Search

The impact of the assimilation of high spatial and temporal resolution atmospheric motion vectors (AMVs) on tropical cyclone (TC) forecasts has been investigated. The high-resolution AMVs are derived from the full disk scan of the new generation geostationary satellite Himawari-8. Forecast experiments for three TCs in 2016 in a western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). Two different ensemble–variational hybrid data assimilation configurations (using background error covariance created by global ensemble forecast and HWRF ensemble forecast), based on the Gridpoint Statistical Interpolation (GSI), are used for the sensitivity experiments. The results show that the inclusion of high-resolution Himawari-8 AMVs (H8AMV) can benefit the track forecast skill, especially for long-range lead times. The diagnosis of optimal steering flow indicates that the improved track forecast seems to be attributed to the improvement of initial steering flow surrounding the TC. However, the assimilation of H8AMV increases the negative intensity bias and error, especially for short-range forecast lead times. The investigation of the structural change from the assimilation of H8AMV revealed that the following two factors are likely related to this degradation: 1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and 2) a drying around and outside the RMW. Assimilating H8AMV using background error covariance created from HWRF ensemble forecast contributes to a significant reduction in negative intensity bias and error, and there is a significant benefit to TC size forecast. [ABSTRACT FROM AUTHOR]

Typhoon Lionrock (2016) was unusual among tropical cyclones (TCs) in that it formed east of the monsoon gyre in the western North Pacific, and moved counterclockwise. It rapidly intensified in the monsoon gyre in an environment of weak upper-tropospheric winds and vertical wind shear. This study used a 3-km mesh nonhydrostatic model to examine the warm-core intensification of Typhoon Lionrock, which was associated with cyclone-scale vigorous convection [i.e., convective bursts (CBs)]. The simulation reproduced the multiple CBs at intervals of 1 day or shorter, which were related to the diurnal cycles and other short time-scale variations in the TC convection. Each CB tended to precede peak temperature anomalies near the TC center by 0–12 h, indicating that the warm-core intensification occurred due to diabatic heating released by the vigorous eyewall convection. Notably, updrafts due to convection during the intensification phase were stronger than those occurring during the mature and decay phases, and the maximum temperature anomaly of the upper-tropospheric warm core rapidly increased during eyewall formation. In addition, this study indicated that most of the asymmetric inner-core vigorous convection associated with CBs, which was induced by the vertical wind shear, contributed to the warm-core intensification. Furthermore, the budget analysis of potential temperature within the TC inner core showed that adiabatic heating due to subsidence from near the tropopause within the eye, often following CBs, was essential in developing the eye. The lag correlation suggested the lag time between the CBs and the subsidence within the eye was 3–9 h. [ABSTRACT FROM AUTHOR]

This study investigates the assimilation impact of rapid-scan (RS) atmospheric motion vectors (AMVs) derived from the geostationary satellite Himawari-8 on tropical cyclone (TC) forecasts. Forecast experiments for three TCs in 2016 in the western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). An ensemble-variational hybrid data assimilation system is used as an initialization. The results show that the assimilation of RS-AMVs can improve the track forecast skill, while the weak bias or slow intensification bias increases at the shorter forecast lead time. A vortex initialization in HWRF has a substantial impact on TC structure, but it has neutral impacts on the track and intensity forecasts. A thinning of AMVs mitigates the weak bias caused by RS-AMV assimilation, resulting in the reduction of intensity error. However, it degrades the track forecast skill for a longer lead time. A decomposition of the TC steering flows demonstrated that the change in TC-induced flow was a primary factor for reducing the track forecast error, and the change in environmental flow has less impact on the track forecast. The investigation of the structural change from the assimilation of RS-AMV revealed that the following two factors are likely related to the intensity forecast degradation: (1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and (2) a drying around and outside the RMW. [ABSTRACT FROM AUTHOR]