Earth System Modeling, Data Assimilation and Predictability
Atmosphere, Oceans, Land and Human Systems

This book provides a broad introduction to numerical models, data assimilation, and predictability for coupled Earth–Human Systems.

Eugenia Kalnay (Author), Safa Mote (Author), Cheng Da (Author)

9781107009004, Cambridge University Press

Hardback, published 31 October 2024

372 pages
25 x 18 x 2.7 cm, 0.81 kg

Review of previous edition: '… this book … is extremely useful, informative, and well-written … there are many instances where items that were only marginally familiar beforehand have now become very clear.' Brian O. Blanton, Senior Scientist/Oceanographer, University of North Carolina, Chapel Hill

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 Earth–human 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.

Dedication
Preface to the Second Edition
Reviews and Comments on the First Edition
Foreword to the first edition
Acknowledgement in the First Edition
List of Variables
List of Abbreviations
1. An overview of numerical weather prediction
2. The continuous equations
3. Numerical discretization of the equations of motion
4. Introduction to the parameterization of subgrid-scale physical processes
5. Data assimilation
6. Atmospheric predictability and ensemble forecasting
A. Coding and checking the tangent linear and the adjoint models
B. Post-processing of numerical model output to obtain station weather fore-casts.

Subject Areas: Meteorology & climatology [RBP]