the multi-scale nature of earth-system and weather prediction: recent results on hurricane vortex...
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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
6 (r
ed c
urve
)
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