QUANTIFICATION OF DRIZZLE FRACTION USING LARGE EDDY SIMULATION MODEL OUTPUT - THE MULTI-PURPOSE OF THE EARTHCARE SIMULATOR
Igor Stepanov, Simone Placidi and Herman Russchenberg Delft University of Technology, Delft, The Netherlands
1 INTRODUCTION
Earth's low altitude, warm, layered clouds play a major role in the climate system, reflect-ing back a large amount of the incomreflect-ing short-wave radiation from the Sun. Stratocumulus (Sc) clouds are typical representatives of such type of clouds. They cover vast areas, exten-sively spreading in horizontal direction, having a considerable impact on the radiation budget. To look into the process of the Sc structure variability and evolution, retrievals of the mi-croscale cloud properties are used. These can be related back to the large scale processes that consequently onset due to a change in the microscale. One of such chain of events is the growth of cloud droplets via process of diffusion until a critical size is achieved, from where process of collisions and coalescense takes over, forming light drizzle drops. This type of feedback could lead to development of pockets of open cells (Stevens et al., 2005), also changing the cloud radiative properties.
Drizzle onset process regarding such clouds is of great importance to comprehend the cloud evolution and its impact on the radiation bud-get. To analyze the consequence chain this microphysical process holds on a large scale, a modified version of the EarthCARE mission Simulator (ECSIM) is used here to ingest cloud scenes from a Large Eddy Simulation (LES) model, represented with microphysical output. Full description of the LES model used is given in Heus et al., 2010. Recreated scenes in EC-SIM were used in order to generate realistic ob-servations for ground-based radar instrument. 2 RADAR OBSERVATIONS IN
DRIZZLE CATEGORIZATION
Previous studies have shown presence of drizzle is very common in water clouds (Fox and Illingworth, 1997). Issue with proper
quan-tification of drizzle is that the retrieved radar re-flectivity when profiling clouds does not match the cloud structure identically, when drizzle is present. This offset happens due to increased sensitivity of radar reflectivity (proportional to the power of six of the cloud droplet radius) to large cloud drops, which are usually located near the bottom of the clouds. These, how-ever do not contribute analogously to the wa-ter content of the cloud itself. For this rea-son drizzle droplets dominate the radar reflec-tivity, obscuring the retrieval interpetation. A method initially created by Krasnov and Russ-chenberg 2002., and used later in Khain et al. 2008., further analyzed the relationship between radar reflectivity (Z) and liquid wa-ter content (LWC). It distinguishes 3 categories of drizzling clouds: ``the cloud without driz-zle fraction'', ``the cloud with light drizdriz-zle'' and ``the cloud with heavy drizzle'', derivated using radar retrievals. Such method adopted cate-gorization of Z-LWC relationship from previous studies (Baedi et al., 2000; Fox and Illingworth 1997; Sauvageot and Omar, 1987 and Atlas, 1954). This technique encouraged extensive classification and potential in improving the mi-crophysical retrieval algorithms.
3 METHODOLOGY
The EarthCARE satellite mission is sched-uled to be launched in 2015. The payload will consist of 4 instruments onboard: a 94GHz cloud profiling radar, a high spectral resolu-tion lidar at 353nm, a broadband radiometer and a multispectral imager. ECSIM is a tool developed as a part of the mission develop-ment process in order to simulate the complete mission instrument observations. It consists of scene creation, orbit, forward, instrument and retrieval models. Put together, they simulate how EarthCARE measurements would be. Ex-tensive description of models and algorithms
ECSIM employs can be found in the documen-tation by Donovan et al., 2008. In our simula-tion, one output from LES model was used to create a cloud scene used as input in ECSIM. LES was utilized to reproduce Sc clouds ob-served during the Atlantic Stratocumulus Tran-sition Experiment (ASTEX) campaign. Input information read in from LES output contained liquid water content values stored in the centre of each model grid box. Resolution of the cloud scene is 50m and 15m in horizontal and verti-cal direction, respectively. Scene domain size is 25.6km x 25.6km in horizontal, and 2.75km in vertical direction.
4 LES to ECSIM parametrizations
In the process of parameterizing the droplet size ditribution, number concentration (N ) was assumed a fixed value in order to calculate droplet effective radius. In order to study driz-zle, four different simulations were run with N shifted towards lower (consequently resulting in higher droplet effective radius values of the cloud scene) and higher (lower effective ra-dius) N values, keeping the LWC values un-modified. Values used for N were 10, 50, 100 and 150cm−3. These are several of the suit-able values to use, as they were observed during the ASTEX campaign measurements (Wood, 2000). Droplet size distribution was modeled by choosing a generalized gamma distribution (Hu and Stamnes, 1993).
N (r) = N Rm 1 Γ(g)( r Rm )Γ−1exp(− r Rm ), (1) where r is the particle radius, Rm is the mode radius, N is the number concentration and g is the width parameter. To recreate the cloud scene in ECSIM we used liquid water content, effective radius values and a fixed value of the distribution shape parameter (g), for liq-uid water clouds. LES model gives informa-tion solely on LWC, meaning effective radius had to be calculated. The effective radius of cloud droplets was subsequently determined, according to equations (2) and (3):
Rm = ( 3ql 4πρN) 1 3 (2) ref f = 4 3(g + 2)Rm, (3)
Figure 1: 3D view of the LES cloud scene used as input in ECSIM.
where ql stands for liquid water content and
ref f for effective radius.
The ``equivalent radar reflectivity'' for 3GHz is calculated from the ECSIM radar forward model, and then related to other - user spec-ified frequency values. It represents a value related to the power of the backscattered coef-ficient at the radar wavelength βrad as:
Ze= λ4rad π5 1 |Kw|2 4πβrad, (4) where Kw = n2 w− 1 n2 w+ 2 , (5)
and nw is the complex index of refraction of water at 3GHz at a fixed temperature (20◦C so that |Kw|2 = 0.92. λrad is the radar wave-lenght.
The hardware configuration profile used was a 32GHz cloud profiling radar with parameters: -Pulse Repetition Frequency: 6800Hz
-Vertical resolution: 25m -Antenna diameter: 1.75m -Pulse length: 100m -Power: 29.5W
Figure 1 shows a three-dimensional overview of the cloud scene used in this case study. Vertical axis is exaggerated by a factor of 10 to emphasise the variability of the cloud shape in both vertical and horizontal direction.
Figure 2:Liquid Water Path of the cloud scene.
Figure 3: Liquid Water Content vertical crossection of the scene at y=25km.
5 RESULTS
To select a vertical crossection of the cloud scene for instrument runs, liquid water path (LWP) profile (Figure 2) was used to locate a single track along the LES modeled domain that reveals high variability in LWP. The loca-tion used is y = 25km, which generated a 25.6km along-track profile. The track where the radar instrument slice was created is indi-cated on Figure 2 with a black horizontal line.
Radar instrument model output for four dif-ferent values of N is shown as four vertical pro-files on Figure 4.
Direct comparison of the LWC for the cho-sen crossection and retrieved radar reflectiv-ity measurement are shown in Fig.3 and Fig.4. A strong spatial correlation of high LWC and radar relectivity values is present. From for-mulation of effective radius calculation (Eqs. (2)and (3)) it is clear this correlation comes di-rectly from relationship of effective radius val-ues to the radar retrievals.
As both are integrated values, relationship (shown on Fig.5) between the integrated radar reflectivity and LWP (red scatterers) indicate a
Figure 4: 32GHz Radar reflectivity profile for different NC values: a)N = 10, b)N = 50, c)N = 100, d)N = 150 (in cm−3)
Figure 5: Relationship of median and integrated re-flectivity simulated retrievals with collocated Liquid Water Path values.
Figure 6: Relationship of of Radar Reflectivity and Liq-uid Water Content for 4 different NC values.
pattern that remains consistent for all four N parametrizations. Median reflectivity relation-ship with LWP (black scatterers), gives a rela-tionship with lower correlation, however stable for all N values. Dispertion of reflectivity val-ues has shifted for different N valval-ues due to inverse relationship with effective radius, thus retrieving higher reflectivities for lower N and opposite for higher N values.
Taking the aforementioned approach as in Krasnov and Russchenberg 2002., ECSIM simulated retrievals combined with LES data have merited a Z-LWC relationship, indicating the same character used in their categorization of drizzle presence (Fig.6).
6 CONCLUSION
The method of utilizing ECSIM for ground based studies of remote sensing using mod-eled cloud structure in terms of drizzle, ap-pears to be valid and gives confidence for fu-ture studies. With such infrastrucfu-ture in place, next step is combining a ground based elastic backscatter lidar instrument simulations with both modeled and observed cloud scenes.
Classification of observed patterns in Sc sheets using combined ECSIM generated radar-lidar profiles should merit drizzle catego-rization for a given cloud scene. This informa-tion is planned to be related to passive satel-lite imagers, in order to create a classification of drizzle categories, observed with such intru-ments as well. This part of the research would
be implemented via ECSIM, using the satellite mode for the simulation and the same cloud scenes - profiling them from above and using the ground module. Simulated observations from the ground are used due to its high spatial and temporal resolution capability, compared to the satellite module. Capturing high driz-zle variability within the cloud and further in-fluence it has on the cloud evolution is to be established using the aformentioned simula-tion infrastructure. The final product would es-tablish a capability of tracking the drizzle frac-tion and its variability using passive satellite im-agers alone, that would allow further insight on the large scale observations and study of the drizzle impact on the Sc clouds mutation.
Acknowledgements Authors of this paper
would like to thank TU Delft Research Group
Clouds, Climate and Air Quality and Johan van
der Dussen for providing the LES model data and generous assistance in adaptation of the data.
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