The Wind Lidar Mission ADM-Aeolus Data Processing

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
Background for ADM-Aeolus Measurement Concept
CALIPSO lidar vertical cross sections of
backscatter
7
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
  lt1 m/s (z0-2 km, for ?z0.5 km) lt2
  m/s (z2-16 km, for ?z 1 km), unknown bias lt0.4
  m/s and linearity error lt0.7 of actual wind
  speed HLOS projection on horizontal of LOS gt
  LOS accuracy 0.6HLOS
• Operating _at_ 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)

HLOS
8
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) gt 1 data-downlink per orbit30
  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
9
Satellite and Instrument ALADIN
Mass and Power Budgetsmass 1100 kg dry
116-266 kg fuelpower 1.4 kW avg. (solar array
2.4 kW peak)mass instrument 470 kgpower
instrument avg. 840 W (laser 510 W)Volume 4.3
m x 2.0 m x 1.9 m Doppler Lidar Instrument ALADIN
NdYAG laser in burst mode operation(120 mJ _at_
355 nm, 100 Hz)1.5 m Cassegrain
telescopeDual-Channel-Receiver with ACCD
detector (Accumulation Charge Coupled
Device) Orbitpolar, sun-synchronous, dawn-dusk
(6 pm LTAN), 97 inclination height 410 km
(395-425 km), 7 days orbit repeat cycle (109
orbits) 92.5 min orbit duration Pointing and
Orbit ControlGPS, Star-Tracker, Inertial
Measurement Unit, Yaw steering to compensate for
earth rotation Launcher tbd 2008
Rockot (Russia), Dnepr (Russia) or
Vega (ESA)
10
LIDAR Instruments for Earth Observation Missions
ADM-Aeolus/ALADINESA, launch 2010 wind profiles,
aerosol and clouds
EarthCARE/ATLID ESA, launch 2013 aerosol and
clouds
Calipso/CALIOPNASA, launch 2006 aerosol and
clouds
IceSAT/GLASNASA, launch 2003 elevation, aerosol
and clouds
Future Lidar Instruments, e.g. A-SCOPE for CO2
11
Comparison of Power-Aperture Products of Space
Lidars
Power-aperture product
Factor 45
Factor 56
adapted from A. Ansmann 2006
12
ADM-Aeolus Ground Segment
ESA-ESRIN
ESA-ESOC
DLR
13
Ground Segment - Svalbard Satellite Reception
Station
Courtesy KSAT
Data-downlink with 5 Mbit/s with X-Band to 2.4 m
antenna to Svalbard, Norway (7815'N)
14
ADM-Aeolus Data Products
Product Contents Processor developerand location Size in MByte/orbit
Level 0 Time ordered source packets with ALADIN measurement housekeeping data MDA (Canada)Tromsø (Norway) 47
Level 1b Geo-located, calibrated observational data preliminary HLOS wind profiles (standard atmosphere used in Rayleigh processing) not suitable for assimilation spectrometer readouts at measurement scale ( 1-5 km ) input for Level 2a/b processing viewing geometry scene geo-location data MDA (Canada) Tromsø (Norway) 10-15 (BUFR) 22 (EE XML Format)
Level 2a Supplementary product Cloud profiles, coverage, cloud top heights Aerosol extinction and backscatter profiles, ground reflectance, optical depth DLR-IMF (Germany) Tromsø (Norway) 12
Level 2b Meteorologically representative HLOS wind observations HLOS wind profiles at observation scale ( 50 km ) suitable for assimilation - temperature T and pressure p (Rayleigh-Brillouin) correction applied with ECMWF (or other) model T and p ECMWF Reading (UK) (and other NWP/research centres) 18
Level 2c Aeolus assisted wind vector product Vertical wind profiles (u and v component) NWP model output after assimilation of Aeolus HLOS wind ECMWF Reading (UK) 22
15
Ongoing ADM-Aeolus Scientific Studies
Title Team
Consolidation of ADM-Aeolus Ground Processing including L2A Products DLR Germany Météo-France, KNMI, IPSL, PSol
Development and Production of Aeolus Wind Data Products ECMWF UK Météo-France, KNMI, IPSL, DLR, DoRIT
ADM-Aeolus Campaigns DLR GermanyMétéo-France, KNMI, IPSL, DWD, MIM
Optimisation of spatial and temporal sampling KNMI Netherlands
Tropical dynamics and equatorial waves MISU Sweden
Rayleigh-Brillouin Scattering Experiment tbd
ESA plans an Announcement of Opportunity AO for
ADM-Aeolus scientific use of data for late 2008
distinct from the AO for Cal/Val
16
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 gt high quantum
  efficiency gt0.8 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 gt retrieval of profiles of
  aerosol/cloud optical properties possible

Fig. U. Paffrath
17
ALADIN Optical Layout
Transmitter laser assemblyReference Laser
Headwith stabilized tunable MISER lasersseeding
the Power Laser Headwith low power
oscillator,two amplifiers and tripling
stagetwo redundant laser assemblies in ALADIN
Telescope1.5 m diameter, Cassegrain,SiC
lightweight structure, afocal, thermally focused
Transmit/receive optics polarizer as T/R
switch, Laser Chopper mechanism, 1 focus as
field stop,interference filter and prism for
broad-band rejection of solar background
Mie receiver Fizeau interferometer, thermally
stable, fringe imaged on single accumulation CCD
Rayleigh receiver Double edge Fabry-Perot
interferometer, sequentially illuminated,tempera
ture tunable Outputs focused on single
accumulation CCD
18
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

19
Product for assimilation L2B 50km hlos
windADM-Aeolus Baseline

• UV lidar (355 nm) with two receivers
• - Mie (aerosol), Rayleigh (molecules)
• - both use direct detection
• Wind profiles from surface to 30 km with
  resolution varying from 0.5 to 2 km
• - vertical bins configurable in flight
• - HLOS component only
• - direction 7º from zonal at equator
• - 6 hour coverage shown

L2C adds along-track wind component after data
assimilation
Burst mode 1 obs 50 meas Scalable system
20
ADM-Aeolus Ground Segment
ESA-ESRIN
ESA-ESOC
L2B HLOS
DLR
21
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
22
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
(T48,T120) Differences significant
(T-test) Supported by information content
diagnostics Cheaper than OSSEs
Pressure (hPa)
Zonal wind (m/s)
23
Global information content - consistent

• Mike Fisher for Entropy Reduction DFS
• S log( det( PA ) )
• tr ( log ( J -1 ) )
• J 4d-var Hessian
• PA analysis error covar.
• DWL data are accurate and fill data gaps
• subject to usual caveats about simulated data

TEMP/PILOT Simulated DWL
Data considered u,v to 55 hPa HLOS
Entropy_Reduction (Info bits) 4830 3123
Deg_Free_Sig 3707 2743
N_Obs 90688 50278
Info bits per obs 0.053 0.062
N_Obs/Deg_Free_Sig 24.5 18.3
Redundancy 2 ? 3
24
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
25
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
26
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
27
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
28
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

29
Retrievals validated for idealized broken
multi-layer clouds E2S simulator operational
processing chain
Specified wind50 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
30
Realistic scenes simulated

• Real scattering measurements obtained from the
  LITE and Calipso missions
• ESAs 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

31
Wind retrieval validated in the presence of
heterogeneous clouds and wind E2S simulation
Retrievals fairly accurate
Level-1B
Rayleigh molecular
Mie particles
Outliers being examined
Backscatter from Calipso
32
Wind retrieval validated in the presence of
heterogeneous clouds and wind E2S simulation
Retrievals fairly accurate
Level-1B
Rayleigh molecular
Mie particles
Outliers being examined
Backscatter from Calipso
33
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
34
Wind retrieval error from ACCD digitization-
theory confirmed by E2S simulation
Photon noise will dominate
35
Level-2B hlos error estimates reqts met
Poli/Dabas Meteo-France
36
Contents

• Summary on 2 Slides
• Background
• Data Processing
• Conclusions

37
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

38
Key references

• Baker et al 1995, BAMS
• ESA 1999 Report for Assessment (Stoffelen et al
  2005, BAMS) and 2008 Science Report
• Weissman and Cardinali 2006, QJRMS
• N. Zagar co-authors, QJRMS Tellus A
• Tan Andersson 2005, QJRMS
• Tan et al 2007, QJRMS
• Tan et al 2008, Tellus A (Special Issue on
  ADM-Aeolus)

39
5.2 Key assimilation operators

• Tan 2008 ECMWF Seminar Proceedings
• HLOS, TL and AD
• H - u sin f - v cos f
• dH - du sin f - dv cos f
• dH ( - dy sin f, - dy cos f )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

40
Overview why expectations are so high

• ADM-Aeolus addresses key observational needs
• Objectives, wind observation requirements, DWL
  instrument, viewing geometry
• Implementation well-advanced for launch in 2010
• Space and ground segments
• HLOS wind product (L2B data, algorithm, portable
  s-ware)
• Cloud and aerosol products (L2A data)
• Experimental campaigns and calibration/validation
• Studies with wind lidar data support theoretical
  expectations
• Data simulations, NWP data impact studies
  (assimilation ensembles as alternative to OSSEs,
  information content)
• Airborne DWL (Weissman). Tropical assimilation
  (Zagar).

41
Background for ADM-Aeolus What is the ADM-Aeolus
Mission ?

• Aeolus objectives
• improve understanding of atmospheric dynamics
  climate processes (global atmospheric transport,
  global cycling of energy, water, aerosols,
  chemicals), and
• improve the quality of weather forecasts (via
  better initial conditions analyses from data
  assimilation), by
• providing global observations of wind profiles
  from space
• Selected in 1999 as the 2nd Earth Explorer Core
  mission in ESAs Living Planet Programme for
  Earth Observation
• Launch 2009 (provisional), duration 3 years
• Currently in Phase C (manufacturing testing)
• RD, pre-operational for future meteorological
  satellites

42
Background for ADM-AeolusObservational
Requirements

• Most important requirements - accuracy
  vertical resolution

43
Background for ADM-Aeolus Measurement Concept
44
ADM-Aeolus Space Segment preparation/testing
of 1) structural-thermal model 2) lidar
transmitter/receiver
45
Assimilation studies for ADM-Aeolus

• Tan et al., QJRMS 133381-390 (2007)
• Assimilation ensembles for data impact assessment
• Original motivation use ensemble spread as proxy
  for short-range forecast errors (background
  errors)
• By extension, good data reduce ensemble spread
• DWL impact
• Radiosonde/profiler impact - provides calibration
• Additional diagnostics related to information
  content
• Entropy reduction
• Degrees of freedom for signal

46
Ack Werner Wergen (DWD)
47
(No Transcript)
48
Data impact on ensemble forecasts - zonal wind
spread at 500 hPa
Sondes
Control

• Radiosondes and wind profilers over Japan,
  Australia, N.Amer, Europe
• DWL over oceans tropics
• Some features more obvious at 200 hPa

ADM-Aeolus
49
Data impact on ensemble forecasts - zonal wind
spread at 200 hPa
Sondes

• Radiosondes and wind profilers over Japan,
  Australia, N.Amer, Europe
• DWL over oceans and tropics

ADM-Aeolus
50
ADM-Aeolus pre-launch campaignswith
development/pre-flight instrument (A2D)
Ack Oliver Reitebuch
Campaign Location Time Instruments
ADM-Aeolus Ground Campaign Lindenberg DWD-MOL 4 weeks Jul 2007 A2D within container (DLR) 2µm lidar within container (DLR) 482 MHz windprofiler radar (DWD) 35.5 GHz cloud radar (DWD) laser ceilometer (DWD) sun-photometer (DWD) 4 operational RASO/day 10 additional (DWD) aerosol lidar 355 nm (MIM) Rayleigh Doppler lidar?
ADM- Aeolus Airborne Campaign 1 DLR-Oberpfaffenhofenover-flights Lindenberg andother sites 15 days Oct 2007 A2D and 2µm in DLR Falcon DWD-MOL instruments as in AGC
ADM- Aeolus Airborne Campaign 2 TBD 17 days2008/9 A2D and 2µm in DLR Falcon additional instruments, if linked to other campaign
51
On-going preparations for ADM-Aeolus

• Level-0 to Level-2B processing
• Rayleigh HLOS retrieval requires auxiliary
  meteorological data (T p profiles) from NWP
  models
• Flexible portable L2B processor being developed
• prototype available to the nwp/scientific
  community
• Error estimates, quality indicators, weighted
  averaging of the measurement scale (lt 3.5 km) to
  produce the observation scale (50 km), signal
  classification
• Potential cloud aerosol products ( algs / code
  for L2Bp)
• Concepts for follow-on future missions
• Scanning vs multiple orbits non-scanning
• Programmatics, data continuity

52
The Level-2B Processor

• Introduction
• What are the Level-2B/2C Wind Products?
• How do they differ from Level-1B Products?
• Strategy and implementation
• Who will make them?
• Why distribute source code for the L2BP?
• Does it work?
• Main algorithm components
• Retrieval examples, future work
• How will L2BP source code be distributed?

53
5.1 Prototype Level-2C Processing

• Ingestion of L1B.bufr into the assimilation
  system
• L1B obs locations within ODB (internal
  Observation DataBase)

• Assimilation of HLOS observations (L1B/L2B)
• Corresponding analysis increments (Z100)

54
2a-4. Other NWP configurations
55
1a/b. What are Level-2B/2C Products?
56
1a/b. What are Level-2B/2C Products?

• 2B Meteorologically representative HLOS profiles
• retrieval algs applied to Level-1B data,
  2B-output suitable as input to data assimilation
• auxiliary input data T p, Rayleigh-Brillouin
  response data, etc
• 2C Meteorologically representative wind vector
  profiles
• result of a data assimilation algorithm,
  combining Level-2B with other data/weather
  forecast model
• How do they differ from Level-1B Products?
• Rayleigh channel retrieval accounts for T p
  effects
• measurements grouped/weighted by features
  detected in the atmospheric scene (primarily
  clouds aerosol)

57
2a. Who will make Level-2B/2C Products?

• ECMWF for operational Level-2B/2C products
• Processing integrated with data assimilation
  system
• Products in ESAs Earth Explorer file format
  available from ESA (Long-Term Archive)
• ESA LTA for Level-2B late- re-processing
• Level-1B missing ECMWFs operational schedule
• New processing parameters/auxiliary inputs
• Other Numerical Weather Prediction centres
• Different operational schedule/assimilation
  strategy
• Different processing params/aux inputs/algorithms
• Research institutes general scientific users
• Different processing params/aux inputs/algorithms

58
2a-1. ECMWF operational configuration
59
2a-2. ESA-LTA late- and re-processing
60
2a-3. Research/general scientific use
61
1.3 Integration of Aeolus L2BP at ECMWF
L1B preprocessing (if required)

62
1.3 ECMWF operational schedule
Processing of L1B 09-21Z starts at 02Z (D1)
dcda-12utc
Orbit from 2000--2140Z is split over two
assimilation cycles

63
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

64
3a. How it works Tan et al Tellus A 2008

• Rayleigh channel HLOS retrieval Dabas et al,
  Tellus A
• R (A-B) / (AB) and HLOS F-1 (RT,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 S ACCDm Wm, Wm between 0 and 1
• Error estimate provided for every Rayleigh Mie
  hlos
• dominant contributions are SNR in each channel

65
Rayleigh-Brillouin spectrum and Aeolus response
curves
Rayleigh Rayleigh-Brillouin
R (A-B)/(AB)
Dabas et al., Tellus accepted
66
3b. Level-2B input screening feature finding
Poli/Dabas Meteo-France
67
3b. Level-2B hlos wind retrievals
Poli/Dabas Meteo-France
68
3b. Level-2B hlos retrieval - error estimates
69
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

70
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.

71
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

72
5.3 L2BP integration within an assimilation system
4DVar
ODB
screening
L1B-odb lat lon
L1B
Background T p
Wrapper module copies odb variables to from
data structures (hidden from screening) C.f.
RTTOV SSMI TCWV
AMD-odb
L1B AMD
L2B Processor
L2B
L2B-odb
Background hlos
Obs - Bg, BgQC, etc
73
5.4 Overview data flow standalone mode
EE
L2BP_standalone
L1B
L1B AMD
L2B Processor
AMD
L2B
L2B
L2C
74
5.5 Principal Guidance to Met Centres

• How to install and test the standalone version
• Source code, documentation, unix scripts and test
  data (EE format) supplied
• Useful tool for inter-comparison purposes
• Interface requirements for integrated-assimilation
  mode
• Generation of auxiliary meteorological data
• Wrapper module between odb and L2B processor
  used as a callable subroutine within
  assimilation.x
• Both to occur during Screening
• Facilitates assimilation of Aeolus data
• Assimilation outputs at discretion of each met
  centre

75
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 _at_ 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

76
Scene classification influences L2B output
P L1 measurements
L2B Profiles
L2bP
24 Mie or Rayleigh height bins
77
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

78
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

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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
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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

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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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Facts and figures for ADM-Aeolus

• ESA point of contact Dr Paul Ingmann
• Mission Experts Division, ESA/ESTEC, The
  Netherlands

Orbit Sun-synchronous Dawn-dusk
- inclination altitude 97 ? 408 km
Mass - total ALADIN lidar component 1100 kg 450 kg
Transmitter - laser type pulse energy NdYAG, frequency tripled to 355 nm 150 mJ
- pulse repetition freq. duty cycle 100 Hz 10 s every 28 s
Receiver - telescope diameter 1.5 m
- spectrometers Fizeau (Mie) Dual edge etalon (Rayleigh)
Average power demand 1400 W
Launch date mission lifetime 2008 3 years
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1 Baseline L2BP Algorithm

• Baseline architecture
• HLOS retrieval - TN2.2, Fig 2
• Generation of aux met data TN2.2, Fig 1
• L1B BRCs processed independently ( possibly in
  parallel)
• No communication of intermediate L2BP results
• L1B data arriving within met centre operational
  schedule
• Met centre produces aux met data, L2B (and L2C)
• L1B data missing the ECMWF schedule
• ECMWF produces aux met data
• at locations inferred from predicted flight
  tracks
• L2B possible via re-processing

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1.1 L2BP Portability considerations

• Common design accommodating three processing modes

Met Centres Operational (ECMWF) Re-proc (ESRIN)
L1B data (input) in EE format (or predicted orbit locations) Received in Q/NRT (30m-3h) Received in NRT (5h) LTA/reprocessing
Auxiliary meteorological input (T p profiles, EE/BUFR) Self-generated Self-generated sent to LTA Oper available (via LTA)
Primary L2BP code Oper available Oper Oper available
Auxiliary parameter input files Oper available Oper Oper available

L2B data output in EE format Yes Yes Yes
L1B/L2B data in BUFR format (for assimilation purposes) EE2BUFR EE2BUFR Not required
85
The ILIAD Study

• Why the ILIAD study ?
• The L1 processing scheme proposed by the industry
  for Rayleigh winds does not take into account the
  impact of the pressure and the potential presence
  of Mie scattering.
• Preliminary studies conducted by DLR (O.
  Reitebuch) and ESA (M. Endemann) suggested the
  impact of both exceed requirements on data
  quality.
• Objectives
• Find a correction scheme.
• Study Team.
• IPSL/LMD (P. Flamant, C. Loth), IPSL/SA (A.
  Garnier), ONERA/DOTA (A. Dolfi-Bouteyre),
  HOVEMERE (D. Rees), MF/CNRM (A. Dabas, M. L.
  Denneulin)

86
ILIAD - Impact of P, T and backscatter ratio on
Rayleigh Responses
87
ILIAD - Baseline Inversion Scheme
Aux. data
88
ILIAD - Simplified correction scheme

• Based on a simplification of baseline inversion.
• Two-step appraoch
• Inverse response RR as if there were no Mie.
• Method Look-up in the 3D matrix Fd (i,j,k)
  giving the inverse frequency (or velocity) for
  pressures PiP0iDP, TjT0jDT and RkR0kDR
• Output parameters
• Vr(Pmod,Tmod,r1) where Pmod and Tmod are the
  pressure and temperature inside the sensing
  volume as predicted by the NWP model.
• dvr/dP, dvr/dT and dvr/dR, that is, the first
  order derivative of vr with respect to P, T and
  the response RR.
• Correct from Mie contamination.
• Method First order, linear correction based on
  the estimation of dvr/dr

89
ILIAD - Practical implementation
ISR
TA(fm), TB(fm) Df25 MHz
Rayleigh-Brillouin spectra
INVERSION
Look-up table
Aux. File Processed every time an ISR is carried
out 2 Mb
Global aux. File Processed once before launch 8
Mb
Part of L2Bp Applied to all Rayleigh responses
90
Aeolus satellite layout
91
ALADIN Structure and Optical Structural Thermal
Model
ALADIN structure has been completed for OSTM and
tested. Mass-dummies have been integrated for
OSTM Power Laser Heads (PLH), Reference
Laser Heads (RLH), and Optical Bench Assembly
(OBA)
92
ALADIN OSTM
Transmit-Receive Telescope
Laser Cooling Radiator
Platform simulator
93
ALADIN Laser Cooling System
Laser Radiator
Heatpipes
OBA
Redundant PLH
Nominal PLH
94
ALADIN OSTM
Before shipment to CSL (Liege) for installation
in vacuum chamber and full thermal vacuum testing
95
Data simulations for ADM-AeolusYield (age of
data meeting mission requirements) at 5 1 km

• 5 km 75 of Rayleigh have accuracy lt 2 m/s (also
  15 Mie not shown)
• 1 km 66 of Mie have accuracy lt 1 m/s (aerosol
  cloud returns)
• Adequate transmission through overlying cloud

96
ADM-Aeolus data simulations - comparison with
radiosondes/mission spec

• Aeolus median like obs error assigned
  operationally to radiosondes
• Aeolus HLOS observations expected to receive
  appreciable weight

97
ADM-Aeolus data simulations Effects of model
cloud cover (2)

• Mid-latitude example
• QC implications, Task 2

• Tails of Rayleigh error distributions
  underestimated, median barely changed

98
Assimilation of prototype ADM-Aeolus data
Quality Control for Aeolus data

• Most QC parameters taken from conventional wind
  obs
• Background errors quality control thresholds
  (BgQCVarQC)
• Aeolus-specific Background Quality Control
  (recommended option)
• Capping of observation error in bg departure
  classification
• Testing with LITE period, LIPAS-simulated
  Level-2B data
• Gaussian non-Gaussian errors (instrument bias,
  input wind bias)
• Operational model (Cy26r1) at full/reduced
  resolution, ERA40/NoSSMI

Set B (obs-bg) / ES(obs-bg), accept obs iff
abs(B) lt 4. In standard BgQC for Aeolus, ES
(so2 sb2)1/2. Aeolus option ES (so2
sb2)1/2, where so min(so, 2.5 ms-1)
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Quality Control Examples Std Aeolus-optional
QC for DWL -- active
Radiosonde U-wind
Option improves departure statistics