English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Advancing ensemble subseasonal forecasting with machine learning

Totz, S., Cohen, J., Flaspohler, G., Orenstein, P., Mackey, L. (2023): Advancing ensemble subseasonal forecasting with machine learning, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-4586

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020996 Version Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5020996_1
Genre: Conference Paper

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Totz, Sonja1, Author
Cohen, Judah1, Author
Flaspohler, Genevieve1, Author
Orenstein, Paulo1, Author
Mackey, Lester1, Author
Affiliations:
1IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations, ou_5011304              

Content

show
hide
Free keywords: -
 Abstract: Improving our ability to forecast the weather and climate is of interest to all sectors of the economy and government agencies. However, accurate forecasts for the subseasonal regime are lacking due to the chaotic nature of the weather. We have recently developed a machine learning based adaptive bias correction (ABC) method combining state of the art dynamical forecasts with observations as well as utilizing regression models. We applied this method to probabilistic precipitation and temperature forecasts with a forecasting lead of 3 to 4 weeks over the continental United States from 1999 to 2016. Using the ranked probability skill for comparison, we can show that the skill of the ABC debiased forecasts is higher than in the traditional debiased forecasts for both space and time averaged skill. Another method to further improve the skill is to combine several different dynamical models into a multimodel ensemble e.g., by weighting all model forecasts equally. However, a better alternative to the latter is to weigh model forecasts based on their skill. We recently developed an approach for adaptive ensembling of numerical weather predictions using a machine learning tool called sequential online learning algorithms. As real-time weather outcomes are observed, the algorithm adjusts the weights of each model member according to its past performance. We evaluated the skill of precipitation and temperature forecasts forecasting at 3 to 4 weeks lead time over the United States from 1999 to 2016 and see that our proposed online learning algorithm can outperform uniform ensembles for probabilistic forecasts.

Details

show
hide
Language(s): eng - English
 Dates: 2023-07-112023-07-11
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.57757/IUGG23-4586
 Degree: -

Event

show
hide
Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Place of Event: Berlin
Start-/End Date: 2023-07-11 - 2023-07-20

Legal Case

show

Project information

show

Source 1

show
hide
Title: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Potsdam : GFZ German Research Centre for Geosciences
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -