TH3.TO4.3.ppt

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TH3.TO4.3.ppt

  1. 1. 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]
  2. 2. Introduction <ul><li>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 ) </li></ul><ul><li>In this work a time series of SWE for 30 years has been produced </li></ul><ul><li>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.) </li></ul><ul><li>The results show significant improvement to traditional algorithms which are based on using either spaceborne derived estimates or interpolated values only </li></ul>
  3. 3. Principle of SWE algorithm I <ul><li>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 </li></ul><ul><li>Spaceborne radiometer data is obtained from National Snow and Ice Data Center (NSIDC). Data is either SMMR, SSM/I or AMSR-E. </li></ul><ul><li>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. </li></ul><ul><li>From spaceborne data estimates of the SWE are obtained using inversion of HUT model. </li></ul>
  4. 4. Principle of SWE algorithm II <ul><li>If snow is dry: weighing different data sources applying their respective statistics an assimilated SWE is estimated </li></ul><ul><li>If snow is wet: only kriging interpolated data is used </li></ul><ul><li>To correctly track down new snow a cumulative dry snow mask has been used </li></ul><ul><li>To correctly track down snow melt snow clearance date product has been integrated to SWE system </li></ul><ul><li>The final product is SWE and SWE variance map of whole Northern Hemisphere in EASE Grid </li></ul>
  5. 5. Principle of SWE algorithm III <ul><li>Example of snow clearance date product for year 2008 </li></ul><ul><li>Time series of 30 years available from author </li></ul><ul><li>For details see Takala et al. 2009 </li></ul>
  6. 6. Example of SWE product
  7. 7. SWE algorithm assesment I <ul><li>Difference between assimilated SWE estimate and kriging interpolation only fields </li></ul><ul><li>Weather stations marked in yellow </li></ul>
  8. 8. SWE algorithm assesment II <ul><li>Histogram of difference between assimilated SWE result and kriging interpolated background field </li></ul><ul><li>Typically increases accuracy in areas with sparse SD data </li></ul>
  9. 9. SWE sensitivity I <ul><li>Density scatterplot </li></ul><ul><li>Ground truth data is INTAS SCCONE SWE path data </li></ul>
  10. 10. SWE sensitivity II
  11. 11. SWE sensitivity III
  12. 12. SWE Animation
  13. 13. Thanks for your attention! <ul><li>SWE data freely available at </li></ul><ul><li>www.globsnow.info </li></ul><ul><li>Manuscript has been submitted to a peer reviewed journal </li></ul>

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