Sustainable Biz - Magazine - Page 15
A common meteorological practice is
downscaling—inferring high-resolution
outputs from low-resolution variables.
Typical data inputs include temperature,
precipitation, and surface winds, all of which
can have varied resolutions. The model can
depict both weather and climate data at
up to 12x resolution, generating localized
forecasts and climate projections. The
fine-tuned downscaling model is available
on the IBM Granite page on Hugging Face.
Gravity wave parametrisation: Gravity waves
are ubiquitous throughout the atmosphere
and can affect many atmospheric processes
related to climate and weather, such as
cloud formation and aircraft turbulence.
Traditionally, existing numerical climate
models have not sufficiently captured gravity
waves, which leads to uncertainties in terms
of how exactly gravity waves can affect
climate processes. This weather and climate
foundation model can help scientists better
estimate gravity wave generation, to improve
the accuracy of numerical weather and
climate models and constrain uncertainty
when simulating future weather and climate
events. This gravity wave parametrisation
model is being released as part of the NASAIBM Prithvi family of models on Hugging
Face."Advancing NASA's Earth science for
the benefit of humanity means delivering
actionable science in ways that are useful
to people, organizations, and communities.
The rapid changes we're witnessing on
our home planet demand this strategy to
meet the urgency of the moment," said
Karen St. Germain, director of the Earth
Science Division of NASA's Science Mission
Directorate. "The NASA foundation model
will help us produce a tool that people
can use: weather, seasonal, and climate
projections to help inform decisions on how
to prepare, respond, and mitigate."
"This space has seen the emergence of
large AI models that focus on a fixed
dataset and single use case — primarily
forecasting. We have designed our weather
and climate foundation model to go beyond
such limitations so that it can be tuned
to a variety of inputs and uses," said Juan
Bernabe-Moreno, Director of IBM Research
Europe (UK and Ireland) and IBM's
Accelerated Discovery Lead for Climate
and Sustainability. "For example, the model
can run both on the entire earth as well as
in a local context. With such flexibility on
the technology side, this model is well-
suited to help us understand meteorological
phenomena such as hurricanes or
atmospheric rivers, reason about future
potential climate risks by increasing the
resolution of climate models, and finally
inform our understanding of imminent
severe weather events."
"As a premier research institution and
computing facility, we're focused on
supporting teams to make research
breakthroughs across many areas of
science," said Arjun Shankar, director of
This open-source
foundation model helps
generate high-resolution
climate projections,
short-term forecasts,
and deeper insights into
atmospheric processes
the National Center for Computational
Sciences at Oak Ridge National Laboratory.
"Our collaboration with IBM and NASA
to support the creation of the Prithvi
weather and climate foundation model was
a key part of our goal to bring advanced
computing and data to problems of national
importance, in this case, for weather and
climate applications, which need continued
computational science and model skill
improvements to be impactful."
IBM has already collaborated with
Environment and Climate Change Canada
(ECCC) with a view to test the flexibility
of the model with additional weather
forecasting use cases. With the model,
ECCC is exploring very short-term
precipitation forecasts using a technique
called precipitation nowcasting that ingests
real-time radar data as input. The team is
also testing the downscaling approach from
global model forecasts at 15 km to km-scale
resolution.
This weather and climate model is part of a
larger collaboration between IBM Research
and NASA to use AI technology to explore
our planet, and joins the Prithvi family
of AI foundation models. Last year, IBM
and NASA made the Prithvi geospatial AI
foundation model the largest open-source
geospatial AI model available on Hugging
Face. This geospatial foundation model
has since been used by governments,
companies, and public institutions to
examine changes in disaster patterns,
biodiversity, land use, and other geophysical
processes. The foundation model and the
gravity wave parametrisation model can be
accessed through the NASA-IBM Hugging
Face page and the downscaling model
can be accessed through the IBM Granite
Hugging Face page.
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