To improve understanding of the convective processes key to the Madden-Julian oscillation (MJO) initiation, the Dynamics of the MJO (DYNAMO) and the Atmospheric Radiation Measurement Program (ARM) MJO Investigation Experiment (AMIE) collected 4 months of observations from three radars-the
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The Madden-Julian oscillation (MJO; Madden and Julian 1971, 1972) is the most prominent intraseasonal (20-100 days) oscillation in the tropics. As it propagates eastward at about 5 m s21 (Weickmann et al. 1985) from the
Global climate models generally underestimate the strength and intraseasonal variability of the MJO (Zhang et al. 2006), while some models fail to produce even half of the observed MJO variance (Lin et al. 2006). The prediction skill is particularly low over the
One of the key hypotheses proposed in the DYNAMO/ AMIE field campaign is that different phases of the MJO are characterized by different cloud populations and that a specific cloud population is essential to the initiation of the MJO (
The main objectives of this paper are to evaluate the ability of the cloud and precipitation radars deployed at Addu Atoll to document the full spectrum of cloud populations and to take advantage of the multiwave- length radar platforms to produce a merged cloud- precipitation dataset (Feng et al. 2009) with continuous hydrometeor microphysics and radiative heating profile retrievals that can be used to test the DYNAMO science hypothesis and to evaluate numerical model simula- tions. The three radar systems that were deployed at Addu Atoll are the ARM Mobile Facility (AMF) ver- tically pointing Ka ARM zenith radar (KAZR; Ka band), the
During DYNAMO/AMIE, both the S-Pol and SMART-R performed regular range-height indicator (RHI) scans over the AMF site where the KAZR was located. Therefore, collocated measurements from all three radars at the AMF site are available during the field campaign. Comparisons between statistics of vari- ous types of clouds, particularly shallow, congestus, and deep convective clouds that are potentially important to the initiation of MJO, will be performed. Through this comparison, the ability of the two scanning pre- cipitation radars in detecting both precipitating and nonprecipitating clouds will be evaluated, and recom- mendations of how to use the comprehensive suite of radar data collectively to address the DYNAMO sci- ence goal related to the initiation of the MJO will be provided.
The paper is organized as follows. Section 2 describes the three radar systems and other ancillary data used in this study, and the collocation of data and qual- ity control; section 3 provides cloud statistics and comparison results; section 4 describes a procedure to produce a merged cloud-precipitation dataset for cloud microphysics and radiative heating rate re- trievals; and a summary and conclusions are given in section 5.
2. Data and methodology
DYNAMO/AMIE successfully completed operations from
Figure 1 shows the geographic locations of the three radars on Addu Atoll. The AMF KAZR is located at the
In addition to the three radar systems deployed at Addu Atoll, radiosondes were launched at
a. AMF KAZR
The AMF KAZR is a zenith-pointing Doppler cloud radar operated at 35 GHz (8-mm wavelength). The main purpose of the KAZR is to provide vertical pro- files of clouds by measuring the first three Doppler moments: reflectivity, radial Doppler velocity, and spectral width (Bharadwaj et al. 2013)aswellasthefull Doppler spectrum though that is not used in this analysis. The KAZR used during DYNAMO/AMIE has an antenna diameter of 2 m, with a 0.328 beam- width. Two simultaneous operating modes (Bharadwaj and Chandrasekar 2012) were used during the field campaign: general mode (detects full range but less sensitive) and cirrus mode (more sensitive but does not detect clouds below 2-km height due to pulse com- pression techniques), with a maximum range of about 18 km. The single-pulse minimum observable re- flectivityis219.7dBZat1kmand215.7dBZ at10km for general and cirrus modes, respectively. Spectral processing is used to enhance the sensitivity of the ra- dar by performing an equivalent coherent integration in the spectral domain, which adds approximately 20 dBZ sensitivity to KAZR (i.e., KAZR data have a sensitivity of approximately 240 dBZ at 1 km and approximately 236 dBZ at 10 km for general and cirrus modes, respectively).
The KAZR data used in this study are the KAZR Active Remote Sensing of Clouds (ARSCL) product produced by ARM (www.arm.gov). KAZR-ARSCL corrects for water vapor attenuation and velocity alias- ing and produces a significant detection mask. By se- lecting the mode with the highest signal-to-noise ratio at a given point, data from the two simultaneous operating modes are combined for each profile to obtain the best- estimate time-height fields of the three radar moments (i.e., reflectivity, Doppler velocity, and spectral width). The KAZR-ARSCL product has vertical and tempo- ral resolution of 30 m and 4 s, respectively. Although KAZR-ARSCL provides cloud boundaries that are derived from a combination of KAZR measurements and observations from the micropulse lidar and ceil- ometer, they are not used for comparison purpose in this study because reflectivity measurements from S-Pol and SMART-R are essentially ''hydrometeor'' echoes that do not separate between cloud and rain droplets. In- stead, hydrometeor echo boundaries are derived using KAZR-ARSCL reflectivity measurements that are designated as ''significant detection,'' which are defined as signal-to-noise ratio above 218 dB and reflectivity values greater than 240 dBZ.
Several other auxiliary datasets at the AMF site are also used in this study. Surface precipitation rate is measured from the
b. NCAR S-Pol
The NCAR S-Pol radar is a dual-polarimetric, dual- frequency (10 cm:
During the DYNAMO/AMIE field campaign, S-Pol operated on a 15-min scanning cycle, starting at minute 0, 15, 30, and 45 of every hour. The scanning sequence started with two RHI scans at 1418 and 1428 azimuth over the AMF site, with oversampled elevation angles of 0.58 up to 658 (Fig. 2a), followed by a 3608 plan position indicator (PPI) surveillance volume scan consisting of eight elevation angles between 0.58 and 118; and finally, two RHI sector scans were performed between the azimuth angles of 48-828 and 1148-1408 with a 28 azi- muthal resolution up to an elevation angle of 408. These two sectors were selected because of the low-level blockage that occurred from the deep-water port at the azimuths in between the sectors. The S-Pol data quality control includes automated ground clutter re- moval (Hubbertetal.2009a,b) and correction of noise based on a beam-by-beam varying noise power algo- rithm (Dixon and Hubbert 2012). The calibration of S-Pol was verified and monitored continuously using daily solar scans, injecting a known power source into the receiver for every azimuth of data collected. Ad- ditionally, the differential reflectivity was calibrated using vertically pointing data in light rain, which is the most accurate and reliable method. The dual- polarimetric self-consistency calibration procedure described by Vivekanandan et al. (2003) was per- formed on data from various times throughout the project. The accuracy of the Vivekanandan et al. (2003) calibration method is about 60.25 dB; thus, the S-Pol reflectivity is considered to be calibrated to better than 1 dB. There were also routine checks to ensure the pedestal was level and the pointing and ranging were accurate.
c. Texas A&M SMART-R
The Texas A&M SMART-R is a 5-cm (C band), single-polarimetric mobile Doppler radar (Biggerstaff et al. 2005) mounted on a diesel flatbed truck. The main purpose of SMART-R was to document the three- dimensional structure of precipitation echoes, detailed rainfall patterns, and Doppler measurements of air motions on the convective and mesoscales. The PRF and peak transmitter power of SMART-R are 300-3000 Hz and 250 kW, respectively. The SMART-R has a 2.54-m antenna with a 1.58 beamwidth and a minimum detect- able reflectivity of about 221 and 27dBZ at a distance of 10 and 50 km, respectively, for a single pulse.
During the DYNAMO/AMIE field campaign, the SMART-R operated on a 10-min scan cycle, starting at minute 0, 10, 20, ... of every hour, for six total scan cycles per hour. Three RHI scans out to 100 km were directed over the AMF site at the beginning of each 10-min cycle. At the start of the campaign, these RHIs were at azimuth angles 1468, 1478,and1488,with elevation angles up to 608 (Fig. 2b). At
d. Collocating measurements at AMF
To compare reflectivity measurements between the three radars, the column above the AMF site was extracted from the S-Pol and SMART-R RHI data for each time they performed an RHI scan over the AMF. Time-height series of the
A hydrometeor echo layer is defined in this study such that at least three continuous vertical grids (i.e., 270-m echo thickness) have significant echo detection (using a minimum reflectivity threshold of 240, 230, and 230dBZ for KAZR, S-Pol, and SMART-R, re- spectively). The top and bottom heights of the hy- drometeor layer are then defined as the echo top and base height, respectively. Because the main focus of this paper is to evaluate the ability of the three radar systems to document the cloud pop- ulations that are key to MJO initiation-namely, shallow, congestus, and deep convective clouds- cloud echoes that are thinner than 270 m are not in- cluded in the comparison in the next section. We note that excluding these echoes in the comparison will likely remove thin tropical shallow clouds, cirrus clouds (Riihimaki and McFarlane 2010), and midlevel clouds (Riihimaki et al. 2012), but those clouds are not the focus of the radar comparison. The merged radar dataset described in section 4 will include all clouds, including those thin ones that are excluded in the comparison.
Figure 3 shows an example of the collocated KAZR, S-Pol, and SMART-R data on
Data from S-Pol (Figs. 3b,e) show excellent sensitivity of the radar in detecting a wide range of hydrometeors from precipitating to nonprecipitating clouds. For ex- ample, most of the cirrus and anvil clouds between 0000 and
Data from the SMART-R (Figs. 3c,f) show consider- ably less sensitivity to nonprecipitating clouds compared to S-Pol and KAZR. This is expected due to the much lower transmitting power and wider beamwidth of the SMART-R. Ground clutter is strongest below 1.5 km and significant echo striations are seen below 4 km and at around 8 km. Data were removed below a threshold value of a signal quality index (SQI) of 0.2 (0.05) below (above) 9.5 km to quality control the reflectivity data. SQI is a measure of coherence of Doppler power in the linear channel and higher SQI thresholds help remove second-trip echoes, since the signal is less coherent. Similar to the S-Pol, these height values were deter- mined through subjective visual comparison between SMART-R and KAZR data to remove as much non- meteorological echoes and retain as much real hydro- meteor echoes as possible. Additionally, reflectivities below 1.5 km that have Doppler velocity values between 20.5 and 0.5 m s21 were filtered to remove ground clutter. Echo striations below 9.5 km that remained after this filtering were removed if their thickness was less than 0.5 km. The resulting quality-controlled SMART-R data (Fig. 3c) show that most of the re- flectivities below 1.5 km and low-level echo striations have been removed.
In the next section, we compare these collocated, quality-controlled S-Pol and SMART-R reflectivity data with measurements from the KAZR to charac- terize the hydrometeor detection capabilities for vari- ous types of clouds during the DYNAMO/AMIE field campaign.
3. Cloud statistics results
a. Overall hydrometeor statistics
Three MJO events were observed at Addu Atoll during the 4-month-long DYNAMO/AMIE field cam- paign. The time-height series of vertical hydrometeor frequency observed by the KAZR is shown in Fig. 4a. The frequency is defined by the ratio of the number of times with significant hydrometeor detection to the total number of observed times (12 h) at each 90-m vertical level. The first two MJO events (10 October-
Figure 4b shows the averaged vertical frequency of hydrometeors at each 90-m level and their base and top heights. The mean frequency of hydrometeors (black line) is 0.13 with a prominent peak of high clouds be- tween 10 and 12 km. Three distinct peaks are seen in the cloud-top frequency (red line), corresponding to shallow clouds (1 km), midlevel clouds (5-6 km, approximately at 08C), and high clouds (13 km). This vertical distribu- tion of cloud-top height frequency is consistent with previous studies in the tropical western Pacific region (Johnson et al. 1999; McFarlane et al. 2007;
b. Hydrometeor detection from all radars by cloud type
To evaluate the hydrometeor detection capabilities from the two scanning precipitating radars, hydrome- teors are separated into precipitating and nonprecipi- tating clouds, each of which are subdivided into three categories (precipitating: shallow, congestus, deep clouds; nonprecipitating: midlevel, cirrus, anvil clouds) based on their echo base and top heights as defined in Table 1.The low- and midlevel heights are selected where KAZR- observed cloud-top frequencies are at their local mini- mum (Fig. 4b). An example of each cloud type observed by KAZR is given in Fig. 5.
A cloud phase classification based on Shupe (2007) with parameters tuned for tropical clouds (Comstock et al. 2013) was applied to the KAZR data. The main purpose of the classification is to identify the presence of precipitation in the hydrometeor echo layers observed by KAZR. This technique uses radar Doppler moments (reflectivity, Doppler velocity, and spectrum width), li- dar backscatter, microwave radiometer, and tempera- ture profiles to identify cloud (liquid, ice), drizzle, and rain. In general, hydrometeors with high (low) lidar backscatter (.0.1) or radar reflectivity (.5dBZ)/ Doppler velocity (.2.5 m s21) are classified as pre- cipitating rain/snow (nonprecipitating cloud/ice) parti- cles, which are further separated by the sounding temperature of 08C. No liquid cloud water is allowed to exist colder than 2128C because aircraft measurements reported in Stith et al. (2002) found that liquid water is rarely observed at colder temperatures in tropical stratiform clouds. Further, for radar reflectivity of 217 ; 5dBZ and Doppler velocity of 1 ; 2.5 m s21, with temperature . 08C, it is classified as drizzle. Al- though mixed-phase cloud likely exists in this dataset, hydrometeors are classified by their radiatively dominate phase. We expect that most clouds will be dominated by either liquid or ice, and therefore small amounts of either will not contribute significantly to the cloud radiative forcing or heating rates. The bottom panels in Fig. 5 show the cloud phase classification for each of the six cloud types defined in this study. As expected, precipitation (drizzle/rain, orange/green colors) appears increasingly frequent from shallow and congestus to deep clouds, while midlevel, cirrus, and anvil clouds mostly consist of nonprecipitating cloud particles (liquid/ice).
Table 1 shows the percentage of cloud profiles with precipitation (drizzle or rain) identified within the hy- drometeor layer and those identified near surface (lowest range gate of KAZR) for the six cloud types, along with their respective average frequency of occur- rence during DYNAMO/AMIE. Note that the defini- tion of cloud types and cloud phase classification are two independent processes. The cloud phase classification is used as an independent evaluation of the precipitating and nonprecipitating cloud types defined using echo boundaries alone. The resulting percentages show that while more than 90% of the shallow, congestus, and deep clouds have precipitation identified within the hydrometeor layer, about 32%, 58%, and 84% of these clouds, respectively, have precipitation actually reach- ing the surface. While some midlevel, cirrus, and anvil clouds have precipitation inside the cloud, by definition there is no precipitation that reaches the surface. This result suggests that the definition of cloud types using hydrometeor-layer top/base heights (section 2d) can separate precipitating and nonprecipitating clouds rea- sonably well.
The averaged frequency of occurrence for the pre- cipitating and nonprecipitating clouds from the three radars during DYNAMO/AMIE (values in Table 1)is plotted in Fig. 6. Note that the sum of all cloud types exceeds 100% because at a given time, multilayer clouds (e.g., shallow clouds and cirrus) could be present and would be counted in both categories. For precipitating clouds, the largest differences in frequency occur for shallow clouds. Both S-Pol and SMART-R overestimate shallow clouds frequency compared to KAZR, particu- larly for S-Pol, where its frequency is over sevenfold more than KAZR. Ground clutter from
c. Cloud detection accuracy by scanning radars
While comparison of frequency of occurrence de- scribes general agreement in cloud detection between the three radars, collocated measurements allow more detail comparisons for clouds with coincident detection by different radars, providing useful insights into the characteristics of clouds, both precipitating and non- precipitating, that can be accurately detected by the scanning radars within a certain range. Moreover, ac- curate detection of the evolution of cloud populations during different life cycles of the MJO is critical to test the DYNAMO/AMIE hypothesis, rather than the mean frequency of cloud occurrence.
To quantify the accuracy of the scanning radars in detecting various types of cloud, a 2 3 2 contingency table is used (Fig. 7). The KAZR-observed cloud types at each instance when S-Pol or SMART-R performs an RHI scan over the AMF site are treated as ''true'' values, and the S-Pol/SMART-R-detected cloud types are considered ''predicted'' values. Therefore, when both KAZR and S-Pol (or SMART-R) detect the same type of cloud at an instance, it is considered as true positive (TP); when S-Pol (or SMART-R) detects a cloud type that KAZR does not detect, it is considered as false positive (FP); when S-Pol (or SMART-R) miss the type of cloud detected by KAZR, it is considered false negative (FN); and finally, when both KAZR and S-Pol (or SMART-R) do not detect the cloud, it is considered true negative (TN). Therefore, the hit rate, accuracy rate, and false discovery rate (
Hitrate -TP/P - TP/(TP + FN),
Accuracy Rate - (TP + TN)/(P + N )
- (TP 1 TN)/(TP + FN
+FP + TN), and
A perfect score for hit rate and accuracy rate will be 1, and a perfect score for false discovery rate will be 0. The results of the scores are shown in Table 2. It is apparent that both S-Pol and SMART-R have relatively high hit rates (more than 0.72) and low false discovery rates (less than 0.25) for congestus and deep clouds, suggesting detection of these clouds agrees well with KAZR. For shallow clouds both S-Pol and SMART-R have high false discovery rates, especially S-Pol (0.91), which is consistent with the overestimated frequency of occurrence. The hit rate and accuracy rate for deep clouds can be under- estimated due to inaccurate KAZR cloud-top estimates in heavy rainfall when its signals are severely attenuated, which will be further addressed in section 3f. For non- precipitating clouds, both S-Pol and SMART-R have relatively high hit rates (.0.66) for anvil clouds, suggest- ing that their upper-level reflectivity data can be used to map the 3D structure of convective anvils. The S-Pol also performs well in detecting cirrus clouds, providing they are thicker than 270 m (section 2d). However, neither of the scanning radars is adequate for detecting tropical midlevel clouds, as they are oftentimes thin and requires high sensitivity for detection (Riihimaki et al. 2012).
d. Cloud thickness comparison with coincident detection
To provide guidance on the physical characteristics of clouds that can be reliably detected by the scanning radars, particularly those precipitating clouds that are important to the initiation of MJO, we compare the cloud thickness in coincident detection of clouds between the three radars. Through this comparison, guidance of using the scanning radar data to map the three-dimensional cloud fields will be provided.
Cloud thickness is defined as the distance between echo top and base height, and therefore for precipitating clouds, the thickness includes the precipitation below the actual cloud base. Figure 8 shows the distribution of cloud thickness with coincident detection from the three radars; that is, at a given instance, if S-Pol (or SMART-R) detects the same type of cloud (Table 1)as KAZR, the cloud thickness is included in the statistics. For shallow clouds, cloud thickness from KAZR shows a roughly exponential decrease. Most of the coincidently detected shallow clouds are less than 2 km thick. Con- gestus clouds thickness ranges between 2.5 and 7 km for both KAZR and S-Pol, while the SMART-R thickness distribution shows an ;1-km shift. This shift is likely because some real low-level echoes from SMART-R data (e.g., Fig. 3) were removed by the clutter filtering (section 2d). Most deep clouds are thicker than 7 km across all three radars; however, S-Pol reports a higher frequency of clouds that are 14 km or thicker compared to KAZR, likely due to signal attenuation of KAZR in these thick clouds; while SMART-R reports much lower frequency of deep cloud thickness above 10 km, possibly due to 1) too much low-level echo removal during data quality control, and 2) lower cloud-top height estimate because SMART-R is not as sensitive to small cloud drops near cloud top as KAZR and S-Pol. For non- precipitating midlevel and cirrus clouds, thickness also follows an exponential distribution with a quick falloff above 2 km, while for anvil clouds the thickness ranges from 1 to 8 km.
This comparison of cloud thickness with coincident detection suggests that minimum cloud thicknesses of 2.5, 7, and 1 km can be added as additional constraints when defining congestus, deep, and anvil clouds, re- spectively, to increase reliability of detecting these clouds when using scanning radar data at locations outside of the AMF KAZR. While RHI scans by the scanning radars can detect 2.5-km-thick clouds, it is more challenging for PPI scans, as the gap between two typical PPI elevation angles (0.58-18) quickly increases with distance from the radar. For example, at the 50-km range the vertical distance between two PPI scans 18 apart is ;1 km, so that a congestus cloud of 3 km thick only contains three vertical data points. Therefore, RHI data from scanning radars should be used whenever possible for accurate detection of congestus and high clouds. Range is also an important factor to consider for scanning radars, which will be addressed in the next subsection.
e. Impact of decreasing sensitivity with distance to cloud detection
The comparisons between S-Pol, SMART-R, and KAZR so far were performed at a distance of about 10 km from the two scanning precipitation radars. As mentioned in section 2, the sensitivity decreases with range from the radar and therefore reducing the de- tectability of clouds, as they are farther away. Consid- ering S-Pol has much higher sensitivity to clouds than SMART-R, we investigate the impact of S-Pol's de- creasing sensitivity with range in detecting various types of clouds. The minimum detectable reflectivity from S-Pol can be estimated by
Zmin 5C 1ZMDS12Ar 120log10r,
where C is the S-Pol radar constant (68.9 dBZ), ZMDS is the S-Pol minimum detectable signal (2113.34 dBm), A is one-way atmospheric attenuation, and r is the range from radar (km). The actual value of A varies with humidity and therefore with height, but it is approxi- mately 0.005 dB km21 in the lowest levels. For exam- ple, at distances of 10, 20, 30, 50, 100, and 150 km, the minimum detectable reflectivity by the S-Pol are ap- proximately 224, 218, 215, 210, 23, and 1 dBZ, re- spectively. To determine the impact of decreasing sensitivity with range to the detection of various cloud types, Fig. 9 shows the cumulative frequency of KAZR radar reflectivity (corrected for droplet attenuation; more details in section 4) by altitude for the six cloud types, overlaid with the minimum sensitivity of S-Pol at several typical distances. The percentages to the left of the ver- tical lines indicate clouds that are potentially missed by the S-Pol due to limitations of its sensitivity.
For precipitating clouds, decreasing S-Pol sensitivity has relatively less impact on cloud detection compared to nonprecipitating clouds due to higher reflectivities from rain- and drizzle-sized particles. For shallow clouds, issues with Bragg scattering likely have a larger effect on S-Pol than sensitivity. For congestus clouds, the S-Pol can potentially detect up to 80% (60%) with a range out to 30 km (50 km), making its data useful for mapping three-dimensional volumes of congestus clouds (precipitating or not) and for investigating their role in preconditioning deep convection. Similarly for anvil clouds between 6- and 12-km height, S-Pol can detect up to 70% (50%) at a range out to 30 km (50 km). Non- precipitating midlevel and cirrus clouds require much higher sensitivity for detection. For example, at 10 km, between 30% and 50% of these clouds could be un- detected by S-Pol. Users of S-Pol radar data for studying various types of clouds should be aware of the impact of distance on cloud detection.
f. KAZR attenuation by rainfall
Finally, to address the KAZR attenuation issues in precipitating clouds mentioned in section 3c, cloud-top height differences between both the
We identify instances of KAZR attenuation by re- quiring cloud-top height from the
As shown in Fig. 10, when KAZR suffers from rainfall attenuation, cloud-top height differences during drizzle to moderate rain rate (0.1-5 mm h21) are most fre- quently between 0.2 and 0.5 km, and only slightly in- crease with rain rate. However, attenuation sharply increases when the surface rain rate is above 5 mm h21, such that in heavy rainfall (.20 mm h21) KAZR can underestimate cloud-top height by more than 2 km. An empirical fit between surface rain-rate and cloud-top height differences are calculated for rain-gauge-measured and radar-derived rain rates separately. The cloud-top height difference is assumed to have a power-law re- lationship with the surface rain rate, such that
where R is the rain rate (mm h21), and DH is the KAZR cloud-top height underestimation (km). The empirical a and b values are indicated in each panel in Fig. 10. Because of sampling limitations, rainfall events are not separated into convective and stratiform rain types, al- though higher rain rates (.10 mm h21) are typically associated with convective rain (Tokay and Short 1996). This relatively simple method provides first-order cor- rection of Ka-band radar cloud-top height estimates in the presence of precipitating clouds and can potentially be applied to other tropical sites (i.e., the ARM tropical western Pacific sites at Darwin,
4. Producing merged dataset
As discussed in the previous section, while KAZR provides observations with high temporal and vertical resolution for nonprecipitating and lightly precipitating clouds, its data are questionable during moderate to heavier precipitation (i.e., rain rate . 10 mm h21), when the signals are severely attenuated and fail to correctly detect the precipitating cloud tops (Fig. 10). Therefore, using KAZR data alone to study the evolution of trop- ical convective clouds could result in biases, particularly in moderate to heavily precipitating clouds.
Fortunately, the collocated S-Pol and SMART-R ra- dar reflectivity profiles with KAZR during the DYNAMO/ AMIE field campaign provide an excellent opportunity to improve the quality of the KAZR dataset in the presence of precipitating convective clouds. Compared to Ka-band radar,
To produce a seamless merged radar reflectivity and PNNL COMBRET dataset between KAZR and S-Pol, an improved method based on Feng et al. (2009) is used. Figure 11 shows the flowchart of the method and dataset used for each step. First, PNNL COMBRET is applied to the original KAZR dataset. This algorithm combines radar Doppler moments, a lidar attenuated backscatter coefficient, sounding, a microwave radiometer, and a surface rain gauge to retrieve cloud water content, ef- fective particle size, and visible extinction coefficient for both liquid and ice clouds (Comstock et al. 2013). Compared to the method used by Feng et al. (2009) that assumes linear liquid water content profiles scaled from microwave-radiometer-retrieved liquid water path, this study uses the liquid water content profiles retrieved by COMBRET instead.
As discussed in section 3b, cloud phase is classified following Shupe (2007) with parameters tuned for tropical clouds (Comstock et al. 2013). For a volume classified as one of the three liquid hydrometeors (cloud, drizzle, or rain), the liquid water content associated with that specific type is retrieved by COMBRET. For ex- ample, only the rainwater content is retrieved if a vol- ume is classified as rain and all other water content is neglected. For liquid clouds, COMBRET follows the ARM baseline cloud retrieval value-added product (
The raindrop size distribution N (D) follows an expo- nential form of N(D) 5 N0e2LD, where the intercept parameter N0 5 0:08 cm24 (Marshall and Palmer 1948), and the slope parameter L542:0854R20:1485. Rain rate R is derived from the following Z - R relationship:
where Z is radar reflectivity in linear units of mm6 m23 and R is in millimeters per hour. Both the Z-R re- lationship and the slope parameter L are obtained from surface disdrometer data on Addu Atoll (
In the second step, the S-Pol reflectivity data are first converted to linear units (mm6 m23), then linearly in- terpolated in height to match the KAZR vertical grid, and finally converted back to log unit (dBZ). An im- portant factor that must be considered when merging
In the fourth step, ''precipitation events'' are defined using surface rain gauge data at the AMF site. An event is defined as a continuous time period when the surface rain rate is above 1 mm h21. Within each precipitation event, if the S-Pol detects precipitating cloud-top (Table 1) heights more than 200 m (two vertical grids) higher than that from the KAZR for more than 10% of the time, KAZR reflectivity profiles within this period are replaced by the S-Pol data. The cloud-top height criteria is intended to capture periods when KAZR data are truly affected by attenuation from precipitation, as shown in the cloud-top height differences in precipitating clouds (Fig. 10). Figure 13 shows two example days of the merged dataset. It is clear that during the heavy pre- cipitation events (indicated by red bars in Figs. 13e,f), the merged dataset provides improved reflectivity pro- file estimates rather than using KAZR alone, as well as better cloud-top height estimates (purple dots). During the DYNAMO/AMIE field campaign, about 28% of the KAZR profiles with precipitation reaching the sur- face were replaced by the S-Pol profiles in the merged dataset.
In the final step, the PNNL COMBRET cloud mi- crophysics retrieval and radiative heating rate retrieval is applied to the merged attenuation-corrected KAZR- S-Pol data, and compared to that retrieved using the original uncorrected KAZR data. Figure 14 shows an example of radar reflectivity, retrieved water content, cloud radiative effects, and the differences between the merged attenuation-corrected and original uncorrected KAZR product. During the ''precipitation event'' when the KAZR signal is severely attenuated by rainfall (e.g.,
Note that a sharp increase of retrieved water content around 5-km height occurs because COMBRET does not explicitly retrieve mixed-phase cloud properties. When snow/ice particles fall below 08C isotherm, they begin to melt and are coated with a liquid shell, which greatly enhances the radar reflectivity (known as the bright band with ;500-m thickness), resulting in a sub- stantial increase in the retrieved rainwater content around that level. There are additional cases (not shown) when a cloud or a cloud with virga below crosses the melting level, which also have a discontinuity due to the type of retrieval applied. Since there is no straight- forward way to retrieve simultaneously the ice and liq- uid properties in this region, we choose to apply the appropriate retrieval based on the phase classification. Although one past study has suggested a method to better represent attenuation of radar signal by these mixed-phase hydrometeors (Matrosov 2008), more research in this area with in situ measurements of melting snow/ice particles for validation is still needed before implementing such a technique into COMBRET.
To further examine the impact to cloud radiative heating rate retrieval from the merged KAZR-S-Pol product and the original KAZR product, we calculated the averaged cloud radiative effect during the DYNAMO/ AMIE field campaign separately for the two products (Fig. 15). For precipitating clouds, the merged product produces 22% stronger longwave cooling (21% stronger heating) above (below) 10 km, and similarly 18% stronger shortwave heating (73% weaker cooling) above (below) 7km(Fig. 15c). The net radiative effect is about 11% stronger (absolute value) from the merged product. For nonprecipitating clouds, there is negligible difference in their radiative effects (Fig. 15f) as expected due to negligible KAZR attenuation, resulting in identical microphysics retrievals. For all clouds, the impact from the merged product is similar to that of the precipitat- ing clouds, except the net radiative effect is about 7% stronger.
This merged KAZR-S-Pol data product provides more accurate cloud-top height estimates and attenuation- corrected radar reflectivity for precipitating clouds compared to the KAZR-ARSCL product. Moreover, the cloud water content, the effective particle size, and the radiative heating rate for both precipitating and nonprecipitating clouds were retrieved by PNNL COMBRET. This data product has been provided to the research community (available in the ARM data archive as a principal investigator product) as best esti- mates of the total hydrometeor microphysics and their radiative effects at
From the comparison results of precipitating clouds between the three radars performed in section 3, this new dataset provides the only reliable estimates of shallow clouds at Addu Atoll. These shallow clouds along with congestus have been suggested as contrib- uting to moistening and preconditioning the atmosphere for deep convection (Johnson et al. 1999; Benedict and Randall 2007). The trimodal tropical convective cloud population (Johnson et al. 1999)anditsevolu- tion with the life cycle of the MJO can be reliably constructed with this merged dataset, because of the improved cloud-top height estimates for precipitating clouds. Together with the DYNAMO/AMIE intensive 3-hourly soundings, the interaction and feedback from shallow and congestus clouds and their large-scale environments can be thoroughly investigated to ad- vance our understanding of their roles in various pha- ses of the MJO. The retrieved cloud microphysics and radiativeheatingratecanalsobeusedtoevaluate model simulations.
5. Summary and conclusions
The DYNAMO/AMIE field campaign successfully completed operations from
Comparisons of cloud statistics observed by collo- cated KAZR, S-Pol, and SMART-R radar observations were performed at
1) Statistics from KAZR show that while more than 90% of the shallow, congestus, and deep clouds have precipitation identified within the hydrometeor layer, about 32%, 58%, and 84%, respectively, have precipitation actually reaching the surface. For pre- cipitating clouds, the largest difference in the fre- quency of occurrence between S-Pol/SMART-R and KAZR is for shallow cloud, where both scanning radars overestimate its occurrence, possibly due to low-level Bragg scatter and/or ground clutter. Fre- quency of congestus and deep clouds agree much better between both scanning radars and KAZR (within 13%, Table 1), along with high coincident detection rates (.72%, Table 2). For nonprecipitat- ing clouds, both S-Pol and SMART-R reported relatively high coincident detection rates (80% and 66%, respectively) for anvil clouds, while S-Pol also detects up to 79% of cirrus clouds (thicker than 270 m) within a 8.5-km range.
2) Comparisons of cloud thickness with coincident de- tection between the three radars suggest that mini- mum cloud thicknesses of 2.5, 7, and 1 km can be used as constraints when identifying congestus, deep, and cirrus/anvil clouds, respectively, using scanning radar data (Fig. 8), in addition to cloud boundary heights. The impact of decreasing sensitivity with range to S-Pol's cloud detection is investigated. At 30-50-km radius, S-Pol can potentially detect up to 80%-60% and 70%-50% of congestus and anvil clouds, re- spectively (Fig. 9). Detection of deep clouds by S-Pol is much less affected by sensitivity due to high reflectivity from precipitation size particles.
3) Cloud-top height comparison in precipitating clouds between KAZR and S-Pol/SMART-R reveals that KAZR underestimates cloud-top heights due to rainfall attenuation in ;34% of the precipitating clouds during the DYNAMO/AMIE field campaign, with an average cloud-top underestimate of 1.15 km. An empirical method of correcting cloud-top height bias for KAZR using surface rainfall rate has been proposed (Fig. 10). This relatively simple method can potentially be applied to other Ka-band vertically pointing cloud radars in the tropics to improve the quality of the ARSCL cloud boundary data product in the presence of precipitation.
4) A merged KAZR-S-Pol dataset is produced to obtain total hydrometeor profile estimates at
Comparisons between KAZR, S-Pol, and SMART-R performed in this study indicate that KAZR data are the only reliable estimates of shallow clouds at Addu Atoll, while the scanning radars can reasonably detect congestus and anvil clouds within a certain range in addition to precipitating deep clouds. To take advantage of the three- dimensional cloud-detecting capability of the scanning radars that is essential to document the degree of con- vective organization, and to understand the role of both precipitating and nonprecipitating congestus clouds that are important for preconditioning deep convection and the initiation of MJO, we provide the following recom- mendations in identifying congestus clouds from the scanning radar data: 1) define cloud-top/cloud-base height using a continuous layer of reflectivity .220 dBZ;2)use cloud-top height between 3 and 8 km, cloud-base height below 3 km, and cloud thickness greater than 2.5 km; 3) limit the use of the data within 30-50-km radius from the radar to keep reasonable detection of various congestus clouds; and 4) use data from RHI scans instead of PPI scans if possible to have higher vertical resolution.
As new research involving the use of DYNAMO/ AMIE radar data continues to emerge, this study hopes to provide quantitative guidance in using these radar data for various cloud and precipitation studies. Pre- vious studies have used millimeter-wavelength cloud radar data to study MJO processes (e.g., Deng et al. 2013), but they are limited in some ways in not being able to fully examine deep convective clouds due to attenua- tion by heavy rainfall and the lack of high-frequency large-scale soundings. The merged KAZR-S-Pol data- set produced in this study alleviates this issue and can be reliably used to construct the trimodal tropical cloud population (shallow, congestus, and deep cloud) because of the improved cloud-top height estimates for precipi- tating clouds. Together with the DYNAMO/AMIE in- tensive 3-hourly sounding arrays, the interaction and feedback from shallow and congestus clouds and their large-scale environments can be investigated to address some of the key DYNAMO/AMIE science hypothesis. The retrieved cloud microphysics and radiative heating rate provide a unique dataset at this remote oceanic region to study the radiative impact of tropical clouds and to evaluate various model simulations. Some of these research activities using this dataset are already underway by the authors of this paper and will be pre- sented in future studies.
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ZHE FENG AND SALLY A. MCFARLANE*
JENNIFER COMSTOCK AND NITIN BHARADWAJ
* Current affiliation: Climate and Environmental Sciences Di- vision,
Corresponding author address: Dr.ZheFeng,
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