A Data Domain to Model Domain Conversion Package (DMCP) for
Sparse Climate Related Process Measurements
This Science Applications project addresses evaluation of high-end climate models. Data collected at the Atmospheric Radiation Measurement Program Climate Research Facility (ACRF) sites are employed mainly in column radiation models, to validate the models and develop new parameterizations. The ACRF observatories are designed to simulate the grid size of a climate model and to support collection of sparsely distributed surface and profile measurements over an area of more than 300 km2. Similarly, a vast majority of the data collected in the DOE Terrestrial Carbon Processes program, such as at AmeriFlux sites, are widely distributed and are very specific to local soil type, vegetation, climatology, and hydrology. Such data are employed primarily to constrain model calculations, often with a single measurement assumed to represent the average for the domain. In a few instances (e.g., in the Single Column Atmospheric Model), measured data sets are used for four-dimensional data assimilation of meteorological variables such as wind velocities, temperature, and humidity, to constrain the dynamics in the model and improve on the boundary forcing derived from the National Centers for Environmental Prediction. However, no single methodology can be used with data collected at the spatial scale of the ACRF sites or for specific AmeriFlux locations, to derive suitable grid average or column mean values of measured variables for model evaluation and data assimilation in climate models. Such a tool would generate statistical error estimates of the mean quantities when averaged from the observation grid to the model grid, as well as correlations in errors across space and time. Here we propose to develop such methods and implement a novel approach for generating data ensembles, by using the latest available statistical modeling tools and our knowledge of relevant physical and chemical process to develop climatologically aware methods for processing ACRF and other spatially sparse data sets. The software tools generated will be documented and distributed under the name Data Domain to Model Domain Conversion Package (DMCP).
For more information, please contact:
Dr. Rao Kotamarthi