- Title Pages
- [UNTITLED]
- Previous sessions
- Preface
- List of participants
- 1 4D-VAR: four-dimensional variational assimilation
- 2 Four-dimensional variational data assimilation
- 3 Introduction to the Kalman filter
- 4 Smoothers
- 5 Observation influence diagnostic of a data assimilation system
- 6 Observation impact on the short-range forecast
- 7 Background error covariances
- 8 Observation error specifications
- 9 Errors. A posteriori diagnostics
- 10 Error dynamics in ensemble Kalman-filter systems
- 11 Short-range error statistics in an ensemble Kalman filter
- 12 Error dynamics in ensemble Kalman filter systems
- 13 Particle filters for the geosciences
- 14 Second-order methods for error propagation in variational data assimilation
- 15 Adjoints by automatic differentiation
- 16 Assimilation of images
- 17 Multigrid algorithms and local mesh refinement methods in the context of variational data assimilation
- 18 Selected topics in multiscale data assimilation
- 19 Data assimilation in meteorology
- 20 An introduction to inverse modelling and parameter estimation for atmosphere and ocean sciences
- 21 Greenhouse gas flux inversion
- 22 Data assimilation in atmospheric chemistry and air quality
- 23 Combining models and data in large-scale oceanography
- 24 Data assimilation in coastal oceanography
- 25 Data assimilation in glaciology
Adjoints by automatic differentiation
Adjoints by automatic differentiation
- Chapter:
- (p.349) 15 Adjoints by automatic differentiation
- Source:
- Advanced Data Assimilation for Geosciences
- Author(s):
L. Hascoët
- Publisher:
- Oxford University Press
This chapter describes how adjoint algorithms can be created by automatic differentiation (AD). Data assimilation makes intensive use of gradients. In many situations, the so-called adjoint approach is generally the most efficient way to compute gradients, by propagating derivatives backwards from the result of the given model or function. Writing an adjoint algorithm by hand is a complex, error-prone task. When the given model is provided in the form of a computer algorithm, AD can build its adjoint algorithm mechanically, for instance by program transformation. This chapter presents the principles of AD, focusing on the adjoint mode. It provides a brief panorama of existing AD tools, and the program analysis and compiler technology that they employ to produce efficient adjoint algorithms.
Keywords: automatic differentiation, program transformation, compiler, program analysis, adjoint algorithm, gradient
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- Title Pages
- [UNTITLED]
- Previous sessions
- Preface
- List of participants
- 1 4D-VAR: four-dimensional variational assimilation
- 2 Four-dimensional variational data assimilation
- 3 Introduction to the Kalman filter
- 4 Smoothers
- 5 Observation influence diagnostic of a data assimilation system
- 6 Observation impact on the short-range forecast
- 7 Background error covariances
- 8 Observation error specifications
- 9 Errors. A posteriori diagnostics
- 10 Error dynamics in ensemble Kalman-filter systems
- 11 Short-range error statistics in an ensemble Kalman filter
- 12 Error dynamics in ensemble Kalman filter systems
- 13 Particle filters for the geosciences
- 14 Second-order methods for error propagation in variational data assimilation
- 15 Adjoints by automatic differentiation
- 16 Assimilation of images
- 17 Multigrid algorithms and local mesh refinement methods in the context of variational data assimilation
- 18 Selected topics in multiscale data assimilation
- 19 Data assimilation in meteorology
- 20 An introduction to inverse modelling and parameter estimation for atmosphere and ocean sciences
- 21 Greenhouse gas flux inversion
- 22 Data assimilation in atmospheric chemistry and air quality
- 23 Combining models and data in large-scale oceanography
- 24 Data assimilation in coastal oceanography
- 25 Data assimilation in glaciology
