PNNL

Abstract

Ensemble Forecast Sensitivity to Observation (EFSO) is a powerful technique that can efficiently identify the beneficial/detrimental impacts of every observation in ensemble-based data assimilation (DA). EFSO has been widely used in atmospheric DA but has not yet been applied to any ocean or coupled DA due to the lack of a proper error norm for oceanic variables. This study first introduces a novel density-based error norm that simultaneously includes sea temperature and salinity forecast errors, by which EFSO becomes available to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2-LETKF for quantifying the individual impact of ocean observations under the weakly coupled DA framework. In the data-denial experiment, the CFS forecasts were significantly improved by removing the detrimental ocean observations detected by EFSO, showing the validation of impact estimation and the great potential of EFSO to be extended as a data selection criterion.