958 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 26

A Method to Merge WSR-88D Data with ARM SGP Millimeter Data by Studying Deep Convective Systems

ZHE FENG,XIQUAN DONG, AND BAIKE XI Department of Atmospheric Sciences, University of North Dakota, Grand Forks, North Dakota

(Manuscript received 18 June 2008, in final form 8 December 2008)

ABSTRACT

A decade of collocated Atmospheric Radiation Measurement Program (ARM) 35-GHz Millimeter Cloud Radar (MMCR) and Weather Surveillance Radar-1988 Doppler (WSR-88D) data over the ARM Southern Great Plains (SGP) site have been collected during the period of 1997–2006. A total of 28 winter and 45 summer deep convective system (DCS) cases over the ARM SGP site have been selected for this study during the 10-yr period. For the winter cases, the MMCR reflectivity, on average, is only 0.2 dB lower than that of the WSR-88D, with a correlation coefficient of 0.85. This result indicates that the MMCR signals have not been attenuated for ice-phase convective , and the MMCR reflectivity measurements agree well with the WSR-88D, regardless of their vastly different characteristics. For the summer nonprecipitating convective clouds, however, the MMCR reflectivity, on average, is 10.6 dB lower than the WSR-88D mea- surement, and the average differences between the two radar reflectivities are nearly constant with height above cloud base. Three lookup tables with Mie calculations have been generated for correcting the MMCR signal attenuation. After applying attenuation correction for the MMCR reflectivity measurements, the averaged difference between the two has been reduced to 9.1 dB. Within the common sensitivity range (210 to 20 dBZ), the mean differences for the uncorrected and corrected MMCR reflectivities have been reduced to 6.2 and 5.3 dB, respectively. The corrected MMCR reflectivities were then merged with the WSR- 88D data to fill in the gaps during the heavy periods. This merged dataset provides a more complete radar reflectivity profile for studying convective systems associated with heavier precipitation than the original MMCR dataset. It also provides the intensity, duration, and frequency of the convective systems as they propagate over the ARM SGP for climate modelers. Eventually, it will be possible to improve understanding of the cloud-precipitation processes, and evaluate GCM predictions using the long-term merged dataset, which could not have been done with either the MMCR or the WSR-88D dataset alone.

1. Introduction with global climate models (GCMs) (Cess et al. 1996; Randall et al. 2006). The deep convection parameteriza- Clouds are one of the most fundamental aspects of tion of GCMs has the greatest uncertainty because the our climate system and have significant impacts on cli- formative and dissipative processes of convective clouds mate change by reflecting solar radiation back to space are poorly understood (Xu and Krueger 1991). A deep and absorbing longwave radiation from the surface and convective system (DCS) normally consists of two parts: the atmosphere (Wielicki et al. 1996). They are also one the convective core (precipitation) and the stratiform of the most important elements in the hydrology and region (cirrus anvil with or without precipitation). The energy cycle of our planet, particularly in the process of former is important for the atmospheric hydrological cy- producing precipitation (Del Genio et al. 2005). Yet, cle, whereas the latter is dominant in atmospheric radia- understanding of the interaction between clouds and the tion because of its large spatial coverage (Lin et al. 2006). climate, namely, cloud feedback, remains the major To understand the fundamental physics and the in- source of uncertainty in estimating climate sensitivity teractions between clouds and their radiative feedbacks in the atmosphere, the Atmospheric Radiation Mea- surement Program (ARM; Ackerman and Stokes 2003) Corresponding author address: Mr. Zhe Feng, Dept. of Atmo- spheric Sciences, University of North Dakota, 4149 University Dr., supported by the U.S. Department of Energy (DOE) Stop 9006, Grand Forks, ND 58202-9006. established the research site at the Southern Great Plains E-mail: [email protected] (SGP) in 1992. The centerpiece of the ARM cloud

DOI: 10.1175/2008JTECHA1190.1

Ó 2009 American Meteorological Society Unauthenticated | Downloaded 09/27/21 01:13 AM UTC MAY 2009 F E N G E T A L . 959 instrument array is the millimeter wavelength cloud the MMCR reflectivity (corrected for attenuation) to fill radar (MMCR; Moran et al. 1998), which has excellent in the gaps during the heavy precipitation periods. This sensitivity and large dynamic range to detect small hy- merged dataset provides a more complete radar re- drometeors, such as cloud droplets and ice crystals, but flectivity profile for convective systems associated with the signals can be significantly attenuated during the heavier precipitation than the original MMCR dataset. heavy precipitation periods (Lhermitte 1990). The It also provides the intensity, duration, and frequency of MMCR provides long-term continuous profiles of the convective systems as they propagate over the ARM equivalent radar reflectivity factor and vertical velocity SGP for climate modelers. Eventually, we can improve at the SGP site. When combined with other ARM in- our understanding of the cloud-precipitation processes, struments, such as the laser ceilometer, micropulse li- and evaluate GCM predictions using the long-term dar, or microwave radiometer (MWR), the hydrome- merged dataset, which could not have been done with teor profiles of the atmosphere can be retrieved (e.g., either the MMCR or the WSR-88D dataset alone. For Dong and Mace 2003; Matrosov et al. 2006). These example, the precipitating part of DCS might be missed valuable long-term datasets facilitate the progress of if using the MMCR only, whereas the cirrus anvil from research on understanding cloud microphysics and their the DCS might not be detected by the WSR-88D. effects on climate (e.g., Clothiaux et al. 2000; Mace and Benson-Troth 2002; Dong et al. 2005, 2006). Because the MMCR signals could be attenuated 2. Data during the heavy precipitating periods, the MMCR a. ARM data reflectivity measurements have some gaps and discon- tinuities, especially when deep convective clouds pass The ARM MMCR operates at a wavelength of 8 mm over the MMCR. Therefore, the MMCR measurements (Ka band) in a vertically pointing mode and measures during the precipitation periods, even for nonprecipitating continuous reflectivity profiles as clouds and hydrome- DCSs with large cloud liquid water path (LWP), are teors pass over the radar field of view (Clothiaux et al. questionable (Moran et al. 1998; Kollias et al. 2007; 2000). It records equivalent radar reflectivity factors

Dong et al. 2008). Dong et al. (2008) found that the (Ze) with a 90-m vertical resolution; a total of 167 levels ARM MMCR-derived cloud-top height is much lower starting from 105 m above ground level. The MMCR (;2 km) than the Moderate Resolution Imaging Spec- uses a 3-m-diameter antenna and has a 0.28 beamwidth, troradiometer (MODIS)-retrieved effective cloud height which yields lateral resolution of 35 m at the height of at the ARM Tropical Western Pacific (TWP) site for 10 km for a vertically directed beam. The minimum nonprecipitating DCSs. They suspected that some radar detectable Ze of MMCR are 255 dBZ at 1 km and signals were attenuated near cloud tops because of the 235 dBZ at 10 km (Moran et al. 1998). For cumulus clouds, 22 high LWP (;5000 g m ) measured at the ARM TWP the MMCR can detect Ze up to about 120 dBZ, but Mie site. The results in the Dong et al. (2008) study moti- scattering due to increasing hydrometeor size and signal vated us to investigate precipitating and nonprecipitating attenuation make it difficult to penetrate deep convec- DCSs at the ARM SGP site. As demonstrated in Fig. 1, tive clouds within heavy precipitation. A constant cor- the MMCR signals have been severely attenuated by rection of 3 dB to all levels as well as near-field cor- heavy rainfall ( rate . 40 mm h21), and completely rections have been added to the MMCR reflectivities attenuated above 2 km between 0250 and 0300 UTC. To (Sekelsky 2002). The MMCR has four operational provide a more complete reflectivity profile, that is, to modes optimized for various clouds types and runs fill in the gaps of the MMCR reflectivity measurements, consecutively in a 36-s cycle (Clothiaux et al. 1999). The other types of radars designed for detecting precipita- data used in this study are 5-min averages (the Mace PI tion are required. product; more information is available online at http:// The Weather Surveillance Radar-1988 Doppler iop.archive.arm.gov/arm-iop/0pi-data/). The Mace PI (WSR-88D) near the SGP site is a good complementary product (Mace et al. 2006) mimics the Active Remote instrument to the MMCR because it has the particular Sensing of Clouds (ARSCL) product (Clothiaux et al. advantage of providing the vertical distribution and 1999, 2000), but the key difference between the two horizontal coverage of precipitation (Miller et al. 1998). products is how the profiles from the four operational In this study, we used the collocated WSR-88D radar modes are merged. For the ARSCL product, they per- volumetric scans over the SGP site and reconstructed formed interpolation to a 9-s temporal grid (the tem- the WSR-88D reflectivity into a time–height series that poral spacing of the individual modes), whereas for the matched the MMCR data format during the period of Mace PI product, they estimated the most reasonable 1997–2006. The WSR-88D data were then merged with measurements for a given 90-m vertical bin from one of

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FIG. 1. (a) ARM SGP MMCR observed reflectivity with (red lines) sounding temperature and (black dots) laser ceilometer–observed cloud base and (b) (black line) surface rain rate and (blue line) MWR- retrieved cloud LWP on 17 Jun 2001. the modes during the 36-s cycle and assigned the three ments and corrections, are denoted as ARM merged Doppler moment measurements from that particular soundings (Mace et al. 2006). The cloud LWP is derived mode to that bin (Mace et al. 2006). from the microwave radiometer brightness tempera- Cloud-base height is derived from a composite of the tures measured at 23.8 and 31.4 GHz using a statistical Belfort laser ceilometers, micropulse (MPL), and retrieval method (Liljegren et al. 2001). The root-mean- MMCR data (CloudBaseBestEstimate; Clothiaux et al. square (RMS) accuracies of the LWP retrievals are 2000). Because the laser ceilometer and lidar are sen- about 20 g m22 and 10% for cloud LWP below and sitive to the second moment of the scatterer size dis- above 200 g m22, respectively (Dong et al. 2000; tributions of the particle, rather than the sixth moment Liljegren et al. 2001). The surface precipitation rate is of the MMCR, they are virtually immune to insect measured from the tipping-bucket rain gauge at the contamination and precipitation particles falling below SGP Central Facility (SCF) closest to the MMCR. All cloud base. Therefore, the estimated cloud-base height ARM data used in this study were averaged to a 5-min from the laser ceilometer and/or lidar is used as the temporal resolution (the Mace PI product). lowest cloud base. Cloud temperatures are estimated b. WSR-88D data from a linear temporal interpolation of ARM SGP ra- winsonde soundings (;4 times per day). The instanta- The Next Generation (NEXRAD) neous soundings are first degraded to a common vertical WSR-88D operates at a wavelength of 10 cm (S band) resolution of 90 m before linear interpolation. The in- and monitors its surrounding environment in a pre- terpolated soundings, combined with other measure- programmed sequence of 3608 azimuthal sweeps at

Unauthenticated | Downloaded 09/27/21 01:13 AM UTC MAY 2009 F E N G E T A L . 961 various elevation angles. Thus the WSR-88D observa- TABLE 1. Number of cases collected under various VCP modes tions represent an instantaneous measurement of radar and their respective characteristics. Volume scan time is the total reflectivity at a given elevation angle, azimuthal angle, time required to complete a volume scan and the number of ele- vation angles is the discrete elevation angles for each volume scan. and range gate. With a 18 beamwidth, the WSR-88D has a spatial resolution of 1831 km over the ARM SGP No. of No. of site. Each complete sequence of azimuthal scan is de- winter summer Vol scan No. of Max VCP Mode (DJF) (JJA) time elevation elevation fined as a ‘‘volume scan.’’ The volume coverage patterns No. name cases cases (min) angles angle (VCPs) describe the different combinations of elevation angles and processing modes used for the volume scan. 32 Clear air 7 0 10 5 4.58 21 Precipitation 21 12 6 9 19.58 Depending on the operating VCPs, the sensitivities of 11 Precipitation 0 25 5 14 19.58 the WSR-88D range from 228 to $ 128 dBZ in clear- 12 Precipitation 0 8 4 14 19.58 air mode (VCP 32) and 15to175 dBZ in precipitation mode (VCP 21, 11) (for more details, see Crum et al. 32 and 21, respectively (Fig. 3). The height of each beam 1993; Klazura and Imy 1993). Deployed in 2004, the new that intersected the MMCR-sampled column was rep- operation mode (VCP 12) provides greater vertical resented by the center of the beam. resolution at lower elevation angles and completes a volume scan in 4.1 min as opposed to 5 min for VCP 11 The criteria for selecting WSR-88D reflectivities are (Brown et al. 2000). as follows: 1) the heading and distance from the KVNX 8 2 8 The WSR-88D radar used in this study is located at WSR-88D (36.7408 , 98.1278 ) to the ARM SGP MMCR (36.60508, 297.48508) are calculated using the Vance Air Force Base, Oklahoma (KVNX; 36.74088, great circle navigation equation (Rinehart 2004); 2) the 298.12788), operated by the Department of Defense WSR-88D reflectivity measurements that fall within a (DOD). At approximately 59.3 km west of the SGP 618 azimuth heading and 61-km range gate centered on MMCR, the KVNX radar is the closest to the SGP site. the SGP MMCR are selected; 3) if more than one The theoretical minimum detectable Ze of the WSR- 88D at this range is between 225 and 230 dBZ for VCP reflectivity measurement is selected within that spatial 32 and from 220 to 215 dBZ for VCP 21 (Miller et al. domain, the one with the closest distance and heading 1998). In this study, the level 2 data were used to re- to the SGP MMCR is used; and 4) if no signal is detected by the WSR-88D, then it is assigned as missing value. construct the time–height series of the WSR-88D data Figure 4 shows two examples, the first (left panel) is over the SGP site. Because of the limited available from wintertime primarily ice-phase clouds with virtu- KVNX radar data archived by the DOD before 2002, a ally no liquid water retrieved by the MWR, and the total of 28 winter [December–February (DJF)] and 45 second (right panel) is from summertime with light summer [June–August (JJA)] DCS cases were collected for this study during the period of 1997–2006. Table 1 precipitation measured by the surface rain gauge and lists the number of cases under different WSR-88D high liquid water path from the MWR. As shown in VCPs, as well as their corresponding characteristics. both Figs. 3 and 4, the vertical resolutions of the re- constructed WSR-88D data, particularly above 6 km, c. Reconstruct time–height series of WSR-88D are much lower than those of MMCR observations. data at the SGP site Notice that there are almost no signal returns in the upper layers of both cases from WSR-88D data (Fig. 4), which is The general approach to collocate the WSR-88D and mainly a result of the lower sensitivity of WSR-88D for MMCR reflectivity measurements is illustrated in Fig. 2, and the method of processing the WSR-88D data is similar to that described by Miller et al. (1998). To compare reflectivities between the two radars, WSR-88D level 2 data were used to reconstruct the reflectivity profile for the vertical column above the SGP MMCR. The portion of each WSR-88D volume scan that inter- sected the MMCR-sampled column was extracted and saved. The vertical resolution of the reconstructed WSR-88D reflectivity over the SGP site is determined by the VCPs used for each case. There are approxi- mately 12 vertical levels of WSR-88D data in VCPs 11 and 12 for reconstructing the time–height series over the FIG. 2. Schematic illustration of common scanning volumes SGP site, whereas there are only 5 and 8 levels in VCPs between WSR-88D and MMCR (courtesy of Miller et al. 1998).

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FIG. 3. WSR-88D sweeps as a function of elevation angle and range from the radar. The ARM SGP MMCR is located at about 60 km away from the WSR-88D as marked by the thick solid line. detecting clouds compared to the MMCR. A more de- to statistical comparisons of the reflectivity measure- tailed comparison between MMCR and WSR-88D re- ments from the two radars. flectivity measurements shows that they agree well for As a consequence of being located 59.3 km away from the winter case, but the MMCR measurements for the the MMCR, the spatial resolution of the WSR-88D is summer case, especially for the convective portions of about 1831 km over the SGP site, and its vertical clouds during the periods of 0700–0800 and 1300–1500 resolution (;1 km) is much coarser than that of the UTC, are much lower than those of the WSR-88D. This MMCR (90 m). To match the two datasets vertically, a difference is due to the vastly different nature of the two Gaussian weighted average (Rinehart 2004; represented radars, such as wavelength, scanning strategy, beam- by a parabolic curve, see Fig. 5a) was applied to the width, resolution, and sensitivity. Also this comparison MMCR reflectivities within each collocated WSR-88D is only from two cases. To provide a statistically reliable beam to represent the MMCR reflectivity measurement comparison, more cases during the winter and summer for that volume. The horizontal resolutions are 35 and seasons are needed. 1000 m at an altitude of 10 km for the MMCR and WSR-88D, respectively. Although this spatial differ- ence can cause some uncertainties in the reflectivity of 3. Statistical comparison convective clouds for a short time period (Straka et al. Comparisons between the MMCR and WSR-88D 2000), a 5-min-averaged MMCR reflectivity should re- observations must be conducted carefully because of duce this difference significantly. the significant vertical, spatial, and temporal differences For the temporal resolutions, the MMCR data are between the two observing platforms. Therefore, it is 5-min averages, whereas the reconstructed WSR-88D necessary to discuss these differences before proceeding data are the instantaneous reflectivity measurements at

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FIG. 4. Two convective systems over the SGP site on 26 Jan 2000 and 1 Jul 2004. (a),(d) Time–height series of SGP MMCR reflectivity, (red lines) sounding temperatures, (black dots) laser ceilometer–derived cloud bases; (b),(e) reconstructed KNVX WSR-88D reflectivity over the SGP site, (black lines) cloud boundaries measured by the MMCR; and (c),(f) (blue line) LWP and (black line) surface rain rate. (a)–(c) 26 Jan 2000. (d)–(f) 1 Jul 2004. successive vertical levels over the SGP during a period panel)] were used as a consistency check between the two of 4–10 min, depending on the VCP mode because the radars, whereas the summer convective cases [e.g., Fig. 4 WSR-88D scans 3608 at different elevation angles. It is (right panel)] were used to quantify the attenuation of assumed that convective cloud systems do not evolve the MMCR measurement for nonprecipitating DCSs. significantly over the course of 4–10 min for the WSR-88D Both winter and summer convective cases in Fig. 5 to complete a full volume scan. Therefore, the recon- were selected during the time periods with no surface structed WSR-88D dataset is considered to be a radar precipitation (rain rate 5 0) detected by the tipping- reflectivity profile comparable to that of the MMCR. bucket rain gauge. There are three additional criteria Moreover, reflectivity measurements depend on abso- for selecting radar reflectivities from the winter cases: 1) lute calibration. In this study, both radars are assumed the cloud temperatures are lower than 273 K; 2) the to be correctly calibrated. MWR-retrieved LWP is less than 20 g m22 (considering From theoretical study, there is no attenuation in the uncertainty of the retrieved LWP; Dong et al. 2000); detecting ice particles from both radars, but there is and 3) the reflectivity measurements are above the light attenuation for the WSR-88D measurements and cloud base. The radar reflectivities from the summer severe attenuation for the MMCR measurements in cases were selected to investigate the signal attenuation detecting precipitation (Moran et al. 1998; Lhermitte of MMCR measurement from the liquid cloud droplets 1990). To quantitatively investigate the difference in rather than rain drops with the following two additional reflectivity measurements from the two radars, the cases criteria: 1) the cloud temperatures are higher than 273 K from both winter and summer seasons have been care- and 2) the contiguous cloud tops exceed 5 km. fully selected from the matched MMCR and WSR-88D Figures 5b and 5c show the scatterplots of MMCR dataset. The cases selected during winter seasons with versus WSR-88D reflectivities for the winter and sum- primarily ice-phase convective clouds [e.g., Fig. 4 (left mer cases, respectively. For the winter cases (Fig. 5b),

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FIG. 5. (a) The weighting function of MMCR reflectivities within a WSR-88D volume. Comparisons between the weighted average MMCR and WSR-88D reflectivities for (b) winter ice-phase convective clouds and (c) summer pure liquid portion of convective clouds. (d),(e) Similar comparisons from (a) and (b), except for the differences between the two radars as a function of height above cloud base up to 5 km. (squares) The averages for every 0.5 km, (bars) standard deviations, (bracketed numbers) bin numbers, and (numbers to right) the number of samples in each bin. the two radars provide very consistent reflectivity mea- 210 and 230 dBZ, respectively, whereas the upper surements for primarily ice-phase convective clouds, bounds are about 130 and 120 dBZ, respectively. The considering the large sampling differences discussed reflectivity measurements from both radars agree better above. The MMCR reflectivity, on average, is only 0.2 dB from 210 to 120 dBZ (from the MMCR measurement lower than the WSR-88D measurement, with a correla- point of view) than other ranges because the two radars tion coefficient of 0.85. The selected samples show that are sensitive to ice crystals and cloud droplets in this the lower bounds for WSR-88D and MMCR are around range. This comparison shows that the WSR-88D

Unauthenticated | Downloaded 09/27/21 01:13 AM UTC MAY 2009 F E N G E T A L . 965 reflectivity measurements agree well with the MMCR zles) is the major concern. The specific attenuation A, measurements. The possibility of both radars’ calibration which is a function of the extinction coefficient sext,is shifting to the same direction (high or low) cannot yet be calculated by integrating over the liquid hydrometeor ruled out, but this possibility is presumed to be very low. drop size distribution (DSD) function n(D) (Bringi and Figure 5c shows the comparison for the pure liquid Chandrasekar 2001): portion of summer nonprecipitating convective clouds. Averaged over all the differences between the matched Dðmax 3 two datasets, the MMCR measurement is 10.6 dB lower A 5 4.343 3 10 sextn(D)dD, (1) than the WSR-88D reflectivity. The correlation coeffi- Dmin cient between the two datasets is 0.64, which is slightly lower than the value for ice-phase convective cases. The where the minimum and maximum diameter Dmin and main differences seem to originate from 0 to 120 dBZ, Dmax are 1 and 5000 mm, respectively. At Ka band (8-mm where the WSR-88D reflectivity can be up to 140 dBZ, wavelength), the size of liquid particles are comparable whereas the maximum MMCR reflectivity is around to the radar wavelength, thus the simple Rayleigh

120 dBZ. If we have a subset of the original two data- theory is not applicable for calculating sext. Therefore, sets within the common sensitivity range (210 to 20 dBZ), Mie theory is used in the calculation of the extinction the mean difference is reduced to 6.2 dB. There are efficiency Qext (Bohren and Huffman 1983), which re- several possible reasons to explain these differences: 1) lates to sext through the MMCR signals may be attenuated by the large LWP values, 2) the MMCR receiver is saturated at around 2 sext 5 Qextpr , (2) 120 dBZ, 3) large particles (.2.5 mm) may cause the MMCR reflectivity to be smaller than Ray- where r is the particle radius. For cloud droplets and leigh assumption as a result of the Mie effect, and 4) the , the hydrometeors under consideration are as- large sample volume difference between the two radars sumed to be spherical particles. The extinction effi- may also be a significant contributor. ciency Qext is a function of the radar wavelength, tem- To further investigate the differences with height, the perature, and hydrometeor type (in this case, liquid two matched datasets were grouped as a function of height water). The DSD function considered for cloud droplets above cloud base, and the differences between WSR-88D and drizzles is the gamma distribution (discussed more and MMCR measurements were averaged for every 0.5 later), which can be expressed as km. As shown in Figs. 5d and 5e, the reflectivity differ- nÀ1 ences between the two radars are nearly constant with Nt D 1 D n(D) 5 exp À , (3) height for both winter and summer cases, although there G(n) Dn Dn Dn are biases near cloud base for both seasons. There are two possible reasons to explain these biases. The first reason is where Nt is the total number concentration; Dn is the that the biases may be caused by large droplets present characteristic diameter, which can be related to the near the cloud base. The second possible reason is that mean diameter Dmean through Dn 5 Dmean/n; and n is the WSR-88D measurements may include the ground the shape parameter. The total number concentration contribution from its sidelobe. However, these biases still Nt can be derived through LWC as exist when the comparison is constrained for the cloud LWC G(n) bases above 1 km (not shown). Thus, it is possible to rule Nt 5 . (4) p r D3 G(n 1 3) out the ground clutter issue of the WSR-88D. The ver- 6 w n tical differences for liquid-phase summer cases (Fig. 5e) are consistently large throughout 5 km above cloud base As described in Eqs. (2)–(4), the specific attenuation with an averaged difference of 10.6 dB and relatively (one way) of MMCR in Eq. (1) depends upon LWC, large standard deviations (;5 dB). Therefore, a further temperature, mean diameter, and shape parameter un- study about the MMCR signal attenuation for the sum- der the assumption of a gamma distribution. Sensitivity mer convective clouds is necessary. studies have been conducted to investigate the depen- dence of specific attenuation on each of the parameters 4. Attenuation correction (Fig. 6). As demonstrated in Fig. 6a, the specific atten- uation increases linearly with increasing LWC for a a. Theoretical calculation given mean diameter and shape parameter. For exam- In this study, the attenuation of MMCR signal by ple, for a fixed temperature of 273 K, the specific at- liquid hydrometeors (mainly cloud droplets and driz- tenuation increases to 6 dB km21 as LWC increases

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FIG. 6. Sensitivity tests for the dependence of specific attenuation on (a) LWC, (b) temperature, (c) mean diameter, and (d) shape parameter. from 0 to 3 g m23. The specific attenuation increases with an exponential distribution using the aircraft in semilogarithmically with decreasing temperature (Fig. 6b). situ measurements over Oklahoma. Other studies For example, the specific attenuation increases from (Chandrasekar and Bringi 1987; Ulbrich 1983) suggested 2.2 to 3.2 dB km21 as temperature decreases from 300 that the particle sizes of convective clouds followed a to 240 K at a fixed LWC of 1 g m23. Specific attenuation gamma distribution with a shape parameter n varying increases less with mean diameter except for those from 1.4 to 2.6 using both modeling and observations. larger than 300 mmasshowninFig.6c.Notethata Based on these previous studies, three lookup tables for broader distribution (i.e., smaller shape parameter n # 2; correcting attenuation have been generated from the see Fig. 6d) can cause more attenuation than a narrow calculations in Fig. 6. They represent different DSDs: 1) distribution. For n 5 1, the gamma distribution in Eq. (3) gamma distribution with Dmean 5 100 mm and n 5 1.5 can be simplified as (Fig. 7a), 2) M–P distribution with Dmean 5 200 mm and n 5 1.0 (Fig. 7b), and 3) M–P distribution with D 5 mean 300 mm and n 5 1.0 (Fig. 7c). For a given DSD, the Nt D n(D) 5 exp À , (5) specific attenuation is only a function of LWC and Dn Dn temperature of the hydrometeors. which is equivalent to the Marshall–Palmer (M–P) dis- b. Applying the attenuation correction tribution (Marshall and Palmer 1948). Heymsfield and Hjelmfelt (1984) found that most of the liquid-phase As discussed in the previous section, temperature and convective cloud droplet diameters were below 450 mm LWC are required to obtain the attenuation for each

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FIG. 7. Similar comparison as in Fig. 5d, but after applying attenuation correction using lookup tables from (a) gamma distribution with

Dmean 5 100 mm and n5 1.5, (b) M–P distribution with Dmean 5 200 mm, and (c) M–P distribution with Dmean 5 300 mm. (d) Averaged attenuation correction profiles for the three lookup tables. range gate of the MMCR. The temperature profiles are calculating the layer 3 attenuation. After correcting given by the ARM merged soundings in this study. To attenuation using the three generated lookup tables, obtain LWC profile, MWR-retrieved LWP was used the MMCR reflectivities during summer (in Fig. 5e) as a constraint. Investigations of some radar reflectivity were then compared with the WSR-88D reflectivities profiles in the selected DCS cases (not shown) from the to quantify the difference between two radar mea- MMCR and WSR-88D show that the differences be- surements. tween the original reflectivity measurements of the two Figure 7 illustrates three DSDs and their corre- radars are the largest near cloud base and then decrease sponding averaged reflectivity differences between the with height. If these differences are mainly caused by MMCR and WSR-88D up to 5 km above cloud base the liquid water attenuation to the MMCR signals, then (same as Fig. 5e). Compared to the original reflectivity we can assume that the LWC profile linearly decreases differences (10.6 dB) in Fig. 5e, the averaged differences with height from cloud base to the level at T 5 240 K in after correcting the MMCR reflectivity attenuation this study. The linearly decreased LWC profile gives a have been reduced by 0.7, 1.6, and 2.5 dB, respectively, larger correction close to the cloud base and less cor- for the assumed gamma and M–P distributions. The 1.6-dB rection to the cloud top, which makes the corrected reduction represents the averaged attenuation correction, MMCR reflectivity closer to that of the WSR-88D. whereas the 0.7 and 2.5 dB may be the lower and upper After obtaining the LWC and temperature profiles, bounds of the correction. The averaged differences the specific attenuation for each MMCR 90-m reflec- from bins 3–8 (1–4 km above cloud base) slightly de- tivity measurement was then estimated using the crease with height, which is expected after applying the aforementioned lookup tables. Because the radar sig- attenuation correction. Figure 7d shows three averaged nals travel through the clouds 2 times, the specific at- attenuation profiles for the selected cases as a function tenuation has been used twice for correcting MMCR of height above cloud base, using the generated lookup reflectivities. The reflectivity attenuation at a given tables with the estimated LWC and temperature pro- level is the result of the attenuation through all un- files. Figure 7d has demonstrated that the attenuations derlying layers. For example, the layer 3 attenuation from three DSDs monotonically increase from cloud of MMCR reflectivity is the sum of layers 1–3 because base up to 8 km as a result of the assumed linearly de- the radar signals have to go through layers 1 and 2, thus creased LWC profile and accumulated attenuation from the attenuation of layers 1 and 2 should be used in cloud base upward.

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Despite the effort to correct the MMCR attenuation Third, during the periods right after heavy precipita- using the methods described above, there are still the tion (e.g., 0310 UTC in Fig. 1), it is possible to have averaged differences of 8.2–10.0 dB between the two water accumulated on the MMCR radome, which could radars depending on the DSD. If we have a subset of the significantly attenuate radar signals. The comparisons two datasets within the common sensitivity range (210 to discussed in Figs. 5 and 7 assume that the accumulated 20 dBZ), the mean differences are reduced to 5.3 dB. water on the MMCR radome has been evaporated after There are several possible reasons to explain these 10 min of any measured precipitation. However, pre- differences. cipitation in some convective cases lasted for hours and First, the MMCR receiver is possibly saturated at such an assumption might be invalid. Last but not least, or before 120 dBZ, thus the MMCR reflectivity WSR-88D in its precipitation mode (VCPs 21, 11, and measurements around 120 dBZ are lower than the 12) has lower sensitivity than the clear-air mode (VCP WSR-88D data. The MMCR data used in this study 32) as mentioned in section 2. The small hydrometeors combine all four operating modes that cover hydrome- (e.g., reflectivities less than 210 dBZ in Fig. 5c) can be teors with reflectivity range of approximately 250 to measured by the MMCR, but they are beyond the lower 120 dBZ (Clothiaux et al. 1999). The first three modes bound of WSR-88D measurement (about210 dBZ). are more suitable in detecting nonprecipitating or weakly reflecting clouds, whereas the robust mode 5. Merging dataset and discussion (mode 4) should be able to detect weakly precipitating clouds. Unfortunately, the robust mode was not set up After investigating the MMCR attenuation, the cor- to avoid saturation until 2004 and a 5-min average of rected MMCR reflectivity measurements using M–P this mode at the edge of sharp convective rain events distribution with Dmean 5 200 mm in this study were then (e.g., Fig. 1) might have a potential problem because merged with the WSR-88D measurements. Because mode 4 is less sensitive to nonprecipitating clouds than only one time stamp was recorded for each WSR-88D other modes (Clothiaux et al. 1999). Therefore, with the volume scan and it is usually not at the exact 5 min, the 5-min combined MMCR reflectivity used in this study, reconstructed WSR-88D vertical profile was approxi- we suspect that the large difference found is because of mated to the closest 5-min temporal resolution to match the saturation of the MMCR receiver. the MMCR data format in the merged dataset. During Second, the assumption of neglecting the volume the heavy precipitation periods (rain rate . 7mmh21) differences between the two radars might partially when the MMCR signals failed to penetrate the entire contribute to the differences. However, in the best-case depth of the convective clouds, the WSR-88D data were scenario of wintertime primarily ice-phase convective used to fill in the gaps. When the MWR-retrieved LWPs clouds where the MMCR is free of attenuation, the are not reliable or there are no LWP retrievals at all, it is averaged difference (Fig. 5b) is 0.2 dB, and the two impossible to implement the attenuation correction radar reflectivity measurements, on average, are almost scheme in this study. To avoid discontinuity during the the same. For the summer convective cases, such dif- transition periods in the merged dataset, the WSR-88D ferences could be much larger when a few large rain- reflectivity measurements during the precipitating pe- drops were present in the large WSR-88D volume but riod 65 min were used to fill in the gaps of MMCR missed by the MMCR. Furthermore, the criteria of se- measurements. The vertical resolution of the WSR-88D lecting data in summer cases for comparison assume data depends upon the VCP modes used at the time that the hydrometeors with sounding temperatures higher of the measurement. As a result of the much lower than 273 K are pure liquid. While this assumption is vertical resolution (;1 km) of the WSR-88D, a distance- generally acceptable, there are some possibilities that weighted linear interpolation was performed between melting ice particles could be present in the selected any two levels of WSR-88D reflectivity measurements data. In such cases, not only would the WSR-88D such that its vertical resolution was comparable to measure much higher reflectivity as a result of higher the MMCR measurements. If the MMCR detected power returned from the liquid shielded melting ice weak reflectivities above the highest WSR-88D level, particles (often referred to as the bright band), but the those reflectivities were then included in the merge attenuation of MMCR signal due to melting particles dataset. could be as much as 3 times higher than equivalent Two examples of the merged dataset have been il- liquid hydrometers (Matrosov 2008). The net effect lustrated in Fig. 8. The case on 17 June 2001 demon- would enlarge the existing difference between the two strates the ability of the WSR-88D reflectivities to fill in radar measurements and could not be corrected by our the MMCR gap in the merged dataset. During the pe- proposed lookup tables. riods of heaviest precipitation (;0300 UTC), the MMCR

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FIG. 8. Example cases of the merge dataset. (a),(e) MMCR reflectivity, (b),(f) reconstructed WSR-88D reflectivity, (c),(g) merged WSR-88D and attenuation-corrected MMCR reflectivity, (d),(h) LWP (blue line) and surface rain rate (black line). (a)–(d) 17 Jun 2001. (e)–(h) 21 Jun 2004. signals were almost completely lost or attenuated; the (;1000 UTC), the cloud bases were assumed to be at WSR-88D reflectivities were used to fill in the gap of the lowest height of MMCR reflectivity measurement, MMCR reflectivity measurements to produce a more with the constraint that either the rain rate was greater complete reflectivity profile. Notice that during the pe- than zero or LWP was larger than 1000 g m22. This riod (;0245 UTC) right before the sharp convective rain, constraint is to ensure that the liquid cloud base is close the LWP increased dramatically up to 104 gm22.This to the surface and the attenuation-correction scheme huge LWP makes the transition from the attenuation- can still be applied. The correction method, in general, corrected MMCR reflectivity to the interpolated WSR- is applicable to the nonprecipitating portion of DCS 88D reflectivity smoothly. However, during the period with the available LWPs and sounding temperatures; (;0315 UTC) right after the rain, there are no LWP for the weakly precipitating portion of the DCS, how- retrievals, thus no attenuation correction method can be ever, the correction method may need further im- applied. provement (e.g., between 0900 and 1100 UTC). The case on 21 June 2004 clearly shows the advantage This merged MMCR and WSR-88D dataset provides of the merged dataset compared with the original a much wider range of hydrometeor reflectivity mea- MMCR data. The merged dataset shows much higher surements from 250 to 160 dBZ compared to the reflectivities than the original MMCR measurements original MMCR and WSR-88D datasets. The most ob- during the periods of high LWPs, such as 0530–0700 vious advantage of the merge dataset is being able to UTC, 0900–1100 UTC, and 1230–1400 UTC, which is provide information that was lost during the period of consistent with the attenuation-correction method de- heavy precipitation, when the MMCR failed to pene- scribed in section 4. If the cloud-base heights were not trate the entire DCS. The WSR-88D data were also available during downtime of the laser instruments used to quantify the MMCR attenuation during the

Unauthenticated | Downloaded 09/27/21 01:13 AM UTC 970 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 26 periods of nonprecipitating convective clouds. By as- original 10.6 dB. For a subset of the original two suming a DSD function, attenuation correction to the datasets within the common sensitivity range (210 MMCR was obtained using the MWR-retrieved LWP to 20 dBZ), the mean differences for the uncorrected and sounding temperature through theoretically calcu- and corrected MMCR reflectivities have been re- lated attenuation lookup tables. This method demon- duced to 6.2 and 5.3 dB, respectively. strates the WSR-88D as a good complementary instru- 4) The merged MMCR and WSR-88D dataset provides ment to the ARM remote sensing arrays in detecting a a more complete time–height evolution of the re- wider range of atmospheric hydrometeors for future flectivity profile of convective systems, which can fill in climate research. the gaps from MMCR measurements during the heavy precipitating periods. It is our hope that this merged dataset can be used by modelers to improve 6. Summary and concluding remarks our understanding of the life cycle of convective sys- tems, as well as their associated water and energy A total of 28 winter and 45 summer DCS cases over budgets. Because satellite data are critical for studying the ARM SGP site have been collected for this study large-scale spatial cloud, precipitation, and radiation during the period of 1997–2006. From the analysis of the properties, we will map the WSR-88D observed hor- merged dataset and the generated lookup tables for izontal reflectivity on the Geostationary Operational correcting MMCR signal attenuation, we have made the Environmental Satellite (GOES)–MODIS-retrieved following conclusions: cloud-top properties to study the spatial structures and 1) For the winter cases, the two radars provide very developmental stages, such as formative and dissipa- consistent reflectivity measurements for primarily tive processes, of convective systems in future studies. ice-phase convective clouds. The MMCR reflectivity, Eventually we can generate a 3D database from both on average, is only 0.2 dB lower than the WSR-88D vertical and spatial distributions of observations for measurement with a correlation coefficient of 0.85 modelers to simulate various stages of DCS and to (both radars are sensitive to ice crystals). This result improve cumulus cloud parameterizations. has indicated that the MMCR signals have not been attenuated for ice-phase convective clouds, and the Acknowledgments. Special thanks to Dr. Gerald WSR-88D reflectivity measurements agree well with G. Mace and Ms. Sally Benson at the University of Utah the MMCR measurements. for providing their PI products, Mr. Christopher 2) For the summer nonprecipitating convective clouds, Theisen and Mr. Aaron Kennedy at the University of however, the MMCR reflectivity, on average, is 10.6 North Dakota for helping with the process of WSR-88D dB lower than that of the WSR-88D, and the aver- data and some comments and discussions, and Dr. Eugene aged differences are nearly constant with height Clothiaux and Dr. Pavlos Kollias for helpful suggestions above cloud base. There are four reasons to explain about the project. 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