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
Numerical Weather Prediction
NWP
NEP
Numerical Environmetnal Prediction
Numerical Weather Prediction
NWP
NEP
Numerical Environmetnal Prediction
‘’Traditional’’ NWP… Plenty of Environmental
Processes
ATMOSPHERIC
DYNAMICS /
CIRCULATIONS
CLOUDS
ATMOSPHERIC
RADIATION
PRECIPITATION
CITIES
VEGETATION
LAND
SEA-ICE
OCEANS
LAKES
GLACIERS
SNOW
‘’Traditional’’ NWP… Characteristics
“In-line” treatment
Single code (most
often)
Same timestep
Same spatial
resolution
Optimized for
meteorology
Incomplete
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
OCEANS and SEA-ICE SYSTEMS
SURFACE PREDICTION SYSTEM
(land, vegetation, cities)
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
OCEANS and SEA-ICE SYSTEMS
SURFACE PREDICTION SYSTEM
(land, vegetation, cities)
Distinct systems
Distinct timesteps
Distinct codes
Distinct spatial resolutions
Coupled (one-way or twoway)
Optimized for own applications
Own assimilation system
An Example: Land Surface Prediction Systems
The Canadian Land Data Assimilation System
(CaLDAS)
CaLDAS
IN
Ancillary land
surface data
Orography,
vegetation, soils,
water fraction, ...
LAND
MODEL
(SPS)
Analyses
of…
xb
ASSIMILATION
EnKF + EnOI
Atmospheric
forcing
OBS
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)
Surface
Temperature
Soil moisture
y
T, q, U, V, Pr, SW, LW
OUT
EnKF
xa = xb+ K { y – H(xb) }
Snow depth or
SWE
with
K = BHT ( HBHT+R)-1
Carrera et al. 2014 (in revision)
Vegetation*
*) not done yet…
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)
GEM 2.5-km with and without CaLDAS :
Dew point temp., Bias, summer, 00 UTC cases
BC
Prairies
North
Maritimes
USA
Que - Ont
GEM 2.5-km with and without CaLDAS:
Dew point temp., STDE, summer, 00 UTC cases
BC
Prairies
North
Maritimes
USA
Que - Ont
CaLDAS-screen (Pan-Canada – 2.5 km)
Near-Surface Soil Moisture (0-10 cm)
Valid on June 25, 2011, at 1200 UTC
Coming… For both global and regional suites
Ensemble
Kalman Filter
(EnKF)
Atmosphere ICs
Ensemble
Prediction
System
Land surface ICs
CaLDAS
Forcing and
first guess
Land surface ICs
EnsembleVariational
(EnVar)
Atmosphere ICs
Deterministic
Prediction
System
Land surface prediction system (SPS)
ATMOS
MODEL
LOW-RES
3D INTEGRATION
ATMOSPHERIC FORCING at FIRST ATMOS. MODEL LEVEL (T, q, U, V)
ATMOSPHERIC FORCING at
SURFACE (RADIATION and
PRECIPITATION)
External
Land Surface
Model
With horizontal
resolution as high as
that of surface
databases (e.g., 100 m)
HIGH-RES
2D INTEGRATION
Computational cost of off-line surface modeling system is
much less than an integration of the atmospheric model
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)
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)
Two-way coupling
GEM 2.5 km
CaLDAS 2.5 km
Lower BCs
Nudging
surface
variables
Forcing +
first guess
Surface Prediction
System
An ‘’horizontal’’ challenge
SINGLE GEM (ATMOSPHERE) GRID AREA (LOW RES)
 1  
 
1  ______
  KT
  

 w' '
t   z 
z 
 z

 ______


 w' '   CT u*  S  f S  NK _ atm

S

 ______

  w' '       NK
_ atm

S
  CT u*  S
  CT u* f S
Spatially
averaged
SPATIAL AVERAGE OF IMPLICIT LOWER BC FOR VERT. DIFFUSION
WATER
LAND / VEG
(ISBA / SVS)
URBAN
(TEB)
MULTIPLE SURFACE GRID AREAS (HIGH RES)
Potential contribution of two-way coupling
~115 Wm-2
95%
75%
25%
~40 Wm-2
5%
Subgrid-scale
variability of turbulent
fluxes for 25-km grid
spacing model based
on external 2.5-km land
surface model
(Provided by M. Rochoux, EC)
~115 Wm-2
~40 Wm-2
INCREASED VERTICAL RESOLUTION
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)
SPATIAL AVG of
TENDENCIES
for EACH
INTERSECTING
LEVEL
WATER
LAND / VEG
(ISBA / SVS)
URBAN
(TEB)
MULTIPLE SURFACE GRID AREAS (HIGH RES)
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)
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.