thomas holzer-popp (dlr), stefan kinne (mpi-m) & the aerosol_cci team

Thomas Holzer-Popp (DLR), Stefan Kinne (MPI-M)& the Aerosol_cci team

aerosol_cci

Aerosol_cci > Thomas Holzer-Popp > ESA Living Planet Symposium, Bergen, 1 July 2010

slide 2

URD - sources

• GCOS as baseline

• CMUG as model-oriented update

Applications: model development, assimiation, decadal forecasting, trend monitoring

• AEROCOM as aerosol_cci CRG

Applications: process studies, trend monitoring

• MACC (currently added)

Application: assimilation (re-analysis)


slide 3

GCOS requirements

• Aerosol optical depthgoal threshold

• accuracy 0.01 0.02• stability 0.005 / decade N/A• resolution 1 km / daily 10 km / weekly

• Other aerosol properties

• to supplement AOD

• e.g. single scattering albedo• accuracy 0.02• stability 0.015 / decade

• Comprehensive ground-based independent validation

-> can not be met (per pixel) by any satellite product


slide 4

URD - CMUG


slide 5

URD - requirements

• Overall user needs:

Easy availability (netCDF)

proven and documented quality

aerosol properties should identify aerosol species linked to source categories

observation of long-term trends (many years) for regions and globally

analysis of specific issues (absorption, aerosol above clouds, vertical profile, …)

prepare for easy and complete re-processing with new versions


slide 6

URD - requirements

• Level2 products for data assimilation:

AOD at 4 wavelengths (440, 550, 670, 870 nm) and several layers

500 (1000) observations per hour

1 - 3 years covered

20 km ( 5 km) horizontal

3 km vertical

accuracy and precision 0.05 (0.02)

with pixel level uncertainty (random + systematic)

consistent with clouds and fire


slide 7

URD - requirements

• Level3 products for process studies and trend monitoring:

AOD at 4 wavelengths

Angstrom coefficient (440-870), fine mode fraction (D<1μm), dust fraction

Absorption aerosol optical depth (or single scattering albedo)

aerosol vertical extinction / AOD profile (any information is valuable)

accuracy: combined absolute (low AOD) and relative (high AOD)

error characteristics


slide 8

URD - requirements

derived stability need

regional AOD range (0.1 – 0.5) * 5% trend detection -> 0.005


slide 9

URD - requirements

Satellite variable (reference dataset)

required RMS at superpixel level of 10x10 km2

required RMS at climate model grid level of 1°x1°

required RMS at regional level of 1000x1000 km2

Aerosol optical depth at 550nm and other wavelengths (Aeronet global dataset daily mean)

20% or 0.05 10% or 0.02 0.02

Fine mode fraction (Aeronet global dataset daily mean)

20% or 0.1 20% or 0.1 0.1

Dust fraction (Coarse fraction from Aeronet global dataset in known dusty conditions daily mean)

30% or 0.2 30% or 0.2 30% or 0.2

Absorption optical depth (Absorption optical depth computed from SSA and size of Aeronet daily mean)

20% or 0.05 20% or 0.02 0.02


slide 10

URD - requirementsSatellite variable (reference dataset)

required monthly RMS month-to-month stability at regional level of 1000x1000 km2

required monthly RMS year-to-year stability at regional level of 1000x1000 km2

required monthly RMS decadal stability at regional level of 1000x1000 km2

Aerosol optical depth at 550nm and other wavelengths (Aeronet regional mean)

0.01 0.005 0.01

Fine mode fraction (Aeronet global dataset daily mean)

0.05 0.05 0.05

Dust fraction (Coarse fraction from Aeronet global dataset in known dusty conditions daily mean)

0.05 0.05 0.05

Absorption optical depth (Absorption optical depth computed from SSA and size of Aeronet daily mean)

0.002 0.005 0.005


slide 11

URD - consistency

• With CMUG requirements

Same basic parameters

CMUG gives higher priority and detail to vertical information

CMUG adds depolarisation ratio

Aerosol_cci adds aerosol type properties

• horizontal / temporal resolution:

CMUG one dimension more demanding

aerosol_cci adds regional level

• accuracy

aerosol_cci links to horizontal resolution and adds relative criterium

aerosol_cci has lower goal for aerosol type variables

agreement on regional level for AOD, difference for AAOD

• stability

aerosol_cci defines target per temporal analysis grid

agreement on decadal level for AOD, difference for AAOD


slide 12

URD - colocation

Done in URD

Done through discussions within AEROCOM

Iteration with CMUG, WMO-GAW SAG and other CCIs initiated, more iteration with AEROCOM to do

Is taken into account

Done through WMO-GAW SAG (membership of several aerosol_cci partners)

Template from ESA used and published on project website

Iteration with CMUG ongoing (March meeting)MACC input upgraded

Done in PSD


slide 13

URD - evolution

Aerosol_cci URD_v1.3 accepted

Continuing iterations adding MACC data assimilation requirements (v1.4)

Further iteration with other ECVs to complete their requirements

Feeding aerosol_cci URD into WMO-GAW SAG aerosol input to GCOS

aim to harmonize between different requirements –> iteration with contributors

SAG chair: “The aerosol_cci document is far superior to anything else that I have seen

concerning requirements.” – including CMUG table

Apreaciates tying to applications (CMUG) and accuracy resolution-dependance (aerosol_cci)


slide 14

URD - harmonization

SAG chair questions and recommendations

• overall process

• use the WMO RRR excel sheet to avoid missinterpretations and gaps

• add 1 line for each application / resolution

• questions

• can we harmonize application domains?

• can we harmonize / justify vertical layers (3km – 5km)?

• CMUG: Why 2 values for accuracy and precision?

• CCI: 1° x 1° : not equal area / shall we restrict requirements to level2 products?

• recommendations

• add duration, coverage (for process studies)

• add requirements for sub-orbital data (model/sat validation, assimilation)


slide 15

PSD - overviewProduct name Parameter(s) sensors level comment

Tropospheric / total column products

Single-sensorAOD / type

Multi-spectral AODAerosol type probability

ATSR-2 / AATSRMERISPOLDER

Level2,3 Multi-spectral AOD depending on instrument capabilities.

Ångstrom coefficient can be derived from multi-spectral AOD.Aerosol type may include information on fine / coarse mode fraction and chemical components, which together best describe the observations

SynergeticAOD / type

Multi-spectral AODAerosol type probability

AATSR/SCIAMACHYATSR-2/GOMEAVHRR/GOME-2

Level2,3

AAI Absorbing aerosol indexaveraging kernel

OMISCIAMACHYGOME

Level2,3

Merged AOD / type Multi-spectral AODAerosol type probability

Combining several level2 with appropriate wighting

Level3

Aerosol type “climatology”

Aerosol type probability / dominant aerosol type

All AOD products Level3 Based on one year of data

Stratospheric products

Extinction Gridded extinction profile

GOMOS(SCIAMACHY)

Level3


slide 16

PSD vs. URD (1)No. User requirement Product specification response

Product parameters

AOD at 4 wavelengths 4 AOD, depending on instrument capabilities

Ångström coefficient(based on AOD at 440 and 870 nm)

Implicit from 4 AODs where feasable

Fine mode fraction(based on 4 AOD)

Implicit from 4 AODs where feasable

(dominant) aerosol speciation or dust fraction

Implicit in aerosol type probability where feasable; focus of synergetic product

Absorption: AAOD or SSA Implicit in aerosol type probability where feasable; focus of AAI product

Extinction profile Not feasible with the sensor suite used

Numerical specifications

Accuracy To be established by validation

Precision To be established by validation

Horizontal resolution 10 x 10 km2 / 1° x 1°

Temporal resolution Daily (finer not possible with sensor suite used)

Stability To be established by validation


slide 17

PSD vs. URD (2)

Data access

Easy availability To be taken into account in system design

Long-term record Only partial (2 years – options to extend further)

Error documentation(indicating source of error)

Will be available via web portal, with metadata of each product pointing to it

System specifications

Easy reprocessing To be taken into account in system design

Technical specifications

Format netCDF as required

Metadata CF1-4 convention as required

Grid lat-lon for level3 as required

Product levels Level3 as requiredLevel2 based on MACC need / assimilation

Anciliary parameters

Cloud fraction(also for pixels without AOD)

Missing value for pixels without AOD result

Local error Pixel error contained in products


slide 18

Planned product use

Model development and inter-comparison -> AEROCOM

Data assimilation -> MACC (as far as possible)

Trend monitoring: only later (longer dataset needed)

options proposed for larger dataset processing


slide 19

ECV consistency

Other ECV participants in Aerosol_cci algorithm workshops

cloud masking (cloud, fire, landcover, ozone)

surface treatment (oceancolour, landcover, fire)

aerosol model -> interest of other ECVs to get our recommendation / tech note)

Consistent 3-level cloud masks with cloud_cci

Need recommendations from oceancolour_cci and landcover_cci

for chlorophyll / sediments and BRDF auxiliary datasets + reference data

exchange of URDs between ECV

aerosol_cci URD distributed to CMUG and ECVs -> collect further requirements

aerosol_cci contributed to cloud_cci URD


slide 20

Product uncertainties

Known uncertainty contributors

cloud screening

surface treatment

(assumed) aerosol optical properties

radiometric calibration

pixel size /atmospheric noise

radiative transfer / look-up tables

Characterization of uncertainty contributions

production of test datasets (1 month) with different algorithm versions

harmonization of critical modules for round robin – where possible


slide 21

Product uncertainties

Error source Absolute error contribution comment

calibration TBD Can be critical

Radiative transfer negligible Error due to Mie calculations for dust TBD

Noise due to pixel size 0.05 Varies for different continents

Cloud fraction TBD Validation showed that pixels up to cloud fraction of 35% can be exploited for the retrieval

Surface reflectance Land: 0.05 – 0.15 (for visible surface reflectance from 0 – 0.25)

Ocean: 0.03 – 0.1 (for visible surface reflectance from 0 – 0.05)

Choice of wrong aerosol type (spectral extinction, absorption, phase function)

0 – 0.2 for AOD550 0. – 1. Is estimated for each pixel by identifying ambiguous aerosol types through comparing quality of fit with difference between aerosol types with similar spectra)

An example: synergetic retrieval algorithm (first estimation based on validation results)


slide 22

ECMWF data needs

These have already iterated with D. Tann -> aerosol_cci DARD

ECMWF CodeOutput field

Units Analysis or Forecast Model levels Model Grid Needed for

165 10m east wind component m s-1 Analysis Surface GG (N128) EO retrieval

16610m north wind

componentm s-1 Analysis Surface GG (N128) EO retrieval

Parameters from ERA Interim, Atmospheric model, Analysiso Requested analysis times: 0000, 0600, 1200, 1800 UTCo Dates: 01/01/1997 to 31/12/1997; 01/10/2008 to 31/12/2008o Requested representation: Lat/long grido Requested representation: 0.7 degreeo Requested area: Global

Grib number

Grib Abbreviation

Units Name

165 10U m s-1 10m east wind component

166 10V m s-1 10m north wind component

thomas holzer-popp (dlr), stefan kinne (mpi-m) & the aerosol_cci team

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