A Study on the Reduction of Forecast Error Variance by Three Adaptive Observation Approaches for Tropical Cyclone Prediction

Abstract

Abstract Three adaptive approaches for tropical cyclone prediction are compared in this study: the conditional nonlinear optimal perturbation (CNOP) method, the first singular vector (FSV) method, and the ensemble transform Kalman filter (ETKF) method. These approaches are compared for 36-h forecasts of three northwest Pacific tropical cyclones (TCs): Matsa (2005), Nock-Ten (2004), and Morakot (2009). The sensitive regions identified by each method are obtained. The CNOPs form an annulus around the storm at the targeting time, the FSV targets areas north of the storm, and the ETKF closely targets the typhoon location itself. The sensitive results of both the CNOPs and FSV collocate well with the steering flow between the subtropical high and the TCs. Furthermore, the regions where the convection is strong are targeted by the CNOPs. Relatively speaking, the ETKF sensitive results reflect the large-scale flow. To identify the most effective adaptive observational network, numerous probes or flights were tested arbitrarily for the ETKF method or according to the calculated sensitive regions of the CNOP and FSV methods. The results show that the sensitive regions identified by these three methods are more effective for adaptive observations than the other regions. In all three cases, the optimal adaptive observational network identified by the CNOP and ETKF methods results in similar forecast improvements in the verification region at the verification time, while the improvement using the FSV method is minor.