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X-STILT is an atmospheric transport model that deals with vertically integrated column concentrations of various trace gases (Wu et al., 2018, 2023). The model code was built upon the Stochastic Time-Inverted Lagrangian Transport (STILT) model (Lin et al., 2003) and its latest version 2 (Fasoli et al., 2018).
The model framework can now work with OCO-2&3 XCO2 and TROPOMI column CO (Wu et al., ACP), CH4 (Li et al., and Tribby et al., in prep), and NO2 (Wu et al., EGUsphere: STILT-NOx) and is testing for TCCON and EM27. 😎
This GitHub repo includes built-in scripts/functions for
run_xstilt.rfor either initial X-STILT or updated STILT-NOx);
Model developments are ongoing and contributions are welcomed and appreciated. Please contact Dien ([email protected]) if you are interested in other column sensors/species or have any questions.
git clone --recursive https://github.com/uataq/X-STILT.git
To automatically install all R packages (requiring R > 3.5.0) and the STILTv2 model with the fortran program for HYSPLITv5, start with the first few lines of
For SATELLITE-dependent column simulation -
For IDEAL column simulation WITHOUT satellite dependence -
time. An example is provided as in
receptor_demo.csv. Check out instructions in recent commit.
(OPTIONAL) For calculating column enhancements [in ppm] -
(OPTIONAL) For performing transport error analyses -
(OPTIONAL) For performing emission error analyses -
Column Footprints [ppm / (umol m-2 s-1)] are the source-receptor sensivities or essentially the Jacobian Matrics between concentration (enhancements) and fluxes (for a given source/sink). Users can start with
run_xstilt.r for model and parameter initializations.
Select a satellite overpass. Either by a manual insertion Or an automatic search. By default, STEP 1 searches for all overpasses that have soundings falling into a spatial domain (e.g., 2 deg x 2 deg) as well as a near-field domain (i.e., 0.6 deg x 0.6 deg).
Select the kind of simulation one would like to conduct in STEP 2. One needs to modify logical flags which included:
run_trajec = T or run_foot = T: Backward trajectories + vertically weighted column footprints;
run_hor_err = T: Horizontal transport error analysis (one needs to calculate wind error statistics first, see below);
run_wind_err = T: Modeled wind error estimates against radiosonde data (one need to download them from NOAA webpage first);
run_ver_err = T: Vertical transport error analysis (via scaling mixed layer height up and down with scaling factors stated in STEP 4);
run_sim = T: Simulate FFCO2 XCO2 enhancements (requires footprint), remember to turn off
run_emiss_err = T: Prior emission error analysis (requires footprint).
Modify parameters for placing column receptors (e.g., max height and vertical spacing between two vertical levels in meters, # of particles per level, receptor locations). By default, X-STILT chooses more receptors within NEAR-FIELD area, see STEP 3.
Modify horizontal resolution and spatial extent of footprint, STEP 5.
Set up SLURM parallel computing, STEP 6.
Start with main script of
compute_bg.r. M3 is the overpass specific background [ppm] by releasing air parcels in a forward fashion from a city and determining urban plume and background region using 2D kernel density. Please refer to details described in Sect. 2.3 in Wu et al. (2018).
run_trajec = T: calculate forward-time trajectories from a small box around the site (see parameter
box.lenand other parameters in STEP 2) no matter if those trajectories had been calculated previously. In this mode, air parcels are released continuously for every
dtime.sephours (see parameter
run_trajec = T & run_for_err = T: add a wind error component (controlled by parameter
siguverr) while calculating forward-time trajectories;
run_wind_err = T: calculate modeled wind errors against radiosonde data (one need to download them first);
run_bg = T: calculate the background values based on OCO-2/3 or TROPOMI data (see parameters in STEP 4); the inner functions will generate plots of plumes and observed enhancements by assuming background region to the north/south/east/west outside the plume. The final estimated background values will be stored in
writeTF = T.
Instead of using model ensembles for estimating errors, X-STILT propagates real-world random u-v- wind errors (with correlation length scale and timescale) into errors in XCO2. The fundamental approach is proposed and documented in Lin and Gerbig, 2005, which is now extended to column CO2. We calculate and propagate XCO2 errors from release levels, to receptor locations, and finally to overpasses.
Briefly speaking, users need to first generate another set of trajectories with wind error statistics (i.e., blue dots in Fig. S4) by turning on
run_hor_err. Once trajectories with wind errors have been generated, one need to turn on
run_sim to let X-STILT calculate the errors in XCO2 based on two sets of trajectories (with/without wind errors). Hard to explain all the steps here, but feel free to contact Dien if one is interested in performing such error estimates. Please also refer to details described in Sect. 2.6 in Wu et al. (2018).
As discussed in Sect. 4.2 in Wu et al. (2018), we could come up the posterior scaling factor for anthropogenic emissions based on 5 overpasses over Riyadh, via a simple Bayesian Inversion. We treated the entire city as a whole and solve for one scaling factor, given biases in near-field wind direction. So, we did not solve for posterior emissions for every grid cell within a city. Codes are not included in this repo.
Figure - Latitude integrated map of weighted column footprints [umol m-2 s-1] on 12/29/2014 from 70+ selected sounding/receptor over Riyadh.
Figure - Latitude integrated XCO2.ff contribution maps [ppm] on 12/29/2014 from 70+ selected sounding/receptor over Riyadh.
Initial X-STILTv1 paper: Wu, D., Lin, J. C., Fasoli, B., Oda, T., Ye, X., Lauvaux, T., Yang, E. G., and Kort, E. A.: A Lagrangian approach towards extracting signals of urban CO2 emissions from satellite observations of atmospheric column CO2 (XCO2): X-Stochastic Time-Inverted Lagrangian Transport model (“X-STILT v1”), Geosci. Model Dev., 11, 4843-4871, https://doi.org/10.5194/gmd-11-4843-2018, 2018.
STILTv2 paper: Fasoli, B., Lin, J. C., Bowling, D. R., Mitchell, L., and Mendoza, D.: Simulating atmospheric tracer concentrations for spatially distributed receptors: updates to the Stochastic Time-Inverted Lagrangian Transport model's R interface (STILT-R version 2), Geosci. Model Dev., 11, 2813-2824, https://doi.org/10.5194/gmd-11-2813-2018, 2018.
Initial STILTv1 paper: Lin, J.C., Gerbig, C., Wofsy, S.C., Andrews, A.E., Daube, B.C., Davis, K.J. and Grainger, C.A.: A near‐field tool for simulating the upstream influence of atmospheric observations: The Stochastic Time‐Inverted Lagrangian Transport (STILT) model. Journal of Geophysical Research: Atmospheres, 108(D16), https://doi.org/10.1029/2002JD003161. 2003.
STILT error uncertainties: Lin, J. C., and Gerbig, C., Accounting for the effect of transport errors on tracer inversions, Geophys. Res. Lett., 32, L01802, https://doi.org/10.1029/2004GL021127, 2005.
X-STILT for TROPOMI XCO: Wu, D., Liu, J., Wennberg, P. O., Palmer, P. I., Nelson, R. R., Kiel, M., and Eldering, A.: Towards sector-based attribution using intra-city variations in satellite-based emission ratios between CO2 and CO, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-1029, in review, 2022.