Ensemble Forecasting of Hurricane Tracks

Z. Zhang Department of Meteorology, The Florida State University, Tallahassee, Florida

Search for other papers by Z. Zhang in
Current site
Google Scholar
PubMed
Close
and
T. N. Krishnamurti Department of Meteorology, The Florida State University, Tallahassee, Florida

Search for other papers by T. N. Krishnamurti in
Current site
Google Scholar
PubMed
Close
Restricted access

Because of the initial data uncertainties, it is inevitable that operational hurricane track forecasting practice would, in the future, follow an ensemble forecast approach. The ensemble technique is becoming increasingly popular for the middle-latitude weather forecasts. This paper focuses on an ensemble forecast methodology for the hurricane track forecast procedure.

In this study, an ensemble perturbation method is applied for hurricane track predictions using the Florida State University's Global Spectral Model with horizontal spectral resolution of T63 and 14 vertical levels.

The method is based on the premise that (a) model perturbation grows linearly during the first few days of model integration, and (b) in order to make a complete set of ensemble perturbations of hurricane forecasts, both hurricane initial position and its structure and environment need to be perturbed. The initial position of the hurricane is perturbed by displacing its original position 50 km equally toward the north, south, east, and west directions. The hurricane environment and structure perturbations can be generated by implementing EOF analysis to the differences between forecasts starting from regular analysis and randomly perturbed analysis. Only the temperature and wind fields are perturbed with the order proportional to the respective observational error. The method generates 15 ensemble members for each hurricane.

The result shows that this ensemble prediction method leads to an improvement in the hurricane track forecasts. The track position errors are largely reduced by the ensemble prediction for most of the hurricane cases that have been tested, and these forecasts are superior to the results from single-model control experiments. It is also noted that the spread of the ensemble track forecasts is useful to assess the reliability of the predictions.

Corresponding author address: Dr. T. N. Krishnamurti, Department of Meteorology, The Florida State University, Tallahassee, FL 32306.

Because of the initial data uncertainties, it is inevitable that operational hurricane track forecasting practice would, in the future, follow an ensemble forecast approach. The ensemble technique is becoming increasingly popular for the middle-latitude weather forecasts. This paper focuses on an ensemble forecast methodology for the hurricane track forecast procedure.

In this study, an ensemble perturbation method is applied for hurricane track predictions using the Florida State University's Global Spectral Model with horizontal spectral resolution of T63 and 14 vertical levels.

The method is based on the premise that (a) model perturbation grows linearly during the first few days of model integration, and (b) in order to make a complete set of ensemble perturbations of hurricane forecasts, both hurricane initial position and its structure and environment need to be perturbed. The initial position of the hurricane is perturbed by displacing its original position 50 km equally toward the north, south, east, and west directions. The hurricane environment and structure perturbations can be generated by implementing EOF analysis to the differences between forecasts starting from regular analysis and randomly perturbed analysis. Only the temperature and wind fields are perturbed with the order proportional to the respective observational error. The method generates 15 ensemble members for each hurricane.

The result shows that this ensemble prediction method leads to an improvement in the hurricane track forecasts. The track position errors are largely reduced by the ensemble prediction for most of the hurricane cases that have been tested, and these forecasts are superior to the results from single-model control experiments. It is also noted that the spread of the ensemble track forecasts is useful to assess the reliability of the predictions.

Corresponding author address: Dr. T. N. Krishnamurti, Department of Meteorology, The Florida State University, Tallahassee, FL 32306.
Save