Physically based data assimilation and fuzzy verification

Gad Levy
NorthWest Research Associates 
1:30 pm March 17 in Room 2551

Ideally, a verification and validation scheme should be able to evaluate and incorporate lower  dimensional features (e.g., discontinuities) contained within a bulk simulation even when not directly  observed or represented by model variables. Nonetheless, lower dimensional features are often ignored.  Conversely, models that resolve such features and the associated physics well, yet imprecisely are  penalized by traditional validation schemes. This can lead to (perceived or real) poor model performance  and predictability and can become deleterious in model improvements when observations are sparse,  fuzzy, or irregular. I present novel algorithms and a general framework for using information from  available satellite data through fuzzy verification that efficiently and effectively remedy the known  problems mentioned above. As a proof of concept, we use a sea-ice model with remotely sensed observations of leads in a one-step initialization cycle. Using the new scheme in a sixteen day simulation experiment introduces model skill (against persistence) several days earlier than in the control run,  improves the overall model skill and delays its drop off at later stages of the simulation. Although sea-ice models are currently a weak link in climate models, the appropriate choice of data to use, and the fuzzy  verification and evaluation of a system’s skill in reproducing lower dimensional features are important  beyond the initial application to sea ice. Our strategy and framework for fuzzy verification, selective use of information, and feature extraction could be extended globally and to other disciplines. As time permits,  examples from tropical climate classification, oil spill detection and modeling will also be presented.