An Artificial Neural Network Downscaling of MERRA Mountain Gap Wind Events

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner
Tuesday, 4 February 2014: 3:30 PM
Room C204 (The Georgia World Congress Center )
Emily Foshee, University of Alabama, Huntsville, AL; and U. Nair, X. Li, D. K. Smith, and K. Keiser

Mountain gap winds (MGW) are a low-level jet feature that results from the interaction between the large-scale flow and mountain gaps. The focus of this study is the MGW events that occur over the Gulf of Tehuantepec, originating from the Chivela Pass in the Sierra Madre mountain range, typically in conjunction with cold surges into the Gulf of Mexico. MGW at this location can attain speeds in excess of 25 meters per second and can extend for a long distance over the Gulf of Tehuantepec. The high winds and seas associated with an MGW event pose a hazard to both aviation and shipping industries within the area.

Whereas numerical models capture the MGW precursor synoptic scale patterns, they often fail to adequately resolve the interactions of large scale flow with small scale terrain features. This study evaluates the utility of an Artificial Neural Network (ANN) for the downscaling of MGW events simulated by models with coarse grid spacing. The inputs to the ANN include MERRA surface fields. The Cross-Calibrated, Multi-Platform (CCMP) ocean surface wind product is used as ground truth to train the ANN. Preliminary results show that the ANN is able to generate wind fields that are very similar to corresponding CCMP observations. The technique is being extended by using 1 kilometer grid spacing numerical model outputs to train the ANN and conduct downscaling to finer spatial scales. Since the ANN is computationally efficient, it can be deployed in an operational setting to downscale low-resolution numerical model output. The training and performance evaluation of the ANN will be presented.