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| title | Satellite Data Assimilation (EUMETSAT/ECMWF)) |
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| | Multiexcerpt |
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| MultiExcerptName | SATTT2018 |
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| Time | Monday | Tuesday | Wednesday | Thursday | Friday |
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9:30 -10:45 | Meet the course participants | The infrared spectrum- measurement, modelling and
information content
Tony McNally
GPS Radio Occulation: Extended applications
Sean Healy
Satellites for environmental
monitoring and forecasting
Antje Inness
Satellite information on the ocean surface (SCAT)
Giovanna De Chiara
11:15...12:30 | Theoretical background (1)
What do satellites measure ?
Tony McNally
GPS Radio Occulation: Principles and NWP use
Sean Healy
The detection and assimilation of clouds in infrared radiances
Tony McNally
Background errors for satellite data assimilation
Tony McNally
Bias correction methods for satellite data
Niels Bormann
14:00...15:15 | Theoretical background (2)
Data assimilation algorithms, Key elements and inputs
Tony McNally
Satellite information on the land surface
Patricia de Rosnay
The detection and assimilation of clouds and rain in microwave radiances
Alan Geer
Observation errors for satellite
data assimilation
Niels Bormann
| Current satellite observing network and its future evolution
Stephen English
15:45...17:00 | The microwave spectrum,
measurement, modelling and
information content
Alan Geer
A Practical guide to IR and MW radiative transfer – using the RTTOV model and GUI
David Rundle (UK Met Office)
Wind information from satellites
(Atmospheric Motion Vectors)
Katie Lean
1DVar theory, simulator + practical
session on background and observation errors
Tony McNally
Question and answer session,
course evaluation | | Expand |
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| title | Using stochastic physics to represent model error |
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After this lecture, students will be able to: explain the physical and practical motivations for using stochastic physics in an ensemble forecast; describe the two stochastic parameterization schemes used in the IFS ensemble, and their respective purposes; be able to identify the improvement in forecasting skill from the inclusion of stochastic physics.
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