1. 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

2. The Aeolus Cal/Val AO Aimed at reducing the uncertainties in the ADM- Aeolus 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

3. AO: Areas solicited for contribution Validation using other satellite, airborne, or ground- based 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

4. US Cal-Val Effort: Investigators 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 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

5. 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

6. 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.

7. 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

8. 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.

9. 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

10. 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

11. Dropsonde and satellite comparisons 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 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

12. 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

13. Next steps 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