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Land Surface: Modelling & Data Assimilation

Land Surface: Modelling & Data Assimilation

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Land Surface: Modelling & Data Assimilation

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  1. Land Surface:Modelling & Data Assimilation Gianpaolo Balsamo European Centre for Medium-Range Weather Forecasts (ECMWF) ARPA-SIM, Bologna, 18 February 2008 Soil schemes: modeling&assimilation - G. Balsamo

  2. Outline • Introduction • The Earth Integrated Forecast System • The role of Land Surface (LS) • The role of data assimilation • LS observational network • Modelling the land surface • Motivations • Simplification vs. Realism in LS parameterizations • TESSEL scheme • Analysing the land surface • Motivations • Current practice in NWP (OI, EKF) • New methods (simplified 2D-VAR, EnKF) • Modelling & data assimilation synergy • The example of soil hydrology (HTESSEL) • The Cal/Val benchmark/strategy (field-site to global simulations + Data Assimilation) • Conclusions and Perspectives Soil schemes: modeling&assimilation - G. Balsamo

  3. Earth energy cascade • The sun emits 4 x 1026 W • the Earth intercepts 1.37 kW/m2 • This energy is distributed between • Direct reflection (~30%) • Conversion to heat, mostly by surface absorption (~43%), re-radiated in the infrared • Evaporation, Precipitation, Runoff (~22%) • Rest of the processes (~5%, Winds, Waves, Convection, Currents, Photosynthesis, Organic decay, tides, … ) Robinson & Henderson-Sellers, 1999 Soil schemes: modeling&assimilation - G. Balsamo

  4. Terrestrial Atmosphere 4.5 Evaporation 71 Rain 107 Runoff 36 Land Chahine, 1992 [•] = 1015 kg = teratons [•] = 1015 kg yr-1 Earth water cycle • Atmosphere recycling time scales associated with land reservoir • Precipitation 4.5/107 = 15 days • Evaporation 4.5/71 = 23 days Soil schemes: modeling&assimilation - G. Balsamo

  5. Water budget RT P Carbon budget (natural) RS Energy budget LE E NEE H Y 65 134 Wm-2 27 40 mmd-1 0.9 1.4 2.2 ERA40 land-averaged values 1958-2001 Role of land surface • Atmospheric general circulation models need boundary conditions for the enthalpy, moisture (and momentum) equations: Fluxes of energy, water at the surface. Soil schemes: modeling&assimilation - G. Balsamo

  6. Role of land surface (2) • Numerical Weather Prediction models need to provide near surface weather parameters (temperature, dew point, wind, low level cloudiness) to their customers. ECMWF model(s) and resolutions Length Horizontal Vertical Remarks resolution levels • Deterministic 10 d T799 (25 km) L91 00+12 UTC • Ensemble prediction 15 d T399 (50 km) L62 2x(50+1) • Monthly forecast 1 m T159 (125 km) L62 (Ocean coupled) • Seasonal forecast 6 m T95 (200 km) L40 (Ocean coupled) • Assimilation physics 12 h T255(80 km)/ L91 T95(200 km) Soil schemes: modeling&assimilation - G. Balsamo

  7. How to initialize the Land Surface: Current Practice in NWP centers • Use of 2m observationswith OI Analysis(ECMWF, Météo-France,HIRLAM, MSC)or Simplified Kalman Filter(DWD) • OFF-LINE Land surface(NLDAS, GLDAS, UK MetOMeteo-France) Who, When and Why ? • Coiffier et al. 1987 (Use of 2m for land surface) • Mahfouf 1991 (OI / Variational formulation of the land surface analysis) • the operational application comes few years later (94-95 at ECMWF, 99 Météo-France.) Soil schemes: modeling&assimilation - G. Balsamo

  8. EVOLUTION OF LAND SURFACE DATA ASSIMILATION SYSTEMS L-band Tb hourly 6-hourly C-band scat. IR Ts C-band Tb T/H 2m Soil schemes: modeling&assimilation - G. Balsamo

  9. OBSERVATIONS FOR SOIL MOISTURE ANALYSIS INFORMATIVITY on SOIL MOISTURE 2008/2012 AVAILABILITY now • + Large Information content • + Global Coverage • + Reduced Atmospheric Contrib. • Not Available ‘till 2009 • + Global coverage • + Relatively reduced Atmospheric contrib. • RFI • Vegetation masking VCW>1kg/m2 • + Large coverage • Cloud Masking • Model Bias • + Wide validation • -Coverage • Variable Information Content L-band Tb C-band scat. IR Ts C-band Tb T/H 2m Soil schemes: modeling&assimilation - G. Balsamo

  10. Outline • Introduction • The Earth Integrated Forecast System • The role of Land Surface (LS) • The role of data assimilation • LS observational network • Modelling the land surface • Motivations • Simplification vs. Realism in LS parameterizations • TESSEL scheme • Analysing the land surface • Motivations • Current practice in NWP (OI, EKF) • New methods (simplified 2D-VAR, EnKF) • Modelling & data assimilation synergy • The example of soil hydrology (HTESSEL) • The Cal/Val benchmark/strategy (field-site to global simulations + Data Assimilation) • Conclusions and Perspectives Soil schemes: modeling&assimilation - G. Balsamo

  11. History of ECMWF 2m T errors Soil schemes: modeling&assimilation - G. Balsamo

  12. The challenges for Land Surface Modeling • Capture natural diversity of land surfaces (heterogeneity) via a simple set of equations • Focus on elements which affects more directly weather and climate (i.e. soil moisture, snow cover). Soil schemes: modeling&assimilation - G. Balsamo

  13. New treatment of snow under high vegetation Revised canopy resistances, including air humidity stress on forest High and low vegetation treated separately Variable root depth No root extraction or deep percolation in frozen soils TESSEL scheme • Tiled ECMWF Scheme for Surface Exchanges over Land + 2 tiles (ocean & sea-ice) Soil schemes: modeling&assimilation - G. Balsamo

  14. Vegetation Type (H and L) at T799GLCC(1998) 6 dominant high veg. type (TVH) 9 dominant low veg. type (TVL) Used to assign: root-distributionLAI and Rs_minroughness lengths by a look-up table Soil schemes: modeling&assimilation - G. Balsamo

  15. Vegetation Cover (H and L) at T799GLCC(1998) Note: the cover CVH and CVL are fraction of land use by TVH and TVL and their sum is equal the unity Used to calculate: bare ground fraction asBare_frac=1-ΣCV(TVi)*RCOV(TVi) with RCOV provided by a look-up table Soil schemes: modeling&assimilation - G. Balsamo

  16. Outline • Introduction • The Earth Integrated Forecast System • The role of Land Surface (LS) • The role of data assimilation • LS observational network • Modelling the land surface • Motivations • Simplification vs. Realism in LS parameterizations • TESSEL scheme • Analysing the land surface • Motivations • Current practice in NWP (OI, EKF) • New methods (simplified 2D-VAR, EnKF) • Modelling & data assimilation synergy • The example of soil hydrology (HTESSEL) • The Cal/Val benchmark/strategy (field-site to glo • Conclusions and Perspectives Soil schemes: modeling&assimilation - G. Balsamo

  17. Day 2 forecasts ECMWF German (DWD) Case study: Europe, May-June1994 (1) Soil schemes: modeling&assimilation - G. Balsamo

  18. Case study: Europe, May-June1994 (2) Soil schemes: modeling&assimilation - G. Balsamo

  19. Case study: Europe, May-June1994 (3) Soil schemes: modeling&assimilation - G. Balsamo

  20. Near surface atmospheric errors • In the French forecast model (~10km) local soil moisture patterns anomalies at time t0 are shown to correlate well with large 2m temperature forecast errors (2-days later) wet soil dry soil Balsamo, 2003 Soil schemes: modeling&assimilation - G. Balsamo

  21. Link between soil moisture and atmosphere • The main interaction of soil moisture and atmosphere is due to evaporation and vegetation transpiration processes. E 0 <SWI< 1 Eg Etr Ws Wp Wpvegetation Wsbare ground Soil schemes: modeling&assimilation - G. Balsamo

  22. T2m t RH2m t Wp t 6-h 12-h 18-h 0-h Optimum Interpolation land surface analysis(oper. surface analysis at Météo-France/MSC/ECMWF…)Mahfouf 1991, Bouttier 1993, Giard and Bazile 2000, Mahfouf et al. 2003, Belair et al 2003 Optimum Interpolation of T2m and RH2m using SYNOP observations interpolated at the model grid-point (by a 2m analysis) D T2m = T2ma - T2mf D RH2m = RH2ma - RH2mf Correction of surface parameters (Ts, Tp, Ws, Wp) using 2m increments between analysed and forecasted values Sequential analysis (every 6h) Tsa - Tsf = D T2m Tpa - Tpf = D T2m / 2p Wsa - Wsf = aWsT D T2m + aWsRHD RH2m Wpa - Wpf = aWpT D T2m + aWpRHD RH2m aWp/sT/RH = f (t, veg, LAI/Rsmin, texture, atm.cds.) Tuning of the OI statistics and regressions and accuracy of 2m analyses are key components Soil schemes: modeling&assimilation - G. Balsamo

  23. Mahfouf (1991), Callies et al. (1998), Rhodin et al. (1999),Bouyssel et al. (2000), Hess (2001), Seuffert et al. (2004), Balsamo et al. (2004) Formalism: T2m t RH2m t Wp t 6-h 12-h 18-h 0-h Variational surface analysis J(x) = J b(x) + J o(x) = ½ (x – xb) TB-1 (x – xb) + ½(y – H(x))TR-1 (y – H(x)) Continuous analysis x is the control variables vector y is the observation vector H is the observation operator The analysis is obtained by the minimization of the cost functionJ(x) is the background error covariance matrix B is the observation error covariance matrix R • Advantages:Easier assim. asynop. obs. Extension on longer assim. Window (24-h) Soil schemes: modeling&assimilation - G. Balsamo

  24. The shape of the cost function J for full 2D-VAR (Bouyssel et al. 2000) J=f(Ws,Wp,Ts,Tp) Soil schemes: modeling&assimilation - G. Balsamo

  25. Wp W’p=Wp + dWp Y=(T2m ,Tb,Ts ) d æ ö ( 1 ) Y ç ÷ d Y W p ç ÷ d ( 2 ) Y ç ÷ d W ç ÷ p T = H ... ç ÷ d ( -1) p Y ç ÷ d W ç ÷ p d ( ) p Y ç ÷ t ç ÷ d W è ø p t=0 1 2 … p ( ) - = D D D D ( 1 ) (2 1 ) ( -1) ( ) P p y H ( x ) Y , Y ,..., Y , Y b How the simplified 2D-VAR method works From a perturbation of the initial total soil moisture d Wp applied on each model land grid-point. Guess G dY (i) = YG (i) - YG’ (i) dWp Guess G’ DY (i) = YG (i) - YO(i) Soil schemes: modeling&assimilation - G. Balsamo

  26. 2D hypothesis The 2D hypothesis is validated with simulated observations on a real situation From a prescribed initial error Wp Analysis error The 6-h forecast errors on T2mand RH2m Soil schemes: modeling&assimilation - G. Balsamo

  27. Convergence of 2D-VAR analysis Simulated observations (consistent to SWI=0.5) are assimilated over a 10-day period A 24-h 2D-VAR analysis with optimised settings Real observations experiments are then considered Soil schemes: modeling&assimilation - G. Balsamo

  28. Soil Moisture produced by the ELDAS project The same comparison is produced for the ELDAS soil moisture obtained with the ARPEGE model. An improved match of soil moisture patterns and gradients is obtained on the SAFRAN-ISBA-MODCOU validation area. Habets et al. (2003) ELDAS cycle Soil schemes: modeling&assimilation - G. Balsamo

  29. June July +positive Index 2003/2002 +negative (CNES, 2003) Images SPOT/VEGETATION Variation of NDVI 2003 within respect to 2002 Drought of summer 2003: Comparison of soil moisture and NDVI anomaly 30 June 2003 (exp. 2D-Var + Ecoclimap (Masson et al. 2003) after 2-month cycle) Variation of SWI at 30 June 2003 compared to 30 June 2000 (ELDAS) Soil schemes: modeling&assimilation - G. Balsamo

  30. How Microwave and Infra-red Radiances may be informative on soil water content? L-band Tb IR Tskin C-band Tb Tb = ε Ts Ws Ws Wp Wp Soil moisture modifies soil dielectric const. emissivity ε Soil moisture affects Skin temperature and heating rate L-band Tb C-band Tb Soil schemes: modeling&assimilation - G. Balsamo IR Ts

  31. IR Tskin(or HR) L-band&C-Band TB G G’ Obs. Tb, H TsIR t Tb, V t t Morning(except Clouds) Every hour (except RFI in C-band) Wp Wp t t 0-h 1-h 2-h 3-h … ………… 23-h 0-h 0-h 1-h 2-h 3-h … …… 23-h 0-h Soil schemes: modeling&assimilation - G. Balsamo

  32. Superficial soil moisture from ISBA TB,h from microwave RT model (Drusch et al. 1999, 2001) t+1/2-h t t-1/2-h SMOS (&SMAP) L-BAND simulated TBH,V PHASE I THE OFF-LINE LSS ISBA is driven by near surface atmospheric forcing to obtain the LAND SURFACE STATE PHASE II THE MICROWAVE RT Model LSMEM is used to compute the brightness temperature at 1.4GHz PHASE III SPATIAL and TEMPORAL location of the simulated TB Soil schemes: modeling&assimilation - G. Balsamo

  33. OSSE: Assimilation of Simulated Brightness Temperature The assimilation of HYDROS simulated H and V polarization L-band brightness temperatureis investigated in a 10-day DA experiment using GSWP-II forcing to create a reference landsurface state (from 1-y ISBA model run). Soil moisture Error (% vol.)Analysis – Reference The 2D-VAR analysis is initialized with a background model error of 10% (SWI) and the observations error is set to 3 K. The analysis plays for about 50% in the convergence towards the ISBA-GSWP-II reference (starting from a medium soil moisture Wp=0.5(Wfc-Wwl) Soil schemes: modeling&assimilation - G. Balsamo

  34. OSSE: Assimilation of HYDROS Simulated Brightness Temperature The assimilation of HYDROS simulated H and V polarization L-band brightness temperature is investigated in a 10-day DA experiment using GSWP-II forcing to create a reference land surface state (from 1-y ISBA model run). Soil moisture Error (% vol.)Analysis – Reference The 2D-VAR analysis is initialized with a background model error of 10% (SWI) and the observations error is set to 3 K. The analysis plays for about 50% in the convergence towards the ISBA-GSWP-II reference (starting from a medium soil moisture Wp=0.5(Wfc-Wwl) Soil schemes: modeling&assimilation - G. Balsamo

  35. H(x) H(x) Data Assimilation Techniques applied for Land Surface Variational Optimum Interpolation Optimal Estimation Theory Simplified VAR/EKF Methods Extended / EnsembleKalman Filter Soil schemes: modeling&assimilation - G. Balsamo

  36. Off-line vs. Atmospheric Coupled LDASBalsamo et al. 2007 • Within CaLDAS an Off-line version of GEM-15km is available, MEC-15km • Same dynamical/physical core GEM-15km • In the Off-line version the forcing is applied at ~50 m (28th level of GEM) • The comparison is proper (same innovations, same atmospheric model trajectory). • A SBL (Delage 1997) is implemented and allows to maintain and interactive layer • A multi-observation OSSE using the simplified 2D-VAR scheme is run. • Diagnostics from Jacobians and the information content theory confirm a good approximation over North America (GEM-core domain) with a reduction of noisy signal which seems beneficial (i.e. no convection). Results are still preliminary (1 day considered) and further tests are in progress. Soil schemes: modeling&assimilation - G. Balsamo

  37. Outline • Introduction • The Earth Integrated Forecast System • The role of Land Surface (LS) • The role of data assimilation • LS observational network • Modelling the land surface • Motivations • Simplification vs. Realism in LS parameterizations • TESSEL scheme • Analysing the land surface • Motivations • Current practice in NWP (OI, EKF) • New methods (simplified 2D-VAR, EnKF) • Modelling & data assimilation synergy • The example of soil hydrology (HTESSEL) • The Cal/Val benchmark/strategy (field-site to Global simulation + Data Assimilation) • Conclusions and Perspectives Soil schemes: modeling&assimilation - G. Balsamo

  38. New treatment of snow under high vegetation Too early snowmelting Revised canopy resistances, including air humidity stress on forest High and low vegetation treated separately Too little surfacerunoff Variable root depth A single soil textureglobally, excessive drainage Inhibited root extraction,or drainage in frozen soils TESSEL land surface scheme: + and - • Tiled ECMWF Scheme for Surface Exchanges over Land + 2 tiles (ocean & sea-ice) Soil schemes: modeling&assimilation - G. Balsamo

  39. Surface Water reservoirs (ERA-40) • DA increments redistribute water and constraint near-surface errors Soilmoisture snow moisture deficit Early snowmelting anticipate moisture supply Soil schemes: modeling&assimilation - G. Balsamo

  40. Cold processes I: Snow DA increments ERA-40 ERA-Interim1992, daily SWE increments Soil schemes: modeling&assimilation - G. Balsamo

  41. HTESSEL scheme • Hydrology-TESSEL • Global Soil Map (FAO) • New formulation of Hydraulic properties • VIC surface runoff Soil schemes: modeling&assimilation - G. Balsamo

  42. Soil Type at T799FAO(2003) 6 dominant soil type Used to assign: hydraulic properties (drainage and surf. runoff)field capacity & wilting point for SM analysis Soil schemes: modeling&assimilation - G. Balsamo

  43. A revised hydrology scheme (H-TESSEL) • A spatially variable hydrology scheme is being tested following Van den Hurk and Viterbo 2003 • Use of a the Digital Soil Map of World (DSMW) 2003 • Infiltration based on Van Genuchten 1980 and Surface runoff generation based on Dümenil and Todini 1992 Van den Hurk and Viterbo 2003 Soil schemes: modeling&assimilation - G. Balsamo

  44. Field Capacity and Permanent Wilting Point Soil Conductivity Soil Diffusivity TESSEL TESSEL Soil schemes: modeling&assimilation - G. Balsamo

  45. The soil texture classification database FAO 2003 from Freddy.Nachtergaele, after a survey of the available datasets. █coarse █medium █med-fine █fine █very-fine █organic The interpolation to model grid is donewithin the IFS by the prepdata (interporoutine) preserving the dominant texture type at various resolution (T21-T799). Important for “upscalability” Dominant soil type from FAO2003 (at native resolution of ~ 10 km) Soil schemes: modeling&assimilation - G. Balsamo

  46. The orography runoff generation Runoff as a function of orography (b is based on standard deviation of orography) 10mm/h Up to ~30% Surfacerunoff in complex orography fraction runoff s of the grid-point area S. Also the standard deviation of orography is scaling with resolution (especially T159-799). ; Soil schemes: modeling&assimilation - G. Balsamo

  47. Field sites (Offline) Catchment (Offline) Global (Offline) Coupled GCM Coupled GCM + DA Verification Strategy for the new Hydrology Soil schemes: modeling&assimilation - G. Balsamo

  48. Field site verification of HTESSEL • observed atmospheric forcing • observed SM/LE/H • observed Tb • Ancillary data as in operational (no local readjustment) Soil schemes: modeling&assimilation - G. Balsamo

  49. SEBEX (sandy soil) Savannah, desert climate HTESSEL show a consistent improvement of soil moisture and evaporation with respect to TESSEL Soil schemes: modeling&assimilation - G. Balsamo

  50. BERMS (Boreal Forest) Forest, snow dominated site HTESSEL show a consistent improvement of Top 1m soil moisture with respect to TESSEL and a better represented interannual variability Soil schemes: modeling&assimilation - G. Balsamo