GEOSChem Integration for vSmartMOM

This document describes the new clean API for integrating GEOSChem NetCDF output files with vSmartMOM radiative transfer calculations.

Overview

vSmartMOM now supports reading atmospheric profiles and trace gas concentrations directly from GEOSChem NetCDF4 output files using a type-safe, extensible IOSource system. This eliminates the need for manual data extraction and conversion.

Architecture

The implementation follows Julia best practices with multiple dispatch:

src/IO/
├── Sources.jl              # IOSource type hierarchy
├── NetCDF/
│   ├── NetCDF.jl          # NetCDF utilities module
│   └── GeosChem.jl        # GEOSChem-specific reader
└── ...

Type Hierarchy

IOSource (from Formats.jl)
├── FileSource              # YAML/TOML files
├── DictSource              # Direct Dict
└── NetCDFSource            # Abstract for NetCDF data
    ├── GeosChemSource      # GEOSChem cubed-sphere
    └── NetCDFGridSource    # Generic gridded data

Usage

Quick Start

using vSmartMOM

# Create a data source
src = GeosChemSource("GEOSChem.Custom.20190101_0000z.nc4", 10, 20, 1)

# Option 1: Use with default RT parameters
params = read_parameters(src)
model = model_from_parameters(params)
R = rt_run(model)

# Option 2: Get Dict and customize
config = geoschem_to_dict(src)
config["radiative_transfer"]["spec_bands"] = [(1e7/777):0.015:(1e7/757)]
params = read_parameters(config)

Complete Example

using vSmartMOM

# Specify GEOSChem grid location
file = "GEOSChem.Custom.20190101_0000z.nc4"
idx, idy, idf = 10, 20, 1  # X, Y indices and cubed-sphere face

# Create source
src = GeosChemSource(file, idx, idy, idf)

# Read and convert to configuration Dict
config = geoschem_to_dict(src)
# This automatically extracts:
#   - Temperature profile (T)
#   - Pressure levels (p)
#   - Specific humidity (q)
#   - Trace gas VMRs (CO2, CO, CH4, N2O, C2H6, H2O)
#   - Geographic location (lat, lon)

# Add radiative transfer parameters
config["radiative_transfer"] = Dict(
    "spec_bands" => [(1e7/777):0.015:(1e7/757)],
    "surface" => ["LambertianSurfaceScalar(0.15)"],
    "quadrature_type" => "GaussLegQuad()",
    "polarization_type" => "Stokes_I()",
    "max_m" => 3,
    "Δ_angle" => 2.0,
    "l_trunc" => 20,
    "depol" => 0.0,
    "float_type" => "Float64",
    "architecture" => "default_architecture"
)

config["geometry"] = Dict(
    "sza" => 60.0,
    "vza" => [0.0, 30.0, 60.0],
    "vaz" => [0.0, 0.0, 0.0],
    "obs_alt" => 1000.0
)

# Convert to parameters and run
params = read_parameters(config)
model = model_from_parameters(params)
R = rt_run(model)

Convenience Function (See examples/geoschem_integration.jl)

include("examples/geoschem_integration.jl")

# Simple usage with defaults
R = run_rt_with_geoschem_v2("GEOSChem.Custom.20190101_0000z.nc4", 10, 20, 1)

# With custom parameters
R = run_rt_with_geoschem_v2(
    "GEOSChem.Custom.20190101_0000z.nc4", 
    10, 20, 1;
    spec_bands = [(1e7/777):0.015:(1e7/757)],
    sza = 45.0,
    vza = [0.0, 30.0, 60.0],
    architecture = GPU()
)

Library Reference

Types

GeosChemSource

GeosChemSource(path::String, idx::Int, idy::Int, idf::Int)

Represents a GEOSChem data source at a specific cubed-sphere grid location.

Arguments:

• path: Path to GEOSChem NetCDF4 file

• idx: X-dimension index (must be > 0)

• idy: Y-dimension index (must be > 0)

• idf: Cubed-sphere face (1-6)

Validation:

• File existence is checked

• Indices must be positive

• Face must be 1-6

Functions

geoschem_to_dict

geoschem_to_dict(src::GeosChemSource) -> Dict

Read GEOSChem file and convert to vSmartMOM configuration Dict.

Returns: Dict with keys:

• "atmospheric_profile": T, p, q, vmr, profile_reduction

• "absorption": molecules, vmr, broadening, CEF, wing_cutoff

• "_metadata": source_file, latitude, longitude, time, etc.

read_geoschem_profile

read_geoschem_profile(file::String, idx::Int, idy::Int, idf::Int) -> Dict

Convenience function that creates a GeosChemSource and calls geoschem_to_dict.

Data Conventions

GEOSChem File Structure

GEOSChem output files have dimensions: (Xdim, Ydim, nf, lev, time)

• nf: Cubed-sphere face (1-6)

• lev: Vertical levels (typically 72)

  • Level 1 = Bottom of Atmosphere (BOA)

  • Level 72 = Top of Atmosphere (TOA)

• time: Time dimension

Variable Mapping

GEOSChem Variable	vSmartMOM Field	Units	Notes
Met_T	T	K	Temperature profile
Met_PS2WET	Surface pressure	hPa	Used to compute pressure grid
Met_DELP	Layer thickness	hPa	Used to compute pressure grid
Met_SPHU	q	g/kg	Specific humidity
SpeciesConcVV_*	vmr[molecule]	mol/mol	Trace gas VMRs
lats, lons	Metadata	degrees	Geographic location

Automatic Processing

1. Vertical Flipping: GEOSChem uses BOA→TOA indexing; vSmartMOM uses TOA→BOA. Data is automatically reversed.

2. Pressure Grid: Computed from surface pressure (Met_PS2WET) and layer thicknesses (Met_DELP).

3. Trace Gases: Automatically extracts available molecules:

• N2O, CH4, C2H6, CO2, CO, H2O

• O2 is added to molecule list but uses vSmartMOM defaults (not in GEOSChem output)

4. Units: All conversions handled automatically to match vSmartMOM conventions.

Extensibility

The IOSource pattern makes it easy to add support for other formats:

# Example: Add WRF support
struct WRFSource <: NetCDFSource
    path::String
    lat_idx::Int
    lon_idx::Int
    time_idx::Int
end

function geoschem_to_dict(src::WRFSource)
    # WRF-specific reading logic
    # ...
    return config_dict
end

Comparison with Old Approach

Old (Hacky) Approach

# Manual struct definition
struct GeosChemData
    data::Dict{String, Any}
    units::Dict{String, String}
end

# Manual reading and conversion
function read_gchp(file, idx, idy, idf)
    # ... lots of manual NetCDF reading ...
    return GeosChemData(data, units)
end

# Manual RT setup
geos = read_gchp(file, idx, idy, idf)
parameters = default_parameters()
parameters.T = geos.data["Met_T"]
parameters.p = geos.data["pressure"]
# ... manual field assignment ...

New (Clean) Approach

# Type-safe source specification
src = GeosChemSource(file, idx, idy, idf)

# Automatic conversion
params = read_parameters(src)

# Or customize
config = geoschem_to_dict(src)
# ... modify config ...
params = read_parameters(config)

Benefits

• Type Safety: IOSource types catch errors at compile time

• Extensibility: Easy to add WRF, GCHP, CLM, etc.

• Clean Separation: I/O logic separated from RT logic

• No Duplication: Reuses existing parameter system

• Idiomatic Julia: Multiple dispatch, clear abstractions

• Backwards Compatible: Old code still works

• Well Documented: Types, functions, examples

• Testable: Easy to mock sources for testing

Dependencies

• NCDatasets.jl: NetCDF file reading (already in Project.toml)

Testing

See examples/geoschem_integration.jl for comprehensive examples and test patterns.

Future Enhancements

Potential additions:

• [ ] WRF output support (WRFSource)

• [ ] GCHP variants (GCHPSource)

• [ ] CLM/CESM data (CLMSource)

• [ ] Caching for repeated reads

• [ ] Lazy loading for large files

• [ ] Interpolation to arbitrary lat/lon

• [ ] Time series support