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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

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


Contents

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


Summary of ecmwf activities for adm aeolus

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


Status summary day 1 system on track

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


Contents1

Contents

  • Summary on 2 Slides

  • Background

  • Data Processing

  • Conclusions


The wind lidar mission adm aeolus data processing

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


The wind lidar mission adm aeolus data processing

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


The wind lidar mission adm aeolus data processing

ADM-Aeolus Ground Segment

[ESA-ESRIN]

[ESA-ESOC]

[DLR]


Adm aeolus data products

ADM-Aeolus Data Products


Ongoing adm aeolus scientific studies

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


The wind lidar mission adm aeolus data processing

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


Contents2

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


The wind lidar mission adm aeolus data processing

ADM-Aeolus Ground Segment

[ESA-ESRIN]

[ESA-ESOC]

L2B HLOS

[DLR]


L2b data simulated using ecmwf clouds

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


Impact studied via assimilation ensembles

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)


The wind lidar mission adm aeolus data processing

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


Assimilation of prototype adm aeolus data 2004 receive l1b data l2b processing at nwp centres

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


The wind lidar mission adm aeolus data processing

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


Retrievals account for receiver properties

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


And for atmospheric scattering properties

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


The wind lidar mission adm aeolus data processing

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


Realistic scenes simulated

  • 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


Wind retrieval validated in the presence of heterogeneous clouds and wind e2s simulation

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


But only after bugs were fixed in earlier versions of the l1b processor

… 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


Wind retrieval error from accd digitization theory confirmed by e2s simulation

Wind retrieval error from ACCD digitization- theory confirmed by E2S simulation

Photon noise will dominate


Level 2b hlos error estimates reqts met

Level-2B hlos error estimates – reqts met

Poli/Dabas

Meteo-France


Contents3

Contents

  • Summary on 2 Slides

  • Background

  • Data Processing

  • Conclusions


Conclusions day 1 system on track

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


5 2 key assimilation operators

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


5 1 prototype level 2c processing

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)


2a 4 other nwp configurations

2a-4. Other NWP configurations


2a 1 ecmwf operational configuration

2a-1. ECMWF “operational” configuration


2a 2 esa lta late and re processing

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


2a 3 research general scientific use

2a-3. Research/general scientific use


1 3 integration of aeolus l2bp at ecmwf

L1B/2B/2C

IFS(ODB) allocation

AuxMet/L2B

in Screening

L2C

1.3 Integration of Aeolus L2BP at ECMWF

L1B preprocessing (if required)


1 3 ecmwf operational schedule

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


2b why distribute l2bp source code

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


3a how it works tan et al tellus a 2008

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


3b level 2b input screening feature finding

3b. Level-2B input screening & feature finding

Poli/Dabas

Meteo-France


3b level 2b hlos wind retrievals

3b. Level-2B hlos wind retrievals

Poli/Dabas

Meteo-France


3c future work

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


4 distribution of l2bp software

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.


Conclusions

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


1 baseline l2bp algorithm

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


  • Scene classification influences l2b output

    Scene classification influences L2B output

    P L1 measurements

    L2B Profiles

    L2bP

    24 Mie or Rayleigh height bins


    1 4 baseline architecture l2bp

    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


    1 5 locations for computing aux met data

    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


    1 4 l2bp auxiliary pre processing

    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


    1 5 l2bp primary processing

    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


    2 future work

    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


    Facts and figures for adm aeolus

    Facts and figures for ADM-Aeolus

    • ESA point of contact – Dr Paul Ingmann

      • Mission Experts Division, ESA/ESTEC, The Netherlands


    1 baseline l2bp algorithm1

    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


    1 1 l2bp portability considerations

    1.1 L2BP – Portability considerations

    • Common design accommodating three processing modes


    The iliad study

    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)


    Iliad impact of p t and backscatter ratio on rayleigh responses

    Response curve

    Function of P, T and r

    Mie

    Rayleigh

    (P,T)

    Transmission

    through the

    Double FP

    Line shape

    Depends on

    P, T and r

    Light transmitted through TA andTB

    Response (function of fD)

    ILIAD - Impact of P, T and backscatter ratio on Rayleigh Responses


    Iliad baseline inversion scheme

    Calibration

    TA(), TB()

    Model

    P,T

    Line shape

    Tenti +Gauss.

    L1Bp

    NA, NB

    Output

    Inversion

    RR(fD,[P,T,r])

    Input

    ILIAD - Baseline Inversion Scheme

    Aux. data


    Iliad simplified correction scheme

    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 Pi=P0+iDP, Tj=T0+jDT and Rk=R0+kDR

      • Output parameters:

        • Vr(Pmod,Tmod,r=1) 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


    Iliad practical implementation

    ILIAD - Practical implementation

    ISR

    TA(fm), TB(fm)

    Df=25 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


    Aeolus satellite layout

    Aeolus satellite layout


    Aladin structure and optical structural thermal model

    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)


    Aladin ostm

    ALADIN OSTM

    Transmit-Receive Telescope

    Laser Cooling Radiator

    Platform simulator


    Aladin laser cooling system

    ALADIN Laser Cooling System

    Laser Radiator

    Heatpipes

    OBA

    Redundant PLH

    Nominal PLH


    Aladin ostm1

    ALADIN OSTM

    Before shipment to CSL (Liege) for installation in vacuum chamber and full thermal vacuum testing


    The wind lidar mission adm aeolus data processing

    Data simulations for ADM-AeolusYield (%age of data meeting mission requirements) at 5 & 1 km

    • 5 km: 75% of Rayleigh have accuracy < 2 m/s (also 15% Mie not shown)

    • 1 km: 66% of Mie have accuracy < 1 m/s (aerosol & cloud returns)

    • Adequate transmission through overlying cloud


    Adm aeolus data simulations comparison with radiosondes mission spec

    Rayleigh

    Mie

    With cross-track representativity

    (cf radiosonde – dashed)

    Without representativity

    (cf mission spec – dash-dot)

    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


    Adm aeolus data simulations effects of model cloud cover 2

    Model

    Observed

    cloud

    cover

    (midlats)

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

    • Tails of Rayleigh error distributions underestimated, median barely changed

    • Mid-latitude example

    • QC implications, Task 2


    Assimilation of prototype adm aeolus data

    Assimilation of prototype ADM-Aeolus data

    Quality Control for Aeolus data

    • Most QC parameters taken from conventional wind obs

      • Background errors & quality control thresholds (BgQC+VarQC)

    • 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) < 4.

    In standard BgQC for Aeolus, ES = (σo2 + σb2)1/2.

    Aeolus option: ES = (so2 + σb2)1/2, where so = min(σo, 2.5 ms-1)


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