A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE

A LATENT HEAT A LATENT HEAT RETRIEVAL IN A RAPIDLY RETRIEVAL IN A RAPIDLY

INTENSIFYING INTENSIFYING HURRICANEHURRICANE

Steve Guimond and Paul Reasor

Florida State University

34th Conference on Radar Meteorology 34th Conference on Radar Meteorology

Background/MotivationBackground/Motivation

• Main driver of hurricane genesis and intensity change is latent heat release

• Observationally derived 4-D distributions of latent heating in hurricanes are sparse – Most estimates are satellite based (i.e. TRMM)

• Coarse space/time• No vertical velocity

– Few Doppler radar based estimates• Water budget (Gamache 1993)

• Considerable uncertainty in numerical model microphysical schemes– McFarquhar et al. (2006)– Rogers et al. (2007)

Current ApproachCurrent Approach• Refined latent heating algorithm (Roux and Ju

1990)

– Model testing: • Non-hydrostatic, full-physics, quasi cloud-

resolving (2-km) MM5 simulation of Hurricane Bonnie (1998; Braun 2006)

– Examine assumptions– Uncover sensitivities to additional data– Uncertainty estimates

Numerical Model TestingNumerical Model Testing

– Goal saturation using production of precipitation (Roux and Ju 1990)

• Divergence, diffusion and offset are small and can be neglected

ZDQQ

z

Vq

z

wvq

z

wqvq

t

q tpp

pp

p

net

tpp

p Qz

Vwqvq

t

q

Structure of Latent HeatStructure of Latent Heat

total precipitation mixing ratio

horizontal winds

hydrometeor fallspeed

source of total precipitation

sink of total precipitation

net source of total precipitation

tu

p

t

net

q

v

V

Q

Q

Q

D

rbulent diffusion

model offset for numerical errorZ

Magnitude of Latent HeatMagnitude of Latent Heat

– Requirements• Temperature and pressure (composite eyewall, high-altitude

dropsonde)• Vertical velocity (radar)

lnp

D JC

Dt T

ln c s

p

L qDw

Dt C T z

where s sc c

Dq qJ L L w

Dt z

gas constant

latent heat of condensation

T temperature

potential temperature

saturation mixing ratio

w vertical velocity

p

c

s

C

L

q

– Positives…• Full radar swath of latent heat in various types of clouds

(sometimes 4-D)

– Uncertainties to consider…• Estimating tendency term

– Steady-state ?

• Thermo based on composite eyewall dropsonde• Drop size distribution uncertainty and feedback on derived

parameters

ln c s

p

L qDw

Dt C T z

)(saturated 0netQ

ed)(unsaturat 0netQln c

netp

LDQ

Dt C T

Putting it TogetherPutting it Together

Model Heating Budget Model Heating Budget ResultsResults

Examining Assumptions Examining Assumptions with Doppler radarwith Doppler radar

• Clouds are not steady state• Guillermo TA tendency term with ~34 min delta T

– Sufficient to approximate derivative?– Typical value of tendency term for ∆t 0 ?

Impact of Tendency on Impact of Tendency on HeatingHeating


All heating removed


net

tpp

p Qz

Vwqvq

t

q

100*

RMW

RMWRMW

S

STP


How to parameterize tendency term?(1) Using 2 minute output from Bonnie simulation

(2) Coincident (flight level) 2 RPM LF data

R2 = 0.714


Including parameterization

P-3 Doppler Radar ResultsP-3 Doppler Radar Results

•Rapidly intensifying Hurricane Guillermo (1997)

•NOAA WP-3D airborne dual Doppler analysis (Reasor et al. 2009)

•2 km x 2 km x 1 km x ~34 min

•10 composite snapshots

Hurricane Guillermo (1997)Hurricane Guillermo (1997)

Uncertainty EstimatesUncertainty Estimates

Mean =117 K/h

• Bootstrap (Monte Carlo method)

• Auto-lag correlation ~30 degrees of freedom

• 95 % confidence interval on the mean = (101 – 133) K/h

• Represents ~14% of mean value

• New version of latent heat retrieval– Identified sensitivities, constrained problem with more

data (e.g. numerical model)– Developed tendency parameterization

• Statistics with P-3 LF data• Validate saturation with flight level data

– Ability to accept somesome errors in water budget– Local tendency, radar-derived parameters, etc.

– Monte Carlo uncertainty estimates (~14 % for w > 5)

• Goal: Understand impact of retrieved forcings on TC dynamics– Simulations with radar derived vortices, heating

• Smaller errors with retrieved heating vs. simulated heating

Conclusions and Conclusions and Ongoing Ongoing WorkWork

AcknowledgmentsAcknowledgments• Scott Braun (MM5 output)• Robert Black (particle processing)• Paul Reasor and Matt Eastin (Guillermo edits)• Gerry Heymsfield (dropsonde data & satellite images)

ReferencesReferences• Roux (1985), Roux and Ju (1990)• Braun et al. (2006), Braun (2006)• Gamache et al. (1993)• Reasor et al. (2009)• Black (1990)

Thermodynamic SensitivityThermodynamic Sensitivity

• Only care about condition of saturation for heating– Some error OKSome error OK– Tendency, reflectivity-derived parameters

Testing algorithm in modelTesting algorithm in modelHow is Qnet related to condensation?

Constructing Z-LWC Constructing Z-LWC RelationshipsRelationships

Hurricane Katrina (2005) particle data from P-3– August 25, 27, 28 (TS,CAT3,CAT5)– Averaged for 6s ~ 1km along flight path

• Match probe and radar sampling volumes

net

tpp

p Qz

Vwqvq

t

q

Below melting level:

Z = 402*LWC1.47 n = 7067 RMSE = 0.212 g m-3

Above melting level (Black 1990):

Z = 670*IWC1.79 n = 1609 r = 0.81

Doppler Analysis QualityDoppler Analysis Quality

• Comparison to flight-level data at 3 and 6 km height– Vertical velocity (eyewall ~1200 grid points)

• RMSE 1.56 m/s• Bias 0.16 m/s

DropsondesDropsondes

• Composite sounding– DC8 and ER2 (high-altitude) total of 10 samples– Deep convection

• Sat IR, AMPR, wind and humidity

• Non-hydrostatic, full-physics, cloud-resolving (2-km) MM5 simulation of Hurricane Bonnie (1998; Braun 2006)

Testing algorithm in modelTesting algorithm in model

ZDQQ

z

Vq

z

wvq

z

wqvq

t

q tpp

pp

p


Testing algorithm in modelTesting algorithm in modelln c s

p

L qDw

Dt C T z


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A LATENT HEAT RETRIEVAL IN A RAPIDLY INTENSIFYING HURRICANE