Collaborative Research: Flexible Statistical Models to Blend Massive Geostationary-Derived Climate Data Records
合作研究:灵活的统计模型来融合大量对地静止轨道衍生的气候数据记录
基本信息
- 批准号:1953168
- 负责人:
- 金额:$ 20万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-07-01 至 2025-06-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Land surface albedo is the fraction of incoming solar radiation reflected by the land surface. It is an essential climate variable as identified by the Global Climate Observing System. An international effort involving institutions from the USA, European Union, Japan, Switzerland and Korea is dedicated to obtaining geostationary images from five different satellites. Those satellites have overlapping areas for which two different measures of albedo are retrieved. The project will develop blended global albedo products that account for the discrepancies between the different retrievals and quantify the uncertainty in the resulting albedo values using probabilities. The project will leverage the expertise of two PIs to develop state of the art methods that can capture the variability in time and space of climate variables that are observed from a constellation of geostationary satellites, like cloud characteristics, wind speed and direction, snow cover, and precipitation, among others.The project will contribute geostatistical methods with a model-based approach to analyze, interpolate and make inferences for a multivariate spatial field, featuring a non-stationary spatial process, indexed in a continuous space. The scalability is achieved thanks to a local neighborhood conditioning structure. Computations will be performed fast and concurrently, satisfying global coherence through a hierarchical structure. The model-based nature of the proposed approach allows to account for complex observational errors, perform non-homogeneous multivariate spatial regression, and combine observations obtained at different levels of spatial aggregation. Extensions to spatio-temporal models are seamlessly obtained incorporating space-time interactions within a parsimonious hierarchical model.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
陆地表面反射率是入射太阳辐射被陆地表面反射的分数。它是全球气候观测系统确定的一个重要气候变量。来自美国、欧盟、日本、瑞士和韩国的机构参与的一项国际努力致力于从五颗不同的卫星获取地球静止图像。这些卫星有重叠的区域,可以检索两种不同的测量方法。该项目将开发综合全球可持续发展产品,以说明不同检索之间的差异,并使用概率量化所得可持续发展值的不确定性。该项目将利用两名专业人员的专业知识,开发最先进的方法,以捕捉从地球静止卫星星座观测到的气候变量的时间和空间变化,如云的特征、风速和风向、积雪和降水等。该项目将提供地质统计方法,并采用基于模型的方法进行分析,插值和推断的多元空间领域,具有非平稳的空间过程,在连续空间索引。可扩展性是通过局部邻域调节结构实现的。计算将快速并行地进行,通过层次结构满足全局一致性。所提出的方法的基于模型的性质,可以考虑复杂的观测误差,执行非齐次多元空间回归,并结合联合收割机在不同层次的空间聚集获得的意见。扩展到时空模型无缝地获得纳入一个简约的层次模型的时空相互作用。这个奖项反映了NSF的法定使命,并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Distributed nearest-neighbor Gaussian processes
分布式最近邻高斯过程
- DOI:10.1080/03610918.2021.1921798
- 发表时间:2021
- 期刊:
- 影响因子:0
- 作者:Grenier, Isabelle;Sansó, Bruno
- 通讯作者:Sansó, Bruno
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Bruno Sanso其他文献
Bruno Sanso的其他文献
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{{ truncateString('Bruno Sanso', 18)}}的其他基金
Multi-Scale Models for Non-Stationary Spatial Datasets
非平稳空间数据集的多尺度模型
- 批准号:
2050012 - 财政年份:2021
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
Bayesian Inference for Peaks Over Threshold Models for Multivariate and Spatial Extremes
多元和空间极值的阈值模型峰值的贝叶斯推理
- 批准号:
1513076 - 财政年份:2015
- 资助金额:
$ 20万 - 项目类别:
Continuing Grant
Travel Support for the 12th ISBA World Meeting on Bayesian Statistics
第十二届 ISBA 贝叶斯统计世界会议的差旅支持
- 批准号:
1401118 - 财政年份:2014
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
CBMS Regional Conference in the Mathematical Sciences - Model Uncertainty and Multiplicity
CBMS 数学科学区域会议 - 模型不确定性和多重性
- 批准号:
1137825 - 财政年份:2012
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
Space and Space-Time Models for Large Datasets
大型数据集的空间和时空模型
- 批准号:
0906765 - 财政年份:2009
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
SGER: Evaluation of Community Climate System Model (CCSM) Constituent Transport Variability
SGER:社区气候系统模型 (CCSM) 成分传输变异性的评估
- 批准号:
0405451 - 财政年份:2004
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
CMG: Improved Bayesian Estimators for Uncertainty in Climate System Properties
CMG:气候系统特性不确定性的改进贝叶斯估计
- 批准号:
0417753 - 财政年份:2004
- 资助金额:
$ 20万 - 项目类别:
Standard Grant
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