8th International Symposium on Data Assimilation (ISDA2020)

When:
2020-09-14 – 2020-09-18 all-day
2020-09-14T00:00:00-06:00
2020-09-19T00:00:00-06:00
Where:
Canvas Stadium, Colorado State University
Fort Collins
Colorado
USA

Data Assimilation is the science of combining prior knowledge in the form of comprehensive numerical models of (components of) the Earth system with observations of the Earth. It has a strong mathematical background in Bayesian Inference and Inverse modelling, and is crucial for accurate prediction of e.g. weather, coastal oceans, and climate. Huge advances in both theory, models, observations and computational resources have revolutionized the field, with clear and direct societal benefits.

The 8th International Symposium on Data Assimilation (ISDA2020) continues a series of well-received and attended events: ISDA2019 in Kobe, ISDA2018 in Munich, ISDA2016 in Reading, ISDA2015 in Kobe, ISDA2014 in Munich and the first two symposia at DWD in Offenbach in 2012 and 2011, following the grant vision of Dr. Roland Potthast. This the first time it is organized in the USA, and we are expecting over 150 participants.

The main objective of the conference is to bring together data-assimilation experts from all over the world to discuss problems and progress in the field of data assimilation and the use of satellite observations for weather and climate. The expertise of the participants will range from fundamental mathematics and developing new data-assimilation methodologies being derived and developed at many academic and research institutions from around the world, through to scientists working at operational weather and climate prediction centers, such as NASA/GMAO, ECMWF, NOAA/NCEP/EMC, ONR/NRL, and Met Offices from all over the world.

Discussion topics will include Method Development and Mathematics of Data Assimilation, Satellite Data Assimilation, Error Covariance Estimation in Observations, State and Model, Coupled Data Assimilation Developments, Convective Scale Data Assimilation, Atmospheric Chemistry, and Aerosol Data Assimilation, Earth Systems Components and Further Applications of Data Assimilation, and Machine Learning for Data Assimilation.