The European Space Agency (ESA) organises a series of summer schools on Monitoring of the Earth System, which promote the exploitation of Earth observation (EO) data across disciplines, with a specific focus on their assimilation into Earth System models.

The two-week course, held in ESA-ESRIN (near Rome, Italy) during August (typically every two years), aims to provide students with an integrated end-to-end perspective going from measurement techniques to end-user applications. Courses include lectures covering issues related to Remote Sensing, Earth System Modelling and Data Assimilation as well as hands-on computing exercises on the processing of EO data. Keynote lectures on global change issues are also given to discuss the current state of the science of global change and its relationship to society in order to help students appreciate how their specific field fits into a broader scientific and political context. For more information, please see the programme.

The event is open to Early Career Scientists (i.e. Ph.D. students, young post-doctoral scientists) who are specialised in a variety of Earth Science disciplines and wish to expand and improve their knowledge and skills.

The third EO Summer School took place between 31 July - 11 August 2006. 

Programme

Michel Verstraete (JRC-IES)

• Fundamentals of Remote Sensing and Direct Modelling
• Information Retrieval By Explicit Inversion
• Information Retrieval By Implicit Inversion

Pierre Brasseur (LEGI)

• Data Assimilation Methods Based on the Kalman Filter: theoretical aspects
• Data Assimilation Methods Based on the Kalman Filter: oceanographic applications
• Data Assimilation Methods Based on the Kalman Filter: operational implementations

Olivier Talagrand (LMD)

• Data Assimilation. Basic Principles
• The variational approach to data assimilation
• Advanced variational assimilation

Bob Scholes (CSIR)

• Modelling emissions from vegetation wildfires
• Desertification monitoring
• Observation systems for biodiversity

David Barber (University of Manitoba)

• Principles of remote sensing in polar regions
• Geophysical and thermodynamic properties of snow covered sea ice
• Integration - microwave remote sensing & numerical process models of ocean-sea ice-atmosphere

Jean-Noël Thépaut (ECMWF)

• Use of Satellite Observations in Operational Numerical Weather Prediction (NWP)
• Future Opportunities and Challenges in Satellite Data Assimilation for NWP
• Reanalysis

Richard Rood (University of Michigan)

• Physical Consistency of Climate Modeling and Data Assimilation
• Assimilation of Atmospheric Trace Constituents
• Use of model information in climate data sets

Bob Su (ITC)

• Surface Energy Balance System and Evaporation/transpiration
• Microwave Remote Sensing of Soil Moisture
• Earth Observation of Water Cycle and Applications in Drought Monitoring and Prediction

Keith Haines (NERC-ESSC)

• Inverse Modelling in Oceanography for Water Mass Tracking
• Assimilation of T&S and Gravity Data into Ocean Models
• New Technology for Data Distribution & Integration

Filippo Giorgi (ICTP)

• Climate Modeling: from the Global to the Regional Scale
• Modeling the Effects of Modeling Atmospheric Aerosols on Climate
• Climate Change Prediction

Hartmut Grassl (Max-Planck-Institut)

• Earth Explorers and Scientific Challenges

Andy Shepherd (University of Edinburgh)

• Satellite Radar: 21st century glaciology
• Abrupt climate change: why did Larsen Ice Shelf collapse?
• Pine Island Glacier: did we solve the problem?

Pierre-Philippe Mathieu (ESA)

• Introduction

Practicals

Remote Sensing Tutorial (Bilko)

Val Byfield, Doug McNeall and David Poulter (NOC)

• Synergistic use of altimetry and sea surface temperature (SST) measurements (AATSR) to study ocean eddies
• Productivity of coastal upwelling systems from MERIS, AATSR and other sensors
• Medspiration: Precise Mediterranean sea surface temperatures from multiple satellite sources
• Cloud clearing in surface temperature images

Data Assimilation Tutorial

Stefano Migliorini and Amos Lawless (DARC)

• Effect of Statistical Analysis Parameters
• Data Assimilation Practicals