Cloud characterization for cloudy radiance
assimilation
Lydie Lavanant1, Arlindo Arriaga2, Nadia Fourrié3, Antonia Gambacorta4, Giuseppe
Grieco5, Sylvain Heilliette6, Fiona Hilton7, Min-Jeong Kim8, Tony McNally9, Hidenori
Nishihata10, Ed Pavelin8, Florence Rabier3, Ben Ruston11, Claudia Stubenrauch12
1Météo-France/CMS
2EUMETSAT
3Météo-France/CNRM
4NOAA
5UNIBAS
6Centre
Météorologique Canadien 7MetOffice 8NCEP 9ECMWF
9Japan Meteorological Agency 11Naval Research Laboratory 12Laboratoire de
Météorologie Dynamique
ISSWG: recommendation for an intercomparison of clouds products
within IASI field of views, october 2009.
In parallel, proposal by WGNE to intercompare the assimilation of cloudy
radiances.
Initial study endorsed by WGNE, which helped embark a few more NWP
centres
IASI Cloud products Intercomparison
Teams:
Rationale:
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The first step is to detect and characterize the clouds
in the footprint of the sounder.
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MF/CMS
MF/CNRM
EUMETSAT
ECMWF (low opaque. 255 sits)
NOAA
CMC
METO
METO oper (cloud amount)
JMA
NRL (clear sit.)
LMD
NCEP
UNIBAS (cloud flag)
One way of investigating the limitations of a particular
methodology is to perform a careful intercomparison of
the results of different processing schemes for the
same observations.
Experimental settings:
All methods are applied to a 12-h global
acquisition on 18 Nov. 2009
IASI Cloud products Intercomparison
ƒ Various methods of detection and characterization:
¾ Mainly CO2-slicing methods (CNRM/GMAP, CMC, EUMETSAT, JMA,
MF/CMS)
¾ Cloud-clearing method using the 2x2 IASI spots in conjunction with AMSU
and MHS (NOAA)
¾ 1D-Var (MetOffice)
¾ minimum residual method (NCEP)
¾ Weighted χ2 method (LMD)
¾ Use of AVHRR information (MF/CMS, CMC)
ƒ Settings differ:
¾ from 8 channels to 92 channels. 165 for NCEP
¾ 1 reference channel for all channels or couples of channels
ƒ And many other differences
¾ single layer cloud or multiple layer (up to 3) clouds (NOAA, MF/CMS)
¾ RT models: RTTOV (from 7 to 9.3), RTIASI, SARTA, 4A, CRTM
¾ FOV to which the method is applied
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¾ A priori: NWP forecast or climatology (NOAA, LMD)
Examples of cloud top pressure over the globe
MetOffice
MF/CMS
The main meteorological structures
have been retrieved by all the schemes
but the cloud heights can be very
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different
NCEP
CMC
Examples of Cloud pressure scatterplots
CNRM
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Large discrepancies can be observed for
small cloud effective amount situations
Effective amount
Cloud pressure statistics. All data
MF/CMS
MF/CNRM
METO
EUMETSA
T
JMA
CMC
ECMWF
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NOAA
nb
mean / sig (hPa)
correlation
MF/CNRM
METO
EUMETSA
T
JMA
CMC
ECMWF
NOAA
NCEP
25990
30211
162120
37752
44335
104
54308
53931
55.7 / 81.8
42.8 / 103.3
-27.8/95.7
64.6 / 148.6
67.4 / 96.5
.94
.92
.88
.37
.89
.90
.81
.91
5984
36964
8064
9498
12354
13468
-4.9 /97.22
33.0 / 93.6
128.7 / 233.5
0.9 / 97.3
0
9.8 / 123.2
20.2 / 86.4
.91
.85
36629
.37
19920
.86
25840
27
.80
10317
.91
18204
33.8 / 132.5
27.5/ 215.0
6.6/ 129.4
10.0/190.8
14.0 / 151.2
22.7 / 94.4
.80
.59
57624
.85
60933
.62
.78
82622
.91
911899
95.3 / 208.0
-42.9 / 85.4
0
-36.4 / 135.2
-7.8 / 104.6
.38
.86
57625
39
.72
13866
.87
27921
-119.9 / 213.7
65.6/197.3
.51
.10
42
.27
15904
.81
31551
11.1/100.2
42.9 / 148.0
23.9 / 106.9
.91
.68
19
.87
56
157.2/160.2
53.3/116.8
.79
.86
27122
80.0 / 108.6 186.1 / 260.0 57.7 / 109.9
-110.6 / 248.2 -19.1 / 244.7
22.0 / 136.8
0.79
Cloud products Intercomparison
Distribution of cloud layers
CNRM
Histogram of the differences in
cloud effective amount with METO
CNRM
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Nb of cloud layers
Based on AVHRR clusters and ΔTcld >1K
| overcast / opaque single layer
Overcast(AVHRR cloud cover >.98) Thick (Tcld)
effective amount can be smaller
Opaque: cloud effective amount >.98
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1 layer
2 layers
3 layers
Cloud distribution for single layer situations
Overcast thick single layer. CMS definition
CNRM
2 peaks, better defined than
with all situations
9
66
Overcast and « thick » single layer. CMS definition
CNRM
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Effective amount:
Overcast « thick » single layer. CMS definition
MF/CMS
MF/CNRM
METO
MF/CNRM
METO
EUMETSA
T
JMA
CMC
ECMWF
NOAA
NCEP
12846
14838
85821
18897
20741
68
17670
28101
39.5 / 58.3
31.1 / 76.7
-37.6/82.6
39.6 / 118.1
46.7 / 74.9
0.97
0.95
1718
0.91
10297
0.42
2131
0.94
2354
.92
0.86
1908
0.95
3409
-8.4 / 78.2
18.9 / 76.0
101.8 / 205.8
-7.3 / 71.2
0
-1.7 / 80.9
7.9 / 72.4
0.95
0.91
10765
0.48
5078
0.93
6110
9
0.91
1469
0.96
4993
1.1 / 111.2
-48.4/91.2
-11.1 / 104.1
12.2 / 70.4
0.91
16043
.72
0.91
14814
0.96
21396
0
-19.8 / 99.7
-1.1 / 103.3
0.92
15631
12
0.83
1673
0.88
6013
-106.1 / 182.3
112/223.8
0.54
.10
13
0.31
1659
0.80
6871
0.3 / 27.5
29.0 / 118.0
19.0 / 96.6
.99
0.82
0.92
20
1
27.2/84.9
53.4 / 89.0 144.8 / 227.2 32.2 / 81.3
19.0 / 119.0 15.1 / 159.8
0.86
EUMETSA
T
0.75
15630
85.8 / 171.9 -31.7 / 69.7
0.48
JMA
CMC
ECMWF
-106.5 / 194.9 -14.4 / 253.6
.89
3198
NOAA
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34.9 / 103.1
0.88
Conclusions
ƒ The main meteorological structures have been retrieved by all the schemes
ƒ Cloud heights:
¾ In spite of different retrieval methods, MetO, NCEP, ECMWF, CMC, MF/CNRM and
MF/CMS outputs are close
¾ Similar methods lead to similar results (CMC and MF/CNRM); reversely, larger
differences come from different methods (CO2-slicing and Cloud-clearing).
¾ Larger differences near the surface: accuracy on Ts and T profile in low levels required
ƒ Single overcast and thick cloud layers:
¾ occurrence of these situations is about 50% in this study
¾ Correlations between schemes larger than .95 for these situations
¾ ECMWF overcast/opaque situations often determined with effective emissivity<1 by the
other teams. But good coherence in cloud height.
Publication:
ƒ In special issue of Quarterly Journal of the Royal Meteorological Society
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Comparison with A-Train on-going
+ Concordiasi
dropsonde
s
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