Assimilation of SeaWiFS data into a global ocean-biogeochemical model using a local SEIK filter

Chlorophyll data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) is assimilated into the three-dimensional global NASA Ocean Biogeochemical Model (NOBM) for the period 1998–2004 in order to obtain an improved representation of chlorophyll in the model. The assimilation is performed by the SEIK filter, which is based on the Kalman filter algorithm. The filter is implemented to univariately correct the concentration of surface total chlorophyll. A localized filter analysis is used and the filter is simplified by using a static state error covariance matrix. The assimilation provides daily global surface chlorophyll fields and improves the chlorophyll estimates relative to a model simulation without assimilation. The comparison with independent in situ data over the seven years also shows a significant improvement of the chlorophyll estimate. The assimilation reduces the RMS log error of total chlorophyll from 0.43 to 0.32, while the RMS log error is 0.28 for the in situ data considered. That is, the global RMS log error of chlorophyll estimated by the model is reduced by the assimilation from 53% to 13% above the error of SeaWiFS. Regionally, the assimilation estimate exhibits smaller errors than SeaWiFS data in several oceanic basins.