Microphysical variability of tropical and mid-latitude rainfall

as revealed by polarimetric radar

S. Rutledge1, D. Wolff2, B. Dolan1, P. Kennedy1, W. Petersen2 and

V. Chandrasekar1

1Colorado State University2NASA/Wallops Is.

2013 AGU Fall MeetingSan Francisco, CA

9-13 December 2013Session H42A

• Reflectivity-based rain estimation central to TRMM and GPM.

• We will investigate the polarimetric radar derived “structure” of rainfall at several locations around the globe. These structures reveal regimes where the melting of graupel and hail contribute strongly to rainfall vs. where coalescence dominates (tropical warm rain). These structures reflect basic differences in drop size distributions.

• These structures have implications for the A coefficient in Z=ARb relationships, which is how rain rate is estimated based on TRMM PR observations.

• We will conclude by discussing how A derived from polarimetric radar compares to that from the PR algorithm. This comparison will be done using both the TRMM V6 and V7 datasets.

• Reflectivity-based rain estimation central to TRMM and GPM.

• We will investigate the polarimetric radar derived “structure” of rainfall at several locations around the globe. These structures reveal regimes where the melting of graupel and hail contribute strongly to rainfall vs. where coalescence dominates (tropical warm rain). These structures reflect basic differences in drop size distributions.

• These structures have implications for the A coefficient in Z=ARb relationships, which is how rain rate is estimated based on TRMM PR observations.

• We will conclude by discussing how A derived from polarimetric radar compares to that from the PR algorithm. This comparison will be done using both the TRMM V6 and V7 datasets.

Rainfall microphysics seen through

combination of polarimetric variables

ZZ

KdpKdp

Oklahoma

Kdp proportional to mass content andmass-weighted oblateness ratio

Based on computationsof Z and Kdp from DSDassumptions

Based on computationsof Z and Kdp from DSDassumptions

0.5 deg elevation angle0.5 deg elevation angle

50

40

30

2 4 6

deg/km Difference in H,V phase in degrees

RainRain

KDP is a measure of the difference in wave propagation between H and V polarizations; sensitive to non-spherical particles

a

b

0

Differential Reflectivity

Reflectivity

Mass weighted…..

Gorgucci et al. (2006, JTECH) showed that a parameter space formed by Kdp / Z vs. Zdr was useful for characterizing precipitation physics.

Figure on the right shows results of scattering simulations for various Gamma DSD’s with mean diameters (Dm) ranging from 1.5 to 3.5 mm. Variations in Dm are evident as well-defined curving paths in Kdp/Z vs. Zdr space.

This technique can also be used to distinguish warm rain-coalescence situations (high freezing level and active drop coalescence processes, smaller drop sizes) from rain derived from the melting of graupel and hail (larger drop sizes), as distinguished by Kdp/ Z; Zdr pairs.

For a given rainfall regime, behavior of Kdp/Z vs. Zdr represents precipitation physics.

Gorgucci et al. (2006, JTECH) showed that a parameter space formed by Kdp / Z vs. Zdr was useful for characterizing precipitation physics.

Figure on the right shows results of scattering simulations for various Gamma DSD’s with mean diameters (Dm) ranging from 1.5 to 3.5 mm. Variations in Dm are evident as well-defined curving paths in Kdp/Z vs. Zdr space.

This technique can also be used to distinguish warm rain-coalescence situations (high freezing level and active drop coalescence processes, smaller drop sizes) from rain derived from the melting of graupel and hail (larger drop sizes), as distinguished by Kdp/ Z; Zdr pairs.

For a given rainfall regime, behavior of Kdp/Z vs. Zdr represents precipitation physics.

Application of polarimetric data……Application of polarimetric data……

Dm, mmDm, mm

Smallerdrops, large liquidwater contentsModest Z; high Kdp

Largedrops frommelting iceLarge Z

An illustrative example; contrasting the FNL flood case with a nearby bow echo storm…

BEC stormFNL storm

NLDN lightning for 5 hour period

10 inches of rain in a 5 hour period

Heavy rain, littlelightning

Lightning withbow echo storm

Ft. Collins flood example; tropical likeheavy rain event. Z=139R1.47

Nearby strong convective storm; Z=300R1.4

Z=139R1.47

Z=300R1.4

All points > 30 dBZ used

Normalized density of points expressed as a percentageYellow 70%Red 50%Blue 30%

High values of KDP/Z indicate large water contentswith low Z; smallZDR, small drops

Larger ZDR

values indicatingmelting iceparticles

Flood (tropical like)event distinguished from bow echo by reducedZ and Zdr.

Kdp/Z shifted to higher values for FNL (flood)case. Implies large LWCconsisting of relatively smalldrops.

Polarimetric variables consistentwith Z-R forms for these events

Small drops, high LWC, small A (FLOOD)

Large drops, large A (BOW ECHO)

TRMM LBA, Jan-Feb 1999

NCAR S-pol radardeployed for TRMM-LBA

Documented east-west regime with 7-10 day variability (Petersen and Rutledge, 2002)

Shift to the tropics…..

26 Jan 1999 EAST case.Stronger convection,higher CAPE.

24 Feb 1999WEST case.Lower CAPE,monsoon-like regime.

EASTWEST Subtle

differencesbetweenEast andWest

LARGER ZDR VALUES IN EAST CASE COMPARED TO WEST. INDICATIVE OF LARGER DROPS (MELTING ICE)CONSISTENT WITH HIGHER CAPE/STRONGER CONVECTION IN EAST PHASE.

IN WEST PHASE, LARGER Kdp/Z INDICATING SUBSTANTIAL LWCCONTAINED BY SMALLDROPS.

EAST

WEST

Following Bringi et al. 2004, J. Atmos. Ocean Technol., the A coefficient in Z=ARb iscalculated via the so-called pole-tune method where a Gamma distribution is assumed

Here A is a function of Z, D0 and μ. Bringi et al. argue that A derived in this mannercontinuously tracks the variability in DSD.

“A” coefficients derived from the TRMM-LBA dataset using the NCAR S-polradar. DSD’S ASSUME BROAD RANGE OF VALUES WITHIN EACH REGIME.

KDP/LIN Z

ZDR

N-Pol in MC3E sporting its new center-fed parabolic antenna and other upgradesN-Pol in MC3E sporting its new center-fed parabolic antenna and other upgrades

Midlatitude Continental ConvectiveClouds Experiment

May-June 2011

Observations from MC3E…..

25 April 2011:

Multiple Convective Cores

25 April 2011:

Multiple Convective Cores

DZ Kdp

ZdrHID

VR Rhohv

24 May 2011

Severe storm

24 May 2011

Severe storm

- 70+ dBZ up to 10 km- Large (+5 º/km) Kdp at the surface- Signature of large hail (in RH and ZDR)- Strong tilted updraft and divergence aloft- Data of high quality at significant ranges

- 70+ dBZ up to 10 km- Large (+5 º/km) Kdp at the surface- Signature of large hail (in RH and ZDR)- Strong tilted updraft and divergence aloft- Data of high quality at significant ranges

DZ Kdp

Zdr

HID

VR

Rhohv

Zdr and Kdp columnZdr and Kdp column

N-Pol in Oklahoma, MC3E

Results consistent withactive coalescence growthand ice-based precipitation.

Resulting from highermoisture contents, higherfreezing level, collisionalbreakup, etc.

N-Pol in Oklahoma, MC3E

Results consistent withactive coalescence growthand ice-based precipitation.

Resulting from highermoisture contents, higherfreezing level, collisionalbreakup, etc.

Colorado events for comparison;Warm rain vs. melting hail examplesColorado events for comparison;Warm rain vs. melting hail examples

Warm rainWarm rain

Ice basedIce based

N-POLN-POL

NASA IFLOODS DEPLOYMENT INSTRUMENTATION

Radars: Rain mapping, 4-D precip structure, DSD, rates

• NPOL S-band transportable, scanning dual-pol radar

• D3R radar: Dual-frequency (KA-KU), dual-polarimetric, Doppler radar.

• 3 Metek Micro Rain Radars (K-band), vertically pointing (one on order)

Point-Network Disdrometer/Gauges: Precip character/reference

• 5 2D Video Disdrometers • 16-20 Parsivel-2 Disdrometer

with MetOne 12” TB Rain Gauge• 25 dual-gauge Met One TB rain

gauges with soil T/Q

Precipitation Video Imager (PVI)

Dual-Gauge Net

JW

May 2, 2013 – Cold, light rain [70 km]

TRMM-LBAWest regimeNote: Axischange

May 26, 2013 – Convection

Very similar to MC3E case

Squall line example from IFLOODS

Large drops frommelting graupel andsmall hail

Trailing stratiform region from same squall line

Smaller drops frommelting of aggregates

IMPLICATIONS FOR TRMM rain mapping

The precipitation physics revealed by these polarimetric data have a direct bearing on Z-R based rain estimation and Z based attenuation correction

Shift to upper left implies smaller “A”coefficient in Z=ARb and more rain for a given Z.

Shift to lower right implies larger “A”coefficient in Z=ARb and less rain for a given Z.

Have seen clear examples of these distinctiveshifts…….which are due to microphysicalvariations in the production of rainfall

Towards small A values

Towards largeA values

Kind of the crux of thematter…..

Comparison of A coefficientin Z=ARb between TRMM 2A25and those derived from S-polpolarimetric radar

Rain physics variability wasnot well captured in Version 6, reflected by the restricted range of A

TRMM V6 underpredictedintense rain

Rainfall physics much better captured in TRMMV7!

For both regimes TRMM derived A has a double peak, coalescence and melting ice. Consistent with the range of polarimetric radar derived A values.

Reasons for the improvement

Introduced NUBF correction,increase in high rain rates

Addition of 0.5 dB to PIA; increase in heavy rain overland.

Changes to α in the k=αZeβspecific attenuation calculation