MSGID: 1:317/3 646ee4b3
PID: hpt/lnx 1.9.0-cur 2019-01-08
TID: hpt/lnx 1.9.0-cur 2019-01-08
 New method predicts extreme events more accurately 
 Columbia Engineers develop machine-learning algorithm to better
understand and mitigate the impact of extreme weather events, which are
becoming more frequent in our warming climate. 

  Date:
      May 24, 2023
  Source:
      Columbia University School of Engineering and Applied Science
  Summary:
      A new study has 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.


      Facebook Twitter Pinterest LinkedIN Email

==========================================================================
FULL STORY
==========================================================================
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.

    * RELATED_TOPICS
          o Earth_&_Climate
                # Weather # Global_Warming # Climate #
                Environmental_Awareness
          o Computers_&_Math
                # Computer_Modeling # Mathematical_Modeling #
                Computer_Programming # Distributed_Computing
    * RELATED_TERMS
          o Global_climate_model
          o Temperature_record_of_the_past_1000_years o
          Climate_model o Computer_simulation o Weather_forecasting
          o Global_warming_controversy o Artificial_neural_network
          o Alan_Turing

==========================================================================
Story Source: Materials provided by
Columbia_University_School_of_Engineering_and_Applied Science. Original
written by Holly Evarts. Note: Content may be edited for style and length.


==========================================================================
Journal Reference:
   1. Sara Shamekh, Kara D. Lamb, Yu Huang, Pierre Gentine. Implicit
   learning
      of convective organization explains precipitation stochasticity.

      Proceedings of the National Academy of Sciences, 2023; 120 (20)
      DOI: 10.1073/pnas.2216158120
==========================================================================

Link to news story:
https://www.sciencedaily.com/releases/2023/05/230524181937.htm

--- up 1 year, 12 weeks, 2 days, 10 hours, 50 minutes
 * Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)
SEEN-BY: 15/0 106/201 114/705 123/120 153/7715 218/700 226/30 227/114
SEEN-BY: 229/110 112 113 307 317 400 426 428 470 664 700 291/111 292/854
SEEN-BY: 298/25 305/3 317/3 320/219 396/45
PATH: 317/3 229/426