Weather Generator Based on Generative AI for Interdisciplinary Probabilistic Downscaling Using Convection-Permitting Model Outputs and Potential Utility in Equitable, Community-focused Climate Scenario-ing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Convection-permitting model outputs offer significant opportunities for training statistical downscaling approaches. The Coordinated Regional Climate Downscaling Experiment (CORDEX) on the urban environment and regional climate change ensemble simulations provide valuable insights into the uncertainties of numerical atmospheric models. Traditional weather generators, based on the Maximum Likelihood for the Generalised Linear Model approach, have been instrumental in modelling precipitation occurrence and amount. This study advances the statistical downscaling method by integrating Generative AI approaches, using deep learning to create stochastic precipitation ensembles.Compared to deterministic simulations, this new probabilistic approach allows for an exploration of the nonstationary statistical properties influenced by regional climate conditions through more feasible nonlinear representation for the weather generator parameters by deep learning. Emphasis is placed on the importance of probabilistic and agnostic methods in exploring, interpreting, and explaining uncertainties.Findings related to temperature variations for daily precipitation extremes attribute the roles of sensible and latent heat, which are further interpreted through regional processes. The integration of generative AI highlights the stochastic uncertainties in weather generators, emphasising the need for consistency between deterministic convection-permitting model outputs and observational data. By examining scaling relationships, the interpretability and explainability of model outputs, particularly concerning energy balance processes, are demonstrated.Through interpretable and explainable statistical downscaling, the approach to modelling precipitation extremes based on maximum likelihood theory fosters international collaboration in the Climate Collaboratorium* project (IIRCC; ‘Exploring climate solutions with interactive theatre’)This includes contributions from Canada, Germany, the UK, and the US, aimed at providing accessible science that can inform climate decisions in partnership with social science/arts and humanities researchers, tailored to place-based user needs. Advocacy for responsible AI in atmospheric and water sciences facilitates interdisciplinary climate adaptation and mitigation with Taiwanese and Brazilian communities. This approach promotes transparency and fairness through explainable and interpretable climate scenarios. By incorporating immersive experiences and smart decision-making processes, the pathway for human oversight remains central to fair climate action to achieve Sustainable Development Goal 13.*https://www.ukri.org/publications/international-science-partnerships-fund-iircc-initiative-funded-projects/international-joint-initiative-for-research-in-climate-change-adaptation-and-mitigation-project-overview/
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it