标题： 基于神经天气模型的热带气旋强度全球预报 显示英文标题

标题： Global Forecasting of Tropical Cyclone Intensity Using Neural Weather Models

摘要： 数值天气预报（NWP）模型通过将耦合的物理方程向前推进时间，是模拟大气过程和预测天气的传统工具。随着深度学习的最新进展，神经天气模型（NeWMs）作为中程NWP模拟器已经出现，其性能与最先进的NWP模型相比具有竞争力。然而，它们通常在有限空间分辨率（例如，0.25{\deg }水平网格间距）的再分析数据上进行训练，这会平滑天气系统的关键特征。例如，热带气旋（TCs）——由于其对人类活动的破坏性影响而成为最具影响力的天气事件之一——难以预测，因为在0.25{\deg }分辨率的确定性预测中，像风速阵风这样的极端值被平滑了。为了解决这个问题，我们使用最佳观测估计的风速阵风和最低海平面气压来训练一系列后处理模型，以处理NeWM的输出。应用于Pangu-Weather和FourCastNet v2，后处理模型能够提前五天准确可靠地预测TC强度。我们的后处理算法与跟踪无关，可以防止完全遗漏，我们证明即使线性模型也能从NeWM输出中提取超出初始条件所编码信息的预测信息。虽然空间掩码提高了概率预测的一致性，但我们并未发现卷积架构在我们的NeWM后处理目的上明显优于简单的多层感知器。总体而言，通过结合NeWM的效率与轻量、与跟踪无关的后处理框架，我们的方法提高了全球TC强度预测的可及性，标志着向其民主化迈出了一步。 显示英文摘要

摘要： Numerical Weather Prediction (NWP) models that integrate coupled physical equations forward in time are the traditional tools for simulating atmospheric processes and forecasting weather. With recent advancements in deep learning, Neural Weather Models (NeWMs) have emerged as competent medium-range NWP emulators, with performances that compare favorably to state-of-the-art NWP models. However, they are commonly trained on reanalyses with limited spatial resolution (e.g., 0.25{\deg} horizontal grid spacing), which smooths out key features of weather systems. For example, tropical cyclones (TCs)-among the most impactful weather events due to their devastating effects on human activities-are challenging to forecast, as extrema like wind gusts, used as proxies for TC intensity, are smoothed in deterministic forecasts at 0.25{\deg} resolution. To address this, we use our best observational estimates of wind gusts and minimum sea level pressure to train a hierarchy of post-processing models on NeWM outputs. Applied to Pangu-Weather and FourCastNet v2, the post-processing models produce accurate and reliable forecasts of TC intensity up to five days ahead. Our post-processing algorithm is tracking-independent, preventing full misses, and we demonstrate that even linear models extract predictive information from NeWM outputs beyond what is encoded in their initial conditions. While spatial masking improves probabilistic forecast consistency, we do not find clear advantages of convolutional architectures over simple multilayer perceptrons for our NeWM post-processing purposes. Overall, by combining the efficiency of NeWMs with a lightweight, tracking-independent post-processing framework, our approach improves the accessibility of global TC intensity forecasts, marking a step toward their democratization.