103
Evaluation of WSR-88D methods to predict warm-season convective wind events at Cape Canaveral Air Force Station and Kennedy Space Center

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Tuesday, 19 January 2010
James J. Rennie, Plymouth State University, Plymouth, NH; and J. P. Koermer, T. R. Boucher, and W. P. Roeder

Handout (1.3 MB)

Forecasting downbursts is challenging since it is difficult to tell which convective cells will generate downbursts, when they will occur, and the expected maximum gust. Nonetheless the 45th Weather Squadron (45 WS) is responsible for predicting wind gusts that exceed 35 kt, 50 kt, and 60 kt for Cape Canaveral Air Force Station (CCAFS) and Kennedy Space Center (KSC). Recently, an attempt has been made to use information from the WSR-88D's Storm Cell Identification and Tracking (SCIT) algorithm. Previous work has shown that attributes from the SCIT algorithm could be used as a precursor in nowcasting a convective wind event. Additionally, results showed an intriguing relationship between the reported wind gust and hail potential. However due to the small sample size, statistical significance was questionable and continued evaluation is needed. Also, the findings were based on the volume scan at or just before the time of the maximum reported peak wind gust. It would be more useful if this information was correlated with previous volume scans, in order to provide a longer lead-time for forecasting these events.

Using WSR-88D storm structure data of warm season convective events from 2003-2007, predicted wind gusts derived from previous methods are tested against actual peak wind gusts reported from one of the 36 weather towers located on the KSC/CCAFS complex. Scatter plots, root mean square error (RMSE), and mean absolute error (MAE) are used to evaluate the methods. In addition, several new techniques to predict wind gusts will be introduced in order to improve forecast skill. Statistical methods, including logistic regression and classification and regression trees (CART), will be used to develop a forecast technique with the maximum skill, i.e. the best combination of high probability of detection and low false alarm ratio. The performance will be verified using independent data not used to develop the technique.

Preliminary results show that the potential of the previous regression-based methods does not appear to be as promising in forecasting convective wind events as originally indicated. For a much larger sample size, high RMSE and MAE values, and low correlation coefficients were measured for the time of onset, with consistent performance for earlier volume scans. However there continues to be some potential for the hail relationship. Whenever the height of the cell's maximum reflectivity is higher than the freezing level, the wind gust tends to be greater than 35 kt. The ability to differentiate between downbursts less than 35 kt and greater than 35 kt has been identified as important to 45 WS operations based on the previous climatology of distribution of convective wind speeds. In addition this is a simple technique to use in operations. These results are consistent for all volume scans. Under these circumstances, there is a high probability that hail has formed, which has been shown to be an indicator of wet microbursts. This is not surprising since large ice particles are one of the ingredients for downbursts since they provide continued cooling to sustain the downdraft over a large depth. This also suggests the opportunity for improved downburst prediction from dual-polarization radar, such as the one being implemented for use by 45 WS.