[CRYOLIST] Call for abstracts: EGU2020 CL5.25 Satellite simulators for evaluating and training geophysical models

Clara Burgard clara.burgard at gmail.com
Tue Oct 29 09:56:56 PDT 2019


Dear colleagues,

Are you working on satellite simulators, observation operators and/or forward models translating a simulated state into an observable state? Then our EGU session is for you!

We invite scientists from all geosciences, not only climate scientists, to submit an abstract to session CL5.25 on
"Satellite simulators for evaluating and training geophysical models" at EGU 2020: https://meetingorganizer.copernicus.org/EGU2020/session/37378 <https://meetingorganizer.copernicus.org/EGU2020/session/37378>

The abstract submission is now open until the 15th of January 2020, 13:00 CET.

Find the session details below:

Session number: CL5.25
Session title: Satellite simulators for evaluating and training geophysical models
Convener: Clara Burgard
Co-conveners: Alejandro Bodas-Salcedo <https://meetingorganizer.copernicus.org/EGU2020/session/37378#>, Andrew Roberts <https://meetingorganizer.copernicus.org/EGU2020/session/37378#>, Abigail Smith

Description:
Geophysical variables are seldom measured directly by satellites. Rather, satellites measure properties such as radiances, backscatter, or reflectance, which are quantities not often simulated by, for example, Earth System Models. Therefore, the geophysical variables of interest are typically derived from satellite measurements through retrieval algorithms. The retrieved variables are then used as observations, representing the “real” state against which models may be evaluated, assimilated, and trained using neural networks in data fusion exercises. However, in many cases, such retrieval algorithms require assumptions to derive the geophysical variable, and the measurement error associated with these assumptions is difficult to quantify. This leads to uncertainty in the application using the set of retrieved “observations”. One possible approach to circumvent the use of retrieval algorithms is to apply satellite simulators or observation operators, which translate the simulated system state in an observable quantity, directly comparable to satellite measurements.
In this session, we invite contributions from studies developing or applying satellite simulators or observation operators for geophysical model evaluation, assimilation and machine learning, including atmospheric, oceanic, sea ice, terrestrial and ice sheet models, and fully coupled earth system models that include them. As this approach can be applied across a range of research fields, we also welcome submissions from a broader class of geophysical models, including of planets and moons, to discuss and exchange common challenges and opportunities that might be solved in one field of research but still remain unsolved in another.

Looking forward to an interesting session!

Best regards,

Clara, Alejandro, Andrew, and Abby

--
Dr. Clara Burgard
Postdoctoral researcher
Department "Ocean in the Earth System"
Max Planck Institute for Meteorology, Hamburg, Germany
Mail : clara.burgard at mpimet.mpg.de <mailto:clara.burgard at mpimet.mpg.de>
Twitter : @climate_clara, @MPI_Meteo
Room : Z 240
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