CCI CMUG Integration Meeting 14.-16.03.2011 Rainer Hollmann Deutscher Wetterdienst Cloud cci

CCI CMUG Integration Meeting 14.-16.03.2011

Rainer HollmannDeutscher Wetterdienst

Cloud cci


• Overview of requirements

• Product specifications

• Uncertainties

• Consistency with needs of climate Research Group

• consistency with other ECVs

• (ECMWF data needs … are documented in DARD)

Overview of talk


User requirements

• URD: compilation of requirements from the users point of view covering ideally the full spectrum of applications

• Product specification: The answer to the URD what is achievable and feasible and how it is designed


User Requirements

• Kent et al (2008): “The definition of user requirements for the marine observing system is not a simple process. When asked, users have a tendency to ask "what can you provide?" or reply "as much as possible“

• Cloud User requirements based on• GCOS

• CMUG

• EPS-SG, MTG, CM SAF

• WMO DOR, Database of observational requirements

• ESA CCI Variables

• Globsnow (a few days ago)


Requirements as stated in GCOS-107, product A-4:

GCOS requirements for clouds

Requirements as stated in GCOS-107, product A-4, update 2011

Accuracy Spatial resolution

Temporal resolution

Stability (target)

JustificationSource

Cloud Cover 0.10 100 km 3 h n/a n/a

Cloud

height

500 m 100 km 3 h n/a n/a

Cloud temperature

0.3 K 100 km 3 h n/a n/a

Cloud ice profile

n/a 100 km 3 h n/a n/a

Cloud water profile

n/a 100 km 1-3 h n/a n/a

Accuracy Spatial resolution

Temporal resolution

Stability (target)

JustificationSource

Cloud Cover 0.05 50 km 1-3 h 0.01/dec CFMIP

Cloud pressure

50 hPa 50 km 1-3 h 0.25 K/dec

NISTIR-7047

Cloud temperature

1 K 50 km 1-3 h 5 hPa/dec

NISTIR-7047

Cloud ice profile

25 % 50 km 1-3 h 2 %/dec NISTIR-7047

Cloud water profile

10 % 50 km 1-3 h 5 %/dec NISTIR-7047


CCI projects

Cloud variable

Time-period

Instruments / Satellites (e.g.

Modis, AVHRR)

Comments(any)

Aerosols CCI

Cloud cover 2008, 1997 A(ATSR)-2, MERIS, AVHRR/3, GOME(-2), SCIAMACHY, OMI, POLDER

(1) the major goal are consistent cloud masks (avoiding double counting of pixels as cloud and as aerosol and allowing for a interim zone which is exploited by neither ECV) for ATSR instruments as baseline(2) More years proposed in options; long-term record feasible 1995 – 2020

Cloud Properties requirementsfrom CCI variables (I)


CCI projects

Cloud variable

Time-period


Modis, AVHRR)

Comments(any)

Sea Surface Temperature CCI

Cloud cover, cloud optical thickness

(within Aug 1991- Dec 2010)

AVHRR GAC and (A)ATSR full resolution

A substantial sample only is required. Cloud cover to identify failures-to-detect in our cloud mask. Cloud optical thickness to assess if there is a critical thickness to be masked.

Fire CCI Cloud cover, cloud top

height

1995 - 2009 ATSR, AATSR, VEGETATION,

MERIS-RR, MERIS-FRS

Cloud cover is needed for masking pixels. In addition, cloud top height would be of interest for cloud shadow identification.

Cloud Properties requirementsfrom CCI variables (II)


CCI projects

Cloud variable

Time-period


Modis, AVHRR)

Comments(any)

Land Cover CCI

x1: cloud cover, cloud top heightx2: cloud optical thickness, cloud top height, cloud top pressure

From 1998 (sure)From 1992 (to be confirmed)

MERIS Full Resolution (from 2005)MERIS Reduced Resolution (from 2005)SPOT-VGT (from 1998)NOAA-AVHRR (from 1992 – to be confirmed)(A)ATSR (from 2005 - to be confirmed)

X1 - pixel identificationX2 - atmospheric correctionrequest: cloud shadow

Cloud Properties requirementsfrom CCI variables (III)


• Cloud CCI URD is reflecting all important Req.

• has been forwarded to open review of science community (GEWEX RP)

• Needs of Cloud CCI Climate Research are included.

Consistency with needs of Climate Research Group


Consistency with other CCI‘s

• Cloud CCI provided URD to all CCI SC for feeback

• received positive feedback from Aerosols, Land Cover, SST, Fire

• needs are documented in URD covering different time periods and instruments

• Most likely cloud CCI will not be able to cover all additional requirements

• Evaluation process ongoing


Consistency on Level 1 Radiances

• CCI URD addresses the GCOS need for FCDR for all instruments

• CCI Cloud is performing Level 1 Intercalibration for AVHRR-AATSR-MODIS instruments

• CCI Variables based on same instruments should agree on the same FCDRs!


Temporal resolution of final products(based on local satellite equator crossing time)

• AATSR/MERIS (ENVISAT): 10:00 am/pm

• AVHRR (N15, N16, N17, N18): 10.00 am/pm (drifting), 02:00 am/pm (drifting), 10:00 am/pm, 01.30 am/pm

• MODIS (AQUA,TERRA): 01:30 am/pm, 10:30 am/pm

Providing the final products:• Monthly means for each individual sensor• Monthly means for each sensor group (e.g. all AVHRRs)• Monthly means for all sensors on all satellites

• AATSR-MODIS-AVHRR• AATSR-MERIS

Current baseline: Specifications


Name Satellite data

Temporalresolution

MM at sat equator crossing time (L2 is swath

based)

Spatialresolution

AccuracyError estimates included

in dataset

Goal Threshold

Cloud cover(high/mid/low-level)

AATSRAVHRRMODIS

- AATSR (ENVISAT): equator crossing times- AVHRR (N15, N16, N17, N18): equator crossing times- MODIS (AQUA,TERRA): equator crossing times

L3: 50 kmL2: swath based

MM:better than 0.08 cloud fraction or 10% bias 20% bc-rms

MM:better than 0.15 cloud fraction or 20% bias40% bc-rms

Cloud top height

AATSRAVHRRMODIS

Same… L3: 50 kmL2: swath based

MM:300m/300m/800m1500m bc-rms

MM:800m/800m/1200m3000m bc-rms

Current baseline: Specifications (I)


Name Satellite data

Temporalresolution

MM at sat. equator crossing time (L2 is swath based)

Spatialresolution

AccuracyError estimates included

in dataset

Goal Threshold

Cloud top temperature

AATSRAVHRRMODIS


MM:2/3/5 K bias5/7/9 K bc-rms

MM:4/6/10 K bias6/9/13 K bc-rms

Liquid water path

AATSRAVHRRMODIS


better than25 g/m2 or15% bias30% rmse

better than25 g/m2 or30% bias50% rmse

Ice water path

AATSRAVHRRMODIS

Same L3: 50 kmL2: swath based

better than 25 g/m2 or25% bias40% rmse

better than25 g/m2 or 40% bias70% rmse

Current baseline: Specifications (II)


Product compliance (I)

ID Horizontal Resolution (km)

Temporal Resolution (h)

Accuracy

TS

GCOSCMU

GWCR

PTS GCOS

CMUG

WCRP

TS GCOS CMUG WCRP

cloud CCI_cc

50

100 G100 BT500 T

10 MD30 TM

50 G85.5 BT250 T

ten times daily

346

13

34.812

10% bias20% bc-rms

10%15%20%

5%5%

5%7.9%20%

cloud CCI_cth

50

100 G200 BT500 T

10 MD30 TM5 DA

100 G171 BT500 T

ten times daily

346

131

1215.124

15% bias1500m bc-rms

0.5km1.0km2.0km

0.1km0.2km0.1km

0.5km0.63km1km

cloud CCI_ctt

50

100 G200 BT500 T

10 MD30 TM

50 G85.5 BT250 T

five times daily

346

13

34.812

2/3/5K bias5/7/9K bc-rms

0.3K0.4K0.6K

0.25K0.25K

0.5K0.794K2K


Product compliance (II)

ID Horizontal Resolution (km)

Temporal Resolution (h)

Accuracy

TS

GCOSCMU

GWCRP TS

GCOS

CMUG

WCRP

TS GCOSCMU

GWCRP

cloud CCI_lwp

50

100 G200 BT500 T

10 MD

50 G85.5 BT250 T

five times daily

346

1 34.812

15% bias30% rmse

10(5)kg/m²20(10)kg/m²50(20)kg/m²

- 10kg/m²17.1kg/m²50kg/m²

cloud CCI_iwp

50

100 G200 BT500 T

10 MD

50 G85.5 BT250 T

five times daily

346

1 34.812

20% bias40% rmse

10g/m²15g/m²20g/m²

- 10g/m²12.6g/m²20g/m²


Product compliance (III)

Summary on compliance for Cloud CCI:

Specification meet the GCOS 107 Requirements

Further discussion on requirements from CMUG with TO & SC needed

• Joint work on cloud simulator approach

• Identifying case studies to supply full inst. data for process studies

• …


• Focus on Level 2 uncertainties

• Reference data will be A-Train data and high quality surface networks

• Possibly include threshold limit to allow filtering of model data

• Started process of evaluation of Level 2 to Level 3 uncertainties supported by international science community (CREW, GEWEX Cloud Assessment)

• Aiming at radiative consistency on Level 3

Uncertainties


Error uncertainties Level 2

Non Systematic errors:• Retrieval error: Uncertainty estimation of the retrieval system itself based

on optimal estimation methodology.• Sampling error: Due to the sampling of temporally and spatially unequally

distributed samples. Systematic errors (bias)• Radiometric Bias: calibration issue• Retrieval Bias: Biases related to shortcomings in a retrieval itself.• Sampling/Contextual Bias: Biases related to where a retrieval is/is not

performed or contextually related uncertainty in a scene.• Aggregation/Data Reduction Bias: Loss of required information during

conversion to a gridded product or during analysis.

Other considerations• Cognitive Bias ?• Correlated error "Independent" products that share similar biases; • Tautology -Circular reasoning or treating non-independent data as

independent during tuning.



Temporal and spatial resolution

N128 reduced Gaussian grid, ERA-Interim reanalysis at 00 UTC and 12 UTCERA-Interim forecast 00 UTC +06h and +12h, ERA-Interim forecast 12 UTC +06h and +12h

Parameter ECMWF parameter description

Availability at ECMWF (comments)

Land/Sea mask Land-sea mask (172) As surface property

Surface temperature Skin temperature (235)Sea surface temperature (34)

As surface property(skin temperature to be used for land surfaces; sea surface temperature to be used for sea surfaces)

Sea ice mask Sea-ice fraction (31) As surface property

Surface pressure Log surface pressure (152) At model levels (requested for surface only)

Snow albedo Snow albedo (32) As surface property

10m winds 10 metre U wind comp. (165)10 metre V wind comp. (166)

As surface property

Cloud CCI requirements from ECMWF (I)


Temporal and spatial resolution

N128 reduced Gaussian grid, ERA-Interim reanalysis at 00 UTC and 12 UTCERA-Interim forecast 00 UTC +06h and +12h, ERA-Interim forecast 12 UTC +06h and +12h

Parameter ECMWF parameter description

Availability at ECMWF (comments)

2m temperature 2 metre temperature (167) As surface property

Temperature profile Temperature (130) At model levels (entire profile requested)

Specific Humidity profile

Specific humidity (133) At model levels (entire profile requested)

Geopotential height Surface geopotential (129) At model levels (requested for surface only; profile to be calculated)

TCWV climatology Monthly means of TCWV (137)

As climatology

Cloud CCI requirements from ECMWF (II)

Documents

CCI CMUG Integration Meeting 14.-16.03.2011 Rainer Hollmann Deutscher Wetterdienst Cloud cci