基于机器学习和物理模型的海洋预报对比
Comparing Ocean Forecasts Driven with Machine Learning-based and Physics-based Atmospheric Forcings
AI总结：
本文比较了基于机器学习和物理模型的大气强迫对海洋预报能力的影响，发现机器学习模型在预测精度和计算效率上具有优势。
AI中文摘要：
传统海洋预报系统通常使用由数值天气预测（NWP）模型驱动的动力海洋模型。近年来，人工智能和机器学习（ML）的进步推动了基于ML的大气天气模型的发展，其中短期预测精度与传统NWP系统相当或更优。本研究通过使用UK Met Office GOSI9配置的NEMO动力海洋模型，评估了基于ML的大气强迫对海洋预报能力的影响。两个10天预报实验共享相同的初始海洋条件，但大气强迫不同：一个使用ECMWF的ML-based AIFS模型，另一个使用澳大利亚气象局的物理模型ACCESS-G3。预报在2023-2024年每个月的第一天初始化。通过将AIFS和ACCESS-G3的预测能力与ECMWF再分析v5（ERA5）和ACCESS-G3分析进行比较，发现AIFS在初始预测时间或数天后均优于ACCESS-G3。海洋预报能力相对于GOSI9再分析和观测进行了评估，重点关注关键表面变量，包括海表温度、盐度、海平面和海洋环流。使用AIFS大气数据驱动的海洋预报与使用ACCESS-G3数据驱动的预报相比，预测能力相当或更优。这些发现表明，基于ML的大气模型有潜力取代传统NWP驱动的海洋预报系统，提供更准确的预测和更高的计算效率。
英文摘要：
Operational ocean forecasting systems conventionally employ dynamical ocean models driven by atmospheric forcing derived from numerical weather prediction (NWP) models. Recent advancements in artificial intelligence and machine learning (ML) have led to the development of ML-based atmospheric weather models, which have competitive, if not better, medium range forecast accuracy compared to traditional NWP systems. This study evaluates the impact of ML-based atmospheric forcing on ocean forecast skill through two sets of 10-day forecasts using the UK Met Office GOSI9 configuration of the NEMO dynamical ocean model. Both experiments share identical ocean initial conditions; but differ in atmospheric forcing: one uses ECMWF's ML-based AIFS model, while the other uses the Australian Bureau of Meteorology's physics-based NWP model, ACCESS-G3. Forecasts were initialized on the first day of each month over the period 2023-2024. The quality of the atmospheric forcing was assessed by comparing AIFS and ACCESS-G3 forecast skill against both ECMWF reanalysis v5 (ERA5) and ACCESS-G3 analyses. Results indicate that AIFS consistently outperforms ACCESS-G3, either from the initial forecast time or after the first few days. Oceanic forecast skill was evaluated against both the GOSI9 reanalysis and observations, focusing on key surface variables including sea surface temperature, salinity, sea level, and ocean currents. The ocean forecasts forced with AIFS atmospheric data exhibit comparable or enhanced predictive skill compared to those forced with ACCESS-G3 data. These findings underscore the potential of ML-based atmospheric models to replace traditional NWP forcing in operational ocean forecasting systems, offering improved accuracy and computational efficiency.
