Bio‐Optical Data Assimilation With Observational Error Covariance Derived From an Ensemble of Satellite Images. Issue 3 (5th March 2018) - Shulman, Igor; Gould, Richard W.; Frolov, Sergey; McCarthy, Sean; Penta, Brad; Anderson, Stephanie; Sakalaukus, Peter
Abstract: An ensemble‐based approach to specify observational error covariance in the data assimilation of satellite bio‐optical properties is proposed. The observational error covariance is derived from statistical properties of the generated ensemble of satellite MODIS‐Aqua chlorophyll (Chl) images. The proposed observational error covariance is used in the Optimal Interpolation scheme for the assimilation of MODIS‐Aqua Chl observations. The forecast error covariance is specified in the subspace of the multivariate (bio‐optical, physical) empirical orthogonal functions (EOFs) estimated from a month‐long model run. The assimilation of surface MODIS‐Aqua Chl improved surface and subsurface model Chl predictions. Comparisons with surface and subsurface water samples demonstrate that data assimilation run with the proposed observational error covariance has higher RMSE than the data assimilation run with "optimistic" assumption about observational errors (10% of the ensemble mean), but has smaller or comparable RMSE than data assimilation run with an assumption that observational errors equal to 35% of the ensemble mean (the target error for satellite data product for chlorophyll). Also, with the assimilation of the MODIS‐Aqua Chl data, the RMSE between observed and model‐predicted fractions of diatoms to the total phytoplankton is reduced by a factor of two in comparison to the nonassimilative run. Key Points: An ensemble approach to specify observational error covariance in the data assimilation of satellite bio‐optical properties is proposed The assimilation run with proposed covariance has higher errors than the run when observational errors equal 10% of the ensemble mean The run with proposed covariance has smaller or comparable errors than the run when observational errors equal to 35% of the ensemble mean