Observability of Flow-Dependent Structure Functions for Use in Data Assimilation

Abstract

One of the objectives of data assimilation is to produce initial conditions that will improve the quality of forecasts. Studies on singular vectors and sensitivity studies have shown that small changes to the initial conditions can sometimes lead to exponential error growth. This has motivated research to include flowdependent structures within the assimilation that would have the characteristics to correctly predict the growth or decay of meteorological systems. This relates to the characterization of precursors to atmospheric instability. In this paper, the observability of such structures by observations is discussed. Several studies have shown that deploying observations over regions where changes in the initial conditions may impact the forecast the most do not lead to the expected benefit. In this paper, it is shown that given the small magnitude of the signal to be detected, it is important to take into account the accuracy of the observations. If the signalto-noise ratio is too low, observations cannot detect and characterize precursors to forecast error growth. From that perspective, the assimilation only has the possibility to extract information about evolved structures of error growth. Experiments with a simple one-dimensional variational data assimilation (1D-Var) system are presented and, then, an adapted three-dimensional variational data assimilation (3D-Var) system with different sensitivity structure functions is used. The results have been obtained by adapting the variational assimilation system of Environment Canada.

Impact of singular‐vector‐based satellite data thinning on NWP

“…If areas subject to growth or decay of meteorological systems are associated with inappropriate error covariance statistics, then the information contained in radiance observations can be filtered out. Even in the case of flow-dependent structure functions, Lupu and Gauthier (2010) showed that observational accuracy must be rather high to avoid a too low signal-to-noise ratio, given the small magnitude of the signal. In summary, an important meteorological signal can be lost since satellite radiances are generally penalized with respect to conventional observations and structure functions are static.…”