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DTSTART;TZID=America/New_York:20190328T080000
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SUMMARY:Big Data Assimilation: A New Science for Weather Prediction and Beyond
DESCRIPTION:This event has passed. See the seminar recording here:\n\n \n\nDr. Takemasa Miyoshi\nRIKEN in Japan\nThursday March 28, 2019, 12:00-1:00 PM\nESSIC Conference Room 4102, 5825 University Research Ct, College Park, MD 20740\nAbstract:\nData Assimilation was introduced in numerical weather prediction to combine computer model forecasts with real-world observations, using dynamical systems theory and statistical mathematics. Computing, remote sensing, and information/communication technologies are all advancing rapidly, and Data Assimilation is becoming more popular as a means to perform cyber-physical fusion in other sciences and technology fields. At RIKEN, the Japan’s flagship research institute for all sciences, we pioneered future possibilities of numerical weather prediction by taking advantage of the powerful K computer, and Big Data from advanced sensing technologies such as the Phased Array Weather Radar and the Himawari-8 geostationary satellite. We thus developed innovative “Big Data Assimilation” (BDA) technology, and made possible a 30-second-update of severe weather prediction at 100-m resolution, two orders of magnitude higher and faster than what is currently used in operational numerical weather prediction centers. I will talk about the exciting results of our BDA efforts in numerical weather prediction, and give a perspective towards Data Assimilation becoming a new science hub – from severe weather forecasting to many other new important applications.\n \nBio-sketch:\nDr. Takemasa Miyoshi received his B.S. degree in theoretical physics from the Kyoto University, and M.S. and Ph.D. degrees in meteorology on ensemble data assimilation from University of Maryland (UMD). Dr. Takemasa Miyoshi started his professional career as a civil servant at the Japanese Meteorological Agency (JMA). He was a tenure-track Assistant Professor at University of Maryland in 2011. Dr. Miyoshi is now leading the Data Assimilation Research Team in RIKEN Advanced Institute for Computational Science, and has been working towards his goals of advancing the science of data assimilation as well as a deep commitment to education. Dr. Miyoshi’s scientific achievements include 96 peer-reviewed publications and more than 100 invited conference presentations including the Core Science Keynote at the American Meteorological Society Annual Meeting (2015). Dr. Miyoshi has been recognized by several prestigious awards such as the Yamamoto-Syono Award by the Meteorological Society of Japan (2008), the Young Scientists’ Prize by the Minister of Education, Culture, Sports, Science and Technology (2014), the Japan Geosciences Union Nishida Prize (2015), and the Meteorological Society of Japan Award (2016) – the highest award of the society.\n \nContact: \nDr. Eugenia Kalnay, ekalnay@umd.edu\n \nWebex info: \nEvent site: http://go.umd.edu/miyoshi \nEvent number: 738 407 907\nEvent password: essic\n——————————————————-\nTo join the online event\n——————————————————-\n1. Click here to join the online event.\nOr copy and paste the following link to a browser: \nhttps://umd.webex.com/umd/onstage/g.php?MTID=e3eed8cccc22a13da59e2f857a561a6cf\n2. Click “Join Now”.\n——————————————————-\nTo join the audio conference only\n——————————————————-\nUS Toll: +1-415-655-0002\nGlobal call-in numbers: \nhttps://umd.webex.com/umd/globalcallin.php?serviceType=EC&ED=774097752&tollFree=0\nAccess code: 738 407 907\n——————————————————-\nFor IT assistance\n——————————————————-\nTravis Swaim: tswaim1@umd.edu\nCazzy Medley: cazzy@umd.edu\n\nFollow ESSIC:\nESSIC homepage: http://essic.umd.edu/\nESSIC seminar calendar: MSQ-4102; http://go.umd.edu/essicseminar\nESSIC twitter: http://twitter.com/ESSICUMD\nESSIC facebook: http://facebook.com/ESSICUMD\nESSIC seminar coordinator: Dr. John Yang, jxyang@umd.edu\n \n \n
URL:https://essic.umd.edu/events/big-data-assimilation-a-new-science-for-weather-prediction-and-beyond/
ORGANIZER;CN=John Xun Yang:MAILTO:jxyang@umd.edu
CATEGORIES:Spring 2019
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