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Earth System Modeling, Data Assimilation and Predictability

by Safa Mote , Cheng Da
Save 17% Save 17%
Current price ₹15,267.00
Original price ₹18,321.00
Original price ₹18,321.00
Original price ₹18,321.00
(-17%)
₹15,267.00
Current price ₹15,267.00

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Book cover type: Hardcover
  • ISBN13: 9781107009004
  • Binding: Hardcover
  • Subject: N/A
  • Publisher: Cambridge University Press
  • Publisher Imprint: Cambridge University Press
  • Publication Date:
  • Pages: 372
  • Original Price: GBP 120.0
  • Language: English
  • Edition: 2 Revised edition
  • Item Weight: 794 grams
  • BISAC Subject(s): Earth Sciences / Meteorology & Climatology

Data Assimilation methods are now applied to many areas of prediction and forecasting. This second edition introduces readers to applications across Earth systems and coupled Earth-Human Systems. It's indispensable for advanced undergraduate and graduate students, researchers, and practitioners working in weather forecasting and climate prediction.

Da, Cheng: - Cheng Da works on Coupled Data Assimilation as a postdoctoral research associate at the University of Maryland and the Global Modeling and Assimilation Office at NASA's Goddard Space Flight Center. Supported by the NASA Earth and Space Science Fellowship, he earned his Ph.D. degree under the supervision of Professor Kalnay at the University of Maryland, focusing on the assimilation of precipitation and nonlocal observations in the ensemble data assimilation system and coupled data assimilation. Before this, he earned his bachelor's and Master's degrees in Meteorology at Florida State University, working on radiance assimilation from spaceborne sensors.

Mote, Safa: - Safa Mote is Assistant Professor of Computational and Applied Mathematics at Portland State University and Visiting Assistant Professor of Atmospheric and Oceanic Sciences at the University of Maryland who has worked on a wide range of challenging interdisciplinary problems. He has two Ph.D. degrees in Physics and in Applied Mathematics and Statistics, and Scientific Computing from the University of Maryland. He designs mathematical models to propose and assess holistic policies that lead to sustainability in interconnected environmental, economic, climate, and health systems. He develops computational methods based on Dynamical Systems, Machine Learning, and Data Assimilation to forecast extreme weather and climate events, improve subseasonal to seasonal predictions, and create projections for the coupled energy-water-food nexus.

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