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Stéphane Bélair

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

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Stéphane Bélair

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

  2. Numerical Weather Prediction NWP NEP Numerical Environmetnal Prediction

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

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

  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 AIR QUALITY MODELS FOREST FIRES ATMOSPHERIC DISPERSION SYSTEMS HYDROLOGY HYDRODYNAMICS WAVES LAKE MODELS (1D and 3D) WAVES SURFACE PREDICTION SYSTEM (land, vegetation, cities) OCEANS and SEA-ICE SYSTEMS

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

  8. An Example: Land Surface Prediction Systems

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

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

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

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

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

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

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

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

  17. Urban Heat Island Modeling (Montreal) Comparison with MODIS MOD11A1 product Resolution: 1km (exactly 928 m) • Atmospheric effects corrected • Satellite View Angle : 15° • Radiative Surface Temperature (°C) July 6th 2008 (10:54 LST) Warm and Sunny Urban off-line modeling system Resolution: 120 m Z0h: Kanda (2007) (Leroyer et al., 2011)

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

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

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

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

  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 Torontoregion 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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