Dynamic Data-Driven Environmental Systems Science
First International Conference, DyDESS 2014, Cambridge, MA, USA, November 5-7, 2014, Revised Selected Papers
Nonfiction, Computers, Advanced Computing, Information Technology, General Computing, Programming
| Author: | ISBN: | 9783319251387 | |
| Publisher: | Springer International Publishing | Publication: | November 26, 2015 |
| Imprint: | Springer | Language: | English |
| Author: | |
| ISBN: | 9783319251387 |
| Publisher: | Springer International Publishing |
| Publication: | November 26, 2015 |
| Imprint: | Springer |
| Language: | English |
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.
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.
