AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus GmbHGöttingen, Germany10.5194/amt-8-3555-2015Marine boundary layer drizzle properties and their impact on cloud
property retrievalWuP.DongX.dong@aero.und.eduXiB.https://orcid.org/0000-0001-6126-2010Department of Atmospheric Sciences, University of North Dakota, Grand Forks,
ND, USAX. Dong (dong@aero.und.edu)3September201589355535628March201529April201526August201527August2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/8/3555/2015/amt-8-3555-2015.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/8/3555/2015/amt-8-3555-2015.pdf
In this study, we retrieve and document drizzle properties, and investigate
the impact of drizzle on cloud property retrieval in Dong et al. (2014a)
from ground-based measurements at the ARM Azores facility from June 2009 to
December 2010. For the selected cloud and drizzle samples, the drizzle
occurrence is 42.6 %, with a maximum of 55.8 % in winter and a minimum of
35.6 % in summer. The annual means of drizzle liquid water path
LWPd, effective radius rd, and number concentration Nd for the rain
(virga) samples are 4.73 (1.25) g m-2, 61.5 (36.4) µm, and 0.38
(0.79) cm-3. The seasonal mean LWPd values are less than 3 % of the LWP values
retrieved by the microwave radiometer (MWR). The annual mean differences in cloud-droplet
effective radius with and without drizzle are 0.75 and 2.35 %,
respectively, for the virga and rain samples. Therefore, we conclude that
the impact of drizzle below the cloud base on cloud property retrieval is
insignificant for a solar-transmission-based method, but significant for any
retrievals using radar reflectivity.
Introduction
Marine boundary layer (MBL) clouds frequently produce light precipitation,
mostly in the form of drizzle (Austin et al., 1995; Wood, 2005, 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 the North Atlantic. Using aircraft data, Fox and Illingworth (1997) found
that drizzle exists ubiquitously in all marine stratocumulus clouds for
cloud thicknesses ≥ 200 m. Mace and Sassen (2000) found that cloud
layers with maximum reflectivity ≥-20 dBZ nearly always
contain drizzle for continental clouds over the ARM Southern Great Plains (SGP) site. Wang and
Geerts (2003) demonstrated that the thresholds varied from -19 to -16 dBZ
for three different cases of maritime clouds. Kollias et al. (2011) and
others suggested that the radar reflectivity threshold should be -30 dBZ or
even lower. As will be discussed later in this paper, however, none of the
thresholds stated above are actually suitable for diagnosing the presence or
absence of drizzle in MBL stratocumulus.
The drizzle effect on the stratocumulus-topped boundary layer is complex (Wood,
2012) because it affects cloud lifetime and evolution (Albrecht, 1993;
Wood, 2000). Zhao et al. (2012) summarized current ARM cloud retrievals. For
the treatment of drizzle, 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 to which drizzle
impacts cloud property retrievals.
When drizzle drops fall out of the cloud base, they either evaporate
before reaching the surface, which is defined as virga (AMS, 2015), or reach
the surface in the form of rain. Rémillard et al. (2012) identified the
virga and rain samples based on whether the lowest range gate of radar
echoes reach near the surface (200 m). Assuming that the drizzle evaporation
rate below the cloud base is the same for both forms of drizzle, the evaporation
cools the sub-cloud layer and generates turbulence between the sub-cloud
layer and the surface. This turbulence can transport moisture from the
surface up to the cloud layer to sustain and enhance the development of
cloud. Wood (2005) found that the sub-cloud layer with drizzle is generally
wetter and cooler than the drizzle-free region, which is a result of drizzle
evaporation and evaporative cooling. On the other hand, the two forms of
drizzle have different effects on cloud life cycle and boundary layer liquid
water budget. Virga drizzle fluxes evaporate completely at a height before
reaching the surface, while rain drizzle fluxes are low enough to reach the
surface. In both forms, drizzle depletes liquid water from the cloud layer
but enhances liquid water in the sub-cloud layer with different net effects.
During the rain period, drizzle drops fall out of the cloud base and reach
the surface, which results in a net decrease of liquid water in the
atmospheric column, shortens cloud lifetime, and consequently changes cloud
microphysical properties. During the virga period, however, all
drizzle drops falling out of the cloud base evaporate, which
provides additional water vapor to sustain cloud development. This has also
been discussed in Dong et al. (2015) in which a conceptual model (their Fig. 7) was developed to demonstrate the differences in total water mixing ratio
for both rain and virga periods. The boundary layer total water mixing ratio
tends to remain constant for virga drizzle while it decreases with height for
rain drizzle. In this study, we refer to “virga” as drizzle fluxes which evaporate
before reaching the surface and “rain” as drizzle fluxes which reach the surface.
In this study, we will first separate all drizzle samples into forms of
virga or rain, and simply analyze drizzle (either virga or rain) underneath
the MBL cloud base over the Azores. We will describe the method of retrieving
both virga and rain microphysical properties in Sect. 2, and present the
seasonal means of drizzle properties and investigate to what extent drizzle
can impact cloud property retrievals given in Dong et al. (2014a) in Sect. 3.
Finally, a brief summary and conclusions are given in
Sect. 4.
Data and methodology
The data sets used in this study were collected by the Atmospheric Radiation
Program Mobile Facility (AMF), which was deployed on the northern coast of
Graciosa Island (39.09∘ N, 28.03∘ W) from June 2009 to December 2010
(for more details, please refer to Wood et al., 2015; Rémillard et al.,
2012; Dong et al., 2014a). The detailed operational statuses 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 reflectivity measured by the W-band Doppler radar (WACR)
and the cloud-base height detected by the Vaisala laser ceilometer (VCEIL). 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 drizzle. When drizzle drops fall 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 drizzle, we adopt the method of O'Connor et al. (2005) to retrieve the
drizzle microphysical properties using both radar reflectivity and laser-ceilometer-attenuated
backscatter coefficient. The cloud base heights used
in this study were determined using a threshold of 10-4 Sr-1 m-1
in attenuated backscatter coefficient (similar to O'Connor et al.,
2004 and Fielding et al., 2015). 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 LWP > 200 g m-2 (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 to be adequately
represented by a normalized gamma distribution. The ratio of radar
reflectivity (Z) to the calibrated-ceilometer-attenuated backscatter
coefficient (β) is proportional to the fourth power of drizzle size
and can be written as
Zβ=2πΓ(7+μ)Γ(3+μ)S(3.67+μ)4D04,
where D0 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
D0 can be estimated assuming µ=0, and
then vary D0 by adjusting µ to
calculate the radar spectral width. The final D0 and
µ values can be retrieved until the calculated radar spectral
width converges to within 10 % of the measured radar spectral width. Once
D0 and µ values are determined,
normalized concentration can be calculated from radar reflectivity. Thus
three drizzle parameters – drizzle liquid water content (LWCd), number
concentration (Nd), and effective radius (rd) – can be calculated.
Note that D0 was provided in O'Connor et al. (2005), and
drizzle particle effective radius rd in this study is calculated
using the following equation:
re=12∫0∞D3n(D)dD∫0∞D2nDdD=12Γ(4+μ)Γ(3+μ)D03.67+μ.
ARM WACR radar data are well calibrated for the AMF Azores according to
the data report in the ARM data archive (http://www.archive.arm.gov/DQR/ALL/D100729.5.html). To assess the impact
of ARM WACR reflectivity on drizzle property retrievals, we adopt
the conclusion of Hogan et al. (2003) in which they found that the
uncertainty of WACR reflectivity measurements during rain drizzle is likely
to be around 1.5 dB from the theoretical calculation. To account for the shift
of backscatter signal between observation and theoretical value and consider
the effect of multiple scattering, the raw Vaisala-ceilometer-attenuated
backscatter coefficients were multiplied by a factor of 2.45. The
uncertainty of the calibrated β is around 20 % (see O'Connor et
al. (2004) and http://cedadocs.badc.rl.ac.uk/77/2/vaisala_ceilometer.html). Following the same error analysis method as O'Connor et
al. (2005), the fractional errors in LWCd, Nd, and rd can be
expressed as
ΔLWCdLWCd=17ΔZZ2+6Δββ212ΔNdNd=17-5ΔZZ2+12Δββ212Δrdrd=27ΔZZ2+Δββ212.
Based on the uncertainties of Z and β, the uncertainties of retrieved
LWCd, Nd, and rd are estimated as 18, 45, and 13 %,
respectively, in this study.
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 on 8 November 2010 to the morning of 9 November 2010) is a rain case (drizzle reaches the surface).
Seasonal and yearly means of drizzle and cloud properties for virga
and rain.
Note there are a total of 1091 h (13 090 samples at 5 min resolution,
including 1270, 1933, 6498, and 3389 5 min samples for winter, spring,
summer, and autumn) daytime single-layered MBL clouds selected from the 19-month
period (Dong et al. 2014a).
Probability distribution functions (PDFs) and cumulative density
functions (CDFs) of daytime drizzle properties at the Azores during the
period from June 2009 to December 2010. PDFs and CDFs of (a) WACR
reflectivities below the cloud base for drizzle from virga and rain in this
study, (b) drizzle particle effective radius rd, (c) number
concentration Nd, and (d) liquid water path (LWPd). The red lines and
black lines represent the results from the selected virga and rain drizzle
episodes, respectively.
The impact of drizzle on cloud property retrievals (daytime only).
The left panel is for the selected virga samples (red line) and the right panel is
for the selected rain samples (black line). Solid dots denote the mean
values of each bin, and the bottom and top of each whisker represent 1
standard deviation. The solid lines are fitted linear regression lines, the
dashed lines indicate upper and lower boundaries of a 95 % confidence
interval for the regression. Δrc and Δτ represent
the differences between the originally and newly retrieved values.
The daytime cloud microphysical properties presented in Dong et al. (2014a)
are retrieved from Dong et al. (1998, hereafter D98). The layer-mean
cloud-droplet effective radius (r‾c) during the daytime was
parameterized as a function of cloud liquid water path (LWPc), solar
transmission ratio (γ), and cosine of solar zenith angle (μ0) (D98). This parameterization is given by the following
expression:
rc‾=-2.07+2.49LWPc+10.25γ-0.25μ0+20.28LWPcγ-3.14LWPcμ0,
where the units of r‾c and LWPc are in µm and 100 g m-2, respectively. Cloud-droplet number concentration (Nc) is given
by
Nc=3LWPC4πρwrc3ΔHexp(3σx2),
where ρw is water density and σx 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:
τc=3LWPC2rcρw.
Dong et al. (2014a) summarized the uncertainties for the retrieved cloud
properties: ∼ 10 % for rc, ∼ 20–30 % for Nc, and ∼ 10 % for τc based on
the comparisons with aircraft in situ measurements at midlatitude
continental sites (Dong et al., 1998 and 2002; Dong and Mace, 2003). Dong et
al. (2014a) also compared the MBL cloud property retrievals with aircraft in
situ measurements during ASTEX (field intensive operational period (IOP) during 1992 at Azores) with
reasonable agreement. Aircraft in situ data are required to directly validate
the MBL cloud microphysical property retrievals in Dong et al. (2014a and b).
The microwave-radiometer-retrieved LWP represents the entire atmospheric column,
including both cloud liquid water path (LWPc) and drizzle liquid water
path (LWPd). Therefore it is necessary to estimate LWPc by eliminating
LWPd from LWP in order to get more accurate cloud property retrievals from Dong
et al. (2014a).
Results and discussions
Figure 1 demonstrates virga and rain drizzle below the cloud base from two
selected cases along with their retrieved microphysical properties. Case I
represents a typical virga case which occurred on 22 November 2009, and Case II
is a typical rain case that occurred on the late afternoon of 8 November
and lasted until the morning of 9 November 2010. Figure 1a and f present WACR
reflectivity profiles and cloud-base heights (CBHs), and Fig. 1b and g
illustrate the ceilometer-attenuated backscatter coefficients for Cases I
and II, respectively. Both cases have significant time periods in which the
radar reflectivities are greater than -37 dBZ below the cloud base, but this happened
more frequently in Case II than in Case I. Comparing Fig. 1a with Fig. 1f,
the radar reflectivities are generally lower in Case I than in Case II, and
the cloud layer in Case I is higher (CBH is 1246 m, cloud top height is 1625
m) and thinner (379 m) than that in Case II (CBH is 698 m, cloud top
height is 1255 m, cloud thickness is 557 m). The retrieved rd values (Fig. 1c) are relatively smaller in Case I than in Case II (Fig. 1h), but the
Nd values are higher in Case I (Fig. 1d) than in Case II (Fig. 1i).
The mean rd in Case I is 30.88 µm, with a range of ∼ 20–50 µm, while it is 42.48 µm for Case II, ranging from 20 to 70 µm.
The larger rd and lower Nd in Case II are anticipated
because drizzle particle sizes are larger when relatively intense drizzling
occurs. For example, the rd values range from 50 to 70 µm during
the period of 07:00–10:00 UTC in Case II. The mean values of rd in both cases
are nearly 3–4 times larger than the mean values of MBL cloud-droplet effect
radius rc at the Azores (12.5–12.9 µm, Dong et al., 2014a and b).
However, their mean Nd values of 0.882 and 0.692 cm-3 are
2 orders of magnitude lower than the mean values of MBL cloud-droplet
number concentration Nc at the Azores (66–82.6 cm-3, Dong et al., 2014a and b).
The retrieved rd and Nd values in both cases are
also of the same magnitude as some previous studies (e.g., O'Connor et al.,
2005; Frisch et al., 1995; Wang, 2002). The drizzle LWC (LWCd) below the cloud
base is about 1–2 orders of magnitude lower than the cloud LWC (LWCc)
above the 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 1c and d show that the rd values below the cloud base are vertically
invariant, however, the Nd values decrease significantly toward to the
surface, indicating that the evaporation of the drizzle particles below the
cloud base occurs for virga. For Case II, the rd values increase toward
the surface, but the Nd values remain either relatively constant or
slightly decrease, which may be a result of either the evaporation of the
drizzle particles or the collision–coalescence process of rain.
To provide statistical results of drizzle microphysical properties and
investigate to what extent drizzle impacts cloud property retrievals given in Dong
et al. (2014a), 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 mode values of 0 and -20 dBZ,
respectively. The mode value (0 dBZ) of rain is consistent with the
definition of intense precipitation type given in Rémillard et al. (2012).
From the CDFs of Fig. 2a, 57 % of the virga and 13 % of rain samples are
less than -15 dBZ, and 36 % of the virga and 10 % 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 rd are shown in
Fig. 2b. The mode values of virga and rain samples are ∼ 35 and
44 µm, respectively. Nearly 81 % of the virga samples and
55 % of the rain samples are less than 50 µm, while there are almost
no virga samples and only 10 % rain samples left for rd > 80 µm. In contrast to the distributions of rd, most of the
Nd values for both virga and rain samples are located at the tail end
with nearly 60 % for virga and 85 % for rain less than 0.5 cm-3 and
more virga samples for large values. About 85 % of virga LWPd values are
less than 4 g m-2 and 90 % of the rain samples are less than 16 g m-2.
To investigate the impact of drizzle on cloud property retrievals given in Dong et
al. (2014a), the cloud liquid water path (LWPc) is calculated by
subtracting LWPd from the microwave-radiometer-retrieved LWP, and then
using it as an input for (4) to retrieve new MBL cloud microphysical properties,
rc′, Nc′, and τ′ without drizzle effect. These newly
retrieved cloud properties (rc′, Nc′, τ′) are then
compared with the original retrievals in Dong et al. (2014a) where the LWP was
used as LWPc in (4). Figure 3 shows the dependence of the differences
between originally and newly retrieved rc and τ on LWPd where both
Δrc and Δτ linearly decrease with increased
LWPd. The slope of the linear regression line (Δrc vs.
LWPd) for the virga samples is 0.13, with a correlation coefficient
(R2) of 0.969 (Fig. 3a), that is, the retrieved rc decreases 0.13 µm
at an increase of 1 g m-3 in LWPd. The rc values will
decrease by up to 0.55 µm with an increase of 4 g m-3 in
LWPd, which is within the uncertainty (∼ 10 %) of
originally retrieved rc values in Dong et al. (2014a). The impact of drizzle on
cloud optical depth retrieval (Fig. 3b) is weak with a slope of -0.06 and
R2 of 0.468. For the rain samples, the slope is -0.08 and the
correlation is 0.831. The rc values can be reduced
∼ 1.5 µm, with an increase of 16 g m-2 in LWPd and relatively larger
fluctuation than for the virga samples. The impact of LWPd on cloud
optical depth retrieval is also weak in rain regions with a R2 of
0.414.
A 95 % confidence interval for each regression line is computed,
indicating that the true best-fit line for the samples has a 95 %
probability of falling 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 and c suggest high
reliability of the regression, whereas the broad interval in Fig. 3b and d
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 have
been selected from the 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. The annual means
of LWPd, rd, and Nd for the rain (virga) samples
are 4.73 (1.25 g m-2), 61.46 (36.45 µm), and 0.38 (0.79 cm-3). For both virga and rain samples, their
LWPd and rd 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
Nd values are highest but their LWPd and rd are at a minimum 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 given in Dong et al. (2014a), we also calculate the ratio of
LWPd to LWP and cloud properties (rc,Nd,τ) using (4) with the
MWR-retrieved LWP and newly calculated cloud LWPc (i.e., LWP - LWPd). Although the
annual mean LWPd from the rain samples is about 4 times as large as
that of the virga samples, their seasonal means are less than 3 % of the
MWR-retrieved LWP. Therefore, their impact on cloud property retrievals given in Dong
et al. (2014a) is insignificant. Notice that the cloud properties in Dong et
et al. (2014a) are retrieved from a solar-transmission-based method (Eq. 4),
which is nearly independent of drizzle, whereas for other methods using
cloud radar reflectivity, their retrievals are heavily affected by any form
of drizzle within and below clouds. As listed in Table 1, the annual mean
differences of (rc-rc′) are 0.09 µm (0.75 %) and 0.38 µm
(2.35 %) for the virga and rain samples, respectively. These differences
fall within the cloud property retrieval uncertainty (∼ 10 %), validated by in situ aircraft measurements at midlatitude
continental sites (Dong et al., 1998, 2002; Dong and Mace, 2003).
Regarding to the impact of the uncertainties of cloud LWP (10 %), γ(5%) and σx(0.13) on the cloud property retrievals in
(4), D98 conducted some sensitivity studies. For example, a 10 % change
(increase or decrease) in LWP will result in a parameterized
re¯ change within 10 %, and a 10 % change in γ can
vary re¯ by 12.4 %. Dong et al. (1997) conducted a sensitivity
study on the impact of σx on the retrieved cloud properties
and found that the retrieved re¯ values are nearly the same, while
the cloud-droplet number concentrations change from 15 to 30 % when
σx varies from 0.2 to 0.5.
From Table 1, the contribution of drizzle LWPd to total LWP is less
than 3 %, thus the impact of drizzle below the cloud base on the cloud
property retrievals given in Dong et al. (2014a) can be ignored when compared to the
uncertainties from γ, σx and LWP values. Therefore, we
conclude that the cloud-droplet effective radius retrieved using D98 is
biased by the presence of drizzle, but the bias is generally very small. But
for some individual cases, the differences can reach as large as
∼ 2 µm, which may cause a large uncertainty, especially in
the study of cloud radiative properties using radiative transfer models
(D98). The impacts of drizzle on the retrieved cloud-droplet number
concentration and optical depth in Dong et al. (2014a) are also relatively
small, presumably due to small changes in both LWPc and rc. The annual
mean differences in cloud-droplet number concentration are -0.78 and -1.17 cm-3, respectively,
for the virga and rain samples.
Summary and conclusion
In this study, we use a similar method as described in Rémillard et al. (2012) to identify virga and rain drizzle samples below the cloud base using 19
months of ground-based observations at the ARM Azores site. Then we adopt
the method of O'Connor et al. (2005) to retrieve drizzle particle effective
radius, number concentration, and liquid water content. Finally we document
the seasonal variations of both drizzle and cloud properties, and
investigate the impact of drizzle on cloud property retrievals in Dong et
al. (2014a). From the 19-month record of ground-based observations and
retrievals, we report the following findings:
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. The annual means of LWPd, rd, and Nd
for the rain (virga) samples are 4.73 (1.25) g m-2, 61.5 (36.4) µm,
and 0.38 (0.79) cm-3, respectively. For both virga and rain samples,
their LWPd and rd 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. On the other hand, their
Nd values are highest but their LWPd and rd
are at a maximum during summer due to the persistent high pressure and dry conditions over the Azores.
To investigate the impact of drizzle on cloud property retrievals given in
Dong et al. (2014a), we calculate the ratio of LWPd to LWP and cloud
properties (rc,Nd,τ) using (4) with the MWR-retrieved
LWP and newly calculated cloud LWPc (i.e., LWP - LWPd).
The seasonal mean LWPd values are less than 3 % of LWP values. The annual
mean relative differences (rc-rc′)/ rc are 0.75 and 2.35 %,
respectively, for the virga and rain samples. These differences fall within the
cloud property retrieval uncertainty (∼ 10 %). The impacts of drizzle
on cloud-droplet number concentration (optical depth) are also small, presumably
due to small changes in both LWPc and rc. Therefore, we
can conclude that the impact of drizzle on cloud property retrievals is insignificant
for a solar-transmission-based method. For other methods using cloud radar
reflectivity, however, their retrievals are heavily affected by any forms of drizzle within and below clouds.
Acknowledgements
The ground-based measurements were obtained from the Atmospheric Radiation
Measurement (ARM) Program sponsored by the U.S. Department of Energy (DOE)
Office of Energy Research, Office of Health and Environmental Research,
Environmental Sciences Division. The data can be downloaded from
http://www.archive.arm.gov/. This research was supported by the DOE ASR
project under grant DE-SC008468 and the NASA CERES project under grant
NNX14AP84G at the University of North Dakota.
Edited by: A. Kokhanovsky
References Albrecht, B. A.: The effects of drizzle on the
thermodynamic structure of the trade-wind boundary
layer, J. Geophys. Res., 98, 7327–7337, 1993.American Meteorological Society: Virga, Glossary of
Meteorology, available at:
http://glossary.ametsoc.org/wiki/Virga, last access: 24 August 2015. Austin, P., Wang, Y., Pincus, R., and Kujala, V.:
Precipitation in stratocumulus clouds: observations and modeling
results, J. Atmos. Sci., 52, 2329–2352, 1995. Chin, H., Rodriguez, D. J., Cederwall, R. T.,
Chuang, C. C., Grossman, A. S., Yio, J. J., Fu, Q., and
Miller, M. A.: A microphysical retrieval scheme for continental
low-level stratiform clouds: impacts of the subadiabatic character
on microphysical properties and radiation budgets, Mon. Weather
Rev., 128, 2511–2527, 2000. Dong, X. and Mace, G. G.: Profiles of Low-level stratus
cloud microphysics deduced from ground-based measurements, J. Atmos.
Ocean. Tech., 20, 42–53, 2003. Dong, X., Ackerman, T. P., Clothiaux, E. E.,
Pilewskie, P., and Han, Y.: Microphysical and radiative properties
of stratiform clouds deduced from ground-based
measurements, J. Geophys. Res., 102, 23829–23843, 1997. Dong, X., Ackerman, T. P., and Clothiaux, E. E.:
Parameterizations of microphysical and radiative properties of
boundary layer stratus from ground-based
measurements, J. Geophys. Res., 102, 31681–31393, 1998. Dong, X., Minnis, P., Ackerman, T. P., Clothiaux, E. E.,
Mace, G. G., Long, C. N., and Liljegren, J. C.: A 25-month database
of stratus cloud properties generated from ground-based measurements
at the ARM SGP site, J. Geophys. Res., 105, 4529–4538,
2000. 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: Part I: Cloud fraction and
single-layered MBL cloud properties, J. Climate, 27, 3665–3682, doi:10.1175/JCLI-D-13-00553.1, 2014a. Dong, X., Xi, B., and Wu, P.: Investigation of diurnal
variation of MBL cloud microphysical properties at the Azores, J.
Climate, 27, 8827–8835, 2014b.Dong, X., Schwantes, A. C., Xi, B., and Wu, P.: Investigation of the marine
boundary layer cloud and CCN properties under coupled and decoupled
conditions over the Azores, J. Geophys. Res.-Atmos., 120, 6179–6191,
10.1002/2014JD022939, 2015.Fielding, M. D., Chiu, J. C., Hogan, R. J., Feingold, G., Eloranta, E.,
O'Connor, E. J., and Cadeddu, M. P.: Joint retrievals of cloud and drizzle in
marine boundary layer clouds using ground-based radar, lidar and zenith
radiances, Atmos. Meas. Tech., 8, 2663–2683, 10.5194/amt-8-2663-2015,
2015.
Fox, N. I. and Illingworth, A. J.: The potential of a spaceborne cloud radar
for the detection of stratocumulus clouds, J. Appl. Meteorol., 36, 676–687,
1997.
Frisch, A. S., Fairall, C. W., and Snider, J. B.: Measurement of stratus
cloud and drizzle parameters in ASTEX with a Ka-band Doppler radar and a
microwave radiometer, J. Atmos. Sci., 52, 2788–2799, 1995.Hogan, R. J., Bouniol, D., Ladd, D. N., O'Connor, E. J., and Illingworth, A.
J.: Absolute Calibration of 94/95-GHz Radars Using Rain, J. Atmos. Ocean.
Tech., 20, 572–580, 10.1175/1520-0426(2003)20<572:ACOGRU>2.0.CO;2,
2003.Kollias, P., Szyrmer, W., Rémillard, J., and Luke, E.: Cloud radar
Doppler spectra in drizzling stratiform clouds:2. Observations and
microphysical modeling of drizzle evolution, J. Geophys. Res., 116, D13203,
10.1029/2010JD015238, 2011.Leon, D. C., Wang, Z., and Liu, D.: Climatology of drizzle in marine boundary
layer clouds based on 1 year of data from CloudSat and Cloud-Aerosol Lidar
and Infrared Pathfinder Satellite Observations (CALIPSO), J. Geophys. Res.,
113, D00A14, 10.1029/2008JD009835, 2008.
Liljegren, J. C., Clothiaux, E. E., Mace, G. G., Kato, S., and Dong, X.: A
new retrieval for cloud liquid water path using a ground-based microwave
radiometer and measurements of cloud temperature, J. Geophys. Res., 106,
14485–14500, 2001.
Mace, G. G. and Sassen, K.: A constrained algorithm for retrieval of
stratocumulus cloud properties using solar radiation, microwave radio-
meter, and millimeter cloud radar data, J. Geophys. Res., 105,
29099–29108, 2000.
O'Connor, E. J., Hogan, R. J., and Illingworth, A. J.: Retrieving
stratocumulus drizzle parameters using Doppler radar and lidar, J.
Appl. Meteorol., 44, 14–27, 2005.
O'Connor, E. J., Illingworth A. J., and Hogan R. J.: A technique for
autocalibration of cloud lidar, J. Atmos. Oceanic Technol., 21, 777–786,
2004.Rémillard, J., Kollias, P., Luke, E., and Wood, R.: Marine Boundary Layer
Cloud Observations in the Azores, J. Clim., 25, 7381–7398. 10.1175/JCLI-D-11-00610.1, 2012.
Sauvageot, H. and Omar, J.: Radar reflectivity of cumulus clouds, J. Atmos.
Ocean. Tech., 4, 264–272, 1987.
Wang, J.: Identifying Drizzle within Marine Stratus with W-Band Radar
Reflectivity profiles, M.S. thesis, Dep. Of Atmos. Sci., Univ. of Wyoming,
Laramie, USA, 2002.
Wang, J. and Geerts, B.: Identifying drizzle within marine stratus with
W-band radar reflectivity, Atmos. Res., 69, 1–27, 2003.Wood, R.: Parameterization of the effect of drizzle upon the droplet
effective radius in stratocumulus clouds, Q. J. Roy. Meteor. Soc., 126,
3309–3324. 10.1002/qj.49712657015, 2000.
Wood, R.: Drizzle in stratiform boundary layer clouds, Part I: Vertical and
horizontal structure, J. Atmos. Sci., 62, 3011–3033, 2005.Wood, R.: Stratocumulus Clouds, Mon. Weather Rev., 140, 2373–2423. 10.1175/MWR-D-11-00121.1, 2012.
Wood, R., Wyant, M., Bretherton, C. S., Rémillard, J., Kollias, P.,
Fletcher, J., Stemmler, J., deSzoeke, S., Yuter, S., Miller, M., Mechem, D.,
Tselioudis, G., Chiu, C., Mann, J., O'Connor, E., Hogan, R., Dong, X.,
Miller, M., Ghate, V., Jefferson, A., Min, Q., Minnis, P., Palinkonda, R.,
Albrecht, B., Luke, E., Hannay, C., and Lin, Y.: Clouds, Aerosol, and
Precipitation in the Marine Boundary Layer: An ARM Mobile Facility
Deployment, B. Am. Meteorol. Soc., 10.1175/BAMS-D-13-00180.1, 2015.Zhao, C., Xie, S., Klein, S. A., Protat, A., Shupe, M. D., McFarlane, S. A.,
Comstock, J. M., Delanoë, J., Deng, Min, Dunn, M., Hogan, R. J., Huang,
D., Jensen, M. P., Mace, G. G., McCoy, R., O'Connor, E. J., Turner, D. D.,
and Wang, Z.: Toward understanding of differences in current cloud
retrievals of ARM ground-based measurements, J. Geophys. Res., 117, D10206,
10.1029/2011JD016792, 2012.