{
  "version": "2.0",
  "updated": "2026-10-16",
  "author": "Adrian Dunkley",
  "status": "Eight linked working manuscripts; empirical component studies, theoretical formulation and proposed extension distinguished",
  "papers": [
    {
      "slug": "00-space-time",
      "title": "Space–time control volumes for Caribbean atmospheric reconstruction",
      "kind": "Theoretical formulation",
      "refs": [],
      "words": 2086
    },
    {
      "slug": "01-conservation",
      "title": "Conservation-constrained learning for local atmospheric prediction in Jamaica",
      "kind": "Mathematical methods paper",
      "words": 2813,
      "refs": [
        "wrf",
        "pinn",
        "power"
      ],
      "development_url": "https://climatephysicsai.com/papers/01-conservation.html#mathematical-development"
    },
    {
      "slug": "02-extreme-nowcasting",
      "title": "Hybrid precipitation nowcasting and its transfer to Caribbean extreme rainfall",
      "kind": "Critical synthesis and experiment protocol",
      "words": 2629,
      "refs": [
        "das",
        "nowcast",
        "dgmr",
        "fss",
        "imerg"
      ],
      "development_url": "https://climatephysicsai.com/papers/02-extreme-nowcasting.html#mathematical-development"
    },
    {
      "slug": "03-observation-scale",
      "title": "Observation-aware downscaling of Caribbean rainfall: support, conservation and identifiability",
      "kind": "Methods and identifiability paper",
      "words": 2938,
      "refs": [
        "imerg",
        "chirps",
        "power"
      ],
      "development_url": "https://climatephysicsai.com/papers/03-observation-scale.html#mathematical-development"
    },
    {
      "slug": "04-connected-cells",
      "title": "Conservative graph coupling for a cell-based atmospheric world model",
      "kind": "Architecture and mathematical analysis paper",
      "words": 3058,
      "refs": [
        "graphcast",
        "wrf",
        "pinn"
      ],
      "development_url": "https://climatephysicsai.com/papers/04-connected-cells.html#mathematical-development"
    },
    {
      "slug": "05-foundation-models",
      "title": "Foundation priors and physics constraints: the Jamaican temperature experiment",
      "kind": "Empirical component study and integration methods",
      "words": 3517,
      "refs": [
        "timesfm",
        "weathernext",
        "graphcast",
        "scoring"
      ],
      "development_url": "https://climatephysicsai.com/papers/05-foundation-models.html#mathematical-development"
    },
    {
      "slug": "06-validation",
      "title": "Verification, uncertainty and reproducibility for physics-informed Caribbean forecasts",
      "kind": "Verification and reproducibility methods paper",
      "words": 2681,
      "refs": [
        "das",
        "fss",
        "scoring",
        "imerg"
      ],
      "development_url": "https://climatephysicsai.com/papers/06-validation.html#mathematical-development"
    },
    {
      "slug": "07-scenarios",
      "title": "Atmospheric world models for infrastructure scenarios in Jamaica",
      "kind": "Theoretical scenario framework",
      "refs": [],
      "words": 1836
    }
  ],
  "sources": {
    "wrf": {
      "citation": "Skamarock et al. (2019). A Description of the Advanced Research WRF Model Version 4. NCAR Technical Note NCAR/TN-556+STR.",
      "url": "https://www2.mmm.ucar.edu/wrf/users/docs/technote/v4_technote.pdf"
    },
    "pinn": {
      "citation": "Raissi, Perdikaris & Karniadakis (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707.",
      "url": "https://doi.org/10.1016/j.jcp.2018.10.045"
    },
    "das": {
      "citation": "Das, P. et al. (2024). Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting. npj Climate and Atmospheric Science, 7, 282.",
      "url": "https://doi.org/10.1038/s41612-024-00834-8"
    },
    "nowcast": {
      "citation": "Zhang et al. (2023). Skilful nowcasting of extreme precipitation with NowcastNet. Nature, 619, 526–532.",
      "url": "https://doi.org/10.1038/s41586-023-06184-4"
    },
    "dgmr": {
      "citation": "Ravuri et al. (2021). Skilful precipitation nowcasting using deep generative models of radar. Nature, 597, 672–677.",
      "url": "https://doi.org/10.1038/s41586-021-03854-z"
    },
    "imerg": {
      "citation": "NASA GPM. IMERG Version 07 Technical Documentation. Product documentation; consult the selected run and version for latency and support.",
      "url": "https://gpm.nasa.gov/resources/documents/imerg-v07-technical-documentation"
    },
    "chirps": {
      "citation": "Funk et al. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific Data, 2, 150066.",
      "url": "https://doi.org/10.1038/sdata.2015.66"
    },
    "power": {
      "citation": "NASA POWER. Hourly point API: historical source response embedded in the DLEGOS workbench.",
      "url": "https://power.larc.nasa.gov/docs/services/api/temporal/hourly/"
    },
    "graphcast": {
      "citation": "Lam et al. (2023). Learning skillful medium-range global weather forecasting. Science, 382, 1416–1421.",
      "url": "https://doi.org/10.1126/science.adi2336"
    },
    "timesfm": {
      "citation": "Das, A., Kong, Sen & Zhou (2024). A decoder-only foundation model for time-series forecasting. Proceedings of ICML, PMLR 235, 10148–10167.",
      "url": "https://proceedings.mlr.press/v235/das24c.html"
    },
    "weathernext": {
      "citation": "Google. WeatherNext model documentation. Living documentation: record the model generation, issue time, licence and dataset version for each experiment.",
      "url": "https://developers.google.com/weathernext/guides/models"
    },
    "fss": {
      "citation": "Roberts & Lean (2008). Scale-Selective Verification of Rainfall Accumulations from High-Resolution Forecasts of Convective Events. Monthly Weather Review, 136, 78–97.",
      "url": "https://doi.org/10.1175/2007MWR2123.1"
    },
    "scoring": {
      "citation": "Gneiting & Raftery (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. Journal of the American Statistical Association, 102, 359–378.",
      "url": "https://doi.org/10.1198/016214506000001437"
    }
  },
  "public_origin": "https://climatephysicsai.com",
  "series_url": "https://climatephysicsai.com/papers/index.html",
  "publication_note": "Public working-paper drafts; not journal-reviewed empirical findings.",
  "development_url": "https://climatephysicsai.com/papers/development.html",
  "development_experiment": "Executed constructed neural-conductance and numerical-verification companion; not climate forecast validation.",
  "bibliography_entries": 211,
  "source_records": 275,
  "literature_cutoff": "2026-10-11",
  "source_search_date": "2026-10-16",
  "empirical_evidence": "evidence/verification.json",
  "manuscript_source_archive": "Thesis-Manuscripts-Source.zip",
  "reference_bibtex": "reference-library.bib",
  "research_inventory": {
    "date": "2026-10-31",
    "experiments_recorded": 14,
    "theories_recorded": 12,
    "experiments_page": "experiments.html",
    "theories_page": "theories.html",
    "source_register": "research-register.json",
    "editorial_note": "Retrospective archive scores are distinct from synthetic verification and proposed model ideas; see evidence-status fields.",
    "paper_reading_style": "journal.css"
  }
}
