Santander Meteorology Group

A multidisciplinary approach for weather & climate

See the follow-on EELA-2 Project

The EELA (E-infrastructure shared between Europe and Latin America) project aims at establishing a bridge between the existing e-Infrastructures in Europe and those emerging in Latin America, through the creation of an interoperable Grid Infrastructure - based on the RedCLARA and GÉANT2 networks - for the development and deployment of advanced applications in Biomedicine, High Energy Physics, e-Learning and Climate. EELA is expected to help reducing the digital divide in Latin-America, making available to researchers a high performance e-Infrastructure for advanced investigations, later extendable to a larger community of users. The EELA project has three main objectives:

• Establish a collaboration network between European institutions where Grid expertise exists (e.g. EGEE Project), and Latin American institutions where Grid activities are emerging.
• Set up a pilot e-Infrastructure in LA, interoperable with the EGEE one in Europe allowing to run enhanced applications thus enabling dissemination of knowledge and experience on Grid technology.
• Set up a steady framework for e-Science collaboration between Europe and Latin America.

The EELA consortium involves 21 leading institutions around Europe and Latin America (the Spanish partners are CIEMAT (Coordinator), CSIC-IFCA, Red.ES, UC and UPV.

The EELA project is organized in four Work Packages (WP):

• WP1: Project Administration and Technical Management.
• WP2: Pilot Test-bed Operation and Support. It implements all necessary services to access the LA-EU e-Infrastructure, provides a framework for the end user and makes available resources to run applications.
• WP3: Identification and Support of Grid Enhanced Applications.
• WP4: Dissemination Activities. It imparts the Grid knowledgeadvertises EELA activities and provides training on Grid technology.

Contribution of the Santander Meteorology Group:
Our group contributed in the following workpackages:

WP2

Modern climate science deals with different sources of global climate simulations and geographically distributed observational data
(surface, atmosphere, ocean, etc.) stored in different platforms and formats. These sources of data can jointly help to solve many important problems, such as the effects of climate change on different regions of
interest. To this aim, efficient problem-driven statistical analysis tools are required for discovering knowledge, or useful information, within the huge amount of information. Data mining and machine learning techniques have been developed in the last decades to deal with this task, and different alternatives have been studied to make easier the process in a distributed environment such as the Grid.

Climate Application is composed of three sub-applications working together for forecast climate:

• CAM Model (Community Atmospheric Model): The Community Atmosphere Model (CAM) is the latest in a series of global atmosphere
  models developed at NCAR for the weather and climate research communities.
  
• WRF Model (Weather Research & Forecasting Model): Is a limited-area model designed to simulate or predict regional atmospheric circulation. This model can work with nested domains with different
  resolutions and require as input the boundary conditions from a global model (e.g., the CAM model).
• SOM (Self Organizing Maps) for climate data: Due to the high-dimensional character of the data involved in the climate simulations, it is necessary to first analyze and simplify the data in
  order to extract some useful knowledge. Some data mining techniques are appropriate for this context. Unsupervised clustering techniques allow partitioning the simulation databases, producing realistic weather or
  climate models of great variability governing the global dynamics. Self-Organizing Maps (SOM) are amongst the most popular clustering algorithms, which are especially suitable for high dimensional data visualization and modelling.

Other Applications: Volcano Sonification

Current knowledge of volcanic eruptions does not yet allow scientists to predict future eruptions. The EGEE and EELA Projects are trying to put the scientific community one step nearer to the prediction asset by means of the sonification of volcano seismograms. Thus, the translation of the patterns of Mount Etna (Italy) and Mount Tungurahua’s (Ecuador) volcanic behaviour into sound waves has been carried out within the context of these Projects.

Data sonification is currently used in several fields and for different purposes: science and engineering, education and training. It acts mainly as data analysis and interpretation tool.

Training and events

Several tutorials and workshops are frequently organised by the project. The former aims to ensures that all users fully understand the characteristics of the offered grid services and that they have enough
technical knowledge to properly use the EELA infrastructure. All training material produced and used in EELA training events has been published in an open repository at http://documents.eu-eela.org/

The main goal of the workshops is to present EELA project to the local authorities, decision makers and scientific community, besides assessing the interest of local institutions to collaborate with EELA.

Innovation

EELA proposes innovative technologies and strategies.
Technologically, the challenge is to efficiently share large amount of data across a wide network area through a global file system. This integrated platform shall enhance the computing capability in Europe
and even more in Latin America, thanks to the possibility of redistributing the global computational workload by migrating jobs across national borders, in a way that is totally transparent to end users.

From the strategic point of view, the EELA project will deploy a computing and storage infrastructure through a deep integration of existing national high-end platforms, tightly coupled to a dedicated
network by means of advanced Grid software. Strategies of coordinated operation have been identified and agreed. The result will be an integrated infrastructure whose capabilities shall be more efficient
than the sum of its constituent parts.

The benefits of the Grid enhanced applications running on the EELA infrastructure are twofold: besides their obvious scientific importance, several of these applications will have a noticeable social impact. New inhibitors for Malaria, Influenza and other neglected diseases (responsible for the daily death of thousands of people), access to Education for isolated people and powerful climate prediction
are some good examples.