STAR Seminars

STAR Science Seminars
Note: This series will be presented online only.

Presenter(s):
Vladimir Krasnopolsky, NWS/NCEP/EMC

Sponsor(s):
STAR Science Seminar Series: Special Seminar Series on AI

Recording:
https://www.star.nesdis.noaa.gov/star/documents/seminardocs/2020/20200401_Krasnopolsky.mp4

Slides:
https://www.star.nesdis.noaa.gov/star/documents/seminardocs/2020/20200401_Krasnopolsky.pdf
https://www.star.nesdis.noaa.gov/star/documents/seminardocs/2020/20200401_Krasnopolsky.pptx

Abstract:

This introductory talk provides basic information about mostly used machine learning (ML) techniques and some ML applications developed to enhance different components of Numerical Weather Prediction (NWP) systems. Basic groups of ML applications that have been already developed for NWP systems are overviewed.Major challenges that NWP currently faces are discussed. It is shown that many of these problems can be resolved or alleviated using ML techniques. ML applications developed for NWP model initialization/data assimilation, model improvements, and model output post processing are discussed. Several examples of such application (ML satellite retrieval algorithm, ML fast parameterizations of subgrid processes, and ML nonlinear ensembles) are introduced to illustrate the capabilities of ML techniques. Advantages and limitations of ML techniques are discussed.


Bio(s):
Dr. Vladimir M. Krasnopolsky got his M.S. in Theoretical and Computational Physics and Ph. D. in Theoretical Nuclear Physics from the Moscow State University (Russia). He worked in the field of theoretical nuclear physics at the Institute of Nuclear Physics (Moscow State University) before coming to the US in 1989. Since 1990 he has been working in the field of numerical weather and climate prediction and AI applications. Vladimir works on applications of remote sensing and satellite data in meteorology, oceanography, and numerical weather and climate prediction. Dr.Krasnopolsky also works with various machine learning techniques. He developed multiple neural network applications for numerical weather and climate prediction. Dr. Krasnopolsky published two books, two book chapters, over 70 papers in refereed scientific journals. He is a member (formerly Chair) of the Committee on “Computational and Artificial Intelligence Applications in Environmental Science” of American Meteorological Society, a member of the IEEE/Computational Intelligence Society Task Force “Computational intelligence in earth and environmental sciences”, and a member of the International Neural Network Society Working Group “Computational intelligence in earth and environmental sciences”. In 2018 Vladimir was awarded AMS Distinguished Scientific Committee award for “Contributions to advancing the application of artificial neural networks to earth science problems and in particular emulations of complex multidimensional mappings.”

Seminar Contact: Stacy Bunin, stacy.bunin@noaa.gov

STAR Science Seminars
Note: This series will be presented online only.

Presenter(s):
David John Gagne, NCAR

Sponsor(s):
STAR Science Seminar Series: Special Seminar Series on AI

Remote Access:
WebEx:Event Number: 909 492 412
Password: STARSeminar

Event address for attendees:
https://noaa-nesdis-star.webex.com/noaa-nesdis-star/j.php?MTID=m8f8f05fb2abf3fd586202cc6644da0b7

Audio:
+1-415-527-5035 US Toll
Access code: 909 492 412

Abstract:
The success of a machine learning system depends on not only the choice of machine learning algorithm but also on how the the whole machine learning pipeline is constructed. In this presentation, the key components of the machine learning pipeline, including problem definition, preprocessing, choosing appropriate algorithms, training, evaluation, and interpretation will be described. Common approaches in the atmospheric sciences for each component will be explained and linked with examples from machine learning applications in the atmospheric sciences. Finally, challenges of transitioning machine learning systems to operational use will be discussed.

Bio(s):
David John Gagne is a Machine Learning Scientist in the Computational Information Systems Laboratory (CISL) and the Research Applications Laboratory (RAL) at the National Center for Atmospheric Research (NCAR). His research focuses on developing machine learning systems to improve the prediction and understanding of high impact weather, and to enhance weather and climate models. During his time at NCAR, he has collaborated with interdisciplinary teams to produce machine learning systems to study hail, tornadoes, hurricanes, and renewable energy. He has also developed short courses and hackathons to provide atmospheric scientists hands-on experience with machine learning. Gagne received his Ph.D. in meteorology from the University of Oklahoma in 2016 and completed an Advanced Study Program postdoctoral fellowship at NCAR in 2018. In addition to his duties at NCAR, he also serves as chair of the American Meteorological Society Artificial Intelligence Committee.

Seminar Contact:
Stacy Bunin, stacy.bunin@noaa.gov