AI Workshop Group Photo, 23 April 2019
(click to enlarge)
Purpose of Workshop
The workshop will help gather scientists, program managers, and leaders from public,
academic and private sectors. It will enable experts involved in the development and
adaptation of AI tools and applications in a number of fields to meet and exchange experiences.
It is important to note that there are fields with similar fundamental issues as the
field of environmental data information (from space- and ground-based platforms), NWP
data assimilation, forecasting, and other environmental prediction systems. These
fundamental issues include image and signal processing, pattern recognition, morphing,
projection mapping, data mining, clustering, etc. This will facilitate the cross-fertilization
of knowledge in different communities to benefit environmental observation and numerical
prediction using machine-learning techniques.
This gathering will allow participants to exchange ideas, share lessons learned,
and to discuss both the future potential and limitations of AI. It will also help to
establish collaborations for using AI tools for many purposes, including:
- Reviewing AI-enabling technology and tools
- Furthering scientific objectives of better utilization of Earth observation
- Sharing ideas to improve NWP skills
- Improving efficiency of environmental data processing and exploitation for cost effectiveness
- Identifying innovative ways to use satellite data and other environmental data to create new products and services and generate new markets
- Expanding commercial markets of high-level environment-related products and services to benefit society and the economy
Who Should Attend
Scientists, program managers, and leaders from the public, academic
and commercial sectors interested in identifying innovative ways to use
satellite and other environmental data.
Workshop Format
The workshop will begin with a keynote address by Dr. Neil Jacobs
(Assistant Secretary of Commerce for Environmental Observation and
Prediction) followed by a series of overview presentations and discussions.
The workshop will have oral presentations as well as posters organized by
sessions. It will also have panel discussions and invited speaker presentations.
Tutorials on AI/ML tools are also planned as part of the workshop.
If you have questions, please contact: Narges Shahroudi
AMS Special Collection for the Workshop
An AMS special collection of articles containing research from the
1st NOAA AI workshop has been compiled.
You can find the collection at AMS - AI Workshop Special Collection
The collection includes overview articles, as well as those covering a
wide range of topics including image processing such as gap filling;
estimation of radar reflectivity, soil moisture, and TC intensity;
detection of tropical and extratropical cyclones, and intense mid-latitude
convection; prediction of streamflow, TC rapid intensification, and
tornados; development of neural networks to replace convective
parameterization schemes; post-forecast correction of long-range
forecasts of precipitation and surface temperature; postprocessing in
the form of nonlinear averaging of precipitation and ocean wave ensemble
forecasts; and explainable AI.
Invited Speakers
- Christopher Bretherton (University of Washington)
- William Collins (University of California, Berkeley)
- Peter Dolan (DeepMind, Google)
- Alan Geer (ECMWF)
- David M. Hall (NVIDIA)
- Sue E. Haupt (NCAR, Research Applications Laboratory)
- Amy McGovern (University of Oklahoma)
- Edward Ott (University of Maryland)
- Philip Rasch (PNNL)
- Philippe Tissot (Texas A&M-Corpus Christi, AMS AI Committee)
- John K. Williams (The Weather Company, an IBM Business)
- Tianle Yuan (UMBC JCET/NASA GSFC)
Background and Workshop Motivations
Artificial Intelligence (AI), machine/deep learning techniques (including
deep neural networks, DNNs) have advanced considerably in recent years
across a number of areas and applications: in medicine, self-driving cars,
social media, the finance industry, etc. The astonishing increase in accuracy
and applicability of AI has been significant in the private sector, driven by
the ease, efficiency, cost-effectiveness, speed and auto-learning features of AI.
Significant advances have also been made in application of AI in different areas
of meteorology and oceanography.
However, until recently, far fewer AI applications were developed in the
area of environmental data exploitation of satellite data, high-level information
extraction in the area of Numerical Weather Prediction (NWP), data assimilation
and forecasting, as well as for extreme weather prediction and nowcasting.
There have been encouraging signs that AI is increasingly considered for
these applications, with promising results—including predictive skills—and
this trend is expected to continue with the ever-increasing volume of satellite
data and the increased societal reliance on improved forecasting accuracy and
resolutions. The increase of data volume comes from higher resolution satellites
and sensors, from a growing list of new sensors (traditional as well as
smallsats/cubesats), and from an explosion of new virtual observing systems
made possible by the internet of things (IoT). These large increases are
expected to present major challenges for exploiting these data sources, and
AI has emerged as a potentially transformational and mitigating technology.
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