Data from: Long-term (1979-present) total water storage anomalies over the global land derived by reconstructing GRACE data ...

This research data is associated with the manuscript entitled “Long-term (1979-present) Total Water Storage Anomalies Over the Global Land Derived by Reconstructing GRACE data (https://doi.org/10.1029/2021GL093492)”. The study focused on the reconstruction of long-term GRACE-like gridded total water...

Full description

Bibliographic Details
Main Author: Li, Fupeng
Format: Dataset
Language:English
Published: Dryad 2021
Subjects:
Antarc*
Antarctica
Online Access:https://dx.doi.org/10.5061/dryad.z612jm6bt
https://datadryad.org/stash/dataset/doi:10.5061/dryad.z612jm6bt
Description
Summary:This research data is associated with the manuscript entitled “Long-term (1979-present) Total Water Storage Anomalies Over the Global Land Derived by Reconstructing GRACE data (https://doi.org/10.1029/2021GL093492)”. The study focused on the reconstruction of long-term GRACE-like gridded total water storage anomalies over the global land surface. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) mission has monitored global total water storage anomalies (TWSA) with an unprecedented accuracy. Yet, many applications require a longer record, i.e. extending prior to the GRACE period. Besides, the Global Climate Observing System (GCOS) Steering Committee has made great efforts towards establishing TWSA as a new Essential Climate Variable (ECV). Here, we produced a new global (excluding Antarctica) total water storage anomaly data set by reconstructing the RL06 CSR mascons using precipitation, land temperature, sea surface temperature, soil moisture, evaporation, surface runoff, subsurface runoff, ... : GRID_CSR_GRACE_REC is derived based on the synthetic methodology framework developed by Li et al. (2020). The synthetic methodology framework contains seven data-driven methods, which could be classified into three groups of techniques – i.e, 1) two statistical decomposition techniques Principal Component Analysis (PCA) and Independent Component Analysis (ICA), 2) two time series decomposition techniques Least Square (LS) and Seasonal-Trend decomposition based on Loess (STL), and 3) three machine learning techniques Artificial Neural Network(ANN), AutoRegressive model with eXogenous variables (ARX), and Multiple Linear Regression (MLR). ...

Similar Items