Soil Moisture and Numerical Weather PredictionSoil Moisture and Numerical Weather Prediction
SMAP Applications Workshop, Silver Spring, September 2009
Stephane BelairScience and Technology Branch,
Environment Canada
LL
MOTIVATION for BETTER SOIL MOISTURE in NWP MODELS
1 2
3
Low-level air Temp. Errors
bias
RMS
(K)
(hour)
(hPa)
(K)
Temp. Errors
Soil moisture + assimilation
Control
Precipitation Threat Score (Day 4)- SHEF (US)
NEAR-SURFACE AIR CONDITIONSBOUNDARY-LAYER andMIXING
CLOUDS and PRECIPITATION
RMSE
BIAS
Control
With improvedsoil moisture
RMSE
BIAS
Improvedsoil moist.
Ctrl
1 and 2: Implementation of short-range regional system in 2001. 48 cases (summertime)3. Implementation of global medium-range in 2006. 116 cases (summertime)
POSSIBLE IMPROVEMENT at MEDIUM-RANGE (GLOBAL MODEL)
m3m-3
(valid at 0000 UTC 15 December 2001)
Near surface soil moisture
m3m-3
(valid at 0000 UTC 15 December 2001)
Near surface soil moisture
Global
Medium-Range prediction system
116 cases
120-h, Northern Hem.
red = improved soil moisture
Zonal wind Wind speed
Temperature Geopotential height
BiasRMSE
POSSIBLE IMPROVEMENT at MEDIUM-RANGE (GLOBAL MODEL)
Global, Medium-Range prediction system
116 cases, Northern Hemisphere
red = improved soil moisture
RMSE, Geopotential Height
500 hPa
POSSIBLE IMPROVEMENT at MEDIUM-RANGE (GLOBAL MODEL)
?
RMSE, Geopotential Height
500 hPa
POSSIBLE IMPROVEMENT at MEDIUM-RANGE (GLOBAL MODEL)
Global, Medium-Range prediction system
116 cases, Northern Hemisphere
red = improved soil moisture
OTHER ASPECTS BECOMING MORE IMPORTANTSOIL MOISTURE and AIR QUALITY / DISPERSION
Dry Soil Dry SoilUrban Center
Humid Soil Humid SoilUrban Center
Solar
Solar
MEAN FLOW
GRADUAL DIMINUTION OF TURBULENCETRANSITION TO FREE ATMOSPHERETRANSPORT POLLUTANT AWAY FROM CITY
MORE SHALLOW BOUNDARY LAYERBUT A SMALLER PORTION OF POLLUTANTS IS INTRODUCED IN THE DOWNWIND WELL-MIXED LAYER
BOUNDARY LAYER IS DEEPERAND “MIXES” MOST OF THE POLLUTANTS PRODUCED BY THE CITY
SMAP DATA in the ENSEMBLE KALMAN FILTER of theCANADIAN LAND DATA ASSIMILATION SYSTEM (CaLDAS)
LAND SURFACEMODEL (M)
OBS OPERATOR (H)
GEO DATA
FORCING DATA
(perturbed)
OBS DATA
(perturbed) Background error covariances (Bt+1)
Obs error covariances (Rt+1)
Background(first guess)
Gain Matrix (Kt+1)
Increments and updates
New analyses
( )−
+1tx ( )+
+1tx
( )i1ty +
Previousanalyses
( )+tx
SMAP DATA in the ENSEMBLE KALMAN FILTER of theCANADIAN LAND DATA ASSIMILATION SYSTEM (CaLDAS)
LAND SURFACEMODEL (M)
OBS OPERATOR (H)
GEO DATA
FORCING DATA
(perturbed)
OBS DATA
(perturbed) Background error covariances (Bt+1)
Obs error covariances (Rt+1)
Background(first guess)
Gain Matrix (Kt+1)
Increments and updates
New analyses
( )−
+1tx ( )+
+1tx
( )i1ty +
Previousanalyses
( )+tx
SMAP
ANCILLARY DATA for the ASSIMILATION of SMAP DATA
FORCING DATA
(perturbed)GEO DATA
Analyses or best estimates of:
Precipitation (quantity and phase)
Radiation incident at surface (LW and SW –direct and diffuse)
Low-level air temperature
Low-level air humidity
Low-level winds
Information on:
Orography (DEM)
Land / water coverage fractions (databases)
Soil texture (databases)
Vegetation type (databases / remote sensing)
Vegetation conditions, i.e., LAI (analysis based on remote sensing)
Urban cover types (databases)
Snow conditions, i.e., coverage at least (analyses)
Surface temperature (analyses)
RESEARCH and DEVELOPMENT ISSUES
Assimilation of SMAP data
Impact of SMAP data
Ancillary data
Specification of background and observations error covariances
Role and impact of other data (i.e., screen-level obs)
Impact on boundary layer, clouds, and precipitation is expected
Impact on large-scale upper-air features?
Atmospheric forcing (e.g., precipitation) of crucial importance
Geophysical data (soil texture not that good)
Vegetation analysis (combination of satellite and model products)
THANK YOU for your ATTENTION
SOIL MOISTURE at ENVIRONMENT CANADAHISTORICAL PERSPECTIVE
1986
1995
2001
2006
2011?
Force-restore scheme with a time-evolving “soil wet ness” variable. Initial conditions provided by a “climat ology”
Simple assimilation of soil moisture based on scree n-level air temperature and humidity
Land surface scheme with a soil moisture variable. Improved method for the initial conditions, but sti ll based on screen-level data (regional, short-range system)
Same but for the global, medium-range system
Assimilation of SMOS passive data
2014? Assimilation of SMAP passive and active data
ASSIMILATION of SPACE-SPACE REMOTE SENSING DATA
Local
Regional
Global
Land Surface
Surface fluxes (H, LE, momentum)
Boundary-layer mixing
Near-surface air conditions
Clouds
Precipitation
Convective systems
Large-scale systems
Spa
tial a
nd T
ime
scal
es
PROCESSES MODELS
• Determine exchanges of heat, water, and momentum between the surface and the atmosphere
• Impact on near-surface conditions, boundary-layer mixing, clouds, precipitation, and maybe even large-scale systems
• Important for local, continental, and global models, … at short, medium, and long ranges
• Land surface is even more important for new environmental systems now in development at EC, e.g., external land surface system, hydrology.
SOIL MOISTURE and NUMERICAL WEATHER PREDICTION
IMPACT on NEAR SURFACE and BOUNDARY LAYER
Near surface soil moisture
(valid at 1200 UTC 22 October 2004)
m3m-3(hPa)
(K)
Temp. Errors
48-h integrations
(Bélair et al. 2003)
CONTROLIMPROVED SURFACE
RMSEBIAS
Low-level air Temp. Errors
bias
RMS
(K)
(hour)
IMPACT on NEAR SURFACE and BOUNDARY LAYER
Near surface soil moisture
(valid at 1200 UTC 22 October 2004)
m3m-3
Low-level air Temp. Errors
bias
RMS
(K)
(hour)
(hPa)
(K)
Temp. Errors
48-h integrations
(Bélair et al. 2003)
CONTROLIMPROVED SURFACE
RMSEBIAS
IMPACT on NEAR SURFACE and BOUNDARY LAYER
Near surface soil moisture
(valid at 1200 UTC 22 October 2004)
m3m-3
Low-level air Temp. Errors
bias
RMS
(K)
(hour)
(hPa)
(K)
Temp. Errors
48-h integrations
(Bélair et al. 2003)
CONTROLIMPROVED SURFACE
RMSEBIAS
IMPACT on PRECIPITATION
Precipitation bias (24-48h)
Control
Soilmoisture assimilation (OI)
Soil moisture + assimilation
Control
Precipitation Threat Score (Day 4)- SHEF (US)
REGIONAL FORECASTING SYSTEM (2001)
48 CASES
GLOBAL FORECASTING SYSTEM (2006)
116 CASES
THERMODYNAMIC versus MOMENTUM SURFACE FLUXES... Based on our experience at EC
� H and LE surface fluxes
� Surface temperatures
� Soil moisture
� Water / land fractions
H LE
Significant impact on boundary layer, clouds, and precipitation. But not clear yet how it influences large-scale upper-air features
� Resolved orography
� Blocking effect (subgrid-scale)
� Turbulent mixing (subgrid-scale)
Significant impact on large-scale features, but not on clouds and precipitation
THERMO MOMENTUM
m3m-3
(valid at 0000 UTC 15 December 2001)
Near surface soil moisture
Global
Medium-Range prediction system
116 cases
120-h, Europe
red = improved soil moisture
Zonal wind Wind speed
Temperature Geopotential height
BiasRMSE
POSSIBLE IMPROVEMENT at MEDIUM-RANGE (GLOBAL MODEL)
m3m-3
(valid at 0000 UTC 15 December 2001)
Near surface soil moisture
Significant impact for this region... but not always like this. Impact on medium-range upper-air prediction still TBD
Zonal wind Wind speed
Temperature Geopotential height
BiasRMSE
POSSIBLE IMPROVEMENT at MEDIUM-RANGE (GLOBAL MODEL)
Global
Medium-Range prediction system
116 cases
120-h, Europe
red = improved soil moisture