Cloud Cover Reanalysis Application

Transcription

1 The 5th ISDA, Reading, UoR, July 2016 Cloud Cover Reanalysis Application Tomas Landelius and Jelena Bojarova Swedish Meteorological and Hydrological Institute

2 European cloud cover reanalysis using best available data at any given time, Horizontal resolution: 5.5 km MESAN EURO4M Time resolution: Hourly for the period Observations: CMSAF polar orbit AVHRR cloud mask CMSAF geostationary SEVIRI cloud mask CMSAF new polar orbit & geostationary CM SAF cloud cover probability product for MFG ( ) and MSG ( ) (in production MeteoSwiss) First guess (alternatives): EURO4M 22 km HIRLAM 3DVar, HIRLAM EURO4M 22 km interpolated to 5.5 km using LSM

3 Processing chain super-observations Polar data GAC orbit data Composite Geostationary data - CLAAS - SARAH OI 5 km EURO4M NWP Fourier transform EURO4M NWP first-guess analysis

4 Data resolution North Polar -orbiting South Geostationary Pilot study : OI scheme on 22km resolution

5 Data coverage around :10 UTC NOAA16 08:05 NOAA18 08:05 NOAA16 09:44 Polar-orbiting GAC T T Tk T+ T Polar GAC Geostationary CLASS Tk 1, Tk, Tk+1 Geo CLAAS 09:45 10:00 10:15

6 Super observations Use the quality and scan geometry information available in CMSAF products to calculate weights: w=f (quality flags, sat angles, time delta) Calculate cloud fractional cover as a weighted fraction of cloudy pixels within a HIRLAM grid box: wi CM i CFC= wi

7 Super observations, continued... Wic( timeliness, view angle, cumulative quality flag ) Polar orbiting Geostationary 3km 1.3km yso yso yso yso y x so b xb yso xb Ci i WicCM yso iwiccmi km CFC x iwic 4 HIRLAM EURO4M Cloud Fraction is used as gap-filler in grids where no CM observations available

8 Optimal Interpolation x a =x b + K ( y H ( x b )) T T 1 K =BH ( HBH +R) B matrix Diagonal in Fourier space, i.e. homogeneous HIRLAM NMC statistics (fc differences) as the first-guess for B and LB R matrix (spatially correlated errors) Diagonal in Fourier space, i.e. homogeneous The first-guess : R = 0.1* B and LR = 0.5 LB Re-estimate statistics based on Desroziers diagnostics from the pilot run H operator : identity matrix extract Cloud Fraction from HIRLAM EURO4M forecasts

9 Estimation of statistics raw data param.fit Dez. Iter. B Desroziers diagnostics dyb = y H(xb); dya = y H(xa) R Tends to underestimate R and introduce more energy on larger scales D = E(dybdybT) = (HBHT + R HεbεyT εyεbtht) R = E(d ad bt) = * y y Ro(HTBoH + Ro) 1(HTBH+R HεbεyT εyεbtht) In the presented results the misfit due to crosscorrelations is not addressed

10 Optimal Interpolation in Fourier Space D*=B+R* Kν = Bν (Bν + Rν) 1 Xaν = Xbν + Kν (yν Xbν) Total 1D spectra : D, B, R Mean 1D filtering and misfit

11 UTC Forecast (cloud fraction) super -obs Optimal Interpolation HIRLAM EURO4M _ analysis

12 Comparison with SYNOP obs for Analysis has lower std than both the first-guess and super-observations. On average the analysis has too much clouds against SYNOP

13 Limitations of the optimal interpolation: Total cloud cover (03 UTC 2009) (one year average) Obs-minus-Forecast Obs-minus-Analysis Mean innovation error : y_so-x_b Mean analysis error : y_so-x_an Note clear response to orography in the error statistics due to homogeneity assumptions

14 Scale-dependent decomposition Large scales Total cloud cover 03 UTC 2009 (one year average; Background forecast) 1936 km 330 km Medium scales 66 km Mean background field (1 year average) noise 5 30 Decomposition on three overlapping 150 spectral bands (scales) 66 Small scales

15 Scale-dependent decomposition Spectral Space Homogen. large Gridpoint Space within scales medium small (large+medium+small cross-scales )/homog. between scales Sample estimate of background error Decomposition of background error variability on scales LargeMedium MediumSmall

16 Data coverage Innovation Super-observation Background

17 Analysis (within scale contribution only) Scale-dependent analysis increment Large dxassd=σ1 j J (Bj)1/2 Σ1 k JΨk [B1/2/(B+R)1/2]dy/D1/2 Medium + + Large Medium +2x Medium Large Small Medium Small +2x Small Medium

18 Impact of scale dependent analysis

19 To conclude... What next? Super observations and OI analysis on 5.5 grid One more overlapping band to model convective scale phenomena? Lessons learned... Space/Scale-dependent decomposition can efficiently be used to model local in space phenomena Space/Scale-dependent decomposition allows to model cross-scale dependencies and to relax homogeneity assumption staying in spectral space. The decomposition on the overlapping bands will induce and impact cross-scale correlations Space/scale localization seems to be a promising technique for flowdependent data assimilation. However more research is needed to understand the impact of space-scale dependent localization on the spectra and the error propagation properties.