Four-dimensional variational data assimilation for a limited area model

Abstract

A B S T R A C TA 4-dimensional variational data assimilation (4D-Var) scheme for the HIgh Resolution Limited Area Model (HIRLAM) forecasting system is described in this article. The innovative approaches to the multi-incremental formulation, the weak digital filter constraint and the semi-Lagrangian time integration are highlighted with some details. The implicit dynamical structure functions are discussed using single observation experiments, and the sensitivity to various parameters of the 4D-Var formulation is illustrated. To assess the meteorological impact of HIRLAM 4D-Var, data assimilation experiments for five periods of 1 month each were performed, using HIRLAM 3D-Var as a reference. It is shown that the HIRLAM 4D-Var consistently out-performs the HIRLAM 3D-Var, in particular for cases with strong mesoscale storm developments. The computational performance of the HIRLAM 4D-Var is also discussed.

A hybrid variational ensemble data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)

“…11 it can be seen that 3D-Var hybrid provides a large positive impact on standard deviation forecast verification scores in comparison with the standard 3D-Var. The standard 4D-Var provides an even larger (doubled) positive impact over the standard 3D-Var, which is consistent with previous comparisons between HIRLAM 3D-Var and 4D-Var (Gustafsson et al, 2012). On the other hand, it is also obvious from Fig.…”

“…The HIRLAM variational data assimilation includes a 3-D version (3D-Var, see Gustafsson et al, 2001 and) and a 4-D version (4D-Var, see Gustafsson et al, 2012). We have incorporated the hybrid variational ensemble technique in both 3D-Var and 4D-Var.…”

Four-dimensional ensemble variational (4D-En-Var) data assimilation for the HIgh Resolution Limited Area Model (HIRLAM)

“…The concept of incremental 4D-Var (Courtier et al, 1994) made it possible to practically solve the computational problem of 4D-Var by introducing simplification and linearization of the forecast model and the observation operators and by introducing a reduced spatial resolution for the 4D-Var minimization. Incremental 4D-Var schemes have been successfully introduced operationally for global NWP models (Rabier et al, 2000) as well as for regional high-resolution models (Kawabata et al, 2007;Gustafsson et al, 2012).…”

“…Although applied during the coarse-resolution tangent linear and ad- joint model integrations, the application of this weak constraint during the data assimilation also efficiently prevents the presence of high-frequency (gravity wave) oscillations induced by the non-linear model during integrations from initial data created by the assimilation (Gustafsson et al, 2012). It is less straightforward to apply a similar digital filter constraint within the framework of 4D-En-Var since there is no model integration applied during the data assimilation process.…”

Influence of targeted observations on short-term forecasts of high-impact weather events in the Mediterranean

“…The parameterization for clouds and condensation processes was reformulated and the new scheme implemented is based on KainFritsch for convection (Kain, 2004) and Rasch-Kristjansson for large-scale microphysics (Zhang et al, 2003). In these experiments we have used the incremental HIRLAM 4-D variational data assimilation (DA) system, 4-D-Var (Gustafsson et al, 2012), as upper-air analysis with a 6 h time window. Physical processes are represented in the tangent linear and adjoint models.…”