The model outputs can be found nested within the out/ directory (see Structure). STILT outputs two files for later use. Particle trajectories are saved to a _traj.rds file and gridded footprints saved to a _foot.nc file.
Particle trajectories and simulation parameter information are packaged and saved in a compressed .rds file (serialized single R object) with the naming convention with the naming convention YYYYMMDDHH_LONG_LATI_ZAGL_traj.rds. This allows regridding of the footprints without recalculating particle trajectories.
This object is can be loaded with readRDS() and is structured as
out <- readRDS('YYYYMMDDHH_LONG_LATI_ZAGL_foot.rds')
str(out)
# List of 4
# $ runtime : POSIXct[1:1], format: "2015-06-16"
# $ file : chr "/uufs/chpc.utah.edu/common/home/u0791983/stilt-sims/test/out/2015061600_-111.847672_40.766189_10/2015061600_-111.847672_40.7661"| __truncated__
# $ receptor:Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 1 obs. of 4 variables:
# ..$ run_time: POSIXct[1:1], format: "2015-06-16"
# ..$ lati : num 40.8
# ..$ long : num -112
# ..$ zagl : num 10
# $ particle:Classes ‘tbl_df’, ‘tbl’ and 'data.frame': 128441 obs. of 26 variables:
# ..$ time: num [1:128441] -2 -2 -2 -2 -2 -2 -2 -2 -2 -2 ...
# ..$ indx: num [1:128441] 1 2 3 4 5 6 7 8 9 10 ...
# ..$ long: num [1:128441] -112 -112 -112 -112 -112 ...
# ..$ lati: num [1:128441] 40.8 40.8 40.8 40.8 40.8 ...
# ..$ zagl: num [1:128441] 61.3 100.6 87.3 98.3 88.6 ...
# ..$ sigw: num [1:128441] 1.15 1.15 1.15 1.15 1.15 ...
# ..$ tlgr: num [1:128441] 8.05 8.05 8.05 8.05 8.05 ...
# ..$ zsfc: num [1:128441] 1532 1532 1532 1532 1532 ...
# ..$ icdx: num [1:128441] 2 2 2 2 2 2 2 2 2 2 ...
# ..$ temp: num [1:128441] 301 301 301 301 301 ...
# ..$ samt: num [1:128441] 2 2 2 2 2 ...
# ..$ foot: num [1:128441] 0.0219 0.0219 0.0219 0.0219 0.0219 ...
# ..$ shtf: num [1:128441] 0 0 0 0 0 0 0 0 0 0 ...
# ..$ tcld: num [1:128441] 87.9 87.9 87.9 87.9 87.9 ...
# ..$ dmas: num [1:128441] 1.104 1.104 1.104 0.991 1.119 ...
# ..$ dens: num [1:128441] 0.968 0.965 0.966 0.965 0.966 ...
# ..$ rhfr: num [1:128441] 0.277 0.282 0.28 0.282 0.28 ...
# ..$ sphu: num [1:128441] 0.00222 0.00222 0.00222 0.00222 0.00222 ...
# ..$ solw: num [1:128441] -99 -99 -99 -99 -99 -99 -99 -99 -99 -99 ...
# ..$ lcld: num [1:128441] -99 -99 -99 -99 -99 -99 -99 -99 -99 -99 ...
# ..$ zloc: num [1:128441] -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 ...
# ..$ dswf: num [1:128441] 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 ...
# ..$ wout: num [1:128441] 0.722 0.722 0.722 0.722 0.722 ...
# ..$ mlht: num [1:128441] 329 329 329 329 329 ...
# ..$ rain: num [1:128441] 4.02e-07 4.02e-07 4.02e-07 4.02e-07 4.02e-07 ...
# ..$ crai: num [1:128441] -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 -0.9 ...
Particle trajectory data is stored in a data frame with columns corresponding with varsiwant and can be accessed with out$particle.
Footprints are packaged and saved in a compressed .nc file conforming to the Climate and Forecast (CF) metadata convention with the naming convention YYYYMMDDHH_LONG_LATI_ZAGL_foot.nc. This object contains information about the model domain, the grid resolution, and footprint values. This object is typically a three dimensional array with dimensions ordered (x, y, z). However, the object will only have dimensions (x, y) for time integrated footprints.
For those familiar with raster operations, the default output adheres to the CF-1.4 metadata conventions which is inherently compatible compatible with the R raster package. For more information about raster manipulation, the Raster R package is a good place to start.
Variable names were changed from the CF convention naming table for consistency with existing STILT distributions. However, the array structure adheres to the convention.
If time_integrate = TRUE, the footprint .nc files can be loaded directly with raster(). If time_integrate = FALSE, the data can be loaded with brick(), which is a three dimensional version of a standard raster. The POSIX time (UTC seconds since 1970-01-01) is stored in the Z dimension and can be easily accessed with getZ().
Using the Raster R package, the data can be loaded with brick() and is structured as
> library(raster)
> out <- brick('2015070211_-111.835_40.763_7_foot.nc')
> out
# class : RasterBrick
# dimensions : 280, 390, 109200, 5 (nrow, ncol, ncell, nlayers)
# resolution : 0.002, 0.002 (x, y)
# extent : -112.3, -111.52, 40.39, 40.95 (xmin, xmax, ymin, ymax)
# ...
> time <- as.POSIXct(getZ(out), tz = 'UTC', origin = '1970-01-01')
> str(time)
# POSIXct[1:5], format: "2015-07-02 05:00:00" "2015-07-02 06:00:00" ...
Alternatively, the data can be loaded using standard netCDF methods.
> library(ncdf4)
> nc <- # nc_open('2015070211_-111.835_40.763_7_foot.nc')
> nc
# File 2015070211_-111.835_40.763_7_foot.nc (NC_FORMAT_CLASSIC):
# 1 variables (excluding dimension variables):
# float Footprint[longitude_center,latitude_center,time]
# units: ppm (umol-1 m2 s)
# _FillValue: -1
# 3 dimensions:
# longitude_center Size:390
# units: degrees_east
# long_name: longitude_center
# position: cell_center
# latitude_center Size:280
# units: degrees_north
# long_name: latitude_center
# position: cell_center
# time Size:5
# units: seconds since 1970-01-01
# long_name: time
# timezone: UTC
# 7 global attributes:
# crs: +proj=longlat +ellpsWGS84
# crs_format: PROJ.4
# Conventions: CF-1.4
# Title: STILT Footprint Output
# Compatibility: raster::raster() and raster::brick()
# Documentation: benfasoli.github.io/stilt
# Author: Ben Fasoli
To get your feet wet, try one of the tutorials such as simulating carbon dioxide for the William Browning Building at the University of Utah.