NOAA GOES-13 satellite Imagery of Hurricane Irene

Improving Hurricane Forecasting

through Air-Sea-Wave Coupling

and Scale-Selective Data

AssimilationBin Liu, NCSU/MEAS/CFDL

Colleagues:Drs. Lian Xie, Huiqing Liu, Shiqiu Peng, and other current and

previous CFDL group members

TC forecast error trends for the Atlantic Basin

From http://www.nhc.noaa.govThe specific goals of the HFIP are to reduce the average errors of hurricane track and intensity forecasts by 20% within five years (2014) and 50% within 10 years (2019) with a forecast period out to seven days. (From HFIP’s Strategic Plan)

Outline

• Air-Sea-Wave Interaction– A Coupled Atmosphere-Wave-Ocean Modeling System

(CAWOMS)• Limited-Area Model Nesting/Downscaling

– A Scale-Selective Data Assimilation (SSDA) technique

Atmospheric Boundary Layer

Oceanic Boundary Layer

From CBLAST Project

The CAWOMS

Schematic of the coupled atmosphere-wave-ocean modeling system

The Model Coupling Toolkit

• The coupling between the model components is carried out by using the Model Coupling Toolkit (MCT, Larson et al., 2005; Jacob et al., 2005)

MCT

Model Comp 1t=t0

Init MCT…

t=cpltime…

t=tendFinalize MCT

Master begin

Master end

Model Comp 2t=t0

Init MCT…

t=cpltime…

t=tendFinalize MCT

Atmosphere-ocean coupling

• Atmospheric model drives ocean model– WRF drives POM through

atmospheric forcing

• Oceanic feedback effects– POM provides sea surface

temperature to WRF to estimate air-sea heat fluxes

– POM provides sea surface currents to determine the relative wind speed for estimation of sea surface wind stress

Wave-ocean coupling

• Wave-ocean interaction– Doppler shift effect of background

current on waves– Water level variation changing water

depth – Modifying upper-ocean currents

through radiation stress– Wave-enhanced bottom stress due to

wave orbital velocity– Wave-enhanced upper-ocean mixing

due to wave breaking– Both wave state and surface ocean

conditions impact air-sea momentum and heat fluxes

Atmosphere-wave coupling

• Atmospheric model drives wave model– WRF drives SWAN through surface wind– WRF provide surface variables for

estimation of sea spray fluxes

• Wave-related feedback effects– Wave state and sea spray affected sea

surface roughness – Wave state affected sea spray heat

fluxes– Dissipative heating

Wind stress

Drag coefficient Sea surface roughness

0

* lnz

zuU zn

)/exp( 2/10 DznCzz

2)(z

Dz U

uC

2

0

2 )ln(

z

zCDzn

2 2* Du w u C U

Air-sea momentum flux

Based on the dependence of sea surface roughness on wind speed, Charnock (1955) proposed the famous Charnock relation

E.g., Wu (1980):

A constant Charnock parameter corresponds to the drag coefficient increasing linearly with wind speed.

Sea surface roughness

2*0 /ugz

0185.0

Cd-U10 relationships

Wave-age-dependent sea surface roughness

• The SCOR workgroup 101 (Jones and Toba 2001) presented a relation between the Charnock parameter and wave age (SCOR relation):

• According to this relation, the non-dimensional sea surface roughness first increases and then decreases with the increasing wave age.

* * *

*

0.03 exp 0.14 , ~ 0.35 35

0.008, 35

The SCOR relation and its comparison with various field and laboratory observations (from Toba et al., 2006)

Sea surface under hurricane winds

Photo by Mike Black, NOAA/AOML/HRD

Wave state and sea spray affected sea surface roughness

Instead of using a constant Charnock parameter, we combined the SCOR relationship (Jones and Toba 2001) with the resistance law of Makin (2005), which took into account of sea sprays impact on air-sea momentum flux, and obtained a sea surface roughness applicable to both low-to-moderate and high wind conditions:

1/3/ 2 1 1/* * * *

1/1 1/*

(0.085 ) 0.03 exp 0.14 , 35

17.60 0.008 , 35

* */pc u *min 1, cra u -10.64 m scra

Wave state and sea spray affected sea surface wind stress

The wave state and sea spray affected Cd-U10 relationship together with the field and laboratory observations

Wave state and sea spray affected sea surface scalar roughness

• As for the sea surface heat and moisture fluxes, we use the parameterization of sea surface scalar roughnesses in COARE algorithm V3.1 (Fairall et al., 2003) to estimate the direct air-sea heat fluxes.

4 5 0.6*min 1.1 10 , 5.5 10 ReT qz z

* 0 *Re /z u The Reynolds number of sea surface aerodynamic roughness

Sea sprays and air-sea heat fluxes

Sea spray droplets

From Andreas (1995)

Droplet Evaporation Layer (DEL)

From Andreas and DeCosmo (2002)

Wave state affected sea spray heat flux

• By using the wave state dependent Sea Spray Generation Function (SSGF) and Andreas (1992)’s algorithm to estimate nominal sea spray heat flux, and considering the feedback effects

• One can now estimate the sea spray sensible and latent heat fluxes that include wave state effect. and are determined following Bao et al. (2000), while is taken as 1 (Andreas, 1992).

,

,

S T S S L

L T L L

H H Q Q

H H Q

,

,

S sp S L

L sp L

Q Q Q

Q Q

Sea state affected air-sea heat fluxes

The given atmospheric and sea surface environment: sea surface pressure: 1000 hPa; air temperature: 25 °C; air relative humidity: 80%;neutral stable atmospheric layer; sea surface temperature: 27 °C; and ocean salinity: 34 psu.

CAWOMS - Idealized TC

• A bogus vortex with a maximum wind speed of 30 m s-1 at a radius of 70 km is implanted in the ambient atmosphere with uniform easterly winds of 5 m s-1.

• The ambient temperature and humidity (salinity) profiles are derived from the monthly averaged vertical profiles for September at the location of (20N, 145E):– NCEP-DOE AMIP-II Reanalysis– One degree WOA05

• The initial SST equals 29 Celsius degree, with a mixed layer depth (MLD) of 40 m.

Typical tropical atmosphere

Typical tropical ocean

u = -5 m/s

Vortex: max wind 30 m/s

Experiment design for an idealized TC

Summary of the experiments

Expts.

Couplings considered

Atmosphere-wave Atmosphere-ocean Wave-ocean

CTRL No No No

CPLAW Yes No No

CPLAO No Yes No

CPLAWO Yes Yes Yes

The simulated TC tracks

45-h results of the control run

Vertical cross section of potential temperature and horizontal wind speed.

CTRL

CPLAO

CPLAW

CPLAWO

CTRL

CPLAO

CPLAW

CPLAWO

CTRL

CPLAO

CPLAW

CPLAWO

Wind and SLP SWH SSC

Effects of atmosphere-wave-ocean coupling

CTRL

CPLAO

CPLAW

CPLAWO

Left: SSTMiddle: MLDRight: HHC

CTRL

CPLAW

CPLAO

CPLAWO

Left: Total upward sensible heat fluxRight: Total upward latent heat flux

CPLAW CPLAWO

CPLAWOCPLAW

HSS

HLL

HSD

HLD

HEE

Effects of atmosphere-wave-ocean coupling

Time series of the simulated (a) minimum SLP, (b) max 10-m wind, (c) maximum SWH, and (d) minimum SST for each experiment.

CTRL: Blue

CPLAW: Red

Expts. Min SLP Max 10-m wind Max SWH Min SST Max SSC Max MLD

CTRL 959.29 41.11 17.83 25.28 0.89 132.48

CPLAW 950.98 49.10 19.07 24.89 0.95 133.70

CPLAO 966.35 40.13 15.98 26.17 0.81 98.83

CPLAWO 962.36 42.05 16.09 25.89 0.89 107.90

CPLAO: Green

CPLAWO: Black

Sensitivity to MLD

*80 => initial MLD of 80 m*120 => initial MLD of 120 m

Summary of AWO interaction and coupling

• A CAWOMS consists of WRF, SWAN and POM, based on atmosphere-wave, atmosphere-ocean, and wave-current interaction processes.

• The AW coupling has overall positive contribution, which strengthens the TC system.

• The AO coupling has overall negative contribution due to the negative feedback of SST cooling, which weakens the TC system.

• The overall effects of AWO coupling on TC intensity depend on the balance between wave-related overall positive feedback and oceanic overall negative feedback.

Outline

• Air-Sea-Wave Interaction– A Coupled Atmosphere-Wave-Ocean Modeling System

(CAWOMS)• Limited-Area Model Nesting/Downscaling

– A Scale-Selective Data Assimilation (SSDA) technique

SSDA—A nesting/downscaling technique

GCM LAM Sub-grid scale

SSDA

L k1000 km 100 km

100 km 12 kmResolution

10000 km 10 km

Traditional sponge zone method and the SSDA approach

Domain boundary

Relaxation zone

Domain interior

The SSDA system and procedure

LAM-WRFt

Components: LAM (WRF), 3DVAR (WRFDA), Low- and band-pass filter

Application to regional climate simulation of North Atlantic (Jun-Oct

2005)

The large-scale wind field at 200 hPa for (a) GFS analysis; (b) control run; and (c) SSDA5 at 00 UTC 24 September 2005 (right after the data assimilation cycle).

CTRLGFS

SSDA

Application to TC track hindcastingHurricane Katrina (2005)

Simulated storm tracks of Hurricane Katrina for experiments (blue ‘o’) CTRL, (green ‘+’) FDDA, and (red ‘x’) SSDA, together with (black ‘s’) the best track

CTRL: Blue

SSDA: Red

BEST: Black

Application to TC track hindcastingHurricane Katrina (2005)

Simulated storm tracks: (‘o’) CTRL1, (‘*’) SSDA1, (‘+’) CTRL2, (‘x’) SSDA2, together with (‘s’) the best track

Model domains

CTRL1: Blue ‘o’SSDA1: Blue ‘*’CTRL2: Green ‘+’SSDA2: Green ‘x’BEST: Black

Application to TC forecastingHurricane Felix (2007)

Mean track (left) and intensity (right) forecast errors for the CTRL and SSDA runs, the GFS global forecasts, the CLIPPER5 or SHF5 model, and the official NHC forecasts, at different forecast periods.

Track Intensity

Application to Hurricane Irene (2011)’s

track simulation

Application to Hurricane Irene (2011)’s

intensity simulation

The Hybrid SSDA System

LAM-WRF

Components: LAM (WRF), 3DVAR (WRFDA), Low- and band-pass filter

Hybrid SSDADA for Hurricane Irene

OBS Surface OBS Upper Air

Hybrid SSDADAHRD for Hurricane Irene

H*Wind TDR wind

Hybrid SSDA for Hurricane Irene

Blue: CTRLGreen: SSDARed: SSDADACyan: SSDADAHRDPurple: BEST

Hybrid SSDA for Hurricane Irene

Valid at 12Z25AUG

CTRL SSDA

SSDADA SSDADAHRD

Summary of SSDA• The SSDA approach drives the regional model from the model

domain interior in addition to through the conventional sponge zone boundary conditions.

• The SSDA approach can be applied in various situations, including regional climate downscaling and tropical cyclone forecasting. The SSDA approach can effectively improve the TC forecasting by assimilating large-scale information from global model results, especially for the track forecasting.

• The Hybrid SSDA approach has an additional function (merit) to assimilate all kinds of available observations, leading to improvements for both TC intensity and track forecasting.

A Hybrid SSDA-CAWOMS

CAWOMS SSDADA

Hybrid SSDA

TC Intensity Forecasting

TC Track Forecasting

TC Track and Intensity Forecasting

Hybrid SSDA-CAWOMS

Thank you!