Mesoscale boundary layer clouds structures as observed during the semaphore campaign

Phys.Chem.Earth (B), Vol. 24, No. 8, pp. 933-938.1999

Q 1999 BlsevierScience Ltd

Fwgamon

All rightsreserved 1464-1909/99/s - see front matter PII: s1464-1909(99)00106-9

Mesoseale Boundary Layer Cloads During the Semaphore Campaign

Structures

as

A. Mathieu’, G. She2, C. Guerin’, H. Dupuis3 and A. Weill’

‘Centre d’Etudes des Environnements Terrestre et Plan&ire, VClizy, France 2Laboratoire de MCt6orologie Dynamique, Paris, France 3D % artement de G6ologie et d’oceanographie, Bordeaux, France Received

24 April 1998; accepted 20 December I998

Abrtract. Firat results on a comprehensive study of Marine Atmospheric Boundary Layer (MABL) clouds during the SEMAPHORE campaign are given. The low level clouds structures are determined from a dynamic cluster analysis applied to four channels of the AVHRR satellite radiometer over five days. To introduce our future objectives and’methods, a tentative synoptic structure analysis of the clouds is presented using a Global Ciiculation Model ‘(GCM) assimilated on the intensive zone of measuremebts. It is found that the GCM cloudiness is not strictly related to the observations, but reveals some correct trends, confirmed by ~JI analysis of the low level ARPEGE velocity convergence; it is thus infnred that the GCM modelling of the mean MABL contains enough information to explain at least partially the olmezvcd cloudb field. 8 1999 Elssvier Science Ltd. All rightsreserved. 1

model. The four-channel satellite AVHRR (Advanced Very High Beaolution Badiometer) data haa been chosen for its high resolution on the sane (1 .l x 1.lkm2 at nadir). Figure 1 gives, in AVHRR pixel representation, the visible 0.72 - l.lpm optical reflectivity. We can identify a bench of low level cellular zone at SE, a clear high clouds bench over the Azores basin at NW. The SEMAPHORE zone is about one tenth of the total surface in the center. The goal was then to examine the relationship (if it exists) between cellular cloud events with turbulent fluxes measured during the experiment. It turned out that most of the observed ever& occured outside of the SEMAPHORE zone; therefore a GCM analysis is used as a tentative link between the zones of cloud occurence and the area of measuremente. For this purpm we use the SEMAPHORE analysis version of ARPEGE (Action de Rechezche, Petite Echelle, Grande Echelle) derived from the French Met O&e operational model code (Giordani, 1997).

Introduction

This study is mai y concerned with the impact of Marine Atmospheric “$0 undary Layer (MABL) secondary fiows on the surface Atmosphere fluxes during the SEMAPHORE experiment (Structure des Echangea Mer-Atmosph&re,Propri&& des H&rog&%6a Oc&nique: Recherthe Experimentale). The SEMAPHORE campaign took place during October-November 1993 South East of the Azores, (Mid Atlaxitic 25W-35N 500)mx500Bm) (L.Eymard et al. 1996). We tried here an approach different from classical structure studiee ting aircraft (Nicolls and Le Mone, 1988), or air and shipbome remote sensing measure ment (Atlas et al., 1986; Bradley et al., 1991) to quote a few. Here, one autiysesspatial cloud signatures to derive information about surface using experimental data (ship and airborne measurements) and field data assimilated with a numerical meteorological operational Comapondence

to: [email protected]

2

Determination data analysis

of MABL clouds from AVHRR

In view of the cellular zone of Fig. 1, at first glance,we can analyse the difficulty of the detection, considering the lack of homogeneity and regularity of the observed zone, the uncertainty of elementary objects d&nition and also a defavorable high incidence of sun angle - most of the images have been taken on early mornings and late evenings. Moreover, we have the task of discriminating between low-level clouds and others since our objective is to analyse MABL clouds. Figure 2 shows an AVHRR image cloud clamiflcation obtained from a method developed by Seae and Deabois (1987) and bssed on a dynamic cluster analysis. This method - which was developed for MBTEOSAT data clsssifiea each pixel using four parameters: - Inst.: one infrared channel which gives information

933

934

A. Mathieu et al.: Mesoscale Boundary Layer Clouds Structures

Fig. 1. Channel 2 (0.72.-1 .lm), reflectance of the AVHRR 88Jellite ladiameter, S/11/93 17h127 Over the Azores region (approx. 3ooa 1 x 0-

504

locm

onclouds altitude. We have taken the 11.5-12.5pm signals (channel 5); - 2nd: one visible reflectance channel which gives information on the clouds optical thickness. The 0.72 - 1. lprn signals is used (channel 2); - 3rd and 4th: spatial local variances for the two pre vious signals to discriminate between uniform and non uniform clouds and, for non uniform clouds be tween thin cirrus and small cumulus. On Fig. 2 zoomed picture (white square of Figure 1, about 700 x 700km2) each colour is associated with a cloud class. From top to bottom, we have high level and cirrus clouds down to sea surface classes. We can appreciate, in particular, how high level clouds are well detected; this was not a surprise - despite of the high incidence angle, as this classification have been intensively used for high class studies (Giraud , 1994; Raffaelli , 1995). More surprising, low-level clouds are well determined, pcesibly due to the spatial variance components of the pixel representation, aud a clear discrimination between uniform (green colours) and cellular clouds (brown colours) is achieved. At first sight this observations suggest that it could be used to initiate the process of cells determination. An intermediate step was to reprocess the classification on images cleared from high clouds, in order to improve the dynamic of low level classes. But

3CHXJkm~).

even after thii step, it turned out that the center of cells are poorly determined. More, if it is true that a contour of one cell can be associated with particular classes, it may not be the same classes for another cell contour, so that the contours cannot in general be associated with a definite set of clssses. The zoom picture of Fig. 3 shows the result of a different classification which uses 4 channels of AVHRR: the first and second components being the same as before, the third being the difference be tween channel 5 and channel 4 signals, to enhance the detection of cirrus and the fourth being channel 2 divided by channel 1 signal for better sea surface deterrnination (Saunders and Kriebel, 1988). We clearly see the improvement, for the small scales structures allowing a better capture of sea surface (in blue and black classes). Hence we finally used thii last classsification in order to determine the structured clouds pattern. The result is illustrated in Fig. 4 where each cell has been identified from its center and where the characteristics of the field are clearly restituted: we obtain a wide size variability, ranging from 8 to 50 km, we have a good statistical representation of cell population, and the original complexity of shapes seems to be kept. Thus, to summarize, the combination of the two classifications leads to both determinations of cloud eusembles and individual constituent recognitions.

A. Mathieu et 01.: Mesoscale Boundary Layer Clouds Structures

0

ioil

zcm

Fig. 2. Zoom on the 1st clauifbtion

so0

400

with variance.

Fig. 4. Identificationof individual cellular low clouds.

20 -40

-20

Fig. 6. AFWEGE iaocontoursof the vertical speed at 3OOmheight with the projected cksees - dp/dt (Pa/s), (November 14th at 17h23UT).

Fig. 3. Zoom on the 2nd classificationwith four channela data.

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A. Mathieu et al.: Mesoscale Boundary Layer Clouds Structures

AFU’EGE Sea Surface Temperature Fit. 6. SEMAPHORE zone.

3

map and the

Combining AVHRR data clouds classification and ARPEGE SEMAPHORE analysis

Figure 6 shows the ARPEGE reanalysed sea surface temperature map with the SEMAPHORE square. The situation of the intensive measurement zone is well identified from the details of the oceanic front meanders, which are obviously not so well described out of the zone. The ARPEGE analysis of the campaign uses a standard world wide operational meteorological data completed by the specific use of the SEMAPHORE measurements. The main different features are: - a position of the pole of the grid over the Azores, and a rising of the spectral truncature from 95 (op erational) to 119, which provides a grid resolution not departing much from 30)m x 30km. - a specilic assimilation on the zone taking a higher resolution function to increase the weight of the SEMAPHORE data. The assimilation method is the same as in the operational version and consists on a statistical minimization of the differences between forecast and propagated measurement grid points every 6 hours. Comparing with aircraft humidity and temperature cross-sections and ship measurement (for turbulent fluxes), the method has been found to give good results either with assimilated data or not. In particular, one can accurately estimate the height of the MABL on the zone. Nevertheless, a careful1 model analysis by Giordani (1997) demonstrated that ARPEGE was not able to give a good estimate of the surface radiative fluxes, with an overestimation of visible fluxes and an under-estimation of

downwards infrared which he attributed to a poor modelisation of low level clouds. However, examination of surface data by the same author has shown that daytime surface budgets were well restituted, this being due to compensating effects. As for our purpose, the first step is to evaluate the ability of using crces information between ARPFGE outputs and AVHRR data. It should allow to get pre cisions on the actual state of the MABL. Within the zone of assimilation, where the GCM is constrained by a maximum of observations to be realistic, it represents a good way to test the feasibility of the GCM to grab the physics of the MABL. Outside of the zone, where the model is closer to operational mode, the challenge is probably more difficult to achieve. Two primary comparisons has been made, the first using ARPEGE cloudiness outputs at three levels as a simple comparison; the second using the vertical speed (w) at 300m height as an index of low level convergence. The period of analysis from November 13 to 15 was chosen for three reasons: 1. because most occurences of low level cellular benches of clouds which appeared to be concentrated there; 2. it has been already intensively studied in the context of SEMAPHORE analysis, giving us the highest possible confidence on gathered data and assimilation; 3. this period stands under an anticyclonic synoptic meteorological situation, for which the physics on the zone stays weakly dependent on large scale atmospheric motion. 3.1

Cloud and cloudiness

comparison

The comparison between. observations and ARPEGE shows that the cloud bench general structure compared to the satellite view looks fine: the warm part of the Azores Oceanic Front is mostly covered with cirrus, the shape organizations of structures are about correct. On a more quantitative point of view, high clouds and sea surface seem to be much more present on ARPEGE results. A quantitative comparison is made on the basis of the ARPEGE grid. For the AVHRR classification, gathering classes in four groups - sea, cellular clouds, uniform low and high clouds - each mesh is labelled for the most present group. For each of these groups of meshes, the mean cloudiness is computed for each of the three ARPEGE cloud levels. The Table 1 shows how the different groups of observed clouds are partitioned into the clear sky and low, midlevel, high cloudiness of the ARPEGE meshes. Clear sky and high level cloudiness meshes seems well represented: the meshes observed as sea surface are found to be clear sky: 69% and only 17% with high cloudiness. As well are computed the observed high clouds meshes,

A. Mathieu et al.: Mesoscale Boundary Layer Clouds Structures

??? SEA

1.0 2.5 2.0 1.5

CELL

4

2

I.0 0.5

0.0 -0.2

fOZ?

0.2

0.2

dP,*iO&,*)

UNIFORM

5

LOW

4 3 2 1 0 -0.2

0.2 dP,dF&,.)

dP/dt

(PC/S)

Hintogamsof AWEGE

vertical speed by clouds observational groupa of mecrhsr(November 13th to 15th, 5 cumulated data and images) (co-ordinate: number of occuraces x10-‘). Fig.

7.

Table 1. AVHFlR cloud obecrvationto ARPEGE cloudinesscorn-

P=iOh. JJ/ AFWEOE (%): 1 Sea

Low

Mid-level

High

1 69.

10.

12.

17.

CdS

81.

a.

6.

9.

uniform low

75.

11.

8.

11.

8.

30.

47.

75.

SCZI

Hich

for which ARPEGE finds 75% of high level cloudineaa. On the contrary, nor the values, nor the tendencies look correct for uniform or cellular low clouda, and ARPEGE givea meetly clear skies: 81% for cells and 75% for low clouds zones, even more than 69% of clear skies correctly found. 3.2

937

Vertical speed tests

In Fig. 5, the AVHRR clasaea, taken on the 15th at 17h35 UT, has been projected on ARPEGE &-contour linea of vertical speed at 300m height defined aa dp/dt (Pa/e) at 17h00, i.e. 5 houra after an asaimiliation; for clarity, only aacente are drawn. Without entering the details on this single illustration, at this stage of analysis, it can be obeerved that strong ascents coincide with cirrua benches (clear purple) in the northern region; the sea clam (in black), the uniform clouds (green classes) and low cellular structurea (light brown classes) are meetly amociated with subsidence (downdraft motion). Figure 7 gives four histograms of the ARPEGE vertical epeed for the same period aa Table 1 for each of the same four classes of AVHRR clouds as previously mentioned. It ia a severe teat, aa it doea not take into account the possible geographical drift of the structures,

which would be acceptable. But fhmlly, considering that the spatial zonea are spread over a fare number of grid me&a, and the spatial drifts of meteorological etructurea are slow, this method can give enough indications. Despite of their width (rms valuea around .05 Pa/s), the hi&gram mean valuee give some tendency to am+ ciate a subsidence with clear sky or low clouda(< w >= .016), and ascenta with high level clouds (< w >= -.022), when the overall average w on the zone ia .008 Pa/s. These features are verified aa well on morning or afternoon histograms, and even when taking any single image. Thie indicator some phydcal relation between obeervation patterns and the ARPEGE low-levelconvergence which looks promising for a deeper analysis and use of the GCM, and gives some confidence to initiate an analysis of stability parameters with ARPEGE. Hence our conclusion ia rather optimistic, and we propcee to analyse other parameters to describe the MABL state as evaluated from the GCM. The first results obtained from the vertical speed only will be a benchmark. 4

Conclusions

and perspectives

The Desboia and Seze classification method, ueed for separating the different cloud types, haa proven to be not too much affected by high light incident angle AVHRR imagea. However, the use. of pixel radiance local spatial variability makes difficult the analyaea of emall cells structure. The four channels procoming hss proven to be efllcient for the detection of cellular cloud organization, making possible the image ax&y& of very inhomogeneous, irregular bench of MABL clouda aa olxerved during the last period of the SEMAPHORE campaign. This method of combining the two classificationa providea a versatile tool to characterize meteorological based clames of MABL clouds from satellite data. The comparison between ARPEGE and obeervation haa suggested that low level clouda are under-estimated, resulting in an over-estimation of downward visible and an under-estimation of infrared radiation on Bea surface. However, the GCM low level convergence ie co herent with structures observation and teems to justify by itself part of the AVHRR cloud analysis, for observed clear sky and MABL clouds correspond meetly with ARPEGE aubeident meshes. We therefore propose to go further and analyse other parameters aa determined with ARPEGE model and in particular some parameters which can be reqonsible of the structure formation ae surface stability parameters and boundary layer characteristics. Acknowledgements. The authon wish to thanh H. Giordani for hia help in retrieving the AFlPEGE sclrimilateddata. The SEMAPHORE campaign has been supported by CNRS, M&so-Fkance, SHOM, DRET, IPFIEMER and ESA; it wan initiated by the program PATOM.

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lte&rerlcee Atlas D., Walter B., Shu-Heien Chou, Sheu P. J. The Structure of the Unstable Marine Boundary Layer Viewed by Lidar and Aircraft Observations. J.Atmor.Sci.,~J 1301-1318,1Q86. Bradley, E. F., Coppin P. A. and Godfrey J. S. Mursurements of sensible and latent heat flux in the Western Equatorial Pacific Ocean, J.Geophy&ier., %S,337~3389,1QQl. Eymard et al. Study of the air-sea interactions at the mesoacale: the SEMAPHORE experiment, Ann. Geophys.,f& 986-1016, 1996. Giordani H., Mod&&ion de la couche limite atmonpbCrique marine en pr&ena d’un front thermique oc&nique: application A lacampagne SEMAPHORE, Ph.D. theair, Toulouse, Sep. 1897. Giraud V., CaractCrintiques deo Prop&t& Optiques et Micro-

physiques den Cirrus: Utilisstion de I’Imagerie Satellite, Ph.D. theria, Lille, Oct. 1994. Nicolls S. and Le Mane M.A. ,The Fair Weather Boundary Layer in GATE: The Relationship of Subcloud Fluxen and Structure to Distribution and Enhancement of Cumulus Clouds, J.Atmos.Sci., 97, 2OSl-2067,lQSO. FlaRaelli J.L., Analyse de la couverturenuageuse de haute altitude A partir de I’imageriesatellite, Ph.D. the&, Pork, Apr. 1995. Saunders R.W. and Kriebel K.T., An improved method for da tecting clear sky and cloudy radiances from AVHRFl data Int.J.Remote Senring, 9, 123-150,lQSS. Seze, G.,Desbois M. 1987: Cloud cover analyaicl from satellite imagery using spatial and temporal characteristics of data,

J.Climote AppLMeteor, 1987.