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The Wind Lidar Mission ADM-Aeolus Data Processing

The Wind Lidar Mission ADM-Aeolus Data Processing. David Tan Research Department ECMWF Acknowledgements: ESA (Mission Science & Aeolus project team) Aeolus Mission Advisory Group Level-1B/2A/2B Development Teams. Contents. Summary on 2 Slides Background Data Processing

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The Wind Lidar Mission ADM-Aeolus Data Processing

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  1. The Wind Lidar Mission ADM-AeolusData Processing David Tan Research Department ECMWF Acknowledgements: ESA (Mission Science & Aeolus project team) Aeolus Mission Advisory Group Level-1B/2A/2B Development Teams

  2. Contents • Summary on 2 Slides • Background • Data Processing • Assimilation of Level-2B hlos wind • Simulations of Level-2B hlos wind data • Assimilation impact study • Level-2B processor development • How to make operational Level-2B hlos • Algorithms & rationale • Validation • Conclusions

  3. Summary of ECMWF activities for ADM-Aeolus • Prepared for assimilating L2B hlos wind • 2002-04, example for other centres • Developing Level-2B processor • ECMWF is lead institute, 5 sub-contractors • 2004-present • Other ongoing work/operational phase • MAG, GSOV, Cal/Val, In-orbit commissioning • ECMWF to generate operational L2B/L2C products, monitor & assimilate Aeolus data, assess impact on NWP • Maintain, develop & distribute L2B processor • On behalf of ESA, using NWP-SAF approach

  4. Status summary: Day-1 system on track • Level-2B hlos winds – primary product for assimilation • Account for more effects than L1B products • Will be generated in several environments • Motivated strategy to distribute source code • Main algorithm components developed & validated • Release 1.33 available – development/beta-testing • Documentation and Installation Tests • Portable – tested on several Linux platforms • Ongoing scientific and technical development • Sensitivity to inputs, QC/screening, weighting options • Contact points – ESA and/or ECMWF

  5. Contents • Summary on 2 Slides • Background • Data Processing • Conclusions

  6. Atmospheric Dynamics Mission ADM-Aeolus • ADM-Aeolus with single payload Atmospheric LAser Doppler INstrument • ALADIN • Observations of Line-of-Sight LOS wind profiles in troposphere to lower stratosphere up to 30 km with vertical resolution from 250 m - 2 km • horizontal averages over 50 km every 200 km (measurements downlinked at 1km scale) • Vertical sampling with 25 range gates can be varied up to 8 times during one orbit • High requirement on random error of HLOS <1 m/s (z=0-2 km, for Δz=0.5 km) <2 m/s (z=2-16 km, for Δz= 1 km), unknown bias <0.4 m/s and linearity error <0.7 % of actual wind speed; HLOS: projection on horizontal of LOS => LOS accuracy = 0.6*HLOS • Operating @ 355 nm with spectrometers for molecular Rayleigh and aerosol/cloud Mie backscatter • First wind lidar and first High Spectral Resolution Lidar HSRL in space to obtain aerosol/cloud optical properties (backscatter and extinction coefficients) [H]LOS

  7. ADM-Aeolus Coverage and Data Availability • 3200 wind profiles per day: about factor 3 more than radiosondes • 3 hour data availability after observation (NRT-Service) => 1 data-downlink per orbit;30 minutes data availability for parts of orbit (QRT-Service with late start of downlink) • launch date May 2010 (consolidated launch date prediction in some months expected) • mission lifetime 39 months: observations from 2010-2012 • ADM-Aeolus Science Report • (ESA publication SP-1311, 2008) • TELLUS 60A(2), Mar 2008 special issue on • ADM-Aeolus workshop 2006 50 km observations during 6 hour period

  8. ADM-Aeolus Ground Segment [ESA-ESRIN] [ESA-ESOC] [DLR]

  9. ADM-Aeolus Data Products

  10. Ongoing ADM-Aeolus Scientific Studies ESA plans an Announcement of Opportunity AO for ADM-Aeolus scientific use of data for late 2008 – distinct from the AO for Cal/Val

  11. Principle of spectrometer for molecular signal principle of spectrometer for aerosol signal Principle of wind measurement with ALADIN • Atmospheric LAser Doppler INstrument ALADIN • Direct-Detection Doppler Lidar at 355 nm with 2 spectrometers to analyse backscatter signal from molecules (Rayleigh) and aerosol/clouds (Mie) • Double edge technique for spectrally broad molecular return, e.g. NASA GLOW instrument (Gentry et al. 2000), but sequential implementation • Fizeau spectrometer for spectrally small aerosol/cloud return • Uses Accumulation CCD as detector => high quantum efficiency >0.8 and quasi-photon counting mode • ALADIN is a High-Spectral Resolution Lidar HSRL with 3 channels: 2 for molecular signal, 1 for aerosol/cloud signal => retrieval of profiles of aerosol/cloud optical properties possible Fig. U. Paffrath

  12. Contents • Summary on 2 Slides • Background • Data Processing • Assimilation of Level-2B hlos wind • Simulations of Level-2B hlos wind data • Assimilation impact study • Level-2B processor development • How to make operational Level-2B hlos • Algorithms & rationale • Validation • Conclusions

  13. ADM-Aeolus Ground Segment [ESA-ESRIN] [ESA-ESOC] L2B HLOS [DLR]

  14. L2B data simulated using ECMWF clouds … Yield (data meeting mission requirements in % terms) at 10 km • 90% of Rayleigh data have accuracy better than 2 m/s • In priority areas (filling data gaps in tropics & over oceans) • Complemented by good Mie data from cloud-tops/cirrus (5 to 10%) • Tan & Andersson QJRMS 2005 LIPAS-simulated HLOS data – operational processors later

  15. ADM-Aeolus p<0.001 NoSondes … & impact studied via assimilation ensembles 12-hr fc impact (Tan et al QJRMS 2007) Spread in zonal wind (U, m/s) Scaling factor ~ 2 for wind error Tropics, N. & S. Hem all similar Simulated DWL adds value at all altitudes and in longer-range forecasts (T+48,T+120) Differences significant (T-test) Supported by information content diagnostics Cheaper than OSSEs Pressure (hPa) Zonal wind (m/s)

  16. Assimilation of prototype ADM-Aeolus data2003/4: introduced L2B hlos as new observed quantity in 4d-Var Observation Processing Data Flow at ECMWF Prototype Level-2B (LIPAS simulation, includes representativeness error) Non-IFS processing “Bufr2ODB” Convert BUFR to ODB format Recognize HLOS as new known observable Observation Screening IFS “Screening Job” Check completeness of report, blacklisting Background Quality Control Assimilation Algorithm IFS “4D-VAR” Implement HLOS in FWD, TL & ADJ Codes Variational Quality Control Analysis Diagnostic post-processing “Obstat” etc (Lars Isaksen) Recognize HLOS for statistics Rms, bias, histograms

  17. Assimilation of prototype ADM-Aeolus data2004-: Receive L1B data & L2B processing at NWP centres Observation Processing Data Flow at ECMWF Level-1B data (67 1-km measurements) Non-IFS processing “Bufr2ODB” Convert BUFR to ODB format Recognize HLOS as new known observable Observation Screening IFS “Screening Job” Check completeness of report, blacklisting Background Quality Control L2BP (1 50-km observation) Assimilation Algorithm IFS “4D-VAR” Implement HLOS in FWD, TL & ADJ Codes Variational Quality Control Analysis Diagnostic post-processing “Obstat” etc (Lars Isaksen) Recognize HLOS for statistics Rms, bias, histograms

  18. Level-2B processor will run in different environments ECMWF will supply source code - use as standalone or callable subroutine Aeolus Ground Segment & Data Flows - schematic view

  19. Retrievals account for receiver properties … • Tan et al Tellus 60A(2) 2008 • Dabas et al same issue • Mie light reflected into Rayleigh channel • Rayleigh wind algorithm includes correction term involving scattering ratio (s) ADM-Aeolus Optical Receiver - Astrium Satellites

  20. Response curve Function of P, T and s Mie Rayleigh (P,T) Transmission through the Double FP Line shape Depends on P, T and s Light transmitted through TA andTB Response (function of fD) … and for atmospheric scattering properties ILIAD – Impact of P & T and backscatter ratio on Rayleigh Responses - Dabas Meteo-France, Flamant IPSL • 1km-scale spectra are selectively averaged • Account for atmospheric variability - improve SNR

  21. Specified cloud layers Retrieved clouds and aerosol 50 km Retrievals validated for idealized broken multi-layer clouds – E2S simulator + operational processing chain Specified wind=50 m/s Retrieved Rayleigh winds are accurate in non-cloudy air Classify scene (threshold) then average cloudy/non-cloudy regions separately Retrieved Mie winds are accurate in cloud and aerosol layers

  22. Real scattering measurements obtained from the LITE and Calipso missions • ESA’s software (E2S) is used to simulate what ADM-Aeolus would ‘see’ • The L1B software retrieves scattering ratio at the 1 km measurement resolution • Our input not perfect Realistic scenes simulated

  23. Wind retrieval validated in the presence of heterogeneous clouds and wind – E2S simulation Retrievals fairly accurate Level-1B Rayleigh molecular Mie particles Backscatter from Calipso Outliers being examined

  24. … but only after bugs were fixed in earlier versions of the L1B processor Level-1B Rayleigh molecular Mie particles Retrieved Mie winds revealed systematic error in L1B input

  25. Wind retrieval error from ACCD digitization- theory confirmed by E2S simulation Photon noise will dominate

  26. Level-2B hlos error estimates – reqts met Poli/Dabas Meteo-France

  27. Contents • Summary on 2 Slides • Background • Data Processing • Conclusions

  28. Conclusions – Day-1 system on track • Level-2B hlos winds – primary product for assimilation • Account for more effects than L1B products • Will be generated in several environments • Motivated strategy to distribute source code • Main algorithm components developed & validated • Release 1.33 available – development/beta-testing • Documentation and Installation Tests • Portable – tested on several Linux platforms • Ongoing scientific and technical development • Sensitivity to inputs, QC/screening, weighting options • Contact points – ESA and/or ECMWF

  29. 5.2 Key assimilation operators • Tan 2008 ECMWF Seminar Proceedings • HLOS, TL and AD • H = - u sin φ - v cos φ • dH = - du sin φ - dv cos φ • dH* = ( - dy sin φ, - dy cos φ )T • Generalize to layer averages later • Background error • Same as for u and v (assuming isotropy) • Persistence and/or representativeness error • Prototype quality control • Adapt local practice for u and v

  30. 5.1 Prototype Level-2C Processing • Assimilation of HLOS observations (L1B/L2B) • Corresponding analysis increments (Z100) • Ingestion of L1B.bufr into the assimilation system • L1B obs locations within ODB (internal Observation DataBase)

  31. 2a-4. Other NWP configurations

  32. 2a-1. ECMWF “operational” configuration

  33. 2a-2. ESA-LTA late- and re-processing

  34. 2a-3. Research/general scientific use

  35. L1B/2B/2C IFS(ODB) allocation AuxMet/L2B in Screening L2C 1.3 Integration of Aeolus L2BP at ECMWF L1B preprocessing (if required)

  36. Aux L1B L2B L2C AP/L2BP within Screening 1.3 ECMWF operational schedule Processing of L1B 09-21Z starts at 02Z (D+1) “dcda-12utc” Orbit from 20:00--21:40Z is split over two assimilation cycles

  37. 2b. Why distribute L2BP Source Code? • Distribution of executable binaries only permits • limited number of computing platforms • different settings in processing parameters input file • thresholds for QC, cloud detection • different auxiliary inputs • option to use own meteorological data (T & p) in place of ECMWF aux met data (available from LTA) • Provide maximum flexibility for other centres/institutes to generate their own products • different operational schedule/assimilation strategy • scope to improve algorithms • feed into new releases of the operational processor

  38. 3a. How it works – Tan et al Tellus A 2008 • Rayleigh channel HLOS retrieval – Dabas et al, Tellus A • R = (A-B) / (A+B) and HLOS = F-1 (R;T,p,s) • T and p are auxiliary inputs • correction for Mie contamination, using estimate of scattering ratio s • Mie channel HLOS retrieval • peak-finding algorithm (4-parameter fit as per L1B) • Retrieval inputs are scene-weighted • ACCD = Σ ACCDm Wm, Wm between 0 and 1 • Error estimate provided for every Rayleigh & Mie hlos • dominant contributions are SNR in each channel

  39. 3b. Level-2B input screening & feature finding Poli/Dabas Meteo-France

  40. 3b. Level-2B hlos wind retrievals Poli/Dabas Meteo-France

  41. 3c. Future work • Quality Indicators • Highlighting doubtful L2B retrievals • More complicated atmospheric scenes from simulations + Airborne Demonstrator • Advanced feature-finding/optical retrievals • Methods based on NWP T & p introduce error correlations • Modified measurement weights • More weight to measurements with high SNR? • Height assignment • In situations with aerosol and vertical shear

  42. 4. Distribution of L2BP software • Software releases issued by ECMWF/ESA • Details & timings to be determined • Probably via registration with ECMWF and/or ESA • Source code and scripts for installation • Fortran90, some C support • Developed/tested under several compilers • Suite of unit tests with expected test output • Documentation • Software Release Note • Software Users’ Manual • Definitions of file formats (IODD), ATBD, etc.

  43. Conclusions • Expectations for ADM-Aeolus are high • On track for producing major benefits in NWP • Meeting the mission requirements for vertical resolution & accuracy • Extending to stratosphere, re-analysis • Our software available to NWP/science community • Combine with other observations • Height assignment for AMVs • Complement other cloud/aerosol missions • Related research • Background error specification

  44. 1 Baseline L2BP Algorithm • Purpose of L2BP • Produce L2B data from L1B data and aux met data • 50 km observations from ~ 1 km measurements • Error estimates and quality indicators • Temperature and pressure corrections via met data • Scene classification and selective averaging • Design a portable source code for three processing modes • Integration at many met centres • Reprocessing @ ESA (ECMWF-supplied met data) • Testing in a range of environments • Simple to use, yet flexible to permit extensions • Auxiliary processing – prepares met data as L2BP input

  45. Scene classification influences L2B output P L1 measurements L2B Profiles L2bP 24 Mie or Rayleigh height bins

  46. 1.4 Baseline architecture – L2BP • Auxiliary L2B processing (centre-dependent) • Profiles of temperature and pressure vs height • At requested locations, full model vertical resolution • L2BP will perform conversion to WGS84 coords • Extract from “first-guess fields” during “screening” • Nearest time (within 15 mins at ECMWF) • At ECMWF, vertical profiles and not slanted • Currently one profile per observation • Pre-processing step • Standardize input for primary L2B processing • Align met data with L1B measurements in horizontal • Could be achieved via extrapolation or interpolation

  47. 1.5 Locations for computing aux met data • Obtain from geolocation information in real L1B data • Offset from the sub-satellite track • Example shows 30 mins x 50 km spacing along-track

  48. P L1 measurement locations N_met raw aux met profiles PreProc 1.4 L2BP - auxiliary pre-processing • Collocation implemented, suitable for 1 met locn per BRC • Sensitivity study to guide extensions, eg interpolation code Aux met height bins, 1 per model level

  49. 1.5 L2BP - primary processing • Primary processing (HLOS retrieval) • L1B product validation (mainly in Consolidation Phase) • Signal classification (+ further code from L2A study) • Assign weights to signals (+ further development) • Apply weights to a general parameter • lat & lon = L2B centre-of-gravity • temperature & pressure = Tref & Pref • HLOS temperature & pressure corrections • Error estimates, quality indices • Output in EE format

  50. 2 Future work • Key inputs from other activities • L1B test datasets • Cloud detection and scene classification • algorithms/codes based on L2AP • Details of temperature & pressure correction scheme • ILIAD results & implementation (e.g. lookup table) • Algorithms for • HLOS error estimates & Quality indicators • Check suitability of interfaces for many met centres • Basic concept ~ screening of radiosonde observations

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