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

TLDR

Land surface and hydrologic models require diverse data input and processing, from consistency checks to advanced analytics. The article presents the development of the Land surface Data Toolkit (LDT), an integrated framework for processing input data for LSMs and hydrologic models. LDT serves as a preprocessor for NASA LIS and stand‑alone models, supports common data formats, processes initial and boundary conditions, ensures data quality, incorporates a machine‑learning layer for predictive modeling, and is built on an extensible object‑oriented framework. The LDT’s machine‑learning layer enables modern data‑science algorithms to develop data‑driven predictive models. Abstract.

Abstract

Abstract. 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.