run_model()

The main entry point for forward radiative transfer simulations.

from biosnicar import run_model

Signature

run_model(input_file="default", **overrides) -> Outputs

Parameters

Parameter	Type	Default	Description
`input_file`	str	`"default"`	Path to YAML config file. `"default"` uses bundled `inputs.yaml`.
`solzen`	int	50	Solar zenith angle (1–89 degrees)
`direct`	int	1	Sky condition: 1 = clear, 0 = overcast
`incoming`	int	0	Irradiance profile index (0–6)
`rds`	int or list	500	Grain/bubble effective radius (µm)
`rho`	int or list	400	Bulk ice density (kg/m³)
`dz`	list[float]	[0.02, 0.146]	Layer thicknesses (m)
`lwc`	float or list	0.0	Liquid water content
`layer_type`	int or list	0	0 = granular, 1 = solid ice + Fresnel
`shp`	int or list	0	Grain shape (0–4)
`grain_ar`	float or list	1.0	Grain aspect ratio
`hex_side`	float or list	50	Hexagonal prism side length (µm)
`hex_length`	float or list	100	Hexagonal prism length (µm)
`black_carbon`	float or list	0	Black carbon concentration (ppb)
`snow_algae`	float or list	0	Snow algae concentration (cells/mL)
`glacier_algae`	float or list	0	Glacier algae concentration (cells/mL)
`dust`	float or list	0	Mineral dust concentration (ppb)

Override behaviour

Scalar ice parameters are broadcast to all layers
Scalar impurity concentrations are applied to the first layer only
List overrides that change the layer count trigger automatic resizing of all per-layer attributes

Returns

An Outputs object.

Examples

# Default configuration
outputs = run_model()
 
# Override parameters
outputs = run_model(solzen=50, rds=1000, black_carbon=500)
 
# Multi-layer
outputs = run_model(
    dz=[0.02, 0.05, 0.05, 0.05, 0.83],
    rds=[800, 900, 1000, 1100, 1200],
    glacier_algae=[40000, 10000, 0, 0, 0],
)
 
# Chain to satellite bands
s2 = run_model(solzen=50, rds=1000).to_platform("sentinel2")
 
# Custom input file
outputs = run_model(input_file="path/to/config.yaml")

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