The Land surface Data Toolkit (LDT) is the new front-end processor for the Land Information System (LIS), versions 7 and greater. It provides an environment for processing land model data and parameters, as well as restart files and data assimilation based inputs (e.g., for bias correction methods) for LIS.
LDT offers a variety of inputs and user options to process datasets and is being designed with not only LIS in mind but for other independent model and modeling systems as well. LDT supports the use of common data formats, like NetCDF, which provide detailed data header information.
Some Major Features
- Processes data inputs for land surface, hydrological, and lake models.
- Writes output in NetCDF, a common descriptive format.
- Supports multiple observational data sources.
- Offers a variety of projections and grid transformation options.
- Includes numerous options for processing parameters (e.g., agreement between parameters).
- Can function as a stand-alone land surface and data assimilation input processor in addition to being a pre-processor for LIS-7.
New Data Options
LDT has been designed and developed to read in what are considered the "native" or raw original data files as how they are provided by a data center, government agency, university group, etc. In the past, the LIS team processed several different model and data parameters into a common binary format standard, which were provided to the community.
With the advent of LDT, we are introducing a "new" philosophy that data and model parameter files should be read in from their "native" grids and file formats and be written to a common descriptive data format, like NetCDF. Several test cases are provided here to help demonstrate some of LDT's capability to read in "native" parameters. Additional information can be found at the main FAQ Page.
LDT's Functional Goals involve developing certain key features, which include:
- Process surface model (e.g., land surface models) parameter on to a common grid domain.
- Generate model initial conditions (e.g., model ensemble initialization).
- Generate CDF statistics that can be used in LIS during data assimilation updates.
- Implement and apply quality control measures to parameters, independent validation datasets, etc.
- Bias correct meteorological forcing datasets (e.g., with independent observations) (coming soon).
- Model parameter optimization support (coming soon).
- Integrate remote sensing product processing algorithms (coming soon).