World-spanning Envisat Summer School concludes in Italy

How can we best use observations and models to quantify the state of the Earth System and better understand the coupled interactions between the ocean, atmosphere, cryosphere and biosphere? This complex but vital question was at the heart of the second ESA Summer School on Earth System Monitoring & Modelling.

This summer, 68 young scientists coming from 21 countries across the world (e.g. Europe, Canada, Australia, Argentine, Brazil, China, India) converged on ESA’s European Space Research Institute (ESRIN) in Frascati (Italy) for a two-week training course on Earth Observation, modelling and data assimilation.

The participants attended keynote lectures about state-of-the-art data assimilation techniques across a range of applications, given by 14 top European scientists. The lectures had a particular focus on utilising data from Envisat, which with its 10 instruments measuring the electromagnetic spectrum is the only truly ‘Earth System’ satellite able to simultaneously monitor the land, ocean, ice and atmosphere. The theory was also considerably reinforced by a set of computing practicals to give students hands-on experience of processing Earth Observation data and real application of assimilation techniques in simplified models.

“When it comes to modelling global processes of whatever type, Earth Observation is an effective control of the model behaviour,” commented lecturer Prof. Chris Schmullius of Friedrich-Schiller University in Jena, Germany. “For example, I am interested in land cover mapping, and using models to see how vegetation changes with climate. Only Earth Observation gives us a regularly updated map of land use changes, including factors such as fire, agriculture, logging and other human interactions that have to be taken account of in models.”

Cathy Trudinger had come from Melbourne, Australia to attend the Summer School. She is currently working on the mathematical modelling of greenhouse gases at Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO): “It’s been a very good overview of the various satellite instruments available, how they work and how the data can be used”. She will be carrying out biosphere modelling in the course of her research.

“ESA has a very good data set from its missions, but up until now I have not known enough about it to make use of it,” explained Manik Bali, originally from India, now working on his Ph.D. thesis at the Max Planck Institute in Germany. “I now plan to assimilate land cover data within a model aimed at improving monsoon prediction, letting affected countries know whether a good or a bad monsoon is coming and making the appropriate preparations.”

All those attending produced posters explaining the subject of their research. The Summer School participants voted among themselves for the best one, and the winner was Christian Beer of Friedrich-Schiller University, dealing with the assimilation of remote sensing data into land models.

“I’ve been working in the field of land studies for two years,” Beer said, “so it has been very educational to come here and meet with different scientists working in other areas and learn first-hand about how they’ve been using satellite data.”

Prof. Alan O’Neill of the Data Assimilation Research Centre at Reading University explained why he thought the summer school had been very successful. “We have kept the focus on Envisat data, but while the first school concentrated on assimilating data from its atmospheric instruments, this time we have covered the full range of its instruments, including land, ocean and ice data. We have also made other changes, including a greater linking of the lectures with practical tutorials.

“Participants came from a diversity of disciplines as well as countries, and half of them were women, which is a positive sign. The span of applications is wide, but everyone here is concerned with similar points: how to turn what are basically measurements of electromagnetic radiation into useful geophysical parameters, what to do about observational errors and how to correct for them in models.

“There’s a commonality of techniques and issues in Earth Observation that makes it a discipline in its own right, and I think the experience of the School shows this very well.”