基于陆面模型的预报进展：LSTM、梯度提升和前馈神经网络模型作为预测状态模拟器的对比研究
Advances in Land Surface Model-based Forecasting: A comparative study of LSTM, Gradient Boosting, and Feedforward Neural Network Models as prognostic state emulators
- University of Freiburg（弗莱堡大学）
- European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
为加速计算成本高昂的陆面过程数值模拟，本研究在物理信息多目标框架下对比了LSTM、XGB和MLP三种代理模型对ECMWF IFS陆面方案ECLand的模拟效果，发现LSTM擅长大陆长期预测、XGB表现稳定、MLP具最佳时间准确度权衡，显著降低运行时间。
中文摘要 AI 辅助
对公众最有用的天气预报是近地表预报。与近地表天气预报最相关的过程也是那些最具相互作用性、表现出正反馈或在能量分配中起关键作用的过程。陆面模型（LSM）将这些过程与地表异质性结合在一起，预测水、碳和能量通量，并与大气模型耦合提供边界和初始条件。这种对大气边界的数值参数化计算成本高昂，因此统计代理模型越来越多地被用于加速实验研究的进展。我们评估了三种代理模型在通过模拟陆面过程来加速实验研究方面的效率，这些陆面过程对于耦合大气模型中预测水、碳和能量通量至关重要。具体而言，我们在物理信息多目标框架内比较了长短期记忆（LSTM）编码器-解码器网络、极端梯度提升和前馈神经网络的性能。该框架模拟了ECMWF综合预报系统（IFS）陆面方案ECLand在大陆和全球尺度上的关键状态。我们的研究结果表明，虽然所有模型在预测期内平均表现出高准确度，但LSTM网络在经过仔细调优后在大陆远程预测中表现突出，XGB在各项任务中得分始终很高，而MLP提供了极佳的实现时间与准确度的权衡。与完整数值模型相比，模拟器实现的运行时间减少是显著的，为进行陆面数值实验提供了一种更快但可靠的替代方案。
英文摘要
Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive and exhibit positive feedback or have key role in energy partitioning. Land surface models (LSMs) consider these processes together with surface heterogeneity and forecast water, carbon and energy fluxes, and coupled with an atmospheric model provide boundary and initial conditions. This numerical parametrization of atmospheric boundaries being computationally expensive, statistical surrogate models are increasingly used to accelerated progress in experimental research. We evaluated the efficiency of three surrogate models in speeding up experimental research by simulating land surface processes, which are integral to forecasting water, carbon, and energy fluxes in coupled atmospheric models. Specifically, we compared the performance of a Long-Short Term Memory (LSTM) encoder-decoder network, extreme gradient boosting, and a feed-forward neural network within a physics-informed multi-objective framework. This framework emulates key states of the ECMWF's Integrated Forecasting System (IFS) land surface scheme, ECLand, across continental and global scales. Our findings indicate that while all models on average demonstrate high accuracy over the forecast period, the LSTM network excels in continental long-range predictions when carefully tuned, the XGB scores consistently high across tasks and the MLP provides an excellent implementation-time-accuracy trade-off. The runtime reduction achieved by the emulators in comparison to the full numerical models are significant, offering a faster, yet reliable alternative for conducting numerical experiments on land surfaces.
