基本信息
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职业迁徙
个人简介
RESEARCH
My group employs advanced numerical, machine learning (ML), and statistical techniques to improve numerical weather prediction models and products. The focus of my research is on the Earth's atmosphere, but the techniques my group develops are also applicable to a wide range of complex physical systems. Our work involves both theoretical investigation and experimentation with simple idealized and complex state-of-the-art models. We carry out research in the following specific areas:
Numerical weather prediction (NWP)
Earth system modeling (ESM)
Data assimilation (DA)
Machine learning (ML)
Predictability of the atmosphere, ocean, and other complex systems
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ISTVAN SZUNYOGH
Istvan Szunyogh
Professor, Graduate Committee Chair
Atmospheric dynamics, predictability, numerical weather prediction, data assimilation, machine learning
szunyogh@tamu.edu
(979) 458-0553
Eller O&M 1010A
RESEARCH
My group employs advanced numerical, machine learning (ML), and statistical techniques to improve numerical weather prediction models and products. The focus of my research is on the Earth's atmosphere, but the techniques my group develops are also applicable to a wide range of complex physical systems. Our work involves both theoretical investigation and experimentation with simple idealized and complex state-of-the-art models. We carry out research in the following specific areas:
Numerical weather prediction (NWP)
Earth system modeling (ESM)
Data assimilation (DA)
Machine learning (ML)
Predictability of the atmosphere, ocean, and other complex systems
Outreach
How well can we know the future? How well can we know the present? MIT Enterprise Forum, Houston, TX, November 12, 2014 https://www.youtube.com/watch?v=mfwRPruwZk4
Panel discussion. MIT Enterprise Forum, Houston, TX, November 12, 2014. https://www.youtube.com/watch?v=PD2BXGCvZ6c
Martian weather analysis and forecasting. Aggies talk. https://youtu.be/_nAEabJXP_c
SELECTED PUBLICATIONS
For a full list of publications, please see CV at the Links Section
* indicates a student and ** indicates a postdoctoral associate, whose research was advised or co-advised by I. S.
Books and Book Chapters
Szunyogh, I., 2014: Applicable Atmospheric Dynamics: Techniques for the Exploration of Atmospheric Dynamics. World Scientific, Singapore, pp. 588.
Kalnay, E., B. Hunt, E. Ott, and I. Szunyogh, 2006: Ensemble forecasting and data assimilation: two problems with the same solution? In Predictability of Weather and Climate. Eds. T. Palmer and R. Hagedorn. Cambridge University Press. Cambridge,157-180.
Selected Articles in Peer-reviewed Journals
Pathak*, J., A. Wikner*, B. Hunt, I. Szunyogh, M. Girvan, and E. Ott, 2021: Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components (under review).
Szunyogh, I., E. Forinash*, G. Gyarmati, Y. Jia, P. Chang, and R. Saravanan, 2021: Evaluation of a coupled modeling approach for the investigation of the effects of SST mesoscale variability on the atmosphere (under review). Preprint: https://doi.org/10.1002/essoar.10504810.1.
Arcomano*, T., I. Szunyogh, J. Pathak*, A. Wikner*, B. Hunt, and E. Ott, 2020: A machine learning-based global atmospheric forecast model. Geophys. Res. Lett., 47, e2020GL087776.
Wikner*, A., J. Pathak*, B. Hunt, M. Girvan, T. Arcomano*, I. Szunyogh, A. Pomerance, and E. Ott, 2020: Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems. Chaos, 30, 053111.
Zagar, N., and I. Szunyogh, 2020: Comments on “What is the predictability limit of midlatitude weather?” by Zhang et al., J. Atmos, Sci., 76, 1077-1091. J. Atmos. Sci., 77, 781-785.
Jia, Y., P. Chang, I. Szunyogh, R. Saravanan, and J. T. Bacmeister, 2019: A modeling strategy for the investigation of the effect of mesoscale SST feedback to the atmosphere. Geophys. Res. Lett., 46, 3982-3989.
Kavulich*, M. J., I. Szunyogh, G. Gyarmati, and R. J. Wilson, 2013: Local dynamics of baroclinic waves in the Martian atmosphere. J. Atmos. Sci., 70, 3415–3447.
Szunyogh, I., E. J. Kostelich, G. Gyarmati, E. Kalnay, B. R. Hunt, E. Ott, E. Satterfield*, and J. A. Yorke, 2008: A Local Ensemble Transform Kalman Filter data assimilation system for the NCEP global model. Tellus, 60A, 113-130.
Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: a Local Ensemble Transform Kalman Filter. Physica D, 230, 112-126.
Ott, E., B. R. Hunt, I. Szunyogh, A. V. Zimin*, E. J. Kostelich, M. Corazza*, E. Kalnay, D. J. Patil, and J. A. Yorke, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428.
Zimin*, A. V., I. Szunyogh, D. J. Patil, B. R. Hunt, and E. Ott, 2003: Extracting envelopes of Rossby wave packets. Mon. Wea. Rev., 131, 1011-1017.
Szunyogh, I., Z. Toth, R. E. Morss, S. J. Majumdar, B. J. Etherton, and C. H. Bishop, 2000: The effect of targeted dropsonde observations during the 1999 Winter Storm Reconnaissance program. Mon. Wea. Rev., 128, 3520-3537.
Szunyogh I., E. Kalnay, and Z. Toth, 1997: A comparison of Lyapunov and optimal vectors in a low-resolution GCM. Tellus, 49A, 200-227.
Szunyogh I., 1993: Finite-dimensional quasi-Hamiltonian structure in simple model equations. Meteorology and Atmospheric Physics, 52, 49-57.
EDUCATION
Ph.D., Earth Sciences, Hungarian Academy of Sciences, Budapest, Hungary
Diploma, Meteorology, Eotvos Lorand University, Budapest, Hungary
AWARDS
College of Geosciences 2017 Distinguished Achievement Award: Faculty Excellence in Research
Certificate of Recognition, January 5, 2015 from the U.S. THORPEX Executive Committee. For his international leadership and predictability and data assimilation research contributions to U.S. participation in the World Meteorological Organization's THORPEX Weather Research Program
Certificate of Appreciation, November 17, 2014 from the World Weather Research Programme (WWRP) of the World Meteorological Organization (WMO). In recognition of an outstanding contribution to the WMO THORPEX program for the years 2005-2014.
My group employs advanced numerical, machine learning (ML), and statistical techniques to improve numerical weather prediction models and products. The focus of my research is on the Earth's atmosphere, but the techniques my group develops are also applicable to a wide range of complex physical systems. Our work involves both theoretical investigation and experimentation with simple idealized and complex state-of-the-art models. We carry out research in the following specific areas:
Numerical weather prediction (NWP)
Earth system modeling (ESM)
Data assimilation (DA)
Machine learning (ML)
Predictability of the atmosphere, ocean, and other complex systems
logo
Geosciences
Departments & Centers
Directory
Giving
News & Events
Search Geosciences
unit logo
About
Academics
Future Students
Undergraduate Students
Graduate Students
Facilities & Resources
People
Research
College News & Events
Inside Geosciences
Search Geosciences
ISTVAN SZUNYOGH
Istvan Szunyogh
Professor, Graduate Committee Chair
Atmospheric dynamics, predictability, numerical weather prediction, data assimilation, machine learning
szunyogh@tamu.edu
(979) 458-0553
Eller O&M 1010A
RESEARCH
My group employs advanced numerical, machine learning (ML), and statistical techniques to improve numerical weather prediction models and products. The focus of my research is on the Earth's atmosphere, but the techniques my group develops are also applicable to a wide range of complex physical systems. Our work involves both theoretical investigation and experimentation with simple idealized and complex state-of-the-art models. We carry out research in the following specific areas:
Numerical weather prediction (NWP)
Earth system modeling (ESM)
Data assimilation (DA)
Machine learning (ML)
Predictability of the atmosphere, ocean, and other complex systems
Outreach
How well can we know the future? How well can we know the present? MIT Enterprise Forum, Houston, TX, November 12, 2014 https://www.youtube.com/watch?v=mfwRPruwZk4
Panel discussion. MIT Enterprise Forum, Houston, TX, November 12, 2014. https://www.youtube.com/watch?v=PD2BXGCvZ6c
Martian weather analysis and forecasting. Aggies talk. https://youtu.be/_nAEabJXP_c
SELECTED PUBLICATIONS
For a full list of publications, please see CV at the Links Section
* indicates a student and ** indicates a postdoctoral associate, whose research was advised or co-advised by I. S.
Books and Book Chapters
Szunyogh, I., 2014: Applicable Atmospheric Dynamics: Techniques for the Exploration of Atmospheric Dynamics. World Scientific, Singapore, pp. 588.
Kalnay, E., B. Hunt, E. Ott, and I. Szunyogh, 2006: Ensemble forecasting and data assimilation: two problems with the same solution? In Predictability of Weather and Climate. Eds. T. Palmer and R. Hagedorn. Cambridge University Press. Cambridge,157-180.
Selected Articles in Peer-reviewed Journals
Pathak*, J., A. Wikner*, B. Hunt, I. Szunyogh, M. Girvan, and E. Ott, 2021: Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components (under review).
Szunyogh, I., E. Forinash*, G. Gyarmati, Y. Jia, P. Chang, and R. Saravanan, 2021: Evaluation of a coupled modeling approach for the investigation of the effects of SST mesoscale variability on the atmosphere (under review). Preprint: https://doi.org/10.1002/essoar.10504810.1.
Arcomano*, T., I. Szunyogh, J. Pathak*, A. Wikner*, B. Hunt, and E. Ott, 2020: A machine learning-based global atmospheric forecast model. Geophys. Res. Lett., 47, e2020GL087776.
Wikner*, A., J. Pathak*, B. Hunt, M. Girvan, T. Arcomano*, I. Szunyogh, A. Pomerance, and E. Ott, 2020: Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems. Chaos, 30, 053111.
Zagar, N., and I. Szunyogh, 2020: Comments on “What is the predictability limit of midlatitude weather?” by Zhang et al., J. Atmos, Sci., 76, 1077-1091. J. Atmos. Sci., 77, 781-785.
Jia, Y., P. Chang, I. Szunyogh, R. Saravanan, and J. T. Bacmeister, 2019: A modeling strategy for the investigation of the effect of mesoscale SST feedback to the atmosphere. Geophys. Res. Lett., 46, 3982-3989.
Kavulich*, M. J., I. Szunyogh, G. Gyarmati, and R. J. Wilson, 2013: Local dynamics of baroclinic waves in the Martian atmosphere. J. Atmos. Sci., 70, 3415–3447.
Szunyogh, I., E. J. Kostelich, G. Gyarmati, E. Kalnay, B. R. Hunt, E. Ott, E. Satterfield*, and J. A. Yorke, 2008: A Local Ensemble Transform Kalman Filter data assimilation system for the NCEP global model. Tellus, 60A, 113-130.
Hunt, B. R., E. J. Kostelich, and I. Szunyogh, 2007: Efficient data assimilation for spatiotemporal chaos: a Local Ensemble Transform Kalman Filter. Physica D, 230, 112-126.
Ott, E., B. R. Hunt, I. Szunyogh, A. V. Zimin*, E. J. Kostelich, M. Corazza*, E. Kalnay, D. J. Patil, and J. A. Yorke, 2004: A local ensemble Kalman filter for atmospheric data assimilation. Tellus, 56A, 415-428.
Zimin*, A. V., I. Szunyogh, D. J. Patil, B. R. Hunt, and E. Ott, 2003: Extracting envelopes of Rossby wave packets. Mon. Wea. Rev., 131, 1011-1017.
Szunyogh, I., Z. Toth, R. E. Morss, S. J. Majumdar, B. J. Etherton, and C. H. Bishop, 2000: The effect of targeted dropsonde observations during the 1999 Winter Storm Reconnaissance program. Mon. Wea. Rev., 128, 3520-3537.
Szunyogh I., E. Kalnay, and Z. Toth, 1997: A comparison of Lyapunov and optimal vectors in a low-resolution GCM. Tellus, 49A, 200-227.
Szunyogh I., 1993: Finite-dimensional quasi-Hamiltonian structure in simple model equations. Meteorology and Atmospheric Physics, 52, 49-57.
EDUCATION
Ph.D., Earth Sciences, Hungarian Academy of Sciences, Budapest, Hungary
Diploma, Meteorology, Eotvos Lorand University, Budapest, Hungary
AWARDS
College of Geosciences 2017 Distinguished Achievement Award: Faculty Excellence in Research
Certificate of Recognition, January 5, 2015 from the U.S. THORPEX Executive Committee. For his international leadership and predictability and data assimilation research contributions to U.S. participation in the World Meteorological Organization's THORPEX Weather Research Program
Certificate of Appreciation, November 17, 2014 from the World Weather Research Programme (WWRP) of the World Meteorological Organization (WMO). In recognition of an outstanding contribution to the WMO THORPEX program for the years 2005-2014.
研究兴趣
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