Strategies to Overcome Filter Divergence in the DRP-4-DVar Approach

This paper discusses an important issue related to filter divergence in the dimension-reduced projection,four-dimensional variational data assimilation(DRP-4-DVar) approach.Idealized experiments with the Lorenz-96 model over a period of 200 days showed that the amplitudes of the root mean square errors(RMSEs) reached the same levels as those of the state variables after approximately 100 days because of the accumulation of sampling errors following the cycle of assimilation.Strategies to reduce sampling errors are critical to ensure the quality of ensemble-based assimilation.Numerical experiments showed that localization and reducing observational errors can alleviate,but cannot completely overcome,the filter divergence in the DRP-4-DVar approach,while the method of perturbing observations and the inflation technique can efficiently eliminate the filter divergence problem.

Assimilating Amounts of Precipitation Using a New Four-Dimensional Variational Method

Observations of accumulated precipitation are extremely valuable for effectively improving rainfall analysis and forecast. It is, however, difficult to use such observations directly through sequential assimilation methods, such as three-dimensional variational data assimilation or an Ensemble Kalman Filter. In this study, the authors illustrate a new approach that makes effective use of precipitation data to improve rainfall forecast. The new method directly obtains an optimal solution in a reduced space by fitting observations with historical time series generated by the model; it also avoids the implementation of tangent linear model and its adjoint. A lot of historical samples are produced as the ensemble of precipitation observations with the fully nonlinear forecast model. The results show that the new approach is capable of extracting information from precipitation observations to improve the analysis and forecast. This method provides comparable performance with the standard fourdimensional variational data assimilation at a much lower computational cost.

A New Typhoon Bogus Data Assimilation and its Sampling Method:A Case Study

In this study,the authors introduce a new bogus data assimilation method based on the dimension-reduced projection 4-DVar,which can resolve the cost function directly in low-dimensional space.The authors also try a new method to improve the quality of samples,which are the base of dimension-reduced space projection bogus data assimilation (DRP-BDA).By running a number of numerical weather models with different model parameterization combinations on the typhoon Sinlaku,the authors obtained two groups of samples with different spreads and similarities.After DRP-BDA,the results show that,compared with the control runs,the simulated typhoon center pressure can be deepened by more than 20 hPa to 30 hPa and that the intensity can last as long as 60 hours.The mean track error is improved after DRP-BDA,and the structure of the typhoon is also improved.The wind near the typhoon center is enhanced dramatically,while the warm core is moderate.

Numerical Simulation of a Landfall Typhoon Using a Bogus Data Assimilation Scheme

A typhoon bogus data assimilation scheme (BDA) using dimension-reduced projection four-dimen-sional variational data assimilation (DRP-4-DVar),called DRP-BDA for short,is built in the Advanced Regional Eta Model (AREM).As an adjoint-free approach,DRP-BDA saves time,and only several minutes are taken for the full BDA process.To evaluate its performance,the DRP-BDA is applied to a case study on a landfall ty-phoon,Fengshen (2008),from the Northwestern Pacific Ocean to Guangdong province,in which the bogus sea level pressure (SLP) is assimilated as a kind of observa-tion.The results show that a more realistic typhoon with correct center position,stronger warm core vortex,and more reasonable wind fields is reproduced in the analyzed initial condition through the new approach.Compared with the control run (CTRL) initialized with NCEP Final (FNL) Global Tropospheric Analyses,the DRP-BDA leads to an evidently positive impact on typhoon track forecasting and a small positive impact on typhoon inten-sity forecasting.Furthermore,the forecast landfall time conforms to the observed landfall time,and the forecast track error at the 36th hour is 32 km,which is much less than that of the CTRL (450 km).