MARC GEORGELIN Laboratoire d Aérologie, CNRS, Toulouse, France

Transcription

1 THE ATMOSPHERIC BOUNDARY-LAYER STRUCTURE WITHIN A COLD AIR OUTBREAK: COMPARISON OF IN SITU, LIDAR AND SATELLITE MEASUREMENTS WITH THREE-DIMENSIONAL SIMULATIONS CYRILLE FLAMANT Service d Aéronomie, Institut Pierre Simon Laplace, CNRS, Tour 15, Boîte 102, Université Pierre et Marie Curie, 4 Place Jussieu, Paris Cedex 05, France MARC GEORGELIN Laboratoire d Aérologie, CNRS, Toulouse, France LAURENT MENUT Laboratoire de Météorologie Dynamique, Institut Pierre Simon Laplace, CNRS, Palaiseau, France JACQUES PELON Service d Aéronomie, Institut Pierre Simon Laplace, CNRS, Paris, France PHILIPPE BOUGEAULT Centre National de Recherches Météorologiques, Météo-France, Toulouse, France (Received in final form 4 July 2000) Abstract. A cold-air outbreak over the Mediterranean, associated with a Tramontane event, has been simulated with the atmospheric non-hydrostatic model Meso-NH using a horizontal resolution of 2 km. Results are compared with in situ aircraft, airborne lidar and satellite measurements. On average, the mean and turbulent parameters simulated in the surface layer and mixed layer compared well with in situ measurements. The model was able to reproduce accurately the Foehn effect in the wake of Cape Creus, as well as the occurence of rolls in the coastal region in connection with cloud streets observed with AVHRR. Over the sea, the threshold value of turbulent kinetic energy defining the height of the atmospheric boundary-layer top in the model (defined as 25% of the maximum turbulent kinetic energy in the profile) enables the simulated atmospheric boundary-layer height to match the one retrieved from lidar measurements. Nevertheless, the model did not handle very well the abrupt gradients of all meteorological parameters observed at the top of the atmospheric boundary-layer. Reasons for this are investigated. Keywords: Airborne lidar, Boundary-layer structure, Cold-air outbreak, Mesoscale numerical modelling, Rolls, Satellite. 1. Introduction Parameterization of atmospheric boundary-layer (ABL) dynamical and turbulent processes in mesoscale and general circulation models is crucial to reproduce the mean flow at the regional and global scale (Brown and Foster, 1994; Garratt et cyf@aero.jussieu.fr Boundary-Layer Meteorology 99: , Kluwer Academic Publishers. Printed in the Netherlands.

2 86 CYRILLE FLAMANT ET AL. al., 1996). Among them, the entrainment process (which controls ABL growth and the transfers of energy between the ABL and the free troposphere) has received a lot of attention both from a numerical perspective (Sullivan et al., 1998, 1999), a laboratory perspective (Deardorff et al., 1980) and an experimental perspective (Nelson et al., 1989; Flamant et al., 1997; Russell et al., 1998). As these processes generally scale with the depth of the ABL (on the order of 1 km, or less), the application of a computational grid, whose horizontal and vertical mesh sizes enable their explicit simulation (20 50 m), has been restricted to large eddy simulations (LES) (Mason, 1994) mostly because of limited computer power. Increased grid-resolution has led to more refined ABL processes parameterizations. To validate these refined ABL parameterization, modellers have turned to high spatio-temporal resolution measurements such as laser remote sensing (lidar). For example, Sullivan et al. (1998) have shown that their simulations of the smallscale structure of the entrainment zone compared well with lidar observations. In this type of process-oriented study, emphasis is placed on the need for high resolution data over a small area. At the same time, the need for operational high resolution weather predictions at the mesoscale has led to the development of non-hydrostatic mesoscale models whose resolution is intermediate between LES and hydrostatic models. These atmospheric models, which now account for complex processes such as anthropogenic forcing and the hydrological cycle, still call for testing and validation. The most challenging aspect of atmospheric model validation at the mesoscale is the requirement for an experiment that must be able to characterize (in a relatively short period of time) the atmosphere on a wide range of scales and dimensions, namely from point (in situ) measurements, to one-dimensional (soundings, level aircraft legs) measurements, to two-dimensional (ground based, airborne and space-borne passive and active remote sensing) measurements, to three-dimensional measurements (via instrumental synergies). The purpose of this paper is to compare high resolution (2 km) simulations of the mesoscale ABL structure made with the atmospheric mesoscale non-hydrostatic model Meso-NH (Lafore et al., 1998) to the extensive set of measurements acquired in the framework of the Pyrénées experiment (PYREX) (Bougeault et al., 1990, 1993). Here, we focus on a case of cold-air outbreak associated with a Tramontane event. The significant thickening of the internal thermal boundary layer (ITBL), as well as the occurence of organized large eddies (OLE) (Brown, 1980; Etling and Brown, 1993) generally associated with cold-air outbreak episodes, provide a meaningful case for testing the evolution of the ABL structure with fetch as simulated by state-of-the-art mesoscale models such as Meso-NH. The validation-testing-comparison approach conducted here benefits from high spatio-temporal resolution measurements of the ABL structure at the mesoscale made with the airborne lidar LEANDRE 1 (Flamant and Pelon, 1996; Flamant et al., 1997).

3 MEASUREMENTS VS. MODEL DURING PYREX IOP Tramontane Study Area and Observational Data The Tramontane flow is a low-level wind associated with northerly synoptic flows, which is accelerated by the pressure build-up on the northern edge of the Pyrénées mountain range. It is further accelerated over the sea and deflected east around the mountain by the Coriolis force (Olafsson and Bougeault, 1996, 1997). Cold air advection over the Mediterranean is associated with the Tramontane flow, which leads to the development of an internal boundary layer over the sea. The Tramontane event, which occurred on 4 November 1990 during PYREX, has been extensively documented by means of ground-based, airborne and spaceborne measurements. The objectives were to understand the mechanisms driving the Tramontane and its evolution over the Mediterranean, as well as to provide a thorough dataset to validate three-dimensional mesoscale models (Bougeault et al., 1990). A description of the experimental set-up during PYREX can be found in Bougeault et al. (1993). The Tramontane dynamics have been investigated by Olafsson and Bougeault (1996, 1997), its structure has been described by Campins et al. (1995) and Flamant and Pelon (1996), and the mechanisms triggering the Tramontane have been discussed by Georgelin and Richard (1996). A detailed description of the operations on 4 November 1990 can be found in Flamant et al. (1997). Information on the structure of the ABL and OLEs within the cold-air outbreak was obtained from in situ measurements carried out from two research aircraft, the multi-agency (CNRS/INSU, CNES, IGN, Météo-France) ARAT and the Météo-France Merlin IV, from lidar measurements (the ARAT was carrying the airborne lidar LEANDRE 1) and from the AVHRR satellite images. Figure 1 shows the observational area with flight tracks of the aircraft (they both flew on the same track) and the location of the AVHRR satellite image. Figure 2 is a zoom on the study area of the AVHRR image received at 1355 UTC on 4 November 1990 in the visible channel. Clouds developed east of the aircraft flight track and were organized as cloud streets aligned approximately with the direction of the mean ABL wind. The full visible AVHRR image taken at 1355 UTC, which covered most of Western Europe and from which Figure 2 is extracted, also evidenced some cloud streets over continental France. 3. Numerical Simulations 3.1. MODEL DESCRIPTION The Meso-NH model (Lafore et al., 1998; see also Bélair et al., 1998) solves the non-hydrostatic and anelastic equation system. It allows for research on a wide range of topics, from LES to α-mesoscale studies (Cuxart et al., 2000). A meso-β scale simulation has first been performed (with a mesh of 10 km) on a domain surrounding the Pyrenees mountain range. The initial and coupling

4 88 CYRILLE FLAMANT ET AL. Figure 1. Flight tracks of the research aircraft ARAT and Merlin IV flown on 4 November 1990 (A0-J1-J2-J3-J4). The larger box marks the location of the AVHRR satellite image shown on Figure 2. The smaller boxes mark the location of the domains for the 2-km simulations (a and b). Superimposed is the Peridot model reanalysis of the wind field at 985 hpa on 4 November 1990 at 1200 UTC. fields were issued from the European Center for Medium-range Weather Forecast (ECMWF) analyses. A higher resolution simulation (with a mesh of 2 km) nested in the previous one was then performed. The domain is centered around the aircraft trajectory (Figure 1). The vertical grid is the same for both simulations: 60 levels with a mesh stretched between 50 and 500 m. To ensure a good description of the ABL, 12 levels are taken below 1000 m. The size of the first 9 meshes is less than 100 m. Above 6400 m, the mesh size is constant and set to 500 m. An absorbing layer is set above m, with the top of the domain located at m. The turbulence scheme, for both simulations, is used in its unidimensional version (Cuxart et al., 2000). Above land, the (effective) roughness length is computed as a function of the subgrid topography (Georgelin et al., 1994), while above sea Charnock s formula is used. The 10-km simulation starts on November 4, 1990 at 0600 UTC and ends at 1500 UTC whereas the 2 km one starts at 1100 UTC and is initialized with the fields from the 10-km simulation.

5 MEASUREMENTS VS. MODEL DURING PYREX IOP6 89 Figure 2. AVHRR radiance on 4 November 1990 at 1355 UTC. The solid line figures the aircraft flight track (J1-J2-J3-J4). The lines marked by R1, R2, P1 and P2 indicate the axes along which the AVHRR radiances have been extracted and analysed DESCRIPTION OF THE TRAMONTANE EVENT SIMULATION In this section we discuss the key features of the Tramontane flow in connection with the 2-km simulation. The results for the 2-km simulation for vertical velocity and water vapor mixing ratio at 1000 m ASL are shown in Figures 3 and 4, respectively. Roll-like patterns (or OLEs) appear as enhanced linear upward vertical motion patterns (associated with enhanced downward vertical motion) in Figure 3. They are associated with enhanced water vapour mixing ratio (Figure 4). The strongest OLEs are observed in the Tramontane flow over the Corbieres (4 overall, oriented almost parallel to A0J1). Their orientation follows the progressive change in wind direction with fetch. Their horizontal extend is limited to approximately 60 km downstream from the coastline. Beyond that distance, the model does not seem to reproduce any organized motion (at 1000 m ASL). According to Figure 3 and Figure 4, none of these OLEs were documented during the flights (at least along J1-J2-J3). This is somewhat surprising since cloud streets (which reveal OLEs when updrafts overshoot the lifting condensation level) are observed downstream from that area on the satellite imagery. This could be caused by border effects in

6 90 CYRILLE FLAMANT ET AL. Figure 3. 2-km simulation of vertical wind speed (m s 1 ) at z = 1000 m above sea level (ASL) at 1400 UTC. the vicinity of the boundary of the simulation domain. OLEs simulated over the continent as well as over the sea do not produce by clouds, which is in agreement with satellite imagery in the region of the simulation (see Figure 2). The sheltering zone behind the Pyrénées is also observed in the 2-km simulation of the wind speed at 1000 m ASL (not shown). It is oriented north-south and extends from the Spanish shoreline (south of Cape Creus) down to in latitude (just West of J4). Its location corresponds almost exactly to that of the shear line depicted by Bougeault et al. (1993). The fluctuation of the wind speed across that shear line is also well simulated (see following section). Over land, the fluctuations of the ABL depth are driven by the underlying topography. Orographic waves have also been observed by in situ measurements made at an altitude of 3500 m. In the simulation, their existence is seen in the form of a perturbation in the vertical velocity field that is perpendicular to the mean wind direction (as opposed to what is observed over the sea) (Figure 3). A somewhat similar phenomenon is observed in the lee of the Pyrénées over Spain. Also associated with these orographic disturbances is a Foehn effect (heating, not shown, and drying see Figure 4). In the simulation, orographic waves disappear above 4000 m, possibly because of the presence of a large wind shear at the top of the ABL.

7 MEASUREMENTS VS. MODEL DURING PYREX IOP6 91 Figure 4. 2-km simulation of the water vapour mixing ratio (kg kg 1 ) at z = 1000 m ASL at 1400 UTC. The strongest Foehn effect simulated at 1000 m ASL is associated with the orography of Cape Creus (Spain). The drying effect extends horizontally over the sea approximately 75 km downstream from the obstacle (Figure 4). The fluctuation of the water vapour mixing ratio in the lee of Cape Creus is also well simulated (see following section). 4. Comparison of Simulations with Observations In this section, we compare mean and turbulent parameters simulated by Meso- NH with those measured along several aircraft legs and soundings. The type of platform from which observations are made and the time of these observations are summarized in Table I. The times of the simulations used to carry out the comparisons are also given TURBULENT SURFACE HEAT FLUXES Surface heat fluxes are computed by the eddy-correlation technique from in situ measurements acquired at 16 Hz on board the Merlin on straight and level runs about 25 to 30 km long. The contribution of scales larger than 3 km to the turbulence is filtered out (Hedde and Durand, 1994). The uncertainty associated with

8 92 CYRILLE FLAMANT ET AL. TABLE I Time (UTC) of comparison carried between observations and 2-km simulations. Profiles Structure of the flow Height of ABL top J2 J3 J4 Horizontal Vertical Platform Merlin Merlin ARAT AVHRR ARAT/LEANDRE Merlin Merlin Observations Simulations the measurements is on the order of 20% at 30 m ASL (Hedde and Durand, 1994). Above the continent, the uncertainty is on the order of 60 to 100% for latent heat fluxes and even larger ( %) for sensible heat fluxes. These large uncertainties can be explained by (i) small values of the fluxes measured over the continent, (ii) the fact that these fluxes are measured above the surface layer and (iii) the enhanced variability of all meteorological parameters above complex terrain. To carry out a meaningful comparison, we have extracted flux values (from the 2-km simulation) along the aircraft flight track and we have averaged them over the same distance as the measurements (i.e., km). We compare the measured and modelled fluxes in Figure 5. The error bars associated with the model-derived fluxes are calculated as the standard deviation of all simulated values over the distance corresponding to the measurements. It gives an idea of the variability of those quantities in the model. The latent heat flux is on the order of 100 W m 2 over land and rapidly increases above the sea to a value of 575 W m 2 at J2 (Figure 5, top right panel). On J2- J3, the surface latent heat flux is relatively constant and equal to 400 W m 2 on average (Figure 5, bottom right panel). The sensible heat flux is on the order of 20 W m 2 over land and rapidly increases above the sea to a value of 100 W m 2 at J2 (Figure 5, top left panel). On J2-J3, the surface latent heat flux is relatively constant and equal to 100 W m 2 on average (Figure 5, bottom left panel). In terms of latent heat flux, we observe a 100 W m 2 difference between the measurements and the model over the sea and the land. However, measured and modelled latent heat flux behave similarly at the mesoscale. In terms of sensible heat flux, we observe a 50 W m 2 difference between the measurements and the model over the sea and a difference of 100 W m 2 or more over the land. However, here also, measured and modelled latent heat fluxes behave similarly at the mesoscale. Given the uncertainty associated with the measurements, the agreement is thought to be encouraging. Note that the fluxes obtained with the 10-km and 2-km simulations are almost identical as previously pointed out by Bélair et al. (1998).

9 MEASUREMENTS VS. MODEL DURING PYREX IOP6 93 Figure 5. Comparison between the surface sensible heat flux (Q0) and the surface latent heat flux (e0) measured by the Merlin (solid line) and derived from the 2-km simulation (dotted line) MEAN PARAMETERS We have also compared measurements of water vapour mixing ratio and wind speed made at 950 m ASL by the Merlin IV with the water vapour mixing ratio and wind speed extracted from the 2-km simulation (at 1000 m ASL, see Figures 3 and 4) along the flight track. Results are shown in Figure 6 for the wind speed and in Figure 7 for the water vapour mixing ratio. For each value of the model, we calculate an average value of the measurements sampled in the corresponding model mesh. The error bars associated with the measurements are calculated as the standard deviation in each mesh. In all cases, the agreement is thought to be promising. The characteristics of the sheltering zone are well reproduced by

10 94 CYRILLE FLAMANT ET AL. Figure 6. Comparison between the wind speed measured by the Merlin at 950 m ASL (solid line) and derived from the 2-km simulation at 1000 m ASL (dotted line). the model as the wind speed decreases from 16 to 5 m s 1 fromj3toj4.the drying associated with the wake of Cape Creus is also well reproduced on J3 J4, even though a difference of approximately 1 g kg 1 is observed between the measurement and the simulation. However, given the relatively small values of

11 MEASUREMENTS VS. MODEL DURING PYREX IOP6 95 water vapour mixing ratio measured, and the large variability associated with them, we may conclude that measurements and simulations compare well. Moreover, the large-scale behaviour is well captured by the model. Figure 7. Comparison between the water vapour mixing ratio measured by the Merlin at 950 m ASL (solid line) and derived from the 2-km simulation at 1000 m ASL (dotted line).

12 96 CYRILLE FLAMANT ET AL. Soundings in J3 and J4 were obtained by the Merlin IV and the ARAT, respectively. These soundings (commonly referred to as race tracks due to the sharp spiral trajectory of the aircraft) covered an area of approximately km 2.In J3, the profiles obtained during the ascent and the descent are almost identical. The comparison between the measured and modelled water vapour mixing ratio and virtual potential temperature profiles are shown in Figures 8 and 9, respectively. Figure 8. Comparison between the water vapour mixing ratio profiles measured by the Merlin and the ARAT at J3 and J4, respectively (dotted line), and derived from the 2-km simulation (solid line). The agreement between the simulated and observed profiles is poor at J3 and quite good at J4. At J3, the simulated gradients of virtual potential temperature and water vapour mixing ratio at the top of the ABL are much weaker than observed. Generally speaking, the transition from the Tramontane flow to the flow aloft is poorly discribed by the model.

13 MEASUREMENTS VS. MODEL DURING PYREX IOP6 97 The differences (2 K and 1 2 g kg 1 in the marine ABL) between the observations and the simulations at J3 are not thought to be due to non-stationary conditions around J3. Flamant and Pelon (1996) reported no difference between the ABL top height derived from nadir lidar measurements made near J3 and the ABL top height derived from zenith lidar measurements 50 minutes later. Moreover, the difference in virtual potential temperature and water vapour mixing ratio measured in the mixed layer by the ARAT and the Merlin (which lagged the ARAT by almost 60 min) were measured to be small (and most likely related to slightly different sensor calibrations). Our 10-km simulation is initialized with the sea surface temperature (SST) derived from AVHRR imagery (which was found in good agreement with the SST measured by a radiometer along the ARAT flight track) so that the differences are not thought to be caused by the specification of an inappropriate SST in the model. It could also be argued that the spin-up time is too short to allow for the formation of the inversion at the top of the ABL. The vertical resolution of the ECMWF data being too coarse, the inversion is not present in the initial fields. Therefore, this inversion needs to be created in response to subsidence in the simulation. In our case, there is a 7-h (5 + 2 h) time lag between the beginning of the 10-km simulation and the time at which we use the 2-km model results (the first 5 h corresponding to the time in the 10-km simulation before starting the 2-km simulation, see Section 3). Near J3, the synoptic vertical velocity at the top of the ABL is estimated to be on the order of 0.03 m s 1 over the sea (Flamant and Pelon, 1996). The lapse rate above the ABL was measured to be equal to 3.5 K km 1 over the sea (Flamant and Pelon, 1996). Then, a time period of 7 h allows for the formation of a 2.6 K temperature inversion at the ABL top, which is of the order of the inversion measured. The fact that the strength of the inversion in the simulation is much weaker could be caused by an incorrect prescription of synoptic vertical velocity above the ABL in the 10-km simulation. Lastly, the observed differences are not related to the representation of the entrainment process at the ABL top (one could argue that entrainment is not being properly handled in the model and that too much entrainment would result in a warmer and dryer ABL mixed layer in the simulation, as seen in Figures 8 and 9). This issue was investigated by comparing the entrainment buoyancy flux (AQ s, where A is a constant and Q s the surface flux) determined experimentally at the top of the ABL by Flamant et al. (1997) and its simulated counterpart. The value of A derived by Cuxart et al. (2000) is equal to 0.23 in the convective (unsheared) ABL. It is slightly smaller than the value of 0.29 obtained by Flamant et al. (1997) near J3 and in sheared conditions. Nevertheless, the relationship A = f( h/l, U)(where h, L and U are the ABL depth, the Obukhov length and the shear across the ABL top, respectively) proposed by Flamant et al. (1997) can be used to estimate the value of A when neglecting the shear term (i.e., in convective conditions close to those reported in Cuxart et al., 2000). The value so

14 98 CYRILLE FLAMANT ET AL. Figure 9. Comparison between the virtual potential temperature profiles measured by the Merlin and the ARAT in J3 and J4, respectively (dotted line), and derived from the 2-km simulation (solid line). obtained ranges from to 0.25, depending on the parametrization used to determine the function f. The source of the discrepancy is further dicussed in Section ATMOSPHERIC BOUNDARY-LAYER STRUCTURE Comparing lidar-derived atmospheric reflectivity and simulated meteorological fields is a difficult thing to do. Ideally, one would need to combine information on aerosol concentration, size distribution and chemical apportionment with information on relative humidity to be able to simulate atmospheric reflectivity in the framework of a model such as Meso-NH. To overcome this and still carry out

15 MEASUREMENTS VS. MODEL DURING PYREX IOP6 99 some meaningful comparisons between lidar measurements and simulations, we have used the turbulent kinetic energy (TKE) field simulated with Meso-NH, to derive quantitative information on the depth of the ABL. A detailed description of the ABL structure over the continent and over the sea as provided by the airborne backscatter lidar LEANDRE 1 can be found in Flamant and Pelon (1996). The lidar-derived ABL top height is determined using the methodology described in Flamant et al. (1997), with a precision of 30 m. The elevation of the topography along the ARAT flight track has been determined from the surface echo in the lidar signal (the precision of the elevation is 30 m as well). The signal backscattered by any dense medium (such as clouds or the Earth s surface) is 3 to 4 orders of magnitude larger than the signal backscattered by particles, and can provide accurate measurements of the distance between the aircraft and the ground. TKE profiles are used to determine the altitude of the ABL top in Meso-NH simulations. Because TKE vanishes above the ABL, one can define a threshold value of the TKE representative of the ABL top. The TKE threshold value defining the height of the ABL top in the model is set to 25% of the maximum of TKE in the profile (Figure 13 of Cuxart et al., 2000, with z/z i = 1, Z i being the depth of the ABL). Over the sea, the threshold value is typically on the order of 0.2 m 2 s 2. In Figure 10, we compare the ABL top heights retrieved from lidar measurements and from the 2-km simulation. To illustrate the sensitivity of the inferred ABL top height to the threshold, we also show the ABL height simulated with a value of 0.3 m 2 s 2. On average, the difference between the ABL top height retrieved using 0.2 rather than 0.3 m 2 s 2 is equal to 103 ± 44 m on J2J3 and 74 ± 38 m on J3J4. A good agreement is found between the ABL top height derived from lidar measurements and from the TKE simulation ORGANIZED LARGE EDDIES In this section, we compare the simulated OLEs seen on the horizontal crosssections of vertical velocity and water vapour mixing ratio at an altitude of 1000 m ASL (near the ABL top) with those seen on the AVHRR. Values of the stability parameter h/l derived along the flight tracks from in situ measurements made by the Merlin IV in the surface layer (between 4 and 9) are compatible with those derived in the presence of rolls in the ABL (Grossman, 1982; Weckwerth et al., 1997, among others) such as those observed in the 2-km simulation and on the AVHRR image. To characterise the aspect ratio of the rolls observed by AVHRR east of the aircraft flight track, we have extracted radiances from two lines perpendicular to the roll direction (denoted P1 and P2 on Figure 2). The size of an AVHRR pixel is 1km 1 km. The software processing the AVHRR images provides radiance series that are projected on a North-South axis. The spacing between two consecutive pixels along P1 and P2 is calculated accounting for the 55 angle between the

16 100 CYRILLE FLAMANT ET AL. Figure 10. Comparison between the ABL top height derived from lidar measurements (dotted line) and diagnosted from modelled TKE profiles along J2 J3 (J3 is to the right) and J3 J4 (J4 is to the left).

17 MEASUREMENTS VS. MODEL DURING PYREX IOP6 101 North and the orientation of the cloud streets. Two wavelengths emerge from the spectral analysis performed on the axis: 6.5 and 13 km (Flamant, 1996). In the simulation, the scales driving the roll spacing over the sea (as derived from the water vapour mixing ratio field) are 6.5 and 13 km, in excellent agreement with the AVHRR radiance analysis. The wavelength derived from the vertical velocity and ABL top height fields in the same region also is 13 km, but the 6.5 km wavelength is not present in the simulation. 5. Discussion and Conclusion The present exercise aimed at providing some insight with the capability of atmospheric non-hydrostatic model Meso-NH to reproduce a variety of 1D airborne in situ measurements, 2D lidar and satellite measurements over the sea. Even though necessary, this type of exercise is necessarly limited in the sense that results can easily be overinterpreted or/and misinterpreted because of the difficulty associated with isolating a particular process. A cold-air outbreak over the Mediterranean, associated with a Tramontane event, has been simulated with the atmospheric non-hydrostatic model Meso-NH using a horizontal resolution of 2 km. Results are compared with in situ aircraft, airborne lidar and satellite measurements. The model was able to reproduce accurately the Foehn effect in the wake of Cape Creus, as well as the occurence of rolls in the coastal region in connection with cloud streets observed with AVHRR. On average, the mean and turbulent ABL parameters simulated in the surface layer and mixed layer compared well with measurements. The agreement is thought to be encouraging given the uncertainty associated with some of the measurements (i.e., turbulent fluxes, for example) and the complexity and the variety of processes parameterized in the model. Over the sea, the height of the ABL top determined from the simulated turbulent kinetic energy profiles is in good agreement with the one retrieved from lidar measurements. Nevertheless, the model did not handle very well the abrupt gradients of all meteorological parameters observed at the top of the atmospheric boundary layer. Some discrepancies were also evident over land for surface turbulent heat fluxes. We believe this can be explained as follows. In our case, the meteorological conditions sampled over the Mediterranean depend to a large extend on the meteorological conditions encountered over land. In this context, one may question the representativity of the initial (large scale) surface temperature and moisture ECMWF fields over complex terrain. Furthermore, subgrid topography could have an impact on ABL structural parameters (such as mixed layer and entrainment zone depths). For example, the ABL depth in the 10-km simulation used to initialize the 2-km simulation might not be representative. The spin-up time for the 2-km simulation (2 hours) might not be long enough to enable the ABL to adjust to the more resolved topography. The evolution of the ABL structure simulated

18 102 CYRILLE FLAMANT ET AL. over the sea by Meso-NH between 1300 and 1400 UTC (not shown) could be an indication of that. Over the Mediterranean, the Tramontane is usually considered as stationary (Georgelin and Richard, 1996 among others). However, the initiation mechanism upstream (over land) needs to be well described for the Tramontane to be properly simulated over the sea. This aspect of the flow has been shown to be fast evolving between 1000 and 1200 UTC (Georgelin and Richard, 1996). The trade-off between the spin-up times for the 2-km and 10-km simulations presented here was the best among those tested. Acknowledgements This paper is dedicated to the memory of Marc Georgelin ( ) who died from cancer on September 4, Marc was personable and had a great sense of humour, and he will be sorely missed by his friends and colleagues in the atmospheric research community. This research has been funded by the Centre National de Recherche Scientifique (CNRS) through the Programme national ATmosphère et Océan à Multi-échelle (PATOM), by the Institut National des Sciences de l Univers (INSU) and by the Centre National d Études Spatiales (CNES). References Bélair, S., Lacarrère, P., Noilhan, J., Masson, V., and Stein, J.: 1998, High-Resolution Simulation of Surface and Turbulent Fluxes during HAPEX-MOBILHY, Mon. Wea. Rev. 126, Bougeault, P., Jansà, A., Bénech, B., Carissimo, B., Pelon, J., and Richard, E.: 1990, Momentum Budget over the Pyrénees: The PYREX Experiment, Bull. Amer. Meteorol. Soc. 71, Bougeault, P. et al.: 1993, The Atmospheric Momentum Budget over a Major Atmospheric Mountain Range: First Results of the PYREX Program, Ann. Geophys. 11, Brown, R. A.: 1980, Longitudinal Instabilities and Secondary Flows in the Planetary Boundary- Layer: A Review, Rev. Geophys. Space Phys. 18, Brown, R. A. and Foster, R. C.: 1994, On PBL Models for General Circulation Models, Global Atmos. Ocean Sys. 2, Campins, J. A., Jansà, A., Bénech, B., and Koffi, E.: 1995, PYREX Observation and Diagnosis of the Tramontane Wind, Meteorol. Atmos. Phys. 56, Cuxart, J., Bougeault, P., and Redelsperger, J.-L.: 2000, A Turbulence Scheme Allowing for Mesoscale and Large-Eddy Simulations, Quart. J. Roy. Meteorol. Soc. 126, Deardorff, J. W., Willis, G. E., and Stockton, B. H.: 1980, Laboratory Studies of the Entrainment Zone of a Convectively Mixed Layer, J. Fluid Mech. 100, Etling, D. and Brown, R. A.: 1993, Roll Vortices in the Planetary Boundary Layer: A Review, Boundary-Layer Meteorol. 65, Flamant, C.: 1996, Étude expérimentale de la couche limite atmosphérique par lidar aéroporté dans un cas de Tramontane, Ph.D. Dissertation, Université Pierre et Marie Curie, Paris, France. Flamant, C. and Pelon, J.: 1996, Atmospheric Boundary-Layer Structure over the Mediterranean during a Tramontane Event, Quart. J. Roy. Meteorol. Soc. 122,

19 MEASUREMENTS VS. MODEL DURING PYREX IOP6 103 Flamant, C., Pelon, J., Flamant, P. H., and Durand, P.: 1997, Lidar Determination of the Entrainment Zone Thickness at the Top of the Unstable Marine Atmospheric Boundary-Layer, Boundary- Layer Meteorol. 83, Garratt, J. R., Hess, G. D., Physick, W. L., and Bougeault, P.: 1996, The Atmospheric Boundary Layer Advances in Knowledge and Application, Boundary-Layer Meteorol. 78, Georgelin, M. and Richard, E.: 1996, Numerical Simulations of Flow Diversion around the Pyrénées: A Tramontana Case Study, Mon. Wea. Rev. 124, Georgelin, M., Richard, E., Petitdidier, M., and Druilhet, A.: 1994, Impact of Subgrid-Scale Orography Parametrization on the Simulation of Orographic Flows, Mon. Wea. Rev. 122, Grossman, R. L.: 1982, An Analysis of the Vertical Velocity Spectra Obtained in the BOMEX Fair- Weather Trade-Wind Boundary-Layer, Boundary-Layer Meteorol. 23, Hedde, T. and Durand, P.: 1994, Turbulence Intensities and Bulk Coefficients in the Surface Layer above the Sea, Boundary-Layer Meteorol. 71, Lafore, J. P. et al.: 1998, The Meso-NH Atmospheric Simulation System. Part I: Adiabatic Formulation and Control Simulation, Ann. Geophys. 16, Masson, P.: 1994, Large-Eddy Simulations: A Critical Review of the Technique, Quart. J. Roy. Meteorol. Soc. 10, Nelson, E., Stull, R., and Eloranta, E. W.: 1989, A Prognostic Relationship for Entrainment Zone Thickness, J. Appl. Meteorol. 28, Olafsson, H. and Bougeault, P.: 1996, Nonlinear Flow Past an Elliptic Mountain Ridge, J. Atmos. Sci. 53, Olafsson, H. and Bougeault, P.: 1997, The Effect of Rotation and Surface Friction on Orographic Drag, J. Atmos. Sci., 54, Russell, L. M., Lenschow, D. H., Laursen, K. K., Krummel, P. B., Siems, S. T., Bandy, A. R., Thornton, D. C., and Bates, T. S. : 1998, Bidirectional Mixing in an ACE 1 Marine Boundary Layer Overlain by a Second Turbulent Layer, J. Geophys. Res. 103(D13), 16,411 16,432. Sullivan, P. P., Moeng, C.-H., and Stevens, B.: 1999, Including Radiative Effects in an Entrainment Rate Formula for Buoyancy-Driven PBLs, J. Atmos. Sci. 56, Sullivan, P. P., Moeng, C.-H., Stevens, B., Lenshow, D. H., and Mayor, S. D.: 1998, Structure of the Entrainment Zone Capping the Convective Atmospheric Boundary Layer, J. Atmos. Sci. 55, Weckwerth, T. M., Wilson, J. W., Wakimoto, R. M., and Crook, N. A.: 1997, Horizontal Convective Rolls: Determining the Environmental Conditions Supporting their Existence and Characteristics, Mon. Wea. Rev. 125,

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