Improving strategies with constraints regarding non-Gaussian statistics in a three-dimensional variational assimilation method |
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| Authors: | Email author" target="_blank">Norihisa?UsuiEmail author Shiro?Ishizaki Yosuke?Fujii Masafumi?Kamachi |
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| Institution: | (1) Oceanographic Research Department, Meteorological Research Institute, Nagamine 1-1, Tsukuba 305-0052, Japan;(2) Global Environment and Marine Department, Japan Meteorological Agency, Tokyo 100-8122, Japan |
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| Abstract: | We assess validity of a Gaussian error assumption, the basic assumption in data assimilation theory, and propose two kinds
of constraints regarding non-Gaussian statistics. In the mixed water region (MWR) off the east coast of Japan exhibiting complicated
frontal structures, a probability density function (PDF) of subsurface temperature shows double peaks corresponding to the
Kuroshio and Oyashio waters. The complicated frontal structures characterized by the temperature PDF sometimes cause large
innovations, bringing about a non-Gaussianity of errors. It is also revealed that assimilated results with a standard three-dimensional
variational (3DVAR) scheme have some issues in MWR, arising from the non-Gaussianity of errors. The Oyashio water sometimes
becomes unrealistically cold. The double peaks seen in the observed temperature PDF are too smoothed. To improve the assimilated
field in MWR, we introduce two kinds of constraints, J
c1 and J
c2, which model the observed temperature PDF. The constraint J
c1 prevents the unrealistically cold Oyashio water, and J
c2 intends to reproduce the double peaks. The assimilated fields are significantly improved by using these constraints. The
constraint J
c1 effectively reduces the unrealistically cold Oyashio water. The double peaks in the observed temperature PDF are successfully
reproduced by J
c2. In addition, not only subsurface temperature but also whole level temperature and salinity (T–S) fields are improved by
adopting J
c1 and J
c2 to a multivariate 3DVAR scheme with vertical coupled T–S empirical orthogonal function modes. |
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| Keywords: | |
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