An End-to-End Framework for Probabilistic Uncertainty Characterization of Climate Satellite Data and Products

Observational data sets have been utilized in various capacities including surface wind stress fields as forcing to ocean circulation models, sea surface temperature fields as boundary conditions to atmospheric circulation models, and high quality in situ data as reference time series for validating or calibrating either models or/and other observational data sets including satellite data.

Although it has been widely-recognized that accurate, global high-resolution observational data sets with uncertainty estimates for each model grid point or cell are crucial in improving numerical weather and climate predictions, it is a challenge providing this uncertainty information systematically due to the nature of the complexity, high dimensionality, and shear-volume of data that one has to deal with in characterizing satellite data and products. In addition, the sources for the uncertainty of any final product may be accumulative but the impacts of all sources/processes are not equal. Therefore, identifying possible source of uncertainties during each stage of creating satellite data and associated products will provide a useful framework for systematically estimating uncertainties and exploring their impact on climate predictions and monitoring.

An end-to-end framework for probabilistically characterizing uncertainty associated with climate satellite data and products is proposed with uncertainty types identified from the level 0 data to level 4 products. Ocean surface winds are used to demonstrate the utility of this end-to-end system.