结合基于物理的建模和深度学习融合GRACE卫星数据：我们能从失配中学习吗？
Combining Physically-Based Modeling and Deep Learning for Fusing GRACE Satellite Data: Can We Learn from Mismatch?
摘要：
全球水文和陆面模型越来越多地用于跟踪陆地总蓄水量（TWS）动态，但现有模型的实用性受到与各种代表性不足和未代表的过程（如地下水储存）相关的概念和/或数据不确定性的阻碍。重力恢复和气候实验（GRACE）卫星任务为在区域和大陆尺度上跟踪TWS提供了宝贵的独立数据源。人们对将GRACE数据融合到全球水文模型中以提高其预测性能有着浓厚的兴趣。在这里，我们开发并应用深度卷积神经网络（CNN）模型来学习从GRACE导出的TWS异常（TWSA）与NOAH（一种广泛使用的地表模型）模拟的TWS异常之间的时空模式。一旦经过训练，我们的CNN模型可以用于校正NOAH模拟的TWSA，而不需要GRACE数据，可能填补GRACE及其后续任务GRACE-FO之间的数据差距。我们的方法是证明了印度，这经历了显着的地下水枯竭，在最近几十年，但没有被捕获的NOAH模型。结果表明，CNN模型显著提高了与GRACE TWSA的匹配，国家平均相关系数为0.94，Nash-Sutcliff有效系数为0.87，比原NOAH TWSA分别提高了14%和52%.在局部范围内，学习的不匹配模式与印度大部分地区观察到的原位地下水储存异常数据相关性很好，这表明深度学习模型有效地补偿了该研究区域NOAH中缺失的地下水成分。
英文摘要：
Global hydrological and land surface models are increasingly used for tracking terrestrial total water storage (TWS) dynamics, but the utility of existing models is hampered by conceptual and/or data uncertainties related to various underrepresented and unrepresented processes, such as groundwater storage. The gravity recovery and climate experiment (GRACE) satellite mission provided a valuable independent data source for tracking TWS at regional and continental scales. Strong interests exist in fusing GRACE data into global hydrological models to improve their predictive performance. Here we develop and apply deep convolutional neural network (CNN) models to learn the spatiotemporal patterns of mismatch between TWS anomalies (TWSA) derived from GRACE and those simulated by NOAH, a widely used land surface model. Once trained, our CNN models can be used to correct the NOAH simulated TWSA without requiring GRACE data, potentially filling the data gap between GRACE and its follow-on mission, GRACE-FO. Our methodology is demonstrated over India, which has experienced significant groundwater depletion in recent decades that is nevertheless not being captured by the NOAH model. Results show that the CNN models significantly improve the match with GRACE TWSA, achieving a country-average correlation coefficient of 0.94 and Nash-Sutcliff efficient of 0.87, or 14\% and 52\% improvement respectively over the original NOAH TWSA. At the local scale, the learned mismatch pattern correlates well with the observed in situ groundwater storage anomaly data for most parts of India, suggesting that deep learning models effectively compensate for the missing groundwater component in NOAH for this study region.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
