Assimilation of Satellite Derived Snow Information in the Canadian Land Data Assimilation System Chris Derksen, Libo Wang Climate Research Division, Environment Canada Toronto, ON Stéphane Bélair, Sheena Solomon, Marco Carrera, and Bernard Bilodeau Meteorological Research Division, Environment Canada, Dorval, QC Sari Metsamaki Finnish Environment Institute, Helsinki
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Assimilation of Satellite Derived Snow
Information in the Canadian Land Data
Assimilation SystemChris Derksen, Libo WangClimate Research Division, Environment CanadaToronto, ON
Stéphane Bélair, Sheena Solomon, Marco Carrera, and Bernard BilodeauMeteorological Research Division, Environment Canada, Dorval, QC
Sari MetsamakiFinnish Environment Institute, Helsinki
Outline
1. Overview: the Canadian Land Data Assimilation System (CaLDAS)
2. Uncertainty characterization for satellite derived snow datasets:
-MODIS fractional snow cover (fSCA)
-AMSR-E snow water equivalent (SWE)
3. Progress towards assimilating satellite snow datasets in CaLDAS
4. The potential impact of emerging datasets
Research Context
• Efforts are underway to improve treatment of the land surface, and include a
hydrological component, to Environment Canada’s (EC) numerical prediction systems.
• Emphasis on assimilation of space-based remote sensing data (for soil moisture,
terrestrial snow, and vegetation).
• Single system for all NWP systems (deterministic and ensemble-based) + hydrology
models.
• In EC’s current prediction systems, snow is initialized using surface observations of
snow depth (sparsely distributed in space; no information on the fractional coverage of
snow on the ground).
• Satellite derived measurements can produce the spatially and temporally continuous
observations necessary to systematically monitor snow cover, and characterize initial
conditions.
A primary objective is to improve the representation of snow in Environment Canada’s
operational prediction systems by including space-based measurements in the
Canadian Land Data Assimilation System (CaLDAS).
LAND DATA ASSIMILATION at ENVIRONMENT CANADA
In DEVELOPMENT
ENSEMBLES(FR, ISBA, CLASS,
WATER, GLACIERS)
GLOBAL REGIONAL LOCAL
GenPhysXSoil texture, orography,
vegetation, water bodies,
glaciers, and cities
TM-LakesHigh-res grid
Satellite
TS, ES, TPExternal system’s
Grid (high-res)
ANALYSES
ASSIMILATION
OTHER MODELS
DATABASES
Vegetation GL-LakesHigh-res grid
Satellite
Canadian Land surface Data Assimilation System (CaLDAS)
High-res global grids (same as external system)Simple Var or EnKF for soil moisture and surface temperature (screen-level + sat)
Snow mass and coverage (surface data + sat)
External Land Surface Modeling System (GEM-Surface)
High-resolution grid over Canada (1 km or less) – Lower resolution grid over world (5 km or less)
(CLASS or ISBA, TEB, WATER, SNOW, GLACIERS, EOLE, blowing snow)
LAND SURFACE MODELS
CaPAModel, surface
and satellite
data
HYDROLOGY
MESH
Initial conditions for land
surface schemes
2-way coupling Forcings
TP
TS,ES
Current Treatment of Snow in CaLDAS:
Canadian Meterological Centre Daily Gridded
Global Snow Depth
Start Date 12-Mar-1998
End Date Ongoing
Domain Global
Temporal Resolution Daily
Spatial Resolution 1/3 degree
Variables Depth; Density
• All available snow depth observations (from synops,
meteorological aviation reports, and special aviation
reports) are ingested into the analysis.
• Updated every 6 hours using optimum interpolation
with an initial guess field provided by a simple snow
accumulation and melt model using analyzed
temperatures and forecast (6 hour) precipitation from
the CMC global forecast model.
• The analysis includes an estimate of the density of
the snowpack.
• Now archived at NSIDC. Known sources of uncertainty: