skills.rest
KaiserWhoLearns
KaiserWhoLearnsCOMMUNITY
Explore all skills by this author →
Website𝕏@KaiserWhoLearnsGitHub

glm-calibration

Calibrate GLM parameters to minimize RMSE between simulated and observed water temperatures.

Updated Jan 15, 2026
Search Tags:#python#optimization#calibration#glm#hydrology#rmse#lake-temperature
One-click install
npx skills add https://github.com/KaiserWhoLearns/skillsbench --skill glm-calibration
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: glm-calibration
Source: https://github.com/KaiserWhoLearns/skillsbench/tree/main/tasks/glm-lake-mendota/environment/skills/glm-calibration
Command: npx skills add https://github.com/KaiserWhoLearns/skillsbench --skill glm-calibration

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Calibrate GLM parameters for water temperature simulations to minimize RMSE between modeled and observed temperatures, enabling more accurate hydrological predictions.

Core Features & Use Cases

  • Parameter tuning for Kw, coef_mix_hyp, wind_factor, lw_factor, and ch to improve model fidelity.
  • Supports both automated optimization using Python (SciPy minimize) and manual calibration per best practices.
  • Use cases include lake temperature forecasting, scenario analysis, and model validation against observational data.

Quick Start

Run the calibration workflow with the default parameters to begin optimizing RMSE against observed data.

Frequently Asked Questions about glm-calibration

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I calibrate GLM parameters to minimize RMSE for lake temperature simulations?▼

You can calibrate GLM parameters by modifying nml files directly or using Python with SciPy minimize. This automates the tuning of variables like wind_factor and Kw to reduce RMSE between simulated and observed lake temperatures.

What is the best way to tune GLM water temperature parameters like Kw and wind_factor?▼

Calibrating GLM for lake temperature forecasting requires tuning Kw, coef_mix_hyp, wind_factor, lw_factor, and ch. This optimizes RMSE against observational data, enabling accurate scenario analysis and hydrological model validation.

Can I use Python and SciPy to automate GLM calibration and modify nml files?▼

Yes, Python and SciPy integrate with GLM calibration by modifying nml files and computing RMSE. This environment allows automated parameter optimization to improve model fidelity for lake temperature forecasting.

When do I need to calibrate GLM parameters for hydrological models?▼

Yes, manual calibration strategies are supported alongside automated Python optimization. You can manually tune GLM parameters like Kw and wind_factor in nml files to improve model fidelity and reduce RMSE for lake temperature simulations.

Does GLM calibration support manual parameter tuning or is automated optimization required?▼

GLM calibration optimizes lake temperature simulations by tuning Kw, coef_mix_hyp, wind_factor, lw_factor, and ch. This minimizes RMSE between modeled and observed water temperatures, enabling more accurate hydrological predictions.

Related Skills

Explore More Data & Analytics Skills→
GeneralReasoningGeneralReasoning
4

glm-basics

Simulate lake temperature dynamics with General Lake Model using namelist configurations.

Official
Basic
GeneralReasoningGeneralReasoning
4

glm-calibration

Tune GLM physical parameters to minimize RMSE against observed water temperatures.

Official
Intermediate
GeneralReasoningGeneralReasoning
4

scipy-curve-fit

Fit nonlinear models to experimental time-series data using scipy.optimize.curve_fit.

Official
Intermediate
BBufBBuf
721

sglang-glm-vlm-ocr-optimization

Aggregate PR-backed evidence and diff history to guide GLM VLM/OCR optimization.

Community
Advanced