Understanding of Atmospheric Systems with Efficient Numerical Methods for Observation and Prediction

By: Contributor(s): Material type: ArticleArticleLanguage: English Publication details: IntechOpen 2019Description: 1 electronic resource (168 p.)ISBN:
  • intechopen.76493
  • 9781838801120
  • 9781838806347
  • 9781838801113
Subject(s): Online resources: Summary: Although the technology of observation and prediction of atmospheric systems draws upon many common fields, until now the interrelatedness and interdisciplinary nature of these research fields have scarcely been discussed in one volume containing fundamental theories, numerical methods, and operational application results. This is a book to provide in-depth explorations of the numerical methods developed to better understand atmospheric systems, which are introduced in eight chapters. Chapter 1 presents an efficient algorithm for tropical cyclone center determination by using satellite imagery. Chapter 2 aims to identify atmospheric systems with a new polarization remote sensing method. Chapters 3-8 place emphasis on enhancing the performance of numerical models in the prediction of atmospheric systems that should be valuable for researchers and forecasters.
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Although the technology of observation and prediction of atmospheric systems draws upon many common fields, until now the interrelatedness and interdisciplinary nature of these research fields have scarcely been discussed in one volume containing fundamental theories, numerical methods, and operational application results. This is a book to provide in-depth explorations of the numerical methods developed to better understand atmospheric systems, which are introduced in eight chapters. Chapter 1 presents an efficient algorithm for tropical cyclone center determination by using satellite imagery. Chapter 2 aims to identify atmospheric systems with a new polarization remote sensing method. Chapters 3-8 place emphasis on enhancing the performance of numerical models in the prediction of atmospheric systems that should be valuable for researchers and forecasters.

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