• Home
  • About
  • Contact us
Tech News, Magazine & Review WordPress Theme 2017
  • Computing
  • Entertainment
  • Gaming
  • Mobile
  • Science
  • Security
  • Services
  • Software
  • Space
No Result
View All Result
  • Computing
  • Entertainment
  • Gaming
  • Mobile
  • Science
  • Security
  • Services
  • Software
  • Space
Technovanguard — Be at the forefront of technology news
No Result
View All Result

Learning from Climate Simulations for Global Seasonal Forecast

Justin Rowell by Justin Rowell
29.09.2022
Home Space

Diagram showing the three barriers in seasonal forecast and the Conditional Generative Forecasting methodology developed to tackle these three barriers.

Editors’ Highlights are summaries of recent papers by AGU’s journal editors.
Source: Journal of Advances in Modeling Earth Systems

Skillful seasonal forecast benefits a broad range of societal sectors. However, current dynamical seasonal forecast systems face three stubborn challenges. First, we may start from a “wrong” place: it is difficult to align a model’s state to match observations at forecast starting time, due to limited coverage of observations, or the state shock problem. Second, we may follow “wrong” paths: the forecasting models cannot perfectly simulate the climate state evolution trajectory, due to poor representation of unresolved climate processes. Third, we may have not tried all possibilities: using limited (~10) forecasting ensemble members, we may not have sufficiently sampled the plausible future climate states. The huge observational, computational, and intellectual cost for resolving these challenges have significantly slowed down our progress toward better seasonal forecast.        

Targeting toward these three challenges, Pan et al. [2022] developed a deep learning model that learns from climate simulation big data for seasonal forecast. Their Conditional Generative Forecasting (CGF) methodology applies a deep generative modeling technique to sample possible climate states a season ahead and adopts a transfer learning technique to account for the data-generating climate model formulation differences. These treatments allow bypassing the aforementioned barriers in dynamical forecast, offering a top-down viewpoint to examine how complicated climate models encode the seasonal predictability information.

Experiments for global seasonal forecast of precipitation and 2 m air temperature show that the CGF methodology achieves competitive performance compared to dynamical forecasts. Using this CGF as benchmark, the authors reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Furthermore, the authors introduced different strategies for composing forecasting ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple climate models to achieve advantageous seasonal forecast.

Citation: Pan, B., Anderson, G. J., Goncalves, A., Lucas, D. D., Bonfils, C. J. W., & Lee, J. (2022). Improving seasonal forecast using probabilistic deep learning. Journal of Advances in Modeling Earth Systems, 14, e2021MS002766. https://doi.org/10.1029/2021MS002766

 —Jiwen Fan, Editor, Journal of Advances in Modeling Earth Systems

Text © 2022. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.


Next Post
Telegram Premium announced: Here are the extra features that it offers

Telegram Premium announced: Here are the extra features that it offers

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended.

The Moon is a Barren and Desolate, but Lunar Caves Could Offer Some Shelter From the Harsh Environment

The Moon is a Barren and Desolate, but Lunar Caves Could Offer Some Shelter From the Harsh Environment

29.09.2022
Arrest in ‘Ransom Your Employer’ Email Scheme

Arrest in ‘Ransom Your Employer’ Email Scheme

29.09.2022

Trending.

Travel Business and Content Marketing: A Match Made in Heaven

Travel Business and Content Marketing: A Match Made in Heaven

07.02.2023
Netflix’s vampire movie Day Shift adds real bite to a classic action throwback

Netflix’s vampire movie Day Shift adds real bite to a classic action throwback

06.01.2023
Staying Ahead of the Game: The Top 10 Most Popular Websites for IT and Modern Technology

Staying Ahead of the Game: The Top 10 Most Popular Websites for IT and Modern Technology

30.01.2023
The Role of Technology in Transforming Healthcare Advertising

The Role of Technology in Transforming Healthcare Advertising

03.01.2023
How did Earth go From Molten Hellscape to Habitable Planet?

How did Earth go From Molten Hellscape to Habitable Planet?

29.09.2022
Technovanguard — Be at the forefront of technology news

Technovanguard - The latest news from the world of IT and modern technologies.

Categories

  • Computing
  • Entertainment
  • Gaming
  • Internet
  • Mobile
  • Science
  • Security
  • Services
  • Software
  • Space
  • Без рубрики

Tags

best bitcoin casino best bitcoin gambling site best crypto casino bitcoin gambling site btc casino FEATUREDNEWS linkedin connection message linkedin connection request template linkedin connect message examples linkedin networking message template linkedin sales message top bitcoin casinos

Recent News

Talents on AI: Kyiv to Host Three-Day Hackathon Connecting Developers and Sponsors in May 2023

Talents on AI: Kyiv to Host Three-Day Hackathon Connecting Developers and Sponsors in May 2023

07.03.2023
Ukrainian NFT Collection Honors Heroes and Raises Funds for Naval Combat Drones

Ukrainian NFT Collection Honors Heroes and Raises Funds for Naval Combat Drones

17.02.2023
  • Home
  • About
  • Contact us

© 2021 technovanguard.com. Submit news release

No Result
View All Result
  • Computing
  • Entertainment
  • Gaming
  • Mobile
  • Science
  • Security
  • Services
  • Software
  • Space

© 2021 technovanguard.com. Submit news release