# Presentation: 059184

## 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

**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**

**Contents**

## Summary of ECMWF activities for ADM-Aeolus

**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 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**

## Contents

**Background**
- Data Processing
- Conclusions

## Background for ADM-Aeolus Measurement Concept

- CALIPSO lidar – vertical cross sections of backscatter

## Atmospheric Dynamics Mission ADM-Aeolus

**       ****ADM-Aeolus with single payload**** A****tmospheric ****LA****ser ****D****oppler ****IN****strument**
**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**

## 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**

**ADM-Aeolus Coverage and Data Availability**

## Satellite and Instrument ALADIN

**Mass and Power Budgets**mass:  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)
**Orbit**polar, 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 Control**GPS, Star-Tracker, Inertial Measurement Unit, Yaw steering to compensate for earth rotation
**Launcher **tbd 2008**                               **Rockot (Russia), Dnepr (Russia) or Vega (ESA)

**Satellite and Instrument ALADIN**

## LIDAR Instruments for Earth Observation Missions

**LIDAR Instruments for Earth Observation Missions**

**ADM-Aeolus/ALADIN**ESA, launch 2010
**wind profiles, aerosol ****and clouds**

**EarthCARE/ATLID**** **   ESA, launch 2013
**aerosol and clouds**

** ****Future Lidar Instruments, e.g.**
**A-SCOPE** **for CO****2**

**Calipso/CALIOP**NASA, launch 2006
**aerosol and clouds**

**IceSAT/GLAS**NASA, launch 2003
**elevation, aerosol ****and clouds**

## Comparison of Power-Aperture Products of Space Lidars

- adapted from A. Ansmann 2006

**Factor 45**

**Factor 56**

**Comparison of Power-Aperture Products of Space Lidars**

**Power-aperture product**

## ADM-Aeolus Ground Segment

**ADM-Aeolus Ground Segment**

**[DLR]**

**[ESA-ESRIN]**

**[ESA-ESOC]**

## Ground Segment - Svalbard Satellite Reception Station

- Data-downlink with 5 Mbit/s with X-Band to 2.4 m antenna to Svalbard, Norway (78°15'N)

**Courtesy KSAT**

## ADM-Aeolus Data Products

| Product | Contents | Processor developerand 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 |

## 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 GermanyMétéo-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

## Principle of wind measurement with 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 wind measurement with ALADIN**

## ALADIN Optical Layout

**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

**Rayleigh receiver: **Double edge Fabry-Perot interferometer, sequentially illuminated,temperature tunable Outputs focused on single accumulation CCD

**Mie receiver:**** **Fizeau interferometer, thermally stable, fringe imaged on single accumulation CCD

**Transmitter laser assembly:**Reference Laser Headwith stabilized tunable MISER lasersseeding the Power Laser Headwith low power oscillator,two amplifiers and tripling stagetwo redundant laser assemblies in ALADIN

**ALADIN Optical Layout**

## Contents

- 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

## Product for assimilation – L2B 50km hlos windADM-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**

**Burst mode      1 obs ~ 50 meas Scalable system**

**L2C – adds along-track wind component after data assimilation**

## ADM-Aeolus Ground Segment

**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

**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**

**L2B data simulated using ECMWF clouds ...**

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

## ... & impact studied via assimilation ensembles

**... ****& 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**

- Pressure (hPa)

- Zonal wind (m/s)

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

## Global information content - consistent

**Mike Fisher for Entropy Reduction & DFS**
**S ~ log( det( P****A**** ) )**
**   ****~ tr ( log ( J’’ ****-1**** ) )**
**J’’ = 4d-var Hessian**
**P****A**** = 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

**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 data****2003/4: introduced L2B hlos as new observed quantity in 4d-Var**

## Observation Processing
Data Flow at ECMWF

**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 data****2004-: ****Receive L1B data & L2B processing at NWP centres**

*L2BP (1 50-km observation)*

## Level-2B processor will run in different environments

**Level-2B processor will run in different environments**

**ECMWF 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

**... ****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**

**Notes:**
Montre comment la réponse du lidar est liée à la vitesse Doppler et mets en évidence l’influence de la température, de la pression et du Mie. Utiliser le dernier transparent si celui-ci ne fonctionne pas.

## Retrievals validated for idealized broken multi-layer clouds – E2S simulator + operational processing chain

- Retrieved Rayleigh winds are accurate in non-cloudy air

- 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

** ****Wind retrieval validated in the presence of heterogeneous clouds and wind – E2S simulation**

-     Outliers being examined

-  Level-1B

- Rayleigh molecular

- Mie particles

-    Retrievals fairly accurate

-     Backscatter from Calipso

##  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**

-     Outliers being examined

-  Level-1B

- Rayleigh molecular

- Mie particles

-    Retrievals fairly accurate

-     Backscatter from Calipso

##  ... 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**

- Retrieved Mie winds revealed systematic error in L1B input

-  Level-1B

- Rayleigh molecular

- Mie particles

##  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

## Contents

- Background
- Data Processing
**Conclusions**

## 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**

## Key references

**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**

## Overview – why expectations are so high

**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).**

**Overview – why expectations are so high**

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

**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 ESA’s Living Planet Programme for Earth Observation**
**Launch 2009 (provisional), duration 3 years**
**Currently in Phase C (manufacturing & testing)**
**R&D, pre-operational for future meteorological satellites**

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

## Background for ADM-AeolusObservational Requirements

**Background for ADM-Aeolus****Observational Requirements**

** ****Most important requirements - accuracy & vertical resolution**

## Background for ADM-Aeolus Measurement Concept

## ADM-Aeolus Space Segment – preparation/testing of	1) structural-thermal model   2) lidar transmitter/receiver

**ADM-Aeolus Space Segment – preparation/testing of****	****1) structural-thermal model   2) lidar transmitter/receiver**

## Assimilation studies for ADM-Aeolus

**Tan et al., QJRMS 133:381-390 (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**

## Ack: Werner Wergen (DWD)

**Ack: Werner Wergen (DWD)**

## Slide 47

## Data impact on ensemble forecasts - zonal wind spread at 500 hPa

**Data impact on ensemble forecasts - zonal wind spread at 500 hPa**

- Sondes

- Control

- ADM-Aeolus

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

## Data impact on ensemble forecasts - zonal wind spread at 200 hPa

**Data impact on ensemble forecasts - zonal wind spread at 200 hPa**

- Sondes

- ADM-Aeolus

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

## ADM-Aeolus pre-launch campaignswith development/pre-flight instrument (A2D)

| 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-Oberpfaffenhofenover-flights Lindenberg andother sites | 15 days 
Oct 2007  | A2D and 2μm in DLR Falcon
DWD-MOL instruments as in AGC
 |
| ADM- Aeolus Airborne Campaign 2 | TBD | 17 days2008/9 | A2D and 2μm in DLR Falcon
additional instruments, if linked to other campaign |

**Ack: Oliver Reitebuch**

## 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 (< 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**

## 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?**

## 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)**

## 2a-4. Other NWP configurations

## 1a/b. What are Level-2B/2C Products?

## 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)**

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

**ECMWF for “operational” Level-2B/2C products**
**Processing integrated with data assimilation system**
**Products in ESA’s Earth Explorer file format available from ESA (Long-Term Archive)**
**ESA LTA for Level-2B late- & re-processing**
**Level-1B missing ECMWF’s 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**

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

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

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

## 1.3 Integration of Aeolus L2BP at ECMWF

** **

** ****L1B preprocessing (if required) **

**Notes:**
The size of the control vector is approx 8M.

## 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**

** **

**Notes:**
The size of the control vector is approx 8M.

## 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**** ACCD****m**** W****m****, W****m ****between 0 and 1**
**Error estimate provided for every Rayleigh & Mie hlos**
**dominant contributions are SNR in each channel**

## Rayleigh-Brillouin spectrum and Aeolus response curves

**Rayleigh-Brillouin spectrum and Aeolus response curves**

- Rayleigh
- Rayleigh-Brillouin

*R* = *(A-B)/(A+B)*

**Dabas et al., Tellus accepted**

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

- Poli/Dabas
- Meteo-France

**3b. Level-2B hlos wind retrievals**

- Poli/Dabas
- Meteo-France

## 3b. Level-2B hlos retrieval - error estimates

## 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

## 5.3 L2BP integration within an assimilation system

- ODB

**4DVar**

**5.3 L2BP integration within an assimilation system**

- screening

- Background   T p

- L1B-odb             lat lon

-                            AMD-odb

- Background hlos

- L2B-odb

- Obs - Bg, BgQC, etc

- L2B Processor

- L1B%  AMD%

- L2B%

- Wrapper module copies “odb” variables to & from % data structures
- (hidden from screening)
- C.f. RTTOV + SSMI
- TCWV

## 5.4 Overview data flow – standalone mode

- AMD

- L2B

- L1B

**5.4 Overview data flow – standalone mode**

- EE

- L2BP_standalone

- L2B Processor

- L1B%  AMD%

- L2B%

## 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**

## 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**

**L2bP**

- 24 Mie or Rayleigh height bins

- 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

**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**

- Aux met height bins, 1 per model level

## 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)**

**Notes:**
Expliquez ici ce que l’on cherche à faire, pourquoi, et qui a participé à l’étude.

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

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

**Notes:**
Montre comment la réponse du lidar est liée à la vitesse Doppler et mets en évidence l’influence de la température, de la pression et du Mie. Utiliser le dernier transparent si celui-ci ne fonctionne pas.

## ILIAD - Baseline Inversion Scheme

**Aux. data**

**Notes:**
Reprendre ici le schéma présenté lors du PM3 d’ILIAD dans lequel on voit les entrées/sorties et le chemin qui mène à l’inversion.

## 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 ****F****d**** ****(i,j,k) **** ****giving the inverse frequency (or velocity) for pressures P****i****=P****0****+i********P, Tj=T****0****+j********T and R****k****=R****0****+k********R**
_**Output parameters**_**:**
**V****r****(P****mod****,T****mod****,********=1)** where Pmod and Tmod are the pressure and temperature inside the sensing volume as predicted by the NWP model.
**dv****r****/dP, dv****r****/dT** and **dv****r****/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 dv****r****/d******

**Notes:**
Montrer le principe en deux étapes. Pour l’étape n°2, se servir du schéma présenté lors du PM3 d’ILIAD 

## ILIAD - Practical implementation

- Global aux. File
- Processed once before launch
- ~8 Mb

**Rayleigh-Brillouin**
**spectra**

- TA(fm), TB(fm)
- f=25 MHz

**ISR**

- Aux. File
- Processed every time
- an ISR is carried out
- ~2 Mb

- Look-up table

- INVERSION

- Part of L2Bp
- Applied to all Rayleigh responses

## Aeolus satellite layout 

- 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

- 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

**1 km: 66% of Mie have accuracy < 1 m/s (aerosol & cloud returns)**
**Adequate transmission through overlying cloud**

**Data simulations for ADM-Aeolus****Yield (%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**

## 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**

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

## Quality Control Examples: Std + Aeolus-optional QC for DWL -- active

**Quality Control Examples: Std + Aeolus-optional QC for DWL -- *****active***

- Radiosonde U-wind

**Option improves departure statistics**