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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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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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L. M. Leslie
,
K. Fraedrich
, and
T. J. Glowacki

Abstract

It is demonstrated that the skill of short-term regional numerical forecasts can be predicted on a day-to-day basis. This was achieved by using a statistical regression scheme with the model forecast errors (MFE) as the predictands and the initial analysis, together with the model forecast, at proximate points, as the predictors.

In a first attempt to assess the utility of the method, the technique was applied in a long-term quasi-operational trial to 24 h forecasts of mean sea level pressure in two seasonal periods (one summer and one winter period) on the Australian region forecast domain. Correlation coefficients were computed between the predicted and observed root-mean-square (rms) MFE and were found to be 0.54 and 0.51, respectively, averaged over the full region, for the 90-day summer and winter periods. Using standard Student's t-tests these correlations were shown to be highly significant. In addition, the regional forecasts were divided into four categories of rms MFE, and were verified against the observed rms MFE. Using a contingency table skill score (relative to chance), it was demonstrated that the category forecasts exhibited a very high level of skill.

The procedure also was applied to subdomains of the Australian region grid and it was found that the predictions of model forecast skill were improved further for these local forecasts.

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