FLEXPART - Plume trajecories

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

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 in the lower 500 m at Newcastle and we will release 1000 kg of material in total.

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 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_r1.csv', will be available (after a minute or so) in folder 'result_tr'. For details about the FLEXPART trajectory 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 using a Table Reader:

#The input file
dIn="result_tr"
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 (i.e. the release) 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 use Input Visualiser and Graph Plotting to plot it:

#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 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_curve,graph_curve,title)

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

Step 2 - Plotting the dates along the mean track

We will improve the trajectory plot by showing 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 to modify and extend it a bit.

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)

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 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 the list of date strings to be plotted 
ttLst=nil
for i=1 to count(tt) do
    d=releaseMidDate + second(tt[i])
    ttLst = ttLst & [ "  " & string(d,"dd") & "/" & string(d,"HH")]
end for    

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

#line attributes
sym_date=msymb(symbol_type: "text",
         symbol_text_list: ttLst,
         symbol_text_font_size: 0.3,
         symbol_text_font_colour: "navy"
        ) 

Finally the plot command has to be extended like this:

plot(view,iv_curve,graph_curve,iv_date,sym_date,title)

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

Step 3 - 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 follow:we plot the track as a curvewe plot the mean trajectory points using symbols of different shape and colour at different timeswe 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_step3.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: