Estimating the Intensity Errors of Tropical Cyclones under the Enkf Framework and Its Impact on Intensity Prediction

In the framework of ensemble Kalman filter (EnKF), the establishment of a tropical cyclone (TC)vortex is derived by assimilating TC-related parameters, with vortex bogusing during the early stageof the assimilation. However, the characteristic of the TC in the analysis is strongly affected by thefeatures of the bogused vortex and distribution of observations and even the spread of the typhoonlocations in the ensemble. In the first stage of our project, we have established several ensemblesbased on the 2010 Typhoon Megi using the WRF EPS and the NC (Nguyenn and Chen, 2011, 2014)vortex initialization scheme to study the uncertainty associated with TC development, especially inthe inner core. Results show that we need to consider both the uncertainties in the environment andTC structure to exhibit error structures that have growing property and are related to intensity. In themean time, we should also avoid having a too large TC position spread in the ensemble.Although combing the EPS and the NC scheme can generate the flow-dependent errorstructures associated with TC intensity, the computational is too high to be feasible for operationalapplication. Therefore, we propose to combine the running-in-place algorithm and the NC methodunder the framework of TCC-LETKF in order to derive the intensity-associated, flow-dependenterror structure at a lower computational cost. In addition, we plan to assimilate the axis-symmetrytangential wind to improve the maximum wind prediction. We would like to investigate whether wecan combine the vortex initialization and assimilation strategies to ameliorate the issues ofinfrequent or sparse observations in the inner core of TCs over northwestern Pacific region. Theimpact of inner-core error structure on intensity prediction will be further justified by assimilation ofthe dropsonde observations.