Prithvi-Precip：将卫星观测数据整合进大气AI基础模型用于降水预报
Prithvi-Precip: Integrating Satellite Observations into an Atmospheric AI Foundation Model for Precipitation Forecasting
AI总结：
本研究基于Prithvi-WxC基础模型开发Prithvi-Precip，通过采用卫星降水训练目标、直接同化卫星观测数据及自回归滚动训练，大幅提升了中期降水预报精度，优于Goddard地球观测系统。
AI中文摘要：
精准降水预报仍是天气预报领域最具挑战性的问题之一。尽管近期的AI天气预报（AIWP）系统在中期预报技巧上取得了显著提升，但降水往往仍是次要预报目标，且通常从包含大量不确定性的再分析数据集中学习。本研究探讨了两种互补策略以改进基于AI的降水预报：以Prithvi-WxC基础模型为基础，开发全球降水预报系统Prithvi-Precip，并研究（1）基于卫星降水估计而非再分析场的训练目标的影响，（2）将卫星观测数据直接同化进预报模型的效果。我们系统评估了对Prithvi-WxC AI基础模型进行降水预报微调的关键设计选择，发现自回归滚动训练比直接以预报提前期为条件生成的预报精度显著更高。使用独立的雷达降水估计进行评估，结果显示，基于卫星衍生降水目标训练相比基于MERRA-2降水场训练，预报精度有所提升；此外，直接摄入卫星观测数据可在短提前期提供额外改进，最大增益出现在热带和亚热带地区。这些进展共同使Prithvi-Precip在可直接对比的降水预报上大幅超越Goddard地球观测系统的结果，我们的研究凸显了改进降水目标与直接整合卫星观测数据作为推进中期AI降水预报的可行路径的潜力。
英文摘要：
Accurate precipitation forecasting remains one of the most challenging problems in weather prediction. While recent AI weather prediction (AIWP) systems have achieved substantial improvements in medium-range forecasting skill, precipitation often remains a secondary target and is commonly learned from reanalysis datasets that contain considerable uncertainty. In this work, we investigate two complementary strategies for improving AI-based precipitation forecasts. Building on the Prithvi-WxC foundation model, we develop Prithvi-Precip, a global precipitation forecasting system, and examine (1) the impact of training targets derived from satellite-based precipitation estimates rather than reanalysis fields and (2) the direct assimilation of satellite observations into the forecasting model. We systematically evaluate key design choices for finetuning the Prithvi-WxC AI foundation model for precipitation forecasting. We find that autoregressive rollout training produces substantially more accurate forecasts than direct conditioning on forecast lead time. Using independent radar-based precipitation estimates for evaluation, we show that training on satellite-derived precipitation targets yields improved forecast accuracy relative to training on MERRA-2 precipitation fields. Furthermore, direct ingestion of satellite observations provides additional improvements at short lead times, with the largest gains occurring in tropical and subtropical regions. Together, these advances enable Prithvi-Precip to substantially improve upon directly comparable precipitation forecasts from the Goddard Earth Observing System. Our results highlight the potential of improved precipitation targets and the direct integration of satellite observations as promising pathways for advancing medium-range AI precipitation forecasting.
