Complementary use of passive and active remote sensing for ...

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D13205, doi:10.1029/2011JD016749, 2012

Complementary use of passive and active remote sensing for detection of penetrating convection from CloudSat, CALIPSO, and Aqua MODIS Alisa H. Young,1 John J. Bates,1 and Judith A. Curry2 Received 24 August 2011; revised 25 April 2012; accepted 6 May 2012; published 6 July 2012.

[1] The study examines penetrating deep convection (PDC), that reach
14 km (PDC14) and
17 km (PDC17), using 1 year of colocated CloudSat, CALIPSO, and Aqua-MODIS observations. The combination of multisensory and multispectral observations is used to examine how well PDC14(17) are captured using cold cloud features (CCFs), defined as groupings of 1 km MODIS pixels with
11 mm brightness temperature (BT) less than or equal to 210 K and 235 K and positive brightness temperature differences (+BTD) between
6.7 mm (BT6.7) and
11 mm (BT11). Cross-comparison of PDC14 with CCFs ≤ 210 K and +BTD signatures according to date, time, and geolocation show that within the tropics 61% (55%) of CCFs ≤ 210 K (+BTD) occur as PDC14. In the case of CCFs ≤ 210 K,
27% of the PDC14 distribution also occur as cold altostratus/anvil clouds. Results show that 50–59% of PDC14 are large enough to be detected from IR observations with a horizontal resolution of 5 km. Although observations are sampled along CloudSat’s narrow swath where CloudSat/CALIPSO and Aqua MODIS observations are colocated, the study provides statistical evidence supporting the use of IR observations to study the long-term temporal and spatial variability of high reaching deep convective cloud activity. Citation: Young, A. H., J. J. Bates, and J. A. Curry (2012), Complementary use of passive and active remote sensing for detection of penetrating convection from CloudSat, CALIPSO, and Aqua MODIS, J. Geophys. Res., 117, D13205, doi:10.1029/ 2011JD016749.

1. Introduction [2] Studies show that multidecadal changes in lower stratospheric water vapor considerably impact surface radiation [Forster and Shine, 2002; Solomon et al., 2010]. According to Brewer [1949], water vapor content in the lower stratosphere is primarily limited by extremely cold tropical tropopause temperatures. However, it is also influenced by cross-tropopause transport from slow ascent of water vapor by large-scale motion and rapid ascent by strong convection or volcanic eruptions [Rosenlof, 2003; Wang et al., 2009]. [3] The later pathway and its association with strong/deep convection is supported by evidence of tropical tropopause layer (TTL) and lower stratospheric hydration from ice crystals lofted from deep convective clouds [Chaboureau et al., 2007; Grosvenor et al., 2007; Corti et al., 2008]. 1

NOAA National Climatic Data Center, Asheville, North Carolina, USA. 2 School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia, USA. Corresponding author: A. H. Young, NOAA National Climatic Data Center, 151 Patton Ave., Asheville, NC 28801, USA. ([email protected]) This paper is not subject to U.S. copyright. Published in 2012 by the American Geophysical Union.

Yet, the extent to which this activity modifies lower stratospheric water vapor content is still unclear—especially from a climate perspective. To address this issue, it is necessary to identify if deep convection penetrates the base of the TTL enough to alter the chemical composition of the lower stratosphere. However, it is equally important to evaluate long-term changes in the frequency of deep convection reaching the TTL for the broader context of climate studies. [4] To work toward these goals and better understand the influence of deep convection on upper tropospheric/lower stratospheric (UTLS) processes, interest has primarily been placed on deep convection that penetrate the base of the TTL at
14 km. While this height level is most often fixed to perform large scale and potentially long-term analysis, the base height of the TTL is not invariable; as is the case of the tropical tropopause [Borsche et al., 2007; Seidel et al., 2001]. Yet the height of 14 km continues to serve as a notable “gateway” for UTLS exchange processes [Fueglistaler et al., 2009]. [5] Given this characteristic of being a “gateway,” the present study examines penetrating deep convection (PDC), that reach 14 km (PDC14) and 17 km (PDC17) following studies including Alcala and Dessler [2002], Hong et al. [2005], Liu and Zipser [2005], Liu et al. [2007], Chaboureau et al. [2007], Hassim and Lane [2010], Hosking et al. [2010], and Russo et al. [2011]. Although deep convective

D13205

1 of 13

D13205

YOUNG ET AL.: COMPLEMENTARY USE OF PASSIVE AND ACTIVE

transport into the UTLS region has been described in other studies, PDC14 are especially important since at
14 km, tropospheric air begins to take on the chemical characteristics of stratospheric air due to weaker influences of convective detrainment [Folkins et al., 1999]. Deep convection that rise above this level can deposit ice-crystal-laden, water-vapor-rich air into upper levels of the TTL where net radiative heating and upward vertical motion can slowly lift the injected air—albeit modified by microphysical processes—into the large scale stratospheric circulation. On the other hand, PDC17 can bypass slow radiative ascent through the TTL by reaching more than
17 km where trace gas species may be injected directly into the lower stratosphere [Danielsen, 1993]. [6] Considering these processes, several studies have addressed the characteristics of PDC14 from radar observations of cloud [Luo et al., 2008] and precipitation size particles [Alcala and Dessler, 2002; Liu and Zipser, 2005; Liu et al., 2007]. Using IR observations, analogs to these events have been examined according to cold cloud features/ pixels [e.g., Gettelman et al., 2002; Rossow and Pearl, 2007], defined by
11 mm brightness temperature thresholds (e.g., 208 K, 210, 215 K, and 235 K) and positive brightness temperature differences (+BTD) between
6.7 mm (BT6.7) and 11 mm (BT11) [e.g., Schmetz et al., 1997; Setvák et al., 2007; Chung et al., 2008; Wang et al., 2009; Bedka and Minnis, 2010]. [7] These two IR approaches respectively denote instances of high reaching deep convection and the lofting of water vapor into the lower stratosphere from deep convective clouds. Yet, radar and IR observations show that the frequency of deep convection in the TTL—which is likely to be a direct indicator of their influence on lower stratospheric water vapor—ranges from 5% to 0.5%  0.25% respectively [cf. Alcala and Dessler, 2002; Gettelman et al., 2002]. Radar and IR observations also show rather strong regional differences [e.g., Liu and Zipser, 2005; Gettelman et al., 2002]. [8] Liu et al. [2007] combined observations from the Tropical Rainfall Monitoring Mission (TRMM) Precipitation Radar (PR) with the TRMM Visible Infrared Scanner (VIRS) to understand whether IR analogs (i.e., cold cloud features and +BTD signatures) and radar based distributions of PDC14 are the same. Although +BTD signatures were not investigated, Liu et al. [2007] show that only
1% of cold cloud features ≤210 K are associated with PDC14. They also suggests that cold cloud features ≤210 K are dominated by non-raining cirrus/anvil cloud fractions, where anvil clouds are defined as widespread (>100 km) cloud systems with horizontal extents that emanate from deep convection. Anvil clouds are also characterized by decaying cloud geometrical depth and cloud tops near the freezing level to the high troposphere. Predominance of cirrus/anvil clouds among cold cloud features ≤210 K imply vast deficiencies in the usefulness of the BT11 ≤ 210 K cloud field to categorically represent PDC14 but is this the case? [9] While the work of Liu et al. [2007] has improved the understanding of radar observations of high reaching deep convection, their results may not be reflective of all deep convective clouds that penetrate the TTL since the TRMM PR chiefly detects precipitation size particles, not typically found within the tops of deep convective clouds [Knollenberg et al., 1993]. To add to this, Li and Schumacher [2011] report that the first spaceborne cloud

D13205

radar, CloudSat, observes many features of vertical cloud structure unapparent from the TRMM PR. More specifically, the authors found that when evaluating anvil clouds, the TRMM PR underestimates anvil area by an average factor of 4 compared with CloudSat. Although CloudSat has a very narrow swath compared with the TRMM PR, cloud top structure and height are key determinants of deep convection entering the TTL. Thus, CloudSat may be better suited to detect PDC14 and higher reaching PDC17. By using more advanced satellite technology to observe and compare radar and IR distributions of high-level clouds, the goal of this study is to 1) better understand the extent to which IR observations capture PDC14 and PDC17, 2) better characterize PDC14(17) and IR based cloud groups associated with them and 3) address if the IR methods evaluated in this study may be used to develop a long-term IR-based climatology that captures the regional and temporal variability of high reaching deep convective clouds. [10] For this analysis, CloudSat and the ModerateResolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite are used to capture radar and IR distributions of PDC14(17). The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) are also used to more accurately define cloud top height. IR approaches evaluated include the IR threshold method using cold cloud features less than 210 K (CCF210) and 235 K (CCF235) and positive brightness temperature differences (+BTD). These specific techniques are evaluated due to debate over their representativeness of PDC14 and because both can easily be applied to 5 km historical IR observations necessary for long-term analysis and climate studies. Although Bedka et al. [2010] and Berendes et al. [2008] provide other IR techniques to capture deep convection involved in UTLS exchange, these studies focus on overshooting tops, which differ from PDC14 and PDC17 according to the assessment of thermodynamic properties (e.g., updraft strength, level of neutral buoyancy, etc.) and cloud top structure (e.g., dome structure above an anvil cloud) not regarded in this work. For an illustration of PDC14 see Figure 1 where the PDC14 (17) observation does not display the classic structure of an overshooting top, but extends high enough into the TTL to participate in UTLS exchange.

2. Data and Methods [11] In this section, methods for sampling PDC14(17) and their IR analogs are described. The evaluation analyzes the period from 1 January 2007 through 31 December 2007 and covers the region between 35 N to 35 S. 2.1. Penetrating Deep Convection From CloudSat/ CALIPSO and Aqua MODIS [12] PDC with cloud top heights ≥14 km (PDC14) and ≥17 km (PDC17) are obtained from the CloudSat 94-GHz, nadir-pointing Cloud Profiling Radar (CPR) and the CALIPSO Lidar using several CloudSat standard data products. A detailed description of these standard products is provided by Stephens et al. [2008] and applied in the following manner. The 2B-CLDCLASS product is used to determine when deep convection is present. According to this product, deep convection are categorized as clouds with cloud base heights between 0 and 3 km, intense shower of

2 of 13

D13205

YOUNG ET AL.: COMPLEMENTARY USE OF PASSIVE AND ACTIVE

D13205

Figure 1. Image of PDC14(17) from CloudSat granule 03682. PDC14 and PDC17 occur in each region where deep convection extends from near the surface to 14 and 17 km (denoted by the heavy black lines). The PDC17 event in this image stretches
131 km and is denoted by the heavy vertical lines. This storm is located in the Timor Sea just northwest of Darwin Australia. rain or hail possible (as suggested by reflectivity values), a horizontal dimension of 10 km, a thick vertical dimension, and liquid water path >0. The 2B-GEOPROF product is used to capture cloud top height and cloud base height where cloud masks values must be ≥30, indicating that relatively strong radar echoes are present. The cloud’s vertical boundaries are further characterized using the 2B-GEOPROF-Lidar product which integrates observations from CloudSat and the CALIPSO Lidar. This product provides the most accurate estimate of hydrometeor layer base and top for up to five layers in each vertical CPR profile so that PDC14 and PDC17 are limited to atmospheric columns with only one cloud layer. [13] To integrate IR observations with radar/lidar observed PDC14 and PDC17, wide swath (100 km) Aqua MODIS/ CloudSat observations from MAC03S1 and MAC021S1 products are used. These products respectively contain full resolution geolocation and radiance data centered on each CloudSat profile. Due to differences in the viewing geometries between Aqua MODIS and CloudSat, proper colocation and sampling of high-level deep convective clouds relies on parallax correction following Wang et al. [2011]. An illustration of this correction is provided in Figure 2 for two satellites with geometries similar to CloudSat (satellite A)

and Aqua MODIS (satellite B). As indicated, colocation between the two satellites is first given at point D where satellite A observes the cloud object and satellite B does not. For satellite B, parallax correction moves colocation from point D to point E to ensure both satellites observe the cloud object. The MAC03S1 product is used to establish 3 km  3 km grids of Aqua MODIS observations that are centered on the parallax corrected point and extracted from the MAC021S1 product at wavelengths of
6.7 mm and 11 mm. [14] PDC14(17) are recorded with time, date, geolocation, reflectivity profile, BTD signature, and cloud brightness temperature (BT11). PDC14(17) properties including cloud vertical structure, cloud geometrical depth, IR characteristics, and areal extent are assessed. To calcuate areal extent, the study assumes that PDC14(17) project circular geometry when seen from CloudSat. The CPR is assumed to transect the cross-sectional length (or diameter), x, of each contiguous group of PDC14(17) and areal extent is defined by (px2)/4. 2.2. IR Analogs of Penetrating Deep Convection [15] To assess the extent to which cold cloud features ≤210(235) K (CCF210(235)), and +BTD signatures represent

3 of 13

D13205

YOUNG ET AL.: COMPLEMENTARY USE OF PASSIVE AND ACTIVE

D13205

Zhang et al., 2007], it is used to further characterize what linkages exist between PDC14 and cold high clouds.

3. Results and Discussion

Figure 2. Schematic representation of viewing geometries for two satellites with configurations similar to CloudSat (Satellite A) and Aqua MODIS (Satellite B) for a deep convective cloud. The configuration demonstrates that the common approach to colocation at point D does not have a direct line of sight from Satellite B through the cloud object. Parallax correction according to cloud top height, h, produces point E, so that Satellites A and B both observe the cloud object. More details on parallax correction are given by Wang et al. [2011].

PDC14 and PDC17, the parallax corrected path associated with point E in Figure 2 is used to evaluate each 3 km  3 km grid of Aqua MODIS observations. CCF210 and CCF235 are then defined by each 3  3 grid with an average BT11 ≤ 210 K and ≤235 K. Corresponding BTD signatures (i.e., negative and positive) are determined in a likewise manner, whereby positive (+BTD) values are retained. All IR analogs are recorded with time, date, geolocation, and cloud type characteristics from the 2B-CLDCLASS product to crossreference their occurrence against combined radar/lidar observations of PDC14(17). To further characterize the IR analogs, optical depth of the BT11 ≤ 210 K cloud field is evaluated using the MODIS Level 2 Cloud Product [King et al., 2006] for January, April, July, and October of 2007. Since optical depth is considered a common variable used to develop cloud classification schemes [Rossow and Schiffer, 1999;

3.1. Evaluation of PDC Profiles [16] In this analysis, 736,443 CloudSat profiles observed between 35 N to 35 S were identified as PDC14. The cloud vertical structure and cloud geometrical depth of PDC14 are demonstrated in Figure 3 using a Contoured Frequency by Altitude Diagram (CFAD). As described by Yuter and Houze [1995], the CFAD communicates information about the width of the frequency distribution at each level for the entire storm volume. The CFAD characterizes PDC14 by an arc-like structure where radar reflectivity tends to increase with decreasing altitude. CloudSat reflectivities of 12–15 dBZ are concentrated between
3–10 km with two primary modes of high reflectivity at
3.8 km and
6.7 km. Contours above
13 km show a broad distribution in frequency among reflectivities between 20 to 10 dBZ. This compares with CFADs provided by Masunaga et al. [2008] who used CloudSat to evaluate active periods of the Madden-Julian Oscillation and show that above
13 km radar reflectivity is concentrated at weaker reflectivities between 28 and 10 dBZ. High frequencies at these reflectivity values suggest cirrus clouds considerably impact the Masunaga et al. storm volume. On the other hand, it indicates that PDC14 have been successfully parsed from other high-level clouds. [17] To further address the vertical extent of PDC14, cloud geometrical depth was evaluated. For this assessment, radar attenuation is a prominent issue as it can severely impact the assessment of cloud base height. Thus the degree of attenuation among PDC14 profiles was evaluated using an approach similar to Battaglia and Simmer [2008] who show that strong attenuation can be determined from radar reflectivity profiles that do not exhibit surface echoes. PDC14 were divided into four categories according to strength to highlight differences among profiles with strong surface echoes (20 to 30 dBZ), moderate surface echoes (10 to 20 dBZ), weak echoes (6 to 10 dBZ), and very weak echoes ( 95. Based on the four month average, t ≥ t DC accounts for
61% of the BT11 ≤ 210 K cloud field. These data indicate that the CCF210 distribution is primarily comprised of deep convective clouds with optical depths far greater than those reported for cirrusanvil cloud fractions according to the characteristics of anvil clouds applied in other studies such as Kubar et al. [2007] who distinguish anvil clouds from more vertically developed deep convection based on cloud tops colder than 245 K and optical depths between 4 and 32.

[25] While recent studies by Yuan et al. [2011] and Yuan and Houze [2010] provide details of anvil cloud vertical structure and areal extent for anvil clouds, the global mean macro- and microphysical properties of anvil clouds are not well known. Without details of the macro- and microphysical properties of anvil clouds, the mean optical depth of all deep convective clouds (as reported by Hong et al. [2007]) has been used to separate the general class of deep convection and anvil clouds from optically thicker deep convective cores associated with PDC14(17). [26] Further analysis of cloud types within the CCF210 cloud field is provided in Figure 8a where other dominant fractions of the CCF210 distribution consist of multilayered clouds (21.1%), altostratus (13.3%), and high-level/cirriform (4.0%) clouds. To complement Figure 8a, Figure 8b shows that the dominant cloud types among multilayered clouds are altostratus, high-level/cirriform clouds, and deep convection, which comprise 64.9%, 17.3%, and 13.4% respectively.

Table 3. Occurrence Frequency of CCFs ≤210 K and +BTD Signatures That Were Cross Referenced Against PDC14 and PDC17 Over 20 N–20 S and 35 N–35 S MODIS–CloudSat/CALIPSO

20 N–20 S

35 N–35 S

No. CCF210 K No. +BTD %CCF210(PDC14) %CCF210(PDC17) %+BTD(PDC14) %+BTD(PDC17)

419,333 699,789 61.26 6.03 54.58 3.51

468,410 829,973 62.09 6.14 52.24 3.37

7 of 13

D13205

YOUNG ET AL.: COMPLEMENTARY USE OF PASSIVE AND ACTIVE

D13205

Figure 7. Normalized frequency distribution of cloud optical depth for all pixels between 35 N–35 S with BT11 ≤ 210 K for January, April, July, and October of 2007 where t DC ¼ 38:3 [cf. Hong et al., 2007]. [27] In a separate analysis (not shown), cases of altostratus clouds with cloud brightness temperatures ≤210 K were shown to fall under the description of anvil clouds as they were found to emanate from deep convection. Based on the description of altostratus clouds defined by the 2BCLDCLASS product, the criteria for the altostratus cloud type differs from other cloud classification schemes [e.g., Rossow and Schiffer, 1999]. Nonetheless, results of this analysis suggest that while 61% of CCF210 are PDC14,
27–30% of the remaining CCF210 distribution is due to cloud types that occur with PDC14 cloud activity. This result supports the conclusions of studies such as Fu et al. [1990] who evaluated the behavior of tropical deep convective clouds using the IR threshold method. [28] Other studies suggest cirrus clouds have a strong influence on the BT11 ≤ 210 K cloud field. In the presence or absence of lower level clouds, cirrus clouds are often contaminated by surface emission so that the cloud brightness temperatures of cirrus clouds are typically not as low as 210 K. Hong et al. [2007] have already shown that the average cloud top temperature—evaluated according to the CO2 cloud slicing method which corrects for cloud semitransparency (described by Menzel et al. [1983])— is 232 K for cirriform clouds. Figure 9 shows differences in cloud top temperature and cloud brightness temperature for cirrus clouds obtained from the CloudSat 2B-CLDCLASS product and the MODIS Level 2 Cloud Product for January 2007. In general, these results show that cloud brightness temperatures (CBT) of cirriform clouds that are uncorrected for cloud semitransparency are much warmer than corresponding

cloud top temperatures (CTT). Only 0.49% of the cirriform clouds represented in Figure 9 have BT11 ≤ 210 K. The average CTT for observations in Figure 9 is 230.8 K while the average cloud brightness temperature is 264.3 K. These results give further indication that the BT11 ≤ 210 K cloud field is not dominated by cirrus clouds. [29] With regard to the other IR technique evaluated in this study, Table 2 shows that 59% (see Table 2) of PDC14 have +BTD signatures. However, Chung et al. [2008] report that 90% of high reaching deep convection with cloud depths greater than 8 km, have +BTD signatures. Given the differences between these statistics which both characterize linkages between +BTD signatures and high reaching deep convective clouds, it is noted that Chung et al. [2008] only evaluate observations of deep convection with cloud brightness temperatures ≤206 K. It is not quite clear how events with cloud brightness temperatures warmer than 206 K were handled or if they were sampled at all. When PDC14 within this study are evaluated according to the same IR threshold of 206 K (see Figure 10), 92% exhibit +BTD signatures. These observations also show that for increasing cloud depth and decreasing cloud brightness temperature +BTD signatures are more likely to occur. Clearly higher +BTD values are associated with higher reaching PDC. 3.3. Areal Extent of Penetrating Deep Convection [30] The areal extents of PDC14(17) are provided in Table 4 and Figure 11 to examine the potential of historical IR observations to spatially resolve PDC14 observations. The data shows that 25% of PDC14 were observed over one

8 of 13

D13205

YOUNG ET AL.: COMPLEMENTARY USE OF PASSIVE AND ACTIVE

D13205

Figure 8. Relative distribution of cloud types within (a) the CCF210 distribution and (b) cloud type dominance among multilayer clouds within the CCF210 distribution. CloudSat footprint while 16% were observed over a slightly larger area associated with 2–3 consecutive CloudSat footprints. Following this pattern, the percentage of PDC14(17)

decreases with increasing number of consecutive CloudSat footprints. Although each CloudSat footprint has a nominal along track dimension of 2.5 km and an across track

Figure 9. Cloud top temperature (CTT) from MODIS Level 2 Cloud Product verses cloud brightness temperature (CBT) for cirroform clouds observed from CloudSat, CALIPSO, and Aqua MODIS during January 2007. 9 of 13

YOUNG ET AL.: COMPLEMENTARY USE OF PASSIVE AND ACTIVE

D13205

D13205

Figure 10. Comparison of (a) BTD signatures verses cloud top height and (b) BTD signature verses cloud brightness for observations with BT11 ≤ 206 K [cf. Chung et al., 2008]. dimension of 1.4 km, the distance between each observation is
1.1 km and has been used to estimate the cross-sectional length, x, associated with each series of consecutive PDC14 observations. According to Table 4, the accumulative distribution of PDC14 with cross-sectional lengths ≤
5 km is
50% of the PDC14 distribution. However, since 41% of the accumulated distribution has cross-sectional lengths
≤3 km it is possible to resolve 50–59% of PDC14 from historical IR observations assuming each PDC14 event is centrally located in each 5-km pixel. According to the percentage of PDC17 with cross-sectional lengths between
3–5 km, 31–42% have areal extents large enough to be resolved from historical IR observations. Although the probability of observing PDC17 is low given the small percentage of PDC14 that reach 17 km (Table 1),
12% of PDC17 have cross-sectional lengths greater than 17.6 km. To illustrate, again refer to Figure 1. During this PDC14(17) event, over the Timor Sea just northwest of Darwin, Australia cloud top height exceeds 17 km over a span of
131 km. 3.4. Evaluation of Warm PDC14 [31] In the final evaluation of PDC14, the study addresses warm observations of PDC14 reflected in Table 2 which shows that
2% of PDC14 were warmer than 235 K. Since this study examined small (3 km  3 km) regions associated with IR observations of PDC14(17), off nadir sampling from Aqua MODIS and partially cloudy subsets may explain the existence of PDC14 with cloud brightness temperatures

warmer than 235 K. To examine the impact of spatial sampling on cloud brightness temperature, PDC14 with cloud brightness temperatures >235 K were further evaluated to determine if these particular events were observed over only one CPR profile or if they exist near the edge of PDC14 events associated with multiple CPR profiles. Approximately 12.6% of all warm PDC14 were observed over only one profile and 72.8% of all warm PDC14 lie at the edge of a much larger PDC14 event account. These observations account for 85.4% of all warm PDC14. Given that each observation from CloudSat represents 1.1 km2 and MODIS observations represent 3 km 2, an inhomogeneous cloud field is the factor most attributable to these occurrences.

4. Conclusions [32] CCFs ≤ 210(235) K, +BTD signatures, and PDC14 (17) were sampled from colocated CloudSat/CALIPSO and Aqua MODIS observations and separately evaluated to 1) better understand the extent to which IR observations capture PDC14 and PDC17 2) better characterize PDC14 (17) and IR based cloud groups associated with them and 3) address if the IR methods evaluated in this study may be used to develop a long-term IR-based climatology that captures the regional and temporal variability of high reaching deep convective clouds. By cross-referencing PDC14(17) with IR analogs according to time, date, and geolocation it is concluded that neither IR scheme completely or exclusively

Table 4. Areal Size Distributions of PDC14 and PDC17 Provided in Figure 11 Percent of PDC Distribution Number of Consecutive CloudSat Footprints 1 2–3 4–5 6–7 8–10 11–13 14–16 >16

PDC14

Accumulated PDC14

24.76 15.88 8.89 6.64 7.56 5.69 4.33 26.25

24.76 40.64 49.53 56.17 63.72 69.41 73.74 100.00

PDC17

Accumulated PDC17

Maximum Cross-Sectional Length x (km)

Maximum Area of Uniform Plume (px2)/4 (km2)

38.10 19.50 11.24 6.18 6.56 4.09 2.64 11.69

38.10 57.60 68.84 75.02 81.58 85.67 88.31 100.00

1.1 3.3 5.5 7.7 11.0 14.3 17.6 >17.6

0.95 8.55 23.76 46.57 95.03 160.61 243.28 >243.28

10 of 13

D13205

YOUNG ET AL.: COMPLEMENTARY USE OF PASSIVE AND ACTIVE

D13205

Figure 11. Areal size distribution of PDC14 and PDC17 assuming the CPR transects each consecutive series of PDC14(17) profiles (also see Table 4). samples PDC14. However, 55% of +BTD signatures and 61% of cold cloud features ≤210 K occurred as PDC14. In the case of the CCFs ≤210 K, it was shown that another 27% of the distribution is represented by cold altostratus/anvil clouds. This result provides more clarity on the cloud type characteristics of the BT11 ≤ 210 K cloud group compared with other studies. Given that the CCF210 distribution predominantly consists of PDC14 and tightly connected anvil clouds, the BT11 ≤ 210 K cloud field may be used to characterize the long-term temporal and spatial variability of high reaching deep convective cloud activity. [33] Results in this study strongly disagree with Liu et al. [2007] who suggest that CCFs ≤ 210 K are dominated by cirrus-anvil cloud fractions. Only 4% of the 210 K cloud field evaluated in this study contained cirrus clouds. In support of this point, we note that the 210 K cloud field is dominated by optically thick clouds and that cirrus clouds evaluated in this study rarely exhibit cloud brightness temperatures as low as 210 K. Differences in detections between PDC14 in this study and Liu et al. [2007] are likely due to differences in the radar characteristics of TRMM and CloudSat. [34] Although different IR thresholds can be used to sample PDC14, more restrictive thresholds will produce more sparsely populated distributions. The tradeoff for using an IR threshold of 210 K is that it allows a relatively larger amount of PDC14 to be sampled in comparison to lower IR thresholds. Although lower IR thresholds will have a weaker signal, a comparison of +BTD signatures, CCFs, or cloud brightness temperatures at varying thresholds may show other interesting results since higher reaching PDC are associated with lower cloud brightness temperatures and more positive BTD signatures. Other work that may enhance the analysis of high reaching deep convection involves the macro- and microphysical properties of anvil clouds and their occurrence as altostratus clouds according to the CloudSat 2B-CLDCLASS product. [35] Approximately 14% of PDC14 clouds sampled in this analysis are found within the extratropics and
4% of

PDC14 reach 17 km (PDC17). These characteristics may be used to simulate the properties of PDC14 using statistical evidence associated with their regional occurrence, areal sizes, vertical cloud reflectivity, and IR properties. [36] Estimation of areal extent for PDC14 show that 50–59% of PDC14 can be observed from historical IR observations with horizontal resolutions of 5 km. This further suggest that IR analogs of PDC14 examined in this study can be combined with historical IR observations to assess the long-term variability of high reaching deep convective cloud activity. Such analysis will build upon recent work by Tselioudis et al. [2010], especially considering the availability of historical IR observations from the International Satellite Cloud Climatology Project that have been re-calibrated and sub-sampled to a higher resolution [Knapp, 2008a, 2008b]. A long–term analysis of the BT11 ≤ 210 K cloud field or +BTD signatures is likely to improve our understanding of PDC14 and their impacts on water vapor in the upper tropospheric/lower stratospheric region. [37] Acknowledgments. The NOAA EPP Graduate Sciences Program supported this work. The first author (A. Young) would especially like to thank Zhengzhao (Johnny) Luo for insightful discussions that influenced the paper and the anonymous reviewer whose comments significantly enhanced the paper.

References Alcala, C. M., and A. E. Dessler (2002), Observations of deep convection in the tropics using the Tropical Rainfall Measuring Mission (TRMM) precipitation radar, J. Geophys. Res., 107(D24), 4792, doi:10.1029/ 2002JD002457. Battaglia, A., and C. Simmer (2008), How does multiple scattering affect the spaceborne W-band radar measurements at ranges close to and crossing the surface-range?, IEEE Trans. Geosci. Remote Sens., 46, 1644–1651, doi:10.1109/TGRS.2008.916085. Battaglia, A., J. M. Haynes, T. L’Ecuyer, and C. Simmer (2008), Identifying multiple-scattering-affected profiles in CloudSat observations over the oceans, J. Geophys. Res., 113, D00A17, doi:10.1029/2008JD009960. Bedka, K. M., and P. Minnis (2010), GOES 12 observations of convective storm variability and evolution during the Tropical Composition, Clouds and Climate Coupling Experiment field program, J. Geophys. Res., 115, D00J13, doi:10.1029/2009JD013227.

11 of 13

D13205

YOUNG ET AL.: COMPLEMENTARY USE OF PASSIVE AND ACTIVE

Bedka, K. M., and P. Minnis, J. Brunner, R. Dworak, W. Feltz, J. Otkin, and T. Greenwald (2010), Objective satellite-based detection of overshooting tops using infrared window channel brightness temperature gradients, J. Appl. Meteorol. Climatol., 49, 181–202, doi:10.1175/ 2009JAMC2286.1. Berendes, T. A., J. R. Mecikalski, W. M. MacKenzie Jr., K. M. Bedka, and U. S. Nair (2008), Convective cloud identification and classification in daytime satellite imagery using standard deviation limited adaptive clustering, J. Geophys. Res., 113, D20207, doi:10.1029/2008JD010287. Borsche, M., G. Kirchengast, and U. Foelsche (2007), Tropical tropopause climatology as observed with radio occultation measurements from CHAMP compared to ECMWF and NCEP analyses, Geophys. Res. Lett., 34, L03702, doi:10.1029/2006GL027918. Brewer, A. W. (1949), Evidence for a world circulation provided by the measurements of helium and water vapour distribution in the stratosphere, Q. J. R. Meteorol. Soc., 75, 351–363, doi:10.1002/qj.49707532603. Chaboureau, J. P., J. P. Cammas, J. Duron, P. J. Mascart, N. M. Sitnikov, and H. J. Voessing (2007), A numerical study of tropical cross-tropopause transport by convective overshoots, Atmos. Chem. Phys., 7, 1731–1740, doi:10.5194/acp-7-1731-2007. Chung, E. S., B. J. Sohn, and J. Schmetz (2008), CloudSat shedding new light on high reaching tropical deep convection observed with Meteosat, Geophys. Res. Lett., 35, L02814, doi:10.1029/2007GL032516. Corti, B., et al. (2008), Unprecedented evidence for deep convection hydrating the tropical stratosphere, Geophys. Res. Lett., 35, L10810, doi:10.1029/2008GL033641. Danielsen, E. F. (1993), In situ evidence of rapid, vertical, irreversible transport of lower tropospheric air into the lower tropical stratosphere by convective cloud turrets and by larger-scale upwelling in tropical cyclones, J. Geophys. Res., 98, 8665–8681, doi:10.1029/92JD02954. Folkins, I., M. Loewenstein, J. Podolske, S. J. Oltmans, and M. Proffitt (1999), A barrier to vertical mixing at 14 km in the tropics: Evidence from ozonesondes and aircraft measurements, J. Geophys. Res., 104(D18), 22,095–22,102, doi:10.1029/1999JD900404. Forster, P. M. F., and K. P. Shine (2002), Assessing the climate impact of trends in stratospheric water vapor, Geophys. Res. Lett., 29(6), 1086, doi:10.1029/2001GL013909. Fu, R., A. D. Del Genio, and W. Rossow (1990), Behavior of deep convective clouds in the tropical Pacific deduced from ISCCP radiances, J. Clim., 3, 1129–1152, doi:10.1175/1520-0442(1990)0032.0.CO;2. Fu, R., Y. L. Hu, J. S. Wright, J. H. Jiang, R. E. Dickinson, M. X. Chen, M. Filipiak, W. G. Read, J. W. Waters, and D. L. Wu (2006), Short circuit of water vapor and polluted air to the global stratosphere, Proc. Natl. Acad. Sci. U. S. A., 103, 5664–5669, doi:10.1073/pnas.0601584103. Fueglistaler, S., A. E. Dessler, T. J. Dunkerton, I. Folkins, Q. Fu, and P. W. Mote (2009), Tropical tropopause layer, Rev. Geophys., 47, RG1004, doi:10.1029/2008RG000267. Gettelman, A., M. L. Salby, and F. Sassi (2002), Distribution and influence of convection in the tropical tropopause region, J. Geophys. Res., 107(D10), 4080, doi:10.1029/2001JD001048. Grosvenor, D. P., T. W. Choularton, H. Coe, and G. Held (2007), A study of the effect of overshooting deep convection on the water content of the TTL and the lower stratosphere from cloud resolving model simulations, Atmos. Chem. Phys. Discuss., 7, 7277–7346, doi:10.5194/acpd-7-72772007. Hassim, M. E., and T. P. Lane (2010), A model study on the influence of overshooting convection on TTL water vapour, Atmos. Chem. Phys., 10, 9833–9849, doi:10.5194/acp-10-9833-2010. Holton, J. R., P. H. Haynes, M. E. McIntyre, A. R. Douglass, R. B. Rood, and L. Pfister (1995), Stratosphere-troposphere exchange, Rev. Geophys., 33, 403–439, doi:10.1029/95RG02097. Hong, G., G. Heygster, J. Miao, and K. Kunzi (2005), Detection of tropical deep convective clouds from AMSU-B water vapor channels measurements, J. Geophys. Res., 110, D05205, doi:10.1029/2004JD004949. Hong, G., P. Yang, B. C. Gao, B. A. Baum, Y. X. Hu, M. D. King, and S. Platnick (2007), High cloud properties from three years of MODIS Terra and Aqua Collection-4 data over the tropics, J. Appl. Meteorol. Climatol., 46, 1840–1856, doi:10.1175/2007JAMC1583.1. Hosking, J. S., M. R. Russo, P. Braesicke, and J. A. Pyle (2010), Modelling deep convection and its impacts on the tropical tropopause layer, Atmos. Chem. Phys. Discuss., 10, 20,267–20,302, doi:10.5194/acpd-10-202672010. King, M. D., S. Platnick, P. A. Hubanks, G. T. Arnold, E. G. Moody, G. Wind, and B. Wind (2006), Collection 005 change summary for the MODIS cloud optical property (06_OD) algorithm, in Collection 005 Change Summary Documents, Level-2 MODIS Atmosphere Products, version 2.2, pp. 1–23, NASA Goddard Space Flight Cent., Greenbelt,

D13205

Md. [Available at http://modis-atmos.gsfc.nasa.gov/C005_Changes/ C005_CloudOpticalProperties_ver311.pdf.] Knapp, K. R. (2008a), Calibration of long-term geostationary infrared observations using HIRS, J. Atmos. Oceanic Technol., 25, 183–195, doi:10.1175/2007JTECHA910.1. Knapp, K. R. (2008b), Scientific data stewardship of international satellite cloud climatology project B1 global geostationary observations, J. Appl. Remote Sens., 2, 023458, doi:10.1117/1.3043461. Knollenberg, R. G., K. Kelly, and J. C. Wilson (1993), Measurements of high number densities of ice crystals in the tops of tropical cumulonimbus, J. Geophys. Res., 98(D5), 8639–8664, doi:10.1029/92JD02525. Kubar, T., D. L. Hartmann, and R. Wood (2007), Radiative and convective driving of tropical high clouds, J. Clim., 20, 5510–5526, doi:10.1175/ 2007JCLI1628.1. Li, W., and C. Schumacher (2011), Thick anvils as viewed by the TRMM precipitation radar, J. Clim., 24, 1718–1735, doi:10.1175/ 2010JCLI3793.1. Liu, C., and E. J. Zipser (2005), Global distribution of convection penetrating the tropical tropopause, J. Geophys. Res., 110, D23104, doi:10.1029/ 2005JD006063. Liu, C., E. J. Zipser, and S. W. Nesbitt (2007), Global distribution of tropical deep convection: Different perspectives from TRMM infrared and radar data, J. Clim., 20, 489–503, doi:10.1175/JCLI4023.1. Luo, Y., R. Zhang, W. Qian, Z. Luo, and X. Hu (2011), Inter-comparison of deep convection over the Tibetan Plateau-Asian monsoon region and subtropical North America in boreal summer using CloudSat/CALIPSO data, J. Clim., 24, 2164–2177, doi:10.1175/2010JCLI4032.1. Luo, Z., G. Y. Liu, and G. L. Stephens (2008), CloudSat adding new insight into tropical penetrating convection, Geophys. Res. Lett., 35, L19819, doi:10.1029/2008GL035330. Masunaga, H., M. Satoh, and H. Miura (2008), A joint satellite and global cloud-resolving model analysis of a Madden-Julian oscillation event: Model diagnosis, J. Geophys. Res., 113, D17210, doi:10.1029/ 2008JD009986. Menzel, W. P., W. L. Smith, and T. R. Stewart (1983), Improved cloud motion wind vector and altitude assignment using VAS, J. Appl. Meteorol., 22, 377–384, doi:10.1175/1520-0450(1983)0222.0.CO;2. Minnis, P., C. R. Yost, S. Sun-Mack, and Y. Chen (2008), Estimating the top altitude of optically thick ice clouds from thermal infrared satellite observations using CALIPSO data, Geophys. Res. Lett., 35, L12801, doi:10.1029/2008GL033947. Rosenlof, K. H. (2003), How water enters the stratosphere, Science, 302, 1691–1692, doi:10.1126/science.1092703. Rossow, W. B., and C. Pearl (2007), 22-year survey of tropical convection penetrating into the lower stratosphere, Geophys. Res. Lett., 34, L04803, doi:10.1029/2006GL028635. Rossow, W. B., and R. A. Schiffer (1999), Advances in understanding clouds from ISCCP, Bull. Am. Meteorol. Soc., 80, 2261–2287, doi:10.1175/1520-0477(1999)0802.0.CO;2. Russo, M. R., et al. (2011), Representation of tropical deep convection in atmospheric models, part 1: Meteorology and comparison with satellite observations, Atmos. Chem. Phys., 11, 2765–2786, doi:10.5194/acp-112765-2011. Schmetz, J., S. A. Tjemkes, M. Gube, and L. van de Berg (1997), Monitoring deep convection and convective overshooting with METEOSAT, Adv. Space Res., 19, 433–441, doi:10.1016/S0273-1177(97)00051-3. Seidel, D. J., R. J. Ross, J. K. Angell, and G. C. Reid (2001), Climatological characteristics of the tropical tropopause as revealed by radiosondes, J. Geophys. Res., 106(D8), 7857–7878, doi:10.1029/2000JD900837. Setvák, M., R. M. Rabin, and P. K. Wang (2007), Contribution of the MODIS instrument to observations of deep convection storms and stratospheric moisture detection in GOES and MSG imagery, Atmos. Res., 83, 505–518, doi:10.1016/j.atmosres.2005.09.015. Solomon, S., K. Rosenlof, R. Portmann, J. Daniel, S. Davis, T. Sanford, and J. Plattner (2010), Contributions of stratospheric water vapor to decadal changes in the rate of global warming, Science, 327, 1219–1223, doi:10.1126/science.1182488. Stephens, G. L., et al. (2008), The CloudSat mission: Performance and early science after the first year of operation, J. Geophys. Res., 113, D00A18, doi:10.1029/2008JD009982. Tselioudis, G., E. Tromeur, W. B. Rossow, and C. S. Zerefos (2010), Decadal changes in tropical convection suggest effects on stratospheric water vapor, Geophys. Res. Lett., 37, L14806, doi:10.1029/ 2010GL044092. Wang, C., Z. J. Luo, and X. Huang (2011), Parallax correction in collocating CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) observations: Method and application to convection study, J. Geophys. Res., 116, D17201, doi:10.1029/2011JD016097.

12 of 13

D13205

YOUNG ET AL.: COMPLEMENTARY USE OF PASSIVE AND ACTIVE

Wang, P. K. (2003), Moisture plumes above thunderstorm anvils and their contributions to cross tropopause transport of water vapor in midlatitudes, J. Geophys. Res., 108(D6), 4194, doi:10.1029/2002JD002581. Wang, P. K., M. Setvák, W. Lyons, W. Schmid, and H. Lin (2009), Further evidences of deep convective vertical transport of water vapor through the tropopause, Atmos. Res., 94, 400–408, doi:10.1016/j.atmosres. 2009.06.018. Webster, P. J., and H. Chang (1988), Equatorial energy accumulation and emanation regions: Impacts of a zonally varying basic state, J. Atmos. Sci., 45, 803–829, doi:10.1175/1520-0469(1988)045 2.0.CO;2. Weisz, E., J. Li, W. P. Menzel, A. K. Heidinger, B. H. Kahn, and C.-Y. Liu (2007), Comparison of AIRS, MODIS, CloudSat and CALIPSO clout top height retrievals, Geophys. Res. Lett., 34, L17811, doi:10.1029/ 2007GL030676.

D13205

Yuan, J., and R. A. Houze Jr. (2010), Global variability of mesoscale convective system anvil structure from A-Train satellite data, J. Clim., 23, 5864–5888, doi:10.1175/2010JCLI3671.1. Yuan, J., R. A. Houze Jr., and A. Heymsfield (2011), Vertical structures of anvil clouds of tropical mesoscale convective systems observed by CloudSat, J. Atmos. Sci., 68, 1653–1674, doi:10.1175/2011JAS3687.1. Yuter, S. E., and R. A. Houze Jr. (1995), Three-dimensional kinematic and microphysical evolution of Florida cumulonimbus. Part II: Frequency distributions of vertical velocity, reflectivity, and differential reflectivity, Mon. Weather Rev., 123, 1941–1963, doi:10.1175/1520-0493(1995) 1232.0.CO;2. Zhang, Y., S. Klein, G. G. Mace, and J. Boyle (2007), Cluster analysis of tropical clouds using CloudSat data, Geophys. Res. Lett., 34, L12813, doi:10.1029/2007GL029336.

13 of 13