Earth System Modeling, Data Assimilation and Predictability : Atmosphere, Oceans, Land and Human Systems
2 Revised edition
Book Details
Format
Paperback / Softback
ISBN-10
1107401461
ISBN-13
9781107401464
Edition
2 Revised edition
Publisher
Cambridge University Press
Imprint
Cambridge University Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Oct 31st, 2024
Print length
372 Pages
Weight
642 grams
Dimensions
16.90 x 24.50 x 2.40 cms
Product Classification:
Meteorology & climatologyClimate changeData capture & analysisComputer modelling & simulation
Ksh 9,250.00
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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.
Since the publication of the first edition of this highly regarded textbook, the value of data assimilation has become widely recognized across the Earth sciences and beyond. Data assimilation methods are now being applied to many areas of prediction and forecasting, including extreme weather events, wildfires, infectious disease epidemics, and economic modeling. This second edition provides a broad introduction to applications across the Earth systems and coupled Earthhuman systems, with an expanded range of topics covering the latest developments of variational, ensemble, and hybrid data assimilation methods. New toy models and intermediate-complexity atmospheric general circulation models provide hands-on engagement with key concepts in numerical weather prediction, data assimilation, and predictability. The inclusion of computational projects, exercises, lecture notes, teaching slides, and sample exams makes this textbook an indispensable and practical resource for advanced undergraduate and graduate students, researchers, and practitioners who work in weather forecasting and climate prediction.
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