The Land surface Data Toolkit (LDT v7.2) - a data fusion environment for land data assimilation systems

被引:44
作者
Arsenault, Kristi R. [1 ,2 ]
Kumar, Sujay, V [2 ]
Geiger, James, V [3 ]
Wang, Shugong [1 ,2 ]
Kemp, Eric [2 ,4 ]
Mocko, David M. [1 ,2 ]
Beaudoing, Hiroko Kato [2 ,5 ]
Getirana, Augusto [2 ,5 ]
Navari, Mandi [2 ,5 ]
Li, Bailing [2 ,5 ]
Jacob, Jossy [2 ,4 ]
Wegiel, Jerry [1 ,6 ]
Peters-Lidard, Christa D. [7 ]
机构
[1] Sci Applicat Int Corp, Mclean, VA 22102 USA
[2] NASA, Goddard Space Flight Ctr, Hydrol Sci Lab, Greenbelt, MD 20771 USA
[3] NASA, Goddard Space Flight Ctr, Sci Data Proc Branch, Greenbelt, MD USA
[4] Sci Syst & Applicat Inc, Lanham, MD USA
[5] Univ Maryland, ESSIC, College Pk, MD 20742 USA
[6] Headquarters 557th Weather Wing, Offutt Air Force Base, NE USA
[7] NASA, Goddard Space Flight Ctr, Earth Sci Div, Greenbelt, MD USA
关键词
SATELLITE SOIL-MOISTURE; INFORMATION-SYSTEM; IRRIGATED AREAS; MODEL; RESOLUTION; CLIMATE; WATER; CIRCULATION; PARAMETERIZATION; FRAMEWORK;
D O I
10.5194/gmd-11-3605-2018
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.
引用
收藏
页码:3605 / 3621
页数:17
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