A B S T R A C TThe Running-In-Place (RIP) method is implemented in the framework of the Local Ensemble Transform Kalman Filter (LETKF) coupled with the Weather Research and Forecasting (WRF) model. RIP aims at accelerating the spin-up of the regional LETKF system when the WRF ensemble is initialised from a global analysis, which is obtained at a coarser resolution and lacks features related to the underlying mesoscale evolution. The RIP method is further proposed as an outer-loop scheme to improve the nonlinear evolution of the ensemble when the characteristics of the error statistics change rapidly owing to strong nonlinear dynamics. The impact of using RIP as an outer-loop for the WRF-LETKF system is evaluated for typhoon assimilation and prediction with Typhoon Sinlaku (2008) as a case study. For forecasts beyond one day, the typhoon track prediction is significantly improved after RIP is applied, especially during the spin-up period of the LETKF assimilation when Sinlaku is developing rapidly from a severe tropical storm to a typhoon. The impact of the dropsondes is significantly increased by RIP at early assimilation cycles. Results suggest that these improvements are because of the positive impact on the environmental condition of the typhoon. Results also suggest that using the RIP scheme adaptively allows RIP to be used as an outer-loop for the WRF-LETKF with further improvements.

Ensemble-based data assimilation (EDA) has been used for tropical cyclone (TC) analysis and prediction with some success. However, the TC position spread determines the structure of the TC-related background error covariance and affects the performance of EDA. With an idealized experiment and a real TC case study, it is demonstrated that observations in the core region cannot be optimally assimilated when the TC position spread is large. To minimize the negative impact from large position uncertainty, a TC-centered EDA approach is implemented in the Weather Research and Forecasting (WRF) Model–local ensemble transform Kalman filter (WRF-LETKF) assimilation system. The impact of TC-centered EDA on TC analysis and prediction of Typhoon Fanapi (2010) is evaluated. Using WRF Model nested grids with 4-km grid spacing in the innermost domain, the focus is on EDA using dropsonde data from the Impact of Typhoons on the Ocean in the Pacific field campaign. The results show that the TC structure in the background mean state is improved and that unrealistically large ensemble spread can be alleviated. The characteristic horizontal scale in the background error covariance is smaller and narrower compared to those derived from the conventional EDA approach. Storm-scale corrections are improved using dropsonde data, which is more favorable for TC development. The analysis using the TC-centered EDA is in better agreement with independent observations. The improved analysis ameliorates model shock and improves the track forecast during the first 12 h and landfall at 72 h. The impact on intensity prediction is mixed with a better minimum sea level pressure and overestimated peak winds.

This study investigates the impact of tropical cyclone (TC) initialization methods on TC intensity prediction under a framework coupling the Weather Research and Forecasting Model with the TC Centered-Local Ensemble Transform Kalman Filter (WRF TCC-LETKF). While the TC environments are constrained by assimilating the same environmental observations, two different initialization strategies, assimilating real dropsonde observations (the DP experiment) and synthetic axisymmetric surface wind structure (the VT experiment), are employed to construct the TC inner-core structure. These two experiments have distinct results on predicting the rapid intensification (RI) of Typhoon Megi (2010), which can be attributed to their different convective burst (CB) development. In DP, the assimilation of the dropsondes helps establish a realistic TC structure with asymmetry information, leading to scattered CB distribution and persistent RI with abundant moisture supply. In VT, assimilating the axisymmetric surface wind structure spins up the TC efficiently. However, the initially excessive CB coverage causes a too-early high-level warm core, and the reduced moisture supply hinders RI. The forecast results imply that if the TC structure is initialized using a scheme considering only the axisymmetric vortex structure, the RI potential can possibly be underestimated due to the inability to represent the realistic asymmetric structure. Finally, assimilation of both the real and synthetic data can be complementary, giving a strong TC initially that undergoes a longer RI period.

This study investigates the impact of assimilating ground-based radar reflectivity and wind data on tropical cyclone (TC) intensity prediction. The effect on a high-impact TC in the western North Pacific region that penetrated the Bashi Channel is examined. A multiscale correction based on the successive covariance localization (SCL) method is adopted to improve the analysis and forecast performance. In addition, GNSS-R wind speed is assimilated jointly in the rapid update assimilation framework to complement the TC boundary layer where radar data are limited. Model experiments are conducted and evaluated using the observing system simulation experiments (OSSEs) framework with a coupled atmosphere–ocean model nature run. Taking the experiment without data assimilation as the baseline, assimilating the radar data with the standard localization and SCL methods reduces the wind speed analysis error by 12% and 44%, respectively. The SCL method dominates the improvement in TC intensity prediction with a lead time longer than 2 days and the TC’s peak intensity forecast is improved by 18 hPa. The additional assimilation of the GNSS-R wind speed observation further reduces the wind speed error in the low-level analysis by 12% and 5% under the standard and SCL radar assimilation framework, respectively. GNSS-R wind assimilation leads to a further 6-hPa improvement in TC’s peak intensity. However, the sampling error introduced by the SCL method restrains the effect of GNSS-R assimilation. Sensitivity experiments with different GNSS-R data arrangements show that better GNSS-R wind coverage and additional wind direction information can further improve the TC analysis. Significance Statement Tropical cyclone (TC) intensity prediction over the western North Pacific (WNP) region remains a significant challenge due to limited observations. This study aims to improve the TC intensity prediction in WNP by assimilating the ground-based radar data using a multiscale correction framework and incorporating with the satellite ocean surface wind speed observation. We particularly focus on a high-impact TC like Typhoon Hato (2017), which penetrated the Bashi Channel and later made landfall in China, causing great damage. Our results showed that the assimilation strategy improved the TC intensity prediction for a lead time longer than 2 days. These results demonstrate the great potential of these observations and can provide guidance for future applications in operation centers.