Description: ITU_GGC16 is a static global gravity field model up to degree order 280 computed from the combination of ITU_GRACE16 (up to d/o 180) and GO_CONS_GCF_2_TIM_R5 (up to d/o 280) by collaboration of various national institutions (YTU, KOU, NEU, SU) lead by ITU and OSU as the international collaborator with the support of research grant no 113Y155 from the Scientific and Technological Research council of Turkey (TUBITAK). The combination is performed at the normal equation level with variance component estimation. No rate terms were modeled, and no corrections for earthquakes have been applied. For additional details on the background modeling, see the GFZ RL05 processing standards document by Dahle et al. (2012).

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: An exponentially increasing number of scientific and commercial Earth orbiting satellites are delivering global and timely sensing of the Earth from space, on its surface or from inside the Earth. The onset of climate change and its dire consequences has been exacerbating the adverse impacts on Earth’s environments and its inhabitants. Timely satellite-based Big Earth observations at adequate spatiotemporal resolution provide a means to monitor the evolutions of more frequent and abrupt climate induced and enhanced hazards. These observations could contribute towards the elucidation of their respective governing climatic processes, and enable improved hazards forecasting, water resources monitoring, and informed hazards management and response. Example satellite geodetic and other observations include satellite gravimetry, altimetry, GNSS, GNSS bistatic altimetry, SAR/InSAR, and Planet PBC's high spatiotemporal (subdaily and 3-5 m) resolution multispectral imageries. We illustrate that the use of deep machine learning analytics can effectively integrating hydrometeorological model and other data, and downscaling the satellite geodetic observations, towards enabling timely monitoring of abrupt weather episode evolutions, including floods, groundwater depletions, cyclone landfall, snowstorms, and meteotsunamis.

Description: The economic and population growth in India accompanied by increasingly frequent and severe disasters, such as floods and forest fires, raises concerns about the future. In recent decades, satellite technology has become an important tool for monitoring the Earth's surface, including natural hazards and land surface changes. Our USAID (United States Agency for International Development) project, REmote Sensing for Forest Renewal, Ecosystem Services, and Sustainable Hydrological Management (REFRESH), aims to improve forest sustainability in India with the help of satellite geodetic and remote sensing data. Here we report the progress for the physical science component of the REFRESH Project. Our team has developed a deep-learning model for classifying 10 types of Land Use/Land Cover (LULC) to detect long-term changes in forest areas over India. With collaboration from Indian partners, we validate our classification results using their in-situ land cover data. We used PlanetScope daily images at 3–5 m spatial resolution to monitor the evolutions of floods and forest fires over India. Other satellite data products include in-land water level virtual stations over rivers, dams, and lakes using three decades of satellite radar altimetry, daily-sampled GRACE/GRACE-FO Level 1B gravimetry data to monitor monsoonal floods and seasonal droughts, spaceborne lidar and GEDI for mapping canopy changes, and deep-learning downscaled satellite gravimetry measured total water storage anomalies over entire India. Finally, we plan to generate spaceborne GNSS-Reflectometry and NASA’s CYGNSS for water/land classification data products over India. The data products can be retrieved, visualized, and analyzed on an elaborate observation portal.

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.