What problem does it solve? Running the Noah-MP land surface model offline requires deep operational knowledge: exact forcing units (Kelvin, mm/s, Pa, specific humidity), HRLDAS setup NetCDF structure, namelist physics options, and recovery from silent failures. This Skill packages that expertise so an AI agent can prepare inputs, execute the real HRLDAS binary, parse outputs, and calibrate parameters without hand-coded approximations. ## Core Features & Use Cases - End-to-end HRLDAS pipeline: Six validated Python tools cover setup-file generation, soil conversion (HWSD/SoilGrids), forcing conversion (CMFD/MSWX/ERA5/FLUXNET to LDASIN), model execution, and LDASOUT parsing. - Diagnostic recovery: 29 symptom-diagnosis-remedy triplets in diagnostics/triplets.yaml map known failure modes (unit errors, rank mismatches, irrigation misconfiguration) to validated fixes. - Site-level calibration: A commissioning-tested calibration contract (calibration.yaml) injects 14 parameters into NoahmpTable.TBL and scores latent heat against FLUXNET observations with NSE, including blocked temporal holdout. - Use Case: Drive a FLUXNET tower site run (e.g., US-Ne1 irrigated maize) from raw tower meteorology to a validated evapotranspiration time series, with crop and irrigation options correctly wired. ## Quick Start Run python preflight_check.py in this directory, then ask the agent to convert FLUXNET forcing for your site and execute the Noah-MP HRLDAS run with the provided tools.