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Ten new Category-1 projects formally invited on G-POD
2007-02-01
This pioneering opportunity, to be sustained in the long-term if successful, has the following objectives:
%u2022 Stimulate the use of global EO mission archives, offering attached computing infrastructure and tools with on-line access to data to assist the generation of %u201Cscientific added value products%u201D.
%u2022 Promote the development of earth science applications requiring high data and data-processing resources
%u2022 Foster new partnerships between ESA and partners in earth science research, algorithm development and validation, and operational deployments of valueadded applications.
%u2022 Promote and demonstrate e-collaborations in Earth Science
%u2022 Encourage the synergetic use of EO data, including synergy with other spaceborne and ground data, models and multidisciplinary applications

In effect, it is considered that while the scientists have the high knowledge and essential tools to develop new earth science algorithms and create new value-added products, they cannot necessarilly invest in the development of end-to-end production chains and associated large and complex data management systems to handle the high quantity of EO data required, and hence validate, publish and promote their products in a sustained and reliable way. The G-POD CAT-1 opportunity has therefore the objective of filling up this gap through the dedicated investment and maintenance by ESA of a common shared infrastructure with access to data and catalogues as well as attached processing power, able to host the processing tasks of the user's data processors. It is beleived also this tight collaboration at technical level between ESA and the scientific community may well evolve towards bringing together the scientists themselves on the common G-POD lab, in particular across EO domains, whereby everyone's algorithm hosted on the system can effortlessly be linked one to another into more complex processing chains, hence giving rise to higher level earth observation products fruit of multi-party and multi-disciplinary collaborations.
We would like thank all the scientists having demonstrated their interest in the G-POD call this year and warmly welcome the ones involved in the ten selected projects. We wish you all a very successful collaboration with us on G-POD. Here is an outline description of the running G-POD CAT-1 projects, with their affiliation and primary research areas:
  • A long time-series of global estimates of surface reflectance and aerosol optical depth from AATSR
    • Principal Investigators: Dr William Grey and Dr. Peter North
    • Institution: Climate and Land Surface Systems Interaction Centre (CLASSIC), UK
    • Application Domain: surface and atmospheric aerosol properties
    • Data: AATSR Level 1 products
    • Area: Global
    The project aim to implement a new method for retrieving bi-directional reflectance and AOD from the AATSR satellite data archive on the ESA G-POD environment. The result will be a 6 year time-series of global estimates of surface and atmospheric aerosol properties.
  • Juelich Rapid Spectral Simulation Code (JURASSIC)
    • Principal Investigators: Dr. Lars Hoffmann
    • Institution: Forschungszentrum Juelich, D
    • Application Domain: Atmospheric trace gas concentrations and aerosol extinction coefficients
    • Data: MIPAS Level 1B and Level 2 products
    • Area: Global
    The system will allow for mass retrievals of atmospheric trace gas concentrations and aerosol extinction coefficients from Envisat MIPAS observations. The project will help to exploit the wealth of information included in the spectrally high-resolved and well-characterized radiance measurements of the Envisat MIPAS instrument. Main focus of the project will be on the retrieval of long-lived trace species, currently not covered by the ESA operational Level-2 data processing
  • GLOBPHY
    • Principal Investigators: David Antoine
    • Institution: Laboratoire d'Oc%E9anographie de Villefranche, FR
    • Application Domain: Oceanography and Land environment
    • Data: MERIS Level 1 and Level 2 products
    • Area: Global
    GLOBPHY aims at analysing the decadal changes in the global ocean phytoplankton through the synergistic use of satellite data and 1-dimensional and 3-dimensional models, and to the understanding of the impact of environmental (climatic) changes on this major compartment of the planetary carbon cycle.
  • Geofit Multi Target Retrieval (GMTR)
    • Principal Investigators: Massimo Carlotti
    • Institution: Inuversit%E0 di Bologna, IT
    • Application Domain: Oceanography and Land environment
    • Data: MERIS Level 2 products
    • Area: Global
    The objective of the proposed study is a systematic analysis of all the MIPAS full-resolution mission using an upgraded version of the two-dimensional (2-D) retrieval system GMTR (Geofit Multi Target Retrieval). GMTR implements the "Geofit" approach for the simultaneous retrieval of several targets, hence avoiding the propagation of the errors due to the overlapping of spectral features of the analyzed targets
  • MERIS Inherent Optical Property Modelling Schemes (MIOPS)
    • Principal Investigators: Dr Tim Smyth
    • Institution: Plymouth Marine Laboratory, UK
    • Application Domain: Oceanography and optical properties from water color
    • Data: MERIS Level 2 products
    • Area: Global
    The objective of this project is to implement the Inherent Optical Property (IOP) models of Smyth et al. and Lee et al. on global MERIS data; and to carry out an intercomparison of the two models.
  • Joint Land and Ocean Environmental Products
    • Principal Investigators: Dr. Gobron Nadine and Dr. Frederic M%E9lin
    • Institution: European Commission-Joint Research Center, IT
    • Application Domain: Oceanography and Land environment
    • Data: MERIS Level 2 products
    • Area: Global
    The objectives of this project are the development and the analysis of common land-sea environmental products, such as the net primary productivity, with respect to different spatial and temporal resolutions using various optical instruments.
  • Semi-Analytical CloUd Retrieval Algorithm (SACURA)
    • Principal Investigators: Dr. Kokhanovsky Alexander
    • Institution: Institution Institute of Remote Sensing, University of Bremen, D
    • Application Domain: Atmospheric trace gas
    • Data: MERIS Level 1 and SCIAMACHY Level 1 products
    • Area: Global
    The project is aimed to the improvement of SCIAMACHY trace gas retrievals for cloudy scenes through the use of advanced cloud products obtained from the synergetic analysis of data from MERIS and SCIAMACHY. This will also contribute to better understanding of global cloud changes and trends as derived from ENVISAT data for last five years.
  • Enhanced and alternative GOMOS retrievals
    • Principal Investigators: Dr. Kyr%F6l%E4 Erkki
    • Institution: Institution Finnish Meteorological Institute, FI
    • Application Domain: Stratosphere
    • Data: MERIS Level 1 and SCIAMACHY Level 1 products
    • Area: Global
    This project aims at the development of a new GOMOS product related to stratospheric internal gravity waves (IGW) and turbulence using next generation retrieval methods.
  • Maximum Chlorophyll Index Products (MCI)
    • Principal Investigators: Dr. Gower Jim
    • Institution: Institute of Ocean Sciences / Fisheries and Oceans, CA
    • Application Domain: Oceanography
    • Data: MERIS Level 1 products
    • Area: Global
    The project aims at producing global daily composites of the MERIS MCI signal (Maximum Chlorophyll Index) to locate and map phytoplankton blooms and other types of aquatic vegetation.
  • Global Validation and Improvements of Neural Networks Procedures for the Analysis of Atmospheric Ozone from Satellite Data.
    • Principal Investigators: Dr. Del Frate Fabio
    • Institution: Tor Vergata University of Rome, IT
    • Application Domain: Atmosphere
    • Data: SCIAMACHY Level 1 and GOME products
    • Area: Global
    The objective of this project is to integrate in the G-POD environment our ready IDL chainable modules to perform a Neural Networks based inversion of GOME and SCIAMACHY Level-1 data. The final goal is a global and long term validation of the algorithms by the comparison of our retrievals with ozonesonde, ground-based and operational Level-2 data
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