Section 3.3: Ensemble Prediction Methods for Tropical Cyclone Forecasting

Rapporteur: Lance M. Leslie
School of Meteorology
Snarkeys Energy Center
University of Oklahoma
100 East Boyd St., Room 1310
Norman, OK 73019

: lmleslie@ou.edu
Fax: 405.325.7689
Members of Group: Robert F. Abbey Jr., Kevin Cheung, Lance Leslie, David
Richardson, Steve Tracton

3.3.1 Background:

Ensemble forecast techniques have been available for use in weather and climate prediction for several decades. However, it is only in the past decade or so that ensemble forecasting methods have developed to the extent that they are now an integral part of both research and operations in numerical weather prediction (NWP) systems at major weather centers. This is particularly true for centers that have responsibility for tropical cyclone (TC) forecasting and issuing of warnings. The techniques are variously purely statistical, purely numerical, or a hybrid of both approaches. The success of the approach continues to grow as the refinement of ensemble prediction methods proceeds and the extension expands.

Apart from some earlier simple attempts at ensemble prediction, the specialist application of ensemble forecast methods to TC prediction has occurred only over approximately the past five years. The application to TCs, like much research in the tropics, followed the extension of ensemble forecasting to severe weather events in the mid-latitudes. In particular, it followed successful research applications to short-range ensemble forecasting (SREF) of explosive mid-latitude cyclogenesis.

From the mid-1990s to the present, the development of ensemble perturbation methodologies for TCs has developed rapidly, especially in the past few years. Ensemble predictions of TC tracks are now becoming available on various websites including some of the major weather centers and other institutions that have responsibility for NWP over the TC basins. In recent years, ensemble techniques have become invaluable and increasingly reliable. The example of TC Lili (below) that affected the Gulf of Mexico in early October, 2002 is a case in point.

An example of an ensemble forecast for TC Lili is shown in Figure 1 below. The ensemble approach was a multiple model ensemble with multiple initial conditions. In this case, the members of the ensemble are forecast tracks from different weather centers, as shown in the legend above the figure. There was very little spread among the ensemble members, so the mean forecast track was little different from any ensemble members. This close correspondence between ensemble members continued over most of the lifecycle of Lili after it reached the southeastern Gulf, near the southwest tip of Cuba. The ensemble consistently predicted landfall in Louisiana out to four days in advance. The fact that the ensemble members are all close together is often, but not always, a sign that the mean error of the ensemble prediction is small. It certainly was true in the case of TC Lili. In a TC context, Aberson et al. (1995) examined the relationship between ensemble spread and ensemble mean error. More recently, Elsberry and Carr (2000) have quantified consensus error as a function of consensus spread, extending earlier work on consensus dynamical TC track forecasts by Goerss (2000). However, as we shall discuss later, it has been established that a small ensemble spread is not always an indicator of a small ensemble mean error and the correlation between ensemble spread and error is an ongoing research area. TC Lili also provided an excellent example of a marked deterioration in the forecast accuracy as Lili approached and made landfall. Both the timing and intensity predictions became significantly less accurate than was the case when Lili was in the Gulf of Mexico, well away from land. This deterioration in forecasts as TCs approach landfall has been documented by various researchers. For example, the recent article by Aberson (2001) discusses in considerable detail the problem of lack of improvement of TC ensemble track forecasts at landfall for the North Atlantic basin.

3.3.2 Methodologies:

It is generally accepted that ensemble prediction methods were developed with at least two main aims in mind. The first goal was to build on established theory for midlatitude baroclinic systems that a probability density function (PDF) could be generated by perturbing the initial state of a forecast model. PDFs could be generated at various times in the future and then be used to generate additional information that a single forecast could not provide. Most notable of the additional information provided is knowledge of the various statistical moments of the forecasts. In particular, it had been shown that the ensemble forecast mean theoretically is more accurate than any individual ensemble forecast member. Additional benefits also were readily available, including valuable estimates of error growth rates and associated predictability time scales. Such information was almost entirely lacking at the time and has since become a key area of research and applications. A second force driving the development of ensemble prediction techniques was the need for much higher resolution models than computational platforms allowed at the time. During the mid 1990s, the modeling of explosive cyclogenesis, whether tropical or extratropical, was severely limited by an inability to achieve model horizontal resolutions commensurate with the spatial scales of the cyclones being modeled. As a consequence, ensemble prediction techniques were viewed as a possible means of achieving the effective skill of a higher resolution forecast without needing the punitive resources required when increasing the model resolution. Remember that for most models an increase in horizontal and vertical resolution of a (modest) factor of two requires a time step reduction of a similar factor and an overall computational demand that is sixteen times as great as the original resolution! When the extra information provided by an ensemble prediction is taken into account, such an alternate approach had obvious appeal. However, it is now widely appreciated that adequate model resolution is a necessity and that rather than replace high resolution models, ensemble methods complement rather than replace the single model approach.

As stated in the Background, the ensemble prediction approach generates a set of forecasts by perturbing the NWP system in various ways and producing forecasts from the perturbations. From the early perturbation generation based on Monte Carlo procedures, or simple weighted averaging of forecasts, a plethora of approaches now exists. At least ten methods have been developed over the years and most are still in use. No attempt will be made here to describe each method as they all are well-documented in the literature. They include:

It is important to note that almost all of the above ensemble prediction schemes are based on perturbing the initial conditions provided to the model(s). The premise is that because the exact state of the atmosphere is not known, perturbing the initial model state can generate a set of equally likely initial states. From these initial states, a set of forecasts can be generated and the statistical mean and higher moments of the forecast can reduce the impact of the initial state uncertainties in the final forecast. However, focusing only on uncertainties in the initial state ignores equally important model formulation uncertainties and also uncertainties in the Geographic Information System (GIS) data base and in surface and lateral boundary parameters and conditions. This latter aspect is now being addressed, with the different models and the Super-Ensemble procedures being examples of that approach.

3.3.3 Some Applications of Existing Ensemble Methods:

As we have seen, there are now at least ten distinct ensemble methodologies still in use for TC forecasting. Each of these techniques has been applied to many cases. It is impossible here to provide examples of each approach. Instead, we will mention just one pair of review papers at present. An excellent starting point is the series of review papers by Cheung and Chan (1999a, b), which traces the development of ensemble prediction schemes in TC forecasting and examines the utility of several of the schemes. The importance of both TC vortex perturbations and the perturbations of the environment are considered. The list of applications of existing ensemble prediction techniques will be expanded greatly by the time of the IWTC meeting.

3.3.4 Current Issues in TC Ensemble Prediction:

Despite the growing success of ensemble prediction methods, many issues remain unresolved, and each issue is a major one. They will only be listed here as the debates are complex and can be obtained from the literature.

3.3.5 Specific Recommendations for Future Work:

Ensemble methods in TC prediction is a growth area in research and applications. As such, the number of possible recommendations is very large. The final IWTC-V report will be much more comprehensive than this draft report, which will mention just a sample of possible focus areas. Specifically, there is a need to carry out the following:


Aberson SD, SJ Lord, M. DeMaria and MS Tracton (1995): Short range ensemble forecasting of hurricane tracks. Preprints, 21st Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 494-496.

Aberson, SD (2001): The ensemble of tropical cyclone track forecasting models in the North Atlantic Basin (1976-2000), Bull. Amer. Meteor. Soc., 82, 1895-1903.

Brooks, HE, MS Tracton, DJ Stensrud, G. DiMego and Z. Toth (1995): Short range ensemble forecasting (SREF): Report from a workshop. Bull. Amer. Meteor. Soc., 76, 1617-1624.

Cheung, KKW and JCL Chan (1999a): Ensemble forecasting of tropical cyclone motion using a barotropic model. Part I: Perturbations of the environment. Mon. Wea. Rev., 127, 1229-1243.

Cheung, KKW and JCL Chan (1999b): Ensemble forecasting of tropical cyclone motion using a barotropic model. Part II: Perturbations of the vortex. Mon. Wea. Rev., 127, 2617-2640.

Elsberry, RL and LE Carr III (2000): Consensus of dynamical tropical cyclone track forecasts – errors versus spread. Mon. Wea. Rev., 128, 4131-3138.

Goerss, J. (2000): Tropical cyclone track forecasts using an ensemble of dynamical models.

Leslie, LM and GJ Holland (1995): On the bogussing of tropical cyclones in numerical models: A comparison of vortex profiles. Meteor. and Atmos. Phys., 56, 101-110.

Richardson, DS (2000): Skill and economic value of the ECMWF ensemble prediction system. Quart. J. Roy. Meteor. Soc., 126, 649-668.

Session 3.3