28 Dec
2021
28 Dec
'21
9:04 p.m.
Annonce postée par : SAUX PICART Stéphane (stephane.sauxpicart(a)meteo.fr)
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Title: Mitigating low signal-to-noise ratio in geostationary satellite ocean colour
observation.
Location: Méteo-France, CNRM, UMR 3589, Lannion, France.
Duration: 4 to 6 months beginning in early 2022. Possibility to do a PhD afterwards.
Supervisor: Dr Stéphane Saux Picart (stephane.sauxpicart(a)meteo.fr)
Co-supervisor: Dr Ewa Kwiatlowska (EUMETSAT)
Summary of the project:
Observation of the Ocean Colour (OC) using remote sensing techniques is possible since the
late
70’s from polar orbiting satellites with revisit time of typically one or two days.
Meteorological
geostationary satellites offer the advantage of providing data at a very high temporal
frequency
(typically every 10 to 15 minutes) necessary for meteorological applications. High
frequency
observation is also key for observing rapidly varying surface parameters (e.g. coastal
dynamics) and
to partially overcome the impossibility to observe the surface under clouds in visible and
infrared
channels.
Currently only the Geostationary Ocean Colour Imager (GOCI), a Korean instrument, is a
geostationary satellite dedicated to observing the colour of water surfaces. Several
studies have
demonstrated the potential of the instrument SEVIRI (on-board the satellites of the
EUMETSAT
Meteosat Second Generation program) to observe water turbidity in highly turbid areas.
EUMETSAT is about to launch its new generation geostationary program (Meteosat Third
Generation): the first imager satellite will be launched in 2022. On-board this platform
is the visible
and infra-red radiometer Flexible Combined Imager (FCI) which has improved capabilities
with
respect to Ocean-Colour applications.
However, geostationary satellites like MTG are placed on a high orbit (altitude of about
36000 km)
and their instrument’s radiometry is not defined considering Ocean Colour specifications.
Therefore, the quality of the acquisitions is poorer than those of instruments on-board of
dedicated
low orbiting satellites. This represents a major limitation in the process of retrieving
Ocean Colour
parameters from such observations. Indeed, water constituents responsible for the colour
of the
ocean contribute to a small extent to the overall top-of-atmosphere reflectance in the
visible
domain. A low signal-to-noise ratio (S/N) therefore results in high uncertainties in the
geophysical
parameters retrieved.
The objective of this internship is to explore ways of mitigating this effect. For
instance, this could
be done by temporally and/or spatially averaging pixel values of top-of-atmosphere
reflectance, of
water-leaving radiance or of geophysical parameter.
Methodology:
• Review the literature on Ocean Colour parameter retrieval from geostationary
observations.
• Design one or more methodologies to mitigate the effect of the low S/N.
• Implement them on MSG or GOES or other proposed satellite data.
• Validate them by comparison to well established higher resolution Ocean Colour products
(for example CCI or CMEMS data).
Requirements:
• Be experienced in using Linux environment and python scientific modules.
• Have some knowledge on remote sensing data processing.
• Have some knowledge about Ocean Colour remote sensing.
• Be highly motivated!
To apply: please send a CV and a cover letter to Stéphane SAUX PICART
(stephane.sauxpicart(a)meteo.fr)
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