CloudSat views the Asian summer monsoon

an opportunistic data celebration

CloudSat views the Asian summer monsoon

an opportunistic data celebration

Brian Mapes

University of Miami

Brian Mapes

University of Miami

CloudSatCloudSat 3 mm wavelength radar, nadir pointing

– Sees cloud and precipitation attenuation in heavy rain

5 mm/h obscures surface

Nadir “curtain” sampling– Vertical (range) sampling 250m

– Horizontal (along track) sampling 1.1 km

– Twice daily (1am, 1pm local time)

Flying since June 2006 – here examine JJAS 2006 data in Asian regionhere examine JJAS 2006 data in Asian region

3 mm wavelength radar, nadir pointing– Sees cloud and precipitation

attenuation in heavy rain 5 mm/h obscures surface

Nadir “curtain” sampling– Vertical (range) sampling 250m

– Horizontal (along track) sampling 1.1 km

– Twice daily (1am, 1pm local time)

Flying since June 2006 – here examine JJAS 2006 data in Asian regionhere examine JJAS 2006 data in Asian region

CloudSat and the A-Train

CloudSat measurements within a few minutes of all other A-train observations

OpportunismOpportunismOpportunismOpportunism

spotty / sparse samplingsampling (nadir ‘curtain’ only)

1pm and 1am1pm and 1am LST only

radar reflectivityreflectivity is a hard to interpret physical measurement

Attenuation; surface clutter issues at low levels

spotty / sparse samplingsampling (nadir ‘curtain’ only)

1pm and 1am1pm and 1am LST only

radar reflectivityreflectivity is a hard to interpret physical measurement

Attenuation; surface clutter issues at low levels

Encourages unbiasedunbiased (whole dataset !) analysis

Part of the A-train

Rich in information - accurate, large dynamic range - retrievals will come

FIRST global profiles !!! Cloud top is best meas. Cloud tops >1km are fine.

Encourages unbiasedunbiased (whole dataset !) analysis

Part of the A-train

Rich in information - accurate, large dynamic range - retrievals will come

FIRST global profiles !!! Cloud top is best meas. Cloud tops >1km are fine.

CloudSat – Measured Return Power

Cloud Mask (20-40 = “yes”)

cloud

This analysis based on cloud objectscloud objectsThis analysis based on cloud objectscloud objects A contiguous region (in the vertical slice) where

cloud mask = “yes”cloud mask = “yes” Each has a bundle of attributes

– mean lat, mean lon, time, top, thickness, width, lowest and highest altitude of surface underneath, etc. etc.

Each is collection of pixels (3-1000’s of them)

accessible using cloud ID #

example (a complicated one):

A contiguous region (in the vertical slice) where cloud mask = “yes”cloud mask = “yes”

Each has a bundle of attributes– mean lat, mean lon, time, top, thickness, width, lowest and highest altitude of surface underneath, etc. etc.

Each is collection of pixels (3-1000’s of them)

accessible using cloud ID #

example (a complicated one):top

width

thk = NPIX/width

All JJAS 2006 cloud centroidsAll JJAS 2006 cloud centroids

First condsider First condsider all JJAS 2006 all JJAS 2006

clouds centered in clouds centered in 10-15N, 75-80E10-15N, 75-80E

Define 7 tropical cloud object typesDefine 7 tropical cloud object types Joint histogram of top height & thickness

– First: all clouds 15N-15S (as a global backdrop)

Joint histogram of top height & thickness– First: all clouds 15N-15S (as a global backdrop)

1 Deep (High Thick)1 Deep (High Thick)0 High thin0 High thin

2 middle 2 middle thinthin 3 mid-top3 mid-top

ThickThick

Low top but tall/thick or drizzlingLow top but tall/thick or drizzling (5:(5: >10km wide) ( >10km wide) (6: <10km wide)6: <10km wide)

4 low4 lowthinthin

JJAS 2006JJAS 2006 day day and and nightnight clouds over S. India clouds over S. India~ true aspect ratio~ true aspect ratio

JJAS 2006JJAS 2006 day day and and nightnight clouds over S. India clouds over S. India~ true aspect ratio~ true aspect ratio

20 km layers20 km layers

One night cloud 350 km wide

dayday

nightnight

Jointhistograms of

pixel-wise z & dBZ

pos. anomalies

(mean histogram & more later)

Jointhistograms of

pixel-wise z & dBZ

pos. anomalies

(mean histogram & more later)

low-dBZ high cloudslow-dBZ high clouds

low-dBZ low cloudslow-dBZ low clouds

high-dbZ (and raining)high-dbZ (and raining)middle cloudsmiddle clouds

Beyond South India Beyond South India 1. ASM pattern & profile of cloud objects

by cloud top altitude

2. Natural cloud “types”

3. Drill down to pixels (z - dBZ joint hists.)

4. Contrasts ocean - coast - land lowland - slope - plateau (over E & W Tibet) day - night East Asia vs. South Asia (Bin Wang request)

5. Dynamical variations MISO from Sep. 2006

1. ASM pattern & profile of cloud objectsby cloud top altitude

2. Natural cloud “types”

3. Drill down to pixels (z - dBZ joint hists.)

4. Contrasts ocean - coast - land lowland - slope - plateau (over E & W Tibet) day - night East Asia vs. South Asia (Bin Wang request)

5. Dynamical variations MISO from Sep. 2006

JJAS OLR climatologyJJAS OLR climatology

22

44 55 66

88 9977

JJAS OLR climatologyJJAS OLR climatology

00

11

33

JJAS 2006 CloudSat-sampled JJAS 2006 CloudSat-sampled cloudcloud volumevolume

00

11 22

33 44 55 66

88 9977

Latitude vs. Latitude vs. top-heighttop-height distribution distributionwhole monsoon (40E-160E)whole monsoon (40E-160E)

Latitude vs. Latitude vs. top-heighttop-height distribution distributionwhole monsoon (40E-160E)whole monsoon (40E-160E)

low-top cloudslow-top clouds

tropical deeptropical deep

middle topsmiddle tops

midlat.midlat.plateau,plateau,midlat.midlat.

in subtropicsin subtropics

1,2 (Eq.) 0 (SH) 3,4,5,6 (NIO, SCS, Phil.)

7,8,9Tibet, E.Asia

JJAS 2006 CloudSat-sampled JJAS 2006 CloudSat-sampled cover (area)cover (area)

00

11 22

33 44 55 66

88 9977

Lon vs. cloudtop Lon vs. cloudtop distributiondistribution

(S Asia: middle-topped clouds enhanced)

Beyond South India Beyond South India 1. ASM pattern & profile of cloud objects

by cloud object top altitude

2. Natural cloud “types”

3. Drill down to pixels (z - dBZ joint hists.)

4. Contrasts ocean - coast - land lowland - slope - plateau (over E & W Tibet) day - night

5. Dynamical variations MISO from Sep. 2006

1. ASM pattern & profile of cloud objectsby cloud object top altitude

2. Natural cloud “types”

3. Drill down to pixels (z - dBZ joint hists.)

4. Contrasts ocean - coast - land lowland - slope - plateau (over E & W Tibet) day - night

5. Dynamical variations MISO from Sep. 2006

Joint histogram of top height & thicknessJoint histogram of top height & thickness

– First: all clouds 15N-15S, Jun06 - Feb07 (as a global backdrop)

– First: all clouds 15N-15S, Jun06 - Feb07 (as a global backdrop)

1 Deep (High Thick)1 Deep (High Thick)0 High thin0 High thin

2 middle 2 middle thinthin 3 mid-top3 mid-top

ThickThick

Low top but tall/thick or drizzlingLow top but tall/thick or drizzling (5:(5: >10km wide) ( >10km wide) (6: <10km wide)6: <10km wide)

4 low4 lowthinthin

Define 7 tropical cloud object typesDefine 7 tropical cloud object types Joint histogram of top height & thickness

– First: all clouds 15N-15S (as a global backdrop)

Joint histogram of top height & thickness– First: all clouds 15N-15S (as a global backdrop)

1 Deep (High Thick)1 Deep (High Thick)0 High thin0 High thin

2 middle 2 middle thinthin 3 mid-top3 mid-top

ThickThick

Low top but tall/thick or drizzlingLow top but tall/thick or drizzling (5:(5: >10km wide) ( >10km wide) (6: <10km wide)6: <10km wide)

4 low4 lowthinthin

0C well above melting level...well above melting level...

5-6 km

7-8 km

Midlevel clouds: a bimodal populationMidlevel clouds: a bimodal populationin global tropicsin global tropics

Midlevel clouds: a bimodal populationMidlevel clouds: a bimodal populationin global tropicsin global tropics

Monsoon cloud object typesMonsoon cloud object types Joint histogram of top height & thicknessJoint histogram of top height & thickness

– All 10 monsoon regions pooled, JJASAll 10 monsoon regions pooled, JJAS

Joint histogram of top height & thicknessJoint histogram of top height & thickness– All 10 monsoon regions pooled, JJASAll 10 monsoon regions pooled, JJAS

1-Deep1-Deep0-High layers0-High layers

2-Middle 2-Middle layerslayers 3-Mid-top3-Mid-top

towerstowers

Low top but tall/thick or drizzlingLow top but tall/thick or drizzling (5:(5: >10km wide) ( >10km wide) (6: <10km wide)6: <10km wide)

4-Low4-Lowlayerslayers

Monsoon cloud object typesMonsoon cloud object types Joint histogram of top height & thicknessJoint histogram of top height & thickness

– All 10 monsoon regions pooled, JJASAll 10 monsoon regions pooled, JJAS

Joint histogram of top height & thicknessJoint histogram of top height & thickness– All 10 monsoon regions pooled, JJASAll 10 monsoon regions pooled, JJAS

1-Deep1-Deep0-High layers0-High layers

2-Middle 2-Middle layerslayers 3-Mid-top3-Mid-top

towerstowers

Low top but tall/thick or drizzlingLow top but tall/thick or drizzling (5:(5: >10km wide) ( >10km wide) (6: <10km wide)6: <10km wide)

4-Low4-Lowlayerslayers

00

11 2233 44 55 66

88 99770

87

6543

21

9

Cloud volumeCloud volumeby cloud typeby cloud type2 times of day2 times of day

Land onlyLand only

Deep convection

1pm

1am

(W. India)

(W. India) (E. India)

Beyond South India Beyond South India 1. ASM pattern & profile of cloud objects

by cloud object top altitude

2. Natural cloud object “types”

3. Drill down to pixels (z - dBZ joint hists.)

4. Contrasts ocean - coast - land lowland - slope - plateau (over E & W Tibet) day - night

5. Dynamical variations MISO from Sep. 2006

1. ASM pattern & profile of cloud objectsby cloud object top altitude

2. Natural cloud object “types”

3. Drill down to pixels (z - dBZ joint hists.)

4. Contrasts ocean - coast - land lowland - slope - plateau (over E & W Tibet) day - night

5. Dynamical variations MISO from Sep. 2006

Joint histogram of dBZ and zall pixels in all JJAS 2006 monsoon cloudsJoint histogram of dBZ and z

all pixels in all JJAS 2006 monsoon clouds

Most frequent cloud: -25 dBZ at 13 km

Minimum of frequency Minimum of frequency in middle of in middle of

measurement space measurement space

tropopause (a true Earth phenomenon)se

nsiti

vity

(in

stru

men

tal)

clutter (instrumental)clutter (instrumental)

rain atten. rain atten. multiple scat. multiple scat. (instrumental)(instrumental)

Reflectivity (dBZe)

z (k

m)

Integrate over dBZ -> cloudiness profile

Integrate over dBZ -> cloudiness profile

Histogram enhancements associated withHistogram enhancements associated with pixels in each of the 7 cloud types pixels in each of the 7 cloud types

Histogram enhancements associated withHistogram enhancements associated with pixels in each of the 7 cloud types pixels in each of the 7 cloud types

Colors show positive Colors show positive anomalies (relative to anomalies (relative to all-monsoon cloud all-monsoon cloud pool) of normalized pool) of normalized probability densityprobability density

Hightop-thin Hightop-thick

Midtop-thin Midtop-thick Lowtop-thin

Low-thick-wide Low-thick-narrow

Heavy rainHeavy rainattenuatesattenuates

rainrain

rainrain rainrain

Beyond South India Beyond South India 1. ASM pattern & profile of cloud objects

by cloud object top altitude

2. Natural cloud object “types”

3. Drill down to pixels (z - dBZ joint hists.)

4. Contrasts geographic boxes ocean - coast - land day - night

5. Dynamical variations MISO from Sep. 2006

1. ASM pattern & profile of cloud objectsby cloud object top altitude

2. Natural cloud object “types”

3. Drill down to pixels (z - dBZ joint hists.)

4. Contrasts geographic boxes ocean - coast - land day - night

5. Dynamical variations MISO from Sep. 2006

0011 22

33 44 55 66

88 9977

Joint Histograms for boxesJoint Histograms for boxes

open contoursopen contoursall-monsoon meanall-monsoon mean

0011 22

33 44 55 66

88 9977

Normalized PD Anomalies >0Normalized PD Anomalies >0

open contours = open contours = all-monsoon meanall-monsoon meancolors = colors =

enhanced normalized frequencyenhanced normalized frequency

sea-coast-land in monsoon tropics (zones 1-6)

sea-coast-land in monsoon tropics (zones 1-6)

Sea

Coast Land

Day vs. night clouds over landDay vs. night clouds over landDay vs. night clouds over landDay vs. night clouds over land

Recall --South India box

Recall --South India box

low-dBZ high cloudslow-dBZ high clouds

low-dBZ low cloudslow-dBZ low clouds

high-dbZ (and raining)high-dbZ (and raining)middle cloudsmiddle clouds

AllAll day day and and nightnight clouds over S. India clouds over S. IndiaAllAll day day and and nightnight clouds over S. India clouds over S. India

20 km layers20 km layers

East AsiaEast Asia(hT type, (hT type, sea, nite)sea, nite)

Bin Wang Bin Wang special special requestrequest

BoBBoB(hT type, (hT type, sea, nite)sea, nite)

East AsiaEast Asia(hT type, (hT type, sea, nite)sea, nite)

Bin Wang Bin Wang special special requestrequest

BoBBoB(hT type, (hT type, sea, nite)sea, nite)

“more frontal”?

“more convective”?

EA vs. EA vs. tropical tropical

monsoon monsoon seasseas

EA vs. EA vs. tropical tropical

monsoon monsoon seasseas

Beyond South India Beyond South India 1. ASM pattern & profile of cloud objects

by cloud object top altitude

2. Natural cloud object “types”

3. Drill down to pixels (z - dBZ joint hists.)

4. Contrasts geographic boxes day - night

5.5. Dynamical variationsDynamical variations MISO from Sep. 2006MISO from Sep. 2006

compare to BoB Onset 1999 (JASMINE)compare to BoB Onset 1999 (JASMINE)

1. ASM pattern & profile of cloud objectsby cloud object top altitude

2. Natural cloud object “types”

3. Drill down to pixels (z - dBZ joint hists.)

4. Contrasts geographic boxes day - night

5.5. Dynamical variationsDynamical variations MISO from Sep. 2006MISO from Sep. 2006

compare to BoB Onset 1999 (JASMINE)compare to BoB Onset 1999 (JASMINE)

Clouds in a monsoon ISOClouds in a monsoon ISO

define a fixed grid on 60-90E OLR time-lat sectiondefine a fixed grid on 60-90E OLR time-lat section

30N 30N

20S 20S

Figure 1: Outgoing Longwave Radiation (OLR), averaged over 60-90E, contoured in time (15 Jun – 5 Nov) vs. latitude (20S - 30N) space. Contours at [220, 200, 180] W m-2.

front

front

afte

raf

ter

1 mo

Sep. 2006

Clouds in front

sorted by top height

true aspect ratio

Clouds in front

sorted by top height

true aspect ratio

front: 581 clouds, 614954 pixels

Clouds in after category sorted by top height

Clouds in after category sorted by top height

after: 296 clouds, 106883 pixels

cf. front: 581 clouds, 614744 pixels

Front vs. AfterFront vs. After

Normalized joint histograms of dBZ and z of pixels

(log color scale)

Normalized joint histograms of dBZ and z of pixels

(log color scale)

Compare to JASMINE shipborne

cloud radar

Compare to JASMINE shipborne

cloud radar

cf. shipborne CPRMay 1999JASMINE

Bay of Bengal

courtesy P. Zuidema

cf. shipborne CPRMay 1999JASMINE

Bay of Bengal

courtesy P. Zuidema

pre-onsetpre-onset

activeactive

atten atten sen sen

atten atten

sen sen

sum joint histograms for each MISO sum joint histograms for each MISO phase over dBZ dimension phase over dBZ dimension

=>=>Cloud fraction profiles across MISOCloud fraction profiles across MISO

sum joint histograms for each MISO sum joint histograms for each MISO phase over dBZ dimension phase over dBZ dimension

=>=>Cloud fraction profiles across MISOCloud fraction profiles across MISO

front after

MISO-relative time (3.5 day bins)

Cloud Fraction

Dynamical data & deductions Dynamical data & deductions Dynamical data & deductions Dynamical data & deductions

front after

MISO-relative time (3.5 day bins)

1.1. ∂∂/∂t (cloud volume) /∂t (cloud volume) ∂/∂p (mass flux) ? ∂/∂p (mass flux) ?

2.2. CloudSat rad. heating productCloudSat rad. heating product

3.3. ECMWF met. interpolated to each pixel ECMWF met. interpolated to each pixel

1.1. ∂∂/∂t (cloud volume) /∂t (cloud volume) ∂/∂p (mass flux) ? ∂/∂p (mass flux) ?

2.2. CloudSat rad. heating productCloudSat rad. heating product

3.3. ECMWF met. interpolated to each pixel ECMWF met. interpolated to each pixel

FindingsFindings CloudSat is good (despite its badnesses)CloudSat is good (despite its badnesses) Cloud object library is a convenient approachCloud object library is a convenient approach

– can drill down to pixels as neededcan drill down to pixels as needed

Tropical clouds fall into types (modes of distributions)Tropical clouds fall into types (modes of distributions) Monsoon clouds are diverseMonsoon clouds are diverse

– low clouds in SIOlow clouds in SIO– deep convection over tropicsdeep convection over tropics

progressively deeper from Arabian Sea -> BoB -> SCS -> Phil. Seaprogressively deeper from Arabian Sea -> BoB -> SCS -> Phil. Sea

– middle clouds enhanced over S. Asian longitudesmiddle clouds enhanced over S. Asian longitudes enhanced at night over land, rainingenhanced at night over land, raining

– East Asian clouds (cb systems over sea have lower tops)East Asian clouds (cb systems over sea have lower tops)

Sep. 2006 MISO cloud structure is tiltedSep. 2006 MISO cloud structure is tilted– in an unsurprising manner, but nice to seein an unsurprising manner, but nice to see

CloudSat is good (despite its badnesses)CloudSat is good (despite its badnesses) Cloud object library is a convenient approachCloud object library is a convenient approach

– can drill down to pixels as neededcan drill down to pixels as needed

Tropical clouds fall into types (modes of distributions)Tropical clouds fall into types (modes of distributions) Monsoon clouds are diverseMonsoon clouds are diverse

– low clouds in SIOlow clouds in SIO– deep convection over tropicsdeep convection over tropics

progressively deeper from Arabian Sea -> BoB -> SCS -> Phil. Seaprogressively deeper from Arabian Sea -> BoB -> SCS -> Phil. Sea

– middle clouds enhanced over S. Asian longitudesmiddle clouds enhanced over S. Asian longitudes enhanced at night over land, rainingenhanced at night over land, raining

– East Asian clouds (cb systems over sea have lower tops)East Asian clouds (cb systems over sea have lower tops)

Sep. 2006 MISO cloud structure is tiltedSep. 2006 MISO cloud structure is tilted– in an unsurprising manner, but nice to seein an unsurprising manner, but nice to see

Cloud volume distribution in Cloud volume distribution in horizontal size vs. top height space horizontal size vs. top height space

(all 15N-15S clouds)(all 15N-15S clouds)

Cloud volume distribution in Cloud volume distribution in horizontal size vs. top height space horizontal size vs. top height space

(all 15N-15S clouds)(all 15N-15S clouds)

0

1 2

3 4 5 6

7 8 9

One equatorial MJO in our database so far

One equatorial MJO in our database so far

year

s si

nce

1-1-

2006