1. Coupling the Canadian Land Surface Scheme to a microwave model to simulate the snow microwave brightness temperature under boreal forest Alain Royer, Alexandre Roy, Benoit Montpetit and Alexandre Langlois Cartel, Université de Sherbrooke With the collaboration of Ghislain Picard, LGGE, Grenoble (DMRT) Samuel Morin, CEN, Météo-France (SSA data) MICROSNOW : Workshop on Microstructure in Snow Microwave Radiative Transfer, University of Reading, UK, 6-8 August 2014

2. Passive microwave (PMW) alone: Simple relationships TB  SWE- a lot of unknowns PMW + in-situ meas. : Globesnow : TB + Hsnow in-situ - need met. stations ; - spatial representativity? Snow model alone : pb of uncertainties in the met. inputs (precips) Snow model + Snow Cover Extent (SCE) (MODIS) Snow model + PMW TB The different approaches for monitoring the snow on the ground

3. SWE retrieval from  Tb Empirical approach for one frequency f : Tb f ≈ e f T snow SWE = F c o (Tb 19 - Tb 37 ) F : fraction of snow cover co: coef. function of grain size IPY transect Voir revue: - Nolin A., J. Glaciol., 2010 - Forster et al., IJRS, 2011 Langlois et al., 2010 1

4. Observed relationships between  Tb and SWE over forested areas (Québec) 1258 SWE measurements over 57 sites (2002-2009, BD HQ) Roy, 2014, PhD

5. Passive microwave (PMW) alone: Simple relationships TB  SWE- a lot of unknowns PMW + in-situ meas. : Globesnow : TB + Hsnow in-situ - need met. stations ; - spatial representativity? Snow model alone : pb of uncertainties in the met. inputs (precips) Snow model + Snow Cover Extent (SCE) (MODIS) Snow model + PMW TB The different approaches for retrieving the snow on the ground

6. Suivi du SWE par télédétection MW Approche par assimilation des obs. in-situ: RMSE ~ 40 mm pour SWE< 150 mm Takala et al., RSE, 2011 2

7. Melting snow, April 2010, Chruchill, Ma. 17-0416-0415-0414-0413-0412-0410-04

8. Passive microwave (PMW) alone: Simple relationships TB  SWE- a lot of unknowns PMW + in-situ meas. : Globesnow : TB + Hsnow in-situ - need met. stations ; - spatial representativity? Snow model alone : pb of uncertainties in the met. inputs (precips) Snow model + Snow Cover Extent (SCE) (MODIS) Snow model + PMW TB The different approaches for retrieving the snow on the ground

9. 1258 SWE measurements over 57 sites (2002-2009, BD HQ) Snow model driven by meteorological reanalysis (NARR) Modèle CLASS Roy, 2014, PhD 3

10. Crocus (ISBA) + Réanalyse ERA-I : ERA-Interim (ECMWF) PGF: Princeton University Global Forcing data set (PGF) (Sheffield et al. 2006) = corrected NCEP-NCAR Brun et al., 2012, J. Hydrometeor. Comparison between the observed snowpack density on 10 March (circles), averaged from 1980 to 1993, and the corresponding simulation driven with ERA- Interim (2D field) 3

11. Passive microwave (PMW) alone: Simple relationships TB  SWE- a lot of unknowns PMW + in-situ meas. : Globesnow : TB + Hsnow in-situ - need met. stations ; - spatial representativity? Snow model alone : pb of uncertainties in the met. inputs (precips) Snow model + Snow Cover Extent (SCE) (MODIS) Snow model + PMW TB The different approaches for retrieving the snow on the ground

12. Hydrological applications HYDROTEL model (CEHQ) 5 state variables: –SWE –Heat deficit –Albedo –Snow height –Liquid water content Threshold on SWE Snow cover MODIS+ AMSR-E

13. Streamflow simulations With RS Obs. Roy et al., J. Hydrol., 2010 Bergeron et al., Hydrol. Proc, 2014 (Mean Nash increase of 0.13 and RMSE decrease by 22%) Without RS RS = SCE assimilation

14. Passive microwave (PMW) alone: Simple relationships TB  SWE- a lot of unknowns PMW + in-situ meas. : Globesnow : TB + Hsnow in-situ - need met. stations ; - spatial representativity? Snow model alone : pb of uncertainties in the met. inputs (precips) Snow model + Snow Cover Extent (SCE) (MODIS) Snow model + PMW TB The different approaches for retrieving the snow on the ground

15. 15 Coupling of a physical snow model with a radiative transfert model 2. DMRT-ML (Picard et al., 2013) Physical model CLASS 1. CLASS-SSA 3. Vegetation Objective T B simulated = T B AMSR-E CLASS / DMRT-ML: Snow grains AMSR-E T B 4. Atmosphere Reanalysis meteorological data SSA = Surface Specific Area ~ S / M ~ 1 / R optique Roy, 2014, PhD CLASS: Canadian Land Atmosphere Surface Scheme

16. 2. SSA in DMRT-ML (Picard et al., 2013) 1. CLASS-SSA (SSA model) 3. Vegetation (τ-ω) Coupling CLASS / DMRT-ML: snow grain CLASS-SSA model : Offline multi-layer model driven by CLASS outputs Layers added when snowfall Snow layers mass, thickness, and density correction as a function of CLASS outputs (weighting functions) SSA evolution as a function of snow aging and temperature gradient (Taillandier et al., 2007. J.Geophys. Res.) Wet snow metamorphism (Brun, 1989. Ann. Glacio.) Roy et al., 2013, TC

17. 17 2. SSA in DMRT-ML (Picard et al., 2013) 1. CLASS-SSA (SSA model) 3. Vegetation (τ-ω) Coupling CLASS / DMRT-ML: snow grain Roy et al., 2013, TC

18. 2. SSA in DMRT-ML (Picard et al., 2013) 1. CLASS-SSA (SSA model) 3. Vegetation (τ-ω) Coupling CLASS / DMRT-ML: snow grain Roy et al., 2013, TC

19. 2. SSA in DMRT-ML (Picard et al., 2013) 1. CLASS-SSA (SSA model) 3. Vegetation (τ-ω) Coupling CLASS / DMRT-ML: snow grain Dense Media Radiative Transfert Theory – Multi layers model Picard et al., 2013

20. 20 ϕ = 3.3 2. SSA in DMRT-ML (Picard et al., 2013) 1. CLASS-SSA (SSA model) 3. Vegetation (τ-ω) Coupling CLASS / DMRT-ML: snow grain Roy et al., 2013, IEEE TGRS For in-situ SSA measurements  will be adjusted for CLASS-SSA

21. 2. SSA in DMRT-ML (Picard et al., 2013) 1. CLASS-SSA (SSA model) 3. Vegetation (τ-ω) Coupling CLASS / DMRT-ML: snow grain Dense Media Radiative Transfert Theory – Multi layers model Picard et al., 2013 Soil parameterization W83: Wang et al., 1983

22. Summer forest transmissivity model 22 ωH 0.050.4 2. SSA in DMRT-ML (Picard et al., 2013) 1. CLASS-SSA (SSA model) 3. Vegetation (τ-ω) Coupling CLASS / DMRT-ML: snow grain Roy et al., 2014, IEEE TGRS Letter

23. Optimisation approach Optimisation of NARR precipitation with in situ SWE measurements Optimisation of soil moisture with 10 GHz (every day) Identification of ice crust with 10 GHz polarisation ratio* Optimisation of  at 37 GHz at the site with low vegetation Inversion of winter γ-ω with 22 sites at 37 GHz Optimisation of ice crust density (700 to 917 kg m -3 ) with 10 and 19 GHz (H-pol and V-pol) LAI winter Location of the 22 sites with in situ SWE measurements (background = LAI winter ). Black : low veg. site; Red: Time series Location of the 22 sites with in situ SWE measurements (background = LAI winter ). Black : low veg. site; Red: Time series Location of the 22 sites with in situ SWE measurements (background = LAI winter ). Black : low veg. site; Red: Time series Location of the 22 sites with in situ SWE measurements (background = LAI winter ). (Montpetit et al., 2013, IEEE TGRS) * > 0.06

24. RESULTS: Snow model CLASS+DMRT-ML =>Tb Tb simulated Tb measured 37 GHz 19 GHz 10 GHz Site 1: LAI winter = 0.06 Roy, 2014, PhD Air temperature

25. RESULTS: Snow model CLASS+DMRT-ML =>Tb Zoom 2008-2009 Tb simulated Tb measured Site 1: LAI winter = 0.06 Roy, 2014, PhD

26. Site 3 : LAI winter = 0.3 Tb simulated Tb measured Zoom 2004-2005 Roy, 2014, PhD RESULTS: Snow model CLASS+DMRT-ML =>Tb

27. Over the 7 years: Roy, 2014, PhD RESULTS: Snow model CLASS+DMRT-ML =>Tb Optimisations Results:   ω winter = 0.13 (ω summer = 0.05)  γ winter > γ summer 37V37H19V19H10V10H Winter RMSE/bias without ice crust 7.1/0.77.5/1.54.1/-0.04.8/1.32.5/-0.73.5/-0.2 Winter RMSE/bias with ice crust 8.1/1.08.4/1.55.0/0.86.5/2.43.2/-0.64.8/0.1 Summer RMSE/bias 2.6/1.02.8/-0.72.4/0.93.0/-0.82.5/0.72.6/-0.8 Mean RMSE/bias (K) for the 22 sites RMSE and bias are relatively good, but the precision is generally higher than the sensitivity to SWE mostly in regions with strong vegetation density.

28. Sensibility analysis 2 3+4+5 7 6 9 8 9 8 37.5% 50.5% (Roy, PhD, 2014) TB variations (δTB) for a variation of ± 0.14 m of the snow depth (CLASS standard error) for different forest density at 37 GHz  Tb≈7 K

29. Conclusion Development of a method for the coupling of CLASS and DMRT-ML for winter boreal forest TB simulations Important impact of ice crust at H-pol Differences in the γ-ω parameterization in winter and summer DMRT-ML, with a  factor on snow grain size, is suitable for modeling satellite-based snow emission in boreal forest Low sensitivity to SWE in dense boreal forest The developed method could be used for SWE retrieval in an assimilation scheme.

30. Correction to apply -Atmospheric effect -Forest effect Snowpack: -Layering -Snow grain size profile -Stickiness (agregates) -Density profile -Temperature profile Soil -Soil temperature under the snow -Soil moisture and Freezing (permittivity) -Soil roughness Remerciements: CRSNG Canada, FCI, Ouranos-HQ, PICS-CNRS France, MRIFCE- France - Québec Correction to apply -OK -To be improved Snowpack: -1 layer model vs X layers? -SSA fitted -Not resolved -OK Soil -OK -Difficult -Fitted Conclusion

31. -Below/above 55°N, under/over estimation of snow model -Large local variability -Root mean square error (Model vs Measurements): 63 mm (30 %) Snow model (SNOWPACK) driven by reanalysis NARR vs in-situ measurements IPY data 3