Weakly Coupled Ocean–Atmosphere Data Assimilation in the ECMWF NWP System

Abstract

Numerical weather prediction models are including an increasing number of components of the Earth system. In particular, every forecast now issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) runs with a 3D ocean model and a sea ice model below the atmosphere. Initialisation of different components using different methods and on different timescales can lead to inconsistencies when they are combined in the full system. Historically, the methods for initialising the ocean and the atmosphere have been typically developed separately. This paper describes an approach for combining the existing ocean and atmospheric analyses into what we categorise as a weakly coupled assimilation scheme. Here, we show the performance improvements achieved for the atmosphere by having a weakly coupled ocean–atmosphere assimilation system compared with an uncoupled system. Using numerical weather prediction diagnostics, we show that forecast errors are decreased compared with forecasts initialised from an uncoupled analysis. Further, a detailed investigation into spatial coverage of sea ice concentration in the Baltic Sea shows that a much more realistic structure is obtained by the weakly coupled analysis. By introducing the weakly coupled ocean–atmosphere analysis, the ocean analysis becomes a critical part of the numerical weather prediction system and provides a platform from which to build ever stronger forms of analysis coupling.

On Temporal Scale Separation in Coupled Data Assimilation with the Ensemble Kalman Filter

“…and the perturbed one at a given time, δx t . Provided that this perturbation is sufficiently small, its dynamics can be described by the linearised equation, dδx dt = ∂f ∂x |xt δx (8) and the formal solution can be written as,…”

“…complete sCDA) and the implicit coupling in the incremental 4DVar has shown that for long assimilation window the latter produces accurate analysis, but the explicit coupling is preferable for short assimilation windows. The transition from a reanalysis to real time prediction is currently under study [8].…”

Assessment and Improvement of Global Gridded Sea Surface Temperature Datasets in the Yellow Sea Using In Situ Ocean Buoy and Research Vessel Observations

“…In June and July, most (93%) of the L3 SST data in the coastal region of the Southeastern YS were assigned as low, bad or worst quality data (Table 6), which resulted in the interpolation of SST data in the coastal region during the production of the L4 gridded SST datasets. The missing SST data in the coastal region were not interpolated from the nearby grid points but they were interpolated using the offshore observation data far away (10-70 km) in warm water and the climatology based on Equationa (3) and (4).…”

“…Sea surface temperature (SST) is one of the main physical variables that provides information regarding the current state of the ocean [1]. SST data have been used for data assimilation in ocean circulation models and as a bottom boundary condition for atmospheric prediction models [2][3][4]. They are also essential for climate modeling and ocean-atmosphere interaction studies [5].…”

Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1)

“…The turbulent surface heat and momentum fluxes are parameterized using stability functions described in Delage and Girard (1992) and Delage (1997). The DA method is a hybrid four-dimensional ensemblevariational (4D-EnVar) (Buehner et al, 2013(Buehner et al, , 2015 using incremental analysis update (IAU) initialization (Bloom et al, 1996). The IAU implementation used with the GDPS system is illustrated in Fig.…”