Orographic mixed-phase stratiform cloud case

Transcription

1 Orographic mixed-phase stratiform cloud case Andreas Muhlbauer and Ulrike Lohmann September 3, 28 Abstract The key issues of this case study focus on the initiation of ice by heterogeneous ice nucleation in orographic clouds and on the liquid to ice mass partitioning in orographic clouds. Since aerosols are important for cloud droplet nucleation and heterogeneous ice nucleation a further objective is to analyze the effect of aerosols on orographic precipitation. In particular, the effect of aerosols on the orographic precipitation distribution, the total orographic precipitation budget, the orographic spillover factor and the drying ratio. In view of observational studies the effect of aerosols on different microphysical processes (e.g., autoconversion, accretion, riming) should be analyzed. From the perspective of numerical modeling, further key issues are the treatment of graupel in the microphysical parameterizations, the conversion of snow to graupel and the potential implications for the orographic precipitation distribution and the orographic spillover factor. A long-term goal is to be able to estimate to what extent the uncertainty in the representation of microphysical processes such as heterogeneous ice nucleation or the treatment of graupel in numerical weather prediction models is important for improving quantitative precipitation forecasting in complex terrain on a cloud or cloud system resolving scale. 1 Idealized 2D simulations of stratiform mixed-phase orographic precipitation 1.1 Main objectives In this experiment a moist 2D flow over an idealized bell-shaped topography is considered. The main focus is put on the development of winter-time mixed-phase orographic clouds and precipitation under idealized conditions. The key objectives are to investigate the orographic precipitation distribution in different dynamical and thermodynamical regimes and to analyze the sensitivity of stable orographic precipitation with respect to the ambient aerosol conditions. Furthermore, the effect of aerosols on microphysical 1

2 processes (e.g., autoconversion, accretion, riming) should be quantified in terms of microphysical conversion rates, total domain precipitation, orographic spillover factor (SP, Jiang and Smith 23) and drying ratio (DR, Smith et al. 25). Two specific cases are considered: Simulation of stable upslope orographic precipitation developing in a linear hydrostatic mountain wave. Simulation of stable upslope orographic precipitation under blocked flow conditions. Within the idealized framework these two distinct flow regimes can be generated by varying the mountain height and/or the ambient wind speed in the initial condition. Two different thermodynamical regimes with varying height of the melting level may be generated easily by varying the sea-level temperature. Optionally, microphysical parameters are prescribed in the form of a log-normal aerosol size distributions as observed at the Jungfraujoch in Switzerland (Weingartner et al. 1999) or in form of a cloud droplet number concentration calculated directly from the aerosol size distribution. Optionally, sensitivity studies with varying heterogeneous ice nucleation parameterizations given in the appendix can be conducted. 1.2 Dynamical initial condition In the following we consider the development of stable orograhic precipitation in a linear hydrostatic mountain wave for different thermodynamical initial conditions. The initial vertical profiles of temperature and dew-point temperature are shown in figure 1 and are similar to the ones used in Muhlbauer and Lohmann (28). The profiles are given analytically by prescribing a sea-level temperature T sl, sea-level pressure p sl and dry Brunt-Väisälä frequency N d (see also Clark and Farley 1984, their eq. (2)). The sea-level temperature is varied between T sl = 273 K and T sl = 28 K to generate two different thermodynamical conditions. The vertical profile of the relative humidity is prescribed by a modified Fermi function (Spichtinger 24) of the type RH(z) = a + b a 1 + exp [ c (z z )], (1) with the parameters a =.95, b =.3, c =.15 m 1, z = 6 m and RH 1. The modified Fermi function gives a vertical profile of relative humidity which starts with the value a =.95 at the surface and decays smoothly with height towards the value b =.3. The windspeed is U = 15 m s 1 and is prescribed constant with height within the first 1 km and increases linearly above with approximately u/ z = 1.84 m s 1 km 1. 2

3 p [hpa] T [ C] 25 m/s Figure 1: Atmospheric soundings for the idealized 2D simulations showing the temperature (red) and dew-point temperature (blue) in a skewt-logp diagram. The soundings are given analytically (Clark and Farley 1984) with the surface temperatures T sl = 28 K (solid) and T sl = 273 K (dashed). The dry Brunt-Väisälä frequency is N d =.11 s 1, the surface pressure is p sl = 1 hpa and the surface relative humidity is RH SL =.95. The windspeed is U = 15 m s 1 and is prescribed constant with height within the first 1 km and increases linearly above with approximately u/ z = 1.84 m s 1 km 1. A small fortran program for creating the idealized initial conditions is provided. The idealized bell-shaped topography has the form h(x) = { h [ cos(π x x )] 4, x x 4a < 4a, x x > 4a, (2) with the peak mountain height h located at x (the middle of the computational domain) and a being the mountain half-width (Kirshbaum and Durran 24). In the first experiment (linear hydrostatic mountain wave) the mountain height is set to 3

4 h = 8 m and the mountain half-width is a = 2 km. In the second experiment (blocked-flow) the mountain height is set to h = 3 m. 1.3 Microphysical initial condition The initial aerosol spectrum is taken from Weingartner et al. (1999) and is shown in figure 2. The aerosol spectrum satisfies a lognormal size ditribution of the form N(ln r) = 2 i=1 N i 2π ln σi exp [ ( ) ] 2 ln r ln ri 2 ln σi with three free parameters, the aerosol number densities N i, the count median radii r i and the geometric standard deviations σ i, specified in table 1. Mode N [cm 3 ] r [µm] σ M [µg m 3 ] Winter Ait Acc Summer Ait Acc Table 1: Parameters of the aerosol size distribution. The mass density in each mode is computed from the aerosol size distribution by assuming a mean aerosol density of ρ = 1.5 cm 3 (Cozic et al. 27). The total aerosol mass densities are M =.51 µg m 3 for the winter aerosol spectrum and M = 2. µg m 3 for the summer aerosol spectrum. The aerosol initial condition is prescribed vertically constant. If one assumes an activation radius of approximately r act = 35 nm beyond which aerosols may get activated to cloud droplets, than the number of cloud droplets can be calculated analytically from the aerosol size distribution. In this case, the cloud dropler number concentrations are approximately N c,winter = 12 cm 3 for the winter aerosol spectrum and N c,summer = 38 cm 3 for the summer aerosol spectrum. Assuming a mean density of the aerosol of ρ = 1.5 g cm 3 (Cozic et al. 27) the mass distribution of the aerosols can be calculated directly from the number distribution. Integration over the mass distributions yields the aerosol mass densities which are M summer = 2. µg m 3 and M winter =.51 µg m 3. In case that models are able to account for the chemical composition of different aerosol species researchers are invited to use the pie-chart information provided in figure (11) of Cozic et al. (27) (Feb/Mar 25, Jul/Aug 25). (3) 1.4 Model setup and simulations The simulations should be conducted in the following way: The idealized bell-shaped topography is given analytically by eq. (2) with a half-width of 2 km and a mountain 4

5 height of h = 8 m (linear mountain wave experiment) and h = 3 m (blocked flow). The full set of simulations for the first experiment is summarized in table 2. Simulation height of mountain [m] Sea-level temperature [K] aerosols Sim ideal 1a 8 28 winter Sim ideal 1b 8 28 summer Sim ideal 2a winter Sim ideal 2b summer Sim ideal 3a 3 28 winter Sim ideal 3b 3 28 summer Sim ideal 4a winter Sim ideal 4b summer Table 2: Parameter specifications for the idealized set of simulations. All simulations are conducted with a sea-level pressure p sl = 1 hpa, a dry Brunt-Väisälä frequency of N d =.11 s 1 and a mountain half-width of a = 2 km. Researchers are encouraged to perform all the suggested simulations but may also restrict themselves to a limited subset. For the idealized simulation we suggest the following model setup which is briefly described below and summarized in table 3. The computational domain is 2D with 4 gridpoints in the horizontal and 6 vertical levels. The idealized topography is centered in the computational domain. In the horizontal direction a grid-spacing of 2 km is applied. Researchers may use the (height-based) vertical model levels which are specified in the example fortran file but may also use their own model level specifications. The model is initialized horizontally homogeneous with the vertical initial conditions given in figure 1. A free-slip lower boundary condition is applied to make the simulations dynamically com Mean winter Mean summer Mean winter Mean summer N (lnr) [cm 3 ] 2 15 M (lnr) [µg m 3 ] radius [µm] radius [µm] Figure 2: Aerosol initial condition for the idealized 2D simulations. The left panel shows the number distribution based on a mean aerosol spectrum for wintertime conditions (solid) and for summertime conditions (dashed). The mass distribution is calculated analytically from the number distribution by assuming a mean density of ρ = 1.5 g cm 3. 5

6 Domain Number of gridpoints 4 Horizontal grid spacing 2 km Timestep 1 s Number of vertical levels 6 Vertical coordinate system SLEVE Boundary conditions Lower bc free-slip Upper bc Rayleigh damping sponge layer Lateral bc Davies-type relaxation Parameterizations Radiation OFF Convection OFF Turbulence Mellor-Yamada scheme Microphysics Two-moment scheme Soil OFF Table 3: Suggested model setup. parable. At the lateral boundaries a Davies-type relaxation boundary condition towards the initial condition is applied. Radiation and convection parameterization is switched off. The simulation time is 1 h and hourly output fields should be provided. A suggestion for the format of the model output is given in the appendix. All fields are compared after 1 h of simulation. Researchers are invited to produce figures of the mixing ratios for the different hydrometeor species (e.g. cloud water mixing ratio q c etc. in units of g kg 1 ), the corresponding hydrometeor number densities (e.g. cloud droplet number concemtration N c in units of m 3 etc.) and the orographic precipitation distribution for the different simulations. In the following we show first model simulations which are performed with the nonhydrostatic limited-area weather prediction model COSMO (Doms and Schättler 22; Steppeler et al. 23) with a horizontal resolution of 2 km. The vertical levels are prescribed as 6 terrain-following (SLEVE) coordiantes (Schär et al. 22) with approximately 2 m grid spacing at the lowermost layer and 14 m grid spacing at the topmost level which is located at a height of m. The boundary conditions are open at the lateral bounds, free-slip at the lower boundary and rigid-lid at the upper boundary. A gravity wave absorbing sponge layer is introduced above a height of 13 m to minimize the effect of gravity wave reflection from the upper boundary of the computational domain. For the microphysics scheme a one-moment scheme with prognostic snow, ice and graupel is chosen. The turbulence parameterization involves a prognostic TKE equation with Mellor-Yamada stability functions. 6

7 8 QC QI distance [km] g/kg distance [km] g/kg 8 QR QG distance [km] g/kg distance [km] g/kg 8 7 QS distance [km] g/kg Figure 3: Cloud mixing ratios after 1 h of simulation for T sl = 273 K, h = 8 m and a = 2 km. The panels show the hydrometeor mixing ratio, the potential temperature (solid black) and the temperature (solid red). Units are [g/kg] for the mixing ratio, [K] for the potential temperature and [ C] for the temperature. 7

8 QC QI e distance [km] g/kg distance [km] g/kg QR QG distance [km] g/kg distance [km] g/kg QS distance [km] g/kg.1 Figure 4: Cloud mixing ratios after 1 h of simulation for T sl = 273 K, h = 3 m and a = 2 km. The panels show the hydrometeor mixing ratio, the potential temperature (solid black) and the temperature (solid red). Units are [g/kg] for the mixing ratio, [K] for the potential temperature and [ C] for the temperature. 8

9 Figure 3 shows the mixing rations for cloud water (QC), ice (QI), rain (QR), graupel (QG) and snow (QS) for the linear mountain wave case (h = 8 m) and for the case with flowblocking (h = 3 m, figure 4) after 1 h simulation time. The precipitation distributions along the topograhy after 1 h of simulation are shown in figure 5 for the case h = 8 m and h = 3 m, respectively. 3 precipitation [mm / 1 h] T =28K 1 precipitation [mm / 1 h] T =28K rain 2 1 T =276K T =273K mountain peak rain 5 T =276K T =273K mountain peak snow graupel total x [km] T =28K T =276K T =273K mountain peak x [km] T =28K T =276K T =273K mountain peak x [km] T =28K T =276K T =273K mountain peak x [km] 1 snow graupel total x [km] 1 5 T =28K T =276K T =273K mountain peak x [km] 1 5 T =28K T =276K T =273K mountain peak x [km] 1 5 T =28K T =276K T =273K mountain peak x [km] 4 h [m] 5 h [m] x [km] x [km] Figure 5: Precipitation distribution after 1 h of simulation for the one-moment scheme with prognostic ice, snow and graupel. The left panel shows the case without flow blocking (h = 8 m) and the right panel shows the case with flow blocking (h = 3 m). Since observational studies by Borys et al. (2) and Borys et al. (23) suggest that aerosols may affect the riming rates in clouds, researchers are encouraged to evaluate the sensitivity of microphysical processes (e.g., autoconversion, accretion, riming) to the varying aerosol conditions by means of timeseries of microphysical conversion rates. Furthermore, the effect of aerosols on orographic precipitation should be analyzed by comparing the orographic precipitation distributions and evaluating metrics such as the total domain precipitation, the orographic spillover factor (i.e., leeward precipitation fraction, Jiang and Smith 23) and the drying ratio (i.e., the ratio of precipitation to the incoming water vapor flux, Smith et al. 23, Smith et al. 25) 9

10 2 Simulations of a mixed-phase orographic precipitation event (2D): Case study 8 March Main objectives In this experiment a case study of an orographic precipitation event in the Alpine region during March 24 is conducted. The main objectives are the investigation of the role of heterogeneous ice nucleation for the liquid-ice mass fractionation in mixed-phase wintertime orographic clouds and the effect on the orographic precipitation distribution. A further objective is to estimate to what extent parameterizing microphysical processes such as heterogeneous ice nucleation and the conversion of snow to graupel improve the forecasted orographic precipitation distribution in complex terrain. A realistic (filtered) topography (north-south transect) with a grid spacing of 2.2 km is used in the simulation. Initial conditions are provided in terms of radiosonde data from the station Payerne (Switzerland). Optionally, microphysical initial conditions such as CCN and/or aerosol spectra are taken from a field campaign at the Jungfraujoch, Switzerland (Verheggen et al. 27). Models are evaluated against precipitation data which is available on a regular 2 km grid over Switzerland (Wüest et al. 28) and, optionally, against radar reflectivities. Furthermore, in-situ measurements of microphysical parameters such as the liquid water content (LWC), ice water content (IWC), cloud droplet number concentrations (CDNC) and ice crystal number concentrations (ICNC) are available at the Jungfraujoch and can be compared. 2.2 Case description The Jungfraujoch target area Figure 6 shows the Jungfraujoch (JFJ) target area. The JFJ area is located along the main Alpine rim in the Bernese alps in central Switzerland. The exact location of the JFJ is N and 7.99 E and the height is approximately 35 m. During February-March 24 a Cloud and Aerosol Characterization Experiment (CLACE 3) has been conducted at the Jungfraujoch research station which is located at the mountain col and is frequently in-cloud (Baltensperger et al. 1998; Choularton et al. 28). In the following we consider 8 March 24 as a case study to investigate the aerosol-cloud interactions and precipitation formation in an winter-time orographic cloud forming at the JFJ Synoptic situation The synoptic flow conditions are given by the ECMWF analysis and are shown in figure 7. 1

11 x Nancy x Stuttgart x Albis Latitude (deg N) x Dole x Payerne x Jungfraujoch x Lema x Milano m Longitude (deg E) Figure 6: The Jungfraujoch target area. The solid line indicates the north-south transect through the Jungfraujoch region. The locations of the three radars (Albis, Dole, Lema) operated by MeteoSwiss are indicated by black crosses. The upper-level flow fields indicate a trough over Switzerland with a north-westerly flow and an easterly propagating upper-level ridge approaching Switzerland. This flow configuration leads to an advection of cold airmasses towards the Alpine region from the north. Due to the orographic flow blocking at lower levels the flow below 65 hpa (the approximate height of the main Alpine rim) is from north-easterly direction which is also indicated in the radiosoundings from Payerne shown in figure 8. During the day the northerly flow intensifies at all levels and the pressure at the JFJ increases by 2 hpa as a consequence of the slowly approaching upperl-level ridge. Radiosoundings are available from the station Payerne (figure 8) at the standard observational times UTC and 12 UTC Observations The in-situ observations are available at the JFJ and are shown in figure 9. 11

12 Latitude (deg N) ECMWF analysis: 8 Mar 24, 3hPa pressure level PVU Longitude (deg E) x Latitude (deg N) ECMWF analysis: 8 Mar 24, 85hPa pressure level K Longitude (deg E) x ECMWF analysis: 8 Mar 24 12::, 3hPa pressure level ECMWF analysis: 8 Mar 24 12::, 85hPa pressure level 315 Latitude (deg N) PVU Longitude (deg E) x Latitude (deg N) K Longitude (deg E) x ECMWF analysis: 9 Mar 24, 3hPa pressure level 8 6 ECMWF analysis: 9 Mar 24, 85hPa pressure level Latitude (deg N) x Latitude (deg N) x PVU Longitude (deg E) K Longitude (deg E) Figure 7: ECMWF analysis fields of the geopotential height, potential vorticity and equivalent potential temperature. Panels (a,c,e) show the geopotential height and the potential vorticity at the 3 hpa pressure surface for 8 March 24 UTC, 12 UTC and 9 March 24 UTC, respectively. Panels (b,d,f) show the geopotential and the equivalent potential temperature at the 85 hpa pressure surface together with the windvectors at this height. Units are gpdm for the geopotential height, pvu for the potential vorticity and K for the equivalent potential temperature. Time is UTC. These observations include measurements such as the temperature T, the dew-point temperature T d and the pressure p from the ANETZ weather station (operated by MeteoSwiss) and the horizontal as well as vertical wind measurements from a 3D wind anemometer. Additonally, measurements of cloud microphysical parameters are available which in- 12

13 Figure 8: The panels show the atmospheric soundings from Payerne from 8 March 24 UTC, 12 UTC and 9 March 24, UTC. The figures are taken from the University of Wyoming archive. clude the cloud liquid water content (LWC) from a Particle Volume Monitor (PVM), the ice water content (IWC) from a Cloud Particle Imager (CPI), the cloud droplet number concentrations from a Forward Scattering Spectrometer Probe (FSSP) and from a Scanning Mobility Particle Sizer (SMPS) as well as ice crystal number concentrations from the Ice-CVI and the CPI (Weingartner et al. 1999; Verheggen et al. 27). Radar reflectivities and the precipitation distribution over Switzerland are available from a precipitation dataset on a regular 2 km grid derived from the MeteoSwiss radar composite (Wüest et al. 28) and are shown in figure Dynamical initial condition The dynamical initial condition is provided by the radiosonde data launced at Payerne at 8 March UTC. Since the data is used as initial condition for a 2D simulation, we suggest to initialize the wind speed from the radiosonde winds but to use the absolute wind such as ff = u 2 + v Aerosol initial condition For the initial aerosol setup we prepared the mean aerosol spectrum from 8 March 24 as measured by the SMPS device at the Jungfraujoch. The mean aerosol spectrum (averaged over 24 h) is shown in figure 11. The total aerosol size distribution is obtained from combining measurements of the SMPS device (total inlet) and an optical particle counter (OPC). The activated aerosol spectrum is determined from the difference of the SMPS measurements behind the total and the interstitial inlet. The averaged activated aerosol spectrum is shown in figure 12. The aerosol spectra are averaged over each size bin and over 24 h. The parameters of the lognormal aerosol size distributions are estimated from robust nonlinear least-squares regression and are summarized in table 4. The total aerosol mass density (determined from the SMPS aerosol size distribution by assuming an average density of ρ = 1.5 g cm 3 ) is M = 4.86 µg cm 3. In case that models 13

14 [ C] p [hpa] T (ANETZ) T (ANETZ) d 18 2 a : 3: 6: 9: 12: 15: 18: 21: : [ms 1 ] [gm 3 ] [gm 3 ] [cm 3 ] : 3: 6: 9: 12: 15: 18: 21: : ff (Sonic) 1 8 c w (Sonic) : 3: 6: 9: 12: 15: 18: 21: :.8 LWC (PVM).6 d LWC error.4.2 : 3: 6: 9: 12: 15: 18: 21: : IWC (CPI) IWC error : 3: 6: 9: 12: 15: 18: 21: : [cm 3 ] b e f CDNC (FSSP) CDNC (SMPS) : 3: 6: 9: 12: 15: 18: 21: : ICNC (CPI) 5 4 g : 3: 6: 9: 12: 15: 18: 21: : Figure 9: Ground-based in-situ observations at the Jungfrauchjoch. The meteorological observations include measurements of the temperature and the dew-point temperature (a), the pressure (b) and the horizontal/vertical wind speeds (c). The microphysical observations include the liquid water content (d), the ice water content (e), the cloud droplet number concentration (f) and the ice-crystal number concentration (g). The shaded regions in the LWC and IWC measurements indicate the uncertainty range as discussed in Verheggen et al. (27). are able to account for the chemical composition of different aerosol species researchers are invited to use the pie-chart information provided in figure (11) of Cozic et al. (27) (Feb/Mar 25). The total number of cloud droplets resulting from the activated aerosol spectrum shown in figure 12 is approximately 16 cm 3. 14

15 a 24h precipitation sum 1 8 longitude (deg N) JFJ latitude (deg E) mm 4 3 b S N transect through JFJ precipitation [mm/d] latitude (deg E) Figure 1: Precipitation distribution over Switzerland. Panel (a) shows the precipitation distribution over Switzerland estimated by the MeteoSwiss radar composite. Panel (b) shows the precipitation distribution along the south-north transect through the Jungfraujoch region. The topography data is taken from a high resolution digital elevation model. The location of the Jungfraujoch is indicated by the black dot. 2.5 Model setup and simulations The following simulations are performed with the numerical weather prediction model COSMO described in section 1.2 but without the free-slip lower boundary condition. A roughness length of z =.5 may be used for the simulations. In this case a horizontal 15

16 number distribution [cm 3 ] SMPS data OPC data a Fit, R 2 =.9923 Aitken mode Acc. mode Coa. mode mass distribution [µg m 3 ] Total aerosol Aitken mode Acc. mode Coa. mode b radius [µm] radius [µm] Figure 11: Mean aerosol spectra of 8 March 24 as measured with the SMPS and OPC devices at the JFJ. The spectra are averaged over 24 h. The panels show the averaged aerosol spectrum from the SMPS total inlet which represents the size distribution of the total aerosol. number distribution [cm 3 ] SMPS data Fit, R 2 =.9362 Aitken mode Acc. mode radius [µm] Figure 12: Mean activated aerosol spectra of 8 March 24 as measured with the SMPS device at the Jungfraujoch. The spectra are averaged over 24 h. The figure shows the averaged activated aerosol spectrum which represents the size distribution of the activated aerosols and is determined from the difference of the total and the interstitial inlet of the SMPS. Mode ˆN [cm 3 ] ˆr [µm] ˆσ M [µg m 3 ] Aitken mode Accumulation mode Coarse mode Table 4: Parameters of the fitted aerosol size distribution. The mass density in each mode is computed from the aerosol size distribution by assuming a mean aerosol density of ρ = 1.5 cm 3 (Cozic et al. 27). The total aerosol mass density is M = 4.86 µg m 3. grid spacing of x = 2.2 km is used. Some first results and comparisons to observations are shown in figure

17 .8.6 LWC model LWC obs. LWC obs. error [gm 3 ].4.2 : 3: 6: 9: 12: 15: 18: 21: :.8.6 IWC model IWC obs. IWC obs. error [gm 3 ].4.2 : 3: 6: 9: 12: 15: 18: 21: : [ C] T model T obs : 3: 6: 9: 12: 15: 18: 21: : [hpa] p model p obs. 64 : 3: 6: 9: 12: 15: 18: 21: : θ model θ obs. [K] : 3: 6: 9: 12: 15: 18: 21: : [ms 1 ] ff model (1m) ff obs. : 3: 6: 9: 12: 15: 18: 21: : [ms 1 ] w model w obs. : 3: 6: 9: 12: 15: 18: 21: : precipitation [mm/d] Radar Model JFJ latitude [degree north] latitude [degree north] Figure 13: The panels show the results of the first model simulations and the comparison to the observations. 17

18 In order to make the different model simulations comparable we encourage researchers to provide the model outputs in table 5. The variables with the asterisk are seen as the key variables and have been compared at the International Cloud Modeling Workshop. Researchers are invited to produce timeseries of temperature, LWC, IWC, CDNC and ICNC at the lowest model level at the JFJ gridpoint and to provide a comparison with the observations (see figure 13 for an example). A suggestion for the format of the model output is given in the appendix. 18

19 Variable (1 h output) variable name units dimension Temperature T K 2D Pressure P Pa 2D Specific humidity QV g kg 1 2D Relative humidity RH % 2D Potential temperature PT K 2D Equivalent potential temperature PTE K 2D Horizontal wind speed U m s 1 2D Vertical wind speed W m s 1 2D Cloud water mix. rat. QC g kg 1 2D Cloud droplet number conc. NC cm 3 2D Ice mix. rat. QI g kg 1 2D Ice crystal number conc. NI cm 3 2D Rain mix. rat. QR g kg 1 2D Rain drop number conc. NR cm 3 2D Snow mix. rat. QS g kg 1 2D Snow crystal number conc. NS cm 3 2D Graupel mix. rat. QG g kg 1 2D Graupel number conc. NG cm 3 2D Radar reflectivity factor Z dbz 2D Rain (gridscale) RAIN GSP mm 1D horiz. Snow (gridscale) SNOW GSP mm 1D horiz. Graupel (gridscale) GRAU GSP mm 1D horiz. Total precipitation (gridscale) TP mm scalar Spillover factor SP scalar Drying ratio DR scalar Output at JFJ gridpoint (15 min output) Total precipitation (gridscale) TOT PREC mm 1D horiz. 2 m temperature T2M K scalar 1 m wind speed U1 K scalar Liquid water content (only cloud droplets) LWC g m 3 1D vert. Liquid water path LWP g m 2 scalar Ice water content (only ice crystals) IWC g m 3 1D vert. Ice water path IWP g m 2 scalar Total water path TWP g m 2 scalar Precipitable water PW g m 2 scalar Table 5: Suggested model output. The key variables are indicated with an asterisk and will be compared at the International Cloud Modeling Worshop. 19

20 Appendix Parameterizations of heterogeneous ice nucleation Fletcher (1962): N i = 1 2 exp [.6 (T T )] (4) Meyers et al. (1992) deposition/condensation and contact nucleation: N i = 1 3 exp ( S i ) (5) N i = 1 3 exp [ (T T )] (6) (7) Cooper (1986): N i = 5. exp [.34 (T T )] (8) Here, N i [cm 3 ] denotes the ice crystal number concentrations, T [K] is the temperature and T = K. S i and s i are the supersaturation (percent) and saturation ratio with respect to ice, respectively. Radar reflectivity For the calculation of the radar reflectivity we define the following radar-type independent reflectivities: Z = D 6 f(d)dd (9) with the hydrometeor diameter D and hydrometeor size distribution f(d). The radar-type independent reflectivity in dbz is then given by: Output formats Z [dbz] = 1 ln Z (1) ln 1 We suggest to produce the model output either in form of netcdf files (each variable and timestep separately) or in form of ascii files with the fortran format specification (DIM(I6,TR2),E13.6) with DIM denoting the number of dimensions of the output field. 2

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