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TH3.TO4.3.ppt
 

TH3.TO4.3.ppt

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    TH3.TO4.3.ppt TH3.TO4.3.ppt Presentation Transcript

    • IMPLEMENTING HEMISPHERICAL SNOW WATER EQUIVALENT PRODUCT ASSIMILATING WEATHER STATION OBSERVATIONS AND SPACEBORNE MICROWAVE DATA M. Takala, K. Luojus, J. Pulliainen, C. Derksen, J. Lemmetyinen, J-P. Kärnä, J. Koskinen, B. Bojkov [email_address]
    • Introduction
      • Properties of snow cover (SCA, SWE, SD, melt) are important in investigating hydrological, climatological, and greenhouse gas processes (such as CO 2 and CH 4 )
      • In this work a time series of SWE for 30 years has been produced
      • The algorithm used is based on data assimilation (Pulliainen 2006) and integrates data of snow clearance (Takala et al. 2009) and auxiliary data (forest coverage etc.)
      • The results show significant improvement to traditional algorithms which are based on using either spaceborne derived estimates or interpolated values only
    • Principle of SWE algorithm I
      • Weather station snow depth data is obtained from European Centre for Medium-range Weather Forecasts (ECMWF) and kriging interpolated over the area in question -> SWE estimate & SWE Var estimate
      • Spaceborne radiometer data is obtained from National Snow and Ice Data Center (NSIDC). Data is either SMMR, SSM/I or AMSR-E.
      • Snow grain size (and variance) is estimated using SD data and HUT Snow model for SD station locations. Values are interpolated over area under investigation.
      • From spaceborne data estimates of the SWE are obtained using inversion of HUT model.
    • Principle of SWE algorithm II
      • If snow is dry: weighing different data sources applying their respective statistics an assimilated SWE is estimated
      • If snow is wet: only kriging interpolated data is used
      • To correctly track down new snow a cumulative dry snow mask has been used
      • To correctly track down snow melt snow clearance date product has been integrated to SWE system
      • The final product is SWE and SWE variance map of whole Northern Hemisphere in EASE Grid
    • Principle of SWE algorithm III
      • Example of snow clearance date product for year 2008
      • Time series of 30 years available from author
      • For details see Takala et al. 2009
    • Example of SWE product
    • SWE algorithm assesment I
      • Difference between assimilated SWE estimate and kriging interpolation only fields
      • Weather stations marked in yellow
    • SWE algorithm assesment II
      • Histogram of difference between assimilated SWE result and kriging interpolated background field
      • Typically increases accuracy in areas with sparse SD data
    • SWE sensitivity I
      • Density scatterplot
      • Ground truth data is INTAS SCCONE SWE path data
    • SWE sensitivity II
    • SWE sensitivity III
    • SWE Animation
    • Thanks for your attention!
      • SWE data freely available at
      • www.globsnow.info
      • Manuscript has been submitted to a peer reviewed journal