Cristin-resultat-ID: 1807450
Sist endret: 22. april 2020, 08:50
Resultat
Vitenskapelig foredrag
2019

Rapid Adjustment of Forecast Trajectories: improving short-term forecast skill through statistical post-processing

Bidragsytere:
  • Nina Schuhen
  • Thordis Linda Thorarinsdottir og
  • Alex Lenkoski

Presentasjon

Navn på arrangementet: European Geosciences Union General Assembly
Sted: Wien
Dato fra: 7. april 2019
Dato til: 12. april 2019

Om resultatet

Vitenskapelig foredrag
Publiseringsår: 2019

Beskrivelse Beskrivelse

Tittel

Rapid Adjustment of Forecast Trajectories: improving short-term forecast skill through statistical post-processing

Sammendrag

The skill of a typical NWP forecast decreases over time, so that forecasts from more recent model runs are generallyconsideredtobemoreskillfulandgivemoreaccuratepredictions.Somepost-processingtechniquesstill make use of older model runs through time-lagging or blending, but with very little relevance, as the newer model runs are preferred. At the same time, technological advances make observations become available in very short time frames and in increasing amounts. We propose a new method, Rapid Adjustment of Forecast Trajectories (RAFT), which works in combination with traditional statistical post-processing techniques and uses short-term observations to improve older forecast runs. As a result, older forecasts match or even surpass the skill of the forecasts from the newest model run. Relying on the inherent correlation structure of the forecast errors between lead times, RAFT updates the tail of a forecast trajectory while the first part verifies. The adaptive regression approach takes into account changesinpredictabilityandlocalpatterns,whilebeingcomputationallyefficient.WewillpresentRAFTversions forhourlysurfacetemperatureand10mwindspeedforecastsfromtheUKMetOffice’sMOGREPS-UKensemble.

Bidragsytere

Nina Schuhen

  • Tilknyttet:
    Forfatter
    ved Avdeling for statistisk analyse og maskinlæring for brukermotiverte anvendelser SAMBA ved Norsk Regnesentral

Thordis Linda Thorarinsdottir

  • Tilknyttet:
    Forfatter
    ved Avdeling for statistisk analyse og maskinlæring for brukermotiverte anvendelser SAMBA ved Norsk Regnesentral

Frank Alexander Lenkoski

Bidragsyterens navn vises på dette resultatet som Alex Lenkoski
  • Tilknyttet:
    Forfatter
    ved Avdeling for statistisk analyse og maskinlæring for brukermotiverte anvendelser SAMBA ved Norsk Regnesentral
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