Soil Moisture Prediction and Assimilation

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1 Soil Moisture Prediction and Assimilation Analysis and Prediction in Agricultural Landscapes Saskatoon, June 19-20, 2007 STEPHANE BELAIR Meteorological Research Division

2 Prediction and Assimilation Atmospheric analyses Geophysical surface fields Atmospheric predictions Land surface model Observations Assimilation (or modeling) Initial conditions Prediction PAST NOW FUTURE time DRAFT Page 2 December 5, 2007

3 Soil Moisture Maps from EC s 15-km Operational System Near-Surface Soil Moisture (kg/m -2 ) Valid at 0000 UTC 13 June 2007 DRAFT Page 3 December 5, 2007

4 Near-Surface Soil Moisture Evolution in EC s Operational System (kg/m -2 ) Central Alberta DRAFT Page 4 December 5, 2007

5 Near-Surface Soil Moisture Evolution in EC s Operational System (kg/m -2 ) Southern Alberta DRAFT Page 5 December 5, 2007

6 Soil Moisture Prediction System INITIAL SURFACE CONDITIONS ATMOSPHERIC FORCING (FORECAST) LAND SURFACE MODEL(S) LAND SURFACE CHARACTERISTICS DRAFT Page 6 December 5, 2007

7 Soil Moisture Prediction System INITIAL SURFACE CONDITIONS ATMOSPHERIC FORCING (FORECAST) LAND SURFACE MODEL(S) LAND SURFACE CHARACTERISTICS Water balance Soil water transport Soil heat transport Evapotranspiration models Infiltration/runoff models Hydrology (baseflow, lateral flow, surface flow) Vegetation interception Snow model Freeze/thaw, dynamic vegetation, mosaic, DRAFT Page 7 December 5, 2007

8 DRAFT Page 8 December 5, 2007

9 Soil Moisture Prediction System INITIAL SURFACE CONDITIONS ATMOSPHERIC FORCING (FORECAST) LAND SURFACE CHARACTERISTICS Topography Land/water fractions Soil texture Land use / Land cover Albedo LAND SURFACE MODEL(S) Water balance Soil water transport Soil heat transport Evapotranspiration models Infiltration/runoff models Hydrology (baseflow, interflow, surface flow) Vegetation interception Snow model Freeze/thaw, dynamic vegetation, mosaic, DRAFT Page 9 December 5, 2007

Land Use / Land Cover 7 22 4 26 15 USGS 4 7 Types

13 Short grass and forbs 15 Crops 24 13 22 24 Tundra

10 Land Use / Land Cover USGS 4 7 Types Evergreen needleleaf trees Deciduous broadleaf trees 13 Short grass and forbs 15 Crops Tundra Bare soil 26 Mixed shrubs DRAFT Page 10 December 5, 2007

11 Soil Texture Sand Fraction (%) In Canada, the soil texture is defined in this case using a database from the Food and Agriculture Organization (FAO) In the US, the soil texture is obtained from the US State Soil Geographic database (STATSGO). DRAFT Page 11 December 5, 2007

12 Soil Moisture Prediction System ATMOSPHERIC FORCING (FORECAST) INITIAL SURFACE CONDITIONS Near-surface air characteristics (temperature, humidity, winds) Surface pressure Incident radiation (solar and infrared) Precipitation (rain and snow) LAND SURFACE CHARACTERISTICS Topography Land/water fractions Soil texture Land use / Land cover LAND SURFACE MODEL(S) Water balance Soil water transport Soil heat transport Evapotranspiration models Infiltration/runoff models Hydrology (baseflow, interflow, surface flow) Vegetation interception Snow model Freeze/thaw, dynamic vegetation, mosaic, DRAFT Page 12 December 5, 2007

13 Numerical Weather Prediction Precipitation Accumulation Downwelling LW Radiation Air Temperature Low-Level Winds DRAFT Page 13 December 5, h numerical prediction valid at 0000 UTC 17 June 2007

14 Soil Moisture Prediction System INITIAL SURFACE CONDITIONS ATMOSPHERIC FORCING (FORECAST) Near-surface air characteristics (temperature, humidity, winds) Surface pressure Incident radiation (solar and infrared) Precipitation (rain and snow) Low res forcing DOWNSCALING MODELS High res forcing LAND SURFACE CHARACTERISTICS Topography Land/water fractions Soil texture Land use / Land cover LAND SURFACE MODEL(S) Water balance Soil water transport Soil heat transport Evapotranspiration models Infiltration/runoff models Hydrology (baseflow, interflow, surface flow) Vegetation interception Snow model Freeze/thaw, dynamic vegetation, mosaic, DRAFT Page 14 December 5, 2007

15 External Land Surface Systems Global (33 km) 10 days Grid size Regional (15 km) Local (2.5 km) Urban (200 m) 2 days 1 day MODEL OUTPUT MODEL OUTPUT MODEL OUTPUT ATMOSPHERIC FORCING External Surface Model With horizontal resolution as high as that of surface databases (e.g., 200 m) 10 days Cost of the external surface modeling system is much less than an integration DRAFT Page of 15 the December full5, atmospheric 2007 model

16 Downscaling: Impact on Ground Snow (GEM) 15 km Orography ALB Snow Water Equivalent ALB (kg/m2) 1 km (MEC) ALB ALB (Valid 0000 UTC 1 December 2006) DRAFT Page 16 December 5, 2007

17 Downscaling Possibilities for Soil Moisture In agricultural landscapes, refinement of soil moisture products using the downscaling approach will mostly come from more detailed information on land use / land cover, albedo, soil texture DRAFT Page 17 December 5, 2007

18 Soil Moisture Prediction System INITIAL SURFACE CONDITIONS Temperatures Soil moisture Soil ice content Snow characteristics Urban surfaces wetness ATMOSPHERIC FORCING (FORECAST) Near-surface air characteristics (temperature, humidity, winds) Surface pressure Incident radiation (solar and infrared) Precipitation (rain and snow) Low res forcing DOWNSCALING MODELS High res forcing LAND SURFACE CHARACTERISTICS Topography Land/water fractions Soil texture Land use / Land cover LAND SURFACE MODEL(S) Water balance Soil water transport Soil heat transport Evapotranspiration models Infiltration/runoff models Hydrology (baseflow, interflow, surface flow) Vegetation interception Snow model Freeze/thaw, dynamic vegetation, mosaic, DRAFT Page 18 December 5, 2007

19 Land Surface Data Assimilation ATMOSPHERIC FORCING T, hu, winds Precipitation Radiation Met analyses and forecasts Precip analyses Adaptation (downscaling) ANCILLARY DATA (databases for soil texture, vegetation, water/land mask, orography, cities) LAND SURFACE MODEL OBSERVATIONS Vegetation Snow Freeze-thaw Soil moisture Surface and soil temp DRAFT Page 19 December 5, 2007 Space-based remote sensing Screen-level met obs in-situ surface measurements Transfer models required

20 Soil Moisture Data Assimilation ATMOSPHERIC FORCING T, hu, winds Precipitation Radiation Met analyses and forecasts Precip analyses Adaptation (downscaling) ANCILLARY DATA (databases for soil texture, vegetation, water/land mask, orography, cities) LAND SURFACE MODEL SOIL MOISTURE OBS DRAFT Page 20 December 5, 2007 Screen-level T air and q air in-situ soil moisture SMOS Hydros and C-band space-based sensors

21 Background, Analysis, and Increments (kg/m -2 ) Moistening Central Alberta Drying Assimilation of screen-level data (EC s operational sequential assimilation) open loop Root-Zone Soil Moisture May June July Aug Sept 2006 DRAFT Page 21 December 5, 2007

Also, the minimum of J(x) is directly obtained from J ( x) = 0 The analyzed state x a is thus given by: x a = x b 2 + K y where K is the gain matrix: K ( H ( x b )) ( 1 T 1 1 )

22 Simplified Variational Approach Cost function 1 b T 1 J ( x) = x x B x x 2 Linear hypothesis b 1 T 1 ( ) ( ) + ( y H ( x) ) R ( y H ( x) ) H ( x + δ x) = H ( x) + Hδx In this technique, the linear observation operator H is evaluated using a finite difference approach, from two perturbed model integrations. Also, the minimum of J(x) is directly obtained from J ( x) = 0 The analyzed state x a is thus given by: x a = x b 2 + K y where K is the gain matrix: K ( H ( x b )) ( 1 T 1 1 ) 1 T B + H R H H = R 15 July October 2005 This formulation of the variational problem could be easily converted to an Extended Kalman Filter (Balsamo, Mahfouf, Bélair, Deblonde, 2006, 2007) DRAFT Page 22 December 5, 2007

23 Information Content from OSSE L-band Screen-Level C-band IR (Balsamo, Mahfouf, Bélair, Deblonde, 2007) DRAFT Page 23 December 5, 2007

24 Ensemble Kalman Filter i k k i+ i ( x ) w x = f + k 1 (From Reichle et al., 2002) k Monte-Carlo approach Sequential assimilation, from one measurement time to the next Forecast and update steps Soil moisture analysis is simply the mean of the ensemble members Error statistics of the model directly evaluated from the ensemble of background forecasts Observation are randomly perturbed Cost depends on the size of the ensemble (and of course on the cost of each member) Attractive for land surface assimilation because of relatively low-cost of 2D integrations DRAFT Page 24 December 5, 2007

An Example of Soil Moisture Assimilation with EnKF Estimated VSM (-) Estimated VSM (+) Std. dev. VSM (-) Std. dev. VSM (+) 0.3 0.06 Measurement Brightness Temperature Estimate Day 169 0.2 0.1 0.3 0.04 0.

25 An Example of Soil Moisture Assimilation with EnKF Estimated VSM (-) Estimated VSM (+) Std. dev. VSM (-) Std. dev. VSM (+) Measurement Brightness Temperature Estimate Day Day Open-Loop Day (From Entekhabi) Day DRAFT Page 25 December 5, 2007 SGP 97 Mean Surface and Rootzone Soil Moisture and Uncertainty Range

26 Issues with Soil Moisture Prediction and Assimilation Prediction Possible to refine soil moisture prediction, but better land surface databases are needed (especially for the soil texture). Progress on soil moisture prediction depends on progress in NWP (precipitation most important forcing) Probabilistic products (e.g., ensemble prediction systems) could be used to provide uncertainty on soil moisture prediction Verification, always verification, DRAFT Page 26 December 5, 2007 Assimilation (and modeling) Physically realistic land surface schemes are required to improve the consistency between surface fluxes and soil moisture This is crucial for the joint assimilation of screen-level observations and of observations more directly related to soil moisture (e.g., remote sensing) Maybe necessary to physically constrain soil moisture analysis increments Ensemble methods provide estimate for model background error statistics, but they are more expensive Need better estimates of the observation errors Atmospheric analyses (especially precipitation) are important Verification, verification

27 Thank you DRAFT Page 27 December 5, 2007

28 Soil Moisture Prediction System INITIAL SURFACE CONDITIONS Temperatures Soil water content Soil ice content Snow characteristics Urban surfaces wetness ATMOSPHERIC FORCING Near-surface air characteristics (temperature, humidity, winds) Surface pressure Incident radiation (solar and infrared) Precipitation (rain and snow) Low res forcing DOWNSCALING MODELS High res forcing LAND SURFACE CHARACTERISTICS Topography Land/water fractions Soil texture Land use / Land cover LAND SURFACE MODEL(S) Water balance Soil water transport Soil heat transport Evapotranspiration models Infiltration/runoff models Hydrology (baseflow, interflow, surface flow) Vegetation interception Snow model Freeze/thaw, dynamic vegetation, mosaic, DRAFT Page 28 December 5, 2007

29 Mean Near-Surface Soil Moisture in the Prairies in Summer (kg/m -2 ) DRAFT Page 29 December 5, 2007

30 Statistical Adaptation for Point Prediction of Soil Moisture INITIALIZATION / ASSIMILATION FORECAST OBS FORCING Precip Tair, qair Wind Radiation OBS SURFACE VARIABLES Tsurf Soil moisture ATMOSPHERIC MODEL STATISTICAL ADAPTATION OF FORCING Precip Tair, qair Wind Radiation LAND SURFACE MODEL + VAR ASSIMILATION LAND SURFACE MODEL Assimilation window Assimilation window Forecast DRAFT Page 30 December 5, 2007

31 General Structure of Land Surface Systems OBSERVATIONS LAND SURFACE DATABASES and HIGH-RES ANALYSES TRANSFER MODELS SURFACE FIELDS GENERATOR GRID PARAMETERS 3 compontents LAND SURFACE DATA ASSIMILATION COMPONENT LAND SURFACE MODELS LAND SURFACE INITIAL CONDITIONS LAND SURFACE MODELS DOWNSCALING MODELS DOWNSCALING MODELS BEST ESTIMATES ATMOSPHERIC FORCING FORECASTS Fields generator Assimilation and analyses Models LAND SURFACE FORECASTS or BOUNDARY CONDITIONS FOR ATMOSPHERIC AND HYDROLOGY DRAFT Page MODELS 31 December 5, 2007 OUTPUT PROCESSOR MODELS or CLIENTS

32 Water Balance in Land Surface Models RAIN veg (1-p snv ) P r SNOW E tr E r E g (1-veg)(1-p sng ) P r p sn P r E S P S SNOW (W S ) freez s melt s LIQ. WAT. RETAINED ON THE CANOPY (W r ) p sn R veg (1-p sn ) R veg LIQ. WAT. IN SNOW (W L ) R surf SOIL LIQUID WATER (w 2 ) R snow melt g freez g Lateral flow FROZEN WATER IN SOIL (w F ) Drain ISBA: Interactions between Surface, Biosphere, and Atmosphere DRAFT Page 32 December 5, 2007

databases do not provide information on LAI (usually specified using a look-up table).

33 Vegetation Analysis LAI with MODIS 2 LAI a LAIo LAIa LAIb LAIa LAIc J ( LAI a ) = + + σ o σ b σ c α o = 2 o LAI 2 σ c σ + σ a 2 c = α LAI + α LAI o o 2 σ o, and α c = 2 2 σ + σ (Gu, Belair, Mahouf, and Deblonde, 2006) c o c 2 c 2 Land cover databases do not provide information on LAI (usually specified using a look-up table). LAI is important for evapotranspiration. Using the LAI analysis from MODIS (or other instruments) could reduce an important source of errors. DRAFT Page 33 December 5, 2007

34 Background, Analysis, and Increments (m -3 m 3 ) Root-Zone Soil Moisture Contents and Increments Increments One point in Southern Alberta (m -3 m 3 ) Open-loop CaLDAS May Jun Jul Aug Sept Oct Nov Dec Jan DRAFT (2006) Page 34 December 5, 2007

35 Information Content of Several Types of Observations for Soil Moisture Assimilation Contribution of each observation source in the Soil Moisture Analysis (N. America): 2D-VAR on 24-h time window using hourly distributed observations (date 01/07/1995) L-band Tb C-band Tb IR Ts T, H 2m N. of observations % of contribution [ 100 (B-A) B-1 ] Tb (L-Band) H Tb (L-Band) V Tb (C-Band) H Tb (C-Band) V T skin (IR) AM+PM T2m (6-h) Q2m (6-h) 0.0 N Analysis Influence (%) sigma Obs (Balsamo et al., 2007) Observation Source N. Analysis Influence (%) DRAFT Page 35 December 5, 2007

The Canadian Land Data Assimilation System (CaLDAS)

The Canadian Land Data Assimilation System (CaLDAS) Marco L. Carrera, Stéphane Bélair, Bernard Bilodeau and Sheena Solomon Meteorological Research Division, Environment Canada Dorval, QC, Canada 2 nd Workshop