Presentation on theme: "DA 22.-31.3. 2006 Surface Analysis (I) M. Drusch Room TT 063, Phone 2759."— Presentation transcript:
DA Surface Analysis (I) M. Drusch Room TT 063, Phone 2759
DA Outline 1.Sea Surface Temperature (SST) 2.Sea Ice 3.Snow 1.2 m Relative Humidity and Temperature 2.Soil Moisture Part 1: Part 2:
DA Overview 1.Sea Surface Temperature (SST) - Reynolds 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
DA SST (1) 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
DA SST (2) Future changes to the Integrated Forecast System: NCEP / MMAB high resolution analysis (1/12 degree) GODAE High Resolution SST data sets
DA Lake SST (1) 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)
DA Lake SST (2) SST Lake (t) = T 2m (t-1)
DA Sea Ice (1) 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.
DA Sea Ice (2) 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
DA Sea Ice (3) (hhtp://polar.wwb.noaa.gov/seaice)
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
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.
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
DA MODIS Fractional Snow Cover (I) sun-synchronous, circular, near polar orbit snow detection based on: Bands 1 ( nm) and 2 ( nm) for NDVI and Bands 4 ( nm) and 6 ( nm) for NDSI snow present if NDSI > 0.4 and reflectance Band 2 > 11% forested areas: canopy reflectance model is used to create NDVI – NDSI polygon mapped to 500 m resolution with 40 % minimum snow cover
DA MODIS Fractional Snow Cover (II) Daily L3 Global 0.05 Deg CMG Products: Frac. Snow Cover := snow covered pixels / visible land pixels Confidence Index := visible land pixels / total land cover Cloud mask Regridding to 0.5 Deg: - SC = FSC × CI for pixels labeled ‚snow covered‘ - SC = FSC + (1. – CI) for pixels labeled,snow free‘ - SC = 1/N SC
DA Motivation for a Revised Analysis (I) MODIS snow extent by NSIDC MODIS vis image
DA Motivation (II) MODIS 16/02/2002 SWE [cm] operational analysis
DA Motivation for a Revised Analysis (III) March 2002May 2002 December 2002
DA General Comments on the Revision NOAA NESDIS satellite data contain no information on snow depth. The parameters for the spatial interpolation and quality checks were developed for T106, they are not ideal for higher resolutions. Satellite data outnumber conventional observations at the snow edges. It is difficult to obtain independent observations and to compare observations with the analyses. Satellite derived snow extent has been available for ~ 20 years. It has not been integrated in any operational analysis ( & there are no papers on combining satellite data, ground based observations and modelled snow depth).
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.
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)
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
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)
DA Snow Depth Analyses for 1/3/2002 SWE [cm] NESDIS Snow Cover [%] MODIS operational revised
DA MODIS Comparison March 2002May 2002 operational revised
DA NOHRSC (I): SNODAS data flow Metar station meteorological obs Rapid Update Cycle (RUC2) NWP analyses / forecasts NCEP stage IV radar precipitation analysis NESDIS GOES solar radiation NOAA GOES AVHRR cloud cover albedo physical downscaling automatic quality control snow model static geophysical data NRCS SNOTEL snow water equivalent CADWR & BC Hydro snow water equivalent NWS / Cooperative Observer snow water equivalent, snow depth automatic quality control NOHRSC Airborne Gamma SWE NOHRSC GOES / AVHRR snow cover [after Carrol et al., 2001]
DA NOHRSC (II): Technical implementation and analysis scheme 1.24 hour model run at 1 hour time steps 2.Snow observations are sampled during the last 18 hours. 3.Satellite data is used to identify snow boundaries. 4.Differences between modelled values and observations are computed and spatially interpolated. 5.Difference fields are analyzed MANUALLY to identify regions to update. 6.Difference fields are divided by 6 to provide hourly increments for the final 6 hourly model run. 7.The model is re-run for 6 hours, at the the end of each time step estimated state variables (snow depth, SWE, and snow pack temperature) are nudged. [after Carrol et al. 2001]
DA Snow Depth Analyses for 2/12/2002 NWS National Operational Hydrologic Remote Sensing Center OperationalRevised NESDIS
DA SNODAS intercomparison SNODAS 30/11/04, 6 UTCCTRL 30/11/04, 6 UTC EXP 30/11/04, 12 UTCEXP 30/11/04, 06 UTC SWE [m]
DA SNODAS snow extent (-124° W to -105° W) (-80° W to -60° W)(-105° W to -80° W)
DA SNODAS mean SWE
DA Impact on the forecast
DA Fractional snow coverage SWE [mm] Fractional snow cover SWE [mm] Fractional snow cover SNODAS at T511 30/11/0331/1/04
DA Natural hazard / severe storm application ‘A surprise storm buried sections of Colorado with as much as 61 centimeters of snow on April 10, According to the Associated Press, the spring storm cancelled flights and closed a major interstate highway, stranding travellers. The MODIS on NASA’s Terra satellite captured this view of the fresh snow on April 12, … Denver, the capital of Colorado, forms a dark circle in the snow near the base of the mountains. The city reported 30 cm of snow.’
DA Natural hazard / severe storm application
DA Summary Climatology can be omitted. The revised analysis using the satellite product results in an improved snow extent compared to MODIS. Higher skill scores for March, December and the first half of May compared to CMS observations (lower skill for the end of May). [not shown] Improved snow extent compared to SNODAS (US domain). In general, ECMWF analyses systematically ‘underestimate’ SWE in the western part of the US. The impact on the forecast is neutral with respect to 1000 and 850 hPa temperatures. The satellite product is not free of errors and can deteriorate the analyses on regional scales.