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Titlebook: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications; Seon K. Park (Prof.),Liang Xu Book 2009 Springer-Verlag Berlin Hei

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书目名称Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications
编辑Seon K. Park (Prof.),Liang Xu
视频video
概述Includes supplementary material:
图书封面Titlebook: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications;  Seon K. Park (Prof.),Liang Xu Book 2009 Springer-Verlag Berlin Hei
描述Data assimilation (DA) has been recognized as one of the core techniques for modern forecasting in various earth science disciplines including meteorology, oceanography, and hydrology. Since early 1990s DA has been an important s- sion topic in many academic meetings organized by leading societies such as the American Meteorological Society, American Geophysical Union, European G- physical Union, World Meteorological Organization, etc. nd Recently, the 2 Annual Meeting of the Asia Oceania Geosciences Society (AOGS), held in Singapore in June 2005, conducted a session on DA under the - tle of “Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications.” nd This rst DA session in the 2 AOGS was a great success with more than 30 papers presented and many great ideas exchanged among scientists from the three different disciplines. The scientists who participated in the meeting suggested making the DA session a biennial event. th Two years later, at the 4 AOGS Annual Meeting, Bangkok, Thailand, the DA session was of cially named “Sasaki Symposium on Data Assimilation for At- spheric, Oceanic and Hydrologic Applications,” to honor Prof. Yoshi K. Sasaki of the University of Ok
出版日期Book 2009
关键词Coast; Meteorology; Ocean; Oceanography; Precipitation; Rain; Scale; Tornado; Weather; cyclone; hydrology; mois
版次1
doihttps://doi.org/10.1007/978-3-540-71056-1
isbn_softcover978-3-642-09009-7
isbn_ebook978-3-540-71056-1
copyrightSpringer-Verlag Berlin Heidelberg 2009
The information of publication is updating

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Favors, “ Merit Ribbons,” and Services the model dynamics and less sensitive to the amount and quality of the observations, while the opposite is true for the DOF for signal. These differences have to be taken into account when comparing the results of different data assimilation methods.
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Information Measures in Ensemble Data Assimilation, the model dynamics and less sensitive to the amount and quality of the observations, while the opposite is true for the DOF for signal. These differences have to be taken into account when comparing the results of different data assimilation methods.
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Radar Rainfall Estimates for Hydrologic and Landslide Modeling,infall in simulating stream discharge. A logit model developed from landslides and radar rainfall for one storm event is able to predict over 80% of landslides triggered by another storm event. This study suggests that radar rainfall, although it still has some shortcomings, can be an alternative to gauged rainfall for modeling applications.
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