Eurainsat Chapter Tools v04

8/13/2019 Eurainsat Chapter Tools v04

1/17

Cloud top microphysics as a tool for precipitation measurements

Daniel Rosenfeld, Hebrew University of Jerusalem, Jerusalem, Israel

Introduction

Rainfall measurements from space are based on the interpretation of theelectromagnetic radiation that is scattered and emitted from the clouds, precipitation andthe underlying surface, and is monitored by the satellite instruments at the variouswavebands. The interaction of the radiation with the cloud and precipitation particlesstrongly depends on their composition and size distribution, as described by the Mietheory. Therefore, variability in the cloud microstructure and precipitation properties forclouds having the same macroscopic properties and rain intensity would result insubstantial changes in the satellite measured radiation that comes from the rain cloud and

hence would cause large variability in the inferred rain intensity for a given actual rainfall.

The variability in precipitation composition and size distribution affects mainly thedirect rainfall measurements. Direct measurements use the portion of the electromagneticspectrum that interacts strongly with the precipitation particles while weakly interacting withthe small cloud particles. The passive microwave band is used for direct measurements ofprecipitation because precipitation size lies within this waveband and radiation interactsmost strongly with particles that are of similar wavelength. The strength of the interactionfor smaller particles decreases with the 6thpower of the particle size (Rayleigh scattering).Using microwave takes us closest to the desirable situation of "visible" precipitation within"transparent" clouds.

Indirect measurements are defined as such that infer the precipitation by observingother cloud properties. Potential rain clouds are sufficiently optically thick to be opaque inthe visible and IR wavebands. Therefore, the radiation that reaches the satellite sensorstypically comes from the cloud droplets and ice particles near the cloud tops, with little orno contribution from the actual precipitation particles. The indirect measurements relatethe precipitation mainly to the cloud top temperatures and its type, i.e., convective orstratiform. However, precipitation in clouds of a given depth can vary greatly in clouds ofdifferent composition, i.e., cloud particle size distribution and phase ice or water. Thesedifferences can determine not only the precipitation properties, but also the differencebetween precipitating or rainless clouds that have otherwise the same cloud top

temperatures and dimensions (Rosenfeld, 1999 and 2000).

Given this state of affairs, it is clear that recognizing cloud microstructure canpotentially be used for improving the accuracy of rainfall measurements from space withdirect measurement methods and even more so for the indirect methods. In this chapterwe will review the ways cloud microstructure affect the precipitation, and the ways bywhich this can be utilized for enhancing the accuracy of precipitation measurements fromspace.

The simplest indirect rainfall measurement is the GOES precipitation Index (GPI)that estimates rainfall from the product of the mean fractional coverage of cloud colder

than 235K in a 2.5 x 2.5 box, the length of the averaging period in hours and a constantof 3 mm h-1 (Arkin and Meisner 1987). This method works very well in the tropicalconvective clouds, but fails in the extra-tropical rain cloud systems. Atlas and Bell (1992)


2/17

suggested that the fundamental reason for the excellent correlation between the area ofcloud tops < 235oC and the surface precipitation on the large scale in the tropics isbecause the cold cloud tops are in fact the anvils of the cumulonimbus (Cb) clouds. In thedeep tropics, with little dynamic complicating factors, the area time integral of the anvilsarea is proportional to the amount of air that is advected to the upper troposphere by the

Cb clouds throughout their lifecycle. The requirement for inclusion of complete lifecycle ofthe convective systems explains why the GPI correlates so highly with the integral surfacerainfall over large areas, but has little skill over small domains.

The robust GPI relation between cold cloud top temperature area and rainfallamount on the surface were competitive with the direct rainfall measurement methods forconvective clouds over the tropical oceans (Ebert et al., 1996). However, over land thiswas no longer the case. McCollum et al. (2000) showed that the both the GPI and passivemicrowave rainfall data overestimated the rainfall by a factor of 2 over Africa, while havingno significant bias over South America. They suggested that this is due to systematicmicrophysical changes in the cloud properties between the two continents, such that the

smaller drops that dominate the clouds over Africa affect the passive microwave signaland the time-area extent of the cold cloud tops in ways that appears as greater rainfallthan the true amount. How that can happen will be the subject of the next sections.

Relations between cloud drop size and precipitation forming processes

Clouds form by condensation of vapor on aerosols that serve as cloud dropcondensation nuclei (CCN). The cloud droplets continue growing by condensation ofvapor. Satellite measurements can detect re, the cloud drop effective radius (re=/,where r is the radius of the cloud droplets in the measurement volume) using methods thatwere pioneered by Arking and Childs (1985) and Nakajima and King (1990). The

probability of collision and coalescence of drops with re< 12 m is very small, to the extentthat raindrops cannot form by this mechanism within the lifetime of clouds. This probability

increases fast with droplet size, so that in clouds with re> ~14 m coalescence of clouddroplets into raindrops leads to fast formation of rainfall (Rosenfeld and Gutman, 1994,Gerber, 1996).

When cloud drops are too small for creating raindrops by coalescence, precipitationcan still form by ice processes. This requires that the cloud will develop to heights abovethe zero isotherm level. The small cloud droplets can remain super cooled (i.e., at a liquidstate but colder than 0C) typically up to -15C to -25C, and in extreme cases all the way

to the homogeneous freezing isotherm of -38C (Rosenfeld and Woodley, 2000). Iceprecipitation develops in such clouds when ice crystals form and grow on expense of thecloud drops due to the smaller vapor pressure on ice than on water. When the crystalsgrow they get rimed by the super cooled cloud droplets, and so become graupel particlesof several mm in diameter. When graupel continues growing beyond about 1 cm they canbecome hailstones.

Larger water drops freeze faster at higher temperatures. Therefore, clouds withlarge droplets that are fast to coalesce into rain drops also produce ice precipitationparticles, typically graupel, at relatively high temperature of -5C to -10C. Therefore, the reof the droplets near cloud top can be used to infer also the presence of ice precipitation

from clouds with cold tops, using the same threshold of re>14 m as for "warm rain"

clouds.In mature or stratiform clouds with slow vertical air motions there is sufficient time

for the cloud drops to freeze and become ice crystals that aggregate into snow flakes. The


3/17

super cooled water at the upper and hence colder portions of such clouds is typicallycompletely converted into ice crystals and precipitation particles, so that the clouds aresaid to be glaciated. Ice crystals that form by heterogeneous freezing in a super cooledcloud grow on expense of the cloud drops, so that in such glaciated cloud each ice crystalcontains water amount that was previously distributed in many drops. Therefore, the ice

crystals are much larger than the cloud droplets from which they were formed, and hencepossess much larger rethan that of the source water cloud. These ice crystals aggregatewith time into snow flakes. When they fall through super cooled clouds the snow flakescontinue growing by accreting the cloud droplets. Distinction must be made here betweenthis situation and ice clouds that form by homogeneous freezing of cloud droplets near orabove the -38C isotherm. Such homogeneously frozen drops retain their mass andbecome similarly small and numerous ice crystals, that have no efficient mechanism toaggregate into precipitation particles at such cold temperatures and small sizes. Thissituation can be detected by the existence of ice clouds with small reat temperatures < -38C. Such clouds would be poor precipitators, because only a small fraction of the cloudwater is converted into rainfall. On the other hand, clouds that form large ice particles at

high temperatures indicate high precipitation efficiency. This principle can be used forassigning relative rainfall amounts for clouds having the same physical dimensions.

Inferring precipitation forming processes from satellite retrieved T-rerelations

The sensors on board the recent satellites have a family of spectral bands in thesolar and terrestrial portion of the radiation spectrum. For example, the geostationarryMeteosat Second Generation (MSG) satellite has 12 spectral bands, from which cloud

composition can be retrieved. These channels, 2.1 and 3.8 m in particular, make itpossible to measure parameters such as thermodynamic phase and rein addition to visiblereflectance and the thermal emission temperature.

Much more information about the cloud microstructure and precipitation formingprocesses in convective clouds can be obtained from analyses of complete cloud clusters,residing in areas containing thousands of satellite pixels. The underlying assumption isthat the microphysical evolution of a convective cloud can be represented by compositionof the instantaneous values of the tops of convective clouds at different heights. This isbased on the knowledge that cloud droplets form mainly at the base of convective clouds,and grow with increasing height or decreasing T. The form of dependence of re on Tcontains vital information about the cloud and precipitation processes, as described below.

The T-rerelations are obtained from an ensemble of clouds having tops covering a largerange of T. Usually many pairs of T-re for each 1

oC interval are observed in a regioncontaining a convective cloud cluster. The points with smaller refor a given T are typicallyassociated with the younger cloud elements, whereas the larger re for the same T areassociated with the more mature cloud elements, in which the droplets growth had moretime to progress by coalescence, and ice particles had more time to develop. Therefore, itis useful to plot not only the median value of T-rerelation, but also, say, the 15

thand 85thpercentiles, for representing the younger and more mature cloud elements within themeasurement region.

Based on the shapes of the T-rerelations, Rosenfeld and Lensky (1998) defined thefollowing five microphysical zones in convective clouds:


4/17

1) Diffusional droplet growth zone: Very slow growth of cloud droplets with depth

above cloud base, indicated by small -dre/dT.

2) Droplet coalescence growth zone: Large increase of the droplet growth with height,

as depicted by large -dre/dT at T warmer than freezing temperatures, indicating rapid

cloud-droplet growth with depth above cloud base. Such rapid growth can occur

there only by drop coalescence.

3) Rainout zone: A zone where re remains stable between 20 and 25 m, probably

determined by the maximum drop size that can be sustained by rising air near cloud

top, where the larger drops are precipitated to lower elevations and may eventually

fall as rain from the cloud base. This zone is so named, because droplet growth by

coalescence is balanced by precipitation of the largest drops from cloud top.

Therefore, the clouds seem to be raining out much of their water while growing. The

radius of the drops that actually rain out from cloud tops is much larger than the

indicated reof 20-25 m, being at the upper end of the drop size distribution there.

4) Mixed phase zone: A zone of large indicated droplet growth with height, occurring at

T


5/17

0 5 10 15 20 25 30 35

-40

-30

-20

-10

0

10

20

reff

T

[oC]

m]

Glaciated

Mixed Phase

Rainout

Coalescence

General

Diffusional growth

0 5 10 15 20 25 30 35

-40

-30

-20

-10

0

10

20

reff

T

[oC]

m]

Glaciated

Mixed Phase

Rainout

Coalescence

Maritime

0 5 10 15 20 25 30 35

-40

-30

-20

-10

0

10

20

reff

T

[oC]

m]

Glaciated

Mixed Phase

Coalescence

Continental - moderate

Diffusional growth

0 5 10 15 20 25 30 35

-40

-30

-20

-10

0

10

20

reff

T

[oC]

m]

Glaciated

Mixed Phase

Continental - extreme

Diffusional growth

Fig. 1: The classification scheme of convective clouds into microphysical zones, according to theshape of the T-rerelations. Note that in extremely continental clouds reat cloud base is very small,

the coalescence zone vanishes, mixed phase zone starts at T


6/17

distinguishing stratified from convective clouds by their microstructure. Typically, aconvective cloud has a larger re than a layer cloud at the same height, because theconvective cloud is deeper and contains more water in the form of larger drops.

Additional vertical information can be obtained by using channels that penetrate to

different depth below cloud tops (Rosenfeld et al., 2004). Chang and Li (2003) pioneeredthis concept using 1.6, 2.1 and 3.7 m channels of MODIS, retrieving the vertical profilesnear the tops of stratiform clouds.

In addition to the microphysical zones, it also can be determined that convective

clouds start precipitating at re> 14 m (Rosenfeld and Gutman, 1994; Gerber, 1996). Thiscan be used quantitatively for improving the accuracy of rainfall measurements fromspace, as demonstrated by Lensky and Rosenfeld (1997) for the NOAA/AVHRR. Thisprinciple was applied to an operational rainfall product (Ba and Gruber, 2001).

The dependence of rainfall remote sensing on hydrometeor size distributions

The previous sections provided a physical basis for indirect measurements ofprecipitation based on the retrieved cloud top composition and temperature, using thevisible and IR wavebands. Direct measurements of precipitation use the microwavefrequencies that interact directly with the precipitation size particles (diameter > 0.1 mm).Direct measurements are divided into active and passive. Active microwavemeasurements require a radar instrument that transmits pulses of radiation and receivesthe back scattered echoes. The echo intensity is converted into precipitation intensityaccording to the radar equation. The backscatter occurs mainly in the Rayleigh regime,

where the intensity of the scattered radiation is proportional to the 6th

power of the particlesize. This highly non-linear relation causes a serious problem of non-uniqueness betweenthe echo and precipitation intensities. Small concentration of large hydrometeors canproduce the same reflectivity factor (Z, [mm6m-3]) as much larger concentration of smallerhydrometeors that form much greater equivalent rain intensity (R, [mm hr-1]). This non-uniqueness in the Z-R relations has been historically the weakest point in radar rainfallmeasurements from both surface and space-borne platforms.

Rainfall measurements from space with passive microwave rely on both thermalemission and back scatter. The thermal emission does not depend so strongly on theparticle size, but because of that it cant distinguish between cloud water and precipitation.The thermal emission can be used mainly on the cold background of the oceans, which

appear cold due to the small microwave emissivity of flat water surfaces. Most of the signalfrom deep convection comes from the backscatter of the upwelling thermal radiation backdownward. This signal strongly depends on the particle size, as in the case for the radar.The larger the hydrometeors the more energy is backscatters to the surface and the lowerthe satellite measured brightness temperature becomes. It can be also viewed as largerparticles backscatter more strongly the 3K background of the outer space, but it isinaccurate in a strict physical sense. Here again the same rain intensity that is associatedwith larger hydrometeors would be interpreted as stronger passive microwave signal andheavier rain. Ice hydrometeors are colder and have smaller emissivity than rain drops, andhence they would create lower brightness temperature for the same precipitation intensity,and more so when they reside higher in the cloud and at lower temperatures. Therefore,the correct interpretation into rainfall of both active and passive microwave measurementsdepends strongly on the relations between the hydrometeor size distributions, types andthe rain intensity.


7/17

It is essential to obtain information about the hydrometeor sizes for achievingreasonable accuracy of the precipitation intensities. This can be achieved by space borneradar measurements with multiple wavelength radar, as planned for the GlobalPrecipitation Mission. The already available space borne radar onboard the TropicalRainfall Measuring Mission (TRMM) satellite has only a single wavelength, and hence

requires external independent information about the Z-R relations. Passive microwavemeasurements are conducted in several wavelengths simultaneously, but have verylimited capability to resolve the particle size, especially in deep convective clouds.

Cloud microstructure and Z-R relationships

In the previous section we have seen that independent external information onhydrometeors type and sizes is essential for improving the accuracy of both the radar andpassive microwave precipitation measurements. Such information can be obtained from

the inferred cloud microstructure and precipitation forming processes as obtained from theT-re relations. We will review here the precipitation evolution in microphysically maritimeand continental clouds. Microphysically maritime clouds are composed of lowconcentration of large cloud drops that coalesce readily into warm rain. In contrast,microphysically continental clouds are composed of small drops that form precipitationmainly by ice processes.

a. Microphysically maritime clouds: Evolution of warm rain

In a hypothetical rising cloud column with active coalescence, the initial dominantprocess would be widening of the cloud drop size distribution into large concentrations ofdrizzle drops; the drizzle continues to coalesce with other drizzle and cloud drops intoraindrops, which will continue to grow asymptotically to the equilibrium rain drop sizedistribution, with the median volume drop diameter D0e =1.76 mm (Hu and Srivastava,1995). Therefore, during the growth phase of the precipitation particles the rain rate Rincreases with D0, median volume drop diameter, and this would increase D0for a given R.Ideally, for rainfall with drops that fall from cloud top while growing, Rwould increase withthe fall distance from the cloud top, mainly by growth of the falling drops due to accretionand coalescence, and to a lesser extent by addition of new small rain drops, until theraindrops grow sufficiently large for breakup to become significant. Shallow orographicclouds can present conditions such as some distance below the tops of convective clouds.

Therefore, similar evolution ofR

can be observed on a mountain slope, such asdocumented by Fujiwara (1965). Different values of R near cloud top or in shalloworographic clouds can come mainly from changing NT, the total concentration of raindrops,because the drop size is bounded by the limited vertical fall distance along which they cangrow. This would cause orographic precipitation to have small drops and for Rto dependmainly on NT, and more so with shallower clouds and stronger orographic ascent, becausethe stronger rising component supplies more water for the production of many smallraindrops not too far below cloud top, which are manifested as a larger R.

b. Microphysically continental clouds: Evolution of cold rain

Microphysically continental clouds are characterized by narrow cloud drop sizedistributions, and therefore having little drop coalescence and warm rain. Most raindropsoriginate from melting of ice hydrometeors that are typically graupel or hail in the


8/17

convective elements, and snowflakes in the mature or stratiform clouds. Graupel and hailparticles grow without breakup while falling through the supercooled portion of the cloud,and continue to grow by accretion in the warm part of the cloud, where they melt. Largemelting hailstones shed the excess melt-water in the form of a rain drop size distribution(RDSD) about which little is known. The shedding stops when the melting particles

approach the size of the largest stable raindrops, which are later subject to further breakupdue to collisions with other raindrops. In fact, new raindrops formation is limited only to thebreakup of pre-existing larger precipitation particles. Therefore, we should expect that insuch clouds there would be, for a given R, a relative dearth of small drops and excess oflarge drops compared to microphysically maritime clouds with active cloud dropcoalescence. Deep continental convective clouds would therefore initiate the precipitationby forming large drops that with maturing approach DSDe from above. This is in contrastwith the approach from below for maturing maritime RDSD.

Recent satellite studies (Rosenfeld and Lensky, 1998) have shown thatmicrophysically maritime clouds are associated typically with a rainout zone, i.e., the fastconversion of cloud water to precipitation cause the convective elements to lose water to

precipitation while growing. This leaves less water carried upward to the super cooledzone, so that weaker ice precipitation can develop aloft. Williams et al. (2002) haverecognized this as a potential cause to the much greater occurrence of lightning incontinental compared to maritime clouds. Williams et al. (2002) noted that frequentlightning occurred also in very clean air during high atmospheric instability, probablybecause the strong updraft leaves little time to the formation of warm rain, and carries thelarge raindrops that manage to form up to the super cooled levels of the clouds, wherethey freeze and participate in the cloud electrification processes (Atlas and Williams,2003).

This difference between continental and maritime clouds means that mostly warmrain would fall even from the very deep maritime convection, which reaches well above thefreezing level, whereas precipitation from continental clouds would originate mainly in iceprocesses. Therefore, the expected difference in RDSD between microphysically maritimeand continental clouds is expected to exist also for the deepest convective clouds thatextend well into the sub-freezing temperatures.

c. Quantifying the role of cloud top reon RDSD

The ultimate test for the role of cloud microstructure is comparing the RDSD ofclouds at the same location, but at different times, when they possess maritime orcontinental microstructure. Rosenfeld and Ulbrich (2003) did exactly that. They used the

VIRS (Visible and Infrared sensor) onboard TRMM (Tropical Rainfall Measuring Mission)satellite to retrieve the microstructure of rain clouds over disdrometer sites. The cloudswere classified into continental, intermediate and maritime, using the methodology ofRosenfeld and Lensky (1998). The RDSDs from the continental and maritime classesduring the overpass time + 18 hours were lumped together and plotted in Fig. 2. Indeed,the continental and maritime RDSDs are well separated in Fig. 2, with the continentalclouds producing greater concentrations of large drops and smaller concentrations of smalldrops. A comparison between the directly measured disdrometer rainfall and thecalculated accumulation by applying the TRMM Z-R relations (Iguchi et al., 2000) to thedisdrometer measured Zresulted in a relative overestimate by more than a factor of two ofthe rainfall from the microphysically continental clouds compared to the maritime clouds.


9/17

0.01

0.1

1

10

100

1000

0 1 2 3 4 5 6

Florida Cont

Florida Mar

LBA Cont

LBA Mar

India Cont

India Mar

Kwaj Mar

N[

m

m

m

-3]

D [mm ]

Fig. 2: Disdrometer measured RDSDs of continental (solid lines) and maritime (brokenlines) rainfall, as microphysically classified by VIRS overpass. The RDSD is averaged for

the rainfall during 18 hours of the overpass time, and the concentrations are scaled to 1mm h-1. The disdrometers are in Florida (Teflun B), Amazon (LBA), India (Madras) andKwajalein (From Rosenfeld and Ulbrich, 2003).

The evidence shows that it is mainly the cloud microstructure that is responsible tothe large systematic difference in the RDSD and Z-R relations between maritime andcontinental clouds. There are several possible causes for these differences, all working atthe same direction:


10/17

a. Extent of coalescence

The cloud drop coalescence in highly maritime clouds is so fast that rainfall isdeveloped low in the growing convective elements and precipitates while the clouds arestill growing. The large concentrations of raindrops that form low in the cloud typically fall

before they have the time to grow and reach equilibrium RDSD, thereby creating therainout zone (Rosenfeld and Lensky, 1998) less than 2 km above cloud base height.Therefore, D0remains much smaller than D0e, as was shown in Fig. 6b of Rosenfeld andUlbrich (2003).

In microphysically continental clouds with suppressed coalescence the cloud has togrow into large depth before start precipitating, by either warm or cold processes. Theraindrops that fall through the lower part of the cloud grow by accretion of small clouddrops, so that they tend to breakup much less than drops that grow mainly by collisionswith other raindrops, as is the case for maritime clouds. This process allows D0to exceedD0ein the growing stages of the precipitation, and later approach it from above when theraindrop collisions become more frequent with the intensification of the rainfall.

b. Warm versus cold precipitation processes

The rainout of the maritime clouds (Rosenfeld and Lensky, 1988) depletes the cloudwater before reaching the super cooled levels (Zipser and LeMone, 1980; Black andHallett, 1986), so that mixed phase precipitation would be much less developed in themaritime clouds compared to the continental. This is manifested in the smaller reflectivityaloft in the maritime clouds (Zipser and Lutz, 1994), which is a manifestation of the smallerhydrometeors that form there (Zipser, 1994). In contrast, the suppressed coalescence incontinental clouds leaves most of the cloud water available for growth of ice hydrometeorsaloft, typically in the form of graupel and hail. These ice hydrometeors can growindefinitely without breakup, until they fall into the warm part of the cloud and melt. Themelted hydrometeors continue to grow by accretion of cloud droplets, until they exceed thesize of spontaneous breakup or collide with other raindrops. Therefore, convective rainfallthat originates as ice hydrometeors would have D0> D0e, and would approach D0e fromabove with maturing of the RDSD.

c. Strength of the updrafts

Updrafts are typically stronger in more continental clouds, and therefore contributeto more microphysically continental clouds and less warm rain processes, as discussed

already above. In addition, stronger updrafts allow drops with greater minimal size to fallthrough them. In addition, stronger updrafts leave less time for forming of warm rain andrainout, and advect more cloud water to the supercooled zone. Therefore, due to thereasons already discussed in (a) and (b), the stronger updrafts are likely to lead toprecipitation with greater D0and smaller Rfor the same Z.

d. Evaporation

More continental environments have typically higher cloud base and lower relativehumidity at the sub-cloud layer. Evaporation depletes preferentially the smaller raindropsand works to increase D0.


11/17

Relations between precipitation measurement biases and cloud microstructure

Now we can return and try explaining the large discrepancies between satellitemeasurements and rain gauge estimates that were found over central Africa, while in theAmazon regions the rain gauges coincide closely with satellite estimates (McCollum et al.,

2000). This is consistent with the in situ microphysical observations showing that clouds inthe Amazon are microphyiscally maritime, similar to equatorial pacific clouds (Stith et al.,2002), except for during periods when they are polluted by smoke from forest fires(Andreae et al., 2004). McCollum et al. (2002) have also shown that remote sensing ofrainfall measurements by both passive microwave (SSM/I) and surface radarmeasurements have relative overestimate when moving from the east coast of the US tocentral US by 25% to 30% (See Fig. 3). McCollum et al. (2002) suggested that this bias iscaused by the greater continentality of the rain clouds in central USA. When they usedmultispectral algorithm that takes into account cloud top microstructure the systematic biassomewhat decreased.

Fig.3. Spatial distribution of area-averaged multiplicative bias for the SSM/I with respect to the

estimates of the bias-adjusted hourly digital precipitation radar rainfall estimates on a national grid

(from McCollum et al., 2002).

Additional indication to the precipitation forming processes is the lightning activity.Clouds are electrified when graupel collides with ice crystals in a super cooled water cloud.Therefore lightning is a manifestation of intense ice precipitation forming processes.Tropical maritime clouds have between one and two orders of magnitude less lightning forthe same amount of rainfall of continental clouds (Petersen and Rutledge, 1998). Satelliterainfall estimates in the Amazon regions and central Africa are comparable in magnitude,while there is much more lightning activity over central Africa with much less rain gaugemeasured rainfall. We postulate that rainfall regime over the Amazon is lessmicrophysically continental than that over central Africa, and hence having smallerhydrometeors and larger extent of cold anvils for the same rainfall amounts. Thissuggestion is further supported by findings of Petersen and Rutledge (2001).

A picture of the bias in the global tropics in relation to the clouds continentality isprovided in Fig. 4. Greater continentality is characterized by larger amount of cloud water


12/17

carried up to the upper portions of the cloud, where it freezes and forms large icehydrometeors, and the released latent heat of freezing invigorates the updrafts and loft thelarge ice particles to great heights (Andreae et al., 2004). The large ice particles aloftproduce smaller passive microwave brightness temperatures that are interpreted asgreater rain intensities, by a factor of 2 to 3 compared to the maritime clouds. The large

raindrops that form when these ice particles melt equally cause radar overestimate of therain intensities by a similar factor of 2 to 3 compared to the maritime clouds. As shownearlier in this chapter and elsewhere (Rosenfeld and Lensky, 1998; Andreae et al., 2004),the continetality of the clouds can be quantified independently of the radar by satelliteretrieved T-rerelationships.

Fig. 4: The measurement bias of the TRMM precipitation radar (PR, middle panel) andTRMM passive microwave (TMI, lower panel) in relation to the continentality of the rainclouds, as given by the mean 30 dBZ echo top height in precipitation features with TMIsignal of ice scattering. Note the large overestimate where large ice hydrometeors existhigh in the clouds. (Presented by S. Nesbitt at the TRMM Hawaii Scientific Conference,Honolulu, HI, 22-26 July 2002).


13/17

Fig. 5: Meteosat Second Generation image from 20 May 2003 13:42 GMT, over centralAfrica at a 1200X1200 km rectangle between 1-12N and 15-26E. The area shows thetransition between the relatively microphysically maritime clouds over the forested area(dark surface) and microphysically continental clouds over the dry lands of the Sahel to thenorth (bright surface). The T-rerelations of the continental clouds (1) show much smaller re

for a given T compared to the maritime clouds (2). The median re of the maritime clouds(the yellow line) saturates near T=-20C, indicating glaciation at that temperature. Thesmall median re at area 1 even above the -40C isotherm indicates homogeneous


14/17

glaciation of the cloud water and hence low precipitation efficiency. The color scheme is

red for the visible, green for 3.9 m reflectance component, and blue for temperature. Forfull description and interpretation of the color table is given in Rosenfeld and Lensky(1998). The T-relines represent percentiles of refor a given T in 10% steps for each line,between 5%-95%. The median is between the yellow and green lines.

Conclusions

Differences in clouds microstructure can explain systematic biases of up to a factorof 3 in passive MW and radar direct rainfall measurements. The cloud microstructure canbe obtained by T-rerelations that are obtained from the operational NOAA orbital satellites.The Meteosat Second Generation, which was commissioned in early 2004, is the first of anew generation of geostationary satellites that have sufficient resolution for providing

useful T-rerelations of convective clouds (see example in Fig. 5). Combining the cloud'smicrophysical continentality from T-reanalyses such as shown in Fig. 5 with the radar andpassive microwave measurements has the potential of eliminating much of themeasurement biases that are shown in Fig. 4. Night time capabilities for microphysicalmeasurements are also emerging (Lensky and Rosenfeld, 2003a).

Indirect rainfall measurements can be also substantially improved using theinformation about cloud top composition. Only the first steps have been done so far in thisdirection during daylight (Lensky and Rosenfeld, 1997; Ba and Gruber, 2001) and night(Lensky and Rosenfeld, 2003b).

The implication for future missions is that rainfall measuring satellite should includeboth microwave and VIS/IR sensors, and the rain estimation should use this addedinformation, without which systematic bias errors greater than a factor of 2 are difficult toavoid.


15/17

References:

Andreae M. O., D. Rosenfeld, P. Artaxo, A. A. Costa, G. P. Frank, K. M. Longo, and M.

A. F. Silva-Dias, 2004: Smoking rain clouds over the Amazon. Science, 303,

1337-1342.

Arking, A., and J. D. Childs, Jeffrey D., 1985: Retrieval of cloud cover parameters from

multispectral satellite images. Journal of Climate and Applied Meteorology,

Boston, 24(4): 322-333.

Arkin, P. A., and B. N. Meisner, 1987: The relationship between large-scale convective

rainfall and cold cloud over the western hemisphere during 1982-84. Mon. Wea.

Rev., 115,51-74.

Atlas, D, and T. Bell, 1992: The relation of radar to cloud area-time integrals and

implications for rain measurements from space. Mon. Wea. Rev.,120(9), 1997-2008.

Atlas D. and C. R. Williams, 2003: An anatomy of a continental tropical convective

storm. J. Atmos. Sci., 60, 3-15.

Ba, M.,and A.Gruber, 2001: GOES Multispectral Rainfall Algorithm (GMSRA).

J.Appl.Meteor., 40, 15001514.

Black, R. A. and J. Hallett, 1986: Observations of the distribution of ice in hurricanes.

J. Atmos. Sci.,43,802-822.

Chang F. L., and Z. Li, 2003: Retrieving vertical profiles of water-cloud droplet

effective radius: Algorithm modification and preliminary application. J.

Geophys. Res., 108 (D24): Art. No. 4763 DEC 18 2003.

Fujiwara, M., 1965: Raindrop-size distribution from individual storms. J. Atmos. Sci.,

22, 585-591.

Gerber, H., 1996: Microphysics of marine stratocumulus clouds with two drizzle modes.

J. Atmos. Sci., 53, 1649-1662.

Hu, Z., and R. Srivastava, 1995: Evolution of the raindrop size distribution by

coalescence, breakup and evaporation: Theory and observations. J. Atmos. Sci.,

52, 1761-1783.

Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and K. Okamoto, 2000: Rain-Profiling

Algorithm for the TRMM Precipitation Radar. J. Applied Meteor.,: 39, 2038

2052.

Ebert E. E., M. J. Manton, P. A. Arkin, R. J. Allam, G. E. Holpin, A. Gruber, 1996:

Results from the GPCP algorithm intercomparison program. Bul. Amer. Met.

Soc., 77, 2875-2887.

Lensky I. M. and D Rosenfeld, 1997: Estimation of precipitation area and rain intensitybased on the microphisical properties retrieved from NOAA AVHRR data.

Journal of Applied Meteorology, 36, 234-242.


16/17

Lensky I. M. and D. Rosenfeld, 2003: Satellite-based insights into precipitation

formation processes in continental and maritime convective clouds at night-time.

Journal of Applied Meteorology 42, 1227-1233.

Lensky I. M. and D. Rosenfeld, 2003: A night rain delineation algorithm for infrared

satellite data.Journal of Applied Meteorology42, 1218-1226.

McCollum, J., A. Gruber, and M. Ba, 2000: Comparison of monthly mean satelliteestimates of precipitation with gauges over Africa. J. Appl. Meteor., 39,666-679.

McCollum J. A., W. F. Krajewski, R. R. Ferraro, M. B. Ba, 2002: Evaluation of biases

of satellite rainfall estimation algorithms over the continental United States. J.

Appl. Meteor., 41, 1065-1080.

Nakajima, T., and M. D. King, 1990: Determination of the optical thickness and

effective particle radius of clouds from reflected solar radiation measurments.

Part I: Theory.J. Atmos. Sci.,47,1878-1893.

Pertersen W. A, and S. A. Rutledge, 1998: On the relationship between cloud-to-ground

lightning and convective rainfall.J. Geophys. Res., 103, 1402514040.

Pertersen W. A, and S. A. Rutledge, 2001: Regional variability in tropical convection:

observations from TRMM.J. Clim., 14, 35663586.

Rosenfeld D., 1999: TRMM Observed First Direct Evidence of Smoke from Forest Fires

Inhibiting Rainfall. Geophys. Res. Lett., 26, No. 20, 3105.

Rosenfeld D., 2000: Suppression of Rain and Snow by Urban and Industrial Air

Pollution. Science, 287(5459), 1793-1796.

Rosenfeld, D., and G. Gutman, 1994: Retrieving microphysical properties near the tops

of potential rain clouds by multispectral analysis of AVHRR data.J. Atmos. Res.,

34, 259-283.

Rosenfeld, D., and A. Gagin, 1989: Factors governing the total rainfall yield of

continental convective clouds. Journal of Applied Meteorology, 28, 1015-1030.

Rosenfeld D., and I Lensky, 1998: Space borne based insights into precipitation

formation processes in continental and maritime convective clouds. BAMS, 74,

2457-2476.

Rosenfeld D. and W. L. Woodley, 2003: Closing the 50-year circle: From cloud seeding

to space and back to climate change through precipitation physics.Chapter 6 of"Cloud Systems, Hurricanes, and the Tropical Rainfall Measuring Mission(TRMM)" edited by Drs. Wei-Kuo Tao and Robert Adler, 234pp., p. 59-80,

Meteorological Monographs 51, AMS.Rosenfeld D. and C. W. Ulbrich, 2003: Cloud microphysical properties, processes, and

rainfall estimation opportunities. Chapter 10 of " Radar and Atmospheric

Science: A Collection of Essays in Honor of David Atlas". Edited by Roger M.

Wakimoto and Ramesh Srivastava. Meteorological Monographs 52, 237-258,

AMS.

Rosenfeld D., E. Cattani, S. Melani, and V. Levizzani, 2004. Considerations on daylight

operation of 1.6 m vs 3.7 m channel on NOAA and METOP Satellites. In

Press.Bulletin of the American Meteorological Society.

Stith J. L., J. E. Dye, A. Bansemer, A. J. Heymsfield, 2002: Microphysical

observbations of tropical clouds.J. Appl. Meteor., 41, 97-117.


17/17

Williams, E., D. Rosenfeld, M. Madden et al., 2002: Contrasting convective regimes

over the Amazon: Implications for cloud electrification. J. Geophys. Res., 107

(D20), 8082, doi:10.1029/2001JD000380.

Zipser, E.J., 1994: Deep cumulonimbus cloud systems in the tropics with and without

lightning,Mon. Wea. Rev., 122, 1837-1851.

Zipser, E.J. and M.A. LeMone, 1980: Cumulonimbus vertical velocity events in GATE.

Part II: Synthesis and Model Core Structure,J. Atmos. Sci., 37, 2458-2469.

Zipser, E.J. and K. Lutz, 1994: The vertical profile of radar reflectivity of convective

cells: a strong indicator of storm intensity and lightning probability. Mon Wea.

Rev., 122, 1751-1759.

Documents

Eurainsat Chapter Tools v04