Selective ensemble-mean technique for tropical cyclone track forecast by using ensemble prediction systems

2013 ◽  
Vol 140 (680) ◽  
pp. 805-813 ◽  
Author(s):  
Liangbo Qi ◽  
Hui Yu ◽  
Peiyan Chen
Keyword(s):  
Tropical Cyclone ◽  
Ensemble Prediction ◽  
Track Forecast ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Selective Ensemble ◽  
Ensemble Mean ◽  
Prediction Systems

Impact of Resolution and Design on the U.S. Navy Global Ensemble Performance in the Tropics

2011 ◽  
Vol 139 (7) ◽  
pp. 2145-2155 ◽  
Author(s):  
Carolyn A. Reynolds ◽  
Justin G. McLay ◽  
James S. Goerss ◽  
Efren A. Serra ◽  
Daniel Hodyss ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Kinetic Energy ◽  
Ensemble Prediction ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Ensemble Performance ◽  
Ensemble Mean ◽  
The Tropics ◽  
The U.S

Abstract The performance of the U.S. Navy global atmospheric ensemble prediction system is examined with a focus on tropical winds and tropical cyclone tracks. Ensembles are run at a triangular truncation of T119, T159, and T239, with 33, 17, and 9 ensemble members, respectively, to evaluate the impact of resolution versus the number of ensemble member tradeoffs on ensemble performance. Results indicate that the T159 and T239 ensemble mean tropical cyclone track errors are significantly smaller than those of the T119 ensemble out to 4 days. For ensemble forecasts of upper- and lower-tropospheric tropical winds, increasing resolution has only a small impact on ensemble mean root-mean-square error for wind speed, but does improve Brier scores for 10-m wind speed at the 5 m s−1 threshold. In addition to the resolution tests, modifications to the ensemble transform initial perturbation methodology and inclusion of stochastic kinetic energy backscatter are also evaluated. Stochastic kinetic energy backscatter substantially increases the ensemble spread and improves Brier scores in the tropics, but for the most part does not significantly reduce ensemble mean tropical cyclone track error.

OBEST: An Observation-Based Ensemble Subsetting Technique for Tropical Cyclone Track Prediction

2016 ◽  
Vol 31 (1) ◽  
pp. 57-70 ◽  
Author(s):  
Lin Dong ◽  
Fuqing Zhang
Keyword(s):  
Tropical Cyclone ◽  
Lead Times ◽  
Track Forecast ◽  
Forecast Errors ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Weather Forecasts ◽  
Ensemble Mean ◽  
Medium Range ◽  
Environmental Prediction

Abstract An observation-based ensemble subsetting technique (OBEST) is developed for tropical cyclone track prediction in which a subset of members from either a single- or multimodel ensemble is selected based on the distance from the latest best-track position. The performance of OBEST is examined using both the 2-yr hindcasts for 2010–11 and the 2-yr operational predictions during 2012–13. It is found that OBEST outperforms both the simple ensemble mean (without subsetting) and the corresponding deterministic high-resolution control forecast for most forecast lead times up to 5 days. Applying OBEST to a superensemble of global ensembles from both the European Centre for Medium-Range Weather Forecasts and the National Centers for Environmental Prediction yielded a further reduction in track forecast errors by 5%–10% for lead times of 24–120 h.

scholarly journals The Impact of Dropwindsonde Data on Typhoon Track Forecasts in DOTSTAR

2007 ◽  
Vol 22 (6) ◽  
pp. 1157-1176 ◽  
Author(s):  
Chun-Chieh Wu ◽  
Kun-Hsuan Chou ◽  
Po-Hsiung Lin ◽  
Sim D. Aberson ◽  
Melinda S. Peng ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Error Reduction ◽  
Track Forecast ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Global Spectral Model ◽  
Track Error ◽  
Typhoon Track ◽  
The Mean ◽  
The Impact

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.

scholarly journals Prediction of Consensus Tropical Cyclone Track Forecast Error

2007 ◽  
Vol 135 (5) ◽  
pp. 1985-1993 ◽  
Author(s):  
James S. Goerss
Keyword(s):  
Tropical Cyclone ◽  
Regression Models ◽  
Average Distance ◽  
Forecast Error ◽  
Dependent Data ◽  
Track Forecast ◽  
Consensus Model ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
National Hurricane Center

Abstract The extent to which the tropical cyclone (TC) track forecast error of a consensus model (CONU) routinely used by the forecasters at the National Hurricane Center can be predicted is determined. A number of predictors of consensus forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic basin in 2001–03. Leading predictors were found to be consensus model spread, defined to be the average distance of the member forecasts from the consensus forecast, and initial and forecast TC intensity. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the CONU TC track forecast error. The percent variance of CONU TC track forecast error that could be explained by these regression models ranged from just over 15% at 48 h to nearly 50% at 120 h. Using the regression models, predicted radii were determined and were used to draw circular areas around the CONU forecasts that contained the verifying TC position 73%–76% of the time. Based on the size of these circular areas, a forecaster can determine the confidence that can be placed upon the CONU forecasts. Independent data testing yielded results only slightly degraded from those of dependent data testing, highlighting the capability of these methods in practical forecasting applications.

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