{
  "abstract": "Local heatwave hazard depends on fine-scale air temperature, but ground stations are sparse and reanalysis products such as ERA5 cannot resolve the terrain and land-surface contrasts that shape real heat exposure. We present a conditional diffusion emulator for high-resolution 2-m temperature downscaling, conditioned on static geography, a training climatology, exact-time ERA5 temperature, and solar and temporal features, guided at inference by score-based data assimilation (SDA): a differentiable Gaussian observation likelihood steers the diffusion score toward sparse revealed temperature observations without any retraining. On a controlled 32-case synthetic-grid protocol over AORC, guidance improves hidden-cell reconstruction over both ERA5 and a strong observation-proximal nearest-neighbor baseline once observation density reaches 1\\% (RMSE 0.318 vs.\\ 0.431~K, winning all 32 cases), while sparser regimes still favor direct interpolation. We further map the full guidance-strength landscape across three observation densities, showing that the optimal strength shifts systematically with density and that over-guiding causes sharp, predictable degradation -- giving a concrete operating recipe rather than a single untuned setting. The resulting fields are intended as a temperature layer for downstream heatwave-hazard products such as threshold exceedance and cumulative heat-burden. The present evidence is a controlled synthetic-grid validation; station-network and held-out-year evaluations are the next steps toward deployment.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.09247",
  "author_total": 7,
  "authors": [
    {
      "name": "Anirudh Avireddy"
    },
    {
      "name": "Manmeet Singh"
    },
    {
      "name": "Shivanshi Singh"
    },
    {
      "name": "Ayush Raj"
    },
    {
      "name": "Saptarishi Dhanuka"
    },
    {
      "name": "Parthasarathi Mukhopadhyay"
    },
    {
      "name": "Sandeep Juneja"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5z6ksj82rs0pf.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09247",
  "fulltext_url": null,
  "id": "5z6ksj82rs0pf",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09247",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "1902|Meteorological Phenomena and Simulations"
    ]
  ],
  "openalex": "W7212157936",
  "pdf_url": "https://arxiv.org/pdf/2609.09247",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5z6ksj82rs0pf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:12Z",
      "native_id": "2609.09247",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09247"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "Steering Diffusion Priors with Sparse Observations for High-Resolution Temperature Downscaling",
  "title_text": "Steering Diffusion Priors with Sparse Observations for High-Resolution Temperature Downscaling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:12Z",
  "url": "https://research.draconitic.com/paper/5z6ksj82rs0pf",
  "venue": "arXiv",
  "versions": []
}