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STAR Seminars

This page lists upcoming STAR Science Forum seminars. Presentation materials for seminars will be posted with each scheduled talk when available.

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To submit a new seminar for the series, fill this form: STAR Seminar Form.

Questions:
Please contact Stacy.Bunin@noaa.gov.

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STAR Seminars During the COVID-19 Pandemic
Until further notice, all STAR seminars will be presented via remote access only. This will be true even if the seminar was originally listed with a physical location.


29 May 2020

Title: Improving NCEP’s Global-Scale Wave Ensemble Averages using Neural Networks: Results and Next Steps
Presenter(s): Ricardo Campos, Centre for Marine Technology and Ocean Engineering
Date & Time: 29 May 2020
11:30 am - 12:30 pm ET
Location: Via webinar only,
Description:

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

Presenter(s):
Ricardo Campos, Centre for Marine Technology and Ocean Engineering
Co-authors: Vladimir Krasnopolsky; Henrique Alves, Stephen Penny

Sponsor(s):
STAR Science Seminar Series

Slides:
https://www.star.nesdis.noaa.gov/star/documents/seminardocs/2020/20200529_Campos.pdf
https://www.star.nesdis.noaa.gov/star/documents/seminardocs/2020/20200529_Campos.pptx

Abstract:
A nonlinear ensemble averaging technique is demonstrated using neural networks applied to one year (2017) of Global ocean Wave Ensemble forecast System (GWES) data provided by NCEP. Post-processing algorithms are developed based on multilayer perceptron neural networks (NN) trained with altimeter data to improve the global forecast skill, from nowcast to forecast ranges up to 10 days, including significant wave height (Hs) and wind speed (U10). NNs are applied as an alternative to the typical use of the arithmetic ensemble mean (EM). The novel method shows that one single NN model with 140 neurons is able to improve the error metrics for the whole globe while covering all forecast ranges analyzed. The bias of the widely used EM of GWES that varies from -10% to 10% for Hs compared to altimeters can be reduced to values within 5%. The RMSE of day-10 forecasts from the NN simulations indicated a gain of two days in predictability when compared to the EM, using a reasonably simple post-processing model with low computational cost.

Bio(s):
Dr. Ricardo Campos has been studying and working in the fields of Ocean Engineering, Physical Oceanography, and Meteorology for the last 17 years, more specifically with ocean waves. He has recently concluded postdoctoral research at the University of Maryland in collaboration with EMC/NCEP/NOAA, working on neural networks to improve wave forecasts. Ricardo is currently a principal investigator and visiting professor at the Centre for Marine Technology and Ocean Engineering (CENTEC/IST) in Portugal.

Seminar Contact:
Stacy Bunin, stacy.bunin@noaa.gov
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17 June 2020

Title: Rescheduled: Validation of the Polarimetric Radio Occultation and Heavy Precipitation (ROHP) data and Potential Application to Weather Modeling
Presenter(s): F. Joseph "Joe" Turk and Chi O. Ao, Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA
Date & Time: 17 June 2020
12:00 pm - 1:00 pm ET
Location: via webinar only
Description:


This seminar was originally scheduled for March 18, 2020.

STAR Science Seminars

Presenter(s):
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: 909 267 721
Password: STARSeminar

Event address for attendees:
https://noaa-nesdis-star.webex.com/noaa-nesdis-star/j.php?MTID=md261cc015eb58948537c9ac63d0f82b7

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.

-----------------------------------

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.Seminar Contact:
Stacy Bunin, stacy.bunin@noaa.gov
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23 June 2020

Title:
New
Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
Presenter(s): Janni Yuval, MIT
Date & Time: 23 June 2020
12:00 pm - 1:00 pm ET
Location: Via webinar only,
Description:

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

Presenter(s):
Janni Yuval, MIT

Co-author: Paul O'Gorman, MIT

Sponsor(s):
STAR Science Seminar Series

Remote Access:

https://noaa-nesdis-star.webex.com/noaa-nesdis-star/j.php?MTID=m8ac1fa6af0ac0738324b961f166a93f6
Meeting number: 199 929 9230
Password: STARSeminar

Join by phone
+1-415-527-5035 US Toll

Access code: 199 929 9230

Abstract:
Global climate models represent small-scale processes such as convection using subgrid models known as parameterizations, and these parameterizations contribute substantially to uncertainty in climate projections. Machine learning of new parameterizations from high-resolution model output is a promising approach, but such parameterizations have been prone to issues of instability and climate drift, and their performance for different grid spacings has not yet been investigated. Here we use a random forest to learn a parameterization from output of a three-dimensional high-resolution idealized atmospheric model. The parameterization leads to stable simulations at coarse resolution that replicate the climate of the high-resolution simulation. Retraining for different coarse-graining factors shows the parameterization performs best at smaller horizontal grid spacings. Our results yield insights into parameterization performance across length scales, and they also demonstrate the potential for learning parameterizations from global high-resolution simulations that are now emerging.

Bio(s):
Janni Yuval is a post-doctoral fellow at MIT at the department of Earth, Atmospheric and Planetary Sciences. At MIT he works with Paul O'Gorman on machine learning parameterization. He is a diverse person with a wide spectrum of interests and skills. He has a BSc. in theoretical physics, an MSc in theoretical soft matter physics, and a PhD in atmospheric dynamics. Furthermore, after finishing his PhD he worked as an algorithm developer at Mobileye. Later, he worked as a data scientist at Clalit Research Institute, where he used machine learning, and causal inference methods to develop personalized medicine. Nowadays, he is excited about the possibility to use machine learning for reducing the uncertainty in climate projections. The work he will present is accepted to Nature Communications (in press).

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