The Multi-Scale Nature of Earth-system and Weather Prediction: recent results on hurricane vortex Rossby-waves predictability and dynamical processes

Dr Gilbert BrunetDirectorMeteorological Research DivisionEnvironment CanadaBergen University, 4 October 2010

Acknowledgements: M. Roch, P. Yau, Y. Martinez, M. Miller, M. Desgagné, S. Bélair, L. Garand, B. Denis and T. Hollingsworth

Outline

• Introduction– The challenge of global seamless prediction: an

historical perspective

• On the predictability and dynamical processes of hurricane intensity

• Conclusion– Numerical Earth-system prediction

The 20th century: Numerical Weather Prediction (NWP)C. Abbe (1901), V. Bjerknes (1904) and L.F. Richardson (1922)

• Numerical weather prediction and modelling are based on the physical laws of fluid,

Horizontal Momentum

Vertical Momentum

Continuity

Thermodynamic

Moisture

State

• These equations are the based for predictability and dynamical processes studies

ddt

p fH

H HVk V F

1

dwdt

pz

g F z 1

ddtln

.

V 0

ddt

F

dqdt

F q

.

kprpTwhereRTp

The 20’s: L.F. Richardson (1922)

La prévision numérique du temps et

du climat, M. Rochas et J.-P. Javelle.

The 50’s

• Von Neumann, pionnier of modern computers, and the American meteorologist Charney were the first to do a NWP forecast.

The 60’s

• The first successful integrations of a global spectral model were performed (Robert, 1969). The ancestor of all existing spectral global climate models

• Unprecedented space and surface based observation systems were put in operation

Integrated Observation System for all space-time scale with global coverage for weather and environmental prediction. These observations are quality controlled and combined together using a state-of-the-art data assimilation system.

Space and surface based observation systems

850 KM

SUBSATELLITE POINT

GOMS (Russian Federation)

76E

MSG

(EUMETSAT) 63 E

MTSAT (J apan)

140E

FY-2 (China)

105E

GOES-E (USA) 75W

NPOESS (USA)

GOES-W (USA) 135W

Oceanographic Missions

AtmosphericChemistryMissions

HydrologicalMissions

High-resolutionLand useMissions

METEOR 3M(Russian Federation)

METEOSAT

(EUMETSAT) 0 Longitude

(China)FY-1

Metop (EUMETSAT)

Polar Communications and Weather (PCW): a Canadian initiative to achieve environmental prediction and integrated monitoring in the North

• We need to observe the Arctic from space with GEO-like imagery (imagery 50-90 N)

• The PCW initiative proposes to use 2 satellites in Molniya orbit

0.5-1 km VIS2 km IR

12-h period63.4 deg. Inclination

Apogee: ~39,500 kmPerigee: ~600 km

Physical Processes

Surface Processes

HEM

G

SolarRadiation

InfraredRadiation

Boundary-Layer Turbulence

DeepConvection

Stratiform Precipitation

Evolution of the HPC provided and the forecast quality

Nb of model calculation needed - HPC provided - Forecast quality yielded

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010

Year

Fo

reca

st q

ual

ity

1.0E-02

1.0E-01

1.0E+00

1.0E+01

1.0E+02

1.0E+03

1.0E+04

1.0E+05

1.0E+06

1.0E+07

1.0E+08

1.0E+09

Quality of 120h forecast

Sustained Mflops

nb of model calculation needed (1976 model = 1)

CDC Cray NEC IBM

SPEC Hem res. 789 km

SPEC Hem res. 350km

SPEC globres. 160km

GEM globres. 100km

GEM globres. 33km

Mflo

ps (

blac

k cu

rve)

Mod

el c

alcu

latio

n w

.r.t

197

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ed c

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)

Global Environmental Multiscale (GEM) Forecasting & Modelling System

2010-2020

Regional and Mesoscale Forecast ( 24-48 h, 10-15 km )

& Data assimilation

Medium-range Forecast ( 240 h, 10 to 35 km )

& Data assimilation

Middle Atmosphere Model &

Data assimilation

Regional Climate Model

Monthly Forecast

Multi- Seasonal Forecast

Ensemble Forecast

Limited-Area Model 0-24h 1-2.5km

& Data assimilation

S P A C E

S C A L E

TIME SCALE

Micro-meteorology (10m-1km)

An unified numerical forecasting system for seamless (e.g. multi-scale and multi-disciplinary) applications

RMS Error at 2 and 5 days of GEM (35km)

Weather Prediction (T1279, ~15 km)compared with Satellite ObservationsECMWF predictions and Meteosat observationsMartin Miller (ECMWF)

Hurricane Juan, 28 September 2003, Halifax

75°N

110°W 10°E5°N

Hurricane climatology: a concern for Canadian.

The challenge of predicting hurricanes at convective permitting scale (~1km)

GLOBAL GEM GRID AT 35km

Instantaneous precipitation rate (mm/hr) for the Operational GEM modelA 5 day animation (20/01/2002 to 25/01/2002) (HR=100km, TR=45 min.)

Acknowledgement to M. Roch and S. Bélair

Instantaneous precipitation rate (mm/hr) for the Meso-Global GEM modelA 5 day animation (20/01/2002 to 25/01/2002) (HR=33km, TR= 15 min.)

Acknowledgement to M. Roch and S. Bélair

On the predictability of hurricane intensity

Motivation and Background

• Andrews (1992) Spiral rainbands have

been reasonably well explained by invoking vortex Rossby waves (VRWs) Dynamics. As VRWs radiate outward, cyclonic angular momentum is transported inward to the parent vortex producing vortex intensification. (Vortex Symmetrization Mechanism. MK97)

Motivation and Background

• Isabel (2003) Mesovortices and polygonal

eyewalls can be explained as a result of VRW instabilities. Then the mature hurricane rapid intensification occurs via eyewall contraction. (Schubert, 1999)

• Can-we characterize and quantify the dynamics and significance of the spiral bands?

• Studies have shown that inner spiral bands have characteristics of vortex Rossby-waves

• Vortex Rossby-waves (VRW) and gravity waves are mixed (Rossby number [U/Lf] is not small)

• Apply Empirical Normal Mode (ENM) method to separate the waves to isolate the effect of VRW on a simulated hurricane (6 km grid size, 24 h simulation sampled every 2 minutes)

Chen, Brunet and Yau 2003: Spiral Bands in a Simulated Hurricane. Part II: Wave Activity Diagnostics. Journal of Atmospheric Sciences: Vol. 60, No. 10, pp. 1239–1256.

Motivation and Background

PV at 6 km

00h

18h12h

06h

Basic State (24 hour mean)

>0

>0

<0

<0

Potential Vorticity

Potential Vorticity Radial Gradient

Wavenumber 2, ENM mode 1 and 2, time series

11.0%

10.5%

T=1hT=1h

T=0.27hT=0.27h

|a|2

(10-3

)

(2 /24H)

1.1h~

2

2,1

2,12,1 A

J

sT

Wavenumber 2, PV ENM mode 1 and 2, spatial patterns

Mode 1 cos Mode 2 cos

Mode 1 sin Mode 2 sin

Period and variance (%) of most important ENMs that contribute to 47% of the total variance

Wave number

ENM number

Period (hour)

Variance (%)

1 1 2.4 11

1 2 2.4 8

1 720 1.6 3

1 721 1.6 4

2 1 1.0 8

2 2 1.0 7

2 720 1.1 3

2 721 1.1 3

00h

18h12h

06h

• Numerical Weather Prediction (NWP) models with timestep less than one hour should start to resolve properly Vortex RossbyWaves (VRWs)

Wavenumber 1 and 2, mode 1+2, Eliassen-Palm flux (24h time mean)

•Wave-mean-flow interaction diagnostic with E-P flux:

Ft

vr

)(

• Acceleration: low and middle troposphere inside the eyewall• Deceleration: upper troposphere in the eyewall• Wave breaking signature

1~2 m/s per hour

•As a hurricane intensifies a secondary eyewall (SE) forms concentrically outside the primary eyewall

• The SE propagates inward and intensifies, the inner eyewall weakens and is eventually replaced by the outer eyewall in a process known as Eyewall Replacement Cycle (ERC)

• About 70% of all major hurricanes have undergone ERC. SE and ERC are usually associated with significant intensity fluctuations

• SE and ERC are vital to understand inner-core dynamics, upgrading land-falling warnings and improve the intensity forecasting

• The rainbands dynamics may play a crucial role on the SE formation (MK97)

Concentric Eyewalls

On the Dynamics of Concentric Eyewall Genesis

• A simulated hurricane obtained using the high-resolution Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) nonhydrostatic mesoscale model version 5 (MM5).

• The simulation uses a four nested domain (27 km, 9 km, 3 km, and 1 km) with two-way interaction between nests.

Wave-mean flow interactionsEliassen-Palm (EP) flux (horiz.) and EP flux divergence

•Two eddy momentum maxima are induced, one slightly inside the eyewall region and the other at about 65 km where the second eyewall eventually develops

• Consistently, two maximum tangential wind acceleration are observed, one associated with the primary eyewall contraction and the other with the second eyewall intensification

Conclusion

• A Vortex Rossby Wave (VRW) wave-mean flow interaction mechanism is proposed for hurricane dynamics to explain the intensification of primary wall and the eye wall replacement cycle

• Consequence for forecast skill:– VRWs need to be properly simulated to obtain correct

momentum redistribution (e.g. wave breaking) inside hurricanes and hence intensification evolution

– We expect the numerical convergence of momentum flux at convective scale (~ 1km)

▪ Chen, Y., G. Brunet and P. Yau 2003 Spiral bands in a simulated hurricane PART II: Wave activity diagnostics J. Atmos. Sci. , 60, 1239–1256.

▪ Martinez, Y., G. Brunet and P. Yau, 2010: On the Dynamics of Concentric Eyewall Genesis: Space-Time Empirical Normal Modes Diagnosis. J. Atmos. Sci., in press.

▪ Martinez, Y., G. Brunet and P. Yau, 2010: On the dynamics of two-dimensional hurricane-like concentric rings vortex formation. J. Atmos. Sci., in press.

▪ Martinez, Y., G. Brunet and P. Yau, 2010: On the Dynamics of Two-Dimensional Hurricane-Like Vortex Symmetrization. J. Atmos. Sci., in press.

A Grand Challenge project on the Earth Simulator: A Japan-Canada collaboration

Tropical PhaseClass2 Hurricane

ET Phase

985 hPa

964 hPa

September 1998: Classified as a very active TC period

Desgagne, Ohfuchi, Brunet, Yau, McTaggart-Cowan and M. Valin, 2004: Large Atmospheric Computation on the Earth Simulator: A report on the LACES project. Annual Report of the Earth Simulator Center (April 2003–March 2004), 225-227. The Earth Simulator Center, Yokohama,

Japan.

EARTH SIMULATORCENTER

inYOKOHAMA, JAPAN

Relative Vorticity at 950 hPa (10km)

Humidity at 350m height is shown over Gulf of Mexico for the first 12-72 hours of the simulation.-Only 1% of the simulation domain is shown!

Numerical Earth-system Prediction

• The progress in computational power, science, telecommunication and observations over the last decade has made it possible to couple Numerical Environmental and Weather Prediction systems with ocean, ice, land surface, hydrological and bio-geochemistry models to achieve an unprecedented number of new Numerical Environmental Prediction and integrated monitoring applications.

04/18/23 37

Why Coupled Models?

• No single group has all the required expertise• Established models exist for most components• Modeling scales are converging

Integrated 2D modelling of the St. Lawrence River EcosystemIntegrated 2D modelling of the St. Lawrence River Ecosystem

River’s physicsAquatic & emergent vegetation

Water quality, turbidity, color, pollutants concentrations

Controlling variables Bio-socio-economic systems

Humans: drinking water, recreation, health and economy

Aquatic and riparian fauna

interactionsExample: Reproduction habitat for the Petit Blongios

Modeling the interactions and predicting the impacts

Velocities, depths, temperature, terrain description, substratum, etc.

Water level and flow scenarios: prediction of the impacts on various aspects such as water intakes, navigation, water quality, plants, mammals, birds, etc.

Acknowledgment to J. Morin and J.-F. Cantin

Bull. Amer. Met. Soc., October 2010

• An Earth-system prediction initiative for the 21st century

Shapiro, Melvyn A., Jagadish Shukla, Gilbert Brunet, Carlos Nobre, Michel Béland, Randall Dole, Kevin Trenberth, Richard Anthes, Ghassem Asrar, Leonard Barrie, Philippe Bougeault, Guy Brasseur, David Burridge, Antonio Busalacchi, Jim Caughey, Delaing Chen, John Church, Takeshi Enomoto, Brian Hoskins, Øystein Hov, Arlene Laing , Hervé Le Treut, Jochem Marotzke, Gordon McBean, Gerald Meehl, Martin Miller, Brian Mills, John Mitchell, Mitchell Moncrieff, Tetsuo Nakazawa, Haraldur Olafsson, Tim Palmer, David Parsons, David Rogers, Adrian Simmons, Alberto Troccoli, Zoltan Toth, Louis Uccellini, Christopher Velden and John M. Wallace.

• Toward a seamless process for the prediction of weather and climate: the advancement of sub-seasonal to seasonal prediction

Brunet, G., M. A. Shapiro, B. Hoskins, M. Moncrieff, R. Dole, G. N. Kiladis, B. Kirtman, A. Lorenc, B. Mills, R. Morss, S. Polavarapu, D. Rogers, J. Schaake, and J. Shukla.

• Addressing the complexity of the Earth system

Nobre, Carlos, Guy P. Brasseur, Melvyn A. Shapiro, Myanna Lahsen, Gilbert Brunet, Antonio J. Busalacchi, Kathy Hibbard, Kevin Noone and Jean Ometto.

• Toward a new generation of world climate research and computing facilities

Shukla, J., T.N. Palmer, R. Hagedorn, J. Kinter, J. Marotzke, M. Miller and J. Slingo

www.ec.gc.ca