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URL: https://earth.stanford.edu/events/ess-wed-seminar-predictability-limits-data-assimilation-and-simultaneous-state-and-parameter
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Title: ESS Wed Seminar: Predictability Limits, Data Assimilation, and Simultaneous State and Parameter Estimation BY Professor Fuqing Zhan, Meteorology and Atmospheric Science, Penn State University
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Texto: ESS Wed Seminar: Predictability Limits, Data Assimilation, and Simultaneous State and Parameter Estimation BY Professor Fuqing Zhan, Meteorology and Atmospheric Science, Penn State University Skip to main navigation Inside Stanford Earth menu Menu list icon Menu list collapse toggle Main navigation Home About Departments & Programs Educational Farm Directory Our Community Commencement Visit Us Contact us Academics Undergraduates Graduate Students Careers Data Science Field Learning K-12 Outreach Working Professionals Faculty & Research Faculty Directory Research Groups Industrial Affiliate Programs Shared Analytical Facilities and Research Resources Undergraduate Research News & Events Stanford Earth Matters magazine School Highlights Media Mentions Events People Spotlights Stanford Earth SCHOOL OF EARTH, ENERGY & ENVIRONMENTAL SCIENCES ESS Wed Seminar: Predictability Limits, Data Assimilation, and Simultaneous State and Parameter Estimation BY Professor Fuqing Zhan, Meteorology and Atmospheric Science, Penn State University When: Wednesday, Apr 24, 2019 12:30 PM Where: Y2E2, Room #111 Audience: Faculty/Staff, Students, Alumni/Friends Sponsor: Department of Earth System Science I will first present our recent progresses in using state-of-the-art global numerical weather prediction models in identifying the predictability limits of the multi-scale atmosphere. We show that an intrinsic predictability limit of about 2 weeks may indeed exist for the prediction of the mid-latitude day-to-day weather and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days beyond the approximately 10-day current practical limit. We further develop a new theoretical framework in understanding and quantifying the error growth and predictability limits of the multi-scale atmosphere that has an approximate -3 energy spectral slope at the synoptic scales and an approximate -5/3 slope at smaller scales. In the second part of my talk, I will first show some of our recent advances in improving the practical predictability of severe weather and hurricanes through cloud-allowing assimilation of high-resolution Doppler radar and/or all-sky satellite radiance observations. I will further advocate for a generalized data assimilation software framework on Ensemble-based Simultaneous State and Parameter Estimation (ESSPE) that will facilitate data-model integration, uncertainty quantification and improved understanding and modeling of physical processes for the broad earth and environmental science communities. I will show example of its applications for improving atmospheric boundary and air-sea flux parameterizations, coupled atmosphere-CO2 data assimilation, coupled hydrology and land surface modeling, and palaeclimate analysis. Through augmenting uncertain model parameters as part of the state vector, the ESSPE framework will allow for simultaneous state and parameter estimation through assimilating in-situ and/or remotely sensed observations. Stanford Earth SCHOOL OF EARTH, ENERGY & ENVIRONMENTAL SCIENCES Join us on Social Media Twitter Twitter Facebook Facebook Instagram Instagram Linkedin Linkedin Alumni Give to Stanford Earth Newsroom Contact Provide Website Feedback Report accessibility issues Login Subscribe to Stanford Earth Matters Our Monthly Research News Alert Your Email Address Stanford University Logo Stanford Home Maps & Directions Search Stanford Emergency Info Terms of Use Privacy Copyright Trademarks Non-Discrimination Accessibility © Stanford University. Stanford, California 94305. mail linkedin double carrot left arrow left double carrot player instagram close carrot quote facebook twitter plus minus search menu arrow cloud clock


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