Wolfram Wobrock, Céline Planche, Delphine Leroy, Andrea I. Flossmann

Transcription

1 COMPARISON BETWEEN RADAR AND DISTROMETER MEASUREMENTS AND PRECIPITATION FIELDS SIMULATED BY A 3D CLOUD MODEL WITH DETAILED MICROPHYSICS FOR A MEDIUM CONVECTIVE CASE IN THE CÉVENNES REGION Wolfram Wobrock, Céline Planche, Delphine Leroy, Andrea I. Flossmann Laboratoire de Météorologie Physique, Université Blaise Pascal-CNRS-OPGC, 24, avenue des Landais, F Aubière Cedex, France W.Wobrock@opgc.univ-bpclermont.fr 1. ABSTRACT The results of a numerical model with detailed microphysics is compared with distrometer measurements (rain rates and size distribution at the ground) and with radar reflectivities for the case of the 27/28 Oct 4 over the Cevennes mountains in southern France. The cloud and rain drop field simulated with DESCAM 3-D considering cold microphysical processes show good agreement with the radar and disdrometer observations for the period between midnight and 4 o clock in the morning. In the model the vertical profiles of the size distribution prove the importance of the ice phase for the formation of the precipitation. However, the simulated reflectivities in the bright band are clearly underestimated. In the case of a polluted air mass, the model produces a precipitation field which is horizontally reduced and has cumulated rain rates lower than the ones resulting from the simulation with a cleaner air mass. The onset of rain is delayed if the concentration of aerosol particles is increased. 2. INTRODUCTION The goal of the present study is to test the performance of the cloud model with spectral (bin) resolved microphysics - DESCAM 3D- for the precipitation at the ground. To forecast in mountainous regions the location and the rate of precipitation is in fact essential in order to study the response of the catchment basins and to predict possible flooding. The volumetric radar of Bollène, installed since 2 in southern France in the region of the Cevennes Mountains allows obtaining a horizontal and vertical distribution of the reflectivity and thus the rain field in an area with is often subject to devastating flash floods. During autumn 4 a disdrometer was added in Alès in order to obtain an additional characterisation of the size distribution of these precipitation events. In order to test the capacity of DESCAM 3D to simulated these rain fields, the event of 27/28 Oct 4 was selected where a cumulative rain rate of 8mm was measured. The top of the clouds stayed below 6km, thus, we will call this situation medium convection. 3. OBSERVATION WITH THE VOLUMETRIC RADAR AT BOLLÈNE AND THE DISDROMETER The disdrometer installed at Alès was run between September and December 4 (Chapon, 6). It classified the rain drops as a function of their size and their terminal velocity. Each of these parameters is distributed in a grid of 32 classes (from 62 µm to 24.5 mm). During the night of 27 / 28 October, the rain event deposited at the ground more than 8 mm (Fig. 1). The maximum intensity was about 65 mm h -1. The volumetric scan of the radar of Météo France at Bollène allows the reconstruction of the vertical profiles of reflectivity. Between 18. and midnight the radar reflectivities were stronger than 4 dbz

2 between the surface and 3 km. After midnight, the reflectivities decreased (around 35 dbz at most) and reached less high altitudes: between midnight and 2 o clock, 3 dbz were found above 3 km while the same value was found around 2.5 km between 2 and 4 o clock in the morning (Fig.2). The simulation was limited to the second phase of precipitation at Alès as only the initialisation with the sounding of Nimes at midnight allowed the formation of precipitation. Using the noon sounding of the 27 October 4 or the analysis of the ECMWF model remained also without success. Height (km) rain amount (mm) rain flux (mm h -1 ) Time (h UTC) Figure 1 : Cumulated rain on the ground (mm) and intensity (mm h -1 ) measured by the disdrometer at Alès on 27 and 28 October 4. A h h3 1h 1h3 2h 2h3 3h 3h3 Time (h UTC) Figure 2 : Time evolution of the vertical profile of radar reflectivity above Alès. The letters A and B correspond to the structure in Fig CLOUD MODEL WITH DETAILED MICROPHYSICS AND INITIALISATION OF THE MODEL The 3D model with detailed (bin) microphysics used herein couples the 3D non-hydrostatic model of Clark and Hall (1991) with the Detailed Scavenging Model DESCAM (Flossmann et al., 1985) A B (dbz) detailed description of the microphysical package, including sensitivity studies of DESCAM under mixed phase conditions can be found in Leroy et al. (7). Below only a brief summary of the essential features is given. The microphysical model employs five distribution functions: three number density distributions functions respectively for the wet aerosol particles (AP), the drops and the ice crystals and two mass density distributions of aerosol particles inside drops and ice particles. The five functions are discretized over 39 bins that cover a range of radius from 1 nm to 6 µm for the wet AP and from 1 µm to 6 mm for the liquid or solid hydrometeors. All together, the detailed microphysics introduces 195 supplementary prognostic variables to the initial code. The microphysical processes that are considered in the model are: condensational growth and activation/deactivation of AP, condensation and evaporation of droplets, coalescence, homogeneous and heterogeneous nucleation, vapor deposition on ice crystals and riming. Droplet nucleation relies on the calculation of the activation radius derived from the Köhler equation (Pruppacher and Klett, 1997), but is also dependent on temperature as described in Leroy et al. (7). Growth rate of drops and ice crystals are given by Pruppacher and Klett (1997). Homogeneous and heterogeneous nucleation follows respectively the works of Koop et al. () and Meyers et al. (1992). Ice crystals are assumed to be spherical and the density of ice is.9 g m -3. Coalescence and riming are treated with the numerical scheme of Bott (1998). The collection kernels for coalescence of drops are calculated with the collection efficiencies of Hall (198) and the terminal velocities of Pruppacher and Klett (1997). Riming description includes collection of droplets by large ice crystals as well as collection of small ice particles by large drops. The collection kernels for riming are set to be the same as those for coalescence of drops, i.e. we assume that the collection efficiency of a spherical ice

3 crystal is equal to the one of a water drop of the same mass. Aggregation and secondary production of ice particles during riming is also neglected in the model for the moment. For the simulations presented in this paper, the model domain is 128 km x 128 km in the horizontal (Δx, Δy= 1km) and 62 vertical level with a vertical resolution of 25 m. The dynamical time step is 4 second. Three simulations will presented below. The first simulation with cold and warm microphysics will be considered as the reference simulation. In the second simulation the cold microphysics processes are turned off in order to study the impact if the ice phase on the formation of rain for the given situation. These two simulations will use the same initial aerosol particle size distribution of Jaenicke (continental case, 1988). The total number of aerosol particles close to the ground is 7 cm -3 and decreases exponentially between the surface up to 3km and stays constant above. For the third simulation the total number of initial aerosol particles is multiplied by a factor of three and the cold microphysical processes are again active, in order to study the impact of a polluted air mass on the ice phase and rain formation. 5. RESULTS 5.1.SIMULATION WITH COLD MICRO- PHYSICS Figure 3 :Vertical profile of the mass spectra of crystals (left in blue) and drops (right in black) at a grid point next to Alès. In Fig. 3 on the left, the distribution of the Due to the spin-up time of the simulation the mass of ice crystals stays mono-modal first precipitation over Alès formed after 2h. irrespective of altitude. The size of the The cloud field reached up to 6km altitude, crystals varies between µm in high rain water was only present below 3.5km. altitudes to 8 µm around 3 km. On the The ice phase is confined to regions above right hand side, the size distribution of the 2.5km. drops shows a bi-model character at some Fig.3 presents the size distributions of places: cloud drops between and 4 µm crystals and drops between the ground and of diameter and rain drops around 1mm of 9.5km altitude in a vertical column of a grid diameter. point close to Alès. We can deduce from Fig.3 the principal microphysical processes responsible for

4 rain formation in this case study. Between 2.5 and 4km, rain drops form by collision/ coalescence inside the cloud. As at these altitudes the cloud is in mixte phase, the riming process transforms large drops into crystals. The total mass of crystals increases, thus, with decreasing altitude. Then, between 2.5 and 2.25km the crystals pass the C isotherm and melt. This process is assumed instantaneous in the model, and, thus, the total mass of crystals can now be found in the drop reservoir. This can be seen in Fig. 3 by a brutal increase in drop mass in this altitude. The formation of rain passes, thus, by the processes of collision/coalescence of drops, riming and then melting. Fig. 4 shows the distribution of rain on the ground after 4h of integration time. The maximum rates can be found on the slopes of the mountains, where the rain starts. As the time passes, the rain extends to the plains and later until the sea. Y in km t = 4 h M Montpellier Alès X in km Nîmes (mm) Figure 4: Cumulative rain on the ground in mm after 4 h of integration time observed simulated C rain accumulation (mm) 15 1 A B t 3 t mm t 2 +1 h 1 min t time (h UTC) Figure 5 : Cumulative rain of the ground recalculated from the disdrometer data (continuous lines) and simulated for Alès (X=79 km, Y=85 km, dotted line). The grey line corresponds to the observations with a temporal delay of +1 h 1 in order to take into account the spin-up of the model. Fig.5 gives the evolution of the cumulative rain on the ground measured by the disdrometer and simulated for a point close to Alès. One can note a strong increase of observed rain between 23h and 23h 4 UTC which can be attributed to the end of

5 the convective period which began before midnight and which the model cannot reproduce as it is initialised with the sounding of 23 UTC. In the following we restrict ourselves to the observations after 23h 4. Due to the spin-up time of the model we will consider a time shift of 1h3 when comparing the model output with the observations. The evolution of the cumulative rain can be divided in three phases A, B, and C in Fig. 5, as a function of the slope of the curve. The first phase A lasts about 2h 3 and the slope is 2 mm h -1. The slope is more important during phase B, around 9 mm h -1, for about 1 h min. Afterwards, the precipitation becomes stronger and a situation resembling phase A reappears. This is phase C with a slope of 2 mm h -1. Between 1h and 1h 3 the simulated cumulative rain for Alès increases linearly and the simulated and observed slope are coherent for phase A (Fig.5). The same applies around 3h 3 integration time when the slope increases (about 7 mm h -1 ) and the cumulated rain increases more strongly. Finally, at 4h in the morning, the model results show also a return to a situation similar to the one between 1 h and 3 h 3 in the morning. Thus, considering the slopes of the three phases the model results agree with the observations of the disdrometer. The main difference lies in the duration of the phase B which is only 3 min in the model but only lasts 1h in the observations. Fig. 6 compares the measured and simulated drop spectra for four different times t 1 to t 4 marked in Fig 5. As in Fig 4 and 5 the simulations are not averaged over a domain but represent the grid point representing Alès in the model. The distributions in Fig. 6a represent phase A (compare Fig.5) where the rain drops are smaller in the simulation than actually observed. This is surprising as the rain rates showed a very good agreement (Fig.5) during this phase. Fig.6b shows the results for phase B. Here, we find a very good agreement between observed and modelled spectra. Finally, the spectra with the diamonds in the Fig. 6b correspond to the end of phase B or the beginning of phase C for the observations, but belong clearly to phase C regarding the model results. Here, the model produces a smaller drops mass, but the agreement for the large drops (diameter larger than 1.1 mm) is good. dm (g m -3 ) dm (g m -3 ) t 2 observed simulated diameter (µm) diameter (µm) t 1 t 4 t 2 t 3 t 1 observed simulated Figure 6 a (top) and b (bottom): Mass distributions of measured rain drops (continuous lines) and simulated rain drops (dotted lines) for 4 times t 1 to t 4 given in Fig. 5. The microphysical processes responsible for the formation of drops in phase A and B are certainly different. In order to find an explanation for the behaviour, an analysis of the radar reflectivities (Fig. 2) is attempted. Fig. 2 shows that the maxima of the reflectivities are not at the same altitude for

6 phase A and phase B. During phase A, the maxima can be found between 2.5 and 3 km altitude. The isotherm C is located in this case study around 2.5 km. During phase A the reflectivites show, thus, a bright band. This bright band can be associated to the fact that the radar attributes the melting crystals to large rain drops and an increase in the radar reflectivity results. The large observed drops result, thus, directly from the melting of large ice crystals. During phase B, however, the maxima of the radar reflectivites are observed close to the ground. This a related to the growth of large drops by collision/coalescence during the sedimentation. The formation of the large drops on the ground is, thus, different in phase A and B which explains the differences noted in the size distribution at the ground. The vertical profile of the radar reflectivities above Alès displays also a different vertical extension of the convection in phase A and B. During phase A the radar reflectivities at 4km are around 3 dbz while those during phase B have values below 4 dbz. Convection is, thus, weaker during phase B which explains a smaller contribution of the ice phase to the precipitation. For phase A the simulated radar reflectivities stay 1-15 dbz below the observations and do not show the bright band at 2.5 km. This is due to the hypothesis of an instantaneous melting of tall ice particles at temperatures above C. During phase B the simulated reflectivities are closer to the observed ones as the drops grow by collision/coalescence in temperatures warmer than C SIMULATION WITHOUT COLD MICROPHYSICS Without cold microphysics, the liquid water can reach altitudes of 7km instead 4.4 km for the cloud water and 6km instead of 3.5 km for the rain water. When the ice phase is active, the crystals consume the water vapour present in high altitudes. The corresponding layers become sub-saturated with respect to liquid, however, stay supersaturated with respect to ice. The Bergeron effect makes the water vapour condense on the ice at the expense of the liquid drops. Consequently, in the case without ice, the air stays supersaturated with respect to liquid up to 7km. The maximum values of RH reach 13.5% in the mixte case as compared to 15.5% in the all liquid case. Y in km Y in km t=4 h X in km t = 4 h M M Montpellier Alès X in km Montpellier Alès Nîmes Nîmes (mm) (mm) Figure 7: Cumulative rain at the ground after 4 h of integration a) (top) without ice and b) (bottom) for the polluted case Fig.7a shows the cumulative rain on the ground after 4h of integration time. The region with rates exceeding 5mm is larger in the all liquid case than in the reference

7 case (Fig.4). This increase is most pronounced in the south around y=5km. Table 1 compares the total mass of rain in megatons integrated over the entire domain for three different times for the mixte and the all liquid case. The simulation without ice always gives more rain, even if the difference in percentage decreases with time. We can, thus, conclude that the absence of ice in this case of medium convection increases the rain over the domain studied and extends the region of the rain towards the south. Table 1: Total mass of rain on the ground in megatons for three different times for the reference case, the all liquid case and the polluted case. The columns difference give the variation in percentage with respect to the reference case. time Ref case All liquid case Difference (%) Polluted case Difference (%) 2 h h h Table 2 : Time evolution of the cumulative rain on the ground for the entire domain for the reference case, the all liquid case and the polluted case. Accumulation on the ground larger than All liquid case Reference case Polluted case 1 mm 45 min 65 min 75 min 5 mm 9 min 11 min 1 min 1 mm 13 min 14 min 17 min 15 mm 18 min 18 min 225 min Table 2 gives the integration times necessary to reach certain values for the cumulative rain on the ground (1, 5, 1 and 15 mm), for the simulations with and without the ice phase. In the absence of the ice phase the first rain appears 1 min earlier and the threshold of 1 and 5 mm are equally reached about min earlier. Then, the time difference decreases, and the threshold of 15 mm is reached at the same moment for the two simulations. After 4h of integration time the maximum accumulation for point M (compare Figs. 4 and 7) is even larger in the mixte phase case (25 mm) than in the all liquid case (16 mm). 5.3 SIMULATION IN A POLLUTED AIR MASS In order to represent polluted air, the number of aerosol particles initially present (7 cm -3 in the first two simulations of sections 5.1 and 5.2) were multiplied by a factor of 3, which results in a particle concentration of 24 cm -3. The shape of the distribution stays unchanged. Some differences can be noted. For the liquid phase, the cloud water content exceeds.5 g m -3 more often in the polluted case. The presence of rain water contents larger than.5 g m -3, however, is less frequent. We find here that with increasing number of particles the cloud water content increases at the expense of the rain water content. Concerning the ice water content, however, we note practically no change with respect to the reference case. This is a consequence of the parameterisation of Koop et al. () and Meyers et al. (1992) for homogeneous and heterogeneous nucleation used in the model. There, the number of crystals formed is only dependant on super-saturation with respect to ice and temperature but stays independent of the number of aerosol particles present. Fig. 7b shows the accumulated rain after 4h of integration time for the polluted case. As

8 before, the main differences can be observed in the south east around Y=55 km. The results concerning the total mass of rain on the ground, as well as the temporal evolution of the accumulation in the model domain are included in Table 1 and 2. Table 1 shows that the cumulative rain on the ground in the polluted case is 1 to % less than in the reference case. Table 2 indicates that the precipitations start later in the polluted case and the time shift increases with integration time. This time shift is certain responsible for the different rain accumulation around Y=55 km (Figs. 4 and 7b): the precipitation in the polluted case started later and is less wide spread, especially in direction to the sea. 6. CONCLUSION The cloud water and rain water fields simulated with DESCAM 3D including cold microphysics show a satisfactory agreement with the observation of the radar and the disdrometer for the episode from midnight to 4 o clock in the morning for the 27/28 Oct 4 in the Cevennes region in southern France. During phase A the radar shows for the region above Alès the presence of a bright band. The drops collected by the disdrometer resulted, thus, from the melting of ice crystals with size above 1mm aloft. During phase B the maxima of radar reflectivities were observed close to the ground. Convection is now weaker and the collision/coalescence of drops can explain the increase of the radar reflectivities close to the ground. In the model, the vertical variation of the size distributions show that the ice phase contributes significantly to rain formation. However, the simulated reflectivities are clearly underestimated when melting is present. The convection in the initial phase is less developed in the model and the drops on the ground are smaller than observed. In the following phase with weaker convection, the model reproduces well the maxima of reflectivity close to the ground and the measurements of the disdrometer. In a polluted air mass the model produces less precipitation, in area as well as in accumulation. Also, this precipitation starts later. Analysing the content of liquid and ice, is seems that in a medium convective situation the warm microphysical processes are more affected by an increase of the aerosol particle number. This can certainly be attributed to the parameterization of the ice nucleation used in the model. This aspect will be studied in the future, taking into account other nucleation ways. 7. LITTERATURE Bott A., 1998 : A flux method for the numerical solution of the stochastic collection equation. J. Atmos. Sci., 55, Clark, T. L., and W. D. Hall, 1991 : Multi-domain simulations of the time dependent Navier-Stokes equations : benchmark error analysis of some nesting procedure, J. Comp. Phys., 92, Chapon B., 6 : Etude des pluies intenses dans la région des Cévennes-Vivarais à l aide du radar météorologique. Régionalisation des traitements radar et analyse granulométrique des pluies au sol. Thèse de l Université Joseph Fourier, Grenoble. 187 p. Flossmann, A. I. and. H. R. Pruppacher, 1988 : A theoretical study of the wet removal of atmospheric pollutants. Part III : The uptake, redistribution, and deposition of (NH4)2SO4 particles by a convective cloud using a twodimensional cloud dynamics model. J. Atmos. Sci., 45, Hall W. D., 198 : A detailed microphysical model within a two-dimensional dynamic framework : Model description and preliminary results. J. Atmos. Sci., 37, Jaenicke, R., 1988 : Aerosol physics and chemistry. In Landolt-Boernstein : Zahlenwerte und Funktionen aus Naturwissenschaften und Technik, V 4b, G. Fischer Editor, Springer, Koop, T., B. Luo, A. Tsias and T. Peter, : Water activity as the determinant for homogeneous ice nucleation in aqueous solutions. Nature, 46,

9 Leroy, D., W. Wobrock, and A. I. Flossmann, 7 : On the influence of the treatment of aerosol particles in different bin microphysical models : a comparison between two different schemes. Atmos. Res. In print. doi: 1.116/j.atmosres Meyers, M.P., P. J. Demott and W. R. Cotton, 1992 : New primary ice nucleation parameterizations in an explicit cloud model. J. Appl. Met., 31, Pruppacher H. R. and J.D. Klett, 1997 : Microphysics of clouds and precipitation. 2 nd ed. Kluwer Academic, 954pp. 8. ACKNOWLEDGEMENTS: The calculations for this paper have been done on computer facilities of the Institut du Développement et des Ressources en Informatique Scientifique (IDRIS, CNRS) in Orsay (France) and the Centre Informatique National de l Enseignement Supérieur (CINES) in Montpellier (France), under project no The authors acknowledge with gratitude the hours of computer time and the support provided. The authors also acknowledge this gratitude the support received from the French CNRS/INSU program OHMCV/Lefe..