Variational bias correction of satellite sea‐surface temperature data incorporating observations of the bias. (13th August 2019)

• Record Type: Journal Article
• Authors: While, James; Martin, Matthew J.
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 145:Number 723(2019)
• Page Start: 2733
• Page End: 2754
• Publication Date: 2019-08-13
• Subjects: bias correction -- SST -- variational methods; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3590
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract : We describe a variational bias‐correction system for satellite sea‐surface temperature (SST) data that includes the use of "observations‐of‐bias". The bias‐correction scheme is designed to work in the historical period, when good quality low‐bias reference data were scarce, but can also take advantage of reference data when they are available. In testing with a simple Lorenz 63 model, our new scheme outperformed traditional variational bias correction. When compared with an offline bias‐correction method, the new scheme showed superior performance both when the bias was large and when reference observations were sparse. The bias‐correction scheme has also been tested using a three‐year assimilative run (2008–2010) of the Nucleus for European Modeling of the Ocean (NEMO) ocean general circulation model, with reference data from the Advanced Along Track Scanning Radiometer (AATSR) instrument withheld in 2009. In these tests, the new scheme was found to be more robust to missing reference observations than an offline scheme. Against AATSR data, the new bias‐correction method had lower biases and root‐mean‐square (RMS) errors than an offline scheme, but was degraded relative to a pure variational technique. However, in comparisons with drifting buoys, the new scheme outperformed both offline and pure variational methods. Abstract : A new method for the bias correction of sea‐surface temperature observations is described that uses "observations‐of‐bias". The scheme is a variational method designed to work both in the early satellite era and in more recent periods, when more and better quality data are available. The new method outperforms the Met Office's historic non‐variational bias‐correction scheme, especially when good‐quality data are sparse. The accompanying figure shows bias fields for the AMSR‐E instrument calculated using three different techniques.