发表机构
Cooperative Institute for Research in the Atmosphere, Colorado State University（科罗拉多州立大学大气合作研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高分辨率LAI预测难题，提出序列到序列ConvLSTM框架，以美国中南部为研究区，实现1公里分辨率30天时效的LAI预测，性能优于基准，为相关应用提供支撑。
AI 中文摘要
叶面积指数（LAI）是控制陆气相互作用的基础生物物理变量；然而，高空间分辨率的LAI预测仍是一个未解决的挑战。尽管近期机器学习方法已能实现点或区域尺度的LAI估算，但尚无一种适用于次季节陆面和气候建模应用的、网格化的、气象驱动的预测模型。本文提出一种序列到序列ConvLSTM框架，该框架可基于历史LAI序列及包含温度、降水在内的每日气象强迫数据，生成提前最多30天的1公里分辨率每日LAI预测。在美国中南部（该区域气候梯度显著且植被类型多样）进行训练与评估后，该模型在30天提前期的域平均RMSE为0.36，比持续性基准降低了三分之一以上。预测技能在季节、地理分布及植物功能类型（包括森林、草原、灌丛和农田）间均保持稳健。据我们所知，这是首次实现1公里分辨率、30天时效的有效LAI预测的演示。
英文摘要
Leaf Area Index (LAI) is a fundamental biophysical variable governing land-atmosphere interactions; however, LAI forecasting at high spatial resolution remains an unsolved challenge. While recent machine learning approaches have demonstrated LAI estimation at point or regional scales, none provides a gridded, meteorology-driven prognostic forecast suitable for subseasonal land surface and climate modeling applications. Here we present a sequence-to-sequence Convolutional LSTM (ConvLSTM) framework that generates daily 1-km LAI forecasts up to 30 days ahead, driven by historical LAI sequences and daily meteorological forcing including temperature and precipitation. Trained and evaluated over the South-Central United States -- a region of strong climate gradients and diverse vegetation -- the model achieves a domain-averaged RMSE of 0.36 at a 30-day lead time, more than a third lower than the persistence baseline. Forecast skill remains robust across seasons, geographic distributions, and plant functional types, including forests, grasslands, shrublands, and croplands. To our knowledge, this is the first demonstration of skillful LAI forecasting at a 30-day horizon at 1-km resolution.
