• HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
Friday, July 25, 2025
BIOENGINEER.ORG
No Result
View All Result
  • Login
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
  • HOME
  • NEWS
  • EXPLORE
    • CAREER
      • Companies
      • Jobs
        • Lecturer
        • PhD Studentship
        • Postdoc
        • Research Assistant
    • EVENTS
    • iGEM
      • News
      • Team
    • PHOTOS
    • VIDEO
    • WIKI
  • BLOG
  • COMMUNITY
    • FACEBOOK
    • INSTAGRAM
    • TWITTER
No Result
View All Result
Bioengineer.org
No Result
View All Result
Home NEWS Science News Chemistry

New method predicts extreme events more accurately

Bioengineer by Bioengineer
May 24, 2023
in Chemistry
Reading Time: 4 mins read
0
ADVERTISEMENT
Share on FacebookShare on TwitterShare on LinkedinShare on RedditShare on Telegram

New York, NY—May 23, 2023—With the rise of extreme weather events, which are becoming more frequent in our warming climate, accurate predictions are becoming more critical for all of us, from farmers to city-dwellers to businesses around the world. To date, climate models have failed to accurately predict precipitation intensity, particularly extremes. While in nature, precipitation can be very varied, with many extremes of precipitation, climate models predict a smaller variance in precipitation with a bias toward light rain.

Rain Storm Colorado Springs Colorado

Credit: Brokentaco/Flickr (https://www.flickr.com/photos/brokentaco/2781330996/in/photostream/)

New York, NY—May 23, 2023—With the rise of extreme weather events, which are becoming more frequent in our warming climate, accurate predictions are becoming more critical for all of us, from farmers to city-dwellers to businesses around the world. To date, climate models have failed to accurately predict precipitation intensity, particularly extremes. While in nature, precipitation can be very varied, with many extremes of precipitation, climate models predict a smaller variance in precipitation with a bias toward light rain.

Missing piece in current algorithms: cloud organization

Researchers have been working to develop algorithms that will improve prediction accuracy but, as Columbia Engineering climate scientists report, there has been a missing piece of information in traditional climate model parameterizations–a way to describe cloud structure and organization that is so fine-scale it is not captured on the computational grid being used. These organization measurements affect predictions of both precipitation intensity and its stochasticity, the variability of random fluctuations in precipitation intensity. Up to now, there has not been an effective, accurate way to measure cloud structure and quantify its impact.

A new study from a team led by Pierre Gentine, director of the Learning the Earth with Artificial Intelligence and Physics (LEAP) Center, used global storm-resolving simulations and machine learning to create an algorithm that can deal separately with two different scales of cloud organization: those resolved by a climate model, and those that cannot be resolved as they are too small. This new approach addresses the missing piece of information in traditional climate model parameterizations and provides a way to predict precipitation intensity and variability more precisely.

“Our findings are especially exciting because, for many years, the scientific community has debated whether to include cloud organization in climate models,” said Gentine, Maurice Ewing and J. Lamar Worzel Professor of Geophysics in the Departments of Earth and Environmental Engineering and Earth Environmental Sciences and a member of the Data Science Institute. “Our work provides an answer to the debate and a novel solution for including organization, showing that including this information can significantly improve our prediction of precipitation intensity and variability.”

Using AI to design neural network algorithm

Sarah Shamekh, a PhD student working with Gentine, developed a neural network algorithm that learns the relevant information about the role of fine-scale cloud organization (unresolved scales) on precipitation. Because Shamekh did not define a metric or formula in advance, the model learns implicitly–on its own–how to measure the clustering of clouds, a metric of organization, and then uses this metric to improve the prediction of precipitation. Shamekh trained the algorithm on a high-resolution moisture field, encoding the degree of small-scale organization. 

“We discovered that our organization metric explains precipitation variability almost entirely and could replace a stochastic parameterization in climate models,” said Shamekh, lead author of the study, published May 8, 2023, by PNAS. “Including this information significantly improved precipitation prediction at the scale relevant to climate models, accurately predicting precipitation extremes and spatial variability.”

Machine-learning algorithm will improve future projections

The researchers are now using their machine-learning approach, which implicitly learns the sub-grid cloud organization metric, in climate models. This should significantly improve the prediction of precipitation intensity and variability, including extreme precipitation events, and enable scientists to better project future changes in the water cycle and extreme weather patterns in a warming climate. 

Future work

This research also opens up new avenues for investigation, such as exploring the possibility of precipitation creating memory, where the atmosphere retains information about recent weather conditions, which in turn influences atmospheric conditions later on, in the climate system. This new approach could have wide-ranging applications beyond just precipitation modeling, including better modeling of the ice sheet and ocean surface.

###

About the Study

The study is titled  “Implicit learning of convective organization explains precipitation stochasticity.”

Authors are: Sara Shamekh, Kara Lamb, Yu Huang, Pierre Gentine

Department of Earth of Environmental Engineering, Columbia University, New York, NY, USA

The study was supported by: SS and PG acknowledge funding from European Research Council grant USMILE, from Schmidt Future project M2LiNES and from the National Science Foundation Science and Technology Center (STC) Learning the Earth with Artificial intelligence and Physics (LEAP), Award  2019625 – STC. KDL acknowledges support from LEAP and DOE Grant DE-SC0022323 “Discovering Physically Meaningful Structures from Climate Extreme Data.”

The authors declare no financial or other conflicts of interest.

### 

LINKS:

Paper: https://www.pnas.org/doi/10.1073/pnas.2216158120 

DOI:  10.1073/pnas.2216158120



Journal

Proceedings of the National Academy of Sciences

DOI

10.1073/pnas.2216158120

Article Title

Implicit learning of convective organization explains precipitation stochasticity

Article Publication Date

8-May-2023

COI Statement

The authors declare no financial or other conflicts of interest.

Share12Tweet8Share2ShareShareShare2

Related Posts

Architecture of VBayesMM

Unraveling Gut Bacteria Mysteries Through AI

July 4, 2025
Visulaization of ATLAS collision

Can the Large Hadron Collider Prove String Theory Right?

July 3, 2025

Breakthrough in Gene Therapy: Synthetic DNA Nanoparticles Pave the Way

July 3, 2025

Real-Time Electrochemical Microfluidic Monitoring of Additive Levels in Acidic Copper Plating Solutions for Metal Interconnections

July 3, 2025

POPULAR NEWS

  • Blind to the Burn

    Overlooked Dangers: Debunking Common Myths About Skin Cancer Risk in the U.S.

    61 shares
    Share 24 Tweet 15
  • AI Achieves Breakthrough in Drug Discovery by Tackling the True Complexity of Aging

    70 shares
    Share 28 Tweet 18
  • USF Research Unveils AI Technology for Detecting Early PTSD Indicators in Youth Through Facial Analysis

    43 shares
    Share 17 Tweet 11
  • Dr. Miriam Merad Honored with French Knighthood for Groundbreaking Contributions to Science and Medicine

    46 shares
    Share 18 Tweet 12

About

We bring you the latest biotechnology news from best research centers and universities around the world. Check our website.

Follow us

Recent News

Additive Manufacturing of Monolithic Gyroidal Solid Oxide Cells

Machine Learning Uncovers Sorghum’s Complex Mold Resistance

Pathology Multiplexing Revolutionizes Disease Mapping

  • Contact Us

Bioengineer.org © Copyright 2023 All Rights Reserved.

Welcome Back!

Login to your account below

Forgotten Password?

Retrieve your password

Please enter your username or email address to reset your password.

Log In
No Result
View All Result
  • Homepages
    • Home Page 1
    • Home Page 2
  • News
  • National
  • Business
  • Health
  • Lifestyle
  • Science

Bioengineer.org © Copyright 2023 All Rights Reserved.