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
Jul

2. Introduction
Properties of snow cover (SCA, SWE, SD, melt) are important in investigating hydrological, climatological, and greenhouse gas processes (such as CO2 and CH4)
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
Jul

3. 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.
Jul

4. 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
Jul

5. 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
Jul

6. Example of SWE product
Jul

7. SWE algorithm assesment I
Difference between assimilated SWE estimate and kriging interpolation only fields
Weather stations marked in yellow
Jul

8. 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
Jul

9. SWE sensitivity I Density scatterplot
Ground truth data is INTAS SCCONE SWE path data
Jul

10. SWE sensitivity II
Jul

11. SWE sensitivity III
Jul

12. SWE Animation
Jul

13. Thanks for your attention!
SWE data freely available at
Manuscript has been submitted to a peer reviewed journal
Jul