Dynamic Data-Driven Environmental Systems Science

Adrian Sandu

ISBN 10: 3319251376 / ISBN 13: 9783319251370

Published by Springer International Publishing Nov 2015, 2015

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This book constitutes the refereed proceedings of the First International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014, held in Cambridge, MA, USA, in November 2014.The 24 revised full papers and 7 short papers were carefully reviewed and selected from 62 submissions and cover topics on sensing, imaging and retrieval for the oceans, atmosphere, space, land, earth and planets that is informed by the environmental context; algorithms for modeling and simulation, downscaling, model reduction, data assimilation, uncertainty quantification and statistical learning; methodologies for planning and control, sampling and adaptive observation, and efficient coupling of these algorithms into information-gathering and observing system designs; and applications of methodology to environmental estimation, analysis and prediction including climate, natural hazards, oceans, cryosphere, atmosphere, land, space, earth and planets. 372 pp. Englisch.

Seller Inventory # 9783319251370

Bibliographic Details

• Title: Dynamic Data-Driven Environmental Systems Science
• Publisher: Springer International Publishing Nov 2015
• Publication Date: 2015
• Language: English
• ISBN 10: 3319251376
• ISBN 13: 9783319251370
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

This book constitutes the refereed proceedings of the First International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014, held in Cambridge, MA, USA, in November 2014.

The 24 revised full papers and 7 short papers were carefully reviewed and selected from 62 submissions and cover topics on sensing, imaging and retrieval  for the oceans, atmosphere, space, land, earth and planets that is informed by the environmental context;  algorithms for modeling and simulation, downscaling, model reduction, data assimilation, uncertainty quantification and statistical learning; methodologies for planning and control, sampling and adaptive observation, and efficient coupling of these algorithms into information-gathering and observing system designs; and applications of methodology to environmental estimation, analysis and prediction including climate, natural hazards, oceans, cryosphere, atmosphere, land, space, earth and planets.