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README.md

splitr

Travis build status DOI

splitr is an R package for conducting trajectory and dispersion modeling with HYSPLIT. We can determine, from one or more receptor sites, where arriving air masses originated. Conversely, it’s possible to model trajectories of air masses from receptor sites. Forward and backward modeling of gas-phase or particulate matter can also be conducted from defined sites. It’s a means to help explain how, where, and when chemicals and materials are atmospherically transported, dispersed, and deposited.

This model has many applications. Some have modeled the atmospheric transport of moisture to determine probable extreme rainfall locations leading to flood events (Gustafsson et al., 2010). Similarly, Creamean et al., 2013 have presented a direct link between long-range transported dust and biological aerosols affecting cloud ice formation and precipitation processes in western United States.

Others have successfully improved understanding of invasive species dispersal abilities to inform conservation and landscape management (Lander et al., 2014). Along similar lines, the long-distance transport of high-risk plant pathogens can be modeled with HYSPLIT to assist with plant disease management decisions, such as applications of fungicide or pesticide to potentially-affected agricultural areas (Schmale and Ross, 2015).

splitr allows you to build and run HYSPLIT models in a fast, easy, and organized manner. A few or, perhaps, thousands of trajectory or dispersion runs can be conducted with minimal code. Because splitr is an R interface to HYSPLIT, we can store output in memory and take advantage of the vast selection of R packages to perform statistical analyses and to generate visualizations. This package furthermore simplifies the process of running HYSPLIT models by automating the retrieval and storage of associated meteorological data files.

Some of the things you can do with splitr are:

  • create and execute model runs with an easily readable magrittr pipeline workflow
  • run multiple trajectory and dispersion model runs (forward or backward) with multiple temporal and spatial variations
  • visualize wind trajectories and particle positions throughout trajectory and dispersion runs
  • use the returned tbl_df object with dplyr to filter(), select(), group_by(), summarize(), mutate(), and transmute() the model output data

HYSPLIT Trajectory Model Runs

To perform a series of HYSPLIT trajectory model runs, one can use the splitr hysplit_trajectory() function:

library(splitr)
library(here)

trajectory <- 
  hysplit_trajectory(
    lat = 42.83752,
    lon = -80.30364,
    height = 50,
    duration = 24,
    days = "2012-03-12",
    daily_hours = c(0, 6, 12, 18),
    direction = "forward",
    met_type = "gdas1",
    extended_met = TRUE,
    met_dir = here::here("met"),
    exec_dir = here::here("out")
  ) 

This use of hysplit_trajectory() sets up four trajectory runs that start at 00:00, 06:00, 12:00, and 18:00 UTC on March 12, 2012 (using days = "2012-03-12" and daily_hours = c(0, 6, 12, 18)). These runs are 24 h in duration (duration = 24).

The receptor/origin locations are set using lat and lon for the latitude(s) and longitude(s). The starting location of 42.83752ºN and 80.30364ºW is set here using lat = 42.83752 and lon = -80.30364. Equal-length vectors of lat and lon values can be used here to create an ensemble of model runs. The starting height of 50 m above ground level is set by height = 50.

The model runs as set above are forward runs (moving forward in time, set here using direction = "forward") and not backtrajectory runs (set with direction = "backward").

The meteorological options include the type of met data to use. The 1º GDAS data is used here with met_type = "gdas1" but there is also the option to use NCEP reanalysis data with the met_type = "reanalysis" setting and NARR (North American Regional Reanalysis) data with met_type = "narr". The necessary meteorological data files relevant to the period being modeled will be downloaded from the NOAA FTP server if they are not present in the working directory.

The function will return a data frame containing trajectory information. The data frame (named here as the object trajectory) will be have the following columns when extended_met is set to FALSE:

  • run the index value for an individual model run
  • receptor a numeric label for the receptor, which is a 3-dimensional position
  • hour_along the integer hour difference (positve for forward trajectories, negative for backward trajectories) for the trajectory position compared to the run starting time
  • traj_dt the date-time value for the trajectory location
  • lat, lon, height the latitude, longitude, and height (meters above ground level) of the air mass along the trajectory
  • traj_dt_i, lat_i, lon_i, height_i the initial values (at the model start) for traj_dt, lat, lon, and height
  • pressure the air pressure at each position and time along the trajectory (in hPa)

If the model is run with extended_met set to TRUE then the following along-trajectory values will also be available in the output tibble:

  • theta the potential temperature (in K)
  • air_temp the ambient air temperature (in K)
  • rainfall the rate of rainfall (in mm/h)
  • mixdepth the mixing depth (or mixing height, in meters)
  • rh the relative humidity
  • sp_humidity the specific humidity (in g/kg)
  • h2o_mixrate the mixed layer depth (in meters)
  • terr_msl the terrain height at the location defined by lat and long
  • sun_flux the downward solar radiation flux (in watts)

Models can also be defined and executed using a modeling object in a magrittr workflow. Here’s an example:

# Create the `trajectory_model` object, add
# various parameters with `add_trajectory_params()`,
# and execute the model runs
trajectory_model <-
  create_trajectory_model() %>%
  add_trajectory_params(
    lat = 43.45,
    lon = -79.70,
    height = 50,
    duration = 6,
    days = "2015-07-01",
    daily_hours = c(0, 12),
    direction = "backward",
    met_type = "reanalysis",
    met_dir = here::here("met"),
    exec_dir = here::here("out")
  ) %>%
  run_model()

Here, we create a trajectory_model object which serves as a container for the model definition and for the results.

This pipeline setup allows for more flexibility as R objects can be piped in for variation in the types of models created. The create_trajectory_model() function creates the trajectory model object. One or more add_trajectory_params() statements can be used to write model parameters to the model object. Ending the pipeline with run_model() runs the model and creates results.

The trajectory data can be be extracted from the trajectory model object using the get_output_tbl() function.

# Get a data frame containing the model results
trajectory_tbl <- trajectory_model %>% get_output_tbl()

trajectory_tbl

…and a tibble of output data is returned:

#> # A tibble: 14 x 21
#>      run receptor hour_along traj_dt               lat   lon height
#>    <int>    <int>      <int> <dttm>              <dbl> <dbl>  <dbl>
#>  1     1        1          0 2015-07-01 00:00:00  43.4 -79.7   50  
#>  2     1        1         -1 2015-06-30 23:00:00  43.4 -79.7   44.1
#>  3     1        1         -2 2015-06-30 22:00:00  43.3 -79.8   39  
#>  4     1        1         -3 2015-06-30 21:00:00  43.3 -79.8   34.5
#>  5     1        1         -4 2015-06-30 20:00:00  43.2 -79.7   30.7
#>  6     1        1         -5 2015-06-30 19:00:00  43.2 -79.7   27.4
#>  7     1        1         -6 2015-06-30 18:00:00  43.1 -79.7   24.5
#>  8     2        1          0 2015-07-01 12:00:00  43.4 -79.7   50  
#>  9     2        1         -1 2015-07-01 11:00:00  43.5 -79.9   44.6
#> 10     2        1         -2 2015-07-01 10:00:00  43.5 -80.1   40.7
#> 11     2        1         -3 2015-07-01 09:00:00  43.5 -80.2   37.7
#> 12     2        1         -4 2015-07-01 08:00:00  43.6 -80.4   35.7
#> 13     2        1         -5 2015-07-01 07:00:00  43.6 -80.5   34.4
#> 14     2        1         -6 2015-07-01 06:00:00  43.6 -80.6   33.8
#> # … with 14 more variables: traj_dt_i <dttm>, lat_i <dbl>, lon_i <dbl>,
#> #   height_i <dbl>, pressure <dbl>, theta <dbl>, air_temp <dbl>,
#> #   rainfall <dbl>, mixdepth <dbl>, rh <dbl>, sp_humidity <dbl>,
#> #   h2o_mixrate <dbl>, terr_msl <dbl>, sun_flux <dbl>

Plotting Trajectory Data

Trajectories can be plotted onto an interactive map. Use the trajectory_plot() function with either the trajectory data frame (created directly by the hysplit_trajectory() function)…

# Plot results using the trajectory data frame
trajectory_tbl %>% trajectory_plot()

…or, with a trajectory model object

# Plot results using the trajectory model object
trajectory_model %>% trajectory_plot()

The visualization will appear in the RStudio Viewer:

The trajectory points and paths are layers where their visibility can be toggled using the Layers icon at the top-right of the view. The following selection of basemaps is also provided:

  • CartoDB Dark Matter
  • CartoDB Positron
  • ESRI World Terrain
  • Stamen Toner

Clicking any of the points along the trajectory will provide an informative popup with time/position info for that location at that point in time:

HYSPLIT Dispersion Runs

Dispersion models can also be conveniently built and executed. Begin the process with the create_disp_model() function. Use one or more add_dispersion_params() statements to write parameters to the model object. The add_grid() function here facilitates the creation of sampling grids. Using the add_emissions() function anywhere in the pipeline will define emissions properties for one or more emitted pollutants. With add_species(), the physical properties and deposition parameters of one or more emitted species can be added to the model.

As with the trajectory model, the pipeline can ended with run_model(). To extract a data frame containing the modeled output data, use the get_output_df() function. An example is in order:

library(splitr)
library(lubridate)
library(here)

# Create the `dispersion_model` object, add
# a grid of starting locations, add run
# parameters, and then execute the model run
dispersion_model <-
  create_dispersion_model() %>%
  add_source(
    name = "particle",
    lat = 49.0, lon = -123.0, height = 50,
    rate = 5, pdiam = 15, density = 1.5, shape_factor = 0.8,
    release_start = lubridate::ymd_hm("2015-07-01 00:00"),
    release_end = lubridate::ymd_hm("2015-07-01 00:00") + lubridate::hours(2)
  ) %>%
  add_dispersion_params(
    start_time = lubridate::ymd_hm("2015-07-01 00:00"),
    end_time = lubridate::ymd_hm("2015-07-01 00:00") + lubridate::hours(6),
    direction = "forward", 
    met_type = "reanalysis",
    met_dir = here::here("met"),
    exec_dir = here::here("out")
  ) %>%
  run_model()

This dispersion model formally begins at 00:00 UTC on July 1, 2015 (using the start_time argument). The model run is a forward run (i.e., moving forward in time, with direction = "forward") and not backwards (which could be set with direction = "backward"). Essentially, running in forward mode means the starting location is a source of emissions; running backward means that the starting location is a receptor.

This run has been set to be modeled for 6 h. The starting location of 49.0ºN and 123.0ºW is set using lat = 49.0 and lon = -123.0; the starting height of 50 m above ground level is set by height = 50. The meteorological options include the type of met data to use (global NCEP Reanalysis data is used here with met_type = "reanalysis).

A single emissions species is set to be emitted (using add_source()) for 2 hours at an emission rate of 5 mass units per hour (rate = 5). Emissions begin at the same time as the start of the model (release_start = lubridate::ymd_hm("2015-07-01 00:00")). The properties of the emitted pollutant are defined in the add_source() call. Here, the physical properties of particle diameter (in micrometers), density (in grams per cubic centimeter), and shape factor (value from 0 to 1), respectively, are defined with pdiam = 15, density = 1.5, and shape_factor = 0.8.

It should be noted that the order of add_source() and add_dispersion_params() does not matter. There can even be several instances of each of these functions throughout the pipeline.

All meteorological data files needed to execute the model during the defined period will be downloaded from the NOAA FTP server if such files are not already present in the working directory.

The output data can be extracted from the dispersion model object…

# Get a tibble containing the model results
dispersion_tbl <- dispersion_model %>% get_output_tbl()

…and the data is conveniently supplied as a tibble object:

dispersion_tbl
#> # A tibble: 13,860 x 5
#>    particle_i  hour   lat   lon height
#>    <chr>      <int> <dbl> <dbl>  <dbl>
#>  1 00001          1  48.9 -123.    152
#>  2 00002          1  48.9 -123.    289
#>  3 00003          1  48.9 -123.    297
#>  4 00004          1  49.0 -123.     53
#>  5 00005          1  48.9 -123.    804
#>  6 00006          1  49.0 -123.     40
#>  7 00007          1  48.9 -123.    754
#>  8 00008          1  48.9 -123.    146
#>  9 00009          1  48.9 -123.    427
#> 10 00010          1  48.9 -123.    375
#> # … with 13,850 more rows

Plotting Dispersion Data

Dispersion data can also be plotted onto a map. Use the dispersion_plot() function with the dispersion model object.

# Plot particle data onto a map
dispersion_model %>% dispersion_plot()

The visualization will appear in the RStudio Viewer:

The dispersed particles at every hour are present as map layers, where their visibility can be toggled using the Layers icon at the top-right of the view.

Installation

splitr is used in an R environment. If you don’t have an R installation, it can be obtained from the Comprehensive R Archive Network (CRAN). It is recommended that RStudio be used as the R IDE to take advantage of its ability to visualize output in its Viewer pane.

You can install the development version of splitr from GitHub using the devtools package.

devtools::install_github("rich-iannone/splitr")

HYSPLIT Citations

Stein, A.F., Draxler, R.R, Rolph, G.D., Stunder, B.J.B., Cohen, M.D., and Ngan, F., (2015). NOAA’s HYSPLIT atmospheric transport and dispersion modeling system, Bull. Amer. Meteor. Soc., 96, 2059-2077, http://dx.doi.org/10.1175/BAMS-D-14-00110.1

Draxler, R.R., 1999: HYSPLIT4 user’s guide. NOAA Tech. Memo. ERL ARL-230, NOAA Air Resources Laboratory, Silver Spring, MD.

Draxler, R.R., and G.D. Hess, 1998: An overview of the HYSPLIT_4 modeling system of trajectories, dispersion, and deposition. Aust. Meteor. Mag., 47, 295-308.

Code of Conduct

Please note that the splitr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

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