Regional geoid model for the area of the subglacial Lake Vostok

We present a regional geoid model for the area of Lake Vostok, Antarctica, from a combination of local airborne gravity, ice-surface and ice-thickness data and a lake bathymetry model. The topography data are used for residual terrain modelling (RTM) in a remove-compute-restore approach together with the GOCE satellite model GOCO03S. The disturbing potential at the Earth's surface, i.e. the quasigeoid, is predicted by least-squares collocation (LSC) and subsequently converted to geoid heights. Compared to GOCO03S our regional solution provides an additional short-wavelength signal of up to 1.48 m, or 0.56 m standard deviation, respectively. More details can be found in Schwabe et. al (2014).

Improved geoid solution for the Weddell Sea region

We present a geoid solution for the Weddell Sea and adjacent continental Antarctic regions. There, a refined geoid is of interest, especially for oceanographic and glaciological applications. For example, to investigate the Weddell Gyre as a part of the Antarctic Circumpolar Current and, thus, of the global ocean circulation, the mean dynamic topography (MDT) is needed. These days, the marine gravity field can be inferred with high and homogeneous resolution from altimetric height profiles of the mean sea surface. However, in areas permanently covered by sea ice as well as in coastal regions, satellite altimetry features deficiencies. Focussing on the Weddell Sea, these aspects are investigated in detail. In these areas, ground-based data that have not been used for geoid computation so far provide additional information in comparison with the existing high-resolution global gravity field models such as EGM2008. The geoid computation is based on the remove-compute-restore approach making use of least-squares collocation. The residual geoid with respect to a release 4 GOCE model adds up to two meters and more in the near-coastal and continental areas of the Weddell Sea region, also in comparison with EGM2008. Consequently, the thus refined geoid serves to compute new estimates of the regional MDT and geostrophic currents.

Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.

Sediment volume variations inferred from DGPS measurements within Bossons glacier proglacial area (Mont-Blanc massif, France)

This dataset presents Differential Global Positioning System data (DGPS) acquired within the Bossons glacier proglacial area. Bossons glacier is a rapidly retreating glacier and its proglacial area is deglaciated for ~30 years. Bossons stream is one of the outlets of the subglacial drainage system. It starts as a 800 m steep cascade reach, then flows through an area with gentler slope : the Plan des Eaux (PdE). PdE is a 300 m long, 50 m wide proglacial alluvial plain with an increasing channel mobility in the downstream direction but decreasing slope gradient and incision. As it may act a sediment trap, studying periglacial and proglacial erosion processes in the Bossons catchment requires to quantify PdE sediment volume evolution. A several meter-sized block located within Bossons proglacial area was set up as GPS base : its location was measured by one antenna (Topcon Hyper Pro) by performing 600 consecutive measurements throughout one day. A second antenna (Topcon Hyper Pro) was then used to measure XYZ location of points in the proglacial area with a ~2 m grid. Radio communication between the two antennas allowed differential calculations to be automatically carried out on field using the Topcon FC-250 hand controller. This methodology yields 3 cm XY and 1.5 cm Z uncertainties. DGPS data have been acquired through 10 campaigns from 2004 to 2014; campaigns from 2004 to 2008 cover a smaller area than those from 2010 to 2014. Digital Elevation Model (DEM) have been interpolated from DGPS data and difference between two DEMs yields deposited and eroded volume within PdE. Maps of PdE volume variation between two campaigns show that incision mainly occurs in the upper and lower sections where as deposition dominates in the middle section. Deposition, denudation and net rate (deposition rate - denudation rate) are calculated by normalizing volumes by DEM areas. Deposition dominates results with a mean net rate of 29 mm/yr. However, strong inter-annual variability exists and some years are dominated by denudation : -36 mm/yr and -100 mm/yr for 2006 and 2011, respectively. Nonetheless, oldest campaigns (2004 to 2008) were carried out on the lower part part of the alluvial plain and ruling them out to keep only complete DEM (2010 to 2014) yields a mean net rate of ~15 mm/yr. This results is coherent with field observations of both strong deposition (e.g. flood deposits) and strong erosion (e.g. 30 cm incision) evidences. Bossons glacier proglacial area is thus dynamic with year-to-year geormorphological changes but may leans toward increasing its mean elevation through a deposition dominated system.

TanDEM-X ice thickness dataset for Wilkins Ice Shelf, Antarctic Peninsula, with link to GeoTIFF

The dataset shows the ice thickness over Wilkins Ice Shelf, Antarctic Peninsula derived from TanDEM-X Interferometry. The data has been acquired between June and August 2012. The TanDEM-X heights have been linked to CryoSAT-2 heights (V. Helm) from the respective time stamp. Elevations have been transformed from WGS84 ellipsoidal heights to the EGM2008 geoid. The ice shelf thickness was estimated assuming hydrostatic equilibrium and a mean ice density of 915 kg/m³.

Mean dynamic topography of the North Indian Ocean

A satellite-only Mean Dynamic Topography (MDT) of the North Indian Ocean is estimated from the DIRR5 geoid and CNES_CLS11 mean sea surface (Schaffer et al. 2012). DIRR5 geoid is estimated from the latest release (Release 5) of GOCE gravity data according to previous studies (e.g., Johannessen et al. 2003; Raj, 2014). Note that this MDT estimated is referenced to a time period of 7 years (1993-1999). A correction data obtained from AVISO is later used to convert the MDT to a time reference of 20 years (1993-2012). More details are given in Raj (2016).