ALBERT

Description: This dataset includes annual mosaics of Antarctic ice velocity derived from Landsat 8 images between December, 2013 and April, 2019, which was updated in 2020 in order to produce multi-year annual ice velocity mosaics and improve the quality of products including non-local means (NLM) filter, and absolute calibration using rock outcrops data. The resulting Version 2 of the mosaics offer reduced local errors, improved spatial resolution as described in the README file.

Description: The rapid rising sea levels and their associated adverse impacts on world’s coastal zones, where approximately 40% of the world's population resides within a 100-kilometer proximity to the coast, have become a subject of increasing global concern as sea level rises are projected to be worsen over time. One of the key conclusions from the latest Intergovernmental Panel on Climate Change (IPCC) Reports is that global sea level changes are experiencing marked acceleration and significant regional variability as a result of both climate change and local factors. Many countries located in the vicinity of the Arabian Peninsula are with low-lying coastal regions, making them highly vulnerable to the adverse effects of sea level rise, including saltwater intrusion and the threat of coastal flooding. It is imperative to assess the rate of sea level rise and its underlying determinants in the region. This study aimed to comprehend the sea level trend in the Arabian Peninsula by analyzing five tide gauge data obtained from the Permanent Service for Mean Sea Level along the coasts of the region and altimetry data from the Archiving, Validation, and Interpretation of Satellite Oceanographic and the Copernicus Climate Change Service for the period 1993-2020 and estimate the vertical land motions at the tide gauge station locations. An accurate determination of the regional relative sea level change allows allows evaluating potential flood patterns and developing long-term strategies for coastal management, and adaptation to the impacts of climate change.

Description: The lunar nodal cycle, causing by moon’s declination variations over the period of 18.61 years, results in changes in the amplitude and phase lags of global tidal constituents. However, the observed range in tidal amplitude is at present not yet well understood, and it is crucial for coastal hazard planning and chart datum. The impact of the nodal tide including its plausibly varying amplitudes and phase changes on the highest astronomical tide along the Northwest Pacific coast in North American could be important as a scientific basis for future coastal disaster planning. Additionally, there is no standard procedure of computing the lowest 18.61-year astronomical tides. Hence, we analyzed the contributions of various factors for calculating the lowest astronomical tide, including time spans, sampling frequencies, number of tidal constituents, and nodal tide correction, and establish a standard procedure which was adopted in Taiwan. Finally, we use a modified two-step harmonic analysis procedure to re-evaluate the seasonality and slope variability of the major constitutes (M〈sub〉2〈/sub〉, S〈sub〉2〈/sub〉, K〈sub〉2〈/sub〉, and O〈sub〉1〈/sub〉) along the Pacific Northwest coast in North Ametrica, and analyze the difference between the observed nodal amplitudes and the values predicted by equilibrium tidal theory.

Description: Satellite altimetry has evolved into a unique and operational geodetic remote sensing measurement system with multi-missions and multi-satellite constellations generating an unprecedented climate data record since 1993, which has fostered seminal research in interdisciplinary Earth sciences. Satellite altimetry is deemed to be operational and sustained, contributing to much geodesy and climate research into climate monitoring, meteorological and ocean circulation forecasting, vertical datum realization, maritime safety, ocean pollution tracking services, flood and water resources management, energy, and many others. The constellations of altimeter missions enable the generation of multi-decadal, continuous, and uniform geodetic and climate data records at unprecedented spatiotemporal resolution and accuracy. Innovative instrumentation has advanced from pulse-limited to Delay-Doppler (or SAR), KaRIn, ATLAS and spaceborne lidar altimeters, as well as to the recent exploitation of SoOP (Signals of Opportunity) satellite sources in bistatic radar enabled altimetry, including L-band GNSS-Reflectometry enabled altimetry, P-band and other radar band SoOP signal-enabled sensors/altimetry. Altimetry advances have offered significant opportunities and challenges to all scientific disciplines and applications, which could afford, more than ever, a need for the establishment of the International Altimetry Service (IAS) under the International Association of Geodesy (IAG).IAS aims to (1) establish IAG Service-Style Coordinating Board, Analysis and Data Centers, and (2) convene and establish formal dialogs with scientists and key technical curators of existing altimetry data product services.

Description: On February 6, 2023, a sequence of earthquakes with Mw 7.8 and Mw 7.5 occurred in southern central Turkey near the northern border of Syria about in 9 hours. The disastrous earthquake sequence and its other aftershocks caused heavy human casualties and devastating building collapses. We employ the Empirical Orthogonal Function (EOF) analysis to capture coherent spatiotemporal features of co-seismic deformation for three components (N, E, U), which is based on the time series of 1-Hz GPS solutions at 20 permanent stations spatially well-distributed around the ruptured Anatolian fault system. The solved EOF modes show patterns which would help to investigate co-seismic rupture of the seismogenic faults. We compare the EOF-derived co-seismic displacement to the modeling results, which is computed from the spherical, elastic dislocation theory and finite fault model inverted from teleseismic waves records. Both GPS-observed and the modeled displacements show high consistency except for that at station EKZ1 (Ekinözü) where ~4.7 m of westward motion was estimated from GPS which we believe does not entirely represent the crustal motion; some other phenomena such as a local co-seismic landslide or a relative motion of the pillar with respect to the ground might have occurred. Moreover, this sequence is a large typical strike-slip faulting, which can generate gravity change above the threshold proposed by some theoretical simulation based on the satellite gravimetry observations. We also compute forward-modeled coseismic gravity changes, and discuss the plausible detection by instrument onboard of GRACE Follow-On gravimetry mission.

Description: Climate-induced natural hazards are thought to be happening more frequent and more intense under an increasingly warmer Earth. Earth-observing satellites including the ones operate in constellations and deliver accurate and sub-daily high-resolution/stereo multispectral satellite imagery, provide an opportunity to rapidly monitoring and quantifying the magnitude of disasters. Planet PBC’s Dove/SuperDove, SkySat, RapidEye (retired in March 2020) satellite constellations have at present over 150 CubeSats providing daily/sub-daily sampled 0.5-5-meter resolution images globally. Here, we use the available multispectral images from Planet CubeSat constellations, other geodetic and remote sensing data with the objective to conduct a feasibility study to monitor hazards. In particular, our goal is to identify, track, and quantify the scopes of active wildfires and rapid flood events even at relatively small scales and relatively short-durations worldwide, to study the feasibility of using timely and multi-platform satellite observations to complement informed disaster responses. In this study, we provide case studies and demonstrations of example brush fire and flood hazards to examine the feasibility of a satellite observation-based decision-support disaster response and management tool.

Description: The time-variable Earth’s gravity field is related to the mass transport and the physical processes within Earth’s system (including the atmosphere, oceans, hydrosphere, and cryosphere), such as melting of ice sheets and glaciers, ocean circulation and sea level variations, hydrological cycle, glacial isostatic adjustment, and earthquake-induced gravity change. The application of precise orbit determination (POD) provides valuable information about the Earth’s mass transport manifested in the temporal variations of the gravity field. This project uses the kinematics orbit-based acceleration approach, which has proven to be effective on the operational temporal gravity field data product generation using the high-low GNSS tracking data collected by the 3-satellite Swarm constellation. This project will utilize geodetic quality, dual-frequency high-low (GNSS-Spire CubeSats) tracking data from Spire Global, Inc.’s ~40 Lemur-2 3U CubeSat constellation. This low Earth orbiting (LEO) constellation is primarily dedicated to operational atmosphere data retrieval using radio occultation, bistatic GNSS-reflectometry forward scattering signals and grazing angle altimetry. The acceleration approach can provide solutions of the Earth’s temporal gravity field in long-wavelength (longer than 1,000 km), but plausibly at higher temporal sampling, weekly or finer with the global coverage of up to 40 simultaneous GNSS-Sensing of Spire CubeSats. This provides an additional observing architecture of gravity field solutions which can be used in conjunction with the more accurate and higher resolution temporal gravity solutions from GRACE-FO to further improve the temporal resolutions of monitoring abrupt-episode natural hazard evolution such as tropical cyclones, wildfires and floods, as well as weekly surface/ground water storage changes.

Description: Starting from April 2002, Gravity Recovery and Climate Experiment (GRACE) and, its successor mission GRACE-FO (FollowOn) have provided irreplaceable data for monitoring mass variations within the hydrosphere, cryosphere, and oceans, and with unprecedented accuracy and resolution for over two decades. However, the long-term products of mass variations prior to GRACE-era may allow for a better understanding of spatiotemporal changes in climate-induced geophysical phenomena, including terrestrial water cycle, ice sheet and glacier mass balance, sea level change and ocean bottom pressure. In this study, total water storage anomalies (TWSA) are simulated/reconstructed globally at 1.0°x1.0° spatial and monthly temporal resolutions from January 1994 to December 2020 with an in-house developed hybrid Deep Learning (DL) architecture using GRACE/-FO mascon and SLR gravimetry, ECMWF Reanalysis-5 (ERA5) data. We validated our simulated mass change data products both over land and ocean, not only through mathematical performance metrics (internal validations) such as RMSE or NSE along with comparisons to previous studies, but also external validations with non-GRACE datasets such as El-Nino and La-Nina patterns, barystatic global mean sea level change, degree (d) 2 order (o) 1 spherical harmonic coefficients (C21, S21) retrieved from Earth orientation parameters, Greenland Ice sheet mass balance and in-situ Ocean Bottom Pressure measurements were carried out. The overall validations show that the proposed DL paradigm can efficiently simulate high-resolution monthly global gravity field both in GRACE/GRACE-FO and pre-GRACE era. The resulting simulated data products are available as monthly mass change grids as well as spherical harmonic models up to d/o 180.

Description: Glacial isostatic adjustment (GIA) is the ongoing response of the solid Earth to the deglaciation of the last Pleistocene ice sheet. In North America near Hudson Bay, the remote region has large-scale GIA-induced deformation, and the GIA modeling is constrained by sparse geodetic measurements from leveling, GNSS, and satellite gravimetry. Satellite-based interferometric synthetic aperture radar (InSAR) has been used to constrain GIA modeling in Iceland. However, measuring surface displacements by InSAR is more challenging in southern Hudson Bay because of the lower-gradient pattern. Such low-gradient displacements in time and space are subject to spatial-correlated biases from tropospheric and ionospheric variations, ocean tide loading, and orbit errors. Here we investigate the feasibility of using InSAR to measure GIA-induced deformation in southern Hudson Bay. We used 5-year (2017-2021) summertime Sentinel-1B SAR data and the persistent scatterer InSAR (PSInSAR) method to estimate surface displacements. We used model-based corrections to alleviate the spatial-correlated errors and further applied the spatiotemporal filter to reduce error residuals. The InSAR-derived vertical velocity shows a consistent pattern with the ICE-6G_D GIA model and shows a good agreement with available GNSS observations (RMS difference is at 2.03 mm/yr). The velocity map reveals distinct regional differences with model prediction, at 2-3 mm/yr higher in the northern and southern areas, and 2-3 mm/yr lower in the middle of the Hudson Bay land area study region. The revealed regional inconsistency between the InSAR-derived deformations and the GIA model could advance the understanding of GIA and potentially constrain the Earth's rheology in this region.

Description: Flood is amongst the hydro metrological hazards most frequently occurring and responsible for massive destruction of lives and properties in developing countries like Pakistan. A most devastating flood episode occurred during 2022 in lower Indus basin where roughly more than 33 million people were affected. The aim of this study was to evaluate previous major floods episodes temporally and extraction of susceptible sites based upon multiple causal parameters, over the lower Indus plain, during past 22 years. To achieve this, we utilized the relevant resolution planet imageries for the study region. The susceptible sites were extracted using multiple causal parameters. A suit of geodetic observations was monitored to enhance the precise extraction of susceptibility. The deep learning (deep boost) and machine learning (naive bayes tree) algorithms were utilized and spatial coverage of vulnerability for each algorithm was calculated and mapped. Both algorithms were compared for the accuracy assessment. Our approach will greatly be beneficial for planners in managing and protecting this river basin from this natural hazard.