Sept. 13th, 2024 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 different sites in the Salt Lake area. These sites are associated with research activities and measurements during summer 2024, as part of multiple field campaigns--e.g., USOS, SLC-SOS, MEEPC. 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 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 at MOST 24 hours--until the air parcels exit the specified domain for each city. To maximize computational efficiency and minimize storage costs, the STILT trajectories and footprints end once they leave the city's domain. ======================= SIMULATED DATES/TIMES ========================= Simulation Months/Years: July & August 2024, hourly time resolution NOTE: missing times generally stem from gaps in the HRRR meteorology ========================== SITES (receptors)=========================== The output for each site in the Salt Lake area is found in its own folder, or its *.tar.gz file to facilitate downloading. The sites are as follows: -WBB (William Browning Building on Univ. of Utah campus): lat = 40.7634; lon = -111.848; zagl = 36 m -MtnMet (Mountain Met site on Univ. of Utah campus): lat = 40.7667; lon = -111.8284; zagl = 4 m -Syracuse (entrance to Antelope Island State Park): lat = 41.089; lon = -112.119; zagl = 4 m -Hawthorne (Hawthorne Elementary School; key long-term UDAQ site): lat = 40.734477; lon = -111.872172; zagl = 4 m -Copperview (Copperview Elementary School; UDAQ site): lat = 40.59794; lon = -111.894; zagl = 4 m -Herriman (Herriman UDAQ site by Fort Herriman Middle School): lat = 40.49639; lon = -112.036; zagl = 4 m -Rosepark (UDAQ & CO2 site at Salt Lake Center for Science Education): lat = 40.79553; lon = -111.931; zagl = 4 m -InlandPort (UDAQ site at Utah State Prison): lat = 40.80791; lon = -112.088; zagl = 4 m -LakePark (UDAQ site): lat = 40.7099; lon = -112.009; zagl = 4 m -TechCenter (UDEQ technical support center; near UDAQ headquarters): lat = 40.77715; lon = -111.946; zagl = 11 m Additional sites where retroreflectors installed by NIST for dual-comb long-path instrument -BryantMiddle (Bryant Middle School): lat = 40.76815; lon = -111.86923; zagl = 15 m -TheShop (The Shop Coworking Space at 350 E 400 S): lat = 40.7605; lon = -111.88098; zagl = 15 m -EastHigh (East High School): lat = 40.75042; lon = -111.85516; zagl = 10 m -Westminster (Westminster College): lat = 40.73258; lon = -111.85488; zagl = 15 m -MtWire (Mt. Wire): lat = 40.7706; lon = -111.79834; zagl = 5 m -UintahElementary (Uintah Elementary School): lat = 40.74201; lon = -111.846; zagl = 10 m ==================== ORGANIZATION OF OUTPUT ===================== The STILT output is separated into different folders: ------------------ 1) by-id --------------- Output arranged as receptor rolders: YYYYMMDDHH_LON_LAT_AGL (e.g., "2017010111_-111.84755_40.76623_33"), in which YYYYMMDDHH 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 -------------- 2) particles (i.e., STILT trajectories) -------- Symlinks to the relevant file stored in one 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 YYYYMMDDHH_LON_LAT_AGL_traj.rds, in which YYYYMMDDHH 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("202002041400_-86.1436_39.7181_40_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$file [1] "/uufs/chpc.utah.edu/common/home/lin-group15/jcl/COVID_urbanGHG/STILT_COVID_UrbanGHG/out/INDY/by-id/202002041400_-86.1436_39.7181_40/202002041400_-86.1436_39.7181_40_traj.rds" > dat$receptor $run_time [1] "2020-02-04 14:00:00 UTC" $lati [1] 39.7181 $long [1] -86.1436 $zagl [1] 40 > dat$particle[1:2,] time indx long lati zagl foot mlht dens samt sigw 1 -1 1 -86.1455 39.7209 52.4091 0.01778287 223.51 1.17631 1 0.325 2 -1 2 -86.1455 39.7204 32.9085 0.03114299 223.51 1.17797 1 0.246 tlgr foot_no_hnf_dilution 1 85.7213 0.0132079 2 7.5186 0.0132079 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] -------------- 3) footprints -------- 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 YYYYMMDDHH_LON_LAT_AGL_foot.nc (e.g., "2017010111_-111.84755_40.76623_33_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. ========================= 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.