FLEXPART - Plume trajectories
This tutorial demonstrates how to generate a single plume trajectory with FLEXPART and how to visualise the results in various ways.
Using FLEXPART with Metview
Note
Please note that this tutorial requires Metview version 5.0 or later.
Preparations
First start Metview; at ECMWF, the command to use is metview (see Metview at ECMWF for details of Metview versions). You should see the main Metview desktop popping up.
The icons you will work with are already prepared for you - please download the following file:
Download
and save it in your $HOME/metview directory.
You should see it appear on your main Metview desktop, from where you can right-click on it, then choose execute to extract the files.
Alternatively, if at ECMWF then you can copy it like this from the command line:
cp -R /home/graphics/cgx/tutorials/flexpart_tutorial ~/metview
You should now (after a few seconds) see a flexpart_tutorial folder. Please open it up.
The input data
The input data is already prepared for you and is located in folder ‘Data’. You will find a FLEXPART Prepare icon that was used to generate the data in folder ‘Prepare’. The corresponding macro code can also be found there.
You do not need to run the data preparation. However, if you wish to do so please note that it requires MARS access and you must set the Output Path parameter accordingly.
Note
Please enter folder ‘plume_trajectory’ to start working. In this example we will generate a forward trajectory by releasing atmospheric tracers from Newcastle.
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.
Note
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 paths and specified the simulation period and the output grid as well. We also told FLEXPART to only generate plume trajectories on output.
Note
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 atmospheric tracer.
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
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_r001.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)
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.
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 we need to determine the middle of the release interval since the trajectory waypoint times are given in seconds elapsed since this date:
#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. Here we use a loop to construct and plot the date labels 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
Note
We had to define the plot for each date label individually (instead of defining just one plot object with a list of values), due to a current limitation for string plotting in Metview’ plotting library. Until this issue is resolved this is the recommended way to plot strings onto a map.
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:
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,title)
Having run the macro you will get a plot like this:
Step 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:
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_step4.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:
