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L. M. Leslie

Abstract

One of the most striking synoptic features of the Australian tropics is the well-developed heat low which persists throughout the summer months. In spite of its significance, the heat low has not, up to the present, been well simulated by numerical forecast models.

In this article, a simple surface heat balance scheme has been incorporated in a large-scale numerical forecast model in order to improve the modeling of the heat low.

The scheme was tested on more than a month of consecutive days that were typical heat low situations. In almost all cases there was a significant improvement in the representation of the heat low.

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K. Fraedrich and L. M. Leslie

Abstract

Probability of precipitation (POP) for the 12 hours 0600 to 1800 local time was predicted for Melbourne each day for the three months (winter) period June-August 1986 using six different techniques. These were: a Markov chain model based on 20 years of three-hourly observations', the Australian region limited-area numerical weather prediction (NWP) model; a weighted linear combination of Markov and NWP models; a model output statistics scheme based on the NWP model; an analogue statistics procedure in which a set of the “best” analogues of the NWP forecast were selected; and the manual “official” Bureau of Meteorology forecast for Melbourne, issued by the duty forecaster.

The six techniques were evaluated and compared in terms of Brier scores and also compared with predictions based on climatology. The results of this operational trial indicate that the skill of the combined Markov-NWP model forecasts considerably exceeds the other techniques. The Markov model was next, followed by the other methods which were close together in skill. Some cam was taken in the interpretation of thew findings as there were differences in lead times associated with the NWP model predictions and the MOS and analogue schemes which were dependent upon it, owing to the operational schedule at Melbourne.

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K. Fraedrich and L. M. Leslie

Abstract

The results of a major real-time trial of techniques for the short-term (12 h ahead) prediction of precipitation for the Australian tropical city of Darwin are described. The trial compared current operational manual forecasting procedures with a range of alternative techniques including statistical methods, numerical weather prediction (NWP), and model output statistics (MOS) which were developed specifically for the trial.

The only technique of those tested which exhibited skill, i.e., consistent superiority over climatology and/or persistence, was a Markov chain model used either individually or in linear combination with other methods. Of particular significance was the relatively poor showing in the tropics of the numerical weather prediction model products, which were worse than both persistence and climatology. The results of this real-time trial should be treated with care because of the small sample size involved.

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K. Fraedrich and L. M. Leslie

Abstract

A “minimal” model is proposed here for the short-term prediction (up to 12 h ahead) of precipitation occurrence in the tropics. The model is purely statistical, consisting of an optimally weighted linear combination of a Markov chain and persistence. It is minimal in the sense that only surface data are needed, and the computing requirements are almost nil.

In this study the skill of the minimal model, i.e., accuracy relative to climatology and/or persistence, is demonstrated in theory and practice. The model was tested in real time during the 1986/87 Australian monsoon season at the tropical city of Darwin.

Results of the real-time experiment reveal that the minimal model was the only model of those available to the Australian Bureau of Meteorology (including manual forecasts, a regional NWP model, and a model output statistics (MOS) scheme) that exhibited forecast accuracy greater than that of both climatology and persistence.

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K. Fraedrich and L. M. Leslie

Abstract

In this article, the theory is presented for a linear combination of two independent predictive techniques (either probabilistic or binary). It is shown that substantial gains might be expected for optimal weighting of the combination. The theory is general but also is applied to several special cases which may be useful for both short-term weather prediction and long-range forecasting. Using data from a recent operational evaluation of techniques for the short-term predicting of rainfall, a linear combination of two independent predictive techniques gives, in practice, improvement in skill compared with the techniques used individually. In the present case, a Markov chain and a numerical weather prediction (NWP) model were combined. The half-Brier wore of the linear combination was 0.142 compared with individual scores of 0.164 for the Markov chain model and 0.258 for the NWP model. The combined Markov-NWP scheme may provide a possible simple alternative to the MOS approach for predictions up to 12 hours ahead.

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J. L. McGregor and L. M. Leslie

Abstract

It is shown that semi-implicit time differencing on a nonstaggered grid using centered time and space derivatives leads to a decoupling into four separate solutions on different subgrids. This deficiency may be successfully overcome by combining weighted averages of Laplacian operators on the subgrids. However, a far more satisfactory approach is shown to be the use of a staggered grid which appears to be a natural choice for the semi-implicit scheme.

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L. M. Leslie and M. S. Speer

Abstract

No abstract available.

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A. F. Bennett and L. M. Leslie

Abstract

The S 1, skill score of the operational Australian Region Primitive Equation Model may be, reduced by a statistical correction scheme, in which the model prognosis of MSL pressure is used to predict errors in the MSL pressure differences. The predicted errors in the differences may be integrated and then added to the model prognosis to produce substantially improved MSL charts. Tests on independent data sets show that the scheme is reliable.

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A. F. Bennett and L. M. Leslie

Abstract

The errors in a barotropic filtered model of the Australian region at 500 mb are shown to be correlated with the numerical prognoses. Using the latter as predictors, an optimal linear prediction of the errors is found to remove about one-third of the error variance. The prognoses have been efficiently represented by the amplitudes of a few Empirical Orthogonal Functions. Tests on independent data sets show that the predictors are reliable. The prediction shows day-to-day utility: it achieves or betters the sample mean-error reduction on two-thirds of the days in the sample.

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A. J. Miller and L. M. Leslie

Abstract

Forecast probabilities of rain were calculated up to 12 hours in advance using a Markov chain model applied to three-hourly observations from five major Australian cities. The four weather states chosen in this first study were three cloudiness states (0–2 oktas, 3–5 oktas and 6–8 oktas) and a rain state. Second-order Markov models with time-of-day dependent transition probabilities were fitted after appropriate statistical testing.

Forecasts were made using transition probabilities for summer and winter seasons. The skill of the Markov chain forecast probabilities of rain was evaluated in terms of Brier scores using to years of independent data, and compared with forecasts based upon persistence and climatology. The skill of the Markov model forecasts appreciably exceeded that of persistence and climatology and a real time trial of the procedure is being planned.

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