STAR Seminars

STAR Science Seminars
Presenter:

F. Joseph "Joe" Turk and Chi O. Ao, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 

Sponsor(s):
STAR Science Seminar Series
Remote access:
WebEx:
Event Number:     902 629 658   
Password: STARSeminar
Event address for attendees:
https://noaa-nesdis-star.webex.com/noaa-nesdis-star/j.php?MTID=me9f3b586d540b847e7aa28c848f6b3e2
Audio:
       
+1-415-527-5035 US Toll
Access code: 902 629 658


Abstract:

As stated  in the recent Decadal Survey for Earth Observations from Space, the climate and weather forecast predictive capability for precipitation intensity is limited by gaps in the understanding of basic cloud-convective processes.  This process lacks several observational constraints, one being the difficulty in obtaining the thermodynamic profile (i.e., vertically resolved pressure,temperature, and water vapor structure) in close proximity to convective clouds.  The objective of the Radio Occultations and Heavy Precipitation (ROHP) experiment, orbiting onboard the Spanish PAZ satellite since May 2018, is to demonstrate the simultaneous capability to detect heavy precipitation along the same RO ray paths used to estimate the thermodynamic profile. While conventional RO does not directly provide this capability, PRO enhances standard RO by receiving the GNSS signals in two orthogonal polarizations (“H” and “V”). Owing to hydrometeor asymmetry, the H- and V-polarized radio signals propagating through heavy precipitation will experience differential phase delays,measurable via the ROHP polarimetric antenna.

In this presentation we will discuss the on-orbit calibration and validation of the ROHP data, and present potential applications for these data in weather modeling. The ROHP calibration is performed with an extensive dataset of one year of observations, co-located with independent information from Global Precipitation Mission (GPM) precipitation products and ionospheric activity.  The validation demonstrates how the calibrated products can be used as a proxy for heavy precipitation.  The PRO signals also exhibit positive differential phase signatures well above the freezing level, indicating possible sensitivity to frozen hydrometeors and the cloud vertical structure.  This knowledge of the presence of precipitation associated with the RO observation is useful for the evaluation and diagnosis of NWP forecast models.  The use of PRO in data assimilation methods will require an observation operator that can simulate all contributions to the differential phase delay along realistic RO propagation paths, taking into account the cloud structure.
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Presenter(s):

F.J. "Joe" Turk is a radar scientist at JPL, where he has been since 2009.  From 1995-2009, he was a member of the meteorological applications group at the Naval Research Laboratory, Marine Meteorology Division, in Monterey, CA. He received his Ph.D. degree from Colorado State University, and his M.S. and B.S. degrees from Michigan Technological University, all in electrical engineering.  His work experience has covered polarimetric weather radar, satellite passive microwave and radar observations and applications, microwave radiative transfer, polarimetric RO, and airborne radar and wind lidar observations. He is a member of NASA's Precipitation Measurement Missions science team.

Chi O. Ao is a research technologist at JPL with over 15 years of experience in GNSS radio occultation (RO) receiver tracking and inversion techniques, simulation methods, data analysis, and climate applications.  He leads the RO processing and applications team from multiple missions including CHAMP and COSMIC at JPL. He is currently the GNSS-RO Scientist of the Jason-CS/Sentinel-6mission, the Principal Investigator of the NASA Earth Science U.S. Participating Program for the ROHP-PAZ experiment, and a member of the Decadal Survey Incubation Study Team for the Planetary Boundary Layer.

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

STAR Science Seminars
Presenter:

Vladimir Krasnopolsky, NWS/NCEP/EMC

Sponsor(s):
STAR Science Seminar Series: Special Seminar Series on AI
Remote access:
WebEx:
Event Number:     905 519 423   
Password: STARSeminar
Event address for attendees:
https://noaa-nesdis-star.webex.com/noaa-nesdis-star/j.php?MTID=mc7f89d898d256f1b2fed2795e488a264
Audio:
       
+1-415-527-5035 US Toll

Access code: 905 519 423



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.

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Bio:

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.”

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