Quantifying Uncertainty in Climate Model Evaluation Using Bootstrap RMSE скачать в хорошем качестве

Quantifying Uncertainty in Climate Model Evaluation Using Bootstrap RMSE

In climate and environmental modeling, uncertainty is a constant—yet too often, it's overlooked in performance evaluation. I recently built a Python-based workflow to assess General Circulation Models (GCMs) using bootstrap-based RMSE. Rather than relying on a single RMSE value, this method generates a full distribution through resampling, allowing us to calculate 95% confidence intervals and rank models based on robustness, not just accuracy. Key highlights: Clean separation of observed vs. simulated data Bootstrapped RMSE distributions for each model Median + confidence intervals (2.5%, 97.5%)