课题基金 / 基金详情

Bayesian Methods in Geodetic Earth System Research

Bayesian Methods in Geodetic Earth System Research
大地测量地球系统研究中的贝叶斯方法
批准号:
226407636
负责人:
Professor Dr.-Ing. Jürgen Kusche
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2012
资助国家:
德国
项目状态:
已结题
起止时间:
2011-12-31 至 2014-12-31

项目摘要

项目成果

Professor Dr.-Ing. Jürgen Kusche的其他基金

相似基金

相关文献

中文摘要
翻译
点击翻译按钮获取中文摘要
英文摘要
In the proposed study, we lay the foundations for the application of Bayesian methods in geodetic Earth system research. A bundle of algorithms and implementation schemes will be developed that allow to formulate Bayesian estimates when geodetic data is analysed or data and physical model output is to be combined, while existing software blocks for geodetic analysis and model integrations can be reused. Uncertainties will be strictly characterized by possibly non-Gaussian probability density functions. Numerically efficient algorithms will be developed, Our work will be generic and not restricted to a particular application within geodetic Earth system research. Yet, through case studies we will deal with the interpretation of satellite-gravimetric data.
期刊论文(5)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1007/s00190-011-0532-5
发表时间: 2012-07-01
期刊: JOURNAL OF GEODESY
影响因子: 4.4
作者: [Forootan, E., Kusche, J.]
通讯作者: Kusche, J.
Multivariate Prediction of Total Water Storage Changes Over West Africa from Multi-Satellite Data
利用多卫星数据对西非总水储量变化进行多变量预测
DOI: 10.1007/s10712-014-9292-0
发表时间:
期刊: Surveys in Geophysics
影响因子: 4.6
作者: [E. Forootan, J. Kusche, I. Loth, W-D. Schuh, A. Eicker, J. Awange, L. Longuevergne, B. Diekkrueger, M. Schmidt, C.K. Shum]
通讯作者: C.K. Shum
DOI: 10.1016/j.rse.2012.05.023
发表时间: 2012-09
期刊: Remote Sensing of Environment
影响因子: 13.5
作者: [E. Forootan;J. Awange;J. Kusche;B. Heck;A. Eicker]
通讯作者: E. Forootan;J. Awange;J. Kusche;B. Heck;A. Eicker
DOI: 10.1016/j.rse.2013.09.025
发表时间: 2014-01-01
期刊: REMOTE SENSING OF ENVIRONMENT
影响因子: 13.5
作者: [Forootan, E., Rietbroek, R., Famiglietti, J.]
通讯作者: Famiglietti, J.
Developing an Ensemble Kalman Filter calibration and data assimilation (EnC/DA) approach for integrating geodetic and remote sensing data into a global hydrological model
Developing a Stabilized Ensemble Kalman Filter for integrating daily GRACE/GRACE-FO data into process models (S-ENKF)
Lunar Reference Systems
Fingerprints of ice melting in geodetic GRACE and ocean modelling
国内基金
海外基金
Computational Methods for Analyzing Toponome Data