A Unified Framework for Bridging the Data Gap Between GRACE/GRACE-FO for Both Greenland and Antarctica

Abstract

The 11-month data gap between gravity recovery and climate experiment (GRACE) and GRACE Follow-On (GRACE-FO) hinders monitoring long-term ice mass change and its further analysis. While many attempts have been made to bridge water storage gaps, few unified frameworks exist to bridge the ice mass change gaps for both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS). This study combined partial least squares regression (PLSR) and the Sparrow Search Algorithm optimized back propagation (SSA-BP) to fill this gap in GrIS and AIS. During this process, seasonal autoregressive integrated moving average (MA) with exogenous variables (SARIMAX) and multiple linear regression (MLR) were introduced as comparison. PSLR is utilized to select key variables for constructing predictive models. We found SSA-BP outperformed SARIMAX and MLR, with correlation coefficients (CCs) and root mean square error (RMSE) at 0.99 and 39.22 Gt for GrIS, and 0.95 and 189.85 Gt for AIS within the testing period. SSA-BP demonstrated a reasonable mass change trend with less noise than other methods. SSA-BP reconstructed result shows superiority than other researches. Moreover, the reconstructed seasonal signals highlight the importance of filling the gap, showing decreased mass loss for GrIS and continuous mass loss acceleration for AIS post-2016.