The terrestrial water storage anomaly (TWSA), derived from the Gravity Recovery and Climate Experiment (GRACE) and its successor, the GRACE Follow-on (GRACE-FO) satellite, presents a remarkable opportunity for extreme weather detection and the enhancement of environmental protection. However, the practical utility of GRACE data is challenged by an 11-month data gap and several months of missing data. To address this limitation, we have developed an innovative transformer-based deep learning model for data gap-filling. This model incorporates a self-attention mechanism using causal convolution, allowing the neural network to capture the local context of GRACE time series data. It takes into account various factors such as temperature (T), precipitation (P), and evapotranspiration (ET). We trained the model using a global dataset of 10,000 time series pixels and applied it to fill all the time gaps. The validation results demonstrate its robustness, with an average root mean square error (RMSE) of 6.18 cm and Nash-Sutcliffe efficiency (NSE) of 0.906. Notably, the Transformer-based method outperforms other state-of-the-art approaches in arid regions. The incorporation of T, P, and ET has further enhanced the accuracy of gap filling, with an average RMSE decrease of 7.5 %. This study has produced a reliable gap-filling product that addresses 11-month data gaps and 24 isolated gaps, ensuring the continuity of GRACE data for various scholarly applications. Moreover, our Transformer approach holds important potential for surpassing traditional methods in predicting and filling gaps in remote sensing data and gridded observations.

中文翻译：

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使用基于 transformer 的创新深度学习方法填补 GRACE 数据空白


源自重力恢复和气候实验 （GRACE） 及其继任者 GRACE 后续 （GRACE-FO） 卫星的地面储水异常 （TWSA） 为极端天气检测和加强环境保护提供了绝佳的机会。然而，GRACE 数据的实际效用受到 11 个月的数据缺口和几个月的缺失数据的挑战。为了解决这一限制，我们开发了一种创新的基于 transformer 的深度学习模型，用于数据空白填充。该模型采用了使用因果卷积的自我注意机制，使神经网络能够捕获 GRACE 时间序列数据的局部上下文。它考虑了各种因素，例如温度 （T）、降水 （P） 和蒸散 （ET）。我们使用包含 10,000 个时间序列像素的全球数据集训练了模型，并将其应用于填充所有时间间隔。验证结果表明其稳健性，平均均方根误差 （RMSE） 为 6.18 cm，Nash-Sutcliffe 效率 （NSE） 为 0.906。值得注意的是，基于 Transformer 的方法在干旱地区优于其他最先进的方法。T、P 和 ET 的加入进一步提高了间隙填充的准确性，平均 RMSE 降低了 7.5 %。这项研究产生了一种可靠的空白填补产品，解决了 11 个月的数据空白和 24 个孤立的空白，确保了 GRACE 数据用于各种学术应用的连续性。此外，我们的 Transformer 方法在预测和填补遥感数据和网格化观测的空白方面具有超越传统方法的重要潜力。