Feb. 4th, 2025 by John C. Lin (John.Lin@utah.edu) Dept. of Atmospheric Sciences University of Utah This folder contains output from the Stochastic Time-Inverted Lagrangian Transport (STILT) model [Lin et al., 2003] for analyses of atmospheric transport of air parcels arriving at the Twin Otter aircraft during the Utah Summer Ozone Study (USOS). STILT is a Lagrangian particle dispersion model that simulates atmospheric transport with ensembles of stochastic air parcels represented by computational particles. In most applications STILT simulates air parcels backward in time, from a receptor of interest, to map out the source region affecting the receptor. In these simulations, STILT was driven by NOAA-NCEP's High Resolution Rapid Refresh (HRRR) meteorological fields, which are gridded at 3-km spacing over CONUS and converted to NOAA-ARL binary format to driven STILT, which was originally based upon NOAA-ARL's HYSPLIT model. In these simulations, STILT was driven by NOAA-NCEP's High Resolution Rapid Refresh (HRRR) meteorological fields, which are gridded at 3-km spacing over CONUS and converted to NOAA-ARL binary format to STILT, which was originally based upon NOAA-ARL's HYSPLIT model. The version of STILT used is the one merged with HYSPLIT [Loughner et al., 2021]. The HRRR-STILT model carried out runs with 200 particles starting from each receptor and transported backward in time for 24 hours. The receptors were selected along the flight track of the NOAA Twin Otter aircraft and selected to pick out instances where significant shifts in horizontal coordinates (lat/lon) OR elevation were detected. A new receptor is selected to make sure that the following thresholds are not exceeded: a) horizontal distance of 0.1-deg b) vertical altitude change of 250 m c) time elapsed of 180 sec ==================== ORGANIZATION OF OUTPUT ===================== The output can be downloaded as gzipped tarballs for each flight day, in YYYYMMDD.tar.gz format. For instance, "20240801.tar.gz" contains all of the simulation output for the flight receptors on Aug. 1st, 2024. The STILT output is separated into different folders under the "by-id" folder: The output arranged as receptor folders, as such: YYYYMMDDHHmm_LON_LAT_AGL (e.g., "202407192234_-111.94539093_40.61921595_849"), in which YYYYMMDDHHmm is the timestamp of the receptor [UTC], and LON/LAT/AGL indicate the receptor location. These includes all of the output for each run for each receptor -------------- particles (i.e., STILT trajectories) -------- Trajectories are stored in the *_traj.rds file stored in each of the by-id/ subfolders These are the trajectories traced out by the STILT air parcel ensemble for each simulated receptor. The filenames follow the convention of YYYYMMDDHHmm_LON_LAT_AGL_traj.rds, in which YYYYMMDDHHmm is the timestamp of the receptor [UTC], and LON/LAT/AGL indicate the receptor location. The trajectory files are in a binary format in R (www.r-project.org) called "RDS" and can be read in R using the "readRDS" command. It can also be converted to other formats (e.g., CSV) in R. Here is some sample R code to do this: > dat <- readRDS("202407192234_-111.94539093_40.61921595_849_traj.rds") > write.csv(x=dat,file="output.csv") #output flight data into CSV file called "output.csv" Here's a look at the contents of the trajectory file from within R: > names(dat) [1] "file" "receptor" "particle" "params" > dat$file [1] "/uufs/chpc.utah.edu/common/home/u0791084/PROJECTS/USOS/USOS_Aircraft/STILT_USOS_Aircraft/out/by-id/202407192234_-111.94539093_40.61921595_849/202407192234_-111.94539093_40.61921595_849_traj.rds" > dat$receptor $run_time [1] "2024-07-19 22:34:03 UTC" $lati [1] 40.61922 $long [1] -111.9454 $zagl [1] 849 > dat$particle[1:2,] time indx long lati zagl foot mlht dens samt sigw 1 -1 1 -111.9459 40.6195 906.8264 0.001363213 3681.16 0.90859 1 2.108 2 -1 2 -111.9484 40.6205 977.0080 0.001370969 3681.16 0.90345 1 2.108 tlgr foot_no_hnf_dilution 1 181.5557 0.0010432 2 181.5557 0.0010432 The different columns in the trajectory data are, as follows: 'time': time since start of simulation [min] 'indx': particle index 'long': particle longitude position [degrees] 'lati': particle latitude position [degrees] 'zagl': particle vertical position [m above-ground-level] 'foot': sensitivity of mixing ratio to surface fluxes [ppm/(micro-moles/m2/s)] 'mlht': mixed-layer height [m] 'dens': air density [kg/m3] 'samt': amount of time that particle spends below 'VEGHT' [min] 'sigw': standard deviation of vertical velocity [m/s] 'tlgr': Lagrangian decorrelation timescale [s] 'foot_no_hnf_dilution': footprint without hyper-near-field correction [ppm/(micro-moles/m2/s)]; see Fasoli et al. [2018] -------------- footprints -------- Footprints are stored in the *_foot.nc file stored in each of the by-id/ subfolders Symlinks to the relevant file stored in one of the by-id/ subfolders "Footprints" indicate the source region of the target receptor--i.e., the upwind regions that atmospheric concentrations at the receptor are sensitive to. The footprints have units of [ppm/(micromole/m2/s)]--i.e., [mixing ratio/(surface flux)]: it indicates the change in mixing ratio at a receptor, given an unit *surface* flux of 1micromole/m2/s from a particular source region. The footprints are derived directly from the trajectory output above. More information can be found in Lin et al. [2003] The filenames follow the convention of YYYYMMDDHHmm_LON_LAT_AGL_foot.nc (e.g., "202407192234_-111.94539093_40.61921595_849_foot.nc"), in which YYYYMMDDHH is the timestamp of the receptor [UTC], and LON/LAT/AGL indicate the receptor location. The footprint files are in netCDF format. The footprint consists of a 3-dimensional array--lat/lon 2D grids at different hours backward in time from the receptor. The footprints are at 0.01-deg x 0.01-deg spacing. The lat/lon coordinates point to the CENTERS of each gridcell. To focus on the Utah domain, the footprint is only gridded in a domain set to: Longitude: -114.5 to -108.6-deg Latitude: 39 to 42-deg ========================= REFERENCES: ========================= Fasoli, B., J.C. Lin, D.R. Bowling, L. Mitchell, and D. Mendoza: Simulating atmospheric tracer concentrations for spatially distributed receptors: updates to the Stochastic Time-Inverted Lagrangian Transport model’s R interface (STILT-R version 2), Geoscientific Model Development, 11, 2813-2824, https://doi.org/10.5194/gmd-11-2813-2018, 2018. Lin, J.C., C. Gerbig, S.C. Wofsy, et al., A near-field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time-Inverted Lagrangian Transport (STILT) model, J. Geophy. Res., 108(D16), 4493, doi:10.1029/2002JD003161, 2003. Loughner, C., B. Fasoli, A.F. Stein, and J.C. Lin: Incorporating features from the Stochastic Time-Inverted Lagrangian Transport (STILT) model into the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model: a unified dispersion model for time-forward and time-reversed applications, Journal of Applied Meteorology and Climatology, 60, 799-810, https://doi.org/10.1175/JAMC-D-20-0158.1, 2021.