Data Fusion Grid Infrastructure

   January, 2007 - December, 2008

NWP WRF

wrf

Objectives

The aim of the project is to develop new methods of data fusion and to provide Grid-based solutions for image processing and geospatial modelling, targeting to improve applied agricultural problems solving.

The project foresees the development of efficient information technologies for integration of computational and data resources, geospatial data processing and indexing and combined use of heterogeneous data that will be based on GRID paradigm and enable the solution of complex problems arising in environmental monitoring using satellite data. Development of such system will allow to unite Ukrainian informational and computational resources and to integrate them to European and world programs of environmental monitoring. To achieve this goal several concrete objectives will be followed:

For solution of complex optimization problems arising in adaptation of environmental models and data assimilation genetic algorithms will be applied. Class of models for which genetic algorithms approach are appropriate ones will be identified. The improvement of yield prediction models will be done using outputs of NWP model which is adapted to particular region. Archive for geospatial data and visualization services will be developed using Grid technology.

Participants

Research programme and tasks

Intelligent methods for adaptation of complex models and data assimilation

Adaptation of environmental models and data assimilation could be represented as complex optimization problem in which functional is defined on hybrid discrete-continuous space with non-convex surface. This limits the use of classical optimization techniques. That is why in this project we propose to use intelligent methods, in particular genetic algorithms [Fogel David B. "Evolutionary computing: toward a new philosophy of machine learning", IEEE Press: NY, 1995, 272 p.], in order to find near-optimal solution for this non-classical optimization problem. The advantages of genetic algorithms lies in the little use of a priori knowledge of optimization surface. However, the methodology allows one to increase the quality of solution by including information given by experts. Expert's knowledge can be used to adjust statistical distribution for initial population, to specify mechanisms of mutation and crossover. Genetic algorithms already proved to be useful for agriculture model transfer [Jacucci G., Foy M., Uhrik C. "Developing transportable agricultural decision support systems: Part 2. An example", Computers and Electronics in Agriculture, 1996, Vol.14, pp. 301-315].

In proposed project genetic algorithms will be applied to solve optimization problems arising in adaptation of NWP models and assimilation process. Class of problems for solution of which genetic algorithms are appropriate ones will be identified.

As an application of proposed approach the problem of yield prediction will be considered. Yield prediction will be done on the basis of bio-productivity model MIDC, NDVI (retrieved from TERRA, AQUA METEOR-3M images) and meteorological parameters derived from adopted NWP models, namely WRF.

Tasks:

Infrastructure for adaptation of complex models and data assimilation

Grid technology will be used since it is-facto standard of integration of resources between different domains. Development will be carried on the basis of existing Grid infrastructure of members of the project [Shelestov Andrey Yu., Kussul Nataliya N., Skakun Serhiy V. "Grid Technologies in Monitoring Systems Based on Satellite Data", Journal of Automation and Information Sciences, 2006], [DOBRUCKY M.: FloodGrid demonstration in the CrossGrid project. In: Proc. of 1st workshop Grid Computing for Complex Problems - GCCP 2005]. Corresponding software will be implemented using open standards (OGC, INSPIRE, ISO) and open-sources software (Globus Toolkit, OGSA-DAI, UNM MapServer). To assess the performance of developed software simulation will be done by the mean of GridSim software [Sulistio A., Poduvaly G., Buyya R., and Chen-Khong Tham, "Constructing a Grid Simulation with Differentiated Network Service Using GridSim", Proc. of the 6th International Conference on Internet Computing (ICOMP'05)].

The separate task of harmonization is required in order to integrate existing resources and provide a basis for newly emerged Wide Area Grid (WAG) project. The WAG project will be considered as the first element of WAG system and as a contribution of WGISS working group to the GEOSS development.

Tasks:

Importance of the project in terms of economic and societal impact

Nowadays, a number of initiatives are being developed in order to integrate different resources (data, informational, and computational ones) and promote shared standards for complex applied problems solving. Among current international initiatives it is worth mentioning the following ones: GEOSS (Global Earth Observation System of Systems) and GMES (Global Monitoring for Environment and Security).

GEOSS will work with and build upon existing national, regional, and international systems to provide comprehensive, coordinated Earth observations from thousands of instruments worldwide, transforming the data they collect into vital information for society. The objectives of the proposed project aim at filling in part gaps in several areas declared in GEOSS 10-Year Implementation Plan Reference Document. In particular, in weather modelling and information technologies the project has focus on:

Wide Area Grid (WAG) project represent an effort aiming at integrating resources provided by space agencies (such as computers, data storage facilities, software, connectivity, etc.) in the framework of GEOSS.

GMES aims to gather together existing data and provide innovative, cost-effective, sustainable and user-friendly services, that enable decision-makers to better anticipate or mitigate crisis situations and issues relating to the management of the environment and security. GMES will achieve this through full use of data collected from space-borne, airborne and in-situ observation systems that is then delivered to service providers through an efficient data integration and information management capacity. The GMES is based on four inter-related components: services; observations from space; in-situ (including airborne) observations; data integration and information management. The objectives of the proposed project conform to the GMES principles for data integration and information management by developing and advancing Grid-enabled solutions for problems of high complexity. In particular:

"The areas of GMES that are data and computationally intensive require high-performance networks and GRID-based computing for the essential data mining, sharing and analysing and visualisation of the results." (from Communication from the Commission to the European Parliament and the Council, "Global Monitoring for Environment and Security (GMES): Establishing a GMES capacity by 2008 - Action Plan (2004-2008)", Brussels, COM (2004) 65).

Thus, the project lies in the framework of important global scientific and technological programmes (such as GEOSS, GMES, WAG) directed to the integration of efforts and resources for the sustainable development.

 
Space Research Institute::Department of Space Information Technologies and Systems