{
  "abstract": "The Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement (IMERG) product combines precipitation estimates from passive microwave (PMW) and infrared (IR) sensors to provide quasi-global precipitation estimates at half-hourly resolution. Because IR observations are primarily sensitive to cloud-top properties, however, IR-based precipitation estimates are generally less accurate than PMW estimates and can introduce artifacts into the merged precipitation fields. We introduce GPROF-IR, a neural-network-based precipitation retrieval designed to provide precipitation estimates from single-channel geostationary IR observations for the upcoming IMERG V08. GPROF-IR uses a convolutional neural network to exploit spatiotemporal information from sequences of half-hourly IR observations. We validate GPROF-IR against independent surface-based precipitation measurements over land and ocean. GPROF-IR substantially improves upon existing single-channel IR retrievals, including the IR retrieval used in IMERG V07 and more recent neural-network-based approaches. During overpasses of flagship PMW sensors, GPROF-IR is more accurate than the corresponding IMERG PMW estimates over CONUS and Austria, but not Korea. Over CONUS, GPROF-IR outperforms the merged IMERG V07 product for hourly and daily precipitation accumulations. Against shipborne disdrometer observations, GPROF-IR is more accurate than IMERG V07 in the tropics but slightly less accurate in the extratropics. Since geostationary IR observations are continuously available across much of the globe since 1983, GPROF-IR provides a promising basis for improving merged precipitation products and developing long-term, temporally consistent precipitation records.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2605.07167",
  "author_total": 4,
  "authors": [
    {
      "name": "Simon Pfreundschuh"
    },
    {
      "name": "Christian D. Kummerow"
    },
    {
      "name": "Jackson Tan"
    },
    {
      "name": "George J. Huffman"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fxvppd5m00361.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2605.07167",
  "fulltext_url": null,
  "id": "fxvppd5m00361",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2605.07167",
  "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",
      "physics.ao-ph"
    ],
    [
      "openalex",
      "1902|Precipitation Measurement and Analysis"
    ]
  ],
  "openalex": "W7160835070",
  "pdf_url": "https://arxiv.org/pdf/2605.07167",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-22",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fxvppd5m00361.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:56Z",
      "native_id": "2605.07167",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2605.07167"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "arxiv:physics.ao-ph",
      "subfield": "atmospheric"
    }
  ],
  "title": "GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products",
  "title_text": "GPROF-IR: An Improved Single-Channel Infrared Precipitation Retrieval for Merged Satellite Precipitation Products",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:56Z",
  "url": "https://research.draconitic.com/paper/fxvppd5m00361",
  "venue": "arXiv",
  "versions": []
}