In this work, a high-resolution triple-nested implementation of the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF) for the 2012 hurricane season is evaluated. Statistics of retrospective experiments for the 2010–11 hurricane seasons show that the new configuration demonstrates significant improvement compared to the 2011 operational HWRF in terms of storm track, intensity, size, dynamical constraints between mass and wind field, and initial vortex imbalance. Specifically, the 5-day track and intensify forecast errors are improved by about 19% and 7% for the North Atlantic basin, and by 9% and 30% for the eastern Pacific basin, respectively. Verifications of storm size in terms of wind radii at 34-, 50-, and 64-kt (17.5, 25.7, and 32.9 m s−1) thresholds at different quadrants show dramatic improvement with most of the overestimation of the storm size in previous operational HWRF versions removed at all forecast times. In addition, dynamical constraints between the storm intensity and the outermost radius in the new configuration are consistent with the best track data. The relationship between minimum sea level pressure and maximum 10-m wind is also improved in both basins, indicating that the storm dynamics and structure have been improved in the 2012 HWRF compared to the previous versions. These significant improvements obtained with the new HWRF implementation are attributed to a number of major changes including a new higher-resolution nest, improved vortex initialization, improved planetary boundary layer and turbulence physics, and some critical bug fixes related to the moving nest. Such improvements show that the new HWRF implementation is a promising upgrade for future hurricane seasons.
This study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble–variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18–24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.
A tropical cyclone initialization method with an idealized three-dimensional bogus vortex of an analytic empirical formula is presented for the track and intensity prediction. The procedure in the new method consists of four steps: the separation of the disturbance from the analysis, determination of the tropical cyclone domain, generation of symmetric bogus vortex, and merging of it with the analysis data. When separating the disturbance field, an efficient spherical high-order filter with the double-Fourier series is used whose cutoff scale can be adjusted with ease to the horizontal scale of the tropical cyclone of interest. The tropical cyclone domain is determined from the streamfunction field instead of the velocities. The axisymmetric vortex to replace the poorly resolved tropical cyclone in the analysis is designed in terms of analytic empirical functions with a careful treatment of the upper-layer flows as well as the secondary circulations. The geopotential of the vortex is given in such a way that the negative anomaly in the lower layer is changed into positive anomaly above the prescribed pressure level, which depends on the intensity of the tropical cyclone. The geopotential is then used to calculate the tangential wind and temperature using the gradient wind balance and the hydrostatic balance, respectively. The inflow and outflow in the tropical cyclone are constructed to resemble closely the observed or simulated structures under the constraint of mass balance. The bogus vortex is merged with the disturbance field with the use of matching principle so that it is not affected except near the boundary of tropical cyclone domain. The humidity of the analysis is modified to be very close to the saturation in the lower layers near the tropical cyclone center. The balanced bogus vortex of the present study is completely specified on the basis of four parameters from the Regional Specialized Meteorological Center (RSMC) report and the additional two parameters, which are derived from the analysis data. The initialization method was applied to the track and the intensity (in terms of central pressure) prediction of the TCs observed in the western North Pacific Ocean and East China Sea in 2007 with the use of the Weather Research and Forecasting (WRF) model. No significant initial jump or abrupt change was seen in either momentum or surface pressure during the time integration, thus indicating a proper tropical cyclone initialization. Relative to the results without the tropical cyclone initialization and the forecast results of RSMC Tokyo, the present method presented a great improvement in both the track and intensity prediction.
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