The recently released suite of AI weather models can produce multi-day,
medium-range forecasts within seconds, with a skill on par with
state-of-the-art operational forecasts. Traditional AI model evaluation
predominantly targets global scores on single levels. Specific prediction
tasks, such as severe convective environments, require much more precision on a
local scale and with the correct vertical gradients between levels. With a
focus on the convective season of global hotspots in 2020, we assess the skill
of three top-performing AI models (Pangu-Weather, GraphCast, FourCastNet) for
Convective Available Potential Energy (CAPE) and Deep Layer Shear (DLS) at
lead-times of up to 10 days against the ERA-5 reanalysis and the IFS
operational numerical weather prediction model. Looking at the example of a US
tornado outbreak on April 12 and 13, 2020, all models predict elevated CAPE and
DLS values multiple days in advance. The spatial structures in the AI models
are smoothed in comparison to IFS and ERA-5. The models show differing biases
in the prediction of CAPE values, with GraphCast capturing the value
distribution the most accurately and FourCastNet showing a consistent
underestimation. In seasonal analyses around the globe, we generally see the
highest performance by GraphCast and Pangu-Weather, which match or even exceed
the performance of IFS. CAPE derived from vertically coarse pressure levels of
neural weather models lacks the precision of the vertically fine resolution of
numerical models. The promising results here indicate that a direct prediction
of CAPE in AI models is likely to be skillful. This would open unprecedented
opportunities for fast and inexpensive predictions of severe weather phenomena.
By advancing the assessment of AI models towards process-based evaluations we
lay the foundation for hazard-driven applications of AI-based weather
forecasts.

近期发布的一套人工智能气象模型能够在几秒钟内预测出与最先进的操作性预测相媲美的多天、中期天气预报，其在局部尺度和级数之间的竖直梯度上需要更高精度的精确性，根据 2020 年全球热点地区的季节性对流性能评估发现，Pangu-Weather、GraphCast 和 FourCastNet 等三个表现最佳的人工智能模型在 10 天的前导时间内，对 Convective Available Potential Energy（CAPE）和 Deep Layer Shear（DLS）的预测结果具有相对的准确性，而且 GraphCast 和 Pangu-Weather 在全球范围内表现最好，能够达到甚至超过 IFS 的性能水平，由于神经气象模型中从垂直压力粗糙层导出的 CAPE 缺乏数值模型的垂直细分辨率，目前的结果表明，人工智能模型中对 CAPE 的直接预测很有前景，这将为快速且低成本的严重天气现象预测开辟前所未有的机遇，通过推进人工智能模型的评估，朝着基于过程的评估方向发展，我们为基于人工智能的天气预报面向灾害驱动的应用奠定了基础。