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Surface Analysis (I)

Surface Analysis (I). M. Drusch Room 1007, Phone 2759. The Current Surface Analysis System. Part 1:. Sea Surface Temperature (SST) Sea Ice (CI) Snow 2 m Relative Humidity and Temperature Soil Moisture. Part 2:. Outline. Overview Part I. Sea Surface Temperature (SST)

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Surface Analysis (I)

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  1. Surface Analysis (I) M. Drusch Room 1007, Phone 2759

  2. The Current Surface Analysis System

  3. Part 1: • Sea Surface Temperature (SST) • Sea Ice (CI) • Snow • 2 m Relative Humidity and Temperature • Soil Moisture Part 2: Outline

  4. Overview Part I • Sea Surface Temperature (SST) • - NCEP / MMAB SST • - lake SST • 2. Sea Ice • 3. Snow • - observation types • - operational Cressman analysis • - revision based on satellite derived snow extent • - analyses’ validation against independent satellite • and in-situ observations • - impact on the forecast

  5. Sea Surface Temperature (SST) - Analysis • The SST analysis is produced by NCEP / MMAB: • daily data set • two dimensional variational interpolation • buoy and ship observations, satellite retrieved SST • Analysis steps: • Satellite retrieved SST values are averaged within 0.5º grid boxes • Bias calculation and removal for satellite retrieved SST • SST from ships and buoys are separately averaged • The first guess is the prior analysis with one day’s climate adjustment added. • Where fractional sea ice cover exceeds 50%, surface temperature is • calculated from Millero’s formula for the freezing point of water: • with s the salinity in psu. • Empirical autocorrelation function has the form: • with d and l in km, grad T K/km

  6. MMAB SST Data

  7. SST – OSTIA Data Sets

  8. [K] OSTIA-NCEP

  9. Lake SST - Data • 22 non analysed lakes at T319 resolution, Great Lakes and Caspian Sea • are included in the NCEP analysis • Current analysis method was developed using: • 18,000 observations of mean monthly surface air temperature compiled by • Legates and Wilmott (1990) • ERA15 monthly mean SST based on satellite and in-situ observations • (NCEP data set) for the Great Lakes and the Caspian Sea • Lake temperatures for 4 African Lakes from Spigel and Coulter (1996)

  10. SST Lake(t) = T2m(t-1) Lake SST - Methodology

  11. NCEP’s algorithm (Grumbine, 1996): • Remapping from scan points to a polar stereographic grid (25km true at 60) • Conversion to brightness temperatures (Hollinger et al., 1987). • Weather filter following Gloersen and Cavalieri (1986). • Sea ice concentration algorithm (Cavalieri et al., 1991). • Polar gap filling. • Quality check (100 % maximum ice cover). • Final filtering based on Reynolds SST (no ice if SST > 2º C). ECMWF post-processing: • Resampling to model grid using a spatial interpolation • (Cressman Analysis). • Final quality check. • Replace sea ice in the Baltic Sea with the high resolution • product from SMHI. Sea Ice – ‘Analysis’ Based on SSM/I (Special Sensor microwave / Imager) antenna temperatures.

  12. Calculate polarization ratio & • spectral gradient ratio first year sea ice tie point ocean tie point multi year sea ice tie point • Calculate fractional • sea ice coverage Sea Ice - Algorithm Sea ice fraction algorithm (Cavalieri et al., 1991) 2. Northern Hemisphere tie points

  13. Sea Ice - Data (hhtp://polar.wwb.noaa.gov/seaice)

  14. Mean sea ice concentration for the period 5-24 January 2004: a) NCEP / ECMWF (CTRL) and b) SMHI / ECMWF (EXP) Sea Ice – Baltic Sea

  15. sensible heat flux latent heat flux CTRL EXP - CTRL Sea Ice – Baltic Sea

  16. Sea Ice – Baltic Sea GP1: thin lines, GP2: thick lines CTRL: solid, EXP: dashed grey

  17. Definitions - snow extent (binary information 1/0) - fractional snow cover (0 – 100 %) - snow depth SD (m) - snow water equivalent (SWE) Observation types - in situ measurements (snow depth and SWE) - remote sensing microwaves (SWE) - remote sensing visible & infrared (snow extent, aggregation gives fractional snow coverage) [m] Snow Analysis - Definitions

  18. 1. Cressman spatial interpolation: with: - SO snow depth from synop reports, - Sb background field estimated from the short-range forecast of snow water equivalent, - Sb‘ background field at observation location, and - wn weight function, which is a function of horizontal distance r and vertical displacement h (model – obs): w = H(r) v(h) with: rmax = 250 km 1 if 0 < h hmax = 300 m if – hmax < h < 0 v(h) = 0 if h < - hmax Cressman Analysis (I)

  19. 2. Quality check for every grid point • If Tb2m < 8 C only snow depth observations below 140 cm are accepted. • If Tb2m > 8 C only snow depth observations below 70 cm are accepted. • Observations which differ by more than 50 cm from the background are rejected. • When only one observation is available within rmax, the snow depth increments are set to 0. • Snow-depth analysis is limited to 140 cm. • Snow-depth increments are set to 0 when larger than (160-16Tb2m) mm, where Tbsm is in C. • Snow-depth analysis is set to 0 if below 0.04 cm • If there is no snow in the background and in more than half of the observations within a • circle of radius rmax, the snow depth increment is kept to 0. 3. Final analysis using climatological values with: - Scli snow depth from climate data set (Foster and Davy 1988), -  relaxation coefficient of 0.02 Cressman Analysis (II)

  20. MODIS snow extent 17.-24.1.2002 MODIS vis image 27.10.2002 by NSIDC by NSIDC Analyses vs Satellite Data

  21. MODIS 16/02/2002 operational analysis SWE [cm] Analyses vs Satellite Data

  22. Interactive Multisensor Snow and Ice Mapping System: • time sequenced imagery from geostationary satellites, • AVHRR, • SSM/I, • station data, • previous day‘s analysis • Northern Hemisphere product • real time • polar stereographic projection • 1024 × 1024 elements NOAA / NESDIS Snow Extent

  23. Revision of the Global Snow Depth Analysis using NESDIS snow extent 1) Comparison between first guess and NESDIS: - NESDIS is interpolated to actual model resolution - fractional snow cover is calculated - snow free f.g. boxes are updated with 10 cm of snow where the NESDIS product has 100% snow cover 2) Cressman Analysis - NESDIS snow free grid boxes are used as observations with 0 cm snow depth. - Observation height is calculated from high resolution ‚ECMWF‘ orography on the corresponding polarstereographic grid. - Climatology is switched off.

  24. NOAA NESDIS snow extent: no snow 00 UTC 06 UTC 12 UTC SYNOP observations SYNOP observations Cressman analysis / quality check (& climatology) Cressman analysis / quality check (& climatology) first guess updated with previous increments 6 hour forecast (first guess) 12 hour forecast (first guess) NOAA NESDIS snow extent: snow present Technical implementation

  25. SWE [cm] SWE [cm] SWE [cm] SWE [cm] 6-h cycling in 12 hour 4DVar 00 UTC 06 UTC first guess: 12 hour fc first guess: 6 hour fc observations 12 UTC 18 UTC first guess: 12 hour fc & update with previous analysis increments & satellite data first guess: 6 hour fc

  26. SWE [cm] NESDIS Snow Cover [%] SWE [cm] MODIS Snow Depth Analyses for 1/3/2002 operational revised

  27. Validation and intercomparison • Research Experiments • November 2003 to May 2004 (Cycle 28R1) • March, May and December 2002 (Cycle 26R3) • Satellite Data ingestion at 12:00 UTC / CTRL • MODIS snow extent • March, May, and December 2002 • 0.05 deg CMG, re-sampled to 0.5 deg • Canadian Met Service daily observations • March, May, December 2002 • Heidke Skill Score (2 class contingency table: snow / no snow) • National Operational Hydrologic Remote Sensing Center • Analysis (SNODAS) • November 2003 to May 2004 • 1km, re-sampled to T511 reduced Gaussian grid

  28. MODIS Comparison March 2002 May 2002 operational revised

  29. NWS National Operational Hydrologic Remote Sensing Center Snow Depth Analyses for 2/12/2002 Operational Revised NESDIS

  30. (-124° W to -105° W) (-105° W to -80° W) (-80° W to -60° W) SNODAS snow extent

  31. Impact on the forecast

  32. Sellers et al. 1996: SiB2 Marshall et al. 1994: CCM2 (NCAR) Yang et al. 1997 z0 : 2 cm; ρ = 300 kg m-3 SNODAS at T511 Fractional snow cover Fractional snow cover 30/11/03 31/1/04 SWE [mm] SWE [mm] Fractional snow coverage

  33. Problems and Limitations … 230 395 285

  34. Operations T799 (May 2006) Operations scaled to T159 (May 2006) ERA 40 T159 (May 1990-2000) ERA Interim T255 (May 1991) Problems and Limitations …

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