Microphysical variability of tropical and mid-latitude rainfall as revealed by polarimetric radar
description
Transcript of Microphysical variability of tropical and mid-latitude rainfall as revealed by polarimetric radar
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