Powerpoint Presentation: Precipitation patterns and aerosols

Precipitation Patterns and AerosolsDaniel Rosenfeld, The Hebrew University of Jerusalem, Israel

Pyro-Cb in Alaska. 7 July 2004, 35,000 feet, -53C

X

Smoke (BC) from Pyro-Cb fires in Alaska. 9 July 2004, 14:54 UT, 36,000 feet, North Labrador

Ship tracks over the North Pacific

Red: Visible reflectance Green: 3.7 µm reflectance Blue: 11 µm temperature

Suppression of Rain and Snow by Urban and Industrial Air Pollution(Rosenfeld, 2000, Science)

VIRS retrieved effectiveradius does not exceedthe 14 µm precipitationthreshold in pollutedclouds within area 2 inthe Australia image.

0 5 10 15 20 25 30

-10

-5

0

5

reff

T [

o C]

[µ m]

12 3

15 20 25 30 35 40 450

1

2

3

4

Reflectivity [dBZ]

H [km]

136

5

VIRS painting yellow pollution tracks in the clouds over SouthAustralia, due to reduced droplets size. PR shows precipitation aswhite patches only outside the pollution tracks, although clouds havesame depth.

TMI shows ample water in the pollutedclouds

PR showsbright band inclean clouds.Therefore,pollutionsuppressedrain and snowin pollutedclouds.

23 May 2003Northeast ChinaVisible

Red: Visible reflectance Green: 3.7 µm reflectance Blue: 11 µm temperature

Hot spot offorest fire

PM, 28 May 2001, Chisolm, Canada

23:30 UT, 28 May 2001

3-km Reflectivities Echo Top Heights


23:40 UT, 28 May 2001

23:50 UT, 28 May 2001


00:00 UT, 29 May 2001


00:10 UT, 29 May 2001


00:20 UT, 29 May 2001


10-5

0.0001

0.001

0.01

0.1

1

0 10 20 30 40 50

DSD20021018_1a

110605Z 400m

111207Z 550m

111700Z 650m

111940Z 1120m

112803Z 1550m

LWC [g m

-3 µm-1]

Drop diameter [µm]

Blue Ocean

10-5

0.0001

0.001

0.01

0.1

1

0 10 20 30 40 50

DSD20021005_2 195200Z 1597m195506Z 1890m195930Z 2338m200551Z 2781m201401Z 2980m202142Z 3640m202635Z 4403m

LWC [g m

-3 µm

-1 ]

Drop diameter [µm]

Warm rain evolution over the western tip of the Amazon, afternoon.

10-5

0.0001

0.001

0.01

0.1

1

0 10 20 30 40 50

DSD20021004_H1

151510Z 1731m

151819Z 1899m

152211Z 2150m

152650Z 3069m

153101Z 3635m

153544Z 4265m

LWC [g m

-3 µm

-1 ]

Drop diameter [µm]

Smoky conditionsNorth of JPR, Noon.

10-5

0.0001

0.001

0.01

0.1

1

0 10 20 30 40 50

190958Z 2300m Ash190840Z 2167m Pyro191405Z 2341m Pyro191950Z 2648m Pyro193318Z 2842m Pyro193724Z 3162m Pyro194018Z 3419m Pyro191049Z 4165m Pyro191818Z 4341m Pyro191926Z 4354m Pyro Dark!

LWC [g m

-3 µm-1]

Drop diameter [µm]

DSD with height in Pyro cumulus2002 10 01 < 4000 m2002 10 04 > 4000 m

Aircraft measured Modal LWC cloud drop diameter as a functionof height above cloud base, for the various aerosol regimes inBrazil [Thailand].

Prec

ipita

tion

thre

shol

d

Andreae, M. O.,D. Rosenfeld,P. Artaxo et al., 2004:Smoking rain cloudsover the Amazon.Science, 303, 1337-1342.

5 10 15 20 25 30 35 400

1000

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3000

4000

5000

6000

7000

8000

Equatorial Atlantic OceanPristine Western AmazonSmoky ThailandClean NW Thai Monsoon

Smoky AmazonPyro Cu AmazonPyro Cb Amazon

Drop size of modal LWC, µm

Height above cloud base, m

Aircraft measured Modal LWC cloud drop diameter as a functionof height above cloud base, for the various aerosol regimes inBrazil [Thailand].

Prec

ipita

tion

thre

shol

d

Andreae, M. O.,D. Rosenfeld,P. Artaxo et al., 2004:Smoking rain cloudsover the Amazon.Science, 303, 1337-1342.

Nature, 25 May2000, 405, 440-442.

5 10 15 20 25 30 35 400

1000

2000

3000

4000

5000

6000

7000

8000

Equatorial Atlantic OceanPristine Western AmazonSmoky ThailandClean NW Thai Monsoon

Smoky AmazonPyro Cu AmazonPyro Cb AmazonSevere hailstorm Argentina

Drop size of modal LWC, µm

Height above cloud base, m

h = 2.72 N

0

1000

2000

3000

4000

5000

6000

0 500 1000 1500 2000

War

m R

ain

Dep

th (m

)

Average Droplet Concentration (cm ) -3

Photo by Andi Andreae

The burned Amazon

Cloud dropRain dropIce crystalIce precipitation

Updraft

Moist surface, Weak Updraft

Updraft

Dry and dark surface,Strong updraft

0oC 0oC


Updraft

Moist surface, Weak Updraft

Updraft

Dry and dark surface,Strong updraft

0oC 0oC

The effect of aerosols on precipitation in clouds wascalculated from the data of the image above. The warmcolors represent efficient precipitation processes, while thecold colors represent suppressed precipitation, due to thepollution. The scale is the maximal cloud top temperature[0C] required for onset of precipitation.

(1) Maritime and Rural aerosolsClouds from clean maritime air develop precipitationefficiently. After interacting with rural aerosols, theclouds are less efficient in developing precipitation.

(2) Urban air pollutionThe blue color indicates detrimental effect of urban airpollution on the precipitation in the clouds.

(3) Smoke from forest firesAnother case of detrimental effect of theinteraction of Clouds with biomass burning smokeon the precipitation in the clouds can be seen inthe blue color over Sumatra and Kalimantan.

The TOMS aerosol index can be seen below:

Bangkok

Ho Chi Minh (Saigon)

KalimantanSumatra


Maritime: Clean, Fast rain, Suppressed updraft


Continental: Polluted,Suppressed rain, Strong updraft

0oC 0oC

Annual average lightning density [flashes km-2]Lightning prevail mostly over land, whereas rainfall is similar over land and ocean,indicates fundamental differences between continental and maritime rainfall.

Can aerosols help explaining the fundamental differences inContinental vs. Maritime convection?

JGR 2002

Et al.

“1.3.The Aerosol Hypothesis” [for cloud electrification]“[8] The present study tests a distinctly different hypothesis for the contrast in lightning activity in continentaland maritime clouds. This explanation, as proposed by D. Rosenfeld for this study, is based on another well-established contrast between continental and maritime boundary layers: the aerosol concentration.”

“[9] The aerosol hypothesis for the land-ocean lightning contrast is illustrated in Figure 2. In contrast with thedominant role of large particles in the traditional hypothesis, this idea focuses on the behavior of the smallercloud droplets. Air drawn from the clean (polluted) boundary layer air will contain a small (large) number oflarge (small) droplets. Active coalescence and rainout of the cloud prevail in the warm portion of the maritimecloud, leading to the depletion of liquid water from the colder mixed phase region. A dominance of diffusionaldroplet growth and suppressed coalescence prevail in the continental CCN-richclouds, preventing rainout and allowing liquid water to ascend to the mixed phase region where it plays a dualrole. First, it can contribute to cloud buoyancy and the updraft strength by the latent heat of freezing. Second, itcan contribute to the growth of graupel particles and catalyze the process of charge separation by ice particlecollisions.[10] In clouds with very large concentrations of small CCN the formation of the ice phase can be delayed tovery high altitudes and low temperatures [Rosenfeld and Lensky, 1998; Rosenfeld,2000; Rosenfeld andWoodley,2002; Khain et al.,2001].The delay in the ice formation to above the -20C isotherm is likely to deprivethe lower part of the mixed phase region of a key ingredient for charge separation in a temperature range whereit is most potent [Takahashi,1978], and thereby addition of aerosols beyond a certain ‘‘optimum’’ may notenhance any more lightning, and may even decrease it with respect to that ‘‘optimum.’’ ”

The hypothesis of dynamic invigoration by delaying precipitation:

“(i) Blue ocean: Low concentrations of cloud condensation nuclei (CCN) in the cleanatmosphere over the ocean produce clouds that are microphysically “maritime,” i.e., haverelatively few but large drops that coalesce rapidly into raindrops. In addition, thetypically weaker updrafts over the ocean allow more time for raindrops to grow andprecipitate before reaching the freezing level. Early precipitation further suppresses theupdraft and vigor of the convection.”

“(iii) Smoky clouds: Vegetation burning produces high concentrations of aerosols, a largefraction of which are capable of nucleating cloud droplets. This results in highconcentrations of small cloud droplets that are slow to coalesce and precipitate. The lackof precipitation, except from the deepest clouds, keeps the particles in the air and creates apositive feedback for maintaining the smoky and rainless conditions. The lack of earlyprecipitation allows updrafts to accelerate and transport cloud water in deep convection tothe high and supercooled regions, where it can release additional latent heat of freezing,which it would not have delivered in the maritime case of early rainout. The added wateris available for production of intense ice precipitation, hail, and lightning, creating moreviolent convective storms.”

Burian and Shepherd, 2004

Steiger et al., JGR 2002

GRL 2003

Disdrometer measured DSD of continental and maritime rainfall, as micophysically classifiedby VIRS overpass. The DSD is averaged for the rainfall during +- 18 hours of the overpasstime. The disdrometers are in Florida (Teflun B), Amazon (LBA), India (Madras) andKwajalein.Application of TRMM Z-R shows a near unity bias in maritime clouds, but overestimates bya factor of 2 rainfall from continental clouds.

0.01

0.1

1

10

100

1000

0 1 2 3 4 5 6

Florida ContFlorida MarLBA ContLBA MarIndia ContIndia MarKwaj Mar

N [mm m

-3 \ mm hr

-1 ]

D [mm]

Rosenfeld and Ulbrich, Met. Monog., 2003

Continentalaerosolsincrease

convectiveupdrafts anddowndrafts

Equatorial maritime air mass: GATE QUADRA Day 261Smoke suppresses rainfall only initially while strongly invigorating theupdrafts. (Khain et al., 2003)

Simulation of extremely continental high base (11oC) clouds

(West Texas, August 1999)

0 0.5 1 1.5 2 2.5

-40

-30

-20

-10

0

10

DMT-LWC [gm-3]

T [

o C]

0 5 10 15 20 25

-40

-30

-20

-10

0

10

FSSP MVD [micron]

T [

o C]

0 200 400 600 800 1000

-40

-30

-20

-10

0

10

FSSP particle conc [cm-3]

T [

o C]

Nature, 25 May 2000, vol 405, p. 440-442.

VIRS T-Re

VIRS+PR, Texas,1998 08 13 22:45

PR H-Z

The “polluted” Cb can have nearly adiabatic water until –38oCTherefore, it has very low precipitation efficiency.

T [o C

]

T [o C

]

T [o C

]

0 200 400 600 800 1000

-40

-30

-20

-10

0

10

FSSP particle conc [cm-3]

T [

o C]

0 0.5 1 1.5 2 2.5

-40

-30

-20

-10

0

10

DMT-LWC [gm-3]

T [

o C]

0 5 10 15 20 25

-40

-30

-20

-10

0

10

FSSP MVD [micron]

T [

o C]

Vertical profiles of maximum values of (a) cloud water content (CWC), (b) the meanvolume diameter and (c) droplet concentration observed in the control run at 250 mbelow the growing cloud top, presented on the background of the aircraft observations(Rosenfeld and Woodley, 2000), shown in green (CWC>0.2 gm-3 and black (CWC<0.2gm-3). The blue and red squares denote model calculated values for the low and highCCN concentrations. The black square in the concentration panel (c) denotes the modelice concentrations.

West Texas: rain accumulation

Depletion of rainin smoky air

West

West

Simulation of PRESTORM Alabama squall line

Prestorm Alabama:

Time evolution of accumulated rain

Squall line forms insmokey air !

No squall line formsin clean air !

Scheme of aerosol effects on precipitation

Aerosol concentration

Dry unstable situation(like Texas clouds)

Maritim

e & m

odera

te (w

et)

contin

ental

cloud

s

(like G

ATE and

PRESTORM)

Acc

umul

ated

rain

GRL 2003

The difference between the cloud clear air equivalent anthropogenic aerosol sulfate concentrations on thetwo days is nearly an order of magnitude, but in absolute terms it is only 1 µg m-3. Astonishingly, thissmall amount of aerosol can reduce the snowfall rate up to 50%.Evidence is presented to demonstrate the possible magnitude of the secondary indirect aerosol effect onprecipitation rates from cold mixed-phase clouds in mountainous regions where a seeder-feeder cloudcouplet is present. Changes as small as 1 µg m-3 in CCN aerosol concentration can cause significantchanges in cloud properties and precipitation efficiencies. (Quoted from Borys et al., GRL 2003).

Polluted

Clean

Rocky Mountains, CO

Droplet diameter [µm]

Droplet diameter [µm]

Crystal diameter [mm]

Crystal diameter [mm]

Dro

plet

con

cent

ratio

n [c

m-3

]

Cry

stal

con

cent

ratio

n [c

m-3

]

Legend

1. San Diego2. Cuyamaca3. Ukiah4. Lake Spaulding5. Los Angeles area6. Los Angeles hills area7. Pacific House8. passes in the dividedownwind to Pac. House9. Fresno10. passes in the dividedownwind to Fresno11. Grant Grove11. Glacier11. Bishop Lake12. Lake Arrowhead13. Morongo14. Bowman15. Boca16. Woodfords17.

1

2

24 25

7

11

23

N E V A D A

A R I Z O N A

C A L I F O R N I A

O R E G O N I D A H OLegend

1E. Ukiah2E. Lake Spaulding3E. Bowman4E. Boca5A. San Francisco6A. Sacramento7A. Pacific House8A. Cluster of snow packsin the divide line downwindto Sacramento9A.Woodfords10B. Fresno11B. Grant Grove12B. Cluster of snow packsin the divide line downwindto Fresno13B. Glacier14B. Bishop Lake15F. Lompoc16F. Mt. Figuroa17F. Santa Barbara18F. Mt. pine19C. Los Angeles area20C. Cluster of stationsdownwind to Los Angeles21C. Lake Arrowhead22C. Big Bear Lake23C. Morongo24. San Diego25. Cuyamaca

5

6

3 4

9

101314

19

8

12

2120 22

15

17

1618

MODIS AQUA7 Dec 2003, 12:50 PST

MODIS AQUA7 Dec 2003, 12:50 PSTSF

9

7

5

3

SF

16

13

11

9

SF

16

18

20

24

LA

SF

San Diego

Cuyamaca

E/S=0.72,P=0.003

Legend

1. San Diego2. Cuyamaca3. Ukiah4. Lake Spaulding5. Los Angeles area6. Los Angeles hills area7. Pacific House8. passes in the dividedownwind to Pac. House9. Fresno10. passes in the dividedownwind to Fresno11. Grant Grove11. Glacier11. Bishop Lake12. Lake Arrowhead13. Morongo14. Bowman15. Boca16. Woodfords17.

1

2

24 25

7

11

23

N E V A D A

A R I Z O N A

C A L I F O R N I A

O R E G O N I D A H OLegend

1E. Ukiah2E. Lake Spaulding3E. Bowman4E. Boca5A. San Francisco6A. Sacramento7A. Pacific House8A. Cluster of snow packsin the divide line downwindto Sacramento9A.Woodfords10B. Fresno11B. Grant Grove12B. Cluster of snow packsin the divide line downwindto Fresno13B. Glacier14B. Bishop Lake15F. Lompoc16F. Mt. Figuroa17F. Santa Barbara18F. Mt. pine19C. Los Angeles area20C. Cluster of stationsdownwind to Los Angeles21C. Lake Arrowhead22C. Big Bear Lake23C. Morongo24. San Diego25. Cuyamaca

5

6

3 4

9

101314

19

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12

2120 22

15

17

1618

1

1.5

2

2.5

3

1950 1960 1970 1980 1990 2000

Cluster of stations: Hills downwind L.A / L.AEnding / Starting ratio = 1.80 / 2.14 = 0.84

y = 14.25 - 0.006x R= 0.27 P= 0.03

Ratio of annual rainfall

year

A

Analysis of the orographic factor trendsaccording to the synoptic conditions:

- 3oC : Expected Clouds Temperatures at 700 mb on rainy days - Los Angeles and San Diego areas

T<-3oC T>-3oC

T >-3oC

T <-3oC

0

20

40

60

80

Number of rain days

ColdairCold airWarm air

-3-17 -15 -13 -11 -9 -7 –5 –3 –1 1 3 5 7 9 11 13 15ToC

The annual ratios of precipitation (Ro) between Cuyamaca and San Diego forclouds occurring when T>-3ºC at 700 hpa (mainly frontal and warm air mass) andwhen T < -3ºC (mainly cyclonic post frontal clouds).

0

1

2

3

4

5

6

7

8

1950 1960 1970 1980 1990 2000

warmcold

Ratio of annual precipitation

Year

Lake Arrowhead (1740 m) vs. Los AngelesCold airmass E/S ratio = 1.38/2.00 = 0.69 P=0.26Warm airmass E/S ratio = 0.91/0.80 = 1.13 P=0.78

0

1

2

3

4

5

6

7

8

1940 1950 1960 1970 1980 1990 2000

ColdWarm


Year

Cuyamaca (1525 m) vs. San DiegoCold airmass E/S ratio = 3.28/5.00 = 0.66 P=0.14Warm airmass E/S ratio = 1.52/1.52 = 1.00 P=0.88

1

1.2

1.4

1.6

1950 1960 1970 1980 1990 2000

y = 10.7 - 0.005x R= 0.54 P= 0.0006

Ratio of annual rainfall

Year

Cluster of stations: Judean Hills / Judea plainsEnding / Starting ratio = 1.17 / 1.38 = 0.85

B

0.5

1

1.5

2

2.5

3

1930 1940 1950 1960 1970 1980 1990 2000

ratio

Years

Startind / End ratio = 1.6/1.63 =0.98y = -0.41486 + 0.0010528x R= 0.05304

1 2 3 4 5 6

1.00

0.80

0.93

1.14

500 4501100 950

475

-65+66

-220

West EastHeight [km]

4

3

2

1

Topographic cross section showing the effects of urban air pollution on precipitation as the clouds moves from west to east across the mountains

Changes in precipitation ratio and trends :Western United states

Back to overview

Utah

Back to overview

4280 Ft, 17.05 In 4820 Ft , 30.32 In

4250 Ft, 15.70 In

5340 Ft, 28.01 In

4820 Ft, 17.48 In

4960 Ft, 23.94 In8710 Ft, 43.00 In

0.5

1

1.5

2

2.5

3

3.5

4

1950 1960 1970 1980 1990 2000

Winter :The ratio netween Ogden Sugar factory to to Pine View

Ending / Starting ratio = 2.08/2.28 = 0.90

Ratio

Year

D

0

1

2

3

4

5

6

1946 1954 1963 1971 1980 1989 1997

Winter :Ratio Silver lake Tooele


Ratio

Year

B

0.5

1

1.5

2

2.5

3

3.5

4

1950 1960 1970 1980 1990 2000

Summer :Ratio Ogden Sugar factory to Pine ViewEnding / Starting ratio = 1.51/1.41 = 1.07

Ratio

Year

D

0

1

2

3

4

5

6

1950 1960 1970 1980 1990 2000

Summer :Ratio Silver lake Tooele


Ratio

Year

D0

0.5

1

1.5

2

2.5

3

3.5

4

1958 1967 1975 1983 1992 2000

Summer : ratio Salt lake city city plant


Rat

io

Year

D

1

1.5

2

2.5

3

1950 1960 1970 1980 1990 2000

Winter : The ratio between City plant and Salt lake city


y = 11.813 - 0.005x R= 0.32

Ratio

Year

D

Long-range trends of the winter ( October – April ) precipitation measured in Tooele (A) and inthe downwind hilly station of Silver Lake (B); the correlation between these two stations (C) andthe ratio of precipitation (Ro) measured between them (D)

Utah

0

10

20

30

40

50

0 4 8 12 16 20

y = 16.626 + 1.1993x R= 0.46

Silver Lake

Tooele

C

0

5

10

15

20

25

30

1950 1960 1970 1980 1990 2000

Trend analysis for TooeleEnding / Starting ratio = 12/8.5 = 1.23

y = -120.12 + 0.066072x R= 0.41

winter rainfall [In]

Year

A

0

10

20

30

40

50

1950 1961 1972 1982 1993 2004

Trend analysis for Silver Lake BrightonEnding / Starting ratio = 26.83/30.01 = 0.86

y = 194.24 - 0.083525x R = 0.21


Year

B

0

1

2

3

4

5

6

1950 1960 1970 1980 1990 2000

Ratio Silver lake TooeleEnding / Starting ratio = 2.30/3.45 = 0.67

Ratio

Year

D

Winter: Winter:

Back to overview

Colorado

Back to overview

6380 Ft, 16.87 In

Haden

10650 Ft, 23.80 In

Steamboat

0.5

1

1.5

2

2.5

1950 1960 1970 1980 1990 2000

Winter :Ending / Starting ratio = 1.38/1.71 = 0.81

y = 13.462 - 0.0060374x R= 0.42


Year

D

0.6

0.8

1

1.2

1.4

1.6

1.8

2

1940 1950 1960 1970 1980 1990 2000

Summer :

Ending / Starting ratio = 1.23 / 1.27 = 0.97

y = 2.1305 - 0.0004339x R= 0.03

ratio of rainfall [In]

Years

D

0

5

10

15

20

25

1950 1960 1970 1980 1990 2000

Winter : Steamboat Springs



Year

B

0

5

10

15

20

25

1950 1960 1970 1980 1990 2000

Winter : Hayden



Year

A

0

5

10

15

20

25

30

0 4 8 12 16 20

y = 2.9224 + 1.2237x R = 0.80

Steamboat

Hayden

C

0

0.5

1

1.5

2

2.5

3

1950 1960 1970 1980 1990 2000



Year

D

Long-range trends of the winter precipitation (Oct- April ) measured in Hayden (A) and in thedownwind hilly station of Steamboat springs (B); the correlation between these two stations (C)and the ratio of precipitation (Ro) measured between them (D)

Colorado

Back to overview

Resolution accepted in 14th Council of World Meteorological Organization,Geneva, May 2003.

Congress noted with concern the new additional evidence, also presented at the8th WMO Scientific Conference on Weather Modification, that was pointing to anapparent substantial reduction of the rainfall efficiency of clouds by plumes ofsmoke caused by biomass burning (agricultural practices, forest fires, cooking andheating) and industrial processes. Congress also noted the evidence that such non-raining clouds could regain their raining ability once they moved over oceans orlarge bodies of water (such as the Aral Sea) because sea-salt was then mixed intothe clouds and overrode the detrimental effect of the smoke particles. Therefore,Congress recommended CAS to establish an ad-hoc Group on Biomass Burningand Smoke Plumes in general, charge it to prepare a summary report forinformation of the Members, addressing relevant issues such as (1) theclimatology of smoke and weather active aerosol (Cloud Condensation Nuclei orCCN) plumes, (2) the in situ and remote measurement of CCN and cloud dropletconcentrations, (3) strategies to reduce biomass burning and hence the density ofsmoke plumes, and (4) the seeding procedures and evaluation methods to re-establish raining ability of clouds affected by smoke plumes, and CAS to report toFifteenth Congress.

IUGG Resolution Sapporo 10 July 2003:Considering biomass burning from agricultural practices, household consumption and

wildfires produces substantial quantities of aerosol particles that can increase smallcloud droplet number concentration.

Realizing that higher concentrations of small cloud droplets affect their coalescence andthe formation of precipitation and thus the water supply.

Welcoming the recognition of the potential effect of all aerosol sources on precipitationby Congress XIV of the World Meteorological Organization, WMO, in May 2003and its projected actions focused on biomass burning plumes.

Urges the scientific community to undertake systematic studies of the impact of biomassburning aerosol on precipitation formation on all scales. Feedback effects on climateas well as the competing effects of industrial fine particle aerosols and natural coarseparticle aerosols such as sea salt and soil dust should be included.

Recommendsthat a body be established to undertake an international program of study and assessment

of the rain related effects of biomass burning in collaboration with WMO and otherinternational organizations.

that this body creates a mechanism to assemble the scientific evidence needed to lay thegroundwork for a UN sponsored conference on pollution effects on precipitation andhence water supply.

that this body reports in the IUGG Newsletters and the GA in 2007 on the step taken andthe progress made.

Documents

Powerpoint Presentation: Precipitation patterns and aerosols