Stéphane Bélair

Numerical Enrivonmental Prediction, on Numerical Enrivonmental Prediction, on the Way Towards More Integrated the Way Towards More Integrated Forecasting of the Earth SystemForecasting of the Earth System

WWOSC, Montreal, August 19th, 2014

Meteorological Research DivisionEnvironment Canada

NWP

NEP

Numerical Weather Prediction

Numerical Environmetnal Prediction

NWP

NEP

Numerical Weather Prediction

Numerical Environmetnal Prediction

Land

surface

+ urban

‘’Traditional’’ NWP… Plenty of Environmental Processes

ATMOSPHERICRADIATION

SEA-ICEOCEANS

LAND

VEGETATIONCITIES SNOW

GLACIERS

LAKES

PRECIPITATION

CLOUDS

ATMOSPHERICDYNAMICS / CIRCULATIONS

‘’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

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

WAVES

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

An Example: Land Surface Prediction Systems

The Canadian Land Data Assimilation System (CaLDAS)

LANDMODEL(SPS)

OBS

ASSIMILATIONEnKF + EnOI

xb

y

EnKFxa = xb+ K { y – H(xb) }

K = BHT ( HBHT+R)-1

with

CaLDASIN OUT

Ancillary land surface data

Atmospheric forcing

Observations

Surface Temperature

Soil moisture

Snow depth or SWE

Vegetation*

Screen-level (T, Td)Surface stations snow depthL-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)

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

GEM 2.5-km with and without CaLDAS :Dew point temp., Bias, summer, 00 UTC cases

Maritimes

Que - OntUSAPrairies

BC North

GEM 2.5-km with and without CaLDAS:Dew point temp., STDE, summer, 00 UTC cases

Maritimes

Que - OntUSAPrairies

BC North

CaLDAS-screen (Pan-Canada – 2.5 km)

Valid on June 25, 2011, at 1200 UTC

Near-Surface Soil Moisture (0-10 cm)

Coming… For both global and regional suites

Ensemble Kalman Filter

(EnKF)

Ensemble-Variational

(EnVar)

Ensemble Prediction

System

Deterministic Prediction

System

CaLDAS

Land surface ICs

Land surface ICs

Atmosphere ICs

Atmosphere ICs

Forcing and first guess

ATMOSMODEL

3D INTEGRATION

ExternalLand SurfaceModel

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 andPRECIPITATION)

LOW-RES

HIGH-RES

Land surface prediction system (SPS)

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)

Urban off-line modeling Urban off-line modeling systemsystemResolution: 120 m

MOD11A1 productResolution: 1km (exactly 928 m) Atmospheric effects corrected Satellite View Angle : 15°

Comparison with MODIS

• Radiative Surface Temperature (°C) July 6th 2008 (10:54 LST) Warm and Sunny

Z0h

: Kanda (2007)

(Leroyer et al., 2011)

Urban Heat Island Modeling (Montreal)

Two-way couplingGEM 2.5 kmCaLDAS 2.5 km

Surface Prediction System

Nudging surface variables

Lower BCs

Forcing + first guess

An ‘’horizontal’’ challenge

LAND / VEG(ISBA / SVS)

URBAN(TEB)

WATER

SINGLE GEM (ATMOSPHERE) GRID AREA (LOW RES)

MULTIPLE SURFACE GRID AREAS (HIGH RES)

______

''11

w

zzK

zt T

atmNK

S

w _

______

''

atmNKSSTS

fuCw _*

______

'' ST uC *

ST fuC *

SPATIAL AVERAGE OF IMPLICIT LOWER BC FOR VERT. DIFFUSION

Spatially averaged

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

~115 Wm-2

(Provided by M. Rochoux, EC)

~40 Wm-2

~40 Wm-2

A ‘’vertical’’ challenge

LAND / VEG(ISBA / SVS)

URBAN(TEB)

WATER

SINGLE GEM (ATMOSPHERE) GRID AREA (LOW RES)

MULTIPLE SURFACE GRID AREAS (HIGH RES)

INC

RE

AS

ED

VE

RT

ICA

L R

ES

OLU

TIO

N

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

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 TOMACSReal-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.