MESOCALE CIRRUS CLOUD MODELING AND COMPARISONS AGAINST REMOTE SENSING DATA COLLECTED FROM SPACE AND AIRCRAFT DURING THE CIRCLE

CAMPAIGN

V. GIRAUD1, G.PENIDE1, A. PLANA2, P.DUBUISSON3, A.PROTAT4, J.PELON5 and JF.GAYET1

1 LaMP/CNRS, Université Blaise Pascal, 63177 Aubière, France 2CETP, Centre d’Etudes des Environnements Terrestre et Planétaires, 78140 Velizy, France

3LOA, Laboratoire d’Optique Atmosphérique, 59655 Lille, France 4BMRC, Bureau of Meteorology Research Centre, Docklands, VIC3008, Melbourne, Australia

4 SA, Service d’Aéronomie, 75252 Paris, France

1. INTRODUCTION The properties and the persistence of cirrus clouds strongly depend on key dynamical and microphysical processes as well as interactions between microphysics, radiation and dynamic. Some major advances in the knowledge of cirrus cloud layers arise from coupling in situ observations with active and passive remote sensing that should described cloud structures at several scales. The capability of the Brazilian Regional Atmospheric Modeling System (BRAMS) to simulate the dissipation phase of frontal cirrus cloud layers is evaluated. The simulation results are compared to aircraft and satellite observations acquired on may 16, 2007, during the CIRCLE-2 campaign. 2. THE MODEL AND THE STRATEGY OF ITS EVALUATION Remote sensing observations revels cloud microphysical properties and their structures. Simultaneous observations at different wavelengths, or by active or passive instruments give access to different cloud characteristics. Our strategy is to simulate a large variety of instruments from the output fields of our model. Then, both real observations signatures and synthetic observation ones are compared to depict the lack (or the

realism) of the model simulation. To be as efficient as possible, the calculations of the synthetic observations must be made with only the information, and the assumption, available from the model. In our study, the BRAMS model is used with the more complete microphysical scheme as possible (Cotton et al., 2001). The cloud microphysics parameterization with two-moment scheme has been activated and the characteristics of the shapes of particles for each species have been take into account (Harrington et al., 2001; Meyers et al., 1997). From model outputs, and with only the hypothesis and parameterizations used in the model, we are able to simulate : radar reflectivity (with the Mie approximation and with absorption, Donovan et al., 2003); radar Doppler signal; lidar backscattering ratio (with multiple scattering, Hogan, 2006) and brightness temperature in the infrared window for classic instruments (with both aborption and diffusion, Dubuisson et al., 2005). Each instrument is simulated for satellite, airborne and ground based observatories. The single-scattering properties reported by Yang et al. (2005) for nonspherical ice particles have been used in the present radiative transfert calculations. Five ice crystal habits namely columns, plates, needles, dendrites and aggregates are considered for pristine and snow species. Crystal habits depend on the diagnostic of

ambient temperature and saturation with respect to water. 2. THE CIRCLE-2 CAMPAIGN The validation of CALIPSO/CLOUDSAT products dedicated to clouds has been performed within the frame of the PAZI/CIRCLE-2 project from DLR (Institute for Atmospheric Physics in Oberpfaffenhofen) in May 2007. During this campaign the LaMP operated a unique combination of cloud in-situ probes on the DLR F20 aircraft including a Polar Nephelometer, a Cloud Particle Imager (CPI) as well as standard PMS probes (FSSP and 2D-C) to measure cloud particle properties in terms of scattering characteristics, particle morphology and size, and in-cloud partitioning of ice and water content. During the CIRCLE-2 campaign, the DLR F20 flights were co-ordinated with the INSU F20 equipped with remote sensing instrumentation (RALI : combination of cloud radar and 3-λ lidar, IR radiometer CLIMAT…) representing an optimum configuration for CALIPSO/CLOUDSAT validation for cirrus clouds studies. The experimental strategy of the combined observations consisted to coordinate the flight plans with the CALIPSO/CLOUDSAT overpass, including the overall objective to validate the retrieved vertical profiles of cloud parameters (backscattering coefficient, extinction, thermodynamic phase including ice and/or liquid water content, effective diameter, …). 3. SITUATION OBSERVED ON MAY 16, 2007. From MetOffice analyses: the situation on may 16 is characterized with a warm front extending southeastward from low pressure in the middle of North Atlantic. This warm front reached the West French coast at 12 UTC; nearly two hours before take-off aircrafts and the overpass of the A-train constellation.

Figure 1: Eumetsat brightness temperature observed on may 16, 2007 at 13:45 UTC over the CIRCLE campaign area. The red line correspond to the ground track of the simultaneous A-train overpass.

The meteorological situation is confirmed by the EUMETSAT 9 observations at 13:45 UTC. Cloudiness draws the warm front that is at this time above the continent. The red line on satellite image (figure 1) shows the ground track of Aqua-train simultaneous observations. Falcon 20 trajectories are superposed to the MODIS image on figure 2. The first aircraft leg is spatially and temporally coordinated with the A-train overpass.

Figure 2: MODIS Brightness Temperatures observed at 12µm channel. Black line shows the FALCON-20 trajectory.

It is clear that the warm front passed over the observed area few hours before the intensive observations. Cirrus clouds have

been generated by the warm front and are probably in a dissipative phase when the observations have been acquired. 3. SIMULATION PROTOCOL Our simulation was set up on May 16 at 0h00 with ECMWF initialization fields and ends at noon the 17. A nudging has been applied every 6 hours from ECMWF analysis at the lateral boundaries of the simulated domain. The model has been configured with 3 nested grids with respectively 25, 5 and 1 km horizontal resolutions. There are 34 vertical levels for the larger grids. The 3th grid, with the higher resolution, was setup with 115 vertical levels extended from ground to 20 km altitude. The vertical resolution of the thinner grid was stretched from 500 m close to ground to 100 m at 10 km altitude (cloud levels). The 3 grids have been centered on 48°N and 5°W. The simulated areas for the 3 grids are represented by the rectangles on figure 3.

4. PRELIMINARY RESULTS: Firstly, the infrared signatures of the simulations have been evaluated. Figure 3 shows the synthetic brightness temperature as should be observed from MODIS channel 32 (centered at 12 µm) on-board Aqua. A direct comparison of figures 2 and 3 shows that simulated temperatures are cooler than those observed. The difference in average throughout the area is close to 20 K. Analysis of the lidar profiles shows that cloud base and cloud top altitudes are correctly simulated. The temperature differences are due to a quantity of ice too important in the simulated cloud. Comparing the arches represented on Figures 4 and 5 corroborates this. These figures correspond to the observations acquired by the radiometer CLIMAT aboard FALCON 20 and the synthetic observations on the same wavelengths

Figure 3: Brightness temperature as seen from MODIS channel at 12µm simulated from simulation at 14UTC. The three rectangles represent the domains covered by the 3 different grids of the model. The red line correspond to the aircraft trajectory

Figure 4 : Brightness temperatures measured by the CLIMAT radiometer aboard FALCON 20. The colours refer to different legs. The brightness temperature differences (between channels focused respectively to 10.6 and 12 microns) are drawn depending on the brightness temperature in the channel centred at 12 µ m.

Figure 5 : Same as Figure 4 but for synthetic brightness temperatures. The colors correspond to the different grids of the model. Red, blue and magenta correspond respectively to grids 1, 2 and 3.

Figure 6 : Same as Figure 5 but for the simulation with terminal fall speed of pristine and snow multiplied by a factor 5.

Figure 4 shows that cirrus overflown by aircraft are optically thin. They are semi-transparent to ground infrared radiation. The simulated cirrus has more varied optical thickness since one can identify an entire arch. In particular, the coldest temperatures, associated with brightness temperature differences closed to zero, correspond to the temperature of the cloud top emission. They therefore correspond to areas of thick clouds. Throughout the simulation domain, differences in brightness temperatures are still less than 2 K. Observations show that, contrary to the simulations, BTD may exceed 5 K. The arch signature on Figure 4 corresponds to small ice particles (Giraud et al., 1997). These initial comparisons show that there are still too many ice in the simulated cirrus. In addition, ice particles are too large. As the first test, we conducted a new simulation by changing only the terminal fall speed velocity of the primary ice and snow. These speeds have been multiplied by a factor of 5. They remain realistic.

Figure 6 shows the results with this new simulation. The colder brightness temperatures have disappeared, and brightness temperature differences have increased. The infrared signature of the simulated cirrus clouds is more realistic, compared to the observed ones. Increasing the speed of falling ice particles increases the rate of sedimentation. Ice water contents decrease. The larger particles are disappearing, resulting in a decrease of the effective particle size and therefore an increase of the brightness temperature differences. 5. CONCLUSIONS AND PERSPECTIVE Our simulation shows that the dissipation of the cirrus cloud is not sufficiently effective. A first test shows that the fall speed velocity of the ice particles impacts strongly the dissipative phase. Others key parameterization evaluations will be shown. All the observations made during the campaign will be used to constrain the choice of parameterizations and improve the dissipation of cirrus clouds in BRAMS.

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Acknowledgements : This work was partially supported by CNES (Centre National d’Etudes Spatiales) and INSU (Institut National des Sciences de l’Univers). The data collected during the experimental campaign were a joint effort of the other partners involved in this project (IPSL, DLR). The researchers of these groups are acknowledged for this work.