Gravity Recovery and Climate Experiment (GRACE) gravimeter satellites observed the Earth gravity field with unprecedented accuracy since 2002. After the termination of GRACE mission, GRACE Follow-on (GFO) satellites successively observe global gravity field, but there is missing period between GRACE and GFO about one year. Many previous studies estimated terrestrial water storage (TWS) changes using hydrological models, vertical displacements from global navigation satellite system observations, altimetry, and satellite laser ranging for a continuity of GRACE and GFO data. Recently, in order to predict TWS changes, various machine learning methods are developed such as artificial neural network and multi-linear regression. Previous studies used hydrological and climate data simultaneously as input data of the learning process. Further, they excluded linear trends in input data and GRACE/GFO data because the trend components obtained from GRACE/GFO data were assumed to be the same for other periods. However, hydrological models include high uncertainties, and observational period of GRACE/GFO is not long enough to estimate reliable TWS trends. In this study, we used convolutional neural networks (CNN) method incorporating only climate data set (temperature, evaporation, and precipitation) to predict TWS variations in the missing period of GRACE/GFO. We also make CNN model learn the linear trend of GRACE/GFO data. In most river basins considered in this study, our CNN model successfully predicts seasonal and long-term variations of TWS change.

This study aims to find a correlation between XRD and Raman result of the oxidized high modulus carbon fibers as a function of its oxidation degrees, and compare with the isotropic carbon fiber reported early. La of the high modulus carbon fiber prepared by oxidation in carbon dioxide gas have been observed using laser Raman spectroscopy. The basic structural parameters of the fibers were evaluated by XRD as well. The La of the original high modulus carbon fibers were measured to be 144 a from Raman analysis and 135 a from XRD analysis. La of the 92% oxidized fiber were 168 a by using Raman and 182 a by using XRD. There was some correlation between the La value obtained from Raman and XRD. However the La value changes of the high modulus carbon fiber through whole oxidation process showed opposite tendency compare with the isotropic carbon fiber because of the fiber structure basically.