Run2 glm config

[COLM'26] SkillLearnBench is the first benchmark for evaluating continual learning methods that automatically generate agent skills.

Install

npx -y skills add cxcscmu/SkillLearnBench --skill run2_glm-config

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Advanced GLM configuration and calibration strategies for vertical temperature profiles.

1.5 KB, as published. Nobody here has run it

The General Lake Model (GLM) performance depends on several key parameters that govern energy balance and mixing.

Kw (Light Extinction): Controls how deep light penetrates. Higher Kw keeps deep layers cooler and surface layers warmer. Range: [0.1, 0.5].
wind_factor: Scales input wind speed. Affects surface mixing and latent/sensible heat fluxes. Range: [0.7, 1.3].
lw_factor: Scales incoming longwave radiation. Affects the overall heat budget. Range: [0.7, 1.3].
ch (Sensible Heat Transfer): Bulk coefficient for sensible heat flux. Affects surface temperature. Range: [0.0005, 0.002].
coef_mix_hyp: Controls mixing below the thermocline. Lower values reduce heating of the hypolimnion. Range: [0.3, 0.7].

Baseline Run: Start with default parameters.
Energy Balance: Adjust lw_factor and ch to align the overall temperature magnitude.
Stratification: Adjust Kw and wind_factor to match the thermocline depth and surface temperatures.
Deep Mixing: Adjust coef_mix_hyp to match deep water temperatures during summer.

Example sed command for precise replacement:

sed -i 's/\bKw\s*=\s*[0-9.e+-]*/Kw = 0.4/' glm3.nml

Or use Python regex for more complex cases.