Investigation of Clouds fromGround-based
and Airborne Radar and Lidar (CARL)

Contribution of the University of Athens (NKUA)

 

1. Introduction

 

On 6 December 1995 the GKSS 95 GHz cloud radar located at Geestacht, northern Germany (called the region of interest hereafter) detected a cloud layer at the layer 4000-5000 m for the time interval 1606 UTC to 1614 UTC (Fig. 1). Although the period of observations was very short (8 minutes), local observers reported that this cloud layer covered the area during almost all the day. This case has been selected as a test case in order to investigate the ability of the Regional Atmospheric Modelling System (RAMS) to resolve this cloud layer.

 

Figure 1: Reflectivity measurements from the GKSS radar, from 1606 to 1614 UTC 6 December 1995.

 

 

 

2. Synoptic Setup

 

On 6 December 1995 a high pressure system prevails over Eastern Europe, which at 1200 UTC exceeds 1040 hPa over western Russia (Fig. 2a). This synoptic setting creates a weak southeasterly flow over central Europe and over the region of interest. Surface temperature is very low during the whole day over a major part of Europe, as low as -7 to -10°C over the area of interest. Light snow was also observed in many synoptic stations. The infrared satellite imagery at 1600 UTC 6 December (Fig. 2b) reveals the presence of medium level clouds over the area of interest, in good agreement with the radar observations, while over the Baltic Sea and offshore Latvia and Estonia, higher clouds are evident.

          

Figure 2: (a) Mean sea level pressure (at 5 hPa intervals), valid at 1200 UTC 6 December 1995,

(b) false-colour infrared imagery from METEOSAT, valid at 1600 UTC 6 December 1995.

 

 

3. Model Setup

 

The analysis of the cloud layer is based on nested-grid simulations performed with the Regional Atmospheric Modelling System. RAMS has been developed at Colorado State University as a research model, but recently is starting also to be used as an operational model (e.g., Cotton et al., 1994; Tremback et al., 1994). A detailed description of the model physics and application fields is given in Pielke et al. (1992).

 

For the present application, RAMS was initialised at 0000 UTC 06 December 1995 and the duration of the simulation was 24 hours. The nonhydrostatic version of the model is employed, while three nested grids have been defined. Indeed the computational domain of the model consists of:

 

(a) the outer grid, with a mesh of 76x62 points and 40 km horizontal grid interval centred at 53N latitude and 10E longitude

 

(b) the second grid with 122x110 points and 10 km horizontal grid interval, centred at 53 41' N latitude and 10 42' E longitude.

 

(c) the inner grid with 82x82 points and 2,5 km horizontal grid interval, centred at 53 41' N latitude and 12 42' E longitude.

 

The inner grid was introduced at 0900 UTC 06 December (after 9 hours of initialisation). The horizontal extension of the grids is shown in Fig. 3. Twenty-eight levels following the topography were used at the outer grid. The vertical spacing varied from 120 m near the surface to 1000 m at the top of the model domain. Vertical nesting was applied to the second and third grid, permitting to resolve adequately the cloud layer. Indeed from 1750 to 9300 m, 18 extra vertical levels were used with approximately 250-300 m vertical resolution. Along with these settings, other RAMS configuration options include:

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3: Extension of the three nested grid of RAMS

 

 

The lateral boundary conditions on the outer grid were the relaxation scheme similar to Davies (1976).

A rigid lid has been set at the model top boundary while top boundary nudging (which dumps gravity waves) has been activated.

A soil layer has been used to predict the sensible and latent heat fluxes at the soil-atmosphere interface (McCumber and Pielke, 1981; Avissar and Mahrer, 1988). Six soil levels have been used down to 50 cm below the surface.

The full microphysical package of RAMS has been activated (Walko et al, 1995). This package includes the condensation of water vapor to cloud water when supersaturation occurs as well as the prognosis of rain, graupel, pristine ice, aggregates, hail, and snow species.

A modified Kuo-type cumulus parametrization developed by Tremback (1990) is used because the model resolved convergence produced at the scales of the outer (40 km) grid is not enough to explicitly initiate convection.

A radiation scheme developed by Chen and Cotton (1983) which takes into account the influence of water vapor and condensate on shortwave nad longwave radiative transfer.

 

The ECMWF 0.5x0.5 gridded analysis fields are objectively analysed by RAMS model on isentropic surfaces from which they are interpolated to the RAMS grids, and they were used in order to initialise the model. The 6-hourly ECMWF analyses were linearly interpolated in time in order to nudge the lateral boundary region of the RAMS coarser grid at a nudging time-scale of one hour. Moreover, the ECMWF analyses were blended with all available surface and upper-air observations. Almost 80 upper-air soundings and more than 1200 surface observations have been used at 6-hour intervals. Observed sea-surface temperature data of 1x1 resolution provided by ECMWF have been used. Moreover, topography derived from a 30"x30" terrain data and gridded vegetation type data of 10'x10' resolution have been used.

 

 

4. Model results

 

This section provides a short description of RAMS results during this event. Figure 4a presents RAMS sea-level pressure and lowest model-level wind, valid at 1600 UTC 6 December. RAMS reproduces well the high pressure system over western Russia (1045 hPa), as well as the weak easterly flow over Central Europe and especially northern Germany where the radar was operating. The wind intensity was about 8 ms-1 over the area of interest, while the flow is accelerated over the North Sea, exceeding 16 ms-1.

 

 

Figure 4: (a) Mean sea-level pressure (at 5 hPa intervals) and wind field at the lowest model level, on the outer grid of RAMS, valid at 1600 UTC 6 December 1995, (b) as in (a) except for temperature (at 2 °C intervals). Negative values are dashed.

 

 

 

RAMS temperature field at the lowest model level (Fig. 4b) reproduces fairly well the negative temperature values characterizing the major portion of the domain, with values of about -6 °C in northern Germany and less than -8 °C in central Germany. Indeed, temperature records at Hamburg (denoted by H in Fig. 4b) and at Erfurt (denoted by E at Fig. 4b) reported -5.8 °C and -7 °C, respectively, while the reported wind at Hamburg is from eastern direction, with an intensity of 8 ms-1 at 1600 UTC, in very good agreement with RAMS results (see Fig. 4a).

 

 

    

 

Figure 5: Vertical cross section inside the second grid of RAMS, following line AB in Fig. 3, valid at 1600 UTC 6 December 1995 of (a) ice mixing ratio (g/kg), (b) pristince ice mixing ratio (g/kg).

 

 

A series of vertical cross sections inside the second grid of RAMS (following line AB in Fig. 3), permits to assess the vertical structure of condensates over the area of interest. Figure 5a presents a vertical cross section of ice mixing ratio at 1600 UTC, bounded vertically at 7 km. Over the western part of the domain, the whole atmospheric depth shown in Fig. 5a is characterised by high concentrations of ice, while on the eastern part of the domain a well defined layer of ice condensates is evident within the layer 4 to 5 km, in good agreement with the radar observations (Fig. 1). The cloud layer is also evident in the cross-section of the pristine ice mixing ratio, where a well defined layer of high values of pristine ice covers the eastern part of the domain shown (Fig. 5b).

 

In order to compare model-generated clouds with the satellite imagery, ice water path (IWP hereafter) can be used as a surrogate of upward longwave radiation (Heckman and Cotton, 1993). Ice water path can be defined as:

 

 

where ri is the ice mixing ratio and r is the air density. This quantity, integrated over a certain layer of  the atmosphere, is a measure of optical thickness. In order to compare the vertical position of clouds, the atmosphere was splited into two layers: 0-6 km and 6-16 km. This splitting permits to identify lower/middle clouds and higher clouds. Figures 6a and 6b present ice-water path within two layers over the second grid of RAMS, valid at 1600 UTC 6 December 1995.  Within the 0-6 km layer, IWP shows clouds over the area of interest, while the region over the North Sea and the Baltic Sea is free from lower clouds (Fig. 6a). Within the 6-16 km layer, IWP shows the absence of higher clouds over the area of interest and the presence of high clouds over the Baltic Sea and the Netherlands, in good agreement with the satellite imagery shown in Fig. 2b.

 

 

 

Figure 6: (a) Ice water path within the layer 0-6 km, valid at 1600 UTC 6 December,

 (b) as in (a) except for the layer 6-16 km.

 

 

 

5. Concluding remarks

 

 

On 6 December 1995, the GKSS polarimetric radar observed a well defined cloud band within the layer 4-5 km. RAMS simulations succeeded to reproduce the general synoptic situation, with the presence of  a high pressure system, a moderate easterly flow and very low surface temperature. The activation of the full microphysics package of RAMS resulted in an accurate reproduction of the cloud band within the layer seen by the radar, while the ice-water path calculated by RAMS output presented a very good agreement with the infrared satellite imagery.

 

It should be noted here that the radar provided data over a very short time period (8 minutes), a fact which makes the comparison with the model results difficult. During July 1998 the TRAC campaign took place near Paris and provided surface and airborne radar and lidar measurements over a longer time period. Thus, an event observed during TRAC will be selected in order to perform additional RAMS simulations, as the available observations would be more appropriate for comparison.

 

 

6. References

 

 

 

Avissar, R., and Y. Mahrer, 1988: Mapping frost sensitive areas with a three dimensional local scale numerical model. Part I: Physical and numerical aspects. J. Appl. Meteor., 27, 400-413.

Chen, C., and Cotton W.R., 1987: The physics of the marine stratocumulus-capped mixed layer. Bound. Lay. Meteor., 25, 289-321.

Cotton, W. B., T.Thompson and P.W. Mielke, 1994: Real time mesoscale prediction on workstations. Bulletin Americ. Meteor. Soc., 75, 349-362.

Davies, H.C., 1976: A lateral boundary formulation for multi-level prediction models. Q. J. R. Meteor. Soc., 102, 405-418.

Heckman, S.T., and W.R. Cotton, 1993: Mesoscale numerical simulation of cirrus clouds-FIRE case study and sensitivity analysis. Mon. Wea.Rev., 121, 2264-2284.

McCumber M.C., and R.A. Pielke, 1981: Simulation of the effects of surface fluxes of heat and moisture in a mesoscale numerical model. Part I: Soil layer. J. Geophys. Res., 86, 9929-9938.

Pielke, R.A, W.R. Cotton, R.L Walko, C.J. Tremback, W.A Lyons, L.D. Grasso, M.E. Nicholls, M.D. Moran, D.A. Wesley, T.J. Lee, and J.H. Copeland, 1992: A comprehensive meteorological modelling system - RAMS. Meteorol. Atmos. Phys., 49, 69-91.

Tremback, C. J., 1990: Numerical simulation of a mesoscale convective complex: Model development and numerical results. Ph.D. dissertation, Atmos. Sci. Paper No. 465, Colorado State University, Dept. of Atmos. Science, Fort Collins, Co 8523.

Tremback, C. J., W.A. Lyons, W.R. Cotton, R.L. Walko, and B. Beitler, 1994: Operational weather forecasting applications using the Regional Atmospheric Modelling System (RAMS). Preprints of the 10th Conference on Numerical Weather Prediction, Portland, OR, 18-22 July 1994.

Walko, R.L., W.R. Cotton, M.P. Meyers and J.Y. Harrington, 1995: New RAMS cloud microphysics parameterization. Part I: the single moment scheme. Atmos. Res., 38, 29-62.