Importance of Model Resolution on Discriminating Rapidly and Non-rapidly Intensifying Atlantic Basin Tropical Cyclones
| Autor: | Alexandria Grimes, Andrew E. Mercer |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Atlantic hurricane
010504 meteorology & atmospheric sciences Meteorology principal component analysis Computer science Storm Statistical model 02 engineering and technology Rapid intensification 01 natural sciences support vector machines Tropical cyclones Weather Research and Forecasting Model 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Tropical cyclone forecast model Tropical cyclone Scale (map) Simulation 0105 earth and related environmental sciences General Environmental Science |
| Zdroj: | Procedia Computer Science. 95:223-228 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2016.09.318 |
| Popis: | The ability to discriminate rapidly intensifying tropical cyclones (TCs) from their non-rapidly intensifying counterparts remains a major forecasting challenge in operational meteorology. Primarily, approaches to this forecast problem utilize dynamic model data as input into either numerical weather prediction models or statistical algorithms. Recent work suggested higher spatial resolution dynamic simulations will have greater success in distinguishing rapid intensification (RI) of TCs from those that do not, owing to the dynamic model's ability to depict smaller scale features explicitly within the simulation. Despite these preliminary findings, this approach has not been tested with a statistical modeling approach. As such, the scope of this work was to identify the importance of spatial resolution on the ability to forecast the onset of RI and non-RI TCs at 24 hour lead times. To accomplish this, 8 storms of each type were simulated using the Weather Research and Forecasting (WRF) model at varying spatial resolutions (54km, 18km, and 6km). Meteorological fields from the WRF were used as input into a support vector machine classification algorithm trained to discriminate RI and non-RI TC environments. |
| Databáze: | OpenAIRE |
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