The Application of Neural Networks in the Earth System Sciences Neural Networks Emulations for Complex Multidimensional Mappings
With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural networkmethods can be applied fruitfully. (...) The broad range of topics covered in this book ensures that researchers/graduate students from ma...
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| Format: | eBook |
| Language: | English |
| Published: |
Dordrecht
Springer Netherlands
2013, 2013
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| Edition: | 1st ed. 2013 |
| Series: | Atmospheric and Oceanographic Sciences Library
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Introduction.- Introduction to Mapping and Neural Networks
- Mapping Examples
- Some Generic Properties of Mappings
- MLP NN – A Generic Tool for Modeling Nonlinear Mappings
- Advantages and Limitations of the NN TechniqueNN Emulations
- Final remarks
- Atmospheric and Oceanic Remote Sensing Applications
- Deriving Geophysical Parameters from Satellite Measurements: Conventional Retrievals and Variational Retrievals
- NNs for Emulating Forward Models
- NNs for Solving Inverse Problems: NNs Emulating Retrieval Algorithms.-Controlling the NN Generalization and Quality Control of Retrievals
- Neural Network Emulations for SSM/I Data
- Using NNs to Go Beyond the Standard Retrieval Paradigm
- Discussion.-Applications of NNs to Developing Hybrid Earth System Numerical Models for Climate and Weather
- Numerical Modeling Background
- Hybrid Model Component and a Hybrid Model
- Atmospheric NN Applications
- An Ocean Application of the Hybrid Model Approach: Neural Network Emulation of Nonlinear Interactions in Wind Wave Models
- Discussion
- NN Ensembles and their applications
- Using NN Emulations of Dependencies between Model Variables in DAS
- NN nonlinear multi-model ensembles
- Perturbed physics and ensembles with perturbed physics
- Conclusions
- Comments about NN Technique
- Comments about other Statistical Learning Techniques