Abstract

With current computational limitations, the accuracy of high-resolution precipitation forecasts has limited temporal and spatial resolutions. However, with the recent development of the superensemble technique, the potential to improve precipitation forecasts at the regional resolution exists. The purpose of this study is to apply the superensemble technique to regional precipitation forecasts to generate more accurate forecasts pinpointing exact locations and intensities of strong precipitation systems. This study will determine the skill of a regional superensemble forecast out to 60 h by examining its equitable threat score and its false alarm ratio. The regional superensemble consists of 12–60-h daily quantitative precipitation forecasts from six models. Five are independent operational models, and one comes from the physically initialized Florida State University regional spectral model. The superensemble forecasts are verified during the summer 2003 season over the southeastern United States using merged River Forecast Center stage-IV radar–gauge and satellite analyses. Precipitation forecasts were skillful in outperforming the operational models at all model times. Precipitation results were stratified by time of day to allow detections of the diurnal cycle. As expected, warm season daytime precipitation is commonly forced by convection, which is difficult to accurately model. Major synoptic regimes, including subtropical highs, midlatitude troughs/fronts, and tropical cyclones, were examined to determine the skill of the superensemble under various synoptic conditions.

Abstract

Forecasting tropical storm intensities is a very challenging issue. In recent years, dynamical models have improved considerably. However, for intensity forecasts more improvement is necessary. Dynamical models have different kinds of biases. Considering a multimodel consensus could eliminate some of the biases resulting in improved intensity forecasts as compared to the individual models. Apart from the ensemble mean, the construction of multimodel consensuses has always contributed to somewhat improved forecasts. The Florida State University (FSU) multimodel superensemble is one that, over the years, has systematically provided improved forecasts for hurricanes, numerical weather prediction, and seasonal climate forecasts. The present study considers an artificial neural network (ANN), based on biological principles, for the construction of a multimodel ensemble. ANN has been used for constructing multimodel consensus forecasts for tropical cyclone intensities. This study uses the generalized regression neural network (GRNN) method for the construction of consensus intensity forecasts for the Atlantic basin. Hurricane seasons 2012–16 are considered. Results show that with only five input models improved guidance for tropical storm intensities may be obtained. The consensus using GRNN mostly outperforms all the models included in the study and the ensemble mean. Forecast errors at the longer forecast leads are considerably less for this multimodel superensemble based on the generalized regression neural network. The skill and correlations of different models along with the developed consensus are provided in our analysis. Results suggest that this consensus forecast may be used for operational guidance and for planning and emergency evacuation management. Possibilities for future improvements of the consensus based on new advances in statistical algorithms are also indicated.

Abstract

A high-resolution nested regional spectral model and an ensemble prediction system are combined to forecast the track, intensity, and flooding precipitation arising from Typhoon Winnie of August 1997, which eventually reached supertyphoon status. The prediction of floods is operationally challenging since rainfall distributions can have a high degree of spatial and temporal variability. Rare event probabilities, however, can be estimated more readily via ensemble forecasting. This technique is used to evaluate a typhoon flood event in which rainfall amounts greater than 200 mm led to landslides and major flooding of crops. Seven-member ensembles were generated using an EOF-based technique. An experiment was conducted with a regional model resolution of 0.5° latitude. A Mercator transform grid with a grid mesh size of approximately 55 km in the east–west and 48 km in the north–south was employed. The results indicated very accurate track and intensity forecasts for both the control and ensemble mean. Track position errors remained below 150 km through 72 h, while intensity errors were approximately 5 m s⁻¹ at landfall. Qualitatively, the overall 5-day precipitation patterns appeared realistic and compared favorably with the observed data, while, quantitatively, the correlation coefficient was near 0.6. For stations near and north of where Winnie made landfall, ensemble-based predictions performed well. While the ensemble mean often underestimated the heaviest rainfall totals by approximately 25%–50%, the maximum values within the ensemble spread either exceeded or came within 10%–15% of the station totals. Finally, in a related experiment the horizontal resolution was increased to 0.25° latitude. Even though more precipitation was produced, especially in northeastern China, the ensemble mean was similar to the 0.5° latitude simulation.

Abstract

This paper examines how combining training-set forecasts from two separate oceanic basins affects the resulting tropical cyclone track and intensity forecasts in a particular oceanic basin. Atlantic and eastern Pacific training sets for 2002 and 2003 are combined and used to forecast 2004 eastern Pacific tropical cyclones in a real-time setting. These experiments show that the addition of Atlantic training improves the 2004 eastern Pacific forecasts. Finally, a detailed study of training-set and real-time model biases is completed in an effort to determine why cross-oceanic training may have helped in this instance.

Abstract

This paper provides an understanding of essential differences between developing and nondeveloping African easterly waves, which was a major goal of NAMMA, NASA’s field program in the eastern Atlantic, which functioned as an extension of the African Monsoon Multidisciplinary Analysis (AMMA) program during 2006.

Three NAMMA waves are studied in detail using FNL analysis: NAMMA wave 2, which developed into Tropical Storm Debby; NAMMA wave 7, which developed into Hurricane Helene; and NAMMA wave 4, which did not develop within the NAMMA domain. Diagnostic calculations are performed on the analyzed fields using energy transformation equations and the isentropic potential vorticity equation.

The results show that the two developing waves possess clear and robust positive barotropic energy conversion in conjunction with positive diabatic heating that includes a singular burst of heating at a particular time in the wave’s history. This positive barotropic energy conversion is facilitated in waves that have a northeast–southwest tilt to the trough axis and a wind maximum to the west of this axis. The nondeveloping wave is found to have the same singular burst of diabatic heating at one point in its history, but development of the wave does not occur due to negative barotropic energy conversion. Such conversion is facilitated by a northwest–southeast tilt to the trough axis and a wind maximum to the east of this axis.

The conclusions about wave development and nondevelopment formulated in this research are viewed as important and significant, but they require additional testing with detailed observational- and numerical-based studies.

Abstract

Ensemble forecasting of hurricane tracks is an emerging area in numerical weather prediction. In this paper, the spread of the ensemble of forecast tracks from a family of different First Global GARP (Global Atmospheric Research Program) Experiment analyses is illustrated. All forecasts start at the same date and use the same global prediction model. The authors have examined ensemble forecasts for three different hurricanes/typhoons of the year 1979. The authors have used eight different initial analyses to examine the spread of ensemble forecasts through 6 days from the initial state. A total of 16 forecasts were made, of which 8 of them invoked physical initialization. Physical initialization is a procedure for improving the initial rainfall rates consistent with satellite/rain gauge based measures of rainfall. The main results of this study are that useful track forecasts are obtained from physical initialization, which is shown to suppress the spread of the ensemble of track forecasts. The spread of the tracks is quite large if the rain rates are not initialized. The major issue here is how one could make use of this information on ensemble forecasts for providing guidance. Toward that end, a statistical framework that makes use of the spread of forecast tracks to provide such guidance is presented.

Abstract

Many frost events over southeastern Brazil are accompanied by a large-amplitude upper trough of the middle latitudes that extends well into the Tropics. This paper first illustrates that a mechanism of downstream amplification across the Pacific into South America is generally accompanied in these situations. This is manifested by troughs and ridges that propagate eastward. An analysis of these situations during frost events shows that these features of downstream amplification, illustrated on a Hovmöller (x–t) plot, can be decomposed into a family of synoptic-scale waves that propagate eastward and a family of planetary-scale waves that acquire a quasi-stationary character during the freeze event. It is shown that a global model, at a resolution of 70 km, can be used to predict these features on the decomposition of scales during freeze events. It became apparent from these features that the growth of the long stationary waves during the freeze events may be due to scale interaction among wave components. This paper discusses the nature of these scale interactions, calculated from the energetics in the wavenumber domain, for periods before, during, and after the freeze events. The salient results are that nonlinear barotropic-scale interactions are an important source for the maintenance of the downstream amplification; however, the baroclinic (in scale) contributions dominate through the life cycle of the downstream amplitude where the large-amplitude troughs are indeed accompanied by baroclinic features. Finally, it is shown that a very high resolution regional spectral model can be used to handle the local aspects of the freeze events. This study offers the possibility for designing prediction experiments on the medium-range timescales for the forecast of these frost events.