Open-Source Toolkit for Arctic d-excess and Sea-Ice Interaction Research: Lagged Correlation, Causal Connectivity, and Assimilated Model Evaluation
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| Autori principali: | , , , , |
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866901983736102912 |
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| author | Heydarizad, Mojtaba Liu, Zhongfang Parker, Mason Mora, Thiago Thakur, Edward |
| author_facet | Heydarizad, Mojtaba Liu, Zhongfang Parker, Mason Mora, Thiago Thakur, Edward |
| contents | <p>This archive contains the analysis and figure-generation code used to study links between Arctic sea-ice variability and precipitation d-excess at multiple stations. The toolkit computes best-lag correlations, extracts top-K “parent” sea-ice regions per station (with sign and lag), visualizes radial edge networks, and compares stations using an <em>F1</em> similarity metric with a ±1-month lag tolerance. It also includes baseline modeling utilities (RFE, LASSO), a simple neural-network + genetic algorithm example, SHAP-based feature importance, an entropy model-averaging (EMA) routine, and a scalar Kalman filter demonstration for data assimilation.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17411473 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Open-Source Toolkit for Arctic d-excess and Sea-Ice Interaction Research: Lagged Correlation, Causal Connectivity, and Assimilated Model Evaluation Heydarizad, Mojtaba Liu, Zhongfang Parker, Mason Mora, Thiago Thakur, Edward Supervised Machine Learning Arctic region Stable isotopes in the atmosphere sea ice <p>This archive contains the analysis and figure-generation code used to study links between Arctic sea-ice variability and precipitation d-excess at multiple stations. The toolkit computes best-lag correlations, extracts top-K “parent” sea-ice regions per station (with sign and lag), visualizes radial edge networks, and compares stations using an <em>F1</em> similarity metric with a ±1-month lag tolerance. It also includes baseline modeling utilities (RFE, LASSO), a simple neural-network + genetic algorithm example, SHAP-based feature importance, an entropy model-averaging (EMA) routine, and a scalar Kalman filter demonstration for data assimilation.</p> |
| title | Open-Source Toolkit for Arctic d-excess and Sea-Ice Interaction Research: Lagged Correlation, Causal Connectivity, and Assimilated Model Evaluation |
| topic | Supervised Machine Learning Arctic region Stable isotopes in the atmosphere sea ice |
| url | https://doi.org/10.5281/zenodo.17411473 |