Aircast-Mars：一种用于全球天气预报的火星基础模型，采用HEALPix感知卷积
Aircast-Mars: A Mars Foundation Model for Global Weather Forecasting with HEALPix-Aware Convolutions
浏览论文内容
中文总结 AI 辅助
研究针对火星天气预报，提出基于EMARS v1.0训练的Aircast-Mars系统。采用HEALPix感知二维U-Net架构，结合自定义填充和ConvNeXt残差块。该模型参数少、速度快，验证MSE低，能稳定递归预测，为行星尺度天气预报提供了基础。
中文摘要 AI 辅助
行星大气的基础模型有望为任务规划和科学探究提供快速、轻量级的昂贵通用环流模型（GCM）替代方案。本文展示了Aircast-Mars，这是一个基于集合火星大气再分析系统（EMARS）v1.0训练的火星深度学习天气预报系统。我们将28个垂直层的温度、纬向风和经向风场重新网格化到Nside = 64（分辨率约110公里）的分层等面积等纬度像素化（HEALPix）网格上，并训练一个受DLESyM架构启发的HEALPix感知二维U-Net来预测下一小时的大气状态。该模型采用了自定义接口填充，尊重12面HEALPix球体的拓扑结构，并使用带有上限高斯误差线性单元（GELU）激活的现代ConvNeXt残差块。虽然包含430万个可训练参数，与地球天气基础模型相比尺寸紧凑，但该网络在归一化单位下实现了最佳验证均方误差（MSE）为1.58e-5。递归自回归滚动在25小时（一个火星日）内保持稳定且物理上连贯，均方根误差（RMSE）从t + 1小时的约0.004单调增长到t + 25小时的约0.031，无发散。与基线三维U-Net相比，HEALPix感知架构在使用更少参数的情况下将验证损失降低了一个多数量级。该模型在单个GPU上大约0.5秒内生成一小时预测，比传统数值GCM快几个数量级。这些结果表明，简约、尊重几何的神经架构可以捕捉火星天气尺度的大气动力学，并为行星尺度天气预报提供基础。
英文摘要
Foundation models for planetary atmospheres promise fast, lightweight surrogates of expensive general circulation models (GCMs) for mission planning and scientific inquiry. Here we present Aircast-Mars, a deep-learning weather prediction system for Mars trained on the Ensemble Mars Atmosphere Reanalysis System (EMARS) v1.0. We regrid temperature, zonal wind, and meridional wind fields across 28 vertical levels onto a hierarchical equal-area isolatitude pixelization (HEALPix) mesh at Nside = 64 (~110 km resolution) and train a HEALPix-aware 2D U-Net inspired by the DLESyM architecture to predict the next hourly atmospheric state. The model employs custom inter-face padding that respects the topology of the 12-face HEALPix sphere and modern ConvNeXt residual blocks with capped Gaussian Error Linear Unit (GELU) activations. While containing 4.3 million trainable parameters, a compact size compared to terrestrial weather foundation models, the network achieves a best validation Mean Squared Error (MSE) of 1.58e-5 in normalized units. Recursive autoregressive rollouts remain stable and physically coherent for 25 hours (one Martian sol), with Root Mean Square Error (RMSE) growing monotonically from ~0.004 at t + 1 h to ~0.031 at t + 25 h without divergence. Compared to a baseline 3D U-Net, the HEALPix-aware architecture reduces validation loss by more than an order of magnitude while using fewer parameters. The model generates a one-hour forecast in approximately 0.5 seconds on a single GPU, offering several orders-of-magnitude speedup over traditional numerical GCMs. These results demonstrate that parsimonious, geometry-respecting neural architectures can capture synoptic-scale Martian atmospheric dynamics and provide a foundation for planetary-scale weather forecasting.
