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Surface Analysis (II)

Surface Analysis (II). M. Drusch Room TT 063, Phone 2759. Overview. Motivation 2. Screen level analysis (2 m T and relative humidity) Operational soil moisture analysis (‘local’ Optimum Interpolation) - Motivation - OI technique

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Surface Analysis (II)

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  1. Surface Analysis (II) M. Drusch Room TT 063, Phone 2759

  2. Overview • Motivation • 2. Screen level analysis (2 m T and relative humidity) • Operational soil moisture analysis (‘local’ Optimum Interpolation) • - Motivation • - OI technique • - Evaluation of the analysis and the impact on the forecast • Satellite observations and future developments • - Remote sensing aspects • - Results from a Nudging experiment • - Design of the future surface analysis

  3. Screen-level analysis: 2D univariate statistical interpolation • Increments Xi are estimated at each observation location i from the • observation and the interpolated background field (6 h or 12 h forecast). 2. Analysis increments Xia at each model grid point j are calculated from: 3. The optimum weights wi are given by: (B + O) w = b b : error covariance between observation i and model grid point j (dimension of N observations) B : error covariance matrix of the background field (N × N observations) B(i1,i2) = 2b×(i1,i2) with the horizontal correlation coefficients (i1,i2) and b = 1.5 K / 5 % rH the standard deviation of background errors. O : covariance matrix of the observation error (N × N observations): O = 2o× I with o = 2.0 K / 10 % rH the standard deviation of obs. errors

  4. Screen-level analysis: Quality controls and technical aspects • Number of observations N = 50, scanned radius r = 1000 km. • Gross quality checks as rH  [2,100] and T > Tdewpoint • Observation points that differ more than 300 m from model • orographie are rejected. • Observation is rejected if it satisfies: with  = 3 • Number of used observations varies from 4000 to 6000 (40% of the • available observations) every 6 hours. • 6. Increments are computed: q = (B + O)-1 X and bTq

  5. Overview • Motivation • 2. Screen level analysis (2 m T and relative humidity) • Operational soil moisture analysis (‘local’ Optimum Interpolation) • - Motivation • - OI technique • - Evaluation of the analysis and the impact on the forecast • Satellite observations and future developments • - Remote sensing aspects • - Results from a Nudging experiment • - Design of the future surface analysis

  6. Rainfall ends Rainfall starts Evaporation and the Hydrological ‚Rosette‘ 3: Motivation

  7. Motivation Climate Simulated July surface temperature for Motivation B) dry soil case (no evapotranspiration) A) wet soil case (actual evapotranspiration is set to potential evapotranspiration) GLAS atmospheric GCM , Shukla and Mintz [1982]

  8. volumetric soil moisture 2 m temperatures [º Celsius] [%] ECMWF long-term forecasts (from ENSEMBLES project) 3. Motivation (monthly averages for North America) Systematic errors in the land surface scheme result in a (dramatic) dry down with summer values close to the permanent wilting point. The corresponding 2 m temperature forecasts show a strong warm bias exceeding 10 K during summer and 5 K during winter.

  9. turbulent surface fluxes fractional cloud coverage [W m-2] [%] ECMWF long-term forecasts (from ENSEMBLES project) 3. Motivation (monthly averages for North America) Latent heat flux is substantially reduced during summer, sensible heat flux is almost doubled. Due to less moisture in the atmosphere cloud coverage is also reduced. Surface pressure is reduced (not shown). The model has to be re-initialized with analysed soil moisture to prevent from drifting into an unrealistic state.

  10. The analysis increments from the screen level analysis are used to produce increments for the water content in the first three soil layers (root zone): and for the first soil temperature layer: Superscripts a and b denote analysis and background ( = forecast), respectively, i denotes the soil layer. Coefficients ai and bi are defined as the product of optimum coefficients i and i minimizing the variance of analysis error and of empirical functions F1, F2, F3. Operational OI soil moisture analysis 3. OI technique [Douville et al. (2000), Mahfouf (1991)]

  11. Operational OI soil moisture analysis:Optimum coefficients Coefficients a, b and c can be written as: a = Cv×  × F1F2F3 b = Cv×  × F1F2F3 c = (1 - F2)F3 with: Cv vegetation fraction (clow +chigh), 3. OI technique From univariate statistical interpolation theory (Daley, 1991).  errors,  correlation of background errors between variables x and y. F1, F2, F3 empirical functions

  12. Based on forecast differences between day 1 and 2 of the net surface water budget. Standard deviation of analysis error: Operational OI soil moisture analysis:Statistics of background errors 3. OI technique Statistics of background errors for soil moisture derived from the Monte Carlo Experiments

  13. 0 r < rmin • Winter / night time correction: • M : cos mean solar zenith angle  = 7 rmin <r < rmax F2 = 2. Weak radiative forcing correction: r : atmospheric transmittance rmin: 0.2 rmax: 0.9 S0 : solar constant M : cos mean solar zenith angle : mean dw surface solar radiation forecast 1 r > rmax 0 Z > Zmax Zmin <Z < Zmax 3. Mountain correction: Z : model orographie Zmin : 500 m Zmax: 3000 m F3 = 1 Z < Zmin Operational OI soil moisture analysis:Empirical functions 3. OI technique

  14. Operational OI soil moisture analysis:Further limitations • Soil moisture increments are set to 0 if: • The last 6 h precipitation exceeds 0.6 mm. • The instantaneous wind speed exceeds 10 m s-1. • The air temperature is below freezing. • There is snow on the ground. 3. OI technique Analysed screen level parameters are used as proxy ‘observations’ for the root zone soil moisture analysis. The relationship between 2 m temperature and relative humidity and soil moisture is often rather weak and intermittent.

  15. Impact study: Soil moisture increments experiment 1: Optimal Interpolation, atmospheric 4DVar vs experiment 2: Open Loop (no analysis), atmospheric 4DVar 3. Evaluation [mm] OI

  16. Humidity increments OI mean humidity increments [%] 3. Evaluation [%] OL – OI difference of mean humidity increments [%]

  17. Temperature at 1000 hPa Forecast skills Root-mean-square error E Significance levels for the Sign test 3. Evaluation grey: OI black: OL solid: North America dotted: Europe dashed: East Asia The proxy ‘observations’ are efficient in improving the turbulent surface fluxes and consequently the weather forecast on large geographical domains.

  18. Validation against OK Mesonet observations 3. Evaluation

  19. daily precipitation daily precipitation daily downward shortwave radiation model forecast (OI) model forecast (OI) observations observations total amount of rainfall: June 87.3 mm model on 19 days 87.8 mm observations on 9 days July 110. mm model on 26 days 79. mm observations on 20 days Correlation : 0.85 Bias : - 0.7 Wm-2 Validation of forcing data area averages for Oklahoma 3. Evaluation

  20. surface soil moisture root zone soil moisture • Too quick dry downs (model problem). • Too much precip in July (model problem). • Too little water added in wet conditions • (analysis problem). • NO water removed in dry conditions • (analysis problem). • Precipitation errors propagate to the • root zone. • Analysis constantly adds water. • The monthly trend is underestimated. model forecast (OI) model forecast (OI) model forecast (OL) model forecast (OL) observations observations Validation of soil moisture area averages for Oklahoma 3. Evaluation The current analysis fails to produce more accurate soil moisture estimates.

  21. Overview • Motivation • 2. Screen level analysis (2 m T and relative humidity) • Operational soil moisture analysis (‘local’ Optimum Interpolation) • - Motivation • - OI technique • - Evaluation of the analysis and the impact on the forecast • Satellite observations and future developments • - Remote sensing aspects • - Results from a Nudging experiment • - Design of the future surface analysis

  22. Wavelengths and soil moisture 4. Remote sensing aspects

  23. ERS-1/2 scatterometer derived soil moisture Data set produced by: Institute of Photogrammetry and Remote Sensing, Vienna University of Technology Basis: ERS scatterometer backscatter measurements Method: change detection method for extrapolated backscatter at 40º reference incidence angle Output: topsoil moisture content in relative units (0 [dry] to 1 [wet]) 4. Remote sensing aspects http://ipf.tuwien.ac.at/radar/ers-scat/home.htm

  24. Typical day with coverage of 28 half orbits. (http://nsidc.org/data/docs/daac/ae_land_l2b_soil_moisture.gd.html) AMSR-E derived soil moisture Data set produced by: National Snow and Ice Data Center (NSIDC), Boulder, Colorado Basis: brightness temperatures at 10.7 and 18.7 GHz horizontal and vertical polarization Method: change detection method for normalized polarization ratios Output: surface soil moisture [g cm-3], vegetation water content [kg m-3] 4. Remote sensing aspects

  25. July 2nd, 1999 0 5 10 15 20 25 30 35 40 45 (%) TMI Pathfinder Data Set Data set produced by: Dept. Civil and Environmental Engineering, Princeton University, NJ Basis: brightness temperatures at 10.65 GHz horizontal polarization Method: physical retrieval based on land surface microwave emission model and auxiliary data sets from the North American Land Data Assimilation Study project Output: surface soil moisture [cm3 cm-3], 4. Remote sensing aspects (Gao et al. 2006)

  26. Oklahoma data sets 2002 4. Remote sensing aspects

  27. Cumulative Distribution Function TMI ECMWF x x’ CDFM(x’) = CDFS(x) Bias correction / CDF matching 4. Remote sensing aspects

  28. transfer funcion 03/2002-10/2002 • CDF matching reduces systematic errors: • The bias has been removed and the dynamic • range has been adjusted. • The random error may increase. r2 = 0.66 r2 = 0.69 r2 = 0.01 r2 = 0.18 x‘-x Bias: -0.35 % x Bias: -11.67 % TMI soil moisture transformation 4. Remote sensing aspects

  29. Corrected TMI soil moisture volumetric surface soil moisture [%] for 06/06/2004 the modelled first guess 4. Remote sensing aspects TMI Pathfinder data corrected TMI data set

  30. 00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 Delayed cut-off 4D-Var (12 h) AN AN AN FC FC FC TMI sampling period (daily) soil moisture analysis 1/4 2/4 1/4 2/4 Nudging set up 4. TMI Nudging experiment

  31. surface soil moisture root zone soil moisture • Nudging / satellite data remove water • effectively and produce a realistic dry • down. • Nudging the satellite results in the most • accurate surface soil moisture estimate. • The information introduced at the • surface propagates to the root zone. • The monthly trend is well reproduced • using the nudging scheme. Validation of soil moisture area averages for Oklahoma 4. TMI Nudging experiment Satellite derived soil moisture improve the soil moisture analysis and results in the most accurate estimate.

  32. OI Nudging OL rH rH T T Forecast skill correlation (observation / fc) bias 4. TMI Nudging experiment The impact of the satellite data on the forecast quality (of screen level variables) is neutral (correlation). The biases obtained from the nudging experiment are slightly higher when compared against the OI and lower when compared against the OL.

  33. Soil moisture increments accumulated increments over June and July 2002 [mm] 4. TMI Nudging experiment Optimal Interpolation (2 m T and RH) Nudging (TMI soil moisture)

  34. 00 03 06 09 12 15 18 21 00 03 06 09 12 15 18 21 00 Delayed cut-off 4D-Var (12 h) AN AN FC FC Early Delivery Analysis 4D-Var (6 h) AN AN SDAS 12 UTC FC 00 UTC FC The future Surface Data Assimilation System 4. Future surface analysis

  35. Land Data Assimilation Systems LDAS Development of advanced systems for the assimilation of satellite observations to improve the analysis of the state of the land surface (and consequently the numerical weather forecasts). 4. Future surface analysis North America : NLDAS, Globe : GLDAS (NASA GSFC, see http://ldas.gsfc.nasa.gov) Canada: CLDAS (Meteorological Service of Canada) Europe: ELDAS (KNMI, see http://www.knmi.nl/samenw/eldas)

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