[email protected] Advances in Land Surface Modelling and Data Assimilation at EC Focus upon...

[email protected]

Advances in Land Surface Modelling and Data Assimilation at EC

Focus upon improved analysis of snow cover

CanSISE Workshop : 30 October – 1 November, 2013Victoria, BC

Marco Carrera, Stéphane Bélair, Nathalie Gauthier, Bernard Bilodeau,Dorothée Charpentier, Chris Derksen, and Libo Wang

Environment Canada

– 13-05-09

MAIN ASPECTS for LAND SURFACE MODELING and ASSIMILATION

Surface characteristics• databases• space-based remote sensing

Modeling• surface processes• interactions with atmosphere• external high-resolution system

Assimilation• CaLDAS (Canadian Land Data

Assimilation System)

Saskatchewan

NorthernTerritories

Toronto

Central Quebec

– 13-05-09

The CANADIAN LAND DATA ASSIMILATION SYSTEM (CaLDAS) The CANADIAN LAND DATA ASSIMILATION SYSTEM (CaLDAS)

ISBALAND-SURFACE

MODEL

OBS

ASSIMILATION

xb

y (with ensemble Kalman filter

approach)

xa = xb+ K { y – H(xb) }

K = BHT ( HBHT+R)-1

with

CaLDASININ OUTOUT

Ancillary land surface data

Atmospheric forcing

Observations

Land surface initial conditions for NWP and hydro systems

Land surface conditions for atmospheric

assimilation systems

Current state of land surface conditions

for other applications

(agriculture, drought, ...

Screen-level (T, Td)Surface stations snow depthL-band passive (SMOS,SMAP)MW passive (AMSR-E)Multispectral (MODIS)Combined products (GlobSnow)

T, q, U, V, Pr, SW, LW

Orography, vegetation, soils, water fraction, ...

– 13-05-09

CMC Operational Snow Analysis (Global, Regional, and LAM)

• Uses an Optimum Interpolation (OI) methodology to combine a first-guess snow field with snow depth observations (Brasnett 1999).

• Simplistic “snow-model” where previous snow-analysis is used as background and operational precipitation is used to compute the new snow accumulation.

• ECMWF has recently revised their operational snow analysis to make use of the OI scheme (instead of Cressman) with the weighting functions of Brasnett (1999).

– 13-05-09

Strategies used at EC to improve snow analyses

1. GEM-Surf : High-resolution near-surface and land surface forecast system. Offline system forced with hourly forcing from CMC deterministic prediction system NWP

2. CaLDAS-EnsOI : Combine Ensemble Kalman filter (EnKF) approach for soil temperature and moisture with ensemble optimal interpolation (OI) for snow depth.

3. CaLDAS-Snow : EnKF approach to assimilate SWE retrievals from microwave passive measurements into GEM-Surf.

– 13-05-09

High resolution GEM-Surf systemComponents and validation

ATMOS MODEL

Atmospheric forcing at first atmos. model level (T,q,U,V)

3D integration

GEM-Surf100 m

2D integration

Downscaled to 100m

Atmospheric forcing at surface.(S,PR, P0)

Adaptation of T,U,V,q,P0 corrected for difference in elevation between forcing model and GEM-Surf. PR phase is adjusted to be coherent with new T.

LOW-RES

HIGH-RES

Land surface characteristicsspecified using High-Res external databases

– 13-05-09

High resolution GEM-Surf systemDeveloped for high-resolution environmental prediction over Canada

→Can run at resolution of the most detailed surface characteristics database available.

→Integrates only land and near-surface processes

→Computational cost of GEM-Surf much less than that of 3D atmospheric models

Good forecast of land surface conditions tied to representation of local surface characteristics such as orography, vegetation type, soil type, snow coverage.

External model of the land surface and near-surface which evolve separately of the 3D operational forecast model in term of resolution and time-step.

SWE – GEM-3DSWE – GEM-Surf

Characteristics

Carrera et al. 2010, Bernier et al. 2011, Leroyer et al. 2011.

– April 11, 2023

DOWNSCALING LAI: SOURCES of INFORMATION

30m 1km 10km200m

TA

RG

ET

RE

SO

LU

TIO

N

LCC-2000Land Use / Land Cover (types)

MODIS 10-year NDVI climatology

Biome-BGC10-year Clim from runs (LAI)

Grass

Forests

– April 11, 2023

LAI (m2m-2)

LAKEONTARIO

GREATER TORONTO AREA

Light grey: pavement fractionDark brown: building fraction

– 13-05-09


Offline Snow Model

Atmospheric forcings

Surface stations snow depth

CaLDAS

SD Analysis

Snow data assimilation currently operational at EC

Orography, vegetation, soils, water fraction, ...

OptimalInterpolation

Assimilation based on optimal interpolationBackground (first guess) snow depth is given by a simple offline snow modelObservations from SYNOP and METAR are used (Brasnett, 1999)

T, hu, winds

Precipitation

Radiation

– 13-05-09


GEM-Surf

Atmospheric forcing

Observations

CaLDAS

SD Analysis

The Canadian Land Data Assimilation System (CaLDAS)

Orography, Vegetation, Soils, Water Fraction, ...

AssimilationEnKFTemp, Humidity,

Winds, PR, and Radiation

AssimilationEnsOI (SD)

xb

y

EnKFxa = xb+ K { y – H(xb) }

K = BHT ( HBHT+R)-1

with

CaLDAS-Snow : BHT covariance between Snow Depth and SWE HBHT : Model Error Variance

– 13-05-09

Forcing: RUN 00Z6-12hr forecasts


00Z 06Z 12Z 18Z



18Z


GEM-SurfGEM-Surf

6h forecasts

CaLDAS: General strategy

PR : spatial shift and additive perturbation using CaPA methodology

Radiation : spatial shift coherent with PR shift

TT : additive Gaussian perturbations

– 13-05-09

Canadian Precipitation Analysis (CaPA) Canadian Precipitation Analysis (CaPA) Generation of an Ensemble of Precipitation analysesGeneration of an Ensemble of Precipitation analyses

Optimum InterpolationAssimilation

PerturbedPrecipitation Gauge

ObservationsMeasurement + Errors

of Representativity

Spatially perturbed Model forecasts of precipitation

Ensemble ofprecipitation analyses

RDPA (Regional Deterministic Precipitation Analysis)http://weather.gc.ca/analysis/index_e.html

– 13-05-09

EnKFxa = xb+ K { y – H(xb) }

K = BHT ( HBHT+R)-1

with

CaLDAS-Snow : BHT covariance between Snow Depth and SWE HBHT : Model Error Variance

CaLDAS-EnsOI •Combine ensemble Kalman filter approach for soil temperature and moisture with ensemble optimal interpolation (OI) for snow depth

•Examine impact of CaLDAS initial conditions in forecast mode for the Global Deterministic Prediction System (GDPS)

– 13-05-09

Improved forecasts of temperature at 2m with CaLDAS

Operational 2m temperature vs CaLDAS-Screen with ensemble of OI analyses

Winter results – 2m temperature (00Z runs – Northern Canada)

Bias STDE

CaLDAS

OP

– 13-05-09

CaLDAS-Snow: General strategy

• Snow in CaLDAS: Ensemble Kalman Filter

• Observations: SWE retrievals from AMSR-E or GlobSnow (1/day)

• Control variable: snow mass

• Other snow variables: snow density and snow albedo cycled

• Background (first guess): GEM-Surf 6h prediction

• Number of members: 24

• Assimilation step: 6h

• Ensemble spread obtained by perturbing the atmospheric forcing, the observations and the analysis

• Observation errors are constant in time and in space: 20mm

• Bias errors (systematic) are removed from SWE observations.

– 13-05-09

Canadian experiments for snow depth

Test period : 1 November 2006 – 21 April 2007

Computational domain : Central part of Canada

Observation network : 50 surface stations (Environment Canada)

Hudson Bay

Grid spacing : 15 km

SWE retrieval algorithms developed from field campaigns for this area

The SWE AMSR-E data were re-gridded on the 15km CaLDAS-Snow grid

– 13-05-09

CMC-OP

Open LoopENS-OI

OBS

Improved analysis of snow depth with

CaLDAS (ENS-OI)

Evaluation against surface snow depth

observations.

Operational snow depth analyses (OI) vs CaLDAS ensemble of OI analyses

Mean snow depth

Bias

STDE

– 13-05-09

CaLDAS with AMSR-E vs open loop

EnKF Assimilation experiment with SWE from AMSR-E

Open Loop

CaLDAS-AMSR-E

Mean snow depth

Bias

STDE

OBSCaLDAS-AMSRE not as good as ENS-OI

shown earlier

– 13-05-09

Preliminary GlobSnow

Open Loop

CaLDAS-GS

OBS

Mean snow depth

Bias

STDE

GlobSnow

(CAREFUL… GlobSnow experiments not leave-one-out type)

– 13-05-09

Summary : Current Status

• CaLDAS has been accepted into CMC operations as an experimental product running offline on a global domain at 25 km resolution.

• January 2014 : final tests will begin for CaLDAS coupled with the new upper-air assimilation system. Goal is to transfer this coupled system into operations in 2014.

• CaLDAS is being coupled with the Global Ensemble Prediction System (GEPS) at 50-km resolution. Individual CaLDAS members providing the surface initial conditions to the GEPS.

[email protected] Advances in Land Surface Modelling and Data Assimilation at EC Focus upon...

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