Building a Land Data Assimilation Community to Tackle Technical Challenges in Quantifying and Reducing Uncertainty in Land Model Predictions

被引：4

作者：

MacBean, Natasha ^{[1
]}

Liddy, Hannah ^{[2
,3
]}

Quaife, Tristan ^{[4
]}

Kolassa, Jana ^{[5
,6
]}

Fox, Andrew ^{[7
]}

机构：

[1] Indiana Univ, Dept Geog, Bloomington, IN 47405 USA

[2] Columbia Univ, Earth Inst, Ctr Climate Syst Res, New York, NY USA

[3] NASA, Goddard Inst Space Studies, New York, NY 10025 USA

[4] Univ Reading, Natl Ctr Earth Observ, Reading, Berks, England

[5] NASA, Goddard Space Flight Ctr, Global Modeling & Assimilat Off, Greenbelt, MD USA

[6] Sci Syst & Applicat Inc, Lanham, MD USA

[7] UCAR, Joint Ctr Satellite Data Assimilat, Boulder, CO USA

来源：

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY | 2022年 / 103卷 / 03期

关键词：

Climate models; Data assimilation; Land surface model; Model initialization; Numerical weather prediction/forecasting; Optimization;

D O I：

10.1175/BAMS-D-21-0228.1

中图分类号：

P4 [大气科学（气象学）];

学科分类号：

0706 ; 070601 ;

摘要：

引用

页码：E733 / E740

页数：8

共 4 条

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[3] Reliable, robust and realistic: the three R's of next-generation land-surface modelling [J].

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[4] Analysis, Integration and Modeling of the Earth System (AIMES): Advancing the post-disciplinary understanding of coupled human-environment dynamics in the Anthropocene [J].

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van der Leeuw, Sander .

ANTHROPOCENE, 2015, 12 :99-106

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