MBL drizzle properties and their impact on cloud property retrievals

Introduction Conclusions References


Introduction
Marine boundary layer (MBL) clouds frequently produce light precipitation, mostly in the form of drizzle (Austin et al., 1995;Wood, 2005Wood, , 2012;;Leon et al., 2008).Radar reflectivity thresholds have been widely used to distinguish between non-precipitating and precipitating clouds.For example, Sauvageot and Omar (1987) and Chin et al. (2000) proposed a threshold of −15 dBZ for continental stratocumulus clouds, and Frisch et al. (1995) used −17 dBZ as a threshold to distinguish non-precipitating and precipitating clouds over North Atlantic.Mace and Sassen (2000) found that cloud layers with maximum reflectivity ≥ −20 dBZ nearly always contain drizzle for continental clouds over the ARM SGP site.Wang and Geerts (2003) demonstrated that the thresholds varied from −19 to −16 dBZ for three different cases of marine time clouds.
The drizzle effect on stratocumulus-topped boundary layer is complex (Wood, 2012) because it involves the cloud lifetime and evolution (Albrecht, 1993;Wood, 2000).Zhao et al. (2012) summarized current ARM cloud retrievals.For the treatment of drizzle, Introduction

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Full some retrieval methods (e.g., COMBRET) classify drizzle from clouds while others just flag the presence of drizzle (e.g., MICROBASE).However, even in COMBRET, they only classify drizzle and do not investigate the impact of drizzle on cloud property retrievals.So far, none of the studies have quantitatively investigated the extent will drizzle impact cloud property retrievals.
When drizzle occur and fall out of the cloud base, they are either evaporated before reaching the surface, which is defined as virga (AMS, 2014), or reach the surface in the form of rain.Rémillard et al. (2012) identified the virga and rain samples based on whether the radar reflectivity exists below cloud base and whether the lowest range gate of radar echoes reach near the surface (200 m).Different physical and feedback processes can be induced by virga and rain periods.The evaporation of virga cools the sub-cloud layer and generates turbulence between sub-cloud layer and surface.This turbulence can transport moisture from the surface to the cloud layer to enhance the development of cloud.Wood (2005) found that the sub-cloud layer with drizzle is generally cooler and wetter than drizzle-free region, which is a result of evaporation cooling.On the other hand, rain depletes water from the cloud layer to the surface.Note that the Azores is surrounded by ocean, so drizzle or rain drops that reach the surface do not increase water vapor.
In this study, we will follow the AMS definition of virga and rain drizzle, and simply analyze drizzle (either virga or rain) underneath the MBL cloud base over the Azores.
We will describe the method to retrieve both virga and rain microphysical properties in Sect.2, and present the seasonal means of drizzle properties and investigate on what extent drizzle can impact cloud property retrievals in Sect.3. Finally, a brief summary and conclusions are given in Sect. 4.

Data and methodology
The datasets used in this study were collected by the Atmospheric Radiation Program Mobile Facility (AMF), which was deployed on the northern coast of Graciosa Island Introduction

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Full (39.09 • N, 28.03 • W) from June 2009 to December 2010 (for more details, please refer Wood et al., 2015;Rémillard et al., 2012;Dong et al., 2014a).The detailed operational status of the remote sensing instruments on AMF were summarized in Fig. 1 of Rémillard et al. (2012) and discussed in Wood et al. (2015).The drizzling status is identified through a combination of the W-band Doppler radar (WACR) measured reflectivity and the laser ceilometer (CEIL) detected cloud-base height.Given the absence of disdrometer measurements at the Azores, we use a similar method as described in Rémillard et al. (2012) to identify the virga and rain.When drizzle falls out of the cloud base and the radar echoes at the lowest range gate (∼ 200 m above the surface) have reflectivities greater than −37 dBZ, the drizzle is defined as rain, otherwise, it is classified as virga.After identifying the virga and rain, we adopt the method of O'Connor et al. (2005) to retrieve the drizzle microphysical properties using both radar reflectivity and laser ceilometer backscatter coefficient.The liquid water path (LWP) is derived from the microwave radiometer with an uncertainty of 20 g m −2 for LWP < 200 g m −2 , and 10 % for Liljegren et al., 2001;Dong et al., 2000).
The method presented by O'Connor et al. ( 2005) is used to retrieve drizzle particle effective radius, number concentration, and liquid water content.The distribution of drizzle particles can be assumed as a normalized gamma distribution.The ratio of radar reflectivity (Z) to lidar true backscatter coefficient (β) is proportional to the fourth power of drizzle size and can be written as D 0 is the median diameter, µ is the shape parameter, and S is the lidar ratio which can be estimated using Mie theory.The retrieval scheme is based on an iterative approach using the radar measured spectral width as a constraint.At first, the initial D 0 can be estimated assuming µ = 0, and then vary D 0 by adjusting µ to calculate the radar spectral width.The final D 0 and µ values can be retrieved until the calculated radar spectral width converges to within 10 % of measured radar spectral width.Once D 0 Introduction

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Full  et al. (1998).The layer-mean cloud-droplet effective radius (r c ) during the daytime was parameterized as a function of cloud liquid water path (LWP c ), solar transmission ratio (γ), and cosine of solar zenith angle (µ 0 ) (Dong et al., 1998).This parameterization is given by the following expression, where the units of r c and LWP c are in µm and 100 g m −2 , respectively.Cloud-droplet number concentration (N c ) is given by: where ρ w is water density and σ x is logarithmic width, which is set to a constant value of 0.38.Cloud optical depth τ can be calculated immediately from the following equation, The microwave radiometer retrieved LWP represents entire atmospheric column, including both cloud liquid water path (LWP c ) and drizzle liquid water path (LWP d ).
Therefore it is necessary to estimate LWP c by eliminating LWP d from LWP in order to get more accurate cloud property retrievals.occurring on 22 November 2009, and Case II is a typical rain case that occurred from the late afternoon of 8 November to the morning of 9 November 2010. Figure 1a and e presents the WACR reflectivity profiles and the CEIL measured cloud-base heights for Cases I and II, respectively.Both cases have significant time periods that the radar reflectivities are greater than −37 dBZ below cloud base, but happened more frequently in Case II than in Case I. Compared Fig. 1a with Fig. 1e, the radar reflectivities are generally lower in Case I than in Case II.The retrieved r d values (Fig. 1b) are relatively smaller in Case I than in Case II (Fig. 1f), but the N d values are higher in Case I (Fig. 1c) than in Case II (Fig. 1g).

Results and discussions
The and N d values in both cases are also in the same magnitude as some previous studies (e.g., O'Connor et al., 2005;Frisch et al., 1995;Wang, 2002).The drizzle LWC (LWC d ) below cloud base are about 1-2 orders of magnitude lower than the cloud LWC (LWC c ) above cloud base (shown in Table 3 of Dong et al., 2014a), and slightly higher in Case II.
High radar reflectivity normally results from large particles because radar reflectivity is proportional to the sixth power of particle size.Figure 1b and c  be a result of the collision-coalescence process for rain.It is also notable that a narrow band appears just below the cloud base, called a "transition layer" from cloud to drizzle, which makes the r d values smaller and the N d values higher than those at lower levels.
To provide statistical results of drizzle microphysical properties and investigate to what extent drizzle impacts cloud property retrievals, we plot Figs. 2 and 3, and list their seasonal means in Table 1. Figure 2 shows the probability distribution functions (PDFs) and cumulative distribution functions (CDFs) of drizzle properties from a total of 353 h of virga and 112 h of rain samples during the 19 month period.As illustrated in Fig. 2a, the reflectivities of rain are generally higher than those of virga with the mode values of 0 and −20 dBZ, respectively.The mode value (0 dBZ) of rain is consistent with the definition of intense precipitation type in Rémillard et al. (2012).From the CDFs of Fig. 2a, 55 % of the virga and 13 % of rain samples are less than −15 dBZ, and 37 % of the virga and 6 % of the rain samples are less than −20 dBZ.Thus, ∼ 45 % of the drizzle samples would be missed if using a threshold of −15 dBZ, and ∼ 30 % for −20 dBZ.Therefore, we conclude that a significant amount of drizzle samples would be missed if using radar reflectivity as a threshold.
The PDFs and CDFs of drizzle particle effective radius r d are shown in Fig. 2b.The mode value of virga samples is ∼ 30 µm, whereas it is not so obvious for rain samples with a broad range of 30 ∼ 150 µm.Nearly 66 % of the virga samples are less than 50 µm and 83 % of the rain samples are less than 100 µm, both with long tails towards large values.In contrast to the distributions of r d , most of the N d values for both virga and rain samples are located at the tail end with nearly 70-80 % less than 0.2 cm −3 and slightly more virga samples for large values.Almost all virga LWP d values are less than 10 g m −2 and ∼ 80 % less than 3 g m −2 , while only 18 % of the rain samples are less than 3 g m −2 .
To investigate the impact of drizzle on cloud property retrievals, the cloud liquid water path (LWP c ) is calculated by subtracting LWP d from the microwave radiometer retrieved LWP, and then used it as an input for (2) to retrieve new MBL cloud microphysical properties, r c , N c , and τ without drizzle effect.These newly retrieved cloud properties Introduction

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Full (r c , N c , τ ) are then compared with the original retrievals in Dong et al. (2014a) where the LWP was used as LWP c in (2). Figure 3 shows the dependence of the differences between newly and originally retrieved r c and τ on LWP d where both ∆r c and ∆τ linearly decrease with increased LWP d .The slope of the linear regression line (∆r c vs. LWP d ) for the virga samples is 0.1 with a correlation coefficient (R 2 ) of 0.987 (Fig. 3a), that is, r c decreases 0.1 µm at an increase of 1 g m −2 in LWP d .The r c values will decrease by up to 0.3 µm with an increase of 3 g m −2 in LWP d , which is within the uncertainty (∼ 10 %) of originally retrieved r c values in Dong et al. (2014a).The impact of drizzle on cloud optical depth retrieval (Fig. 3b) is similar to that of r c with a slope of −0.02 and R 2 of 0.901.For the rain samples, the slope is −0.07 and the correlation is 0.896.The r c values can be reduced 2 ∼ 3 µm with an increase of 40 g m −2 in LWP d and relatively larger fluctuation than for the virga samples.The impact of LWP d on cloud optical depth retrieval is weak with a R 2 of 0.568.
A 95 % confidence interval for each regression line is computed, indicating that the true best-fit line for the samples have 95 % probability to fall within the confidence intervals.The two dashed lines in Fig. 3 represent the upper and lower 95 % confidence bounds for each of the regression.The narrow intervals for Fig. 3a-c suggest high reliability of the regression, whereas for the broad interval in Fig. 3d indicates relatively large uncertainty of the regression.
The sample numbers and seasonal means of retrieved cloud and drizzle microphysical properties for the virga and rain periods are listed in Table 1.A total of 1091 h (13 090 samples at 5 min resolution, including 4237 virga samples and 1345 rain samples) daytime single-layered MBL clouds has selected from 19 month period (Dong et al., 2014a).For the cloud and drizzle samples, the overall drizzle occurrence is 42.6 % with a maximum of 55.8 % in winter and a minimum of 35.6 % in summer.For and their corresponding r d (N d ) values are 71.08 µm (0.14 cm −3 ), 71.97 µm (0.09 cm −3 ), 63.88 µm (0.21 cm −3 ), and 67.74 µm (0.13 cm −3 ).The annual means of LWP d , r d and N d for the rain (virga) samples are 5.48 g m −2 (1.29 g m −2 ), 68.7 µm (39.5 µm), and 0.14 cm −3 (0.38 cm −3 ).For both virga and rain samples, their LWP d and r d are largest during winter because the dominant low pressure systems and moist air masses during winter result in more deep frontal clouds associated with midlatitude cyclones, which will make the MBL clouds deeper and thicker (Dong et al., 2014a).On the other hand, their N d values are highest but their LWP d and r d are minima during summer due to the persistent high pressure and dry conditions over the Azores (Dong et al., 2014a).
To investigate seasonal variations of the impact of drizzle on cloud property retrievals, we also calculate the ratio of LWP d to LWP and cloud properties (r c , N d , τ) using (2) with the MWR-retrieved LWP and newly calculated cloud LWP c (= LWP−LWP d ).
Although the annual mean LWP d from the rain samples is about four times as large as that from the virga samples, their seasonal means are less than 4 % of the MWRretrieved LWP.Therefore, their impact on cloud property retrievals is insignificant.As listed in Table 1, the seasonal differences (r c − r c ) are 0.21 (0.47), 0.11 (0.38), 0.09 (0.32), and 0.12 (0.43) µm for the virga (rain) samples with annual mean differences of 0.12 and 0.38 µm, respectively.These differences fall within the cloud property retrieval uncertainty (∼ 10 %), validated by in situ aircraft measurements at midlatitude continental sites (Dong et al., 1997(Dong et al., , 1998(Dong et al., , 2002;;Dong and Mace, 2003).Therefore, the impact of drizzle on cloud-droplet effective radius, in general, can be negligible.However, for some individual cases, the differences can reach as large as 2 ∼ 3 µm, which may cause a large uncertainty especially in the study of cloud radiative properties using radiative transfer models (Dong et al., 1998).The impacts of drizzle on cloud-droplet number concentration (and optical depth) are also small, presumably due to small changes in both LWP c and r c .The annual differences in cloud-droplet number concentration are −0.93 and −1.50 cm −3 for the virga and rain samples, respectively.Introduction

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Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |and µ values are determined, normalized concentration can be calculated from radar reflectivity.Thus three drizzle parameters: drizzle liquid water content (LWC d ), number concentration (N d ), and effective radius (r d ) can be calculated.The uncertainties of retrieved LWC d , N d and r d are 10, 13, and 14 %, respectively, in this study.The retrieval method of daytime cloud microphysical properties is from Dong

Figure 1
Figure 1 demonstrates virga and rain below cloud base from two selected cases along with their retrieved microphysical properties.Case I represents a typical virga case 4311 mean r d in Case I is 33.73 µm with a range of ∼ 20-50 µm, while it is 48.25 µm for Case II, ranging from 20 to 100 µm.The larger r d and lower N d in Case II are anticipated because drizzle particle sizes are larger when relatively intense drizzling occurs.For example, the r d values range from 50 to 100 µm during the period of 07:00-10:00 UTC in Case II.The mean values of r d in both Cases are nearly 3-4 times larger than the mean values of MBL cloud-droplet effect radius r c at the Azores (12.5-12.9µm,Dong et al., 2014a, b).However, their mean N d values of 0.885 and 0.535 cm −3 are two orders of magnitude lower than the mean values of MBL cloud-droplet number concentration N c at the Azores (66-82.6 cm −3 ,Dong et al., 2014a, b).The retrieved r d shows that the r d values below cloud base are vertically invariant, however, the N d values decrease significantly toward to the surface, indicating that the evaporation of the drizzle particles below cloud base occurs for virga.For Case II, the r d values increase toward the surface, but the N d values remain either relatively constant or slightly decrease, which may Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | the virga samples, the seasonal mean LWP d values (winter to autumn) are 1.87, 1.23, 0.90, and 1.16 g m −2 , and their corresponding r d (N d ) values are 42.27 µm (0.36 cm −3 ), 40.67 µm (0.35 cm −3 ), 37.25 µm (0.48 cm −3 ), and 37.68 µm (0.32 cm −3 ).For the rain samples, the seasonal mean LWP d values are 6.83, 4.93, 4.98 g m −2 , and 5.19 g m −2 , Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | gation of drizzle impact to other cloud property retrieval algorithms are out of the scope of this study because of lacking to access to other algorithms.Discussion Paper | Discussion Paper | Discussion Paper | Dong, X., Minnis, P., Mace, G. G., Smith Jr., W. L., Poellot, M., Marchand, R., and Rapp, A. D.: Comparison of stratus cloud properties deduced from surface, GOES, and aircraft data during the March 2000 ARM Cloud IOP, J. Atmos.Sci., 59, 3265-3284, 2002.Dong, X., Xi, B., Kennedy, A., Minnis, P., and Wood, R.: A 19-month Marine Aerosol-Cloud_Radiation Properties derived from DOE ARM AMF deployment at the Azores: Discussion Paper | Discussion Paper | Discussion Paper |

Figure 1 .
Figure 1.Drizzle properties observed by ARM radar-lidar and retrieved from this study at the ARM Azores site.Two cases have been selected: Case I (left panel, 22 November 2009) is a typical virga case, and Case II (right panel, from late afternoon of 8 November 2010 to the morning of 9 November 2010) is a rain case (drizzle reaches the surface).

Table 1 .
Seasonal means of drizzle and cloud properties for virga and rain.