Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal (UQAM)

Transcription

1 Pierre Gauthier Department of Earth and Atmospheric Sciences Université du Québec à Montréal (UQAM) Contributions: Katja Winger, Kamel Chikhar, René Laprise, Laxmi Sushama Department of Earth and Atmospheric Sciences Université du Québec à Montréal (UQAM) M.K. Yau and K. Menelaou Department of Atmospheric and Oceanic Sciences McGill University, Montréal (Québec), Canada

2 Outline High performance computing resources available in Canadian universities The Global Environmental Multiscale (GEM) atmospheric model of Environment Canada Participation of the Canadian Regional Climate Model to the CORDEX intercomparison experiment Hurricane simulations at high resolution Validation of atmospheric models through data assimilation Atmospheric modeling and its extension to the Earth System Conclusions

3 Applications of high performance computing in Québec: Calcul Québec Aerodynamics - cars, aircraft, wind turbines; Understanding our environment - Climate and environmental modeling, geophysics, hydrology Fundamental research and technology Quantum mechanics and nanoelectronics, biochemical modeling Social sciences - Data mining in large databases, game theory Medical research - Identification of genes responsible for diseases, biochemical mechanisms leading to symptoms, development of new medications

4 Servers Site Mise en service Cœurs Mammouth parallèle Université de Sherbrooke mai Cray XD1 Université de Montréal novembre Cirrus Université Concordia octobre Krylov Université McGill janvier Altix 4700 Université de Montréal février Mammouth série II Université de Sherbrooke février Cottos Université de Montréal septembre Colosse Université Laval janvier TOTAL cores En cours d installation Site Mise en service Cœurs ETS/McGill/UQAM Mai Université Concordia Université de Montréal Université de Sherbrooke TOTAL Infrastructure of Calcul Québec > cores

5 M. Charron s presentation this morning Global Environmental Multiscale (GEM) model Every 10 th grid point shown grid (66% in 15-km uniform area) 80 vertical levels Heart of the operational weather prediction model of the Meteorological Service of Canada

6 Distribution of vertical levels in the GEMstratospheric global model

7 CORDEX A Coordinated Regional Downscaling Experiment Sponsored by the World Climate Research Programme

8 GEM-LAM (limited-area model over North America)

9 Validation of CRCM5 Simulations over Africa K. Winger 1, R. Laprise 1, B. Dugas 1,2, L. Sushama 1 and L. Hernandez-Diaz 1 1 Centre ESCER, University of Québec at Montréal 2 Environment Canada The Canadian Regional Climate Model version 5 the latest version of the climate model used at UQAM the climate library of Environment Canada's numerical weather prediction model GEM (General Environmental Multiscale) Collaboration with Ouranos Physical parameterization: Very similar to the deterministic global model (800x600) used at Environment Canada except for land-surface model

10 Simulation setups for CORDEX domains [Winger et al., Presentation 3C6.5, Thursday 15:15, Ovation] North America 0.44 North America 0.11 ERA-Interim lateral and lower BC 212 x 200 points (free 172 x 160) 668 x 560 (free 628 x 520) (9x 0.44 ) 56 atm. vertical levels, top at 10 hpa 26 soil layers, bottom at 60 m Δt = 1200 sec Δt = 300 sec 108 cores (6x6x3): 360 cores (10x12x3): => 20 years in 3 ½ days => 20 years in 30 days Africa_0.44deg Africa_0.22deg Only every 5 th grid box displayed

11 (Peter) M.K. Yau, K. Menelaou Department of Atmospheric and Oceanic Sciences McGill University, Montréal, Canada [Menelaou, Yau and Martinez: presentation yesterday and poster tomorrow]

12 Background Intense hurricanes often develop an inner and an outer eyewall. The outer eyewall contracts and eventually replaces the inner eyewall which dissipates. This is called the eyewall replacement cycle (ERC). If a hurricane undergoes ERC during landfall, the radial extent of strong winds and heavy precipitation can increase significantly. It is then important to forecast and understand the mechanism for the ERC.

13 Validation of models with respect to observations: data assimilation Numerical models of the atmosphere and the Earth system need to be validated through detailed comparison to observations Observations and model (a.k.a. theory) meet in a numerical laboratory through data assimilation experiments Necessary to validate the processes themselves and their interaction

14 Aircraft data SCA-7212 Assimilation de données

15 Surface (SYNOP) and ship (SHIP) data SCA-7212 Assimilation de données

16 Horizontal data coverage of ATOVS AMSU-a radiance data (every 6-h) SCA-7212 Assimilation de données

17 Winds derived from cloud motion (SATOB, SATWINDS) SCA-7212 Assimilation de données

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19 Verification of 5-day forecasts Northern Hemisphere

20 Approaches to measuring the impact of assimilated observations Information content * based on the relative accuracy of observations and the background state [Sessions on Remote sensing on Friday] Observing System Experiments * Data denials * Global view of the impact of observations on the quality of the forecasts Observation impact on the quality of the forecasts * Sensitivities with respect to observations based on adjoint methods (Baker and Daley, 2000; Langland and Baker, 2003) * Ensemble Kalman filter methods

21 Combined Use of ADJ and OSEs (Gelaro et al., 2008) ADJ applied to various OSE members to examine how the mix of observations influences their impacts Removal of AMSUA results in large increase in AIRS (and other) impacts Removal of AIRS results in significant increase in AMSUA impact Removal of raobs results in significant increase in AMSUA, aircraft and other impacts (but not AIRS)

22 Evaluation of the impact of observtions The Fifth WMO Workshop on the Impact of Various Observing Systems on NWP Sedona, AZ, United States 22 to 25 May 2012 The value of targeted data o Majumdar et al., 2012: WMO report from the THORPEX Data assimilation and observing system working group o Majumdar: presentation Thursday at 14:00 in session Data Assimilation V: predictability

23 Initial tendency diagnostic [Chikhar and Gauthier: presentation Thursday 14:30] Proposed by Rodwell and Palmer (2007). The diagnostic parameter applied to temperature is defined as m m k 1 total 1 ( p) T& i = T& i m m i= 1 i= 1 p= 1 m total number of simulations total T & i total temperature tendency (in black) ( ) p T & i individual temperature tendencies associated with each physical process considered in the model (radiation, convection, advection, vertical diffusion and large scale condensation) 23

24 Era-Interim vs 4D-Var (MSC) Mean 6 hours initial tendencies Tropics Global 24

25 Conclusion Numerical simulations of the atmosphere are central to better understanding the complexities of the Earth system Climate simulations (global and regional) Weather predictions at increasingly higher resolution (simulation of the detailed structures of hurricanes) Comparison to observations require the best validated model available to produce analyses which is the best estimate of the atmosphere one can get Reanalyses are extremely important to validate climate models Climate and weather forecasting systems now need to take into account interactions with the oceans, the land, ice, snow, atmospheric chemistry Modeling the Earth system with data assimilation is certainly the challenge of this century to better understand our changing environment

26 (Hollingsworth, 2004)

27 Thank you for your attention 27