inter-departmental collaboration mou

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GOVST Canadian National Report 2011 Canadian Operational Network of Coupled Environmental PredicTion Systems CONCEPTS November 16 th 2011 Authors Fraser Davidson, Greg Smith Hal Ritchie, Denis Lefaivre, Youyu Lu, Fred Dupont, Jean Francois Lemieux, Youyu Lu

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GOVST Canadian National Report 2011 Canadian Operational Network of Coupled Environmental PredicTion Systems CONCEPTS November 16 th 2011 Authors Fraser Davidson, Greg Smith Hal Ritchie, Denis Lefaivre, Youyu Lu, Fred Dupont, Jean Francois Lemieux, Youyu Lu. Data Assimiliation Codes - PowerPoint PPT Presentation

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Page 1: Inter-Departmental  Collaboration MOU

GOVST Canadian National Report2011

Canadian Operational Network of Coupled Environmental PredicTion Systems

CONCEPTSNovember 16th 2011

Authors Fraser Davidson, Greg Smith

Hal Ritchie, Denis Lefaivre,Youyu Lu, Fred Dupont,

Jean Francois Lemieux, Youyu Lu

Page 2: Inter-Departmental  Collaboration MOU

Inter-Departmental Collaboration MOU

Partnership

Data Assimiliation CodesMecator Ocean Modelling AlgorithmsData Exchange Agreements

CONCEPTSBudget linked to Annual workplan and report

Page 3: Inter-Departmental  Collaboration MOU

CONCEPTS Structure

Steering Committee A Director General from each Department

Secretariat A Division Manager/ Director

from each Department

Annex 1 PredictionSystems

Ritchie Davidson Lefaivre

Annex 2

OceanMonitoring

Gilbert et al.

Project Management

Team

Page 4: Inter-Departmental  Collaboration MOU

The Gulf of St. Lawrence (GSL) Coupled Regional Deterministic Prediction System

-5°C-5°C

-15°C-15°C

-25°C-25°C

• Operational regional forecasting system (GEM-Ops) has tendency to overestimate cold events in winter.

• Increased heat fluxes in coupled system buffers air temperatures and improves forecasts

• Demonstrates importance of air-sea-ice coupling even for short-range weather forecasts

• Coupled GSL system now operational at CMC

– Since summer

S. Desjardins

Page 5: Inter-Departmental  Collaboration MOU

CONCEPTS Canadian Operational Network of Coupled Environmental Prediction • Project Targets:

– Global coupled medium-to-monthly forecasting system:▪ GEM atmospheric model and 4DVAR/EnKF analysis system▪ Coupled to 1/4° resolution (ORCA025) NEMO ice-ocean model▪ Ocean initialized using Mercator analysis system (PSY3)▪ Initially: produce 10 day uncoupled ice-ocean forecasts

– Regional short-term forecasting system▪ Build on V-0 developments / CNOOFS (F. Davidson et al.)

– Ocean initialized using Mercator analysis (PSY2) output▪ NEMO on ORCA12 sub-domain NW Atlantic + Arctic▪ Support MET/NAVAREAS 17 & 18▪ To be coupled to CMC regional forecasting system ▪ Initially: produce 10 day uncoupled ice-ocean forecasts

– Products and Client Product Validation ▪ Build on developments made

– by CNOOFS and Observatoire Global du St. Laurent▪ Consolidation of CONCEPTS output with distribution tools▪ Outreach and Client Interaction

Page 6: Inter-Departmental  Collaboration MOU

CONCEPTS Global - V0

• Ice-ocean model:– NEMO v3.1 : OPA9 ocean model and LIM2-EVP sea ice– ORCA025: Global tri-polar 1/4° resolution

• Atmospheric forcing from GEM Global (GDPS; 33km)– Forced using CORE bulk formula– 3hrly forcing frequency (including diurnal cycle)

• Initialization:– Ice and ocean fields taken from Mercator (PSY3V2) analysis

• Output:– Weekly 10-day forecasts of ocean and ice fields

Page 7: Inter-Departmental  Collaboration MOU

Comparison with AVHRR SST observations

• Differences taken between AVHRR SST data and hourly output from weekly forecasts.

• Statistics accumulated for each day for forecasts made from May 20, 2009 to Mar 23, 2010.

• Results shown for day 10 of forecasts

• Poor coverage in polar regions and due to cloud cover

Mean

Std. dev.

Number of comparisons

F. Roy

Page 8: Inter-Departmental  Collaboration MOU

Comparison with AVHRR SST observations

Mean

Std. dev.

Mean

Std. dev.

Day 1 Day 10

Development of warm bias

Page 9: Inter-Departmental  Collaboration MOU

Comparison with AVHRR SST observations

Mean

Std. dev.

Mean

Std. dev.

Day 1 Day 10

Cold bias present in analysis

Page 10: Inter-Departmental  Collaboration MOU

Comparison between CMC and RTG SST analyses

Figures show difference from GHRSST ensemble median for period May 21-28, 2010

RTG-GHRSSTensCMC-GHRSSTens

www.ghrsst.org

Page 11: Inter-Departmental  Collaboration MOU

Regional RMS differences with AVHRR SST

• CMC SST analysis has smaller RMS diff for day1

• Similar error growth in both persistence curves

• Forecast beat persistence of analysis for most regions

• Forecasts show smaller diff as compared to persistence of CMC SST analyses for N. Atl, N. Pac and T. Ind.

Forecasts

Persistence of SAM2 analyses

Persistence of CMC analyses

Page 12: Inter-Departmental  Collaboration MOU

CONCEPTS Global V1

• AIM: Produce daily analyses and 10day forecasts.

• Based on PSY3V2, with following modifications:– Updated SAM2 to NEMOv3.1, with LIM2-EVP– Assimilate CMC-SST analysis (in place of RTG)– Ocean analysis merged with 3DVAR-FGAT ice analysis– Daily analysis updates (planned)

• Status:– Modifications to SAM2 ongoing– Routine production of ice-ocean analyses since Dec. 2010– Evaluation of ice-ocean forecasts underway

▪ CLASS4 metrics GODAE IntercomparisonTT

– Starting initial trials of coupled runs.

Page 13: Inter-Departmental  Collaboration MOU

Verification against NOAA IMS analyses

• Evaluation of 5km North American analyses

• Based on contingency tables values

• Uses threshold of 0.4 for ice/noise

• Overestimation of ice cover in CMC operational analysis during melt

M. Buehner

Ice Ice

Ice Wat

Wat Ice

Wat Wat

Forecast Observation

PersistenceForecast

Page 14: Inter-Departmental  Collaboration MOU

Verification against Radarsat

• Evaluation of 5 day ice forecasts

• Model appears to have some skill in predicting mean ice cover, but ice dynamics is still a challenge…

• Careful analysis required to understand small-scale details represented in Radarsat image analyses

CIS Radarsat image analysis

Labrador Sea

Model (mean error)

Model (std. dev.)

Persistence of CMCICE (mean error)

Persistence of CMCICE (std dev)

PersistenceForecast 1/4o

Page 15: Inter-Departmental  Collaboration MOU

CONCEPTS Regional V0forecasting system

• C-NOOFS: Canadian-Newfoundland Operational Oceanographic Forecasting System

• Lead: F. Davidson (NAFC)• Produces daily 10-day forecasts at 1/12°

resolution for the Northwest Atlantic • Initialized using Mercator data assimilation

system (PSY2). • Merged with 3DVAR-FGAT ice analysis• Designed to meet needs of Coast Guard and

Navy, as well as variety of applications influenced by sea ice

C-NOOFS

Page 16: Inter-Departmental  Collaboration MOU

Recreating Drifter track data 23 Drifter tracks [surface + 10m]

MMB5410 201012_20110222.avi

For Coast Guard Applications of Ocean Forecasts

Page 17: Inter-Departmental  Collaboration MOU

CNOOFS Comparison with Spring Survey

• Comparison of bottom temperature from 2010 Spring Survey of Grand Banks for

– NWA025 (~PSY3V2R2)– NWA12 (~PSY2V3R1)

F. Davidson

Page 18: Inter-Departmental  Collaboration MOU

Uses NEMO code as other CONCEPTS projects

Coupled to Hydrodynamic Model for rivers.

Planned coupling to 2 D River model of Saint Laurence System

2km grid

Great Lakes Forecast SystemCONCEPTS Regional

•Required to improve weather prediction in highly populated areas

Page 19: Inter-Departmental  Collaboration MOU

CONCEPTS Regional Great Lakes Forecast System

• Required to improve weather prediction in highly populated areas

• Uses same tools as Regional and Global Systems

• Ice Forecast also important

– Forced model run for great lakes shows ability of NEMO system

Page 20: Inter-Departmental  Collaboration MOU

Great Lakes Forecast SystemThermocline Issue

Mo

del

Ob

serv

ed

Typical Model Results for Stratification in Lake Erie. Modelled T is for 2006.Observed T is for 2008

Page 21: Inter-Departmental  Collaboration MOU

CONCEPTS REGIONAL V1

• Impetus from 30 M$ METAREA project development. – Integrated marine Arctic

prediction system– marine forecast system using a

regional high resolution coupled multi-component modelling (atm., land, snow, ice, ocean, wave) and data assimilation system

• Improved Arctic monitoring• Predict:

- Atmospheric conditions,- Sea ice (concentration, pressure, drift, ice edge) - Waves and Freezing spray,- Ocean conditions (temperature and currents)

Page 22: Inter-Departmental  Collaboration MOU

CONCEPTS Regional V1

• Build on CNOOFS and Coupled GSL

• Develop coupled forecasting system for N. America/Arctic

• Couple NEMO to GEM regional (10km)

• 5km LAM over METAREAS 17&18

• with 5km Atm 4DVAR/EnKF

• 1/12th regional SAM2

• Produce 48hr weather and marine forecasts

C-NOOFS

1/12°

GEM RDPS

10km

Page 23: Inter-Departmental  Collaboration MOU

CONCEPTS Regional Ocean data assimilation

1/12° Atlantic/Arctic system

• Evaluate/develop configuration

• Add tides (wt conservative non-linear free surface)

• Develop 1/12° coastal SAM2

• Combine SAM2 with 3DVAR ice analysis

Page 24: Inter-Departmental  Collaboration MOU

CONCEPTS RECAP

• CONCEPTS Global:– Running 1/4° global 10day forecasting system since Dec. 2010– Operational transfer in coming year– Next steps:

▪ produce daily analyses and ▪ improve consistency of ice and ocean analyses

• CONCEPTS Regional– Develop/evaluate N.Atl/Arctic coastal 1/12th NEMO– Great Lakes Work continues– Begin work on 1/12th regional data assimilation system– Improve Ice Rheologies to better represent fine-scale ice deformations over short

lead times?– How do we constrain the ocean under-ice and in the Marginal Ice Zone?– Ice thickness (Radarsat, Cryosat, AVHRR)

• Product Development and Dissemination – Central CONCEPTS server being lit up this month

▪ Serving C-NOOFS products to CCG and DND

Page 25: Inter-Departmental  Collaboration MOU

Target Clients

• Canadian Ice Service, Canadian Coast Guard,

• Canadian Department of National Defense: Ocean Feature Analysis and Acoustic Situational Awareness

• Offshore Oil and Gas Industry. Fog prediction, Pack Ice and Iceberg Management, Deep Water Riser Vibration Prediction, Site planning

• Improvements to regional and global weather forecasts (timescales of days to seasonal) are expected.

• Fisheries and Oceans ecosystem science initiatives

• Oil and Gas Regulation Boards: Oil spill drift and deep well blowout scenarios

Page 26: Inter-Departmental  Collaboration MOU

Extras…