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Stéphane Bélair Numerical Enrivonmental Prediction, on the Way Towards More Integrated Forecasting of the Earth System WWOSC, Montreal, August 19 th, 2014.

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Presentation on theme: "Stéphane Bélair Numerical Enrivonmental Prediction, on the Way Towards More Integrated Forecasting of the Earth System WWOSC, Montreal, August 19 th, 2014."— Presentation transcript:

1 Stéphane Bélair Numerical Enrivonmental Prediction, on the Way Towards More Integrated Forecasting of the Earth System WWOSC, Montreal, August 19 th, 2014 Meteorological Research Division Environment Canada

2 NWP NEP Numerical Weather Prediction Numerical Environmetnal Prediction

3 NWP NEP Numerical Weather Prediction Numerical Environmetnal Prediction Land surface + urban

4 ‘’Traditional’’ NWP… Plenty of Environmental Processes ATMOSPHERIC RADIATION SEA-ICE OCEANS LAND VEGETATION CITIES SNOW GLACIERS LAKES PRECIPITATION CLOUDS ATMOSPHERIC DYNAMICS / CIRCULATIONS

5 ‘’Traditional’’ NWP… Characteristics “In-line” treatment Single code (most often) Same timestep Same spatial resolution Optimized for meteorology Incomplete

6 The Larger and more Modular View of NEP SURFACE PREDICTION SYSTEM (land, vegetation, cities) OCEANS and SEA-ICE SYSTEMS AIR QUALITY MODELS ATMOSPHERIC DISPERSION SYSTEMS HYDROLOGY HYDRODYNAMICS LAKE MODELS (1D and 3D) FOREST FIRES WAVES

7 The Larger and more Modular View of NEP SURFACE PREDICTION SYSTEM (land, vegetation, cities) OCEANS and SEA-ICE SYSTEMS AIR QUALITY MODELS ATMOSPHERIC DISPERSION SYSTEMS HYDROLOGY HYDRODYNAMICS LAKE MODELS (1D and 3D) FOREST FIRES WAVES Distinct systems Distinct codes Coupled (one-way or two- way) Distinct timesteps Distinct spatial resolutions Optimized for own applications Own assimilation system WAVES

8 An Example: Land Surface Prediction Systems

9 The Canadian Land Data Assimilation System (CaLDAS) LAND MODEL (SPS) OBS ASSIMILATION EnKF + EnOI xbxb y EnKF x a = x b + K { y – H(x b ) } K = BH T ( HBH T +R) -1 with CaLDAS IN OUT Ancillary land surface data Atmospheric forcing Observations Surface Temperature Soil moisture Snow depth or SWE Vegetation* Screen-level (T, Td) Surface stations snow depth L-band passive (SMOS, SMAP) MW passive (AMSR-E) *Optical / IR (MODIS, VIIRS) Combined products (GlobSnow) T, q, U, V, Pr, SW, LW Orography, vegetation, soils, water fraction,... Analyses of… *) not done yet… Carrera et al. 2014 (in revision)

10 Coupling CaLDAS with GEM 2.5-km model 4DVAR– (10 km regional) Upper-air assimilation system Atmospheric model (GEM 2.5 km) Land data assimilation system (CaLDAS) UA ICs and LBCs Land surface ICs Forcing and first guess

11 GEM 2.5-km with and without CaLDAS : Dew point temp., Bias, summer, 00 UTC cases Maritimes Que - OntUSA Prairies BC North

12 GEM 2.5-km with and without CaLDAS: Dew point temp., STDE, summer, 00 UTC cases Maritimes Que - OntUSA Prairies BC North

13 CaLDAS-screen (Pan-Canada – 2.5 km) Valid on June 25, 2011, at 1200 UTC Near-Surface Soil Moisture (0-10 cm)

14 Coming… For both global and regional suites Ensemble Kalman Filter (EnKF) Ensemble- Variational (EnVar) Ensemble Prediction System Deterministic Prediction System CaLDAS Land surface ICs Atmosphere ICs Forcing and first guess

15 ATMOS MODEL 3D INTEGRATION External Land Surface Model With horizontal resolution as high as that of surface databases (e.g., 100 m) ATMOSPHERIC FORCING at FIRST ATMOS. MODEL LEVEL (T, q, U, V) 2D INTEGRATION Computational cost of off-line surface modeling system is much less than an integration of the atmospheric model ATMOSPHERIC FORCING at SURFACE (RADIATION and PRECIPITATION) LOW-RES HIGH-RES Land surface prediction system (SPS)

16 100-m SPS for the 2010 Vancouver Games (Thanks to Juan Sebastian Fontecilla) 100-m snow analyses Great decrease of T2m errors (bias shown here) (Bernier et al. 2011, 2012)

17 Urban off-line modeling system Resolution: 120 m MOD11A1 product Resolution: 1km (exactly 928 m) Atmospheric effects corrected Satellite View Angle : 15° Comparison with MODIS Radiative Surface Temperature (°C) July 6 th 2008 (10:54 LST) Warm and Sunny Z 0h : Kanda (2007) (Leroyer et al., 2011) Urban Heat Island Modeling (Montreal)

18 Two-way coupling GEM 2.5 km CaLDAS 2.5 km Surface Prediction System Nudging surface variables Lower BCs Forcing + first guess

19 An ‘’horizontal’’ challenge LAND / VEG (ISBA / SVS) URBAN (TEB) WATER SINGLE GEM (ATMOSPHERE) GRID AREA (LOW RES) MULTIPLE SURFACE GRID AREAS (HIGH RES) SPATIAL AVERAGE OF IMPLICIT LOWER BC FOR VERT. DIFFUSION Spatially averaged

20 Potential contribution of two-way coupling Subgrid-scale variability of turbulent fluxes for 25-km grid spacing model based on external 2.5-km land surface model 95% 5% 25% 75% ~115 Wm -2 (Provided by M. Rochoux, EC) ~40 Wm -2

21 A ‘’vertical’’ challenge LAND / VEG (ISBA / SVS) URBAN (TEB) WATER SINGLE GEM (ATMOSPHERE) GRID AREA (LOW RES) MULTIPLE SURFACE GRID AREAS (HIGH RES) INCREASED VERTICAL RESOLUTION SPATIAL AVERAGE of IMPLICIT LOWER BC for VERT. DIFFUSION (to be applied over atmospheric level just above canopy / soil water / ice) SPATIAL AVG of TENDENCIES for EACH INTERSECTING LEVEL

22 Coupling Urban Canopy w/ Atmosphere  CaM-TEB (Canadian Multilayer version of TEB)  Several model levels intersect the buildings.  Variable building heights exist within a grid cell. (Husain et al. 2013)

23 To be tested with Pan Am and TOMACS Real-time 250-m GEM runs over the Toronto region in preparation of the Pan American Games. Here, precip rates and surface winds for 17 June 2014. Offline runs with SPS over Tokyo. Here, surface air temperature for 26 August 2011.

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