|
Showing 1 - 4 of
4 matches in All Departments
Mathematics of Planet Earth (MPE) was started and continues to be
consolidated as a collaboration of mathematical science
organisations around the world. These organisations work together
to tackle global environmental, social and economic problems using
mathematics.This textbook introduces the fundamental topics of MPE
to advanced undergraduate and graduate students in mathematics,
physics and engineering while explaining their modern usages and
operational connections. In particular, it discusses the links
between partial differential equations, data assimilation,
dynamical systems, mathematical modelling and numerical simulations
and applies them to insightful examples.The text also complements
advanced courses in geophysical fluid dynamics (GFD) for
meteorology, atmospheric science and oceanography. It links the
fundamental scientific topics of GFD with their potential usage in
applications of climate change and weather variability. The
immediacy of examples provides an excellent introduction for
experienced researchers interested in learning the scope and
primary concepts of MPE.
Mathematics of Planet Earth (MPE) was started and continues to be
consolidated as a collaboration of mathematical science
organisations around the world. These organisations work together
to tackle global environmental, social and economic problems using
mathematics.This textbook introduces the fundamental topics of MPE
to advanced undergraduate and graduate students in mathematics,
physics and engineering while explaining their modern usages and
operational connections. In particular, it discusses the links
between partial differential equations, data assimilation,
dynamical systems, mathematical modelling and numerical simulations
and applies them to insightful examples.The text also complements
advanced courses in geophysical fluid dynamics (GFD) for
meteorology, atmospheric science and oceanography. It links the
fundamental scientific topics of GFD with their potential usage in
applications of climate change and weather variability. The
immediacy of examples provides an excellent introduction for
experienced researchers interested in learning the scope and
primary concepts of MPE.
In this book the authors describe the principles and methods behind
probabilistic forecasting and Bayesian data assimilation. Instead
of focusing on particular application areas, the authors adopt a
general dynamical systems approach, with a profusion of
low-dimensional, discrete-time numerical examples designed to build
intuition about the subject. Part I explains the mathematical
framework of ensemble-based probabilistic forecasting and
uncertainty quantification. Part II is devoted to Bayesian
filtering algorithms, from classical data assimilation algorithms
such as the Kalman filter, variational techniques, and sequential
Monte Carlo methods, through to more recent developments such as
the ensemble Kalman filter and ensemble transform filters. The
McKean approach to sequential filtering in combination with
coupling of measures serves as a unifying mathematical framework
throughout Part II. Assuming only some basic familiarity with
probability, this book is an ideal introduction for graduate
students in applied mathematics, computer science, engineering,
geoscience and other emerging application areas.
In this book the authors describe the principles and methods behind
probabilistic forecasting and Bayesian data assimilation. Instead
of focusing on particular application areas, the authors adopt a
general dynamical systems approach, with a profusion of
low-dimensional, discrete-time numerical examples designed to build
intuition about the subject. Part I explains the mathematical
framework of ensemble-based probabilistic forecasting and
uncertainty quantification. Part II is devoted to Bayesian
filtering algorithms, from classical data assimilation algorithms
such as the Kalman filter, variational techniques, and sequential
Monte Carlo methods, through to more recent developments such as
the ensemble Kalman filter and ensemble transform filters. The
McKean approach to sequential filtering in combination with
coupling of measures serves as a unifying mathematical framework
throughout Part II. Assuming only some basic familiarity with
probability, this book is an ideal introduction for graduate
students in applied mathematics, computer science, engineering,
geoscience and other emerging application areas.
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
Higher
Michael Buble
CD
(1)
R487
Discovery Miles 4 870
|