Journal
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
Volume 66, Issue -, Pages -Publisher
TAYLOR & FRANCIS LTD
DOI: 10.3402/tellusa.v66.23294
Keywords
data assimilation; correlated observation errors; ensemble transform Kalman filter
Categories
Funding
- European Space Agency (ESA)
- NERC National Centre for Earth Observation (NCEO)
- Met Office through a CASE studentship
- Natural Environment Research Council [earth010008, NE/K008900/1] Funding Source: researchfish
- NERC [NE/K008900/1, earth010008] Funding Source: UKRI
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For certain observing types, such as those that are remotely sensed, the observation errors are correlated and these correlations are state- and time-dependent. In this work, we develop a method for diagnosing and incorporating spatially correlated and time-dependent observation error in an ensemble data assimilation system. The method combines an ensemble transform Kalman filter with a method that uses statistical averages of background and analysis innovations to provide an estimate of the observation error covariance matrix. To evaluate the performance of the method, we perform identical twin experiments using the Lorenz '96 and Kuramoto-Sivashinsky models. Using our approach, a good approximation to the true observation error covariance can be recovered in cases where the initial estimate of the error covariance is incorrect. Spatial observation error covariances where the length scale of the true covariance changes slowly in time can also be captured. We find that using the estimated correlated observation error in the assimilation improves the analysis.
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