11 July 2008 Data Processing for ADM-Aeolus – JCSDA Slide 1 The Wind Lidar Mission ADM-Aeolus Data Processing David Tan Research Department ECMWF Acknowledgements:

11 July 2008 Data Processing for ADM-Aeolus – JCSDA Slide 1

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


Summary on 2 SlidesBackgroundData 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

Contents


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

Summary of ECMWF activities for ADM-Aeolus


Status summary: Day-1 system on track1. Level-2B hlos winds – primary product for assimilation

a. Account for more effects than L1B products

b. Will be generated in several environments

c. Motivated strategy to distribute source code

2. Main algorithm components developed & validated

a. Release 1.33 available – development/beta-testing

b. Documentation and Installation Tests

c. Portable – tested on several Linux platforms

3. Ongoing scientific and technical development

a. Sensitivity to inputs, QC/screening, weighting options

4. Contact points – ESA and/or ECMWF


Summary on 2 SlidesBackgroundData ProcessingConclusions

Contents


Background for ADM-Aeolus Measurement Concept

• Backscatter signal

• Aeolus winds are derived from Doppler shift of aerosols and molecules along lidar line-of-sight

• Error estimates, cloud & aerosol properties derived from signal strength

Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations

Orbit: 705 km, 98° inclination,in formation with Aqua,CloudSat and Parasol

Launch beginningof 2005

Mission duration: 3years

Threeco-alignedinstruments:

• 3-channel lidar– 532 nm||– 532 nm– 1064 nm

• Imaging IR radiometer• Wide-field camera

Mission Concept

Complementary Instruments

• CloudSat radar (cloud profiles)• Aqua CERES (top-of-the-atmosphere radiation)• Aqua AIRS /AMSU-A/HSB (atmospheric state)• Aqua MODIS (aerosol/cloud properties)• PARASOL (aerosol/cloud properties)• Aura OMI (aerosol absorption)

AquaCALIPSOCloudSat

PARASOL

Aura

Vertical distribution ofaerosols andclouds

Aerosol / cloud properties

CALIPSO lidar – vertical cross sections of backscatter


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)

Atmospheric Dynamics Mission ADM-Aeolus

[H]LOS


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

ADM-Aeolus Coverage and Data Availability


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 Nd:YAG laser in burst mode operation(120 mJ @ 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)

ALADIN

Satellite and Instrument ALADIN


LIDAR Instruments for Earth Observation Missions

ADM-Aeolus/ALADINESA, launch 2010

wind profiles, aerosol and clouds EarthCARE/ATLID

ESA, launch 2013

aerosol and clouds

Future Lidar Instruments, e.g.

A-SCOPE for CO2

Calipso/CALIOPNASA, launch 2006

aerosol and clouds

IceSAT/GLASNASA, launch 2003

elevation, aerosol and clouds


ADM-Aeolus Ground Segment

[DLR]

[ESA-ESRIN]

[ESA-ESOC]


ADM-Aeolus Data ProductsProduct Contents Processor developer

and locationSize 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 productVertical wind profiles (u and v component);

NWP model output after assimilation of Aeolus HLOS wind

ECMWF Reading (UK)

22


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


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

Principle of spectrometer for molecular signal

principle of spectrometer for aerosol signal

Principle of wind measurement with ALADIN


Summary on 2 SlidesBackgroundData 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

Contents


ADM-Aeolus Ground Segment

[DLR]

[ESA-ESRIN]

[ESA-ESOC]

L2B HLOS


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

60°S60°S

30°S 30°S

0°0°

30°N 30°N

60°N60°N

150°W

150°W 120°W

120°W 90°W

90°W 60°W

60°W 30°W

30°W 0°

0° 30°E

30°E 60°E

60°E 90°E

90°E 120°E

120°E 150°E

150°E

Rayleigh channel Level 8ECMWF Analysis VT:Friday 7 February 2003 00UTC Surface: **high cloud cover

0.5

0.6

0.7

0.8

0.9

1.0

1%

25%

50%

75%

100%

60°S60°S

30°S 30°S

0°0°

30°N 30°N

60°N60°N

150°W

150°W 120°W

120°W 90°W

90°W 60°W

60°W 30°W

30°W 0°

0° 30°E

30°E 60°E

60°E 90°E

90°E 120°E

120°E 150°E

150°E

Mie channel Level 8ECMWF Analysis VT:Friday 7 February 2003 00UTC Surface: **high cloud cover

0.5

0.6

0.7

0.8

0.9

1.0

1%

25%

50%

75%

100%

LIPAS-simulated HLOS data – operational processors later

L2B data simulated using ECMWF clouds …

Yield (data meeting mission requirements in % terms) at 10 km


S.Hem

0.0 0.5 1.0 1.51000

100

… & impact studied via assimilation ensembles

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

ADM-Aeolus

NoSondes

Pre

ssur

e (h

Pa)

Zonal wind (m/s)

p<0.001

12-hr fc impact (Tan et al QJRMS 2007)


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 %


Observation Processing

Data Flow at ECMWF

Non-IFS processing

Observation Screening

Assimilation Algorithm

Diagnostic post-processing

“Bufr2ODB”Convert BUFR to ODB format

Recognize HLOS as new known observable

IFS “Screening Job”Check completeness of report, blacklisting

Background Quality Control

IFS “4D-VAR”Implement HLOS in FWD, TL & ADJ Codes

Variational Quality Control

“Obstat” etc (Lars Isaksen)Recognize HLOS for statistics

Rms, bias, histograms

Prototype Level-2B (LIPAS simulation, includes representativeness error)

Analysis

Assimilation of prototype ADM-Aeolus data2003/4: introduced L2B hlos as new observed quantity in 4d-Var


Observation Processing

Data Flow at ECMWF

Non-IFS processing

Observation Screening

Assimilation Algorithm

Diagnostic post-processing

“Bufr2ODB”Convert BUFR to ODB format

Recognize HLOS as new known observable

IFS “Screening Job”Check completeness of report, blacklisting

Background Quality Control

IFS “4D-VAR”Implement HLOS in FWD, TL & ADJ Codes

Variational Quality Control

“Obstat” etc (Lars Isaksen)Recognize HLOS for statistics

Rms, bias, histograms

Level-1B data

(67 1-km measurements)

Analysis

Assimilation of prototype ADM-Aeolus data2004-: Receive L1B data & L2B processing at NWP centres

L2BP (1 50-km observation)


Aeolus Ground Segment & Data Flows - schematic view

Level-2B processor will run in different environmentsECMWF will supply source code - use as standalone or callable subroutine


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

Line shapeDepends on

P, T and s

Rayleigh(P,T)

Mie

Transmissionthrough the Double FP

Light transmitted through TA and TB Response (function of fD)

Response curveFunction of P, T and s

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


Retrievals validated for idealized broken multi-layer clouds – E2S simulator +

operational processing chainSpecified cloud layers

Retrieved clouds and aerosol

Retrieved Rayleigh winds are accurate in non-cloudy air

50 km

Retrieved Mie winds are accurate in cloud and aerosol layers

Specified wind=50 m/s

Classify scene (threshold) then average cloudy/non-cloudy regions separately


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

Outliers being examined

Level-1B

Rayle

igh

mole

cula

rM

ie p

art

icle

s

Retrievals fairly accurate

Backscatter from Calipso


-100 -80 -60 -40 -20 0 20 40 60 80 100

0

5

10

15

20

25

HLOS wind [m/s]Al

titude

[km]

BRC #2

E2S input (238)2b Rayleigh Cloudy (11)2b Rayleigh Clear (14)1b Rayleigh obs (24)2b Mie Cloudy (12)2b Mie Clear (14)1b Mie obs (8)

… but only after bugs were fixed in earlier versions of the L1B processor

Retrieved Mie winds revealed systematic error in L1B input

Level-1B

Rayle

igh

mole

cula

rM

ie p

art

icle

s


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

Photon noise will dominate


Level-2B hlos error estimates – reqts met

Poli/Dabas

Meteo-France


Summary on 2 SlidesBackgroundData ProcessingConclusions

Contents


Conclusions – Day-1 system on track1. Level-2B hlos winds – primary product for assimilation

a. Account for more effects than L1B products

b. Will be generated in several environments

c. Motivated strategy to distribute source code

2. Main algorithm components developed & validated

a. Release 1.33 available – development/beta-testing

b. Documentation and Installation Tests

c. Portable – tested on several Linux platforms

3. Ongoing scientific and technical development

a. Sensitivity to inputs, QC/screening, weighting options

4. Contact points – ESA and/or ECMWF


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)

Key references


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 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)

1480

1640

60°S60°S

30°S 30°S

0°0°

30°N 30°N

60°N60°N

120°W

120°W 60°W

60°W 0°

0° 60°E

60°E 120°E

120°E

NH=0.93 SH= 2.92 Trop= 1.35 Eur=-0.64 NAmer= 0.12 NAtl= 1.58 NPac= -0.29Lev=100, Par=Z, anDate=20041002-20041002 0Z, Ana Step=0, Fc Step=6Diff in RMS of an-Incr: RMS(an_erhg - bg_erhg) - RMS(an_ercp - bg_ercp)

-2.5

-2

-1.5

-1

-0.5

-0.25

-0.10.1

0.25

0.5

1

1.5

2

2.5


2a-4. Other NWP configurations

L2C Product

(optional)

Assimilation

(L2C Processor)

Other observations Forecast ModelL2B Product

L2B Processor

L1B ProductProcessing

ParametersAuxiliary Data

Meteorological

products

Integrated or Standalone, other options possible.

Possibility to modify algorithm source code.


2a-1. ECMWF “operational” configuration

L2C Product

Assimilation

(L2C Processor)


L2B Processor



Meteorological

products

Integrated with assimilation system


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

L2B Product

L2B Processor



Standalone

configuration


2a-3. Research/general scientific use


L2B Processor



Standalone configuration.

Possibility to modify algorithm source code.


1.3 Integration of Aeolus L2BP at ECMWF

L2C

AuxMet/L2B

in Screening

L1B preprocessing (if required)

L1B/2B/2C

IFS(ODB) allocation


1.3 ECMWF operational scheduleProcessing of L1B 09-21Z starts at 02Z (D+1) “dcda-12utc”

AP/L2BP

within

Screening

Aux

L1B L2B L2C

Orbit from 20:00--21:40Z is split over two assimilation cycles


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 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

3

Figure1: Scattering ratio input to theE2S (top) and scattering ratio calcu-lated by the level-1b processor (bottom)

5

Figure3: Level-2b classi cation map for each measurement bin (top) and re-sult of theL1B Input Screeningprocess for each measurement bin (bottom),for Rayleigh

Poli/Dabas

Meteo-France


3b. Level-2B hlos wind retrievals5

Figure3: Level-2b classi cation map for each measurement bin (top) and re-sult of theL1B Input Screeningprocess for each measurement bin (bottom),for Rayleigh

Poli/Dabas

Meteo-France

4

Figure2: Level-2bscatteringratioused in theRayleigh inversions(calculatedby weighting over the level-1b scattering ratios) for the clear and cloudyobservations

9

- 100 - 80 - 60 - 40 - 20 0 20 40 60 80 100

0

2

4

6

8

10

12

14

16

18

20

HLOS wind [m/s]

Alti

tude

[km

]

BRC #2

E2S input (206 excld:0)2b Rayleigh Cloudy (4 excld:0)2b Rayleigh Clear (16 excld:0)1b Rayleigh obs (24 excld:0)2b Mie Cloudy (3 excld:3)2b Mie Clear (2 excld:2)1b Mie obs (2 excld:0)

Figure7: Retrievals for the2nd BRC


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 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 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 Purpose of L2BP

Produce L2B data from L1B data and aux met data50 km observations from ~ 1 km

measurementsError 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

L2bP

24

Mie

or

Rayle

igh h

eig

ht

bin

s

P L1 measurements L2B Profiles


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


Obtain from geolocation information in real L1B data

Offset from the sub-satellite track

Example shows 30 mins x 50 km spacing along-track

1.5 Locations for computing aux met data


1.4 L2BP - auxiliary pre-processing Collocation implemented, suitable for 1 met locn per BRC

Sensitivity study to guide extensions, eg interpolation code

PreProc

Aux m

et

heig

ht

bin

s, 1

per

model le

vel

P L1 measurement locationsN_met raw aux met profiles


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 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 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

Nd:YAG, 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


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 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


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

Line shapeDepends on

P, T and

Rayleigh(P,T)

Mie

Transmissionthrough the Double FP

Light transmitted through TA and TB Response (function of fD)

Response curveFunction of P, T and


ILIAD - Baseline Inversion Scheme

ModelP,T

L1Bp

Tenti +Gauss.

CalibrationTA(), TB()

Line shape

NA, NB

RR(fDP,TInversion

dP

vd,

dT

vd,v rr

r

RR

USR

DB,ADB,A df,T,P;ffIfT,T,P;fN

,T,P,fN,T,P,fN,T,P,fN,T,P,fN

,T,P,fRdBdA

dBdAdR

,T,P;RR

2,T,Pv R

1RR

Input

Output

Au

x.

data

dMdRd fI1T,P;fI,T,P;fI


ILIAD - Simplified correction schemeBased on a simplification of baseline inversion.

Two-step appraoch:

1. 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+iP, Tj=T0+jT and

Rk=R0+kR

Output parameters:

Vr(Pmod,Tmod,=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.

2. Correct from Mie contamination.

Method: First order, linear correction based on the estimation of dvr/d


ILIAD - Practical implementation

K1T

hPa50P

MHz25f

T,P;fI jimR

Global aux. FileProcessed once before launch

~8 Mb

Rayleigh-Brillouinspectra

TA(fm), TB(fm)f=25 MHz

ISR

01.0R

R,T,P kjid

F

Aux. FileProcessed every timean ISR is carried out

~2 Mb

Look-up table

T,P

R

modmod

R

INVERSION

Tv

Pv

ˆ,T,Pv

rr

modmodr

Part of L2BpApplied to all Rayleigh responses


Aeolus satellite layout


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

Platform simulator

Laser Cooling Radiator

Transmit-Receive Telescope


ALADIN Laser Cooling System

Redundant PLH

Nominal PLH

OBA

Heatpipes

Laser Radiator


ALADIN OSTM

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


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

60°S60°S

30°S 30°S

0°0°

30°N 30°N

60°N60°N

150°W

150°W 120°W

120°W 90°W

90°W 60°W

60°W 30°W

30°W 0°

0° 30°E

30°E 60°E

60°E 90°E

90°E 120°E

120°E 150°E

150°E

Rayleigh channel Level 13ECMWF Analysis VT:Friday 7 February 2003 00UTC Surface: **high cloud cover

0.5

0.6

0.7

0.8

0.9

1.0

1%

25%

50%

75%

100%

60°S60°S

30°S 30°S

0°0°

30°N 30°N

60°N60°N

150°W

150°W 120°W

120°W 90°W

90°W 60°W

60°W 30°W

30°W 0°

0° 30°E

30°E 60°E

60°E 90°E

90°E 120°E

120°E 150°E

150°E

Mie channel Level 18ECMWF Analysis VT:Friday 7 February 2003 00UTC Surface: **low cloud cover

0.5

0.6

0.7

0.8

0.9

1.0

1%

25%

50%

75%

100%

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


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

Rayleigh

Mie

With cross-track representativity(cf radiosonde – dashed)

Without representativity(cf mission spec – dash-dot)


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

Model

Observedcloudcover

(midlats)


Assimilation of prototype ADM-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

Quality Control for Aeolus data

Set B = (obs-bg) / ES(obs-bg), accept obs iff abs(B) < 4.

In standard BgQC for Aeolus, ES = (σo2 + σb

2)1/2.

Aeolus option: ES = (so2 + σb

2)1/2, where so = min(σo, 2.5 ms-1)

Documents

11 July 2008 Data Processing for ADM-Aeolus – JCSDA Slide 1 The Wind Lidar Mission ADM-Aeolus Data Processing David Tan Research Department ECMWF Acknowledgements: