Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2018-63
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
23 Apr 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
The Land surface Data Toolkit (LDTv7.2) – a data fusion environment for land data assimilation systems
Kristi R. Arsenault1,2, Sujay V. Kumar2, James V. Geiger3, Shugong Wang1,2, Eric Kemp4,2, David M. Mocko1,2, Hiroko Kato Beaudoing5,2, Augusto Getirana5,2, Mahdi Navari5,2, Bailing Li5,2, Jossy Jacob4,2, Jerry Wegiel1,6, and Christa Peters-Lidard7 1Science Applications International Corporation, McLean, VA, USA
2Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
3Science Data Processing Branch, NASA Goddard Space Flight Center, Greenbelt, MD, USA
4Science Systems and Applications, Inc., Lanham, MD, USA
5ESSIC, University of Maryland, College Park, MD, USA
6Headquarters 557th Weather Wing, Offutt Air Force Base, NE, USA
7Earth Sciences Division, NASA Goddard Space Flight Center, Greenbelt, MD USA
Abstract. The effective applications of land surface model (LSM) 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 pre-processor 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, 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 data sets and data analytics algorithms to aid land surface modelling and data assimilation.
Citation: Arsenault, K. R., Kumar, S. V., Geiger, J. V., Wang, S., Kemp, E., Mocko, D. M., Beaudoing, H. K., Getirana, A., Navari, M., Li, B., Jacob, J., Wegiel, J., and Peters-Lidard, C.: The Land surface Data Toolkit (LDTv7.2) – a data fusion environment for land data assimilation systems, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-63, in review, 2018.
Kristi R. Arsenault et al.
Kristi R. Arsenault et al.
Kristi R. Arsenault et al.

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Short summary
The Earth’s land surface hydrology and physics can be represented in highly sophisticated models known as land surface models. The Land surface Data Toolkit (LDT) software was developed to meet these models’ input processing needs. LDT supports a variety of land surface and hydrology models, and prepares the inputs (e.g., meteorological data, satellite observations to be assimilated into a model), which can be used for inter-model studies and to initialize weather and climate forecasts.
The Earth’s land surface hydrology and physics can be represented in highly sophisticated...
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