotting to define the plot:

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

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

    d=releaseMidDate + second(tt[i])
    ttLst = ttLst & [ label="  " & string(d,"dd") & "/" & string(d,"HH")]
end    for
    

#visualiser
    iv_date = input_visualiser(
	   input_plot_type	:	"geo_points",
	   input_longitude_variable	:	mLon[i],
	   input_latitude_variable	:	mLat[i]	   	  	  
	)

#line    
    #text attributes
    sym_date=msymb(symbol_type: "text",
         symbol_text_list: ttLstlabel,
         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    

Finally we define the title and mapview in the same way as in Step 1 and generate the plotFinally the plot command has to be extended like this:

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_track,graph_track,pltRmsLst,pltDateLst_date,sym_date,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

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follows:

• we plot the track as a curve
• we plot the mean trajectory points using symbols of different shape and colour at different times
• we use use the same symbols and colour-coding for the cluster centres but we use smaller a smaller symbol size for better readability

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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