Perhaps the most common challenge to the systematic and routine comparisons of data from the model output/analysis with NASA remote sensing data is that of CO-locating diverse datasets, often obtained…

This federal contract award was granted by the National Aeronautics and Space Administration (NASA) Shared Services Center to Bayesics, LLC, a small, minority-owned business. The $103,434.68 grant is for research and development services in support of NASA's Earth systems science and digital twin modeling efforts.

Bayesics has provided specialized services to NASA under this contract, including developing frameworks and models utilizing advanced analytics and machine learning techniques to enhance NASA's modeling, simulation, and prediction capabilities. The company has also delivered event-based data stores and analytics to support NASA's technology innovation goals. This contract work aligns with NASA's focus on areas like phase I SBIR research and development for small businesses, as well as NASA-funded academic partnerships exploring advanced snow observation strategies for hydrological applications. The contract does not appear to have a set-aside designation.

Perhaps the most common challenge to the systematic and routine comparisons of data from the model output/analysis with NASA remote sensing data is that of CO-locating diverse datasets, often obtained from instruments with different observation characteristics, for the purposes of detailed comparison, e.g. OBS4MIPS (teixeira ET AL 2014), and use, e.g. adaptation to climate change. Indeed, while a myriad of tools exist to locate, access, and visualize observational data, inter-comparison of disparate data sources requires tedious and often computationally intensive gridding or regridding, which is generally implemented in an ad hoc manner by individual users and becomes an obstacle to robust comparisons. In addition to the duplication of effort stemming from the lack of standard gridding tools, expediency on the part of individual researchers often results in sub-optimal gridded products that (1) limit accuracy due to failure to adequately account for spatial/temporal sampling bias, (2) lack robust gridding uncertainty estimates, and (3) omit the provenance, all of which limit the value of the products to other researchers. Simplicity and familiarity also drive researchers to apply traditional latitude-longitude (LAT-LON) grids rather than better alternatives. Moreover, the trend in the modeling community is to transition to next-generation grid systems, such as geodesic and cubed-sphere, that possess superior quasi-equiareal, scalable characteristics. To maintain numerical stability, the CFL criterion for the time step used in the numerical integration of the general circulation models must decrease with the smallest distance between any two grid points. Since the meridians converge to a single point at each pole, as the model s spatial resolution increases, the smallest grid distance of a LAT-LON grid system quickly approaches zero. Considerable computation will thus be wasted for high-resolution models based on LAT-LON grids, for the short time step required near the poles becomes, progressively, a greater overkill for grid cells toward the equator. Approaches to filter the high-frequency signal to sidestep this concern introduce severe constraints on parallel performance.