By Gillian Law
25 January 2006
LINKED Environments for Atmospheric Discovery (LEAD), a project funded by the US National Science Foundation, is using OGSA-DAI in a far-reaching effort to improve the forecasting of medium-scale weather phenomena such as tornados and severe storms.
"It's a large project, involving nine institutions and over 100 students and faculty," says Beth Plale, a Professor at Indiana University who is developing myLEAD, an OGSA-DAI-based metadata catalogue that forms the backbone of the data subsystem LEAD.
The goal of the project, Plale says is to "change the paradigm of meteorology research and education," by making it easier to investigate large scale datasets in order to carry out weather forecasting. We have adopted a service-oriented architecture (SOA) for the infrastructure and this has given us the flexibility to plug in intelligent data management, workflow, and run time data stream filtering into a single software framework to bring about dynamic and real time responsiveness to weather prediction." Plale says.
At the moment, weather forecasting is done on a very static basis, with measurements taken at set times. The LEAD project aims to improve that by putting algorithms in place that will examine the data coming from a group of measuring sites. If a set threshold - for wind, rain or heat, say - is breached, then a fresh forecast will be produced.
"This requires data mining of the streaming data, the observational data, and it also requires grabbing considerable compute resources, on demand, when the conditions require it," Plale says.
By reacting to real-time information, weather forecasting could save hundreds of lives a year across the United States, where floods, tornados, high winds and other weather 'mesoscale weather events' regularly cause havoc.
The technology is, however, likely to be used first in schools and other educational establishments.
"We have the National Weather Service here in the US but they're understandably conservative in adopting new technology. And this is a research project, really pushing the edge. I can see it being deployed, at least in parts, further down the road [by the weather service] but they're unlikely to be our first users," she says.
"The more immediate users are going to be classrooms. We have a very strong educational component, and we will make this available to high school and undergraduate classes, and also to mesoscale meteorology researchers that are working on these models and making advancements," Plale says.
One of Plale's parts of the project, MyLEAD, aims to provide a 'personal workspace' for users faced with enormous amounts of information.
"The atmosphere community has a lot of community data products, open to anyone in the community. What we want to do is make it easy to access those parts of that data that are relevant to the individual and their experiments. So MyLEAD is essentially a personal workspace, conceptually a collection of data products - some from the community data products and some that the user generates themselves, in the course of their investigation. And the user can then share that personal data with the wider community, if they choose," she says.
The personal workspace is implemented as a metadata catalogue and storage repository, with OGSA-DAI implementing the metadata catalogue and providing a rich search space for the user.
"We chose OGSA-DAI because it was one of the leaders in the field at the time we started, and because, being open source ourselves, the open source point of view was important to us," Plale says.
There were a few hitches to begin with. "The early versions where the data service had to be created on a per-request basis introduces a tremendous amount of overhead, and we did have problems, in some of the early versions, with scalability," she says.
"However, we have just finished some scalability studies and we seem to be seeing better performance on the scalability parts of the project," Plale says.
The LEAD project is now entering its third year and will have a major release of functionality this Spring. "This project is quite a bit of fun." Plale says. "We have leveraged some of the great ideas and tools emerging from the e-Science effort in the UK, and this has enabled us to develop strong working relationships with our peers around the world."
The three-year LEAD project, launched in 2003, is funded by the US National Science Foundation.
The aim of the OGSA-DAI project is to develop middleware to assist with access and integration of data from separate sources via the grid. The project was conceived by the UK Database Task Force and is working closely with the Global Grid Forum DAIS-WG, the OMII and the Globus team.