The US Proposal for ADM Calibration
and Validation
Mike Hardesty, Dave Bowdle, Jason Dunion,
Ed Eloranta, Dave Emmitt, Brian Etherton,
Rich Ferrare, Iliana Genkova, Bruce Gentry,
Gary Gimmestad, Russ Hoffman, Chris
Hostetler, John Hair, Michael Kavaya, Matt
McGill, Lars Peter Riishojgaard, Chris
Velden
The Aeolus Cal/Val AO
• Aimed at reducing the uncertainties in the ADMAeolus measurements by thoroughly assessing all
aspects of instrument performance and stability,
accuracy, and suitability of the data processing, and
comparison with independently acquired
measurements
• Not for science – a second AO will be issued closer to
launch for, e.g., cloud and aerosol retrieval, regional
studies, extreme weather events monitoring, etc.
• No funding – Investigators must bring their own
funding
• ESA will not release the data without evidence of
funding
AO: Areas solicited for contribution
• Validation using other satellite, airborne, or groundbased experiments providing independent
measurements of wind profiles, clouds, and aerosols
• Experiments to assess accuracy, resolution, and
stability of the ADM-Aeolus instrument ALADIN
• Assessment and validation of Aeolus retrieval and
data processing
US Cal-Val Effort: Investigators
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Mike Hardesty, NOAA/ESRL
Dave Bowdle, University of
Alabama Huntsville
Jason Dunion, NOAA/AOML
Ed Eloranta, University of
Wisconsin
Dave Emmitt, Simpson Weather
Associates
Brian Etherton, University of
North Carolina Charlotte
Rich Ferrare NASA Langley
Iliana Genkova, University of
Wisconsin
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Bruce Gentry, NASA Goddard
Gary Gimmestad Georgia Tech
Research Instrument
Ross Hoffman, AER, Inc.
Chris Hostetler NASA Langley
John Hair, NASA Langley
Michael Kavaya, NASA Langley
Matt McGill, NASA Goddard
Lars Peter Riishojgaard JCSDA
Chris Velden, University of
Wisconsin
Zhaoxia Pu, University of Utah
Goals of the US Aeolus Cal/Val Effort
• Obtain and analyze aircraft measurements of wind speed,
aerosol structure, aerosol backscatter, aerosol extinction, cloud
climatologies and relevant parameters under the Aeolus flight
track using remote sensors and dropsondes,
• Develop a data set extending over the life of the mission from
surface remote sensors and in situ sensors (radiosondes,
dropsondes, aircraft winds) by gathering and analyzing
measurements when Aeolus measurement volume coincides with
sensor observational locations,
• Investigate correlations, differences and synergisms between
Aeolus and Atmospheric Motion Vector winds derived from cloud
and water vapor motion
• Investigate Aeolus data quality based on data assimilation
studies
Airborne Wind Studies
• Lower troposphere studies (Hardesty and Emmitt)
– Apply low energy, high prf systems (HRDL and TODWL to
investigate Aeolus performance in high aerosol regions
– Study effects of mesoscale atmospheric inhomogeneities on
Aeolus measurements
• Structured aerosol field
• Broken cloud fields
• Wind shear and horizontal circulations, vertical motions
• Full tropospheric studies (Gentry and Kavaya)
– Apply TwiLite instrument for comparisons with Aeolus of
direct detection winds as opportunities present (focus on
severe storms)
– Apply DAWN lidar for free tropospheric studies to
investigate effects of clouds, wind gradients, etc., as
opportunities present
– Potentially compare Aeolus with notional hybrid of DAWN
and TwilLite can fly together.
Airborne Aerosol Comparisons
• Apply LaRC Airborne High Spectral Resolution Lidar to validate
Aerolus aerosol/cloud extinction and backscatter data products
(Hostetler, Hair, Ferrare)
– Conduct flights along the Aeolus sampling curtain under different
atmospheric conditions and measurement scenarios
– Compare estimates of backscatter and extinction directly computed
by HSRL with Aerolus measurements
Comparisons with
photometer
Measurements from Milagro campaign
Airborne Aerosol Comparisons
• Apply NASA Cloud Physics lidar for Aeolus studies
(McGill)
– Operate NASA CPL from a high altitude aircraft
– Provide observations of cloud and aerosol layers at 1064,
532, and 355 nm
– For elevated layers direct determination of optical depth is
provided without assumptions on lidar ration
Figure 1: NASA ER-2 high-altitude research aircraft (left). The Cloud Physics Lidar
(CPL) instrument (right). When used on the ER-2, CPL flies in a forward superpod, as
indicated. CPL can also be used on the WB-57 or other suitable aircraft.
Surface Wind Comparisons
• Use surface or in situ instruments (lidars, wind
profilers, radiosondes) for long term comparison over
the life the mission (Hardesty, Bowdle, Kavaya)
• Measurements taken when Aeolus measurement
volume coincides with instrument location
• Perform comparisons when Aeolus measurements are
within the domain of a mesoscale atmospheric model
(Bowdle)
– Use local observations to validate the model, then use the
model to validate the instrument
– Comparison of model, surface instruments, and Aeolus will
address validity of applying models for instrument validation
Surface Aerosol Comparisons
• Develop a data set for comparison of cloud and aerosol
backscatter and extinction from a visible HSRL lidar
operating in far northern latitudes, investigate wavelength
differences in HSRL measurements (Eloranta)
• Apply a 355 nm backscatter lidar and a sun photometer to
retrieve aerosol backscatter to extinction ratios and
optical depths (Gimmestad). Apply a forward model to
compare Aeolus and locally measured raw data
characteristics
Dropsonde and satellite comparisons
•Investigate Aeolus performance in
the Saharan Aerosol Layer and in
the vicinity of tropical cyclones
through comparisons with
dropsondes (Dunion and Etherton).
Investigate capability of Aeolus to
represent winds in the clean tropical
environment
• Compare Aeolus global-coverage line of sight winds with
current state of the art feature tracked atmospheric
motion vectors (Genkova and Velden).
– Investigate complementarities of the two data sets by
comparing ADM winds with the global AMV data
– Investigate how ADM wind profiles can be used to assess
uncertainty in AMV’s, based on assumption that cloud and
water vapor features are ideal tracers
Data Assimilation
• Joint Center will study ADM observations in the
context of two data assimilation systems: GFS and
GEOS-5 (Riischojgaard)
– Monitor innovation statistics for level 1 and 2 products
– Make available two different level-2 ADM wind data
products
– Implement KNMI-developed level-2 processor to create its
own alternative level product
• Perform data impact experiments with ADM Level 2
LOS observations
• Three phases: Preparation, data acquisition, extended
analysis
Next steps
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Proposals being reviewed now
Notification sometime in spring
First meeting of cal/val team likely in mid summer
If proposal is accepted, US team will have to develop
funding strategy to support the effort