Canadian Historical Contributions and Future Perspectives in Numerical Weather Prediction

Michel BélandACSDMeteorological Service of CanadaEnvironment CanadaQuébec, Canada

Monday, October 29, 2001, CAS 2001 meetingContributors: Dr. G. Brunet, Dr. R. Benoit, CANARIE INC., Dr. P. Gauthier, M. Jean, Dr. H. Ritchie and A. Simard

L.F. Richardson

La prévision numérique du temps et

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

NWP Research topics 30 years ago.

Ninth Stanstead SeminarJuly 1971St-Anne-de-Bellevue, Québec

NWP Research topics today2001 Workshop on the Solution of Partial Differentail Equations on the sphere, May 2001, Montréal

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Major Contributions to Numerical Weather Prediction (NWP) and Data Assimilation Since 1960 with a Canadian Perspective

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The Starting Point: the 50’s and 60’s

Early operational NWP models were quasi-geostrophic and filtered some types of atmospheric motions (e.g. the Charney 1954 model used by the U.S. Weather Bureau)

The Starting Point: the 50’s and 60’s Provides initial conditions for NWP models: Correct a

forecast by direct insertion of observations (Cressman, 1959)– lead to inconsistencies in the 3D meteorological fields

Univariate statistical interpolation (Gandin, 1963) Increased computer power permitted a return to more

general primitive equations (PE) grid-point models (e.g. the Schumann & Hovermale model implemented in the U.S. in 1966)

The first successful integrations of a spectral model were performed (Robert, 1969)

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Contributions during the 1970’s Gravity waves present in the PE’s were stabilized by the

semi-implicit method (Robert & Kwizak, 1971) permitting a 4-fold increase in efficiency of NWP models

Contributions during the 1970’s First operational spectral model implemented (Daley,

Girard et al.) First variable resolution finite element method applied for

limited area modelling (Staniforth, Daley and Mitchell) Multivariate statistical interpolation

(Rutherford, 1972; Schlatter, 1975; Lorenc, 1981)

– takes into account the relative accuracies of both the observations and the forecast

– analysis corrections are more dynamically consistent

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Contributions during the 1980’s

Semi-implicit semi-Lagrangian (SI-SL) technique increased efficiency of grid-point NWP models by another factor of 4 or 5 (Robert et al.)

First operational turbulent kinetic energy planetary boundary layer model implemented (Benoit et al.)

Ultra-fast FFT’s developed (Temperton) Ensemble prediction (Hollingsworth, Buizza et

al., Toth et Kalnay)

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Contributions during the 1990’s

SI-SL method implemented in spectral models (Ritchie et al.)

Unified GEM (SI-SL, global, uniform or variable resolution, non- hydrostatic and hydrostatic) model (Staniforth, Côté, Gravel)

Contributions during the 1990’s

First SI-SL fully non-hydrostatic model (became MC2) developed (Tanguay, Laprise, Robert)

MC2 internationally recognized for mesoscale modelling (Benoit et al.)

Variational data assimilation: 3D-Var Motivation: assimilation of satellite data

– difficult to assimilate with the explicit formulation of “optimal interpolation”

– variational framework permits the direct assimilation of indirect measurements (e.g., radiances)

Implementation of 3D-VarNCEP (1992)ECMWF (1996)Météo-France and CMC (1997)UKMO (1999)

4D variational data assimilation

0

X

t

X

Impact of the time dimension

– Assimilation of observations at their exact time– Extract information from a time series of

observations» wind information is obtained from a time series of

humidity and ozone measurements

– Precursors to synoptic development» time series of surface pressure

– Small corrections to the initial conditions can have a tremendous impact on the resulting forecast

Observation coverage

Current– Radiosondes, surface stations, aircraft and ships

(100,000 data /6-h)– Satellite radiance data ATOVS/RTOVS (24,000/6-h)

» 40 infra-red and microwave channels(but only 4 are assimilated)

Assimilation of new satellite instruments– AIRS: Atmospheric Infrared Sounder

High resolution (2400 bands) to provide information mostly on

temperature and humidity (EOS-Aqua 2002)

– IASI: The Infrared Atmospheric Sounding Interferometer (~ 8000 bands) (METOP ~2006)

Increase in computational cost Augmentation of the volume of data by a

factor of 50 to 100 by 2006 CPU time of single 4D-Var analysis will

take at least 50% of the time of the operational suite compared to the 17% it now takes

Implemented at ECMWF in Nov. 1997 and Météo-France in June 2000

Planned to become operational at CMC in 2003

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Present Trends

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Coupled Environmental Modeling

To conduct collaborative R&D for environmental prediction in Canada.

Now feasible due to advances in numerical modelling in various domains, together with advances in computer power.

We now have the scientific and technical capabilitiesto build comprehensive environmental prediction systems integrating expertise from a wide range ofdisciplines and addressing important R&D andoperational issues.

Key projects Atmosphere-hydrology Model (Waterloo U., IML, MAP, Ontario/MSC

Region, …) Regional Ocean Modeling and Prediction (Dalhousie U., BIO,IML,...) 3-D ocean circulation models being coupled with MSC models for

atmosphere-ocean prediction (Dr. Greatbatch, Dalhousie U.,...) Coastal Modeling Systems for Storm surge forecasts (Atlantic/MSC region,

Dr. Thompson, Dalhousie U.,...) Atmosphere-wave Modeling (Atlantic/MSC Region,...) Marine Environmental Prediction System: Coupled

atmosphere/ocean/biology/chemistry ecosystem model to be developed for demonstration site for Lunenburg Bay, NS (Dalhousie U., Bedford Institute of Oceanography,...)

St. Lawrence Estuary Models (IML) Extra-tropical hurricane transition (Dalhousie U., McGill U., ...)

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Storm Surge Prediction System

Based on Dalhousie University coastal ocean data assimilation and prediction system (Thompson et al.)

Driven by CMC regional model surface pressures and winds

System has been transferred and implemented at MSC’s Maritimes Weather Center

The January 21st 2000 Storm Powerful storm

hit Maritimes Significant

flooding in Charlottetown

Successful prediction by forecast model

Forecast

St. Lawrence Estuary Models Two projects in progress with Institut

Maurice Lamontagne (IML) “Gulf of St. Lawrence Ice-Ocean-

Atmosphere Climate Change, Detection and impact on the Canadian Energy Sector”

“Modèles atmosphérique et hydrologique couplés à l’échelle régionale: Région des lacs des Deux-Montagnes et Saint-Louis”

Community Of Mesoscale Modeling(COMM) group Leader: R. Benoit

Community model (MC2) support is essential in order to partner effectively with universities and benefit from funds provided through Canadian Foundation Climate and Atmospheric Science ($10 000 000 per year)

Community model would be configured to focus on region with potential active or extreme weather events at 1-3km horizontal resolution.

MC2 is worldwide recognized as one of the most computer efficient non-hydrostatic model

Mesoscale Alpine Project (MAP) Leader: Dr. Robert Benoit

To improve the understanding of orographically influenced precipitation events and related flooding episodes involving deep convection, frontal precipitation and runoff.

To improve the understanding of three-dimensional gravity wave breaking and associated wave drag in order to improve the parametrization of gravity wave drag effects in numerical weather prediction and climate mvvodels.

To improve data sets for the validation and improvement of high-resolution numerical weather prediction, hydrological and coupled models in mountainous terrain.

MAP, Friday, October 1, 1999 The model domain

Animation of PV at 850 hPa

Mesoscale downscaling of wind energy climate --development of new tools for the industry--

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Pionneering in HUGE atmospheric problems

(a)First realtime 10 km North-American forecast

MC2 10km(precipitation rate)

GOES -9(cloud albedo)

achieved:June 1997

(27 June 1997 18 UTC)

753 x 510 x 31 grid1 day forecast at 10 km8 GB memory----------------------40 mins wall clock on 30 NEC SX4 PEs

35Planned for:Spring 2002

Pionneering in HUGE atmospheric problems(b) Quasi-global meso-gamma forecast (~realtime)

20000 x 6250 x 60 grid1 day forecast at 4 km3 Tb memory----------------------24 Hours wall clock on 2500 NEC ES PEs

Earth Simulator, JapanEarth Simulator, Japan640 * 8 PE640 * 8 PE

Current support to Health Canada (context: FNEP)

Automated trajectories in prediction mode

– 151 nuclear reactors worldwide

– 77 nuclear reactors in the United States

– 5 Canadian reactor sites

Simulating the Chernobyl Radioactive Plume

Experimental GEM-HIMAP Experimental GEM-HIMAP

•10-km res. topography•24-hour forecasts•daily developmental run•internal web products

GEM-HIMAP (10-km)24-h fcst based on 17 Oct 00 UTC(EER Toolbox visualization)

Iran

TurkmenistanOuzbekistan

Tadjikistan

Pakistan

China

What the future holds for us? GRIDS and distributed computing for e-

science. Unified Model Concept (Climate, NWP ,

Chemical, Hydrological, Ocean) through coupling .

Highly parallel scalar (vectorial?) codes. Conservative algorithms (climate,chemical) Invisible models for decision making...

Increasing computer power Increasing computer power

1960 ’s - Bendix G20, IBM370. 1970’s: Control Data 7600, Control Data 176 1980’s: Cray 1S, Cray XMP-2/8, Cray XMP-4/16 1990’s: NEC SX-3/44, SX-3/44R, SX-4/64M2 and SX-5/32M2 2000’s: Requirement for a new contract & new HPC systems

SGIO2000's28 PEs

(MIPS R10K)10x FC

dualattach

1.2TB

Front Ends

2x ea

7x ea.

Supercomputing Cluster

HIPPIRAIDs

(0.8 TB)

24x

HIPPI 1000 Mb/s switched128 ports. Each host haslinks to ops & dev networks

Central File Server

IXS (8GB/s)

NECSX-4/64M2

FC switched 32 ports

IXS (16 GB/s)

NECSX-5/32M2

0.7 TB RAID

Climate Archive

1.2 TB14x SCSIfrom OSS

O2000 4 xR10K PEs

145 TB4 DST drives20 MB/s ea.

SGI O2000: 4 PEs

ADIC AML-E (Tape Robot)0.7 TB4xFC

Max StratGen5RAID

Trend in skill 1958-2000