{
 "about": "A model takes an observing system's measurements as its starting state (data assimilation), or a machine-learned model is trained on or started from another product. Every edge: the page that says so (`source`), when we read it (`read_utc`), whether the phrases we looked for were on it (`state`), a short quote. Stamped by tools/meteorology-read.mjs.",
 "by": "incident.meteorology@web-summit.dinis",
 "updated": "2026-11-11T11:35Z",
 "verb": "assimilates",
 "inverse": "assimilated_by",
 "edges": [
  {
   "from": "model-ecmwf-ifs",
   "to": "obs-meteosat",
   "source": "https://www.ecmwf.int/en/about/media-centre/focus/2020/fact-sheet-earth-system-data-assimilation",
   "expect": [
    "from satellites"
   ],
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "e3bb4bb80cada5f454bb3c8d02601aedb4cf98929f8693e1ef07ef08afd3ae7a",
   "state": "stated",
   "note": "ECMWF's fact sheet says satellites in general; that these are among them is in ECMWF's monitoring pages, still to be read: the edge stands on the general statement.",
   "quote": "stations, aircraft, radiosondes, ships or buoys. Other observations are based on remote sensing, for example from satellites or ground-based radar. Most satellite instruments measure electromagnetic radiation emitted from the Earth’s surface and the atmosphere. The signals received"
  },
  {
   "from": "model-ecmwf-ifs",
   "to": "obs-metop",
   "source": "https://www.ecmwf.int/en/about/media-centre/focus/2020/fact-sheet-earth-system-data-assimilation",
   "expect": [
    "from satellites"
   ],
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "e3bb4bb80cada5f454bb3c8d02601aedb4cf98929f8693e1ef07ef08afd3ae7a",
   "state": "stated",
   "note": "ECMWF's fact sheet says satellites in general; that these are among them is in ECMWF's monitoring pages, still to be read: the edge stands on the general statement.",
   "quote": "stations, aircraft, radiosondes, ships or buoys. Other observations are based on remote sensing, for example from satellites or ground-based radar. Most satellite instruments measure electromagnetic radiation emitted from the Earth’s surface and the atmosphere. The signals received"
  },
  {
   "from": "model-ecmwf-ifs",
   "to": "obs-radiosondes",
   "source": "https://www.ecmwf.int/en/about/media-centre/focus/2020/fact-sheet-earth-system-data-assimilation",
   "expect": [
    "radiosondes"
   ],
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "e3bb4bb80cada5f454bb3c8d02601aedb4cf98929f8693e1ef07ef08afd3ae7a",
   "state": "stated",
   "quote": "at the location of the instrument. Such in-situ measurements come for example from weather stations, aircraft, radiosondes, ships or buoys. Other observations are based on remote sensing, for example from satellites or ground-based radar. Most satellite instruments measure electromagnetic"
  },
  {
   "from": "model-ecmwf-ifs",
   "to": "obs-aircraft",
   "source": "https://www.ecmwf.int/en/about/media-centre/focus/2020/fact-sheet-earth-system-data-assimilation",
   "expect": [
    "aircraft"
   ],
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "e3bb4bb80cada5f454bb3c8d02601aedb4cf98929f8693e1ef07ef08afd3ae7a",
   "state": "stated",
   "quote": "conditions at the location of the instrument. Such in-situ measurements come for example from weather stations, aircraft, radiosondes, ships or buoys. Other observations are based on remote sensing, for example from satellites or ground-based radar. Most satellite instruments measure"
  },
  {
   "from": "model-ecmwf-ifs",
   "to": "obs-buoys-ships",
   "source": "https://www.ecmwf.int/en/about/media-centre/focus/2020/fact-sheet-earth-system-data-assimilation",
   "expect": [
    "ships or buoys"
   ],
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "e3bb4bb80cada5f454bb3c8d02601aedb4cf98929f8693e1ef07ef08afd3ae7a",
   "state": "stated",
   "quote": "location of the instrument. Such in-situ measurements come for example from weather stations, aircraft, radiosondes, ships or buoys. Other observations are based on remote sensing, for example from satellites or ground-based radar. Most satellite instruments measure electromagnetic radiation"
  },
  {
   "from": "model-ecmwf-ifs",
   "to": "obs-surface-stations",
   "source": "https://www.ecmwf.int/en/about/media-centre/focus/2020/fact-sheet-earth-system-data-assimilation",
   "expect": [
    "weather stations"
   ],
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "e3bb4bb80cada5f454bb3c8d02601aedb4cf98929f8693e1ef07ef08afd3ae7a",
   "state": "stated",
   "quote": "observations reflect conditions at the location of the instrument. Such in-situ measurements come for example from weather stations, aircraft, radiosondes, ships or buoys. Other observations are based on remote sensing, for example from satellites or ground-based radar. Most satellite"
  },
  {
   "from": "model-gfs",
   "to": "obs-radiosondes",
   "source": "https://www.ncei.noaa.gov/products/weather-climate-models/global-data-assimilation",
   "expect": [
    "balloon",
    "satellite"
   ],
   "note": "The GFS starts from GDAS, the Global Data Assimilation System.",
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "53902e7dc4c1622543277996145921432e1b5cbd6cd5ea566dabe6029477507b",
   "state": "stated",
   "quote": "binary Output Data Format World Meteorological Organization (WMO) Gridded Binary (GRIB) Observation Types Surface Balloon Wind Profiler Aircraft Reports Buoy Radar Satellite Related News May 20, 2026 Rhode Island Receives New 24-Hour State Snowfall Record Read More April 30, 2026 New"
  },
  {
   "from": "model-gfs",
   "to": "obs-jpss",
   "source": "https://www.ncei.noaa.gov/products/weather-climate-models/global-data-assimilation",
   "expect": [
    "satellite"
   ],
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "53902e7dc4c1622543277996145921432e1b5cbd6cd5ea566dabe6029477507b",
   "state": "stated",
   "quote": "Products Product Grid/Scale Period of Record Model Cycle Output Timestep Data Access Links GDAS Snow, Ice, SST, Satellite, Ship, Aircraft (GRIB and BUFR) 1°x1° 0.5°x0.5° 0.25°x0.25° 01Jan2001–Present (approx. one year to present online) 4/day: 00, 06, 12, 18UTC Anl, +00, +03, +06, +09"
  },
  {
   "from": "model-gfs",
   "to": "obs-surface-stations",
   "source": "https://www.ncei.noaa.gov/products/weather-climate-models/global-data-assimilation",
   "expect": [
    "surface"
   ],
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "53902e7dc4c1622543277996145921432e1b5cbd6cd5ea566dabe6029477507b",
   "state": "stated",
   "quote": "(IEEE) binary Output Data Format World Meteorological Organization (WMO) Gridded Binary (GRIB) Observation Types Surface Balloon Wind Profiler Aircraft Reports Buoy Radar Satellite Related News May 20, 2026 Rhode Island Receives New 24-Hour State Snowfall Record Read More April 30, 2026"
  },
  {
   "from": "model-gfs",
   "to": "obs-aircraft",
   "source": "https://www.ncei.noaa.gov/products/weather-climate-models/global-data-assimilation",
   "expect": [
    "aircraft"
   ],
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "53902e7dc4c1622543277996145921432e1b5cbd6cd5ea566dabe6029477507b",
   "state": "stated",
   "quote": "Grid/Scale Period of Record Model Cycle Output Timestep Data Access Links GDAS Snow, Ice, SST, Satellite, Ship, Aircraft (GRIB and BUFR) 1°x1° 0.5°x0.5° 0.25°x0.25° 01Jan2001–Present (approx. one year to present online) 4/day: 00, 06, 12, 18UTC Anl, +00, +03, +06, +09 hours HTTPS GDAS"
  },
  {
   "from": "model-ecmwf-aifs",
   "to": "model-ecmwf-ifs",
   "source": "https://huggingface.co/ecmwf/aifs-single-1.0",
   "expect": [
    "ERA5",
    "IFS"
   ],
   "note": "Trained on ERA5 and on IFS analyses; each run starts from the IFS analysis: a machine-learned model has no observations of its own.",
   "verb_note": "is trained on and starts from",
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "31d15b8b93fcc28fa4cee0a36fc2bb064bf909ecf0f6b6934ae528bede7cbbf6",
   "state": "stated",
   "quote": "neural network (GNN) encoder and decoder, and a sliding window transformer processor, and is trained on ECMWF’s ERA5 re-analysis and ECMWF’s operational numerical weather prediction (NWP) analyses. It has a flexible and modular design and supports several levels of parallelism to enable"
  },
  {
   "from": "model-graphcast",
   "to": "data-era5",
   "source": "https://raw.githubusercontent.com/google-deepmind/graphcast/main/README.md",
   "expect": [
    "ERA5"
   ],
   "verb_note": "is trained on",
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "9f0879fac17bda69b021bb8239241e488b91943d7c813c8727253ff9847853b1",
   "state": "stated",
   "quote": "the per-model READMEs for model-specific code. ### Training Data Full model training requires downloading the [ERA5](https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) dataset from [ECMWF](https://www.ecmwf.int/), best accessed as Zarr via [WeatherBench2](https://weatherbench2.rea"
  },
  {
   "from": "model-pangu",
   "to": "data-era5",
   "source": "https://raw.githubusercontent.com/198808xc/Pangu-Weather/main/README.md",
   "expect": [
    "ERA5"
   ],
   "verb_note": "is trained on",
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "b21aeee735b3196efd1334fc7f34caa0a5fb1e549d06d7ae3738db7b237a19a1",
   "state": "stated",
   "quote": "0.25 degrees. For each 721x1440 slice, the data format is exactly the same as the `.nc` file download from the ERA5 official website. Note that the numpy arrays should be in single precision (`.astype(np.float32)`), not in double precision. We support ERA5 initial fields and ECMWF"
  },
  {
   "from": "model-aurora",
   "to": "data-era5",
   "source": "https://raw.githubusercontent.com/microsoft/aurora/main/README.md",
   "expect": [
    "ERA5"
   ],
   "verb_note": "is trained on",
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "73535361d411738555fba1054f88e97aed8a746b6174391e0feb11db08c04c92",
   "state": "stated",
   "quote": "examples.](https://microsoft.github.io/aurora) You can also directly go to [a full-fledged example that runs the model on ERA5](https://microsoft.github.io/aurora/example_era5.html). Cite us as follows: ``` @article{bodnar2025aurora, title = {A Foundation Model for the Earth System}, author ="
  },
  {
   "from": "model-arome",
   "to": "model-arpege",
   "source": "https://open-meteo.com/en/docs/meteofrance-api",
   "expect": [
    "ARPEGE model combined with high resolution AROME"
   ],
   "note": "Open-Meteo's description of how it serves the two together; Météo-France's own page on AROME's boundaries moved and is still to be read.",
   "verb_note": "is combined with",
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "aee66fa1cef0fce0966d609aa7cd67f826dcaad2b8389a8baf76c90a32527088",
   "state": "stated",
   "label": "evidence",
   "quote": "Open-Meteo.com Open-Meteo Home Features Pricing API Docs Animations GitHub X Toggle theme Météo-France API Global ARPEGE model combined with high resolution AROME model Météo-France Weather Forecast DWD Germany NOAA U.S. Météo-France ECMWF UK Met Office JMA Japan MeteoSwiss MET Norway"
  },
  {
   "from": "model-ecmwf-ens",
   "to": "model-ecmwf-ifs",
   "source": "https://www.ecmwf.int/en/forecasts/datasets/open-data",
   "expect": [
    "both produced from the same IFS model"
   ],
   "verb_note": "is the same model as",
   "note": "The ensemble and the high-resolution forecast are one model, the IFS, run differently.",
   "read_utc": "2026-11-11T11:28Z",
   "http": 200,
   "sha256": "85d2592ad1fd7c023d9645e9b5c13e65b8facc25079b463529b0a66d5d579f10",
   "state": "stated",
   "quote": "are published at two resolutions: the existing 0.25 degrees and, from 20 October 2026, a finer 0.1 degrees - both produced from the same IFS model and interpolated to a lat/long grid. The lower resolution data are released at the end of the real-time dissemination schedule , while"
  }
 ]
}