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• Autour des Observations Spatiales, 20120203
• Equipe Intro
• Personnes concernées : G. Cesana, H. Chepfer, C. Hoareau, P. Messina, V. Noel, M. Reverdy, S. Stromatas, S. Turquety...
2
PRESSU
RE (hP
a) CALIPSO
Observa5ons
0 8.13.4 40.516.5
CLOUDFRACTION
CLOUD OPTICAL DEPTH (CALIOP)
-‐ op&cally thick clouds : mul&layer-‐ very thick clouds : abundance of high clouds
0
0.1
0.3
0.2
0.4
800
600
200
400
300
500
700
900
LMDZ5-‐NP + SIM
CLOUD FRACTION
Objec&f : Observer et comprendre les nuages, leur évolu5on naturelle(à différentes échelles spa5o-‐temporelles) et leur évolu5on en réponse aux forçages externes
LEVEL1A-‐train, CALIPSO, GOCCP,PARASOL, CERES, CLOUDSAT, MODIS COSP
Collabora&on: CFMIP & Atrain
Perspec5ve: A-‐train, Megha-‐Tropique, EarthCare, ADM-‐Aeolus
D. Konsta Modèle (pour info)
0
0.1
0.3
0.2
0.40 8.13.4 40.516.5
800
600
200
400
300
500
700
900
0 0.40.2 0.80.6 0 0.40.2 0.80.6CLOUDY REFLECTANCECLOUDY REFLECTANCE (MODIS)
-‐ op&cally thin clouds : low level, low CF
Objec&f:Comprendre la transi5on de la phase de l’eau dans les nuages et ses implica5ons pour la sensibilité clima5que
OBS GOCCP
LMDZ
LMDZ +
SIM
Latitude
LIQ CLOUDICE CLOUD
Altitude
CALIPSO L1CALIPSO-‐GOCCP/LMD (L2 & L3)COSP simulateur Lidar
Collabora5on: GEWEX-‐CACFMIP
Perspec5ve: Obs décennales CALIPSO+Earth-‐Care
G. Cesana
Latitude
liquid fraction f(T°)
LMDZ+SIM
OBS GOCCP
-40°C 0°C
4
NUAGES DE GLACE ET VAPEUR D'EAUV. Noel, H. Chepfer, M. Reverdy, C. Hoareau
Q1 Quelles sont les propriétés et la distribution des nuages de glace(y compris subvisibles)
• à l'échelle globale ? aux pôles ? aux Tropiques ?• spatialement ? temporellement ?
• Observations profils L1 CALIOP
• coefficient de retrodiffusion• rapports de dépolarisation / couleur
• moyennage, filtrage, aggrégation
• temporel, géographique
• restitution de propriétés (optiques, microphysiques)
• détection / seuillage
• statistique data mining, identification de populations, classification, interprétation
3° in November 2007 [Hu et al., 2009]; such orientedcrystals are however almost nonexistent in clouds colderthan −40°C [Noel and Chepfer, 2010] and their removalshould not impact the present results. The permitted colorratio range is voluntarily large in order to account forfluctuations in the 1064 nm channel calibration [Hunt et al.,2009]. However, due to these fluctuations, the color ratiowill only be used for cloud filtering purposes.3.1.4. Application of the Cloud Layer Detectionto a Single Orbit[23] As an example, Figure 1 shows the nighttime orbit
1155 UT on 1 January 2007. Figure 1 (top) shows b′532(using a logarithmic color scale) between 60°S and 60°Nfrom ground to 22 km, Figure 1 (middle) shows the result ofthe cloud detection and filtering described above. Cloudsthat completely attenuate the signal (e.g., around 5°N at15 km and 30°–45°N at 10 km) are entirely removed exceptat their edges (because of their lower geometrical thickness)while the highest and thinnest clouds, mostly found in thetropical latitudes, keep an accurate structure. Figure 1(bottom) shows b′532 inside cloud layers after applying thesame selection and filtering, but using cloud boundariesfrom the NL2 data set. There is little difference betweenFigure 1 (middle) and Figure 1 (bottom) except between15°N and 25°N where NL2 algorithms detect a smaller partof the thin high cloud at ∼18 km of altitude.
3.2. Statistical Comparison of Retrieved CloudFraction With CALIOP NASA Level 2 Products[24] The algorithm described in section 3.1 was applied on
2.5 years of CALIOP level 1 observations (June 2006 toDecember 2008). The resulting ensemble of cloud detectionsis called the Subvisible‐Enhanced level 2 (SEL2) data sethereafter. We then define cloud fraction as the ratio betweenthe number of CALIOP profiles in which cirrus clouds weredetected according to the criteria in section 3.1.3 and the totalnumber of profiles in each longitude‐latitude bin of the grid(section 2.2). Zonal variations of total cirrus cloud fraction,for each season, from the NL2 and SEL2 data sets arecompared in Figure 2, and averages are compared for severallatitude bands in Table 1. In the two data sets, high cloudfractions appear at tropical latitudes (45%–55% comparedto 5%–25% in midlatitudes) for all seasons, and cloud frac-tion maximum follows the movements of the IntertropicalConvergence Zone (ITCZ), near the equator in DJF andaround 10°N in JJA. The cloud fraction maximum is lower inJJA (45% in CALIOP NL2 and 51% in SEL2) than in DJF(48% and 55%, respectively). The cloud fraction minimumin the subtropics (∼5%–15%) also moves northward fromDJF to JJA. The cloud fraction is higher at all latitudes inSEL2 than in NL2 data for the optically thin clouds con-sidered in the present study. Differences in cloud layers in the
Figure 1. Time series of b′532 in log scale from CALIPSO level 1 (top) before and (middle) after clouddetection from the algorithm described in the text and (bottom) from CALIPSO level 2 for the nighttimeorbit 1155 UT on 1 January 2007. Only nontotally attenuating clouds, with maximum temperature colderthan −40°C, 0.1 < dp < 0.7, 0.7 < cp < 1.5, base < (tropopause + 1 km), and t > 0.001 were selected in thetwo data sets.
MARTINS ET AL.: CIRRUS AND SVC FROM CALIOP D02208D02208
4 of 19
3° in November 2007 [Hu et al., 2009]; such orientedcrystals are however almost nonexistent in clouds colderthan −40°C [Noel and Chepfer, 2010] and their removalshould not impact the present results. The permitted colorratio range is voluntarily large in order to account forfluctuations in the 1064 nm channel calibration [Hunt et al.,2009]. However, due to these fluctuations, the color ratiowill only be used for cloud filtering purposes.3.1.4. Application of the Cloud Layer Detectionto a Single Orbit[23] As an example, Figure 1 shows the nighttime orbit
1155 UT on 1 January 2007. Figure 1 (top) shows b′532(using a logarithmic color scale) between 60°S and 60°Nfrom ground to 22 km, Figure 1 (middle) shows the result ofthe cloud detection and filtering described above. Cloudsthat completely attenuate the signal (e.g., around 5°N at15 km and 30°–45°N at 10 km) are entirely removed exceptat their edges (because of their lower geometrical thickness)while the highest and thinnest clouds, mostly found in thetropical latitudes, keep an accurate structure. Figure 1(bottom) shows b′532 inside cloud layers after applying thesame selection and filtering, but using cloud boundariesfrom the NL2 data set. There is little difference betweenFigure 1 (middle) and Figure 1 (bottom) except between15°N and 25°N where NL2 algorithms detect a smaller partof the thin high cloud at ∼18 km of altitude.
3.2. Statistical Comparison of Retrieved CloudFraction With CALIOP NASA Level 2 Products[24] The algorithm described in section 3.1 was applied on
2.5 years of CALIOP level 1 observations (June 2006 toDecember 2008). The resulting ensemble of cloud detectionsis called the Subvisible‐Enhanced level 2 (SEL2) data sethereafter. We then define cloud fraction as the ratio betweenthe number of CALIOP profiles in which cirrus clouds weredetected according to the criteria in section 3.1.3 and the totalnumber of profiles in each longitude‐latitude bin of the grid(section 2.2). Zonal variations of total cirrus cloud fraction,for each season, from the NL2 and SEL2 data sets arecompared in Figure 2, and averages are compared for severallatitude bands in Table 1. In the two data sets, high cloudfractions appear at tropical latitudes (45%–55% comparedto 5%–25% in midlatitudes) for all seasons, and cloud frac-tion maximum follows the movements of the IntertropicalConvergence Zone (ITCZ), near the equator in DJF andaround 10°N in JJA. The cloud fraction maximum is lower inJJA (45% in CALIOP NL2 and 51% in SEL2) than in DJF(48% and 55%, respectively). The cloud fraction minimumin the subtropics (∼5%–15%) also moves northward fromDJF to JJA. The cloud fraction is higher at all latitudes inSEL2 than in NL2 data for the optically thin clouds con-sidered in the present study. Differences in cloud layers in the
Figure 1. Time series of b′532 in log scale from CALIPSO level 1 (top) before and (middle) after clouddetection from the algorithm described in the text and (bottom) from CALIPSO level 2 for the nighttimeorbit 1155 UT on 1 January 2007. Only nontotally attenuating clouds, with maximum temperature colderthan −40°C, 0.1 < dp < 0.7, 0.7 < cp < 1.5, base < (tropopause + 1 km), and t > 0.001 were selected in thetwo data sets.
MARTINS ET AL.: CIRRUS AND SVC FROM CALIOP D02208D02208
4 of 19
3° in November 2007 [Hu et al., 2009]; such orientedcrystals are however almost nonexistent in clouds colderthan −40°C [Noel and Chepfer, 2010] and their removalshould not impact the present results. The permitted colorratio range is voluntarily large in order to account forfluctuations in the 1064 nm channel calibration [Hunt et al.,2009]. However, due to these fluctuations, the color ratiowill only be used for cloud filtering purposes.3.1.4. Application of the Cloud Layer Detectionto a Single Orbit[23] As an example, Figure 1 shows the nighttime orbit
1155 UT on 1 January 2007. Figure 1 (top) shows b′532(using a logarithmic color scale) between 60°S and 60°Nfrom ground to 22 km, Figure 1 (middle) shows the result ofthe cloud detection and filtering described above. Cloudsthat completely attenuate the signal (e.g., around 5°N at15 km and 30°–45°N at 10 km) are entirely removed exceptat their edges (because of their lower geometrical thickness)while the highest and thinnest clouds, mostly found in thetropical latitudes, keep an accurate structure. Figure 1(bottom) shows b′532 inside cloud layers after applying thesame selection and filtering, but using cloud boundariesfrom the NL2 data set. There is little difference betweenFigure 1 (middle) and Figure 1 (bottom) except between15°N and 25°N where NL2 algorithms detect a smaller partof the thin high cloud at ∼18 km of altitude.
3.2. Statistical Comparison of Retrieved CloudFraction With CALIOP NASA Level 2 Products[24] The algorithm described in section 3.1 was applied on
2.5 years of CALIOP level 1 observations (June 2006 toDecember 2008). The resulting ensemble of cloud detectionsis called the Subvisible‐Enhanced level 2 (SEL2) data sethereafter. We then define cloud fraction as the ratio betweenthe number of CALIOP profiles in which cirrus clouds weredetected according to the criteria in section 3.1.3 and the totalnumber of profiles in each longitude‐latitude bin of the grid(section 2.2). Zonal variations of total cirrus cloud fraction,for each season, from the NL2 and SEL2 data sets arecompared in Figure 2, and averages are compared for severallatitude bands in Table 1. In the two data sets, high cloudfractions appear at tropical latitudes (45%–55% comparedto 5%–25% in midlatitudes) for all seasons, and cloud frac-tion maximum follows the movements of the IntertropicalConvergence Zone (ITCZ), near the equator in DJF andaround 10°N in JJA. The cloud fraction maximum is lower inJJA (45% in CALIOP NL2 and 51% in SEL2) than in DJF(48% and 55%, respectively). The cloud fraction minimumin the subtropics (∼5%–15%) also moves northward fromDJF to JJA. The cloud fraction is higher at all latitudes inSEL2 than in NL2 data for the optically thin clouds con-sidered in the present study. Differences in cloud layers in the
Figure 1. Time series of b′532 in log scale from CALIPSO level 1 (top) before and (middle) after clouddetection from the algorithm described in the text and (bottom) from CALIPSO level 2 for the nighttimeorbit 1155 UT on 1 January 2007. Only nontotally attenuating clouds, with maximum temperature colderthan −40°C, 0.1 < dp < 0.7, 0.7 < cp < 1.5, base < (tropopause + 1 km), and t > 0.001 were selected in thetwo data sets.
MARTINS ET AL.: CIRRUS AND SVC FROM CALIOP D02208D02208
4 of 19
tropics ∼75% of cirrus clouds are colder than −60°C, whilein midlatitudes this is true for only ∼10% of cirrus clouds. Inthe tropics, SVC are slightly colder on average (−66°C) thancirrus clouds (−63°C) whereas in midlatitudes their averagemidlayer temperature is the same (−52°C). It has to be keptin mind that the midlayer point is lower for a cirrus cloudthan for a SVC, which might explain in part this result.[28] Figure 6 shows distributions of particulate depolar-
ization ratio dp of ice crystals in SVC (dashed lines) andcirrus clouds (solid lines). Values are typical of cirrus clouds[e.g., Sassen and Benson, 2001] and show latitude depen-dence: dp is higher in the tropics (0.4–0.5 in average) than inmidlatitudes (0.3–0.4) for both cloud types. The mean dp islower by 0.03 for SVC compared to cirrus clouds either intropics or midlatitudes, which implies that crystals in SVChave slightly less complex shapes; that is, crystals in SVCare either conceptually simpler, like plates, or complexshapes whose sharp edges were smoothed out by sublima-
tion. Previous lidar studies [e.g., Noel et al., 2006] haveoften concluded the depolarization ratio varies with tem-perature, since it influences crystal nucleation and growthmechanisms and therefore directs particle shape; coldertemperatures are usually linked to higher dp. This is con-sistent with present results that show higher dp for cirrusclouds in the tropics, which are colder, than in midlatitudes(see Figure 5); however this is inconsistent with presentresults that show lower dp for SVCs than for cirrus clouds,which are warmer. This suggests that, at least in SVC,temperature is not the only influence driving particle shapeand depolarization.
4. Cirrus Clouds and Vertical Motionsin the Tropics
[29] In this section we investigate the impact of synopticvertical air motions (convection or subsidence) on cloud
Figure 3. Seasonal maps of subvisible cirrus cloud fraction (0.001 < t < 0.03) between ±60°, in percent.
MARTINS ET AL.: CIRRUS AND SVC FROM CALIOP D02208D02208
6 of 19
NUAGES DE GLACE ET VAPEUR D'EAUV. Noel, H. Chepfer, M. Reverdy, C. Hoareau
Q2 Comment les nuages de glace interagissent avec
• la vapeur d'eau ?
• les injections d'aérosols (éruptions, feux de biomasse) ?
• la convection ?
CALIOP L1 (profils retrodiff/rapports)MLS L2 (profils H2O), AIRS ?
OMI L2 (total NO2/SO2)
METEOSAT, TRMM L4 (imagerie IR)
• Approche :
• traitement + restitution/détection + statistique
• colocalisation géographique / temporelle
• correlations, classification, interprétation
+ WRF, ERA-I, in-situ, ground obs
• ClimServ
• Icare
Q3 Quelles sont les propriétés et la répartition des PSC ?
Q4 Comment interagissent-ils avec leur environnement chimique (HNO3, SO2) et dynamique (ondes de gravité) ?
• CALIOP L1 (profils retrodiff/rapports)• MLS L2 (profils chimie)+ WRF, ERA-I
NUAGES STRATOSPHÉRIQUES POLAIRES (PSC)V. Noel, H. Chepfer, A. Hertzog
90°W 75°W 60°W 45°W 30°W
70°S
60°SGravity waves frequency, JJA 2006-2010
0.0
2.5
5.0
7.5
10.0
12.5
15.0
17.5
20.0
22.5
25.0
Freq
uenc
y [%
]
100°W 80°W 60°W 40°W 20°W 0°E 20°E 40°E 60°E 80°E 100°E0
25
50
75
100
125
Ice
PSC
vol
ume
per p
rofil
e -
km
0
50
100
150
200
250
NA
T PS
C v
olum
e pe
r pro
file
- km
100°W 80°W 60°W 40°W 20°W 0°E 20°E 40°E 60°E 80°E 100°E0
25
50
75
100
125
Ice
PSC
vol
ume
per p
rofil
e -
km
0
50
100
150
200
250
NA
T PS
C v
olum
e pe
r pro
file
- km
WRF
NAT PSCH2O + HNO3
ice PSC
vortex
8
composition chimique et qualité de l’air
Questions scientifiques• Impact des feux de végéta&on sur la composi&on chimique (global) & sur la qualité de l’air (Europe) ? => Emissions, transport, évolu&on de la composi&on, etc.• Quan&fica&on de l’importance rela&ve des sources naturelles (dust, feux, biog.) et anthropiques sur la qualité de l’air en Euro-‐Méditerranée (S. Stromatas)
• Transport à longue distance HN: impact des principales sources hautes la&tudes, biais modèles globaux
Observations L2 Surface / feux: • Végéta&on MODIS • Ac&vité et puissance feux MODIS, SEVIRI• Surfaces brûlées MODIS
Approche généraleCalcul « boeom-‐up » des émissions par les feux :gaz traces et aérosols
à Intégra&on dans un modèle de chimie-‐ transport (régional CHIMERE, global LMDz-‐INCA)
Gaz traces • IASI: O3, CO & panaches feux NH3, C2H4, etc. • OMI / GOME-‐2 (NO2+formaldéhyde)+ in situ / surface : campagnes, MOZAIC-‐IAGOS, Airbase, etc.
Aérosols• A-‐Train: PARASOL, MODIS, CALIPSO L1 • SEVIRI/MSG : à venir• IASI ?• Surface: Airbase, AERONET, EARLINET
Evalua&on panaches / niveaux de fondè Comparaison simula&ons vs observa&ons-‐ Pondéra&on ver&cale pour les gaz traces-‐ Calcul des propriétés op&ques -‐ Calcul du signal lidar (SR, CR)
Ajustement des processus en fonc&on des différences constatées.
Exemple de travaux en cours: analyse de l’impact des feux
MODIS
Projet APIFLAME (PRIMEQUAL): Quan&fier l’impact des feux sur l’ozone et les PM de surface région Euro-‐Mediterranée & intégra&on dans les prévisions (www.lmd.polytechnique.fr/apiflame)1. Inventaire régional des émissions 2. Evalua&on u&lisant observa&ons satellitaires dans les
panaches – cas d’étude été 2007 3. Cartographie : fréquence et intensité de la contribu&on dans
la région sur 2003-‐2010
IASI CO(Turquety et al., 2009)
TOSCA IASI / EECLAT:Exploita&on des observa&ons satellitaires pour mieux contraindre les émissions; applica&on à différentes régions d’étude (tropiques, forêts boréales)
CHIMERE
PARASOL
SR: CALIOP vs CHIMERE
OMI
CHIMERE
NO2 tropo
ref + feux
advec&on distribu&on ver&cale photochimie
MODIS