Coming From X-STILT#

PYSTILT has the main pieces of X-STILT’s column and satellite workflow: column and slant receptors, sounding selection, averaging kernels, and pressure weighting. In X-STILT you configure one large script. In PYSTILT these pieces are Python functions and objects that you combine in your own script. PYSTILT does not try to copy every X-STILT feature. The table shows the ones that have an equivalent.

X-STILT concept	X-STILT API / file	PYSTILT equivalent
Column receptor (`minagl` / `maxagl`, `agl` levels)	`get.recp.sensorv2.r`	`stilt.ColumnReceptor` from the sounding’s coordinates
Slant column (`run_slant`)	`get.recp.sensorv2.r`	`stilt.observations.slant_points()` + `stilt.Receptor.from_points()`
Sounding selection (near-field + background)	`sel.obs4recpv2`	`stilt.observations.select_observations_spatial()`
Jittered receptors in a pixel (`jitterTF`)	`jitter.obs4recp.r`	`stilt.observations.jitter_points()`
Overpass grouping	`get_timestr` / overpass search	`stilt.observations.group_by_overpass()`, then `df.groupby("overpass")`
Vertical weighting (AK × PWF)	`wgt.trajec.foot*.r`	`averaging_kernel` and `pressure_weighting` transforms (Particle Weighting (Transforms))
Per-sounding averaging kernels (`get.wgt.funcv3`)	`wgt.trajec.foot*.r`	`averaging_kernel` with `table:` (`stilt.transforms.averaging_kernel_table()`)
First-order chemistry	`chem_lifetime`	`first_order_lifetime` transform
Column footprint outputs	X-STILT column products	standard PYSTILT footprints from column or slant receptors
Product readers (OCO-2/3, TROPOMI, TCCON)	`column_obs/*`	`read_oco2()`, `read_tropomi_ch4()`, `read_tccon()` (Reading Retrieval Products). For other products, write a reader that returns the same table.
Transport error on the modelled column (`cal.trajfoot.stat`, `cal.trans.err`)	`error_functions/`	`stilt.observations.transport_error()` on the particles of an unperturbed variant and a wind-error variant (Transport Error). X-STILT’s separate `outerr_` tree becomes one more variant.
Modelled enhancement from an inventory (`ff.trajfoot`)	`error_functions/`, `run.xco2ff.sim`	`stilt.Footprint.enhancement()`, `stilt.flux.particle_enhancement()`
Wind error statistics from radiosondes and surface stations	`get.uverr`, `get.siguverr`, `cal.wind.err`, `grab.raob`	`variogram()` and `fit_variogram()` estimate all four error settings. X-STILT estimates only `siguverr` and fixes the others. The met is sampled at the observations with `arlmet.sample_points` instead of one HYSPLIT run per sonde, and sondes come from IGRA2 through siphon. See Wind Error Statistics.
Mixing-height scaling for the vertical transport error (`run_ver_err`, `zisf`)	`get.zierr`	`ziscale`, one variant per factor in the same project (see the mixed-layer height section of Transport Error). X-STILT uses one constant factor and leaves the random `sigzierr` unset. PYSTILT supports both.
Background from trajectory endpoints (`endpts.trajfoot`, CarbonTracker)	`background/`	`stilt.observations.background()` with any xarray field (Background)
Emission-error propagation (`cal.emiss.err`, footprint × inventory spread)	`error_functions/`	the same product as the enhancement, `foot.enhancement(sigma)`. With a spatial correlation, use fips’s `prior_obs_error` (see the emission error section of Transport Error).
Satellite-derived plume background (`compute_bg`, forward trajectories)	`background/`	`stilt.observations.plume_polygon()` and `stilt.observations.plume_background()` on forward runs (Plume Background)

Moving a workflow over#

Write a reader that turns your product into a DataFrame with one row per sounding (see Adding your own instrument in Satellite And Column Observations).
Group the soundings by overpass and select the ones to run with the helpers above.
Build one receptor per row. For slant columns, use slant_points.
Build the averaging-kernel table with averaging_kernel_table and save it in the project. Then list averaging_kernel (with table:) and pressure_weighting under transforms in config.yaml.