Aimed at mapping time variations in the Earth’s gravity field, the Gravity Recovery and Climate Experiment (GRACE) satellite mission is applicable to access terrestrial water storage (TWS), which mainly includes groundwater, soil moisture (SM), and snow. In this study, SM and accumulated snow water equivalent (SWE) are simulated by the Global Land Data Assimilation System (GLDAS) land surface models (LSMs) and then used to isolate groundwater anomalies from GRACE-derived TWS in Pennsylvania and New York States of the Mid-Atlantic region of the United States. The monitoring well water-level records from the U.S. Geological Survey Ground-Water Climate Response Network from January 2005 to December 2011 are used for validation. The groundwater results from different combinations of GRACE products (from three institutions, CSR, GFZ and JPL) and GLDAS LSMs (CLM, NOAH and VIC) are compared and evaluated with in-situ measurements. The intercomparison analysis shows that the solution obtained through removing averaged simulated SM and SWE of the three LSMs from the averaged GRACE-derived TWS of the three centers would be the most robust to reduce the noises, and increase the confidence consequently. Although discrepancy exists, the GRACE-GLDAS estimated groundwater variations generally agree with in-situ observations. For monthly scales, their correlation coefficient reaches 0.70 at 95% confidence level with the RMSE of the differences of 2.6 cm. Two-tailed Mann-Kendall trend test results show that there is no significant groundwater gain or loss in this region over the study period. The GRACE time-variable field solutions and GLDAS simulations provide precise and reliable data sets in illustrating the regional groundwater storage variations, and the application will be meaningful and invaluable when applied to the data-poor regions.

Water vapor is one of the most variable atmospheric constituents. Knowledge of both the spatial and temporal variations of atmospheric water vapor is very important in forecasting regional weather and understanding the global climate system. The Moderate Resolution Imaging Spectroradiometer (MODIS) is the first space instrument to obtain precipitable water vapor (PWV) with near-infrared (nIR) bands and the traditional IR bands, which provides an opportunity to monitor PWV with wide coverage during both daytime and nighttime. However, the accuracy of PWV measurements obtained with IR bands is much lower than that with nIR bands. Moreover, seldom have studies been devoted to the calibrations of MODIS IR PWV. In this paper, the accuracy of MODIS IR water vapor product during the nighttime is assessed by ERA-Interim data, Global Positioning System, and radiosonde observations. Results reveal that the performance of MODIS IR water vapor product is much poorer than that from the other observations, and the MODIS IR PWV needs to be calibrated. As such, we propose a differential linear calibration model (DLCM) to calibrate the MODIS IR water vapor product during the nighttime. Case studies under both dry and moist atmosphere in midlatitude and equatorial regions are used to test and assess the performance of the DLCM. Results show that the DLCM can effectively enhance the accuracy of MODIS IR retrievals at nighttime. Furthermore, while the traditional least square model may over calibrate the MODIS IR PWV measurements occasionally, the DLCM can avoid that defect successfully.

Water vapor variations affect the interferometric synthetic aperture radar (InSAR) signal transmission and the accuracy of the InSAR measurements. The Moderate Resolution Imaging Spectroradiometer (MODIS) near infrared (nIR) water vapor product can correct InSAR atmospheric effects effectively, but it only works for the synthetic aperture radar (SAR) images acquired during the daytime. Although the MODIS infrared (IR) water vapor product owns poorer accuracy and spatial resolution than the nIR product, it is available for daytime as well as nighttime. In order to improve the accuracy of water vapor measurements from the MODIS IR product, a differential linear calibration model (DLCM) has been developed in this paper. The calibrated water vapor measurements from the IR product are then used for wet delay map production and nighttime overpass SAR interferogram atmospheric correction. Results show that the accuracy of the MODIS IR product can be improved effectively after calibration with the DLCM, and the derived wet delays are more suitable for InSAR atmospheric correction than original measurements from the IR product. Furthermore, a MODIS altitude-correlated turbulence model (MATM) is incorporated to correct the atmospheric effects from another descending ASAR interferogram. Results show that the MATM can reduce altitude-dependent water vapor artifacts more effectively than the traditional correction method without the need to incorporate the altitude information.

Oceanfront is a typical and important sea surface feature that is reported to be associated with marine ecosystems and can be used as a reference for locating fishing grounds. Frontal zone extraction is often performed using a gradient threshold to classify image pixels and the result can sometimes contain too many spikes and become chaotic, leading to a negative effect for visual interpretation. And most conventional methods that focus on extracting the ridges of fronts struggle with false fronts due to imperfect data. Also, choosing appropriate thresholds for them is another dilemma, which sometimes leads to too many frontal ridges in unwanted areas or too little than needed in the region of interest. To meet the needs for visual interpretation and automatic front detection in significant frontal areas, a novel method based on deep learning is proposed in this letter. In this method, a deep learning model with U-Net architecture was designed to detect and locate significant frontal zones in grayscale sea surface temperature (SST) images. Then, an area threshold was adopted to filter the output of the model to improve the result. To demonstrate the effect of the proposed method, it was applied to an example SST image. The results show that the proposed method can not only merge messy fronts but also capture the overall patterns of frontal zones and work with conventional methods to get a better frontal ridge extraction result.