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

DA Overview 1.Motivation 2.Screen level analysis (2 m T and relative humidity) 3.Operational soil moisture analysis (‘local’ Optimum Interpolation) - Motivation - OI technique - Evaluation of the analysis and the impact on the forecast 4.Satellite observations and future developments - Remote sensing aspects - Results from a Nudging experiment - Design of the future surface analysis

DA Screen-level analysis: 2D univariate statistical interpolation 1.Increments  X i are estimated at each observation location i from the observation and the interpolated background field (6 h or 12 h forecast). 2. Analysis increments  X i a at each model grid point j are calculated from: 3. The optimum weights w i 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(i 1,i 2 ) =  2 b ×  (i 1,i 2 ) with the horizontal correlation coefficients  (i 1,i 2 ) and  b = 1.5 K / 5 % rH the standard deviation of background errors. O : covariance matrix of the observation error (N × N observations): O =  2 o × I with  o = 2.0 K / 10 % rH the standard deviation of obs. errors

DA Screen-level analysis: Quality controls and technical aspects 1.Number of observations N = 50, scanned radius r = 1000 km. 2.Gross quality checks as rH  [2,100] and T > T dewpoint 3.Observation points that differ more than 300 m from model orographie are rejected. 4.Observation is rejected if it satisfies: with  = 3 5.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 b T q

DA Overview 1.Motivation 2.Screen level analysis (2 m T and relative humidity) 3.Operational soil moisture analysis (‘local’ Optimum Interpolation) - Motivation - OI technique - Evaluation of the analysis and the impact on the forecast 4.Satellite observations and future developments - Remote sensing aspects - Results from a Nudging experiment - Design of the future surface analysis

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

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

DA ECMWF long-term forecasts (from ENSEMBLES project) 3. Motivation volumetric soil moisture2 m temperatures [%] [º Celsius] 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. (monthly averages for North America)

DA ECMWF long-term forecasts (from ENSEMBLES project) 3. Motivation turbulent surface fluxesfractional cloud coverage [W m -2 ] [%] 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. (monthly averages for North America)

DA Operational OI soil moisture analysis 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 a i and b i are defined as the product of optimum coefficients  i and  i minimizing the variance of analysis error and of empirical functions F 1, F 2, F 3. [Douville et al. (2000), Mahfouf (1991)] 3. OI technique

DA Operational OI soil moisture analysis: Optimum coefficients Coefficients a, b and c can be written as:a = C v ×  × F 1 F 2 F 3 b = C v ×  × F 1 F 2 F 3 c = (1 - F 2 )F 3 with:C v vegetation fraction (c low +c high ), F 1, F 2, F 3 empirical functions From univariate statistical interpolation theory (Daley, 1991).  errors,  correlation of background errors between variables x and y. 3. OI technique

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

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

DA Operational OI soil moisture analysis: Further limitations Soil moisture increments are set to 0 if: 1.The last 6 h precipitation exceeds 0.6 mm. 2.The instantaneous wind speed exceeds 10 m s The air temperature is below freezing. 4.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.

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

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

DA Forecast skills 3. Evaluation Temperature at 1000 hPa grey: OI black: OL solid: North America dotted: Europe dashed: East Asia Root-mean-square error E areaheight24 h72 h120 h168 h216 h Northern Hemisphere Europe East Asia North America Significance levels for the Sign test The proxy ‘observations’ are efficient in improving the turbulent surface fluxes and consequently the weather forecast on large geographical domains.

DA Validation against OK Mesonet observations 3. Evaluation

DA Validation of forcing data 3. Evaluation area averages for Oklahoma daily precipitation model forecast (OI) observations total amount of rainfall: June87.3 mm modelon19 days 87.8 mm observationson 9 days July 110. mm modelon26 days 79. mm observationson20 days daily downward shortwave radiation model forecast (OI) observations Correlation: 0.85 Bias: Wm -2

DA Validation of soil moisture 3. Evaluation area averages for Oklahoma surface soil moisture model forecast (OI) observations model forecast (OL) 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). root zone soil moisture model forecast (OI) observations model forecast (OL) Precipitation errors propagate to the root zone. Analysis constantly adds water. The monthly trend is underestimated. The current analysis fails to produce more accurate soil moisture estimates.

DA Overview 1.Motivation 2.Screen level analysis (2 m T and relative humidity) 3.Operational soil moisture analysis (‘local’ Optimum Interpolation) - Motivation - OI technique - Evaluation of the analysis and the impact on the forecast 4.Satellite observations and future developments - Remote sensing aspects - Results from a Nudging experiment - Design of the future surface analysis

DA Wavelengths and soil moisture 4. Remote sensing aspects WavelengthprosCons IR good temporal resolution good spatial resolution cloud free situations only model is needed to infer the energy balance at the surface (indirect information) Microwave (scatterometer) acceptable temporal resolution acceptable spatial resolution all weather tool strong dependency on incidence angle effects of surface roughness and vegetation radiative transfer complex Microwave (radiometer) acceptable temporal resolution all weather tool most direct signal radiative transfer established coarse spatial resolution

DA ERS-1/2 scatterometer derived soil moisture 4. Remote sensing aspects 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])

DA AMSR-E derived soil moisture 4. Remote sensing aspects Typical day with coverage of 28 half orbits. ( 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 ]

DA TMI Pathfinder Data Set (%) July 2 nd, 1999 (Gao et al. 2006) 4. Remote sensing aspects Data set produced by: Dept. Civil and Environmental Engineering, Princeton University, NJ Basis: brightness temperatures at 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 [cm 3 cm -3 ],

DA Oklahoma data sets Remote sensing aspects

DA Bias correction / CDF matching xx’ CDF M (x’) = CDF S (x) Cumulative Distribution Function TMI ECMWF 4. Remote sensing aspects

DA TMI soil moisture transformation r 2 = 0.66 r 2 = 0.69 r 2 = 0.01 r 2 = 0.18 CDF matching reduces systematic errors: The bias has been removed and the dynamic range has been adjusted. The random error may increase. transfer funcion 03/ /2002 x‘-x x Bias: % Bias: % 4. Remote sensing aspects

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

DA Nudging set up 4. TMI Nudging experiment Delayed cut-off 4D-Var (12 h) AN FC AN FC TMI sampling period (daily) soil moisture analysis 1/4 2/41/42/4

DA TMI Nudging experiment Validation of soil moisture area averages for Oklahoma surface 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. root zone soil moisture The information introduced at the surface propagates to the root zone. The monthly trend is well reproduced using the nudging scheme. Satellite derived soil moisture improve the soil moisture analysis and results in the most accurate estimate.

DA Forecast skill 4. TMI Nudging experiment correlation (observation / fc)bias OI OL Nudging rH T T 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.

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

DA The future Surface Data Assimilation System 4. Future surface analysis Delayed cut-off 4D-Var (12 h) AN FC AN Early Delivery Analysis 4D-Var (6 h) 00 UTC FC 12 UTC FC SDAS

DA 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). North America : NLDAS, Globe : GLDAS (NASA GSFC, see Canada: CLDAS (Meteorological Service of Canada) Europe: ELDAS (KNMI, see 4. Future surface analysis