تقرير
An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting
| العنوان: | An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting |
|---|---|
| المؤلفون: | Weyn, Jonathan A., Kumar, Divya, Berman, Jeremy, Kazmi, Najeeb, Klocek, Sylwester, Luferenko, Pete, Thambiratnam, Kit |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning |
| الوصف: | We present an operations-ready multi-model ensemble weather forecasting system which uses hybrid data-driven weather prediction models coupled with the European Centre for Medium-range Weather Forecasts (ECMWF) ocean model to predict global weather at 1-degree resolution for 4 weeks of lead time. For predictions of 2-meter temperature, our ensemble on average outperforms the raw ECMWF extended-range ensemble by 4-17%, depending on the lead time. However, after applying statistical bias corrections, the ECMWF ensemble is about 3% better at 4 weeks. For other surface parameters, our ensemble is also within a few percentage points of ECMWF's ensemble. We demonstrate that it is possible to achieve near-state-of-the-art subseasonal-to-seasonal forecasts using a multi-model ensembling approach with data-driven weather prediction models. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2403.15598 |
| رقم الأكسشن: | edsarx.2403.15598 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |