This tutorial demonstrates how to generate plume trajectories with FLEXPART and how to visualise the results in various ways.


Please enter folder 'plume_tr' to start working.


In this tutorial we will release tracers from Newcastle and visualise the resulting plume trajectories.

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. 

The macro starts with defining the release like this:

rel_ncastle = flexpart_release(
	name	:	"NEWCASTLE",
	starting_date	:	0,
	starting_time	:	15,
	ending_date	:	0,
	ending_time	:	18,
	level_units	:	"agl",
	top_level	:	500,
	bottom_level	:	0,
	particle_count	:	10000,
	masses	:	1000,
	area	:	[54.96,-1.6,54.96,-1.6]
	)

This says that the release will happen over a 3 h period between heights in the lower 500 m at Newcastle and we will release 1000 kg of material in total.

Please note that

  • the species is not defined here (will be defined in flexpart_run())
  • we used dates relative to the starting date of the simulation (see also in flexpart_run())

The actual simulation is carried out by calling flexpart_run():

#Run flexpart (asynchronous call!)
r = flexpart_run(
	output_path	:	"result_tr",
	input_path	:	"../data",
	starting_date	:	20120517,
	starting_time	:	12,
	ending_date	:	20120519,
	ending_time	:	12,
	output_field_type	:	"none",
	output_trajectory	:	"on",
	output_area	:	[40,-25,66,10],
	output_grid	:	[0.25,0.25],
	output_levels	:	500,
	release_species	:	1,
	releases	:	rel_ncastle
 
print(r)

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

The actual species that will be released is defined as an integer number (for details about using the species see here). With the default species settings number 1 stands for tracer.

If we run this macro (or alternatively right-click execute the FLEXPART Run icon) the results (after a minute or so) will be available in folder 'result_tr'. The computations actually took place in a temporary folder then Metview copied the results to the output folder. If we open folder 'result_tr' we will see the 'tr_r1.csv' CSV file containing the plume trajectories. For details about the FLEXPART outputs click here.

Step 1 - Plotting the mean track

The macro to plot the mean trajectories is 'plot_tr_step1.mv'. We will see how this macro works.

First, we read the CSV file.

#The input file
dIn="result_tr_single"
inFile=dIn  & "/tr_r1.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 start date and time from the table header:

#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)

Next we read the coordinates of the mean track and define an Input Visualiser to plot it:

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

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

#line attributes
vd=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 define the title:

txt="Mean trajectory starting at: " & 
             string(releaseDate,"yyyymmdd") & " " &
             string(releaseDate,"HH") & " UTC"

title=mtext(text_line_1: txt,
            text_font_size: 0.4)

the mapview:

#Define coastlines
coast_grey = mcoast(
	map_coastline_thickness	:	2,
	map_coastline_land_shade	:	"on",
	map_coastline_land_shade_colour	:	"grey",
	map_coastline_sea_shade	:	"on",
	map_coastline_sea_shade_colour	:	"RGB(0.89,0.89,0.89)",
	map_boundaries	:	"on",
	map_boundaries_colour	:	"black",
	map_grid_latitude_increment	:	5,
	map_grid_longitude_increment	:	5
	)

#Define geo view
view = geoview(
	map_area_definition	:	"corners",
    area	:	[47,-16,57,0],
	coastlines: coast_grey
	)

and finally generate the plot:

plot(view,iv,vd,title)

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

Step 2 - Plotting the dates along mean track

We will improve the trajectory plot by plotting the waypoint dates along the track. 

The macro to use is 'plot_tr_step2.mv'. This macro is basically the same as the one in Step 1, but we have an additional data layer for the waypoint dates:



We will improve the trajectory plot by plotting the waypoint dates along the track. The macro to use is 'plot_tr_step2.mv'. We will see how this macro works.

First, we read the CSV file.

#Read relase 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 input file

Next, we determine the trajectory start date and time from the table header: