Incorporating Uncertainties in Satellite-Derived Chlorophyll into Model Forecasts
NAVAL RESEARCH LAB STENNIS DETACHMENT STENNIS SPACE CENTER MS OCEANOGRAPHY DIV
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We describe and apply an ensemble approach, similar to that used in environmental modeling, to quantify errors and produce uncertainty maps for satellite-derived ocean color chlorophyll, and we incorporate these uncertainties into hydrodynamic and biophysical models. For an ocean color image, we first apply realistic noise to the satellite top-of-atmosphere radiances, which leads to an ensemble of chlorophyll images. From this ensemble, we derive mean and standard deviation uncertainty images for the chlorophyll, which we then incorporate into both hydrodynamic and biophysical forecast models. For both these cases, we create forecast ensemble suites the ensemble variance provides an indication of uncertainty, or confidence in the chlorophyll forecast. We examine mean and individual forecast ensemble members R2, spread-skill statistics to assess predictive value. Thus, we produce a final chlorophyll forecast field that includes uncertainties in both the initial satellite chlorophyll values as well as uncertainties in the hydrodynamic and biological models.
- Biological Oceanography
- Physical and Dynamic Oceanography
- Optical Detection and Detectors