LWG, Destin (Fl) 27/1/2009 Observation representativeness error ECMWF model spectra Application to ADM sampling mode and Joint-OSSE

LWG, Destin (Fl) 27/1/2009 Motivation ESA is re-considering burst vs. continuous mode for ADM-Aeolus Information content of various sampling modes for NWP Effective model resolution –Number of degrees of freedom of a model ADM observation representation –Observations should represent this model resolution –ADM representativeness error

LWG, Destin (Fl) 27/1/2009 Observation weight in data assimilation Observation impact in atmospheric analysis is determined by the relative weight of the observation and the model in the analysis observation NWP model Data assimilationAtmospheric analysis

LWG, Destin (Fl) 27/1/2009 Perfect observation Perfect observation has no observation error: R=0 For simplicity, assume the observation directly related to a model parameter and located on a model grid point: H=I  K=I y = Hx t +  = x t (no observation error is assumed) x a =x b +I(x t -x b ) = x t  The analysed state equals the true atmospheric state at the measurement location  Sounds good ……….. or?

LWG, Destin (Fl) 27/1/2009 Perfect observation Model perfectly fits observation, but no constraint elsewhere (overfitting) The model state is a smooth representation of the real atmospheric state assimilation of perfect observation

LWG, Destin (Fl) 27/1/2009 Perfect observation What goes wrong? Model information (including information from observations in previous cycles) is ignored Model is forced to fit the small-scale structures present in the (point) observation But –model is a smooth representation of the real atmosphere, not representing small-scale features –Small-scale structures are not well treated by the model (noise) and should be avoided in the NWP analysis step.  Weight given to the observation is too large  How to determine a more appropriate weight?

LWG, Destin (Fl) 27/1/2009 Observation representativeness error Representativeness error = the small scale atmospheric variability which is sampled by individual observations, but which the model is incapable of representing To avoid ingesting small-scale structures in the model state, the impact (weight) of the observation in the analysis is reduced by increasing the observation error with the representativeness error, i.e., observation error variance = measurement error variance + representativeness error variance. How to determine the observation representativeness error?

LWG, Destin (Fl) 27/1/2009 Wave number spectra near tropopause k -5/3 k km 5000 km cyclones 2 km shifted Nastrom and Gage (1985) GASP aircraft data near tropopause  Wind spectra follow a k -5/3 spectrum for horizontal spatial scales below 500 km  atmospheric variability (m 2 s -2 ) is found by the surface below the spectrum

LWG, Destin (Fl) 27/1/2009 ECMWF model spectra ECMWF model does not well resolve the atmospheric variability on scales smaller than ~300 km Lorenc curve (1992): k -5/3 atmosphere wind variability spectrum (ESA study by Lorenc on ADM) based on Nastrom and Gage 1000 hPa 500 hPa ECMWF (2008, T799) Power law and amplitude determine unresolved model variance

LWG, Destin (Fl) 27/1/2009 ECMWF comment (1)

LWG, Destin (Fl) 27/1/2009 ECMWF comment (2)

LWG, Destin (Fl) 27/1/2009 ECMWF comment (3)

LWG, Destin (Fl) 27/1/2009 Illustration representativeness error model Resolved wind variability: ECMWF and scatterometer Jur Vogelzang (2006)

LWG, Destin (Fl) 27/1/2009 Tropical cyclone Ike ECMWF T799 ~ 25 km HARMONIE ~ 2.5 km HARMONIE More small-scale structures in high-resolution (LAM) models

LWG, Destin (Fl) 27/1/2009 Implication for Joint OSSE Nature run (NR): ECMWF T511/T799 –Lacking atmospheric variability on scales smaller than ~250km Simulate atmospheric variability for missing NR scales –representativeness error Observation simulation: o = intpol(NR) + instrument error + representativeness error

LWG, Destin (Fl) 27/1/2009 Model resolution cell Introduce Model Resolution Cell (MRC): –spatial scales below the MRC are not well resolved by the model –ECMWF model: MRC ~250km –unresolved wind variability: –UKMO 1992: unresolved wind variability: 3.95 m 2 s -2 computational grids of global NWP models have increased substantially over the last 15 years, but the horizontal scales that are resolved by these models have increased to a much lesser extent

LWG, Destin (Fl) 27/1/2009 ADM representativeness error Assumption: along and across track variabilities are independent and of equal size Total error error variance o 2 = r 2 across + r 2 along + m 2 /N MRC across track along track burst mode MRC continuous mode representativeness error instrument error ~ photon counts with r 2 = atmospheric variability in MRC Increasing the sample length reduces the along track representativeness error !

LWG, Destin (Fl) 27/1/2009 ADM information content Analysis equations Observation impact  [0,1]; 0: no impact, 1: maximum impact (analysis equals true atmosphere)

LWG, Destin (Fl) 27/1/2009 Numerical example Square model area of 2,500 km 2, 25 km model grid, model grid points single layer at 500 hPa No clouds  b = 2.5 ms -1 L B = 250 km B

LWG, Destin (Fl) 27/1/2009 Numerical example (2) – burst mode sampling R A Observation impact = 0.52

LWG, Destin (Fl) 27/1/2009 ADM continuous mode Pulse repetition frequency: 50 Hz (100 Hz for burst mode) Same energy per shot  Double the energy along a 200 km track in continuous mode Continuous mode offers more flexibility −50/100/200/ …. km accumulation −50/100/200/ …. km observation distance Increasing the accumulation length reduces the representativeness error BUT, observation correlation increases with decreasing observation distance

LWG, Destin (Fl) 27/1/2009 Numerical example (3) – continuous mode sampling R A 100 km accumulation, 100 km spacing 200 km accumulation, 200 km spacing 50 km accumulation, 50 km spacing observation impact = 0.61 observation impact = 0.63 observation impact = 0.60 Closely separated observations => highly correlated => reduced impact

LWG, Destin (Fl) 27/1/2009 LAM model resolving small-scales Assume that models ARE capable to resolve 50 km scales; L B =50 km

LWG, Destin (Fl) 27/1/2009 LAM model resolving small-scales – ctd Models capable of resolving small-scale structures => high effective model resolution => small representativeness errors, closely separated observations are less correlated => continuous mode substantially better than burst mode

LWG, Destin (Fl) 27/1/2009 Conclusion Spatial scales that can be resolved by global NWP models has not decreased a lot over the last 15 years; model resolution cell ~ 250 – 300 km  Burst mode is still a useful scenario, despite the increased model grid resolution  100 km accumulations provide independent information on model degrees of freedom (model resolution cells) The quality of ADM-Aeolus HLOS winds is expected to be better, on average, in continuous mode than in burst mode –About double the energy is transmitted into the atmosphere –Similar instrument noise (for 100 km accumulation) –Reduced representativeness error Continuous mode offers a variety of accumulation scenarios (possibly depending on cloud coverage) –More advanced processing needed to get the maximum out of it

LWG, Destin (Fl) 27/1/2009 Backup slides

LWG, Destin (Fl) 27/1/2009 Effective model resolution Effective model resolution is not the same as model grid mesh size Effective model resolution is related to the spatial scales that can be resolved by the model Model grid mesh size ECMWF 1992: 100 km grid box ECMWF 2008: 25 km grid box ECMWF 2010: 15 km grid box

LWG, Destin (Fl) 27/1/2009 Model resolution cell/representativeness error summary Model resolution ~ number of degrees of freedom of the model Number of degrees of freedom is limited because –Limited computer capacity –Limited observation coverage to measure atmosphere non-linearity  model is a smooth representation of the real atmosphere, not representing small- scale features  area (MRC) mean variables (model of a model)  Small-scale structures are not well treated by the model (noise) and should be avoided in the NWP analysis step. Observations should “feed” these degrees of freedom, i.e. the area mean model variables  Observed scales smaller than the MRC (model resolution cell) are treated as noise, i.e. the representativeness error Representativeness error small scale variability which is sampled by an observation, but which the model is incapable of representing

LWG, Destin (Fl) 27/1/2009 Model resolution (3) Wind component variability –integration of the spectra in the previous image Lorenc curve P (hPa)MRC size (km)Tunresolved wind variability (m 2 s -2 ) resolved wind variability (m 2 s -2 ) Model resolution cell (MRC) spatial scales below the MRC are not well resolved by the model MRC computational grids of global NWP models have increased substantially over the last 15 years, but the horizontal scales that are resolved by these models have increased to a much lesser extent

LWG, Destin (Fl) 27/1/2009 ADM representativeness error (2) Numerical example ADM HLOS error: –ADM burst mode: sample length = 50 km –ADM continuous mode : sample length = 100, 170 km –m 2 /14 = 1.64 (ms -1 ) 2 ~ 1 ms -1 LOS observation error standard deviation –r 2 = 3.3 (ms -1 ) 2 –MRC = 250 km 500 hPa representativeness error (ms -1 )  0 of ADM (ms -1 ) sampled variance (m 2 s -2 ) NWP resolved (% of sampled) 50 km (Granada)1.7 (Granada) km burst (2008)1.66 (0.84 r 2 ) 1/ km continuous1.57 (0.75 r 2 ) 1/ km continuous1.44 (0.63 r 2 ) 1/

LWG, Destin (Fl) 27/1/2009 ADM impact  doubling of the energy in continuous mode does not double the additional impact as compared to burst mode.  Observation correlation reduces impact of individual observations (redundancy of sampling the degrees of freedom)  Highly correlated observations (last row) should be avoided Observation length (km)Observation spacing (km)Number of observationsObs. Impact