Academic Journal
Application of numerical weather prediction with machine learning techniques to improve middle latitude rapid cyclogenesis forecasting
| العنوان: | Application of numerical weather prediction with machine learning techniques to improve middle latitude rapid cyclogenesis forecasting |
|---|---|
| المؤلفون: | Snyder, Colin Matthew |
| المصدر: | Theses and Dissertations |
| بيانات النشر: | Scholars Junction |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Bomb Cyclone, Global Forecast System, Machine Learning, Model Verification, Support Vector Machine |
| الوصف: | This study goal was to first determine the baseline Global Forecast System (GFS) skill in forecasting borderline (non-bomb:0.75-0.95, bomb: 1.-1.25) bomb events, and second to determine if machine learning (ML) techniques as a post-processor can improve the forecasts. This was accomplished by using the Tempest Extreme cyclone tracking software and ERA5 analysis to develop a case list during the period of October to March for the years 2008-2021. Based on the case list, GFS 24-hour forecasts of atmospheric base state variables in 10-degree by 10-degree cyclone center subdomains was compressed using S-mode Principal Component Analysis. A genetic algorithm was then used to determine the best predictors. These predictors were then used to train a logistic regression as a baseline ML skill and a Support Vector Machine (SVM) model. Both the logistic regression and SVM provided an improved bias over the GFS baseline skill, but only the logistic regression improved skill. |
| نوع الوثيقة: | text |
| وصف الملف: | application/pdf |
| اللغة: | unknown |
| Relation: | https://scholarsjunction.msstate.edu/td/6237; https://scholarsjunction.msstate.edu/context/td/article/7249/viewcontent/Colin_Snyder_Thesis.pdf |
| الاتاحة: | https://scholarsjunction.msstate.edu/td/6237 https://scholarsjunction.msstate.edu/context/td/article/7249/viewcontent/Colin_Snyder_Thesis.pdf |
| رقم الانضمام: | edsbas.843F0E3F |
| قاعدة البيانات: | BASE |
| الوصف غير متاح. |