cloudsat views the asian summer monsoon an opportunistic data celebration
DESCRIPTION
CloudSat views the Asian summer monsoon an opportunistic data celebration. Brian Mapes University of Miami. CloudSat. 3 mm wavelength radar, nadir pointing Sees cloud and precipitation attenuation in heavy rain 5 mm/h obscures surface Nadir “curtain” sampling - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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CloudSat and the A-Train
CloudSat measurements within a few minutes of all other A-train observations
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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.
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CloudSat – Measured Return Power
Cloud Mask (20-40 = “yes”)
cloud
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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
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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
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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
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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
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One night cloud 350 km wide
dayday
nightnight
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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
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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
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JJAS OLR climatologyJJAS OLR climatology
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JJAS OLR climatologyJJAS OLR climatology
00
11
33
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JJAS 2006 CloudSat-sampled JJAS 2006 CloudSat-sampled cloudcloud volumevolume
00
11 22
33 44 55 66
88 9977
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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
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JJAS 2006 CloudSat-sampled JJAS 2006 CloudSat-sampled cover (area)cover (area)
00
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88 9977
Lon vs. cloudtop Lon vs. cloudtop distributiondistribution
(S Asia: middle-topped clouds enhanced)
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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
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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
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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
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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
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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
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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
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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)
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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
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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)
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Integrate over dBZ -> cloudiness profile
Integrate over dBZ -> cloudiness profile
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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
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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
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0011 22
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88 9977
Joint Histograms for boxesJoint Histograms for boxes
open contoursopen contoursall-monsoon meanall-monsoon mean
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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
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sea-coast-land in monsoon tropics (zones 1-6)
sea-coast-land in monsoon tropics (zones 1-6)
Sea
Coast Land
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Day vs. night clouds over landDay vs. night clouds over landDay vs. night clouds over landDay vs. night clouds over land
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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
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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
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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”?
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EA vs. EA vs. tropical tropical
monsoon monsoon seasseas
EA vs. EA vs. tropical tropical
monsoon monsoon seasseas
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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)
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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
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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
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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
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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)
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Compare to JASMINE shipborne
cloud radar
Compare to JASMINE shipborne
cloud radar
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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
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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
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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
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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
![Page 48: CloudSat views the Asian summer monsoon an opportunistic data celebration](https://reader035.vdocuments.net/reader035/viewer/2022062500/568159e6550346895dc730e3/html5/thumbnails/48.jpg)
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)
![Page 49: CloudSat views the Asian summer monsoon an opportunistic data celebration](https://reader035.vdocuments.net/reader035/viewer/2022062500/568159e6550346895dc730e3/html5/thumbnails/49.jpg)
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One equatorial MJO in our database so far
One equatorial MJO in our database so far
year
s si
nce
1-1-
2006