STAR Seminars

STAR Science Seminars
Presenter:
Isaac Moradi, NASA

Sponsor(s):

STAR Science Seminar Series

Remote Access:
WebEx:
Event Number:    905 458 573   
Password: STARSeminar
Event address for attendees:
https://noaa-nesdis-star.webex.com/noaa-nesdis-star/j.php?MTID=m9876a81238d7e49c4b1dc3d202e9d614

Audio:
  
    +1-415-527-5035 US Toll
    Access code: 905 458 573

Abstract:
We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the rainbands of tropical cyclones (TC). These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated into numerical models to provide accurate initial conditions for the NWP models. The BMCI technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI).

Bio:

Dr. Isaac Moradi is a remote sensing scientist with over fifteen years of experience specializing in radiative transfer modeling; Observing System Simulation Experiments (OSSE); data assimilation; satellite data analysis and bias correction; atmospheric humidity and ice clouds; inverse methods and retrieving geophysical variables from satellite observations; solar radiation resource assessment; quality assurance of solar radiation and in-situ radiosonde measurements; and developing new instrument concepts especially for measuring tropospheric humidity. Please also see https://science.gsfc.nasa.gov/sed/bio/isaac.moradi

POC:
Stacy Bunin, stacy.bunin@noaa.gov

STAR Science Seminars
Presenters:
Imme Ebert-Uphoff of CIRA and Elizabeth Barnes and Ben Toms of Colorado State University
(presenting remotely)

Sponsor(s):

STAR Science Seminar Series

Remote Access:
WebEx:
Event Number:    904 841 535   
Password: STARSeminar
Event address for attendees:
https://noaa-nesdis-star.webex.com/noaa-nesdis-star/j.php?MTID=ma4af12891805bfb155ba26ba1d4a4330

Audio:
  
    +1-415-527-5035 US Toll
    Access code: 904 841 535

Abstract:
TBD
About the

Presenter(s):

Imme Ebert-Uphoff received B.S. and M.S. degrees in Mathematics from the Technical University of Karlsruhe (known today as Karlsruhe Institute of Technology or KIT).  She received M.S and Ph.D. degrees in Mechanical Engineering from the Johns Hopkins University. She was a faculty member in Mechanical Engineering at Georgia Tech for over 10 years, before joining the Electrical & Computer Engineering department at Colorado State in 2011 as research professor.  Her research interests are in applying data science methods to climate applications.  She is also very involved in activities to build bridges between the AI community and the earth science community, including serving on the steering committee of the annual Climate Informatics workshop, and of the NSF sponsored research coordination network (RCN) on Intelligent Systems for the Geosciences.  Starting July 1, 2019, she is spending 50% of her time with CIRA to support their machine learning activities.
Dr. Elizabeth (Libby) Barnes is an associate professor of Atmospheric Science at Colorado State University. She joined the CSU faculty in 2013 after obtaining dual B.S. degrees (Honors) in Physics and Mathematics from the University of Minnesota, obtaining her Ph.D. in Atmospheric Science from the University of Washington, and spending a year as a NOAA Climate & Global Change Fellow at the Lamont-Doherty Earth Observatory. Professor Barnes' research is focused on large scale atmospheric variability and the data analysis tools used to understand its dynamics. Topics of interest include jet-stream dynamics, Arctic-midlatitude connections, subseasonal-to-seasonal (S2S) prediction of extreme weather events (she is currently Task Force Lead for the NOAA MAPP Subseasonal-to-Seasonal (S2S) Prediction Task Force), health-related climate impacts, and data science methods for climate research (e.g. machine learning, causal discovery). She teaches graduate courses on fundamental atmospheric dynamics and data science and statistical analysis methods.
Ben Toms is a fourth year PhD student in the Barnes research group in the Department of Atmospheric Science at Colorado State University.  His PhD research focuses on using neural networks to improve our understanding of decadal predictability within the climate system.  This research requires a fundamental understanding of neural networks and techniques for their interpretation, so he enjoys testing which methods proposed by the computer science community are transferrable to atmospheric science.

POC:
Stacy Bunin, stacy.bunin@noaa.gov