The US Proposal for ADM Calibration and Validation

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  • The US Proposal for ADM Calibration and ValidationMike 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 AOAimed 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 measurementsNot 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 fundingESA will not release the data without evidence of funding

  • AO: Areas solicited for contributionValidation 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

  • US Cal-Val Effort: InvestigatorsMike Hardesty, NOAA/ESRL Dave Bowdle, University of Alabama HuntsvilleJason Dunion, NOAA/AOMLEd Eloranta, University of WisconsinDave Emmitt, Simpson Weather AssociatesBrian Etherton, University of North Carolina CharlotteRich Ferrare NASA LangleyIliana Genkova, University of WisconsinBruce Gentry, NASA GoddardGary Gimmestad Georgia Tech Research InstrumentRoss Hoffman, AER, Inc.Chris Hostetler NASA LangleyJohn Hair, NASA LangleyMichael Kavaya, NASA LangleyMatt McGill, NASA GoddardLars Peter Riishojgaard JCSDAChris Velden, University of WisconsinZhaoxia Pu, University of Utah

  • Goals of the US Aeolus Cal/Val EffortObtain 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 StudiesLower troposphere studies (Hardesty and Emmitt)Apply low energy, high prf systems (HRDL and TODWL to investigate Aeolus performance in high aerosol regionsStudy effects of mesoscale atmospheric inhomogeneities on Aeolus measurementsStructured aerosol fieldBroken cloud fieldsWind shear and horizontal circulations, vertical motionsFull 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 presentPotentially compare Aeolus with notional hybrid of DAWN and TwilLite can fly together.

  • Airborne Aerosol ComparisonsApply 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 scenariosCompare estimates of backscatter and extinction directly computed by HSRL with Aerolus measurementsComparisons with photometerMeasurements from Milagro campaign

  • Airborne Aerosol ComparisonsApply NASA Cloud Physics lidar for Aeolus studies (McGill)Operate NASA CPL from a high altitude aircraftProvide observations of cloud and aerosol layers at 1064, 532, and 355 nmFor elevated layers direct determination of optical depth is provided without assumptions on lidar ration

  • Surface Wind ComparisonsUse 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 locationPerform 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 instrumentComparison of model, surface instruments, and Aeolus will address validity of applying models for instrument validation

  • Surface Aerosol ComparisonsDevelop 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

    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 dataInvestigate how ADM wind profiles can be used to assess uncertainty in AMVs, based on assumption that cloud and water vapor features are ideal tracersInvestigate 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

  • 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 productsMake available two different level-2 ADM wind data productsImplement KNMI-developed level-2 processor to create its own alternative level productPerform data impact experiments with ADM Level 2 LOS observationsThree phases: Preparation, data acquisition, extended analysis

  • Next stepsProposals being reviewed nowNotification sometime in springFirst meeting of cal/val team likely in mid summerIf proposal is accepted, US team will have to develop funding strategy to support the effort