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J. Michael Fritsch and Robert L. Vislocky

Examples of current surface-analysis problems and opportunities are presented. A prototype analysis procedure that simulates some of the new automated analysis capabilities at the National Centers for Environmental Prediction is demonstrated. The new procedure simplifies and enhances the depiction of important surface weather features, especially mesoscale phenomena.

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Robert L. Vislocky and J. Michael Fritsch

Abstract

Several methods of generating very short term (0–6 h) probabilistic forecasts of ceiling and visibility are investigated: 1) an observations-based (OBS-based) system in which potential predictors consist of weather observations from a network of surface stations along with several climatic terms; 2) the traditional model output statistics (MOS)-based approach in which potential predictors consist of nested grid model (NGM) output, the latest observation from the forecast site, and climatic variables; and 3) persistence climatology in which potential predictors consist of the latest observation of the predictand variable from the forecast site and several climatic terms.

Forecasts are generated for each technique on 2 yr (1993–94) of independent data for 25 stations in the eastern United States. Two variables (ceiling and visibility) are forecasted for eight thresholds, two initial times (0300 and 1500 UTC), and three lead times (1, 3, and 6 h). Results show that the OBS-based method is superior to persistence climatology at all lead times and all variable thresholds. This is encouraging since persistence climatology is widely recognized as a formidable benchmark for very short range prediction of ceiling and visibility. Verifications also show that the OBS-based system outperforms the traditional MOS-based technique at the 1- and 3-h lead times with skill improvements of four percentage points. Based upon historical values of improvements in skill, this gain corresponds to roughly a half decade of scientific advancement. Performance of the OBS- and MOS-based systems are similar at the 6-h projection, which appears to be near the crossover point when the NGM guidance becomes more important than the observations in terms of predictive input.

These findings indicate that traditional MOS-based techniques for the very short term prediction of aviation weather parameters can be improved significantly by considering information from a network of surface observations. Furthermore, the improvements of the OBS-based system over the MOS-based method represent the minimum that can be expected since the test comparison of the two methods was intentionally constructed to maximize the performance of the MOS-based procedure. Still further, forecasts from an OBS-based system can be available within seconds of receiving the observations. Therefore, OBS-based systems are likely to be of far greater utility for making very short term forecasts than other traditional forms of guidance.

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Robert L. Vislocky and J. Michael Fritsch

A prototype advanced model output statistics (MOS) forecast system that was entered in the 1996–97 National Collegiate Weather Forecast Contest is described and its performance compared to that of widely available objective guidance and to contest participants. The prototype system uses an optimal blend of aviation (AVN) and nested grid model (NGM) MOS forecasts, explicit output from the NGM and Eta guidance, and the latest surface weather observations from the forecast site. The forecasts are totally objective and can be generated quickly on a personal computer. Other “objective” forms of guidance tracked in the contest are 1) the consensus forecast (i.e., the average of the forecasts from all of the human participants), 2) the combination of NGM raw output (for precipitation forecasts) and NGM MOS guidance (for temperature forecasts), and 3) the combination of Eta Model raw output (for precipitation forecasts) and AVN MOS guidance (for temperature forecasts).

Results show that the advanced MOS system finished in 20th place out of 737 original entrants, or better than approximately 97% of the human forecasters who entered the contest. Moreover, the advanced MOS system was slightly better than consensus (23d place). The fact that an objective forecast system finished ahead of consensus is a significant accomplishment since consensus is traditionally a very formidable “opponent” in forecast competitions. Equally significant is that the advanced MOS system was superior to the traditional guidance products available from the National Centers for Environmental Prediction (NCEP). Specifically, the combination of NGM raw output and NGM MOS guidance finished in 175th place, and the combination of Eta Model raw output and AVN MOS guidance finished in 266th place. The latter result is most intriguing since the proposed elimination of all NGM products would likely result in a serious degradation of objective products disseminated by NCEP, unless they are replaced with equal or better substitutes.On the other hand, the positive performance of the prototype advanced MOS system shows that it is possible to create a single objective product that is not only superior to currently available objective guidance products, but is also on par with some of the better human forecasters.

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Robert L. Vislocky and George S. Young

Abstract

A method of improving the accuracy of model output statistics (MOS) probability of precipitation (POP) forecasts was investigated. The method uses a perfect prog (PP) forecast as a potential predictor in a MOS equation. The PP method, with its larger developmental databases has the potential of incorporating additional information about local climatology, seasonality, and synoptic pattern type, which might be otherwise lacking in the MOS predictor dataset.

Three PP models were developed: an analog model, a 1ogistic regression model and an analog/regression hybrid model. The POP forecasts were generated by the three PP models and the MOS model at four Pennsylvania stations by using 6 months of independent limited-area fine mesh (LFM) forecasts. Three MOS/PP combination models were derived by linearly combining MOS with each of the three PP models. The MOS/PP combination model forecasts were generated with the independent MOS and PP forecasts by using a cross-validation technique. The three MOS/PP combination models showed a small improvement over the MOS model. The probability that thew improvements were from random chance ranged from 6% to 33%.

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Robert L. Vislocky and J. Michael Fritsch

Consensus forecasts are computed by averaging model output statistics (MOS) forecasts based on the limited-area fine-mesh (LFM) model and the nested grid model (NGM) for the three-year period 1990–92. The test consists of four weather elements (max/min temperature, wind speed, probability of cloud amount, and 12-h probability of precipitation) at four projection times from each initialization (0000 and 1200 UTC) for roughly 250–350 stations. Verification results clearly indicate a substantial improvement for the consensus MOS over both the LFM and NGM MOS forecasts for all variables and all lead times. The accuracy increase is on par with a 2–8-yr scientific advancement and a 4–12-h lead time improvement. Moreover, performance of the consensus MOS forecasts is similar to subjective forecasts issued by the National Weather Service.

These results are illustrative of the broad need to adopt a strategy of statistically combining available forecast products rather than relying upon the single most superior product (such as the newest numerical model). Furthermore, there appears to be strong justification to continue support for the entire LFM MOS product both in terms of its full availability and its equation upgrade.

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Robert L. Vislocky and J. Michael Fritsch

Abstract

The skill of probabilistic Model Output Statistics forecasts generated from Generalized Additive Models (GAM) is compared to that of traditional multiple linear regression techniques. Unlike linear regression, where each predictor term in the additive model is assumed to vary linearly with the predictand (unless specified otherwise by the developer), GAM is a nonparametric tool that makes use of the data to automatically estimate the appropriate functional (curvative) relationship for each predictor term. This relieves the developer from the chore of identifying and computing the correct predictor transformations and helps uncover certain nonlinearities that may have been missed.

Forecast equations for each statistical technique are developed for nine regions encompassing a total of 90 stations in the northeastern United States. Three parameters (cloud amount, ceiling height, and visibility) are forecast for eight thresholds and two lead times (12 h and 24 h). The developmental dataset consists of limited-area fine-mesh numerical model output and surface observations for the period 1984–1989. Verification on 3 yr (1990–1992) of independent data indicates a clear and consistent superiority of the GAM model over linear regression, with mean square errors generally 3%–4% lower and lead time gains of 2–9 h.

To some extent, GAM's additional computational burden relative to linear regression has deterred its operational implementation. However, as computer power and memory continue to increase while prices continue to fall, the time is drawing near when the use of such modern statistical techniques will be operationally feasible.

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