Mesoscale wind divergence profiles in convecting regions Brian Mapes, Jialin Lin, Paquita Zuidema Brian Mapes, Jialin Lin, Paquita Zuidema data thanks

Mesoscale wind divergence Mesoscale wind divergence profiles in profiles in

convecting regionsconvecting regions

Brian Mapes, Jialin Lin, Paquita Brian Mapes, Jialin Lin, Paquita ZuidemaZuidema

data thanks to: CSU Radar met group, UW mesoscale group, data thanks to: CSU Radar met group, UW mesoscale group,

TRMM ground validation officeTRMM ground validation officeOutlineOutline

1.1. Motivations and methodsMotivations and methods2.2. Results in some easy and hard casesResults in some easy and hard cases3.3. Statistical resultsStatistical results4.4. A well-measured oddity: JASMINE May 22A well-measured oddity: JASMINE May 225.5. Summary and future plansSummary and future plans

MotivationsMotivations

Heating in tropical convective clouds drives larger-Heating in tropical convective clouds drives larger-scale circulations (LSCs) of many scalesscale circulations (LSCs) of many scales

Heating profiles are important to LSC structure, Heating profiles are important to LSC structure, including feedbacks to convectionincluding feedbacks to convection

Divergence profiles (inflow/outflow) are closely Divergence profiles (inflow/outflow) are closely linked to heating; and affect layer cloudinesslinked to heating; and affect layer cloudiness

Clear-air (unheated) component of divergence is Clear-air (unheated) component of divergence is smaller, but especially important to feedbackssmaller, but especially important to feedbacks

Divergent flow is hard to measure accurately, so Divergent flow is hard to measure accurately, so better observations may lead to new discoverybetter observations may lead to new discovery

Continuing hope: better obs. glimpses of a Continuing hope: better obs. glimpses of a unique aspect of deep moist atmospheric unique aspect of deep moist atmospheric convection: it’s embedded in a convection: it’s embedded in a stratifiedstratified

environment, where gravity waves disperse environment, where gravity waves disperse by vertical wavelengthby vertical wavelength

Wind divergence: Wind divergence: interpretationinterpretation

= ∂/∂p( Q/)

Mapes and Houze 1995 JAS

Rearranging,

For Q >> dhT/dt (tropical convection scaling), w/the

Wind divergence : Wind divergence : measurementmeasurement

V dA

A=

perim

Vnormal dP

A

The divergence theorem for an area A with perimeter P:

Area averaged divergence =

Defining the perimeter-mean

velocity Vnormal,

Vnormal x P/A

For a circular area, the overbar

is an azimuth mean:

= [Vradial] x 2R/R2

= [Vradial] x 2/R

Velocity-Azimuth Display Velocity-Azimuth Display (VAD)(VAD)

At fixed horizontal range (radius) R and At fixed horizontal range (radius) R and altitude, consider radial velocity Vr vs. altitude, consider radial velocity Vr vs. azimuth angleazimuth angle

Vr

azimuth (deg from N)0 360

0

wd

ws

Mean wind = sine wave

Area-avg divergence= [Vr] *2/R((NOTE: no uniformity NOTE: no uniformity assumption within circleassumption within circle))

[Vr]

wave-2 is wave-2 is deformationdeformation; real flows ; real flows may have may have jets, etc. jets, etc. etc.etc.

Background: Doppler radarBackground: Doppler radar

Precipitation radarPrecipitation radar– Radio pulses bounce off hydrometeorsRadio pulses bounce off hydrometeors– Z is 6th moment of the DSD, log scale dBZZ is 6th moment of the DSD, log scale dBZ– For applications, rainrate R ~some power law in ZFor applications, rainrate R ~some power law in Z

Doppler (coherent) radarDoppler (coherent) radar– Pulse pairs sent; Doppler spectrum P(Pulse pairs sent; Doppler spectrum P( received received– A mean A mean gives along-beam velocity:gives along-beam velocity:

VVrr = = t t +/- n+/- nt =2nVt =2nVnyqnyq

– For applications, remove fallspeed For applications, remove fallspeed VVtt(Z)cos(zenith)(Z)cos(zenith)

Cylindrical data binning for Cylindrical data binning for VADVAD

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

r = 8 km (12 bins, 0-96 km)az = 15° (24 bins)z = 500 m (36 bins)

-->p = 50 hPat = 1 hour

dBZ bins(for Z-R)

m/s bins (only need

nadjust)

m/s bins

Further pool Further pool data, e.g. data, e.g. height to height to pressure: pressure:

simply sum simply sum the histogram the histogram

arraysarrays..

(same for (same for range or time range or time

pooling)pooling)

VAD plot for each hour, layer, range VAD plot for each hour, layer, range (pool)(pool)

[Vr] <0: convergence

-2m/s *2/(40km)= -1e-4 /s wind: 7 m/s

from 240 deg

unfolding guide for

absolute Vr(after histogramcompactification)From GOF, or ind.

ws,wd guess

samplingerror bar

used for rel.weighting inleast-square3-param fit: [Vr], ws, wd

Repeat, repeat, repeat…

research radar deploymentsresearch radar deployments

TEPPS: July 1997TEPPS: July 1997 JASMINE: May 1999JASMINE: May 1999

EPIC: September 2001EPIC: September 2001 several 10s of days = many 100s of hours eachseveral 10s of days = many 100s of hours each





EPIC 2001: EPIC 2001: time-time-

pressurepressure

(Vr data (Vr data everywhere, everywhere,

but sometimes but sometimes just noise)just noise)

IvoSparse 48h

An An example example hourly hourly

productproductQuickTime™ and a

TIFF (LZW) decompressorare needed to see this picture.

convection convection developing developing near radarnear radar

48 glorious hours: Sept 23-48 glorious hours: Sept 23-2424

RHB-C RHB-C





All points on all curves All points on all curves at all times are from at all times are from independent data: independent data: Continuity in range, Continuity in range, time, altitude time, altitude encouragingencouraging10x sonde div; decent mass balance ------>

ice sat

dQ/dp >0

R=44km div

dQ/dp >0‘onion’

RH soundings, sfc rain, mm cloud radar, VAD div: Paquita Zuidema, ETL

RH profiles

ascending convection tops

mass flux (-omega)

Hard case: lots of Doppler Hard case: lots of Doppler foldingfolding

Tropical Storm IvoTropical Storm IvoSept. 10, 2001Sept. 10, 2001







convergence

TS Ivo, Sept. 10, 2001TS Ivo, Sept. 10, 2001cyclone moves westward, N of shipcyclone moves westward, N of ship











13Z 15Z 17 18 19 21Z

u>0v<0 u>0v>0

wind

convergence 10-4s-1 x 7h = 2.5, ~10-folding of ~100km scale vorticity

Vr folding problem at Vr folding problem at 925mb925mb





looks OK

clutter in S-Wazimuths:

Vr ~0.

Spike histsdominate fit ~OK

N E S W N






1.1. Motivations and methodsMotivations and methods2.2. Results in some easy and hard casesResults in some easy and hard cases3.3. Statistical results (EPIC, cloud model)Statistical results (EPIC, cloud model)4.4. A well-measured outlier: JASMINE May 22A well-measured outlier: JASMINE May 225.5. Summary and future plansSummary and future plans

EPIC simple-mean EPIC simple-mean divergencedivergence



small circles have fallspeed

overcorrection error(steep beam tilt)

noisy - sea clutter

iceliq

smaller circles’ fluctuations havelarger amplitude; convergent times are undersampled--> divergent bias



Mapes and Houze 1995COARE airborne (mid-troposphere)

Doppler radar over W. Pac.

mean s.d.(dQ/dp)









To isolate latent To isolate latent heat associated heat associated

signal, regress div signal, regress div on R(Z)on R(Z)

all data

rain >0.5

standarderror

overshoot cooling?

Regress div(p) to surface Regress div(p) to surface rainfall: reduces scale rainfall: reduces scale dependence, biasesdependence, biases scale-dependent fluctuation intensities cancel outscale-dependent fluctuation intensities cancel out

can pool or average ranges for more robustnesscan pool or average ranges for more robustnessQuickTime™ and a


Badness of crude ice/liq Vt(Z) assumption

is ~uncorrelated w/surface rainrate, sincethat mainly varies with

coverage, not Z.

Uses data when available. Undersampled

timesw/upper convergence

& R~0 don’t affect slope

div per rain (1e-6 s-1 per mm/h)



Moisture Moisture convergence convergence

per unit per unit rainfall: rainfall:

should be ~1should be ~1Gross variations of Gross variations of magnitude in rain-magnitude in rain-convergence regression convergence regression slopes between slopes between experimentsexperiments

e.g. COARE-MIT Z-R rainrate e.g. COARE-MIT Z-R rainrate is about half the linearly is about half the linearly regressed moisture regressed moisture convergence, convergence, 5 dbZ too 5 dbZ too lowlow??

(Radar has always been low (Radar has always been low outlier of rain estimates - outlier of rain estimates - Weller et al. 2004)Weller et al. 2004)

R(Z) too small

R(Z) too great

re-calibrated

re-calibrated



all data

rain >0.5

sigma

Why is rain weakly associated w/ low-Why is rain weakly associated w/ low-level convergence? level convergence? (hourly, simultaneous, ~100km (hourly, simultaneous, ~100km

scale)scale)

Convective vs. Convective vs. stratiformstratiform by horiz.

echo texture (thanks S. Nesbitt, CSU)



• div>0

° div<0



MultipleMultiple linear linear regression regression teases apart teases apart their pure their pure signaturessignatures

C,S rainrates C,S rainrates are are correlated correlated but have but have independent independent variability variability too. too.

C autocorrelation

C-S cross correlation

hours convergence~525mb

Con-strat Con-strat evolution in total evolution in total

precip precip laglag regression regression

Rapid rise of Rapid rise of convergence to convergence to middle levels at middle levels at time of precip maxtime of precip max

Trimodal cloud Trimodal cloud tops? (divergence) tops? (divergence) rising with time in rising with time in advance of advance of precipitationprecipitation

precipitable waterfrom microwaveradiometer (ETL)

0

1 mm per mm/h



3D cloud model 3D cloud model Marat Marat

Khairoutdinov Khairoutdinov KWAJEX case KWAJEX case

Divergence of Divergence of cloudy updraft cloudy updraft mass fluxmass flux

Trimodal cloud Trimodal cloud tops, PW rises then tops, PW rises then fallsfalls



3D cloud model’s 3D cloud model’s KWAJEX KWAJEX forcingforcing

Sonde-array div vs. Sonde-array div vs. model rainratemodel rainrate

Triple divergence Triple divergence levels, sounding levels, sounding PW rises then fallsPW rises then falls

Note 55h time axis! Note 55h time axis!

internalinternal variations variations in 3D cloud model in 3D cloud model

divergence and divergence and rain in 64x64 km rain in 64x64 km subset of domain subset of domain (1/16 of area)(1/16 of area)

(about VAD size)(about VAD size)

specific humidity in specific humidity in same areasame area

Tracing back to Tracing back to cases cases







day 270

(fold)



Similar pictureSimilar picturefrom other from other

radar radar deployments deployments

(convergence (convergence rises up over rises up over

several hours as several hours as convective convective clouds turn clouds turn stratiform)stratiform)

note JASMINE oddity



Convective, stratiform,…?Convective, stratiform,…?Are these archetypal components of convectiverainfall rooted in physical processes…?

detrainment

entrainment

ice physics

water physics

…or in dynamical modes? (strictly, spectral bands)

m=1/2 m=1

m=3/2

One clue:Does this exist?

(…and is there a physical process behind it)?

(A: Yes, and (A: Yes, and Maybe)Maybe)



A nice touch: interleaved tilts, A nice touch: interleaved tilts, for buttery-smooth vert. res.for buttery-smooth vert. res.when pooled over an hourwhen pooled over an hour

(2+ volumes)(2+ volumes)






1.1. Motivations and methodsMotivations and methods2.2. Results in some easy and hard casesResults in some easy and hard cases3.3. Statistical resultsStatistical results4.4. A well-measured outlier: JASMINE May 22A well-measured outlier: JASMINE May 225.5. Summary and future plansSummary and future plans

equator

40N

JASMINE JASMINE Bay of Bay of Bengal Bengal

May 1999May 1999

EasternGhats

Dave Lawrence

Meteosat-5 Infrared Imagery

Mean diurnal cycle of 210K

low level w

ind

figs by Paquita Zuidema

Meteosat-5 Infrared Imagery

Mean diurnal cycle of 210K

prevailing w

ind

figs by Paquita Zuidema



JASMINE JASMINE May 1999, May 1999, spanning spanning monsoon monsoon onset in onset in

the Bay of the Bay of BengalBengal

JASMINE JASMINE squallsquall(wave?)(wave?)

May 22, 1999(figs from U. of Washington webpages on JASMINE)

~15 m/s

Webster et al. 2003, Zuidema 2003

JASMINE div(p)JASMINE div(p)

1604 Z


are needed to see this picture.QuickTime™ and a


17-18Z

massflux

JASMINE wavy div(p): JASMINE wavy div(p): Double-decker convection?Double-decker convection?

from UW JASMINE web pages



JASMINE div(p)JASMINE div(p)20-21Z



bad

bad

JASMINE May22 time-height JASMINE May22 time-height sectionsection



x - old/folded

mm cloud radar data and overlay presentationby Paquita Zuidema, ETL

note very low drying

Is it dynamical wave# 3/2?Or is it moist (dry air) processes?

Recalling the EPIC case…Recalling the EPIC case…

dry intrusion: a physical process,not simply relatedto a dynamicalmode…

but notedeeper layer ofdescent drying

in this case, onion RH min

~3km (725 mb)JASMINE case IS significantly unusual….JASMINE case IS significantly unusual….

SUMMARY & CONCLUSIONS:

Sensibly consistent w/ surface rain & zenith cloud radar obs.(full EPIC and JASMINE overlay sections at http://www.etl.noaa.gov/~pzuidema)

We see good ol’ convective & stratiform profiles (ho-hum), but also

•Anvils snowing into dry layers, moistening and cooling their tops (cooling flattens the dry layer by diabatic divergence dQ/dp>0)

•Steady convergence below 700mb in TS Ivo, to ~10-fold in 7h

•m=3/2 divergence profile in JASMINE propagating squall/wave: double-decker convection, and deviations from that pillar of tropical meso-meteorology, middle level convergence in stratiform rain

•Wavy profiles of div regressed on shallow convective rainfall - are high and low tropospheric clouds/heating dynamically coupled on the mesoscale? If so, is random overlap a good assumption?

Hourly VAD gives simple, automatic, good divergence measurements

Mapes and Houze 1992 airborne Doppler, EMEX





•Better top!

•Whole troposphere shorter in EPIC than in Australian monsoon

•More complete life cycle: MH92 obs were in mature MCSs and rarely saw fresh development

1km res

meansd

(subjectively categorized samples)

EPIC MLR for EPIC MLR for 4 4 categories categories

of of precipitation: precipitation:

shallow, shallow, deep, very deep, very

deep deep convection; convection; stratiformstratiform

~~Vertical waviness?~~~~~~



convective rainfrom 0-5km top

cells

stratiform

5-10km tops

>10km tops

(like MH92“intermediary”?)

convective rainfrom 0-5km top

cells

CorrespondinCorresponding omegag omega

EPIC MLR for EPIC MLR for 4 4 categories categories

of of precipitation: precipitation:

shallow, shallow, deep, very deep, very

deep deep convection; convection; stratiformstratiform

5-10km tops

>10km tops stratiform



Difficulty of measuring Difficulty of measuring divergence: sounding arraysdivergence: sounding arrays

Erroneous barotropic partof sonde-array

divergence iscomparable

to signalssought inbaroclinic

component…low bar for our work!

Hard case: Hard case: a sparse a sparse

timetime




are needed to see this picture.N E S W N


are needed to see this picture.(sea clutter?)

Documents

Mesoscale wind divergence profiles in convecting regions Brian Mapes, Jialin Lin, Paquita Zuidema Brian Mapes, Jialin Lin, Paquita Zuidema data thanks