Aqua (satellite)
; areal interpolation
; coastal zone
; covariance analysis
; data assimilation
; ecosystem dynamics
; empirical orthogonal function analysis
; error analysis
; MODIS
; observational method
; optical property
; prediction
; satellite altimetry
; satellite imagery
; upwelling
; Bacillariophyta
英文摘要:
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. Published 2018. This article is a US Government work and is in the public domain in the USA.
Oceanography Division, US Naval Research Laboratory, Stennis Space Center, Hancock County, MS, United States; Meteorology Division, US Naval Research Laboratory, Monterey, CA, United States
Recommended Citation:
Shulman I.,Gould R.W.,Jr.,et al. Bio-Optical Data Assimilation With Observational Error Covariance Derived From an Ensemble of Satellite Images[J]. Journal of Geophysical Research: Oceans,2018-01-01,123(3)