matko-iv/vrijeme
Corrected weather model with higher accuracy using 11 model-ensemble and XGBoost.
What's novel
Corrected weather model with higher accuracy using 11 model-ensemble and XGBoost.
Code Analysis
0 files read · 5 roundsThis project implements a sophisticated weather forecasting system for Budva, Montenegro, that blends NWP model outputs (GFS, ECMWF) with XGBoost correction models trained on historical observations and previous forecast runs to generate highly accurate 48-hour predictions.
Strengths
The project demonstrates high modularity and architectural clarity with a clear separation of concerns between data ingestion, feature engineering, model inference, and analysis. The innovation lies in the advanced blending technique using XGBoost to correct NWP biases dynamically, leveraging both current observations and historical context, which represents a significant step beyond standard bias correction methods.
Weaknesses
The lack of visible test files suggests limited automated testing coverage, which is a common weakness in data science projects but can impact reliability. Additionally, without direct access to the source code, specific implementation details regarding error handling and variable naming conventions could not be fully verified beyond high-level assumptions.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
804
Contributors
2
Files
85
Active weeks
5
Repository
Language
Python
Stars
2
Forks
0
License
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