Marco Carrera;
Maziar Banishahabadi;
Bernard Bilodeau;
Sylvain Heilliette;
Stephane Belair;
Peter Houtekamer
Meteorological Research Division, Environment and Climate Change Canada
The 8th EnKF Data Assimilation Workshop
Preliminary experiments in the coupling of the Canadian Land Data Assimilation System (CaLDAS) with the Global Ensemble Prediction System (GEPS) at Environment and Climate Change Canada
Talk:
CARRERA_8_ENKF.ppt
Within the Meteorological Research Division (MRD) of Environment and Climate Change Canada (ECCC), historically the land surface data assimilation has been done separately from the upper-air assimilation. The land-surface and upper-air analyses are combined to serve as initial conditions for the various numerical weather prediction (NWP) model runs at ECCC.
The Canadian Land Data Assimilation System (CaLDAS) has been in development over the last several years with an emphasis on a 2D-offline land-surface modeling capability, driven by atmospheric forcing from a 3D-atmospheric model, assimilating space-based remote sensing observations sensitive to the land surface, and utilizing the Ensemble Kalman Filter (EnKF) methodology. Currently, CaLDAS is run operationally at ECCC to generate the land-surface initial conditions for the High-Resolution Deterministic Prediction System (HRDPS) over North America.
In an effort to enhance the coupling between the upper-air and land-surface assimilation systems, this study will report upon preliminary tests with CaLDAS forced with the individual members of the Global Ensemble Prediction System (GEPS) on a 39-km global grid for the December 2016 to February 2017 period. The 2D-offline land-surface model integrations in CaLDAS require air temperature, humidity and wind forcing at the lowest atmospheric level along with precipitation, incident radiation, and surface pressure from the GEPS members. Soil moisture brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions will be assimilated along with skin temperature retrievals from several polar orbiting platforms. Emphasis will be placed upon quantifying any enhanced ensemble spread generated at the land-surface along with a quantitative evaluation of the analyses produced (e.g., screen-level temperatures, soil moisture and snow). These one-way coupled experiments will serve as a precursor to two-way fully coupled integrations in the future, in which surface variables (i.e., snow, soil moisture, surface temperature) will be provided to the ensemble of atmospheric integrations.
Within the Meteorological Research Division (MRD) of Environment and Climate Change Canada (ECCC), historically the land surface data assimilation has been done separately from the upper-air assimilation. The land-surface and upper-air analyses are combined to serve as initial conditions for the various numerical weather prediction (NWP) model runs at ECCC.
The Canadian Land Data Assimilation System (CaLDAS) has been in development over the last several years with an emphasis on a 2D-offline land-surface modeling capability, driven by atmospheric forcing from a 3D-atmospheric model, assimilating space-based remote sensing observations sensitive to the land surface, and utilizing the Ensemble Kalman Filter (EnKF) methodology. Currently, CaLDAS is run operationally at ECCC to generate the land-surface initial conditions for the High-Resolution Deterministic Prediction System (HRDPS) over North America.
In an effort to enhance the coupling between the upper-air and land-surface assimilation systems, this study will report upon preliminary tests with CaLDAS forced with the individual members of the Global Ensemble Prediction System (GEPS) on a 39-km global grid for the December 2016 to February 2017 period. The 2D-offline land-surface model integrations in CaLDAS require air temperature, humidity and wind forcing at the lowest atmospheric level along with precipitation, incident radiation, and surface pressure from the GEPS members. Soil moisture brightness temperature data from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions will be assimilated along with skin temperature retrievals from several polar orbiting platforms. Emphasis will be placed upon quantifying any enhanced ensemble spread generated at the land-surface along with a quantitative evaluation of the analyses produced (e.g., screen-level temperatures, soil moisture and snow). These one-way coupled experiments will serve as a precursor to two-way fully coupled integrations in the future, in which surface variables (i.e., snow, soil moisture, surface temperature) will be provided to the ensemble of atmospheric integrations.
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