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Sim D. Aberson

Abstract

The National Hurricane Center (NHC) does not verify official or model forecasts if those forecasts call for a tropical cyclone to dissipate or if the real tropical cyclone dissipates. A new technique in which these forecasts are included in a contingency table with all other forecasts is presented. Skill scores and probabilities are calculated. Forecast verifications with the currently used technique have shown a slight improvement in intensity forecasts. The new technique, taking into account all forecasts, suggests that the probability of a forecast having a large (>30 kt) error is decreasing, and the likelihood of the error being less than about 10 kt is increasing in time, at all forecast lead times except 12 h when the forecasts are already quite good.

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Sim D. Aberson

Abstract

Linear multiple regression and discriminant analyses provide estimates of the errors of track forecasts from a nested barotropic hurricane track forecast model (VICBAR), which was run in the North Atlantic Basin during the 1989–94 hurricane seasons. Predictors are determined from the synoptic situation, the magnitude of atmospheric changes in the environment of the tropical cyclone, the consistency between current and past predictions, and the past performance of the model for each particular storm. This technique distinguishes cases in which VICBAR performs well from those for which it performs poorly and can provide skillful operational predictions of model performance to forecasters.

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Sim D. Aberson

Abstract

A simple linear discriminant analysis scheme using climatological predictors is derived for the Atlantic basin as a no-skill baseline for operational phase forecasts from the National Hurricane Center (NHC). The model with independent data correctly classifies 80% of the cases at 12 h, and this value decreases to about 45% by 60 h, remaining steady thereafter. Using the same cases, NHC-issued phase predictions were more frequently accurate than the baseline, so their forecasts are said to have skill.

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Sim D. Aberson

Abstract

Statistical analyses of the most recent 40 yr of hurricane tracks (1956–95) are presented, leading to a version of the North Atlantic climatology and persistence (CLIPER) model that exhibits much smaller forecast biases but similar forecast errors compared to the previously used version. Changes to the model involve the inclusion of more accurate historical tropical cyclone track data and a simpler derivation of the regression equations. Nonlinear systems analysis shows that the predictability timescale in which the average errors increase by a factor e is approximately 2.5 days in the Atlantic basin, which is larger than that found by similar methods near Australia. This suggests that 5-day tropical cyclone track forecasts may have some benefit, and therefore a version of CLIPER extended to 5 days to be used as a baseline to measure this skill is needed.

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Sim D. Aberson

Abstract

In 1997, the National Hurricane Center and the Hurricane Research Division began operational synoptic surveillance missions with the Gulfstream IV-SP jet aircraft to improve the numerical guidance for hurricanes that threaten the continental United States, Puerto Rico, the Virgin Islands, and Hawaii. During the first two years, 24 missions were conducted. Global positioning system dropwindsondes were released from the aircraft at 150–200-km intervals along the flight track in the environment of each tropical cyclone to obtain profiles of wind, temperature, and humidity from flight level (nearly 150 hPa) to the surface. The observations were processed and formatted aboard the aircraft and sent to NCEP to be ingested into the Global Data Assimilation System, which subsequently served as initial and boundary conditions for a number of numerical models that forecast the track and intensity of tropical cyclones. The current study is an attempt to mimic this process to assess the impact of these operational missions on the numerical guidance. Although the small number of missions flown in 1997 showed error reductions of as much as 32%, the improvements seen in the 2-yr sample are not promising. The additional dropwindsonde data from the synoptic surveillance missions provided statistically significant improvements in the GFDL forecasts only at 12 h. The “VBAR” and Global Forecast System (AVN) forecasts were not significantly improved at any forecast time. Further examination suggests that the AVN synthetic vortex procedure, combined with difficulty in the quantification of the current storm-motion vector operationally, may have caused the mediocre improvements. Forecast improvements of 14%–24% in GFDL forecasts are shown in the subset of cases in which the synthetic vortex data do not seem to be a problem. Improvements in the landfall forecasts are also seen in this subset of cases. A reassessment of tropical cyclone vortex initialization schemes used by forecast centers and numerical modelers may be necessary.

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Sim D. Aberson and J. Kaplan

Abstract

The relationship between the Madden–Julian oscillation (MJO) and tropical cyclone rapid intensification in the northern basins of the Western Hemisphere is examined. All rapid intensification events in the part of the Western Hemisphere north of the equator and the MJO phase and amplitude are compiled from 1974 to 2015. Rapid intensification events and the MJO tend to move in tandem with each other from west to east across the hemisphere, though rapid intensification appears most likely during a neutral MJO phase. The addition of this information to an operational statistical rapid intensification forecasting scheme does not significantly improve forecasts.

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Zhaoxia Pu, Xuanli Li, Christopher S. Velden, Sim D. Aberson, and W. Timothy Liu

Abstract

Dropwindsonde, Geostationary Operational Environmental Satellite-11 (GOES-11) rapid-scan atmospheric motion vectors, and NASA Quick Scatterometer (QuikSCAT) near-surface wind data collected during NASA’s Tropical Cloud Systems and Processes (TCSP) field experiment in July 2005 were assimilated into an advanced research version of the Weather Research and Forecasting (WRF) model using its three-dimensional variational data assimilation (3DVAR) system. The impacts of the mesoscale data assimilation on WRF numerical simulation of Tropical Storms Cindy and Gert (2005) near landfall are examined. Sensitivity of the forecasts to the assimilation of each single data type is investigated. Specifically, different 3DVAR strategies with different analysis update cycles and resolutions are compared in order to identify the better methodology for assimilating the data from research aircraft and satellite for tropical cyclone study.

The results presented herein indicate the following. 1) Assimilation of dropwindsonde and satellite wind data into the WRF model improves the forecasts of the two tropical storms up to the landfall time. The QuikSCAT wind information is very important for improving the storm track forecast, whereas the dropwindsonde and GOES-11 wind data are also necessary for improved forecasts of intensity and precipitation. 2) Data assimilation also improves the quantitative precipitation forecasts (QPFs) near landfall of the tropical storms. 3) A 1-h rapid-update analysis cycle at high resolution (9 km) provides more accurate tropical cyclone forecasts than a regular 6-h analysis cycle at coarse (27 km) resolution. The high-resolution rapidly updated 3DVAR analysis cycle might be a practical way to assimilate the data collected from tropical cyclone field experiments.

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Chun-Chieh Wu, Kun-Hsuan Chou, Po-Hsiung Lin, Sim D. Aberson, Melinda S. Peng, and Tetsuo Nakazawa

Abstract

Starting from 2003, a new typhoon surveillance program, Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR), was launched. During 2004, 10 missions for eight typhoons were conducted successfully with 155 dropwindsondes deployed. In this study, the impact of these dropwindsonde data on tropical cyclone track forecasts has been evaluated with five models (four operational and one research models). All models, except the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model, show the positive impact that the dropwindsonde data have on tropical cyclone track forecasts. During the first 72 h, the mean track error reductions in the National Centers for Environmental Prediction’s (NCEP) Global Forecast System (GFS), the Navy Operational Global Atmospheric Prediction System (NOGAPS) of the Fleet Numerical Meteorology and Oceanography Center (FNMOC), and the Japanese Meteorological Agency (JMA) Global Spectral Model (GSM) are 14%, 14%, and 19%, respectively. The track error reduction in the Weather Research and Forecasting (WRF) model, in which the initial conditions are directly interpolated from the operational GFS forecast, is 16%. However, the mean track improvement in the GFDL model is a statistically insignificant 3%. The 72-h-average track error reduction from the ensemble mean of the above three global models is 22%, which is consistent with the track forecast improvement in Atlantic tropical cyclones from surveillance missions. In all, despite the fact that the impact of the dropwindsonde data is not statistically significant due to the limited number of DOTSTAR cases in 2004, the overall added value of the dropwindsonde data in improving typhoon track forecasts over the western North Pacific is encouraging. Further progress in the targeted observations of the dropwindsonde surveillances and satellite data, and in the modeling and data assimilation system, is expected to lead to even greater improvement in tropical cyclone track forecasts.

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