Report of the Second BEDMAP Workshop
on Antarctic Bed Mapping
Cambridge 24-25 July 1999
Sponsored by:
Scientific Committee on Antarctic Research
British Antarctic Survey
Report of the Second BEDMAP Workshop |
Sponsored by: Scientific Committee on Antarctic Research British Antarctic Survey |
On 24 and 25 July 1999, a working group of 21 scientists and data management specialists from seven countries met at the British Antarctic Survey (BAS), Cambridge, UK in the second BEDMAP workshop. Under the sponsorship of the Scientific Committee on Antarctic Research (SCAR), they met to discuss the generation of a new topographic model of the bed beneath the Antarctic Ice Sheet and the seabed of the surrounding continental shelf. Scheduled at the conclusion of Phase 1 of the BEDMAP programme (data collection and storage), the workshop was able to evaluate the suitability of the ice thickness database, compiled over the past year, to meet this objective. Other issues addressed included recent developments in Digital Elevation Models (DEMs), the status of bathymetry over the Antarctic continental shelf, upcoming ice thickness surveys and the establishment of a long term ice thickness data archive for Antarctica. The following points summarise the principal outputs from the workshop.
Progress on the initiative was rather slow immediately following the first workshop. However in July 1998 David Vaughan (BAS) secured funding from the UK Natural Environment Research Council (NERC) to support a BEDMAP Database Manager. Matt Lythe (formerly of the International Centre for Antarctic Information and Research, New Zealand, ICAIR) was appointed to a one-year full-time post which has since been supplemented from BAS core funding for a further nine months.
The focus of the first phase of work was the establishment of a relational database management system (RDBMS) to archive the ice thickness data and subsequently, the compilation of ice thickness data into this repository. It is estimated that approximately 90% of the entire ice thickness measurements over the continent have been assembled into this database. While this work has been carried out a number of procedures to evaluate the data have also been developed.
This workshop was scheduled at the completion of the first phase of work, mainly to enable the BEDMAP consortium to review the database, evaluate the data and discuss the generation of the gridded topographies. In this report we will firstly review the current status of the programme including the data compilation and data evaluation before presenting some of the results from the workshop discussion.
2.0. Ice thickness data compilation
The BEDMAP relational database and assembled ice thickness data sets are summarised below. Although several data sets may be added before the next phase of work begins, this summary represents essentially the final list of data to be used in the production of the ice thickness grid.
2.1. BEDMAP Relational database
The data contributed to BEDMAP are divided into Missions to allow broad groupings of similar data. The missions generally consist of data from a single oversnow traverse, (or series of oversnow traverses), or a group of airborne flights for which acquisition parameters remain similar. Typically a mission is viewed as a season of work using a particular set of equipment and procedures. The BEDMAP data archive is maintained within an Oracle relational database at BAS, Cambridge, UK. The database consists of two principal tables; the MISSION_DATA table, which contains the location and the measured parameters (ice_thickness, surface_elev, bed_elev, watercolumn) and the MISSION_SUMMARY table, which contains the descriptive metadata pertaining to each mission. Note that only final interpreted data sets have been submitted to BEDMAP, it does not contain seismic traces or radar film records. The complete specification for BEDMAP data sets is provided in Appendix 2.
2.2. Data compilation
Almost 2 million ice thickness observations, covering an area of approximately 10.5 million square kilometres (75% of the continent), have been assembled within the BEDMAP database (Figure 1). These data have been collected on over 100 separate expeditions conducted by 12 countries, carried out over the past fifty years up to and including the 1998/99 field season. Overall the coverage is patchy and uncoordinated reflecting the regional nature of surveys undertaken by many nations in support of other scientific activities. Although a few areas have detailed coverage, e.g. Amery Ice Shelf, Ronne-Filchner Ice Shelf and Siple Coast the flightline spacing in many areas is still only 50km. The areas which remain largely unsurveyed include Wilkes Land, Queen Mary Land, coastal Marie Byrd Land and the area of the polar plateau north of South Pole Station between 45ºW and the Greenwich Meridian. A summary list of the data sets archived within the BEDMAP repository is provided in Appendix 3.
The data within the BEDMAP database have been assembled from a number of data sources including hard-copy tabulation (mainly early seismic traverse data in the 1950s and 60s), published data and field reports, paper maps, existing databases and digital files submitted from scientists within the BEDMAP consortium. The overwhelming majority of data within the database are derived from airborne radio-echo sounding (RES) although in terms of number of expeditions more have been collected through oversnow surveying (Table I).
|
Datatype |
Missions |
Records |
Percentage |
| Airborne RES |
51 |
1903763 |
98.57% |
| Ground-based RES |
20 |
20731 |
1.07% |
| Seismic reflection |
34 |
4769 |
0.25% |
| Gravimetric |
9 |
2168 |
0.11% |
| Ice core |
4 |
6 |
<0.01% |
Table I. Summary of data within BEDMAP database by data type.
The development in the 1970s of airborne radio-echo sounding (RES) techniques capable of covering extensive areas and collecting large data sets is further evident with a summary of the database by decade (Table II). Despite there being a similar number of missions in each of the last three decades as in the 1960s, the amount of data collected is primarily airborne RES and therefore the volume of these data is an order of magnitude greater.
|
Period |
Missions |
Records |
Percentage |
| 1950s |
11 |
1153 |
0.06% |
| 1960s |
24 |
14055 |
0.73% |
| 1970s |
15 |
321134 |
16.64% |
| 1980s |
29 |
884718 |
45.83% |
| 1990s |
36 |
709366 |
36.75% |
Table II. Summary of data within BEDMAP database by decade.
Figure 1. Distribution of ice thickness data in BEDMAP compilation.
In Figure 2 the density of data in the BEDMAP compilation is compared to the widely adopted SPRI Folio 'The Bedrock Surface of Antarctica' (Drewry and Jordan, 1983). Represented by the number of measurements in a 100km x 100km grid, the limitations of the SPRI folio, and the ability of BEDMAP to make substantial advances, especially in East Antarctica, are clearly evident from this comparison.
To gain a better understanding of the density of the BEDMAP database at a scale closer to the final output grids, two density measures were determined at grid nodes spaced 10km across the continent. Calculated for the principal regions and also the entire continent the parameter NEAR represents the mean distance to the nearest observation from the grid node while STDEV represents one standard deviation of this distribution (Table III).
The most densely covered areas are the Ronne-Filchner and Ross ice shelves (6 and 10km mean distance respectively) while there is only a slightly lower density for West Antarctica and the
Antarctic Peninsula. In East Antarctica on the other hand the average distance to the nearest observation is almost 40 km while the larger standard deviation also indicates much sparser coverage in this area. As we will see later this has many implications for the identification of a suitable grid spacing for the final data sets. It should be noted that there are still several Russian and Australian data sets in East Antarctica which have not yet been incorporated into the database. It is hoped that we will be able to include these before the gridding phase begins.
|
Region |
Area (km2) |
NEAR (km) |
STDEV (km) |
| East Antarctica |
10,353,800 |
37.64 |
44.61 |
| West Antarctica |
1,974,140 |
18.6 |
25.6 |
| Antarctic Peninsula |
521,780 |
18.6 |
25.6 |
| Ronne-Filchner Ice Shelf |
532,200 |
6.05 |
6.63 |
| Ross Ice Shelf |
536,070 |
10.41 |
13.4 |
| Continent |
13,918,070 |
31.96 |
38.55 |
Table III. Density characteristics of data within BEDMAP database.
The identification and elimination of erroneous observations is a necessary step prior to the generation of the ice thickness grid. Errors may be classed as random where the nature of the error is not consistent from observation to observation for a particular data set or systematic where the magnitude of the error is a function of some technique or processing step that has been consistently applied. Random errors might arise as a result of inaccuracies in the device used to measure ice thickness or the method used to position the observation. Systematic errors might arise from factors such as the processing of the raw data to determine the ice/bed interface, the algorithms used to determine ice thickness, or a near-linear drift in navigation error along a flight line. These errors may manifest themselves through a consistent bias when compared to other observations in the same area. Further errors might also arise in the subsequent data handling and processing of data including recording, digitising and conversion errors.
Probably the most important sources of error are those derived from inaccuracies in the navigation systems. A wide range of navigation methods have been used to fix the ice thickness data sets, including astronomical fixes, dead reckoning, inertial avionics, doppler avionics and global positioning systems (GPS). Positional uncertainty in these methods range from several metres for differential GPS to several km for the inertial and dead reckoning navigation systems. The other principal error source is the precision to which the actual ice thickness observations are determined. Documented precision of the observations in the database ranges from 10 to 180m.
We have developed a number of automated techniques using C programming and Geographic Information Systems (GIS) to detect gross errors and erroneous values in the source data. The quality control procedure consists of three tasks, each of which identifies and flags poor data points. Any data observation flagged as erroneous is recorded in the QUALITY field in the BEDMAP relational database. Note: no observations are removed from the archive - they are simply flagged.
To enable direct comparison of the source data and identification of anomalous values all source data were adjusted to the WGS84 datum. Quality control was then carried out using the three-step procedure described below. Note; in most cases the original return times, raw data or films are not available so a re-calculation of the ice thickness from the primary data is not possible.
3.1. Along-track consistency check for high frequency track-line data
This step is designed to identify anomalous observations within high-density airborne radio-echo sounding data. The procedure is described schematically in Figure 3. In the first instance the distance between sequential observations (samples) along the flight path is determined. This returns a file containing sample number, distance from previous observation and ice thickness. A one-dimensional 7-point filter is then moved through each track-line. The mean and standard deviation of the 7 ice thickness values within the filter window and the total along-track length of the window are then computed. The ice thickness value of the central point within the filter window is then compared to these parameters. If the total along-track length is greater than six times the mean ice thickness (there are six intervals within the window) processing discontinues and the filter increments to the next filter window. Alternatively, if the window distance is less than 6 * mean ice thickness and the ice thickness value of the central point differs from the mean by more than 2 standard deviations, the observation is removed from the interpolation.
Figure 3. Along-track consistency check for high frequency track-line data.
3.2. Crossover analysis
Implemented in a GIS environment, ice thickness differences are determined for all observation pairs that lie within an isotropic distance of 2500m. Crossovers derived from the same airborne sortie are first removed from consideration as these have been dealt with in the first quality assurance check. All crossovers with an ice thickness difference of less than 50m are then purged. All observations constituting the remaining crossovers are flagged as being potentially anomalous and carried forward to the next quality control stage.
3.3. Focused neighbourhood check for high variance
The final quality assurance task takes the results from the crossover analysis and undertakes a local neighbourhood evaluation of ice thickness variance around those points. For each point flagged the mean and variance of all observations (excluding those which belong to the same sortie) within an isotropic distance of 5000m is determined. The value of the point itself is then compared to this output. If the ice thickness magnitude is more than 2 standard deviations different from the neighbourhood mean and the neighbourhood contains at least 5 measurements the point is flagged as inconsistent in the QUALITY table in the database and subsequently removed from the interpolation.
3.4. Results
Data evaluation is not yet complete however preliminary results provide an indication of the data quality. The along-track consistency check shows that the continuity of data along flight lines is excellent in nearly all cases with less than 1% of all points being picked up along any flight line. It is assumed that in most cases the data providers would have carried out similar checks. The cross-over analysis has been applied in several areas with relatively good success. Figure 4 shows an example of the ice thickness difference for data points separated by 1000m or less. The cone shape shows how spatial correlation decreases as distance lag increases. The outliers are potentially erroneous values.
Figure 4. Variogram cloud for ice thickness data, 180W - 135W.
Figure 5 shows the distribution of crossover errors (distance lag < 500m) between separate flights across different missions for the same area. Of those data points so far identified in the cross-over analysis, approximately 75% failed the final check for neighborhood variance.
Over the past decade significant advances have been made in mapping the topography of the Antarctic ice sheet through satellite altimetry, airborne altimetry, terrestrial surveying and satellite image analysis. Continental Digital Elevation Models (DEMs) are now available which incorporate these data sources with grid spacings of several kilometres. The workshop was in full agreement that the topographic model describing bed elevation beneath the grounded ice sheet should be derived through the subtraction of the ice thickness grid from the best available DEM rather than through the construction of bed elevation model. The rationale for this approach is as follows;
Ice sheet modellers, oceanographers, geologists and geoscientists interested in gravity and magnetic anomalies on a continental scale have a requirement for gridded data sets of ice sheet bed and sea bed elevation that cover not only the continent but also the continental shelf of Antarctica. It was hoped that BEDMAP would be able to make use of an existing compilation over the continental shelf. However, although there are several digital data sets describing bathymetry over the Antarctic continental shelf, none of these is sufficiently precise in its current state to be used within BEDMAP. A principal task remaining therefore is the preparation of a new gridded data set describing sea bed elevation over the continental shelf.
Several data sets, which have been identified as source data for this work, are described in Appendix 4. It is envisaged that the principal data sets to be used in the interpolation of the topographic model would be the extensive bathymetric sounding measurements from the National Geophysical Data Center's (NGDC) marine trackline database (GEODAS) and other distributed national data holdings. Where data distribution is sparse, data from the other sources (e.g. ETOPO5, GEBCO) should be evaluated and included where plausible. The predicted seafloor topography from satellite altimetry could be used mainly as an interpolating field to provide an indication of structures and trends rather than absolute depths.
At the workshop Matt Lythe (BEDMAP Database Manager) and Glenn Johnstone (Australian Land Information Group), Canberra, Australia agreed to begin work on the compilation of the data sets necessary for the generation of this data set. Time and resources permitting, we hope to be able to develop a suitable data set. If however this proves impossible we will use the most accurate data set currently available.
In addition to the measured ice thickness from seismic and radar sounding measurements, gravimetric observations and boreholes there are other sources of ice thickness data which will be used by BEDMAP in the final compilation phase. Data hitherto compiled include;
7.0. Geographic framework
All ice thickness data assembled within the BEDMAP database have been referenced to the WGS84 ellipsoidal reference frame. Where data were not already referenced to this datum, and the reference framework is known, a transformation has been applied. It was agreed that the final ice thickness grid should be referenced to this framework. As the ice thickness grid does not describe absolute heights the reference frame of the final bed elevation grid will depend on what the DEM is referenced to. The DEM produced by Liu et al. (in press) is referenced to both the WGS84 ellipsoid and also the most recent geoid model, which at present is OSU91A (Rapp et al. 1991).
In vertical datum adjustment, the minor differences between mean sea level elevation and geoid orthometric height will be ignored. The bathymetric model (referenced to mean sea level) will therefore be interpreted as depths on the OSU91A geoid. The absolute heights in the bathymetry data set will be converted to WGS84 ellipsoidal heights to bring it in line with the continental bed elevation grid. Note in the Antarctic, the geoidal undulation, as calculated from the OSU91A geoid model, ranges from -67m to +42m
The final bed elevation DTM will be referenced to the WGS84 ellipsoid. Users of the final gridded data sets can then apply different geoid models to the data array as required.
8.0. Generation of gridded data sets
The generation of the final bed elevation grid for the continent and continental shelf will involve the construction of a number of intermediate products. These products and the key processing steps involved in their construction are shown schematically in Figure 6. The principal output from the assembled ice thickness database will be a grid of ice thickness for the grounded ice sheet and ice shelves. This data set will be generated through the application of a spatial interpolation procedure to the input ice thickness and other relevant data. This model will not describe absolute heights. The bed elevation grid will be derived through the subtraction of the ice thickness grid from the surface elevation grid. Resources and time permitting a digital terrain model of seabed elevation then be interpolated using the bathymetric data sources described in Appendix 4 and the seismic reflection derived sub ice shelf sea bed depths. If we do not have the time to achieve this, the best of the currently available data sets will be used to describe seabed elevation. Finally the two bed elevation grids will be combined to produce the complete bed elevation grid.
8.1. Determination of grid size for final data sets
Perhaps the most immediate benefit of BEDMAP will be in the field of ice sheet modelling. Numerical modelling of the ice sheet covering Antarctica is the goal of many researchers and our politicians and taxpayers are expecting these modellers to provide concrete answers to difficult questions about the future ice sheet and sea level. The present generation of whole continent models requires a variety of gridded datasets of ice sheet bed / seabed elevation that cover both the continent and the continental shelf of Antarctica. It seems likely that for most of these models a grid resolution of 5 km will be adequate for the near future.
Theoretically the determination of grid spacing is dependent primarily on the distribution and density of the input data. If grid spacing is too large information will be lost while if the grid spacing is too small the model will mis-represent the spatial resolution of the input data while in addition data volume will expand rapidly. As we have seen, in the case of the ice thickness data over Antarctica the spatial resolution is extremely variable. The density parameters presented in Section 2.2 show data are reasonable well distributed at least over West Antarctica and the Ross and Ronne-Filchner ice shelves although for large areas of East Antarctica there are considerably fewer data points.
Workshop participants agreed that it is desirable to produce a gridded data set on a 5km grid as long as the precision and spatial resolution of the data are adequate. If the data distribution in particular could not be proved to be of the required standard then a 10 km post spacing would suffice. An evaluation of ice thickness variability versus distance lag (e.g. Figure 4) indicates that, at least in terms of positioning, the data are suitable for gridding to 5km. The root mean square error (RMSE) in ice thickness difference out to a separation of 10 km for this same data set also shows excellent correlation (Figure 7).
Figure 7. RMSE in ice thickness difference versus distance lag (BEDMAP database 180-135W)
Fine resolution models require more detailed bed elevation data. It seems unlikely that any single product that could be designed by BEDMAP would be adequate for all these models. What would be of greater value would be for modellers to eventually have access to the original data, in the form of point measurements and digitized profiles. This would allow the generation of bespoke datasets in collaboration with the BEDMAP Consortium.
8.2. Spatial interpolation algorithms
As has been discussed the distribution of data points in BEDMAP is extremely non-uniform. In some areas detailed coverage potentially affords generation of sub km topography. However in most parts of the ice sheet the across track distance is at least 50km while some regions contain only a handful of observations from overland seismic or radio echo sounding traverses. The spatial interpolation procedure must therefore be able to deal with anisotropically distributed data, (closely spaced observations along flight lines but widely separated flight tracks), extensive areas of either totally unsurveyed territory or areas where only limited data are available and clustered groupings of ice free ground (zero ice thickness).
A number of procedures are currently being evaluated to cope with the particular requirements of the ice thickness data distribution. These include inverse distance weighted averaging with quadrant and octant searching, kriging with linear and quadratic drift and triangulated irregular networks (TINs). The final choice of interpolation method will be based on several considerations:
A typical variogram generated from the ice thickness data is presented in Figure 8. It indicates a correlation length of around 65km and a nugget effect of 200m. This correlation length provides us with a useful idea of what search distance is appropriate for the spatial interpolation procedure. The number of data points within an average correlation length (as calculated through several variograms) across the continent is presented in Figure 9. The areas with fewer data points within this search radius indicate the 'data holes' and would be characterised by a high estimation variance.
In addition several ice sheet modellers in discussion with BEDMAP have expressed an interest in the computation of several additional grids describing parameters such as the estimation variance for each grid node, the number of points used in the estimation and their standard deviation. It is noted that the determination of the estimation variance is straightforward using the geostatistical interpolation algorithms (kriging) however it is not easily determined using the deterministic procedures.
Figure 8. Sample variogram for ice thickness data (75W - 90W).
Figure 9. Data points within 80km (mean correlation length) of grid nodes on a 10km grid.
9.0. Specification of final BEDMAP products
The working group reached agreement on the following specifications for the final BEDMAP data sets (Table IV).
| Domain: | continent and the continental shelf (also sub ice shelf) |
| Reference frame: | WGS84 ellipsoid |
| Projection: | conformal with minimum area distortion |
| Smoothness: | no strong slope breaks, no high nunataks |
|
Consistency: |
ice thickness = surface elevation - bed elevation inclusion of watercolumn beneath marine ice shelves grounding line position from flotation condition |
| Masks: | grounding line, - coastline (shelf edge) |
| Format: | data array - transformation parameters (i,j) to (?, ?) |
Discussion at the workshop and an identification of all available resources has led to a revised schedule for the BEDMAP project. The updated work programme is provided in Table V. Under this schedule the final deadline for submission of data to BEDMAP is September 30, 1999.
|
Task |
Period |
|
|
6/98 - 09/99 |
|
|
01/99 - 10/99 |
|
|
01/99 - 09/99 |
|
|
06/99 - 09/99 |
|
|
04/99 - 10/99 |
|
|
10/99 - 11/99 |
|
|
10/99 - 11/99 |
|
|
10/99 - 12/99 |
|
|
10/99 - 12/99 |
|
|
12/99 - 12/99 |
|
|
01/00 - 02/00 |
|
|
02/00 - 03/00 |
|
|
02/00 - 03/00 |
|
|
04/00 - ?? |
|
|
04/00 - ?? |
11.0. Protocol concerning the use of data from BEDMAP
The following protocols were generally agreed by the participants as providing adequate safeguards for data-collectors, whilst allowing other BEDMAP partners and, eventually, the wider community the opportunity to use BEDMAP data for research purposes. These guidelines are broadly similar to those adopted by the ADMAP consortium for magnetic anomaly data (ADMAP, 1995).
The BEDMAP relational database is now in place and almost 2 million data records have been archived. We estimate this represents approximately 90% of all ice thickness data. Initial data evaluation work suggests that extensive data refixing is not required for the continental scale BEDMAP products. In general, the data distribution appears adequate for the generation of a 5 km ice thickness digital terrain model while variogram estimation indicate correlation lengths are greater than 60 km. It was recognised that to generate the required model an interpolation procedure needed to be developed that could cope adequately with a patchy data coverage, anisotropic data distribution, sparse data areas and several large 'data holes'. There was general consensus at the workshop that an existing DEM should be used for the determination of the final bed elevation grid. The workshop also decided that BEDMAP should extend the model to the continental shelf and that, if time and resources permitted, a new seabed elevation model should be prepared otherwise an existing model should be used.
We would like to thank SCAR for providing financial support for the second BEDMAP workshop. We would also like to thank the British Antarctic Survey for making their conference room available for the meeting. Liz Edwards helped to organise this workshop.
Appendix 1: Summary of results from BEDMAP Workshop 1
Appendix 3: Summary of ice thickness data sets compiled in BEDMAP database
Appendix 4: Sources of bathymetry data
(http://zenith.ngdc.noaa.gov/mgg/geodas/geodas.html)
A number of national databases containing additional bathymetric data from soundings have been identified. These include the Japanese National Oil Company, Antarctic Support Associates (ASA), South African Hydrographic Office, South African Data Centre for Oceanography, Russian Navy. Contacts have been established with data managers in each of these data centres regarding the procurement of data for the purposes of BEDMAP.
Collaborative work between Alfred Wegener Institute for Polar and Marine Research and the British Antarctic Survey. Bathymetric data derived from the AWI research vessel Polarstern (the only ice breaker equipped with a multi-beam sonar) and other vessels operating in the region. These data form the basis for 6 sheets in the Weddell Sea at 1:1,000,000. Data from gravity anomalies used to in fill where data are not available and digital data gridded with bicubic splines.
Published by W.H.F. Smith and D.T. Sandwell the Predicted Seafloor Topography is an inferred data set and is not true bathymetry. The gravity field as measured by satellite altimetry mimics the seafloor topography in the 15-160 km wavelength band if sediment cover on the ocean floor is thin. Long-wavelength (greater than 160 km) topography is isostatically compensated and is not correlated with the gravity field. The satellite gravity field and the available depth measurements were used to determine the correlation between gravity and the seafloor topography. By applying this correlation to the gravity field seafloor topography is predicted in the 15-160 km wavelength band. This topography is combined with a long-wavelength component estimated directly from ship depth measurements. The coverage for this data base is 30 degrees S - 70 degrees S. The grid spacing is 3 minutes of longitude by 1.5 minutes of latitude (7200 x 1600).
First published in 1905 GEBCO is the authoritative Bathymetric Chart of the World. In 1994 the GEBCO Digital Atlas (GDA) on CD-ROM was published. Digitizing the bathymetric contours, coastline and shiptracks from the printed sheets of the 5th Edition produced the first release of the GDA. It represented the first seamless, high quality, digital bathymetric contour chart of the world's oceans. A second release was published in 1997. The next planned development for the GEBCO will be the preparation of a gridded data set covering the world's oceans. Some errors in the Antarctic coastal region of GDA 1997 have been evaluated and removed by BAS however this data set still contains many spurious features. Lowest continuous contour is 500m.
Appendix 6: Participants at the second BEDMAP workshop
| PARTICIPANT | AFFILIATION | COUNTRY | |
| Robin Bassford | University of Bristol | UK | Robin.Bassford@bristol.ac.uk |
| Paul Cooper | MAGIC, British Antarctic Survey | UK | paul.cooper@bas.ac.uk |
| Hugh Corr | Geosciences, British Antarctic Survey | UK | h.corr@bas.ac.uk |
| Irina Filina | Polar Marine Geological Research Expedition | RU | irina@petrovich.stud.pu.ru |
| Shuji Fujita | Department of Applied Physics, Hokkaido University | JP | sfujita@nd-ap.eng.hokudai.ac.jp |
| Ned Grace | University of Delaware | US | ngrace@udel.edu |
| Richard Hindmarsh | Physical Sciences, British Antarctic Survey | UK | r.hindmarsh@bas.ac.uk |
| Adrian Jenkins | Physical Sciences, British Antarctic Survey | UK | a.jenkins@bas.ac.uk |
| Glenn Johnstone | SCAR Working Group on Geodesy and Geographic Information | AU | GlennJohnstone@auslig.gov.au |
| Matt Lythe | AEDC, British Antarctic Survey | UK | matt.lythe@bas.ac.uk |
| Yuri Macheret | Moscow University | RU | Macheret@gol.ru |
| Dave Morse | University of Texas at Austin | US | morse@ig.utexas.edu |
| Uwe Nixdorf | Alfred-Wegener-Institut fuer Polar-und Meeresforschung | DE | unixdorf@awi-bremerhaven.de |
| Sergey Popov | Polar Marine Geological Research Expedition | RU | RES@Polarex.Spb.Ru |
| Gordon Robin | Scott Polar Research Institute | UK | |
| Ted Scambos | National Snow and Ice Data Center | US | teds@icehouse.colorado.edu |
| Ignazio Tabacco | University of Milano | IT | tabacco@imiucca.csi.unimi.it |
| Laurent Testut | Toulouse, Cedex, France | UK | testut@pontos.cst.cnes.fr |
| Janet Thomson | MAGIC, British Antarctic Survey | UK | jwth@pcmail.nerc-bas.ac.uk |
| Mark Thorley | AEDC, British Antarctic Survey | UK | m.thorley@bas.ac.uk |
| David Vaughan | Physical Sciences, British Antarctic Survey | UK | d.vaughan@bas.ac.uk |
Appendix 7: List of abbreviations and acronyms
ADD Antarctic Digital Database
ADMAP Antarctic Digital Magnetic Anomaly Map
AUSLIG Australian Survey and Land Information Group
BAS British Antarctic Survey
CRC Cooperative Research Centre
EISMINT European Ice Sheet Modelling Initiative
GEBCO General Bathymetric Chart of the Oceans
GEODAS GEOphysical DAta System
GIS Geographic Information System
GPS Global Positioning Systems
GLOCHANT Global Change in Antarctica
ICAIR International Centre for Antarctic Information and Research
IHO International Hydrographic Office
NERC Natural Environment Research Council
NGDC National Geophysical Data Center
NSF National Science Foundation
RDBMS Relational Database Management System
SCAR Scientific Committee on Antarctic Research
SPRI Scott Polar Research Institute
TUD Technical University of Denmark
Appendix 8: References
ADMAP, 1995. Report of the SCAR/IAGA Working Group on the Antarctic Digital Magnetic Anomaly Map. Cambridge, 18-19 September, 1995.
BEDMAP, 1996. Report of the 1st BEDMAP Workshop on Antarctic Bed Mapping. Cambridge, 21-22 October 1996.
BAS, SPRI and WCMC, 1993. Antarctic Digital Database (CD-ROM). Cambridge, Scientific Committee on Antarctic Research.
Drewry, D.J. and S.R. Jordan, 1983. Sheet 3: The bedrock surface of Antarctica. Glaciological and Geophysical Folio Series. Scott Polar Research Institute, Cambridge.
Liu, H., Jezek, K.C., Li, B. Development of an Antarctic Digital Elevation Model by integrating cartographic and remotely sensed data: a GIS-based approach. Journal of Geophysical Research (in press).
Rapp, R.H., Wang, Y.M., Pavlis, N.K. The Ohio State 1991 geopotential and sea surface topography Harmonic Coefficient Models. Rep 410, Dept. of geodetic Science and Surveying, The Ohio State University, 1991.
