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Training Course DA 28.4.-29.4. 2008 Surface Analysis (I) M. Drusch Room 1007, Phone 2759.

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Presentation on theme: "Training Course DA 28.4.-29.4. 2008 Surface Analysis (I) M. Drusch Room 1007, Phone 2759."— Presentation transcript:

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

2 Training Course DA The Current Surface Analysis System

3 Training Course DA Outline 1.Sea Surface Temperature (SST) 2.Sea Ice (CI) 3.Snow 1.2 m Relative Humidity and Temperature 2.Soil Moisture Part 1: Part 2:

4 Training Course DA Overview Part I 1.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 Training Course DA 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: 1)Satellite retrieved SST values are averaged within 0.5º grid boxes 2)Bias calculation and removal for satellite retrieved SST 3)SST from ships and buoys are separately averaged 4)The first guess is the prior analysis with one day’s climate adjustment added. 5)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. 6)Empirical autocorrelation function has the form: with d and l in km, grad T K/km

6 Training Course DA MMAB SST Data

7 Training Course DA SST – OSTIA Data Sets

8 Training Course DA OSTIA-NCEP [K]

9 Training Course DA 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 Training Course DA Lake SST - Methodology SST Lake (t) = T 2m (t-1)

11 Training Course DA Sea Ice – ‘Analysis’ Based on SSM/I (Special Sensor microwave / Imager) antenna temperatures. 1.Remapping from scan points to a polar stereographic grid (25km true at 60) 2.Conversion to brightness temperatures (Hollinger et al., 1987). 3.Weather filter following Gloersen and Cavalieri (1986). 4.Sea ice concentration algorithm (Cavalieri et al., 1991). 5.Polar gap filling. 6.Quality check (100 % maximum ice cover). 7.Final filtering based on Reynolds SST (no ice if SST > 2º C). NCEP’s algorithm (Grumbine, 1996): ECMWF post-processing: 1.Resampling to model grid using a spatial interpolation (Cressman Analysis). 2.Final quality check. 3.Replace sea ice in the Baltic Sea with the high resolution product from SMHI.

12 Training Course DA Sea Ice - Algorithm Sea ice fraction algorithm (Cavalieri et al., 1991) ocean tie point multi year sea ice tie point first year sea ice tie point SSM/I channel open water first year sea ice multi- year sea ice 19 H V V Northern Hemisphere tie points 1.Calculate polarization ratio & spectral gradient ratio 3.Calculate fractional sea ice coverage

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

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

15 Training Course DA Sea Ice – Baltic Sea sensible heat fluxlatent heat flux CTRL EXP - CTRL

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

17 Training Course DA Snow Analysis - Definitions 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]

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

19 Training Course DA Cressman Analysis (II) 2. Quality check for every grid point 3. Final analysis using climatological values with:- S cli snow depth from climate data set (Foster and Davy 1988), -  relaxation coefficient of If T b 2m < 8 C only snow depth observations below 140 cm are accepted. - If T b 2m > 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 r max, 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 (160-16T b 2m ) mm, where T b sm 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 r max, the snow depth increment is kept to 0.

20 Training Course DA Analyses vs Satellite Data MODIS snow extent by NSIDC MODIS vis image

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

22 Training Course DA NOAA / NESDIS Snow Extent 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 elements

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

25 Training Course DA h cycling in 12 hour 4DVar SWE [cm] 00 UTC06 UTC 18 UTC12 UTC first guess: 12 hour fc first guess: 6 hour fc observations first guess: 12 hour fc & update with previous analysis increments & satellite data first guess: 6 hour fc

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

27 Training Course DA 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 National Operational Hydrologic Remote Sensing Center Analysis (SNODAS) November 2003 to May km, re-sampled to T511 reduced Gaussian grid MODIS snow extent March, May, and December 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)

28 Training Course DA MODIS Comparison March 2002May 2002 operational revised

29 Training Course DA Snow Depth Analyses for 2/12/2002 NWS National Operational Hydrologic Remote Sensing Center OperationalRevised NESDIS

30 Training Course DA SNODAS snow extent (-124° W to -105° W) (-80° W to -60° W)(-105° W to -80° W)

31 Training Course DA Impact on the forecast

32 Training Course DA Fractional snow coverage SWE [mm] Fractional snow cover SWE [mm] Fractional snow cover SNODAS at T511 30/11/0331/1/04 Sellers et al. 1996: SiB2 Marshall et al. 1994: CCM2 (NCAR) Yang et al z 0 : 2 cm; ρ = 300 kg m -3

33 Training Course DA Problems and Limitations …

34 Training Course DA Problems and Limitations … ERA Interim T255 (May 1991)ERA 40 T159 (May ) Operations scaled to T159 (May 2006)Operations T799 (May 2006)


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