In this study, the liquid water path (LWP) below the melting layer in
stratiform precipitation systems is retrieved, which is a combination of
rain liquid water path (RLWP) and cloud liquid water path (CLWP). The
retrieval algorithm uses measurements from the vertically pointing radars
(VPRs) at 35 and 3 GHz operated by the US Department of Energy
Atmospheric Radiation Measurement (ARM) and National Oceanic and Atmospheric
Administration (NOAA) during the field campaign Midlatitude Continental
Convective Clouds Experiment (MC3E). The measured radar reflectivity and
mean Doppler velocity from both VPRs and spectrum width from the 35 GHz
radar are utilized. With the aid of the cloud base detected by a ceilometer,
the LWP in the liquid layer is retrieved under two different situations: (I) no cloud exists below the melting base, and (II) cloud exists below the
melting base. In (I), LWP is primarily contributed from raindrops only,
i.e., RLWP, which is estimated by analyzing the Doppler velocity differences
between two VPRs. In (II), cloud particles and raindrops coexist below the
melting base. The CLWP is estimated using a modified attenuation-based
algorithm. Two stratiform precipitation cases (20 and 11 May 2011)
during MC3E are illustrated for two situations, respectively. With a total
of 13 h of samples during MC3E, statistical results show that the
occurrence of cloud particles below the melting base is low (9 %);
however, the mean CLWP value can be up to 0.56 kg m
Clouds in stratiform precipitation systems are important to the Earth's radiation budget. The vertical distributions of cloud microphysics, ice and liquid water content (IWC and LWC), determine the surface and top-of-the-atmosphere radiation budget and redistribute energy in the atmosphere (Feng et al., 2011, 2018). Also, stratiform precipitation systems are responsible for most tropical and midlatitude precipitation during summer (Xu, 2013). However, the representation of those systems in global climate and cloud-resolving models is still challenging (Fan et al., 2015). One of the challenges is due to the lack of comprehensive observations and retrievals of cloud microphysics (e.g., prognostic variables IWC and LWC) in stratiform precipitation systems. Liquid water path (LWP) is defined as an integral of LWC in the atmosphere. It is a parameter used to provide the characterization of liquid hydrometeors in the vertical column of atmosphere and study clouds and precipitation. The estimation of LWC and LWP is one of the critical objectives of the US Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Program (Ackerman and Stokes, 2003).
LWP can be retrieved using a ground-based microwave radiometer (MWR), which sensed downwelling radiant energy at 23.8 and 31.4 GHz (Liljegren et al.,
2001). In the last two decades, ARM has been operating a network of two-channel
(23.8 and 31.4 GHz) ground-based MWRs to provide a time series of LWP at the
ARM Southern Great Plains (SGP) site (Cadeddu et al., 2013).
Absorption-based algorithms using multichannels of MWRs have been widely
used to retrieve cloud LWP (e.g., Liljegren et al. 2001; Turner, 2007), and
they are known to be accurate methods to estimate LWP of nonprecipitating
clouds, with a mean LWP error of 15 g m
In the precipitating system, the liquid water cloud droplets and raindrops often coexist in the same atmospheric layer (e.g., Dubrovina, 1982; Mazin, 1989; Matrosov, 2009, 2010), indicating that the LWP consists of both cloud liquid water path (CLWP) and rain liquid water path (RLWP). However, the discrimination between suspended small cloud liquid water droplets and precipitating large raindrops is a very challenging remote-sensing problem. Even though the partitioning of LWP into CLWP and RLWP is important in cloud modeling (Wentz and Spencer, 1998; Hillburn and Wentz, 2008), there are few studies in which RLWP and CLWP were retrieved simultaneously and separately (Saavedra et al., 2012; Cadeddu et al., 2017). Battaglia et al. (2009) developed an algorithm to retrieve RLWP and CLWP from the six Advanced Microwave Radiometer for Rain Identification (ADMIRARI) observables under rainy conditions. Saavedra et al. (2012) developed an algorithm using both ADMIRARI and a micro rain radar to retrieve and analyze the CLWP and RLWP for midlatitude precipitation during fall. In addition to these RLWP and CLWP estimations mainly from passive microwave radiometers, there are several studies estimating the LWP using active radar measurements only. Ellis and Vivekanandan (2011) developed an attenuation-based technique to estimate LWC, which is the sum of cloud liquid water content (CLWC) and rain liquid water content (RLWC), using simultaneous S- and Ka-band scanning radars measurements. However, using these techniques to retrieve LWC is not always applicable. If raindrop diameters are comparable to at least one of the radar's wavelength, the “Mie effect” will be included in the measured differential reflectivity; however this Mie effect is not very distinguishable from differential attenuation effects (Tridon et al., 2013; Tridon and Battaglia, 2015).
Matrosov (2009) developed an algorithm to simultaneously retrieve CLWP and layer-mean rain rate using the radar reflectivity measurements from three ground-based W-, Ka-, and S-band radars. The CLWP was retrieved based on estimating the attenuation of cloud radar signals compared to S-band radar measurements. Matrosov (2010) developed an algorithm to estimate CLWP using a vertical pointing Ka-band radar and a nearby scanning C-band radar. The layer-mean rain rate was first estimated with the aid of a surface disdrometer, and then CLWP was retrieved by subtracting the rain attenuation from total attenuation measured from two radars. For the estimation of RLWP, Williams et al. (2016) developed a retrieval algorithm for raindrop size distribution (DSD) using Doppler spectrum moments observed from two co-located vertically pointing radar (VPRs) at frequencies of 3 and 35 GHz. The retrieved air motion and DSD parameters were evaluated using the retrievals from a co-located 448 MHz VPR.
In this study, the CLWP retrieval algorithms in Matrosov (2009, 2010) have been modified given the available radar measurements, vertical pointing Ka- and S-band radars, during the Midlatitude Continental Convective Clouds Experiment (MC3E) field campaign. For the estimation of RLWP, we will basically follow the idea described in Williams et al. (2016) to retrieve microphysical properties for raindrops. However instead of retrieving vertical air motion and rain DSDs (Williams et al., 2016), this study aims at retrieving RLWC and then integrating RLWC over the liquid layer to estimate RLWP. Overall, in this study, algorithms from three former publications are modified and combined to estimate the LWP in the stratiform precipitating systems.
The goals of this study are to retrieve the LWP below the melting base,
which includes both RLWP and CLWP retrievals using radars measurements, and
tentatively answer two questions based on observations and retrievals in the
stratiform precipitation systems during MC3E: (1) what is the occurrence of
cloud below the melting base in the stratiform precipitation systems? (2) What are the values of simultaneous CLWP, RLWP, and LWP, and how does CLWP or
RLWP contribute to the LWP? Note that the CLWP and RLWP are constrained in a
stratiform precipitation layer below the melting base and above the surface.
The LWP estimations in this study are primarily aimed at stratiform
precipitating events exhibiting melting-layer features from radar
measurements with lower-to-moderate rain rates (RR < 10 mm h
Acronyms and abbreviations used in this study.
The MC3E field campaign, co-sponsored by the NASA Global Precipitation Measurement and the US DOE ARM programs, was conducted at the ARM SGP (northern Oklahoma) during April–June 2011 to study convective clouds and improve model parametrization (Jensen et al., 2015). MC3E provided an opportunity to develop new retrieval methods to estimate cloud microphysics and precipitation properties in precipitation systems (Giangrande et al., 2014; Williams, 2016; Tian et al., 2016, 2018). Several stratiform rain cases were observed by the VPRs during MC3E (as shown in Fig. 1). Distinct signatures of the “bright band” are detected by VPRs. To retrieve LWP associated with stratiform precipitation, this study mainly uses the observations from two co-located VPRs operating at 3 and 35 GHz at DOE ARM SGP Climate Research Facility.
Time series of
The 3 GHz (S-band) VPR was deployed by NOAA Earth System Research Laboratory
for the 6 weeks during the MC3E. The NOAA 3 GHz VPR is a vertical pointing
radar with 2.6
The Ka-band ARM zenith radar (KAZR) is also a vertical pointing radar,
operating at 35 GHz and permanently deployed by DOE ARM at the SGP site. The
KAZR measurements include reflectivity, vertical velocity, and spectral
width from the near-ground to 20 km. The KAZR data used in this study are the
KAZR Active Remote Sensing of Clouds (ARSCL) product produced by the ARM
(
The DOE ARM program maintains a suite of surface precipitation disdrometers.
Measurements and estimations from the Distromet model RD-80 disdrometer and
the NASA two-dimensional video disdrometer (2DVD) deployed at the ARM SGP site
are used in this study. The RD-80 disdrometer provides the most continuous
raindrop size distribution (DSD) measurements at high spectral (20 size bins
from 0.3 to 5.4 mm) and temporal resolutions (1 min), and its minimal
detectable precipitation amount is 0.006 mm h
The Vaisala Laser Ceilometer (CEIL) operates at the SGP Central Facility, sensing cloud presence up to a height of 7700 m with 10 m vertical resolution. The laser ceilometer transmits near-infrared pulses of light, and the receiver detects the light scattered back by clouds and precipitation. It is designed to measure cloud-base height.
As mentioned earlier, both RLWP and CLWP contribute to the LWP. With the aid
of the cloud-base height detected by the ceilometer, LWP is retrieved under two
different situations: (I) the cloud base is higher than the melting base, and
(II) the cloud base is lower than the melting base. For situation (I), there
are almost no cloud droplets below the melting base (CLWP
Algorithm flowchart to retrieve liquid water path (LWP) below the melting base.
The algorithm from Williams et al. (2016) was developed based on an
assumption that the 3 GHz VPR operates within the Rayleigh scattering regime
for all raindrops, while the 35 GHz VPR operates within the Rayleigh
scattering regime for small raindrops (diameters <
In situation (II), substantial cloud particles exist below the melting base, and
both RLWP and CLWP retrievals are needed to estimate the LWP. The total
two-way attenuation of 35 GHz VPR signals,
Based on Eq. (1), CLWP can be written as
The attenuation (
Notice that the absolute calibration of the radar was not important to the retrieval results since the retrieval of CLWP used S- and K-band differential attenuation. This avoids radar calibration (Tridon et al., 2015, 2017), which is a serious issue limiting the accuracy of radar retrievals.
The
The uncertainties of retrieved CLWP are mainly due to the uncertainties of
estimated
Even though situation (I) is dominated (Fig. 1), especially in Case A, the ceilometer cloud-base estimates can be lower than the melting base (Cases B to D). Two case studies (20 and 11 May 2011) are given as examples to demonstrate the estimation of LWP in stratiform precipitation systems for two different situations.
On 20 May 2011, an upper-level low-pressure system in the central Great Basin moved into the central and northern plains, while a surface low pressure in southeastern Colorado brought warm and moist air from the southern plains to a warm front over Kansas, and a dry line extended southward from Texas to Oklahoma. With these favorable conditions, a strong north–south-oriented squall line developed over the Great Plains and propagated eastward. The convection along the leading edge of this intense squall line exited the ARM SGP network around 11:00 UTC on 20 May, leaving behind a large area of stratiform rain (Case A in Fig. 1). This stratiform system passed over the ARM SGP site and was observed by two VPRs and disdrometers as shown in Fig. 1a–c. It clearly shows the 3 GHz radar echo tops are much lower than those from the 35 GHz VPR. Even though there is attenuation at 35 GHz by the raindrops and melting hydrometeors, the 35 GHz radar can still detect more small ice particles near the cloud top. The bright band, which occurs in a uniform stratiform rain region, is clearly seen from the 3 GHz VPR (a sudden increase and then decrease in radar reflectivity) but is not obvious from the 35 GHz VPR due to the non-Rayleigh scattering effects at 35 GHz (Sassen et al., 2005; Matrosov, 2008).
Figure 1a–b clearly show that the ceilometer-detected cloud base is in the
middle of the melting layer, indicating almost no cloud particles below the
melting layer, and the LWP in the liquid layer is equal to the RLWP. The RLWP is
retrieved using the DVD algorithm introduced in Sect. 3.1 and Appendix A. Figure 3
shows an example of the DVD retrieval algorithm at 13:40 UTC on 20 May 2011. Radar reflectivity from 3 GHz, Doppler velocities from 3 and 35 GHz, and spectrum variance from 35 GHz are the inputs of the DVD algorithm. The
Doppler velocity differences (3–35 GHz) from the surface to 4 km are
also plotted in Fig. 3d. The melting base is defined as the height of
maximum curvature in the radar reflectivity profile at 3 GHz (Fabry and
Zawadzki, 1995), which is clearly seen at 2.5 km in Fig. 3. Below 2.5 km,
the Doppler velocity differences between the two VPRs become relatively
uniform, indicating that the process of melting snow/ice particles into
raindrops is completed. Retrieved profiles of rain microphysical properties
and their corresponding uncertainties (horizontal bars at different levels)
in the rain layer (0–2.5 km) are shown in Fig. 3f–h. In general, the
retrieved
An example of illustrating the Doppler velocity
difference (DVD) retrieval algorithm at 13:40 UTC on 20 May 2011. The
inputs of the DVD retrieval algorithm are
To evaluate the rain property retrievals, we compare the retrieved rain
microphysical properties, the
Time series of
Statistics (mean differences, 95 % confidence interval
of mean differences, and RMSEs) of
On 11 May 2011, a surface cold front moved across the Oklahoma–Texas area,
and then convections were initiated. At 16:00 UTC, a mesoscale convective
system formed with a parallel stratiform precipitation region. A period of 2–3 h later (
Firstly, the surface rain microphysics (
Time series of
Secondly, the CLWP is retrieved using the attenuation algorithm introduced
in Sect. 3.2. Figure 5c shows the time series of RLWP, CLWP, and LWP
retrievals. It is found that the CLWP values (when they exist) are usually
larger than RLWP values in the same vertical column. When cloud droplets and
raindrops coexist below the melting base, the mean values are 0.11 and 1.64 kg m
Box and whisker plots of retrieved RLWP, CLWP, and LWP for situations (I),
(II), and all samples during MC3E are shown in Fig. 6. The horizontal dashed orange
and red lines indicate the median and mean, boundaries of the box
represent the first and third quartiles, and the whiskers are the 10th and 90th percentiles. During MC3E, a total of 13 h of stratiform
rain was observed by VPRs at the ARM SGP Climate Research Facility, in
which 91 % and 9 % the samples are categorized into situations (I) and (II), respectively. The mean RLWPs are 0.32 and 0.10 kg m
Box and whisker plots of retrieved RLWP, CLWP, and LWP for situation (I), (II), and all samples. The horizontal orange line within the box indicates the median, boundaries of the box represent the 25th and 75th percentile, and the whiskers indicate the 10th and 90th percentile values of the results. The dashed red lines represent the mean values.
We also processed the ARM MWR-retrieved LWPs during MC3E and compared them
with our retrievals as illustrated in Fig. 7a. The corresponding LWP
uncertainties are also provided as the grey error bar for each retrieval,
with the rain rate indicated by colors. It is noticed that the MWR has no LWP
estimation when the rain rate is large. The MWR-retrieved LWPs increase with
increased rain rate, and the retrievals from the MWR are much larger than the
new LWP retrievals at high rate rates. The newly retrieved LWPs weakly
correlate with rain rates, and most values are less than 1.0 kg m
LWP is a critical parameter for studying clouds, precipitation, and their life cycles. LWP can be retrieved from microwave radiometer measured brightness temperatures during cloudy and light precipitation conditions. However, MWR-retrieved LWPs are questionable under moderate and heavy precipitation conditions due to the wet radome and the large extinction in the unit volume caused by large raindrops. LWPs below the melting base in stratiform precipitation systems are estimated, which include both RLWP and CLWP. The measurements used in this study are mainly from two VPRs, 35 GHz from ARM and 3 GHz from NOAA during the MC3E field campaign.
In this study, the microphysical properties of raindrops, such as
The applicability of retrieval methods is illustrated for two stratiform
precipitation cases (20 and 11 May 2011) observed during MC3E.
Statistical results from a total of 13 h samples during MC3E show that
the occurrence of cloud droplets below the melting base is low (9 %),
while the CLWP value can be up to 0.56 kg m
Reliable retrievals of RLWC and RLWP are critical for model evaluation and improvement, as RLWC (rain mixing ratio) is an important prognostic variable in weather and climate models. Furthermore, retrievals in the whole rain layer would be useful to understand the microphysical processes (i.e., condensation, evaporation, autoconversion, and accretion, etc.) and have great potential to improve model parametrizations in the future. Overall, the LWP (CLWP and RLWP) retrievals derived in this study can be used to evaluate the models that separately predict cloud and precipitation and contribute comprehensive information to study cloud–precipitation transitions. Note that the attenuation by liquid is more profound at 94 GHz, and the ratio of attenuation by liquid clouds and by rain is larger at 94 GHz compared to 35 GHz (Matrosov, 2009). Thus, using 94 GHz (W-band) radar measurements to develop a retrieval algorithm may be promising if the W-band radar signals are not fully attenuated. In addition, analyzing co-located multiple-frequency VPRs would also better define the uncertainties of retrievals made with co-located radars operating at different frequency pairs.
NOAA S-band vertical profile radar datasets are publicly available in the DOE archives (
Retrieving RLWC and other rain microphysical properties (i.e., drop size and
rain rate) is based on the mathematics of DSD radar reflectivity-weighted
velocity spectral density
The
Brightness temperatures (TB) at 23.8 and 31.4 GHz for different assumptions of CLWP and RLWP values.
The
Comparisons of
The intrinsic (non-attenuation) reflectivity factor and the mean velocity
and the spectrum variance are the zeroth, first, and second
reflectivity-weighted velocity spectrum moments:
The Doppler velocity difference (DVD) is defined as
The RLWC and rain rate (RR) can also be described using the DSD:
In addition, there are two newly defined radar-related parameters (
In this study, four variables, DVD, SV at 35 GHz (SV
The observed radar Doppler velocity difference can be assumed to be equal to
the DSD velocity difference for two reasons: (1) even though the radar
observed Doppler velocity spectrum can be broaden by the air motion, this
spectrum broadening variance is small (within 2 %) relative to the DSD
velocity spectrum because of the narrow beamwidth (0.2
The variabilities of 3 and 35 GHz VPR observations within each 1 min temporal resolution and 60 m vertical resolution bin are regarded as the measurement uncertainties and will
be propagated through the retrieval to produce retrieval uncertainties. The
retrieval uncertainties are estimated following two steps: (1) construction of a
distribution of input radar measurements. For example, the temporal
resolution for 3 GHz VPR is 7 s; thus there are about nine radar
reflectivities observed for 1 min. A normal distribution is generated
first using the mean and standard deviations of these nine observed radar
reflectivities within 1 min and 60 m observations. (2) The DVD
retrievals are repeated using samplings from distributions of all input measurements. We
randomly select 100 groups of members from those (DVD, SV
The uncertainties of RLWP are estimated based on the uncertainties of RLWC. More specifically, we first estimated the RLWC uncertainties at each height level, and then we calculated the ratios of RLWC uncertainties to the mean retrieved RLWC at each height level, which represent percentage values of retrieval uncertainties. Finally, we calculated the mean ratio of the uncertainties in the whole liquid layer below the melting base and regarded this mean ratio as the uncertainty of RLWP.
It is noted that the uncertainty here only considers estimates of instrument noise, not the uncertainties associated with assumptions used in the retrieval. For example, the gamma size distribution used in Eq. (A2) is an approximation which may introduce error into the retrieval. However, it is very difficult to quantify this type of retrieval uncertainty. In this study, we further compared our retrievals with independent surface disdrometer measurements to estimate the uncertainties of retrievals. Also, when both radars are observing within the Rayleigh scattering regime for small raindrops, the reflectivity-weighted radial velocities for these particles should be the same. In order to have a difference in radial velocity during the retrieval, large droplets must exist. The maximum diameters in drop size distribution measured by the disdrometer for all the stratiform cases during MC3E are investigated. It is found that the occurrence of small droplets only (maximum diameter < 1.3 mm) is very low (less than 3 %). Thus, it will not have a significant impact on the retrieval results. Notice that this algorithm is not suitable for strong convective rain due to the wind shear and strong turbulence as well as severe attenuation and extinction of the Ka-band radar signal.
CLWP can be simplified and estimated as follows:
The attenuation (
For given uncertainties of attenuation (
The extinction cross section per unit volume as a function of the drop equivolume diameter for the two frequencies in the MWR (23.8 and 31.4 GHz).
To better explain the “overestimation” issue of LWP retrieved by the microwave radiometer, several examples are given in this appendix. Firstly,
we calculated the extinction cross section per volume as a function of the
drop equivolume diameter for the two frequencies in the MWR (23.8 and 31.4 GHz) with a T-matrix method (Fig. B1). It is clearly shown that the
extinction cross section increases with diameter when the diameter
is smaller than 3 mm. This indicates the extinction (cross section) for raindrops (diameter >
In addition, the brightness temperatures in 23.8 and 31.4 GHz channels are
calculated using the MicroWave Radiative Transfer (MWRT) model. Five
different sensitivity tests are generated with five combinations of CLWP and
RLWP values (Table A1). Table A lists the results and clearly demonstrates
that the brightness temperatures in channels increase with increased cloud
water amount (larger CLWP) and rainwater amount (larger RLWP).
Comparing the results from test no. 2 and no. 3, it is clearly seen that the
brightness temperatures contributed by raindrops are 31 and 51 K more than
those contributed by cloud droplets at the frequencies of 23.8 and 31.4 GHz,
even though their LWPs are the same (1 kg m
The extinction coefficient as a function of RLWC for precipitation with three different drop size distributions (DSDs), which are for heavy precipitation (thunderstorm), moderate precipitation (MP), and drizzle precipitation (drizzle).
JT and XD conceptualized this study. JT developed the retrievals, analyzed the data, and drafted the manuscript. XD contributed to the interpretation of the results and the editing of the paper. CW contributed to the discussions on the methodology in rain and cloud microphysics retrieval and the editing of the paper. BX and PW contributed to the discussions of the results.
The authors declare that they have no conflict of interest.
Jingjing Tian and Xiquan Dong are supported by the DOE CMDV project under grant DE-SC0017015 at the University of Arizona, and Baike Xi is supported by the NASA CERES project under grant NNX17AC52G at the University of Arizona. Christopher R. Williams is supported by the DOE ASR project under grant DE-SC0014294. Special thanks to Sergey Matrosov from NOAA Earth System Research Laboratory (ESRL) for his suggestions. Special thanks are given to Michael Jensen, PI of MC3E. The authors gratefully acknowledge the constructive comments by Alessandro Battaglia and one anonymous referee who helped improve the paper.
This research has been supported by the DOE CMDV project (grant no. DE-SC0017015), the NASA CERES project (grant no. NNX17AC52G), and the DOE ASR project (grant no. DE-SC0014294).
This paper was edited by Marcos Portabella and reviewed by Alessandro Battaglia and one anonymous referee.