ESA Intended Invitation To Tender

18.155.20


Title: 4DANTARCTICA
AO Number: 1-9570
Program ref.: EO-Science for Socie
Tender Type: C
Quarter: 184
Tender Status: EVALUATION
Price Range: > 500 KEURO
Budget Ref.: E/E105-E5 - EO-Science for Socie
Proc. Prop.: NO
Special Prov.: AT+BE+CZ+DK+EE+FI+FR+DE+GR+IE+IT+LU+NL+NO+PL+PT+RO+ES+SE+CH+GB+CA+SI
Establishment: ESRIN
Directorate: Directorate of EO Programmes
Department: Science, Applications & Climate Dep.
Division: Data Applications Division
Responsible: Desnos, Yves Louis
Products: Satellites & Probes / Other
Techology Domains: Others
Industrial Policy Measure: N/A - Not apply
Publication Date: 17-SEP-18

This project aims at capitalizing on the latest developments in Antarctic science and on the different activities that ESA has funded in the last few years over Antarctica. These projects cover a wide range of topics from lithospheric modeling to surface dynamicsproviding a wide spectrum of new results and novel capabilities that open the door to a new multi-disciplinary approach to Antarctic research from space. 4DAntarctica aims at exploiting these new capabilities and advance towards a better understanding of Antarctica, its processes and the interactions between its different components from the lithosphere to the surface. The objectives of theproject include: Generate a Product Portfolio of geo-information datasets over the whole Antarctica charactering the different components of the ice-sheet lithosphere interactions system, with special attention to Antarctic hydrology. Perform a scientific analysis at continental scale of the interactions among the different components of the lithosphere, ice-sheet and hydrological system enhancing our current understanding of the main processes taken place in Antarctica and its impacts in the future evolution of theAntarctic ice sheets. Advance the connection between EO data products and Ice Sheets models establishing close links with the ice Sheet modelling community and demonstrating the potential of EO data products to enhance model perforce and predictions.