a census of precipitation features in the tropics...

1 DECEMBER 2000 4087N E S B I T T E T A L .

q 2000 American Meteorological Society

A Census of Precipitation Features in the Tropics Using TRMM:Radar, Ice Scattering, and Lightning Observations

STEPHEN W. NESBITT AND EDWARD J. ZIPSER

Department of Meteorology, University of Utah, Salt Lake City, Utah

DANIEL J. CECIL

Department of Atmospheric Sciences, Texas A&M University, College Station, Texas

(Manuscript received 5 August 1999, in final form 23 December 1999)

ABSTRACT

An algorithm has been developed to identify precipitation features ($75 km2 in size) in two land and twoocean regions during August, September, and October 1998. It uses data from two instruments on the TropicalRainfall Measuring Mission (TRMM) satellite: near-surface precipitation radar (PR) reflectivities, and TRMMMicrowave Imager (TMI) 85.5-GHz polarization corrected temperatures (PCTs). These features were classifiedby size and intensity criteria to identify mesoscale convective systems (MCSs), precipitation with PCTs below250 K, and other features without PCTs below 250 K. By using this technique, several hypotheses about theconvective intensity and rainfall distributions of tropical precipitation systems can be evaluated. It was shownthat features over land were much more intense than similar oceanic features as measured by their minimumPCTs, maximum heights of the 30-dBZ contour, and 6-km reflectivities. The diurnal cycle of precipitation featuresshowed a strong afternoon maximum over land and a rather flat distribution over the ocean, quite similar tothose found by others using infrared satellite techniques. Precipitation features with MCSs over the oceancontained significantly more rain outside the 250-K PCT isotherm than land systems, and in general, a significantportion (10%–15%) of rainfall in the Tropics falls in systems containing no PCTs less than 250 K. Volumetricrainfall and lightning characteristics (as observed by the Lightning Imaging Sensor aboard TRMM) from thesystems were classified by feature intensity; similar rain amounts but highly differing lightning flash rates werefound among the regions. Oceanic storms have a bimodal contribution of rainfall from two types of systems:very weak systems with little ice scattering and moderately strong systems that do not produce high lightningflash rates. Continental systems that produce the bulk of the rainfall (as sampled) are likely to have higherlightning flash rates, which are shown to be linked to stronger radar and ice-scattering intensities.

1. Introduction

The classification of precipitation features often grewout of the instruments used to observe and quantifythem. Maddox (1980) defined the mesoscale convectivecomplex (MCC) using infrared satellite brightness tem-perature thresholds of size, duration, and shape; how-ever, cloud-top temperatures are not very well relatedto the physical processes within storms, and thus, therainfall contained in the system. These systems, despitebeing responsible for less than 1% of total cloud clustersby size (Mapes and Houze 1993), contribute much moresignificantly to the rainfall in regions where they areobserved. However, infrared measurement of these sys-tems may not be the best tool to determine the radiation

Corresponding author address: Stephen W. Nesbitt, Departmentof Meteorology, University of Utah, 135 South 1460 East Room 819,Salt Lake City, UT 84112-0110.E-mail: [email protected]

budgets and rainfall produced by these systems. In con-trast, Houze (1993) defines a mesoscale convective sys-tem (MCS) ‘‘as a cloud system that occurs in connectionwith an ensemble of thunderstorms and produces a con-tiguous precipitation area ;100 km or more in hori-zontal scale in at least one direction.’’ Here the defi-nition is constrained by the size and intensity of theprecipitation created within it. Convective intensity and/or rainfall measurement is a means of distinguishing thedistribution of systems that produce the bulk of the heat,momentum, and moisture fluxes in the Tropics and sub-tropics (i.e., quantifying the ‘‘hot towers,’’ Riehl andMalkus 1958).

Large-scale rainfall measurement with ground- or air-craft-based radars or gauge data is not feasible, but pas-sive microwave satellite techniques do allow a larger-scale quantification of convective processes. At micro-wave frequencies greater than 30 GHz, ice hydrometeorsprimarily scatter radiation upwelling from the surface.This results in a depression of brightness temperatures

4088 VOLUME 13J O U R N A L O F C L I M A T E

in areas where significant optical depths of ice hydro-meteors are present (Spencer 1986); at 85-GHz emissionfrom rain is minimized, leaving signatures from ice hy-drometeors. The use of polarization corrected temper-ature (PCT; Spencer et al. 1989) allows the removal ofsurface polarization effects, leading to PCT being anunambiguous precipitation-sized ice particle quantifier.Mohr and Zipser (1996, hereafter MZ) used the 85-GHzPCT from the Special Sensor Microwave Imager(SSM/I) to obtain a sample of MCSs (noting their lo-cation, size, and intensity) as defined by their ice scat-tering signatures. They defined the ice scattering MCSas a contiguous area greater than 2000 km2 with PCTless than 250 K with at least one data bin with a PCTof 225 K or less (to ensure convection in the system).Because the ice scattering signature (#250 K) of a sys-tem is almost always smaller than the precipitation area,the MZ (1996) criteria are roughly analogous to theHouze (1993) MCS definition. Using these criteria, aclimatology was created for four months of observedMCSs throughout the Tropics from 1993.

Despite the creation of a large sample of undoubtedlyprecipitating systems, MZ’s analysis of their MCSs didnot allow an accurate quantification of MCSs role in thetropical precipitation budget. Ice scattering signature at85 GHz, notwithstanding its correlation to relative iceparticle size, optical depth, and concentration, does notrelate as directly to surface rainfall as other measure-ments. Significant precipitation may also occur at 85PCTs greater than 250 K, especially at the coarse res-olution of the SSM/I (13 3 15 km2 at 85 GHz). So,this approach certainly introduces sensitivity issues inestimating the properties of ice scattering MCSs.

Mohr et al. (1999) furthered their previous work withthe SSM/I by quantifying the contribution of rainfallfrom ice scattering MCSs versus other ice scatteringprecipitation systems. Systems with PCT #250 K weresubdivided into four classes, ranging from the large MZ(1996) MCS to smaller systems containing only one 133 15 km2 SSM/I data bin #250 K. The rainfall wasestimated from these systems using the GSCAT algo-rithm where brightness temperatures were related to arain rate. Using the ice scattering method (which is notas direct a rain measurement as a radar estimation, forexample), it was found that while MCSs contributedonly 10%–20% of the sampled systems by number, theycontributed 70%–80% of the rainfall throughout theTropics. Also, the oceanic regions’ precipitation systemsthat contributed the most toward the total rainfall con-tained minimum PCTs closely centered at 200 K, whilethe land regions had a broader distribution of minimumPCTs centered between 150 and 175 K. An exceptionto this is the Amazon region. Despite being a large landregion, it had rainfall characteristics similar to thoseover the ocean.

As before, the employment of an ice scattering pre-cipitation algorithm introduces significant uncertaintiesin rainfall estimation and contribution to total rainfall

for large and small systems alike. For small systems,using the SSM/I resolution raises serious beamfillingissues. In general, the occurrence of precipitation fea-tures without an ice scattering signature (i.e., warm rainor light rain) is often observed in the Tropics and is notable to be quantified using an ice scattering techniquealone. One of the goals of this study is to evaluate theextent of this particular shortcoming of ice scatteringrainfall estimation techniques.

To better estimate the tropical rainfall budget, a moredirect and sensitive rainfall estimation technique is nec-essary. Rickenbach and Rutledge (1998) used TropicalOcean and Global Atmosphere Coupled Ocean–Atmo-sphere Response Experiment (TOGA COARE) ground-based radar data to classify precipitating systems by typeand estimate their rainfall contribution. Using space-borne radar [the precipitation radar (PR)] will allow theuse of this direct rainfall estimation technique through-out the Tropics, without being restricted to the fixedlocations of ground-based radars.

In spite of difficulties with rainfall estimation in theTropics, issues of land versus ocean convective intensityhave been somewhat better understood. Lightning flashrates can be a surrogate for the convective intensity ofthe lightning-producing storm. The magnitude of chargeseparation leading to cloud electrification is likely dueto the occurrence of ice particle collisions in the pres-ence of supercooled water in the mixed phase region(between 08 and 2408C, Williams et al. 1991). Labo-ratory (e.g., Takahashi 1978) and field (e.g., Dye et al.1986) measurements have corroborated this. Observa-tions of storms have shown a disparity between the light-ning flash rates of continental versus oceanic convectiontheorized to be linked to the difference in mean updraftspeeds between the two areas (Jorgensen and LeMone1989; Lucas et al. 1994; Zipser and Lutz 1994). Light-ning over the ocean is thought to be uncommon becausethe updrafts are too weak to loft sufficient amounts oflarge particles into the mixed phase region of the cloud(Williams et al. 1992; Zipser and Lutz 1994). Field pro-gram data suggest that the strongest 10% of updraftsover the ocean are less than half as strong as severestorm updrafts observed in a continental regime (Lucaset al. 1994; Zipser and LeMone 1980; Jorgensen et al.1985; Jorgensen and LeMone 1989). The lack of icealoft is reflected in observations of 85-GHz PCT, sincePCT is correlated to the optical depth of ice in the fieldof view of the radiometer. Both MZ (1996) and Mohret al. (1999) estimated the intensity of precipitation fea-tures using their algorithms and found distinctly lowerminimum PCTs in convective cores in land versus oceanMCSs. The availability of large-scale radar measure-ments over the Tropics would allow the 3D quantifi-cation of these intensity differences.

The larger questions of tropical convective intensityand rainfall may be addressed with the coincident re-mote sensing observations of the Tropical Rainfall Mea-suring Mission (TRMM) satellite. By utilizing the PR


to quantify the three-dimensional structure of tropicalprecipitation, we can answer the following questions.

R What are the intensity1 differences between precipi-tation features of various spatial scales in specific trop-ical regions? The PR is used to compare reflectivitycharacteristics of systems (from small features toMCSs) over land and ocean, and regionally through-out the Tropics. For these same systems, the TRMMMicrowave Imager (TMI) is used to assess the inten-sity of ice scattering. By using similar definitions ofice scattering MCSs as in MZ and Mohr et al. (1999),previous work can be related to observations usingthe improved resolution of the TMI, and in additioncompared with the PR’s statistics on the vertical extentof strong radar echoes.

R What is the contribution of various precipitation fea-ture classes to the total population of features and totalrainfall in the various regions of the Tropics? The PRhas many advantages in the detection and estimationof rainfall contribution over microwave techniques:1) detection of near-surface rainfall by radar yields amuch more direct estimate of precipitation than byice scattering techniques, 2) higher resolution (by afactor of 10 over the SSM/I) tempers the beamfillingproblems previously suffered, 3) sampling throughoutthe diurnal cycle allows better quantification of rain-fall than the sunrise/sunset orbital pattern of theSSM/I, and 4) using radar for the measurement ofprecipitation allows the detection of rain occurring atTB . 250 K that is unable to be measured using pre-vious ice scattering techniques. Precipitation missedby ice scattering techniques may include light rainfall,and heavy rainfall that may be occurring either onsmall scales or without significant ice scattering sig-natures (‘‘warm rain’’). In this way we can evaluatesome of the shortcomings in the previous use of icescattering precipitation techniques. Potentially thiskind of validation of ice scattering precipitation es-timation techniques can improve rainfall estimates

1 Intensity is used here in a general qualitative sense to refer to theupdraft magnitudes associated with a particular convective system.We believe that such use is at least as plausible as the more commonassociation of intense convection with high-reflectivity cores or verycold cloud tops.

Aircraft remote sensing observations (e.g., Hakkarinen and Adler1988; Heymsfield et al. 1996) have shown that large densities ofscatterers above the freezing level (i.e., high radar reflectivities aloft)scatter a large portion of the upwelling 85-GHz radiation out of thefield of view of the radiometer, thus lowering PCTs. Radiative transfer(Vivekanandan et al. 1991) and dynamical modeling studies (Smithet al. 1992; Mugnai et al. 1993) have also shown a direct relationshipbetween low brightness temperatures in frequencies near 85 GHz andlarge optical depths of high-density ice particles (graupel as param-eterized in Smith et al.). These large optical depths of graupel aloftmust originate, in large part, from strong updrafts with high super-cooled water content with the ability to grow ice particles by rimingand loft them well above their formation zone. Thus, intense icescattering and intense radar echo aloft both imply intense convection.

from more than 10 years of SSM/I data that has beencollected.

R What are lightning frequency differences for the typesof precipitation features as identified by the PR andTMI? The Lightning Imaging Sensor (LIS) instrumentcan measure flash rate differences among the precip-itation features observed. Differences in observedflash rate over the different tropical regions can thenbe compared with the PR and TMI intensity charac-teristics.

This study will use an algorithm that identifies andclassifies individual precipitation features usingTRMM’s PR and TMI. The characteristics of the iden-tified features will then be used to address the abovequestions.

2. Data and methods

a. Satellite and instrumentation

The TRMM satellite was launched in 1997 and hasbeen providing nearly continuous observations of trop-ical precipitation (between 358N and 358S) since itslaunch date. It orbits at an altitude of approximately 350km. Due to the orbit not being sun-synchronous, the fulldiurnal cycle can be sampled by TRMM with a suffi-ciently large sample of orbits (as opposed to the sunrise/sunset passes of the SSM/I). It takes about 47 days forthe instruments to sample the complete cycle. This studyemploys 92 days of data to avoid biases introduced byincomplete diurnal sampling. Kummerow et al. (1998)provides complete specifications of the PR and TMIinstruments, while Christian (1999) details the LIS in-strument.

The PR is the first quantitative spaceborne radar. ThePR was developed by the Japanese space agency (theNational Space Development Agency) and the Com-munications Research Laboratory in Tokyo, Japan. Itutilizes a 2-m phased-array antenna that provides a hor-izontal resolution of 4.3 3 4.3 km2 at nadir. With aminimum detectable signal of approximately 15 dBZ,the PR can reliably detect precipitation despite its low-transmission power (;500 W). It has a 215-km swathwidth that extends 20 km above the earth ellipsoid witha 250-m vertical resolution. It has a frequency near 13.8GHz, which means that the beam is subject to attenu-ation in heavy precipitation areas. This study utilizesthe 2A-25 version 4.0 algorithm2 reflectivity profiles thatcorrect for attenuation using two methods. A surfacereference method, used most often in moderate to heavyprecipitation, utilizes the decrease in the intensity of thesurface ground clutter to estimate total path attenuation.Mainly in light precipitation, this first method is aided

2 Specifications of the 2A-25 algorithm may be found on the WorldWide Web at http://tsdis02.nascom.nasa.gov/tsdis/Documents/ICSV-ol4.pdf.


by a modified Hitchfield–Borden attenuation correctionscheme that uses the reflectivity profile itself to estimateattenuation (Iguchi and Meneghini 1994). Both thesemethods are used to avoid the calculation of a divergentreflectivity solution. Also, the 2A-25 algorithm removesthe surface ground clutter to leave an attenuation-cor-rected reflectivity profile of precipitation.

The TMI similar in many respects to the SSM/I in-strument orbiting the earth since the mid-1980s. How-ever, because of TRMM’s lower altitude orbit, the TMIachieves higher resolution than the SSM/I. It is a con-ically scanning passive microwave radiometer with a759-km swath width. At 85.5 GHz, the TMI has aneffective field of view of 4 3 7 km2 in the across-trackand down-track directions, respectively. This investi-gation uses the ice scattering signature of precipitationat 85.5 GHz. At this frequency, there is some loweringin PCT from cloud water effects (by about 108), but thevolume scattering by precipitation (large ice particles)will result in decreases in brightness temperature below275 K. With the surface appearing warm at these fre-quencies, ice particles scatter radiation out of the sat-ellite’s field of view (FOV) as well as reflect the coldcosmic background radiation (with a brightness tem-perature 5 2.7 K) into the satellite’s FOV.

At these high frequencies, the ocean’s emissivity isa strong function of polarization at oblique viewing an-gles like the TMI’s. In addition, nonuniform surfacecharacteristics over land cause emissivity differences.Because of this, the horizontally and vertically polarizedbackground brightness temperatures greatly differ de-pending on surface emissivities, which causes discon-tinuities (especially at coastlines). Spencer et al.(1989)developed the PCT that largely removes these ambi-guities. This parameter is calculated by the following(from Spencer et al. 1989):

bT 2 TB Bh yPCT 5 .b 2 1

The parameter b was derived to be 0.45 to yield back-ground PCTs between 275 and 290 K. PCTs depressedbelow 250 K are likely precipitation and not contami-nation from a cold ocean background. Surface snowcover may lead to false precipitation retrievals, and atechnique will be defined below to remove these arti-facts. With all artifacts removed, examining distribu-tions of 85-GHz ice scattering of precipitation featuresin the Tropics gives insight into the microphysical char-acteristics of the systems observed.

The LIS instrument is a staring optical imager thatidentifies changes in radiance in the FOV that it ob-serves. Using the oxygen emission line at 777.4 nm,LIS achieves a 3–6-km resolution (3 km at nadir). Thedetector attains a 90% viewing efficiency of the light-ning ‘‘flashes’’ it observes (Christian 1999). It hasroughly a 600 3 600 km2 viewing area, which is ori-ented parallel to the satellite track to allow roughly equal

viewing time (;90 s) of all areas in the swath. This‘‘viewtime’’ is useful for calculating flash rates at aparticular location. However, this work simply presentsobservations of flashes to relate relative lightning fre-quencies to storm type. This method allows detectionof total lightning; both in-cloud and cloud-to-groundlightning flashes are measured. Because of lightning–updraft strength hypotheses (e.g., Williams et al. 1992;Zipser and Lutz 1994; and others), lightning flash in-formation can be used as a proxy for convective vigorin comparing storms’ intensities.

b. Experimental design

In order to identify discrete precipitation features inthe Tropics, data from the PR 2A-25 near-surface re-flectivity product is used in combination with the TMI85-GHz PCT. Three months of TRMM data from Au-gust, September, and October 1998 have been selectedfor analysis in this study. Four regions have been se-lected to compare: Africa, South America, east Pacific,and west Pacific. (The boundaries of these areas appearin the results in Fig. 3.) These regions were selected toenable the sampling of precipitation features in clima-tologically rainy areas in the deep Tropics in or nearthe ITCZ. The features contained within them consistof a large, archetypical sample of continental and oce-anic precipitation systems. This analysis also allow thecomparison between the four regions to examine dif-ferences in all regions’ precipitation features.

The selection of observational criteria to identify pre-cipitation features was conducted with several goals inmind. A complete sampling of precipitation was desired,reaching from the individual convective shower to thelarge MCC. However, the PR’s relatively coarse reso-lution compared to the scale of individual cumulonim-bus clouds does not allow the sampling of the very smallfeatures. Also, low-altitude random noise near the min-imum detectable signal of the PR inhibits the selectionof individual data bins as precipitation features. (Notethat a data bin corresponds to an area of 18.5 km2 cor-responding to the horizontal area of a PR FOV at thesurface.) Inclusion of this noise would bias any statisticsof the storms sampled. For this reason, a minimum pre-cipitation feature size of four data bins has been chosento remove noise from the dataset, albeit arbitrarily. Thiscorresponds to an area of approximately 75 km2.

To sample all precipitation and the surrounding deep-er cloud systems, both the ice scattering signature andlow-level radar signature are used to select precipitationfeatures. As mentioned previously, these two TRMMinstruments have different scan strategies that inhibitdirect matching of the raw data. So, an interpolationscheme is required to match the retrievals. In this study,a ‘‘nearest neighbor’’ interpolation is used with the PRdata bin locations (on a quasi-regularly spaced 4.3 34.3 km2 grid) used as a basis for the interpolation. With-in the PR swath only, TMI brightness temperatures are


collocated with the closest PR data bin locations. Be-cause the TMI resolution at this frequency places databins roughly every 14 km in the along-track direction,typically two or three PR data bins are assigned thesame TMI brightness temperature. This nearest neighborcollocation was done to ensure inclusion of the extremain brightness temperatures so crucial in the determi-nation of relative ice concentration.

Once the two fields are matched, then a selection ofthe precipitation features ($4 data bins in size) wasperformed. These are systems with contiguous data binstouching (i.e., edges or corners adjacent). All areas withnear-surface reflectivity $20 dBZ or PCT #250 K wereselected to ensure each system contained both the low-level precipitation field and associated areas of ice scat-tering aloft (i.e., anvils). This was motivated by thedesire to sample the entire extent of the system, whichoften extends beyond either the surface rain or ice scat-tering signatures alone. Snow cover in high-elevationregions of the Tropics (e.g., the Andes Mountains) mayartificially depress PCTs outside areas of real precipi-tation (due to the snow’s low emissivity compared withsnow-free land areas). Maintaining that the maximumnear-surface reflectivity or 6-km reflectivity in a pre-cipitation feature be greater than 15 dBZ (close to theminimum detectable signal of the PR) in a feature witha data bin with PCT #250 K ensures that these spurioussnow features were removed. This is justified as it isvery unlikely that precipitation is occurring (with a PCT,250) without some appreciable radar echo at either ofthese two levels .15 dBZ.

Once these systems have been selected and snow ar-tifacts have been removed, they were classified by thesize and intensity criteria appearing below.

c. Description of the precipitation featureclassifications

Figure 1 presents a schematic of the three types ofprecipitation feature classifications based on their near-surface reflectivity and PCT characteristics. The specificcriteria used for identification of the systems using thedual-instrument method appear in Table 1a. In addition,this study used the PCT characteristics of the precipi-tation features to compare with the MCSs and intenseMCSs identified by MZ (1996). Hereafter, these featuresidentified by their ice scattering signature will be re-ferred to as MZ equivalent features; the criteria usedfor their identification appear in Table 1b. However, thefollowing is a description of each of the precipitationfeatures identified using the their near-surface reflectiv-ity and PCT characteristics.

1) PRECIPITATION FEATURE WITHOUT ICE

SCATTERING

The precipitation feature without ice scattering cat-egory includes systems containing surface precipitation

without an associated ice scattering signature (with PCT#250 K). This is not to say that there are no ice pro-cesses occurring in the system, simply that they wereeither too weak or too small to be detected at the TMIresolution. This type of feature was chosen in order toidentify warm rain and weak and/or decaying convec-tion.

2) PRECIPITATION FEATURE WITH ICE SCATTERING

The precipitation feature with ice scattering categoryincludes features that do not meet MCS criteria, butinclude at least one data bin with PCT #250 K. Thisensures that there is significant ice scattering occurring,meaning that significant optical depths of precipitation-sized ice particles (more likely graupel than snow asmodeled by Vivekanandan et al. 1991; Smith et al. 1992;Mugnai et al. 1993) are present in the feature. Thiscategory identifies precipitation features ranging in sizefrom small precipitation features 75 km2 in size to largesystems not meeting the MCS area or intensity criteria,as defined in the next section.

3) PRECIPITATION FEATURE WITH AN MCS

The precipitation feature with an MCS category isbased upon the classification of MZ (1996), who sam-pled the properties of ice scattering MCSs using theSSM/I instrument. This work seeks to match MZ’s icescattering MCS definition. This category requires 108contiguous data bins (area ;2000 km2) with PCT #250K to be contained within the rain or ice scattering areaof the system. In addition, 10 noncontiguous data bins(area ;185 km2) within the 2000-km2 area must containPCT #225 K to ensure the presence of a large opticaldepth of ice and thus a deep convective layer within thestorm. Note that the precipitation feature with an MCScategory may contain more than one area meeting theMZ equivalent PCT criteria below, and the rain fallingat PCTs $250 K contiguous with the depressed ice scat-tering area.

4) MZ EQUIVALENT MCSS

As a sensitivity test, a comparison was performedcomparing MCSs analyzed with their ice scattering andrainfall characteristics (the precipitation feature with anMCS category defined above) and those defined by theTMI ice scattering signature alone as done by MZ(1996). By performing such a comparison, it is possibleto compare the combined TMI and PR estimate of rain-fall and other physical characteristics with those esti-mated by the TMI alone. The features identified by theirPCT characteristics are called MZ equivalent MCS andMZ equivalent intense MCS because of their similaritywith those features identified by MZ. The criteria forselection of these features appears in Table 1b. It isimportant to note that the identification of the MZ equiv-


FIG. 1. Vertical cross-section schematic of the three precipitation feature types. Above eachexample cloud (ice and liquid hydrometeors indicated by asterisks and dots, respectively) lies theidealized 85-GHz PCT trace associated with each feature type. The schematic location of the 20-dBZ contour is labeled.

alent features excludes any link to rainfall occurring atPCTs $250 K.

d. Precipitation feature characteristic definitions

Once these precipitation features have been selected,quantifying their properties has been accomplished sev-eral ways, which will appear in the following sections.Six parameters have been used to quantify the structuraldifferences between them: area, minimum PCT, maxi-mum height of the 30-dBZ echo, maximum 6-km re-flectivity, lightning frequency, and volumetric rainfall.Area is simply the number of data bins in a particularfeature multiplied by the nominal area of a PR data bin.Minimum PCT is the minimum 85-GHz brightness tem-perature assigned to a feature after the nearest neighbortechnique has matched TMI brightness temperatures to

the location of the PR data bins. Maximum height ofthe 30-dBZ echo is the greatest height the 30-dBZ echoextends above MSL in a system. This statistic has beenlinked to several convective intensity criteria, includingupdraft strength, rainfall intensity, and cloud-to-groundlightning frequency (Zipser 1994; Petersen et al. 1996).Also, DeMott and Rutledge (1998) reason that stormscontaining higher 30-dBZ heights likely contain moreintense electric fields (and therefore more lightning),larger supercooled water and ice water contents; thesecond condition leading to lower 85-GHz PCTs.

Maximum 6-km reflectivity is defined as the highestreflectivity value in a feature in the radar bin 6 km aboveMSL. This statistic has been most notably correlated toa threshold for storm electrification by Dye et al. (1989).They reported that storms became electrically activewhen 6-km reflectivities (about 2108C) reached 40 dBZ.


TABLE 1a. Criteria used to classify the three TMI–PR-identified precipitation features from Mohr and Zipser (1996).

Category name Data Criteria

PF without ice scattering (#250 K) PR near-surface reflectivity and nearest neighborTMI 85-GHz PCT (within the PR swath)

• 4 or more contiguous data bins (area $75km2) with a PR near-surface reflectivity $20dBZ

• No data bins contain PCTs #250 KPF with ice scattering and without

an MCSPR near-surface reflectivity and nearest neighbor

TMI 85-GHz PCT (within the PR swath)• 4 or more contiguous data bins (area $75

km2) with a PR near-surface reflectivity $20dBZ or a PCT #250 K

• At least one data bin with PCT #250 K• Does not meet the PF with an MCS criteria

belowPF with an MCS PR near-surface reflectivity and nearest neighbor

TMI 85-GHz PCT (within the PR swath)• 108 or more contiguous data bins (area $2000

km2) with PCT #250 K with associated PRrain area

• 10 or more data bins (area $185 km2) withPCT #225 K

TABLE 1b. Criteria used to classify the ice-scattering-only criteria from Mohr and Zipser (1996).

Category name Data Criteria

MZ equivalent MCS TMI 85-GHz PCT only, within the PR swath • 108 or more contiguous data bins (area $200km2) with PCT #250 K without associatedPR rain area


MZ equivalent intense MCS TMI 85-GHz PCT only, within the PR swath • 108 or more contiguous data bins (area $200km2) with PCT #225 K without associatedPR rain area


In addition, Zipser and Lutz (1994) showed that thedistribution of derived 6-km reflectivities varied signif-icantly among the tropical oceanic, midlatitude, andtropical continental storms observed. This provides ad-ditional justification of using 6-km reflectivities as aproxy indicator for lightning probability and convectiveintensity.

Lightning data from LIS is reported as point data, thatis, the position of the flash is reported along with otherretrieved parameters. The LIS instrument has the ad-vantage that it views most locations within its swath forvery nearly 90 s, so flash counts are linearly related toflash densities across the swath. All flashes within 8 kmof a precipitation feature have been assigned to the clos-est feature data bin to give the number of flashes within8 km of each precipitation feature. Volumetric rainfallis determined from the near-surface rainfall output fromthe 2A-25 algorithm (units of mm h21 km2). It uses avariational Z–R relationship that depends on the strat-iform or convective classification of the PR data bin(among other parameters). This study only considers therelative rainfall contribution of each precipitation fea-ture as a whole; there is no attempt to partition rainfallamong convective and stratiform portions of the fea-tures.

3. Precipitation feature example

This section will present an example of several pre-cipitation features and the interrelation of the datasets.Figure 2a presents a 0.67-mm visible image from thevisible infrared scanner (VIRS) aboard TRMM, overlaidwith PR near-surface reflectivity and collocated TMI 85-GHz PCT over Africa. The image is from 1455 UTC[;1500 local time (LT)] on 18 September 1998. Thereis a large contiguous area of deep convection apparentin the figure, as well as some isolated cells to the westof this line (near 108N). The large feature extends wellbeyond the extent of the PR swath. However, this studyacknowledges biases in the sampling of the areas ofthese features. The PR does, however, sample the ver-tical reflectivity and rainfall profiles within the swath.The large feature at the center happens to meet the MZequivalent intense MCS criteria, with more than 108contiguous data bins less than 225 K and more than 10data bins less than 175 K. In this study, this system isclassified as a precipitation feature with an MCS. Thissystem appears to be a classic squall line system, witha leading convective line to the west and a large strat-iform region extending eastward. An along-satellitetrack cross section of this feature, indicated by the ‘‘Ray29 cross section’’ indication on Fig. 2a, appears in Fig.


FIG. 2. (a) 1455 UTC 18 Sep 1998 VIRS visible image, overlaid with PR near-surface reflectivity(white, contoured at 20 and 40 dBZ ), TMI collocated 85-GHz PCT (gray, contoured at intervals of25 K beginning at 250 K), and LIS flash locations (1 symbols). The cross section in (b) is indicatedby Ray 29 label. (b) Shaded PR reflectivity cross section through the precipitation feature with anice scattering MCS pictured in (a).


TABLE 2. Number and percentage of total precipitation features classified by the criteria in Table 1.

Region

No. PFs

No.

No. PFs without ice scattering

No. %

No. PFs with ice scattering

No. %

No. PFs with an MCS

No. %

AfricaSouth AmericaEast PacificWest PacificTotal

12 83812 60417 40129 66872 511

95969775

15 99325 90961 273

74.777.691.987.384.5

2904263212333568

10 337

22.620.9

7.112.014.3

338197175191901

2.61.61.00.61.2

TABLE 3. MZ equivalent MCSs and intense MCSs found in theTRMM precipitation feature population.

RegionMZ equivalent

MCSsMZ equivalentintense MCSs % intense


373221236235

1065

36824

50

9.73.60.81.74.7

2b. The left y axis gives the height of the shaded re-flectivity features, while the right y axis indicates the85-GHz brightness temperature at the position indicatedby the reflectivity profile. This cross section gives agood example of a squall line’s vertical structure. Thedeep leading convective line, transition zone, and strat-iform region are all apparent as defined by Biggerstaffand Houze (1991). The brightness temperature trace iswell correlated with reflectivity structures above themelting level (keeping in mind the parallax problemassociated with the TMI’s 52.18 incident angle), exceptin the transition zone where reflectivity weakens aloftwhile strong to moderate ice scattering remains in thiszone. Features like the ones presented in this examplewill be quantified by their intensity, rainfall, and light-ning distributions in the following sections.

4. Precipitation feature characteristics

a. Population, geographic distribution, and diurnalcycle

This study found 72 511 precipitation features withinthe TRMM August, September, and October 1998 da-taset. Table 2 details the breakdown of the features intothe subcategories given above. A large fraction (84.5%)of the features found do not contain ice scattering, witha higher portion of features over the ocean not contain-ing a data bin with PCT #250 K. There is almost doublethe fraction of features with ice scattering over land,both features with ice scattering and MCSs. This indi-cates a higher likelihood of finding convective processessufficiently vigorous to loft large ice particles into themid- to upper troposphere over continental areas. Table3 identifies the MZ equivalent MCSs and MZ equivalentintense MCSs to select precipitation features using onlyice scattering criteria. Here we find more MZ equivalentMCSs than precipitation features with MCSs because it

is possible to have multiple MZ equivalent MCSs(closed 250-K isotherms following the ice scatteringdefinition) contained in one contiguous rain area. It wasalso found that 4.7% of the features were MZ equivalentintense MCSs, which is consistent with the 4% foundby MZ (1996) that used an instrument with differentspatial resolution (SSM/I).

Figure 3 indicates the geographic distribution of pre-cipitation features that were identified in this study. Thethree figures from top to bottom indicate the locationof features without ice scattering, features with ice scat-tering, and features with MCSs, respectively. It is ap-parent that these precipitation features occur in preferredlocations in each geographic region. Over the ocean, theITCZ is a favorable environment for deep convection,while there seems to be less focusing over the landregions. Over Africa, regions from 108S northward tothe north boundary of the box contain the lion’s shareof the MCS activity in that region. In South America,features with ice scattering seem to favor the westernside of the continent during this season, away from theAtlantic coast. The foothills of the Andes Mountainsseem to be a focusing mechanism for MCSs. CentralAfrica contains the greatest concentration of intenseconvection found in the dataset. The bulk of the eastPacific’s MCSs are near 108N, near where the NorthernHemisphere warm season ITCZ is found. This is trueto a lesser degree over the west Pacific, where MCSscover a much broader area.

The diurnal cycle of precipitation features by typewas investigated for the four regions sampled. The localtime of observation of each feature was placed in 2-hbins centered on the odd hours of the day. The relativefrequency of all precipitation features and relative fre-quency partitioned by each precipitation feature typewithin each 2-h bin is plotted in Fig. 4. The continentalregions’ features show a strong afternoon response todaytime solar forcing and associated low-level desta-bilization. Examination by feature type indicates thatthe features without ice scattering rise in frequency firstduring the daytime (1000–1200 LT), followed by fea-tures with ice scattering rising 2–4 h later. Both thesefeatures peak in frequency near 1400–1600 LT, and di-minish slowly throughout the local evening. For pre-cipitation features with an MCS, frequencies rise laterin the afternoon than for smaller features, beginningbetween 1400 and 1600 LT over both regions, remaining


FIG. 3. Precipitation feature locations by type. (a) Precipitation features without ice scattering,(b) precipitation features with ice scattering, and (c) precipitation features with an MCS areindicated by a small dot. The boundaries of the four analysis regions used in this study are shownin (c).

FIG. 4. Diurnal cycle (in 2-h local time bins) of TRMM precipitation features over the fouranalysis regions. Solid line indicates the relative frequency of all precipitation features, whiledotted, dashed, and dash–dotted lines indicate the relative frequency of precipitation features withMCSs, with ice scattering, and without ice scattering, respectively.


TABLE 4. Selected physical characteristics of the precipitation features.

Region/type No. of TPFsMedian area

(km2)Median min PCT

(K)Median max height

of 30-dBZ echo (km)Median max 6-kmreflectivity (dBZ )

Africa (all PFs)Without ice scatteringWith ice scatteringWith an MCS

12 83895962904

338

166130684

10 946

268272220138

4.754.57.75

12.5

22.919.137.945.5

South America (all PFs)Without ice scatteringWith ice scatteringWith an MCS

12 60497752632

197

148130647

10 539

271274225150

4.754.257.25

11.25

19.916.736.244.4

East Pacific (all PFs)Without ice scatteringWith ice scatteringWith an MCS

17 40115 993

1233175

148130

155319 914

281281231176

1.751.55.57.0

0.0*0.0*

28.434.4

West Pacific (all PFs)Without ice scatteringWith ice scatteringWith an MCS

29 66825 909

3568191

129111684

15 846

279281228163

3.253.06.258.5

15.313.831.439.2

* Median reflectivity value of 0.0 indicates the value is lower than the minimum detectable signal of the PR (;15 dBZ ).

TABLE 5. Selected characteristics of the MZ equivalent MCSs andintense MCSs.

Region

Medianarea

(km2)

Medianmin PCT

(K)

Median maxheight of the30-dBZ echo

(km)

Medianmax 6-kmreflectivity

(dBZ)

AfricaSouth AmericaEast PacificWest Pacific

4530371642343975

140154183169

11.7510.25

6.57.75

43.741.533.137.1

steady throughout the evening over Africa until mid-night, but diminishing quickly by 2200–2400 LT overSouth America. During nocturnal hours, there is a hintof an overnight second maxima of MCSs over bothregions (0200–0800 LT); however, the reader should bestrongly cautioned of the small number of MCSs ob-served in this study (especially when divided among 12diurnal bins).

Over the ocean, there is a weak diurnal signal amongprecipitation features. Excluding MCSs, there is nevermore than a 2%–3% deviation in the relative frequencyof feature-type observations, with a slight rise in activityamong the non-MCS systems in local nighttime between2000 and 0200 LT. MCSs show a relative peak in activityin the 0400–0600 LT bin, with a relative minima in thelocal afternoon. Again, caution must be used when in-terpreting the MCS diurnal cycle, as there are less than200 precipitation features with an MCS divided among12 time bins in each oceanic region. These findingsagree qualititively with the findings of Chen and Houze(1997) and Hall and Vonder Haar (1999), who used IRsatellite brightness temperatures to identify an overnightmaximum in the spatially large, long-lived precipitationfeatures in the west Pacific warm pool.

The previous works using the SSM/I to classify pre-cipitation features (MZ 1996; Mohr et al. 1999; and

others) relied on the sunrise/sunset orbital pattern of theDefense Meteorological Satellite Program (DMSP) sat-ellite. Not only does this orbital pattern not enable sam-pling of the complete diurnal cycle, but it also integratesbias into the estimation of the frequency and rainfallfrom different classes of systems. By sampling at 0600and 1800 LT only, it is evident from Fig. 4 that a sig-nificant portion of the precipitation features not meetingMCS criteria are missed over land due to the SSM/I’sobservation times. This may have important implica-tions on the SSM/I’s ability to obtain a representativesample of precipitation features and to estimate theirrainfall and intensity free of diurnal cycle bias effects.

b. Structure statistics

Table 4 gives median values of the four statistics usedto compare storm characteristics among the regions andfour types of systems. Table 5 summarizes the statisticsfor the MZ equivalent MCSs. Given are the medianvalues of feature area, minimum PCT, maximum heightof the 30-dBZ contour above mean sea level, and max-imum reflectivity at 6 km above mean sea level.

1) AREA

Median precipitation feature area statistics (Table 4)show that these systems are comparable in size whenall features are taken into account (within three PR databins in size for all features), but when the more intensesystems are compared, these features are significantlylarger over the ocean. For precipitation features con-taining MCSs, for example, features size is about 1.5–2times larger comparing the west Pacific (15 846 km2)and the east Pacific (19 914 km2) to the continentalregions (10 946 and 10 539 km2 for Africa and SouthAmerica, respectively).


FIG. 5. (a) CDFs of precipitation feature area (solid), MZ equivalent MCS area (long dash), and precipitation feature with MCS area(dash–dot). Also, CDFs of (b) minimum PCT, (c) maximum height of the 30-dBZ echo, and (d) maximum 6-km dBZ for all features (solid)and features with MCSs (dash–dot).

The area statistics in Table 5 considers only the MZequivalent MCSs. The area enclosed by the 250-K PCTisotherm leads to similar area values between regions.This suggests that over the ocean the most intense sys-tems contain a larger area of rainfall outside the 250-Kisotherm, but do not contain a larger area of the moreintense ice scattering and rainfall occurring #250 K.This, along with higher ratios of features without icescattering to features with ice scattering over the oceansuggest that there is more rainfall occurring at higherPCTs over ocean than over land.

For all precipitation features (Fig. 5a), area distri-butions among all feature types are similar in shape,with a large number of small systems dominating thedataset. Continental regions have slightly larger systemsat a given percentile than oceanic systems. The areadistributions for features with MCSs include the area ofice scattering (#250 K) and the associated rainfall out-side the scattering area. It is apparent that the oceansystems are larger at a given percentile, consistent withthe median statistics that were discussed above.

Areas of the systems as defined by MZ equivalentcriteria (PCT # 250 K) do not reflect this size differ-ence. Rain areas of the systems as defined by ice scat-tering signatures alone are not similar in size. Thiscontradiction between the areas contained in MZ equiv-alent MCSs and the total area of rain surrounding themsupports the hypothesis that a higher fraction of rain isfalling at higher PCTs over oceanic regions. This willbe examined further when rainfall distributions are dis-cussed below.

Area distributions of precipitation features are limitedby the 215-km swath width of the PR. Tables 6 and 7give the number and percentage of precipitation featuresand MZ equivalent MCSs and intense MCSs, respec-tively, that are truncated by the edge of the swath. Inall, about 17% of all precipitation features were artifi-cially cut by the swath, with the more intense (and larg-er) systems truncated more often (82% for features withMCSs). Many times, only the rain area outside the MZequivalent MCS area was cut; only 54% of the actualMZ equivalent MCS areas #250 K were sliced. This


TABLE 6. Narrow swath effects on precipitation features.

Region

PF total

Edge ofswath %

PF without ice scattering

Edge ofswath %

PF with ice scattering

Edge ofswath %

PF with an MCS

Edge ofswath %

AfricaSouth AmericaEast PacificWest PacificAll regions

2731246527234314

12 233

21.319.615.614.717.0

14451399192828887660

15.114.312.111.312.6

1013905628

12523798

34.934.550.935.136.8

273161167174775

81.082.195.491.186.2

TABLE 7. Narrow swath effects on the MZ equivalent MCS andintense MCSs.

Region

MZ equivalent MCS

Edge of swath %

MZ equivalentintense MCS

Edge of swath %

AfricaSouth AmericaEast PacificWest PacificAll regions

217114129118578

58.251.654.450.054.3

11620

19

30.675.0

100.00.0

38.0

limitation causes some truncation of the area distributionof precipitation features, but this distribution is accurateand unbiased in its portrayal of 1) a greater number ofsmall systems compared to the number of large systemsand 2) broader systems over the oceanic regions whencomparing the larger, more intense systems. This edgeof swath effect does not cause a systematic bias in thecomparisons of storm structure that appear in the fol-lowing sections. However, since the rain areas associ-ated with precipitation features with MCSs are morelikely to be truncated than the MZ equivalent MCSsthemselves, this likely causes an underestimate in theland/ocean difference in rain areas outside features withMCSs.

2) MINIMUM PCT

The following three parameters use TMI and PR ob-servations to compare intensities among the precipita-tion features. MZ (1996) analyzed minimum PCTs ofice scattering MCSs. It was found for the sunset passesof the SSM/I that at the 10th percentile of storms byintensity (as measured by their low 85-GHz tempera-tures), continental MCSs had minimum PCTs about 25K lower than their oceanic counterparts. TRMM pre-sents many advantages over the SSM/I in this compar-ison. Ability to sample throughout the diurnal cycle, thePR’s sampling beyond the microwave signature, andincreased passive microwave resolution enables TRMMto better differentiate among storms by intensity.

Table 4 reveals that continental regions produce sig-nificantly lower median values of precipitation featureminimum PCT than oceanic storms for all categories.This varies by type from 11 to 13 K for all precipitationfeatures to near 30 K for features with MCSs. The MZ

equivalent MCSs alone (Table 5) also demonstrate thisdistinct difference in median intensity between land andocean storms (;40 K between Africa and the easternPacific). Differences (;12 K among MCSs) also existwhen examining Africa versus South America and eastversus west Pacific. Figure 5b contains a cumulativedistribution function (CDF) of precipitation feature min-imum PCT (solid line). While there is a ‘‘modest’’ PCTdifference at the 50th percentile of storms (;12 K), the90th percentile reveals a 40–45-K difference betweenland and ocean storms. Removing the weak systemsover all regions can be accomplished by looking at fea-tures with MCSs distributions. These features are shownby the dash–dot line in Fig. 5b. Here there is a 40–45-Kdifference between the land and ocean storms consis-tently below the 60th percentile. This suggests that, intheir most intense ice scattering cores, these continentalprecipitation features and MCSs contain a significantadditional optical depth (by size or concentration) ofice. This is particularly apparent in the stronger systemsin the precipitation feature distribution and the MCSs.Mohr et al. (1999) shows a 30–40-K difference at theintense percentiles among the ice scattering MCSs sam-pled with the SSM/I, which shows consistency with thisstudy.

3) MAXIMUM HEIGHT OF THE 30-DBZ ECHO

Median values of the maximum height of the 30-dBZecho appear in Tables 4 and 5. For continental regions’precipitation features, this parameter takes the value of4.75 km above MSL for both Africa and South America,while over the oceanic regions the height is much lower(1.75 and 3.25 km for the east and west Pacific, re-spectively). This is accentuated in data for the featureswith MCSs, where the median values are 2.5–5.5 kmhigher for continental regions than over the oceans. Fig-ure 5c shows the feature distribution of the maximumheight of the 30-dBZ echo. Again, continental regionsshow higher 30-dBZ echo heights for the most intenseconvective data bins, extending about 3 km higher, onaverage, at the 90th percentile (see Fig. 5c, solid line).The east Pacific region’s distribution has a differentshape than the other three; this is likely due to the largenumber of features without ice scattering found in thenorthern part of the analysis region (Fig. 3). The largernumber of precipitation features without ice scattering


TABLE 8. Number of lightning flashes within 8 km of eachprecipitation feature category.

RegionPF without

ice scatteringPF with icescattering

PF withan MCS Total


388251

07

646

11 8198815

40138

20 812

12 7965260

45127

18 228

25 00314 326

85272

39 686

over the ocean (which must have weaker reflectivityprofiles above the melting level) likely diminishes therange of the distribution of the 30-dBZ heights over theocean.

Again to remove the weaker systems from the com-parison, the dash–dot portion of Fig. 5c shows the max-imum 30-dBZ echo heights for precipitation featureswith MCSs over the study regions. The oceanic distri-butions become more similar in shape in this plot, withthe east Pacific still containing the lowest echo heights.Here there is a distinct difference in the shapes betweenland and ocean regions. Ocean regions’ MCSs 30-dBZecho heights remain below 10 km for most of the spec-trum, but land convection rises to near 15 km by the90th percentile.

Despite similarities between the land regions and be-tween the ocean regions themselves, there are notabledifferences between them. African precipitation featureswith MCSs consistently contain 1–2 km higher maxi-mum heights of the 30-dBZ echo than South Americanones at a given percentile. Also, the distribution of eastPacific region maximum heights of the 30-dBZ echoshow many differences compared to the west Pacific.For example, 70% of east Pacific systems have max 30-dBZ echo heights less than 3 km. Compare this to only40% of the systems over the west Pacific being weakenough to contain the maximum 30-dBZ echo heightsless than 3 km. This highlights a few of the significantdifferences between the individual regions that need fur-ther investigation.

4) MAXIMUM 6-KM REFLECTIVITY

Tables 4 and 5 again reveal consequential differencesin 6-km reflectivity structure among the land and oceansystems. For all precipitation features, continental sys-tems exhibit more than 4 dBZ higher median reflectiv-ities at 6 km than ocean systems. Note that over the eastPacific, precipitation features without ice scattering con-tained such weak reflectivities at 6 km as to be belowthe minimum detectable signal of the PR at the medianlevel. Because these systems were so numerous, theypushed the median reflectivity for all features in thatregion below the minimum signal. For the MCSs, con-tinental median maximum 6-km reflectivities exceededoceanic ones by 5–10 dBZ. Figure 5d’s solid portionshows a CDF of maximum 6-km reflectivities for allprecipitation features. Between 20%–52% of the fea-tures, depending on the region, do not have reflectivitiesabove the PR minimum detectable reflectivity. This isespecially apparent over the east Pacific box. However,once systems extend above this intensity level, the con-tinental systems are again stronger by several dBZ. Forthe MCSs, Fig. 5d (dash–dot) shows maximum 6-kmreflectivity differences at or exceeding 10 dBZ through-out the distribution above about the 30th percentile. Thisclearly shows that the continental updrafts are loftingmuch larger concentrations of hydrometeors to higher

altitudes in these intense systems. Again, differencesamong the land and ocean regions themselves are ap-parent. Here there is nearly as much difference betweenthe east and west Pacific as there is between the westPacific and the continental regions.

These land–ocean differences indicate that despiteoceanic precipitation features with MCSs containingsignificantly more rainfall outside the 250-K contour,they are much weaker and likely contribute differentlyto the heating and moisture budgets than those systemsover land. The larger number of features without icescattering over the ocean (which are likely be shallowin order to avoid scattering radiation at 85 GHz) willbe shown to contribute much more to the total rainfallover these regions.

c. Lightning statistics

With LIS aboard TRMM, it is possible to calculatethe contribution of each precipitation feature type to thetotal lightning observed from the sample of features.Knowing the intensity distributions of the precipitationfeatures from the preceeding analysis can help to explainthe observed lightning patterns. LIS recorded 39 686flashes within 8 km of precipitation features during the3-month period. The contribution to the total lightningobserved by region and by feature type is presented inTable 8. There is nearly two orders of magnitude dif-ference betwen lightning in land and ocean regions inthe statistics. This lightning ratio of approximately 100:1 holds for all types of features analyzed in the dataset.There is about one-third more lightning contained withinprecipitation features with ice scattering than with anMCS.3 Even when considering the largest of featuresby median area, the oceanic features with an MCS, only136 flashes occurred from 366 features, compared with13 519 flashes for 535 features with MCSs over land.Clearly, the intensity differences quantified above havecrucial implications on the lightning probability for agiven oceanic or continental MCS.

3 Also note that 61 273 precipitation features without ice scatteringproduce a total of 646 flashes, with all but 7 of those occurring overland. (We interpret this result to mean that the features that do producelightning in this category possibly contain an ice scattering signaturebut that it is too spatially small to be quantified at TMI resolution.)


FIG. 6. Locations of lightning associated within 8 km of precipitation features for the four analysis regions.

TABLE 9. Lightning frequency observations from the precipitation feature types.

Region Feature typeWithout any

flashesWith 1–9

flashesWith 10–99

flashesWith $100

flashesNumber of databins per flash

Africa

South America

Without ice scatteringWith ice scatteringWith an MCSWithout ice scatteringWith ice scatteringWith an MCS

93771369

4496241388

29

2191182

22150984

41

0351194

1258113

01

39017

2912022

4452229

East Pacific

West Pacific

Without ice scatteringWith ice scatteringWith an MCSWithout ice scatteringWith ice scatteringWith an MCS

15 9931217

16025 902

3501166

01514

76522

011023

000000

`52555337

35 83620511683

Figure 6 plots the geographic distribution of the flash-es in each region. It is apparent that over the ocean thereis a clustering of lightning north of the equator in agree-ment with the climatological position of the ITCZ (asshown by the presence of precipitation features in Fig.3). The evident high concentrations of lightning overland dominates the total amount of lightning observed.

To further quantify the distribution of flashes per sys-tem, Table 9 quantifies the regional number of precip-itation features in each category containing no flashes,between 1–9 flashes, 10–99 flashes, and more than 100flashes. Between 9% and 12% of precipitation featureswith ice scattering over land contain 10 or more flashes,while only 83 out of 4801 features (1.7%) with icescattering contain even one flash (with 3 containing 10or more). Thirty-nine of Africa’s 338 precipitation fea-tures with MCSs contain more than 100 flashes, whileSouth America only has 7 out of 197 meeting suchcriteria. Only 4 oceanic MCSs contain 10 or more flash-es, with none containing more than 100.

The last column in Table 9 shows the number offeature data bins (as a proxy for area, each data bin is18.5 km2) per lightning flash in each region by featuretype. Precipitation features without ice scattering showthe same trend as before, containing a large area ofrainfall per flash. Except over the west Pacific, precip-itation features with ice scattering contain less rain areaper flash than features with MCSs. This is likely due tothe larger fraction of stratiform rain contained in MCSs

compared with smaller multicell storms (Houze 1993),thus containing lower flash rates. Oceanic systems withice scattering and MCSs contain about 150 times morerain area per flash than features over land. This againhighlights the differences in intensity as observed bythe other TRMM instruments. It is also interesting tonote that over the ocean, only about 0.3% of the pre-cipitation features contain lightning, while over land14.1% contain lightning.

d. Rainfall statistics

As mentioned above, the TRMM PR 2A-25 algorithmrainfall estimate was used to quantify the rainfall con-tribution of each feature. This section presents the con-tribution of each classification of feature to the totalrainfall measured during this analysis period. Again,note that there is no attempt to separate rainfall amongstratiform or convective areas within precipitation fea-tures. Figure 7a shows the relative abundance of pre-cipitation features by type as classified in Table 1. Notethat precipitation features without ice scattering aremore abundant over the ocean, accounting for 92% offeatures over the east Pacific, for example. Features withice scattering are more profuse over the continental re-gions, accounting for 20%–25% of the features. MCSsare quite rare, accounting for less than 3% of the totalfeatures by number over all regions. As one would ex-pect, the smaller systems must contribute less to total


FIG. 7. Contribution by each feature category to (a) total featurepopulation and (b) total regional rainfall.

rainfall than they do by number, and that is shown inFig. 7b. This figure shows the percentage of the totalvolumetric rainfall for each type of system in each re-gion. The features without ice scattering contribute only9% of the rainfall in Africa, with a maximum of 23%over the west Pacific. It appears that these weak systemsare a more important contributor to the rainfall over theocean, with their contribution related to their relativepopulation. Despite features with some ice scatteringaccounting for about twice the percentage over the landareas than over the ocean, their contribution to rainfallis roughly the same over both land and ocean. Featureswith MCSs contribute 38%–55% of the total rainfall inthe various regions. It is especially noteworthy that just1.5% of the systems are contributing about half therainfall in a given region. Likewise, precipitation fea-tures meeting MZ intense MCSs criteria (but containingthe associated ‘‘warm’’ rainfall) contribute much morein precipitation than they do in number. The 35 precip-itation features with MZ intense MCSs (0.3% by num-ber) contribute 12% of the total African box rainfall(not shown).

In order to further quantify the distribution of rainfallfrom the precipitation features found in this study, anexamination of the rainfall from features according totheir estimated intensity follows. Features will be par-titioned by their minimum PCTs and maximum heights

of the 30-dBZ echo to compare differences among theregions’ precipitation features.

1) RAINFALL BY MINIMUM PCT

Figure 8 is a CDF of precipitation feature rainfallaccording to their minimum PCT for each region. Forthe PCT CDFs, the scale has been reversed to indicateincreased ice scattering intensities to the right (decreas-ing brightness temperatures). The curves have been con-structed using 10-K PCT intervals plotted at the mini-mum PCT contained in that interval. For each of thefollowing categories, precipitation features without icescattering (diamonds), precipitation features with icescattering (plus symbols), and precipitation featureswith MCSs (filled circles), the CDF of features by theirminimum PCT (dashed line) and the relative rainfallcontribution by those features is plotted (solid line).Subtracting the dashed from the solid curve for a givensystem type yields the difference in the contribution tothe population and the total rainfall distributions at agiven PCT range. For example over the east Pacific,there is about a 25% difference between the number andrainfall distributions at 260 K. Looking at the drop ineither distribution over a given range indicates the rel-ative occurrence of features in that bin. Again over theeast Pacific, the almost 60% difference between the 280-and 270-K minimum PCT bins in the feature withoutice scattering number distribution show the large num-bers of features in this category. However, these featuresonly account for less than 25% of the rainfall in thecategory.

For each region, there are shape similarities in theminimum PCT number and rainfall distributions. Thereare significant differences, especially when comparingthe intensities of the features. Over the ocean, featureswithout ice scattering occupy a sample space about 10–20 K higher than over land. The 270–280-K range con-tains more than half of these features over the ocean,while these weak features are most likely located in the260–270-K bin over land. For both land and ocean,rainfall is more likely to originate from features withmore ice scattering, as evidenced by the rightward shiftin the rainfall distribution.

Precipitation features with ice scattering range tomuch lower PCTs over land than ocean. By number,both Africa and South America have about 20% of fea-tures containing ice scattering signatures less than 175K. Over the ocean, this part of the distribution is morenearly 190 K. This has implications on the rainfall dis-tribution. For all of the regions, there is a 15%–25%difference in the number and rainfall distributions formost minimum PCTs. However, the larger number ofintense features over land shifts the rainfall distribu-tions. For example, between 20% and 25% of the con-tinental rainfall comes from features with minimumPCTs less than 150 K, compared with less than 10% ofoceanic rainfall. Note that these features do not meet


FIG. 8. CDF of number (dashed) and rainfall (solid) by minimum PCT for the four analysis regions by system type (see legend).

MCS criteria, so these small features are more intenseover land, with more rainfall coming from these strongerfeatures.

For MCSs, again there is a significant regional dif-ference in the intensity distributions, with a significantportion of the features below 100 K over land. However,the rainfall distributions are very similar to the popu-lation distributions, indicating that minimum PCT is nota significant indicator of rainfall in these large systemswhere certain intensity criteria have been already sat-isfied. However, there seems more of a difference amongthe two distributions over land, indicating more of apeak intensity–rainfall relationship.

2) RAINFALL BY MAXIMUM HEIGHT OF THE 30-DBZECHO

As in the previous section, here (Fig. 9) number andrainfall distributions are analyzed for the precipitationfeature types, but by the maximum height of the 30-dBZ echo. Like the maximum 6-km reflectivity, this

parameter presents a scale of feature intensity indepen-dent of the criteria used to define the features them-selves. DeMott and Rutledge (1998) analyzed the max-imum 30-dBZ echo heights and rainfall during TOGA-COARE in the west Pacific warm pool and found oce-anic peaks in the number distribution of 30-dBZ heightsat 4–5 km with peaks in the rainfall distribution ac-cording to 30-dBZ heights at 5–6 km (their Figs. 5 and6). From TRMM, it is again apparent that there is largenumber of weak, shallow features that contribute littletoward the total regional rainfall compared to the deepersystems (Fig. 9).

There is a large number (33%) of very shallow pre-cipitation features without ice scattering over the eastPacific with a 30-dBZ echo height of 1 km or less thatcontribute 10% of the rainfall in that region. Over land,about 90% of the features without ice scattering havemaximum 30-dBZ echo heights less than 7 km. The 90thpercentile lowers to about 5 km over the west Pacificand 4 km over the east Pacific. This independentlyshows that these features without ice scattering (#250


FIG. 9. CDF of number (dashed) and rainfall (solid) by the maximum height of the 30-dBZ echo for the four analysis regions by systemtype (see legend).

K) are indeed shallow reflectivity features with rela-tively little mass above the freezing level. Also note thedifferences in the shapes of the distributions among theregions, which is more significant than the PCT distri-bution differences. This difference is apparent in theother classifications and is likely due to the fact thatecho height is not a criteria in precipitation feature se-lection and is not reflected in the sample selection inany way. For all distributions, it is clear that the veryshallow features do not contribute to the rainfall sig-nificantly despite their abundance.

Precipitation features with ice scattering can rangefrom somewhat shallow features (with maximum 30-dBZ echo heights around the freezing level) to very deepconvective features that just did not meet MCS criteria.Number and rainfall distributions over the ocean aremuch steeper than over land, indicating the rarity of thedeep features (with the maximum 30-dBZ height $10km) over water. By percentile, rainfall is contributedmore by deeper features over the land than over the

ocean (as portrayed by the wider gap between the num-ber and rainfall curves). As with the PCT classification,MCSs seem to contribute to the rainfall proportionatelyby their abundance. Land and ocean differences in in-tensity are apparent, but South America is the only re-gion that shows a significantly increased contributionto rainfall from deeper features. These results show thatpeak intensity (i.e., minimum or maximum) values maynot be the best indicator of rainfall parameters (espe-cially for MCSs).

These rainfall distributions elucidate several keypoints about differences in rainfall among the regionssampled in this study. Despite oceanic regions contain-ing more systems, a large number of these produce littlerainfall. The intense features over the ocean that producethe bulk of the rainfall tend to reach minimum PCTsbetween 175 and 225 K and have maximum 30-dBZecho heights between 5 and 9 km. Land features pro-ducing most of the rainfall have more widely varyingproperties; contribution to rainfall is spread out over a


range of lower minimum PCTs and echo heights reach-ing greater heights than the oceanic systems.

5. Conclusions and future work

The precipitation features examined using TRMMover differing regions of the Tropics have several dis-tinguishing features. This 3-month dataset containedover 70 000 precipitation features, with about 90% and80% of those over ocean and land, respectively, con-tained no TMI 85-GHz PCTs less than 250 K. Featureswith and without ice scattering were numerous whencompared to the number of precipitation features con-taining MCSs (around 1% of the total number over allregions except Africa, which contained 2.6%).

Important differences were found in the intensity andrainfall patterns of land versus ocean convection. Areadistributions revealed that when all precipitation fea-tures were accounted for, similar area distributions werefound for all regions, with continental regions havingslightly larger areas. The MZ equivalent MCSs (whenconsidered only for their area with PCT #250 K) ex-hibited a similar pattern with continental systems havinglarger areas. When the rain area associated with theMCSs were investigated, this pattern reversed with oce-anic intense systems containing a larger area of rainfallthan systems over land. With this rain occurring at highbrightness temperatures at 85 GHz (i.e., little ice scat-tering), increased ambiguity is introduced in ice scat-tering-based precipitation retrieval. This highlights theinherent uncertainties in using ice scattering methods inestimating light precipitation, or heavy rainfall occur-ring at small spatial scales or without a significant icescattering signature.

Using properties retrieved from the PR, differencesin reflectivity structure among the regions could bequantified. With significantly higher values of maximum6-km reflectivity and maximum height of the 30-dBZecho, land convection was shown to be significantlystronger than convection over the ocean, with Africahaving the strongest and the east Pacific containing theweakest. This was also reflected in the minimum PCTdistributions that signaled much more ice mass aloft incontinental features. This was shown to a higher degreewhen the larger (and likely more intense) storms (MCSs)were taken into consideration. All this information sup-ports hypotheses about the lack of lightning over theocean being linked to slower updraft speeds unable tocreate large optical depths of ice above the melting level.

Implications of the differing intensity parameters ob-served on the rainfall distribution were examined. De-spite similar retrieved rainfall amounts over the fourregions when normalized by area, the modes of rainfallwere very different when comparing the regions. Theoceanic regions had significant rainfall contributionfrom a large number of warm systems without signifi-cant ice scattering. Again, these features are either warmrain features in the classic definition, features with in-

sufficient ice processes to lower PCT below 250 K, orsmeared systems too small to be resolved by TRMMresolution. The second mode of oceanic rainfall comesfrom large, moderately intense systems with ice scat-tering. These systems are almost never intense enoughto produce lightning, but do produce heavy rainfall.Over continental areas, the bulk of the rainfall comesfrom a broad spectrum of moderately strong to veryintense features, with more of the rainfall coming fromareas with lower minimum PCTs and higher maximumheights of the 30-dBZ echoes than over the ocean. Rain-fall from smaller systems was a more direct function oftheir intensity than for stronger systems, for which rain-fall was a function of their abundance and size. In spiteof differences in the rainfall distributions for the storms,lightning flash densities for storms with similar rainfallfollowed similar distributions over both regions. Thisrelated the crucial need for more detailed microphysicalstudy and modeling in comparing land and oceanstorms. This may be done with more detailed analysisof the TRMM satellite and ground validation dataset,which can aid in improving conceptual and numericalmodels.

Future work will include a more detailed examinationof the reflectivity and passive microwave signatures ofprecipitation in the Tropics. Full datasets of radar re-flectivity profiles as well as multiple TMI channels willbe used to more closely investigate hypotheses aboutmicrophysical differences in convection throughout theTropics. Also, high-resolution aircraft and ground-basedreflectivity, cloud physics, and other environmentalmeasurements obtained in recent TRMM field cam-paigns can be used to aid in answering questions aboutstorm structure and microphysics.

Future work in progress includes expanding the tem-poral and spatial dimensions of the TRMM precipitationfeature dataset throughout the Tropics to examine pre-cipitation features measured during the first two yearsof the satellite’s operation. A larger dataset will allowthe investigation of diurnal cycle issues in depth, a largerregional comparison, and a more detailed comparisonof the structure of precipitation features (i.e., convectiveversus stratiform properties). Also, incorporating theLIS data in a more quantative manner will allow thetesting of lightning–ice scattering–radar reflectivity re-lationships throughout the Tropics. In addition, the gen-eration of detailed, high-resolution case studies from theTRMM field programs of 1998–99 will allow those ob-served precipitation features to be placed in the contextof the Tropicswide TRMM precipitation feature dataset.

Acknowledgments. The authors would like to thankother members of the Tropical Convection ResearchProgram at Texas A&M University (where the bulk ofthis research was conducted): E. Richard Toracinta,Chris West, and David B. Wolff. Data were obtainedfrom the TRMM Science and Data Information System(TDSIS) at the NASA Goddard Space Flight Center


Distributed Active Archive Center (DAAC) and theGlobal Hydrology Resource Center (GHRC) at theGlobal Hydrology and Climate Center (GHCC). Thanksare also extended to Dr. Ramesh Kakar for his supportof TRMM research, also Dr. Karen I. Mohr, Dr. MichaelBiggerstaff, Dr. Thomas Over, Dr. Richard Orville, Dr.Dennis Boccippio, Dr. Robert Meneghini, Dr. Jun Awa-ka, and Dr. Baike Xi. One of the anonymous reviewersdeserves credit for unusually detailed and constructivecriticisms (and the suggestions to include Table 9 andFig. 4), which greatly increased the clarity of this work.This work was supported by NASA TRMM GrantNAG5-4796.

REFERENCES

Biggerstaff, M. I., and R. A. Houze Jr., 1991: Kinematic and pre-cipitation structure of the 10–11 June 1985 squall line. Mon.Wea. Rev., 119, 3034–3065.

Chen, S. S., and R. A. Houze Jr., 1997: Diurnal variation and life-cycle of deep convective systems over the tropical Pacific warmpool. Quart. J. Roy. Meteor. Soc., 123, 357–388.

Christian, H. J., 1999: Optical detection of lightning from space. Proc.11th Int. Conf. on Atmospheric Electricity, Guntersville, AL,NASA/CP-1999-209261, 715–718.

DeMott, C. A., and S. A. Rutledge, 1998: The vertical structure ofTOGA COARE convection. Part I: Radar echo distributions. J.Atmos. Sci., 55, 2730–2747.

Dye, J. E., and Coauthors, 1986: Early electrification and precipitationdevelopment in a small, isolated Montana cumulonimbus. J.Geophys. Res., 91, 1231–1247., W. P. Winn, J. J. Jones, and D. W. Breed, 1989: The electri-fication of New Mexico thunderstorms. 1. Relationship betweenprecipitation development and the onset of electrification. J.Geophys. Res., 94, 8643–8656.

Hakkarinen, I. M., and R. F. Adler, 1988: Observations of convectiveprecipitation at 92 and 183 GHz: Aircraft results. Meteor. Atmos.Phys., 38, 164–182.

Hall, T. J., and T. H. Vonder Harr, 1999: The diurnal cycle of westPacific deep convection and its relation to the spatial and tem-poral variation of tropical MCSs. J. Atmos. Sci., 56, 3401–3415.

Heymsfield, G. M., I. J. Caylor, J. M. Shepherd, W. S. Olson, S. W.Bidwell, W. C. Boncyk, and S. Ameen, 1996: Structure of Floridathunderstorms using high-altitude aircraft radiometer and radarobservations. J. Appl. Meteor., 35, 1736–1762.

Houze, R. A., 1993: Cloud Dynamics. Academic Press, 573 pp.Iguchi, T., and R. Meneghini, 1994: Intercomparison of single-fre-

quency methods for retrieving a vertical rain profile from air-borne or spaceborne radar data. J. Atmos. Oceanic Technol., 11,1507–1516.

Jorgensen, D. P., and M. A. LeMone, 1989: Mesoscale and convec-tive-scale characteristics of oceanic convection. J. Atmos. Sci.,46, 621–640., E. J. Zipser, and M. A. LeMone, 1985: Vertical motions inintense hurricanes. J. Atmos. Sci., 42, 839–856.

Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998:The Tropical Rainfall Measuring Mission (TRMM) sensor pack-age. J. Atmos. Oceanic Technol., 15, 809–817.

Lucas, C., E. J. Zipser, and M. A. LeMone, 1994: Vertical velocityin oceanic convection off tropical Australia. J. Atmos. Sci., 51,3183–3193.

Maddox, R. A., 1980: An objective technique for separating mac-roscale and mesoscale features in meteorological data. Mon.Wea. Rev., 108, 1108–1121.

Mapes, B. E., and R. A. Houze Jr., 1993: Cloud clusters and super-clusters over the oceanic warm pool. Mon. Wea. Rev., 121, 1398–1415.

Mohr, K. I., and E. J. Zipser, 1996: Mesoscale convective systemsdefined by their 85-GHz ice scattering signature: Size and in-tensity comparison over tropical oceans and continents. Mon.Wea. Rev., 124, 2417–2437., J. S. Famiglietti, and E. J. Zipser, 1999: The contribution totropical rainfall with respect to convective system type, size, andintensity estimated from the 85-GHz ice-scattering signature. J.Appl. Meteor., 38, 596–606.

Mugnai, A., E. A. Smith, and G. J. Tripoli, 1993: Foundations forstatistical–physical precipitation retrieval from passive micro-wave satellite measurements. Part II: Emission-source and gen-eralized weighting-function properties of a time-dependentcloud-radiation model. J. Appl. Meteor., 32, 17–39.

Petersen, W. A., S. A. Rutledge, and R. E. Orville, 1996: Cloud-to-ground lightning observations from TOGA COARE: Selectedresults and lightning location algorithms. Mon. Wea. Rev., 124,602–620.

Rickenbach, T. M., and S. A. Rutledge, 1998: Convection in TOGACOARE: Horizontal scale, morphology, and rainfall production.J. Atmos. Sci., 55, 2715–2729.

Riehl, H., and J. S. Malkus, 1958: On the heat balance in the equatorialtrough zone. Geophysica, 6, 503–538.

Smith, E. A., A. Mugnai, H. J. Cooper, G. J. Tripoli, and X. Xiang,1992: Foundations for statistical–physical precipitation retrievalfrom passive microwave satellite measurements. Part I: Bright-ness-temperature properties of a time-dependent cloud-radiationmodel. J. Appl. Meteor., 31, 506–531.

Spencer, R. W., 1986: A satellite passive 37-GHz scattering-basedmethod for measuring oceanic rain rates. J. Climate Appl. Me-teor., 25, 754–766., H. M. Goodman, and R. E. Hood, 1989: Precipitation retrievalover land and ocean with the SSM/I: Identification and char-acteristics of the scattering signal. J. Atmos. Oceanic Technol.,6, 254–273.

Takahashi, T., 1978: Electrical properties of oceanic tropical cloudsat Ponape, Micronesia. Mon. Wea. Rev., 106, 1598–1612.

Vivekanandan, J., J. Turk, and V. N. Bringi, 1991: Ice water pathestimation and characterization using passive microwave radi-ometry. J. Appl. Meteor., 30, 1407–1421.

Williams, E. R., R. Zhang, and J. Rydock, 1991: Mixed-phase mi-crophysics and cloud electrification. J. Atmos. Sci., 48, 2195–2203., S. A. Rutledge, S. G. Geotis, N. Renno, E. Rasmussen, and T.Rickenbach, 1992: A radar and electrical study of tropical ‘‘hottowers.’’ J. Atmos. Sci., 49, 1386–1395.

Zipser, E. J., 1994: Deep cumulonimbus cloud systems in the Tropicswith and without lightning. Mon. Wea. Rev., 122, 1837–1851., and M. A. LeMone, 1980: Cumulonimbus and vertical velocityevents in GATE. Part II: Synthesis and model core structure. J.Atmos. Sci., 37, 2458–2469., and K. R. Lutz, 1994: The vertical profile of radar reflectivityof convective cells: A strong indicator of storm intensity andlightning probability? Mon. Wea. Rev., 122, 1751–1759.