DATA ASSIMILATION OF VIS / NIR REFLECTANCE INTO THE DETAILED SNOWPACK MODEL SURFEX/ISBA- CROCUS

Transcription

1 DATA ASSIMILATION OF VIS / NIR REFLECTANCE INTO THE DETAILED SNOWPACK MODEL SURFEX/ISBA- CROCUS Luc CHARROIS 1,2 PhD 2013 / 2016 E. Cosme 2, M. Dumont 1, M. Lafaysse 1, S. Morin 1, Q. Libois 2, G. Picard 2 and L. Arnaud 2 1 Météo-France / CNRS, CNRM-GAME URA 1357,. CEN, St. Martin d hères, France 2 Université Joseph Fourier Grenoble CNRS, LGGE UMR 5183, Grenoble, France World Weather Open Science Conference Montreal

2 Introduction IMPROVING SNOWPACK MODEL OUTPUT Snow management Avalanche risk forecast [Spandre et al., 2014] Hydrology Climatology Snow cover area [ [Lafaysse et al., 2014]

3 Introduction COMPARISONS AT COL DE PORTE (1326M)

4 Introduction GOOD AGREEMENT WITH IN-SITU OBSERVATIONS

5 Introduction BUT, ERROR ACCUMULATION

6 Introduction BUT, ERROR ACCUMULATION

7 Introduction NEED DATA ASSIMILATION Why? Good agreement between model & observations But, errors come mainly from meteorological Input [Essery et al., 2013] no corrections using observations obs obs obs obs obs obs analysis model model model analysis analysis [Bouttier et al., 1999]

8 Introduction OUTLINE 1. Snowpack Model: Crocus 2. Satellite Data: MODIS 3. Ensemble methods 4. Data Assimilation: Particle Filter 5. Conclusion

9 Tools & Data SNOWPACK MODEL: SURFEX/ISBA - Crocus Details 1D Vertical Multi layer Up to 50 layers Dynamic vertical discretization New optical scheme (TARTES) to calculate spectral reflectance [Libois et al., 2013] [Brun et al., 1989,1992; Vionnet et al., 2007

10 Tools & Data WHICH OBSERVATIONS FOR ASSIMILATION? Snow features Strong spatial variability Strong temporal variability Punctual measurements not enough to reflect these variabilities Satellite data Surface albedo information Col du Lac Blanc, 2720m, Grandes Rousses mountain range, 02/07/2014 Good temporal resolution

11 Tools & Data MODIS: MODerate Imaging Spectradiometer MODIS features Temporal resolution: 1 overpass / day 36 spectral bands with different spatial resolutions Available freely on Internet MODIS snow reflectance accuracy has been evaluated by several studies [Dumont et al., 2012; Brun et al., 2014] [

12 Ensemble Methods ENSEMBLE METHOD TO MODEL ERROR ESTIMATION Ensemble Method: interpretation of statistical nature of the errors in ensemble estimations - Need to know uncertainty sources Model runs from Meteorological forcing perturbed Model Model error Ensemble representing error model distribution

13 Ensemble Methods 1. ENSEMBLE OF METEOROLOGICAL FORCING 1 st step SW LW Tair Qair Wind Snow fall Rel. Hum. Ensemble simulations impact of meteo variables Snowpack model sensitivity

14 Ensemble Methods 1. ENSEMBLE OF METEOROLOGICAL FORCING 2 nd step SW LW Tair Qair Wind Snow fall Rel. Hum. Ensemble simulations impact of meteo variables Snowpack model sensitivity New Ensemble of meteorological forcing considered

15 Ensemble Methods 2. ENSEMBLE SIMULATIONS SW LW Tair Qair Wind Snow fall Rel. Hum. Ensemble simulations impact of meteo variables Snowpack model sensitivity New Ensemble of meteorological forcing considered Assimilatio n scheme Ensemble simulations Relationship between variables Errors distribution

16 Ensemble Methods 1. ENSEMBLE OF METEOROLOGICAL FORCING 1 st step Perturbations Method Tair Ensemble First Order Auto Regressive model 500 Members Original forcing X t c φx t 1 ε t ε t N 0,σ 2 Uncertainty of each forcing Statistics on 18 years between obs & simulated data σ 2 Additive or multiplicative method to generate the ensemble of meteorological forcing. Near Surface Air Temperature (K)

17 Ensemble Methods 1. ENSEMBLE OF METEOROLOGICAL FORCING 1 st step SW LW Tair Qair Wind Snow fall Rel. Hum. Ensemble simulations impact of meteo variables Snowpack model sensitivity

18 Ensemble Methods 1. ENSEMBLE OF METEOROLOGICAL FORCING 1.1. SW ensemble SW Ensemble simulations impact of meteo variables Snowpack model sensitivity

19 Ensemble Methods 1. ENSEMBLE OF METEOROLOGICAL FORCING 1.1. SW ensemble, 10 days of perturbations SW Ensemble Col du Lautaret, French Alps 2010/2011 Altitude: 2100m One point simulation Slope: members MODIS Band 1: 640nm

20 Ensemble Methods 1. ENSEMBLE OF METEOROLOGICAL FORCING 1.2. SW ensemble scatter plots, 6 & 7 days of perturbations Spectral albedo Band 1: 640nm Nov : Spectral albedo Band 1: 640nm Nov : SW Ensemble Spectral albedo Band 1: 640nm Spectral albedo Band 1: 640nm Bi-modality Threshold effect Non Gaussian distribution errors Non linearity between variables

21 Ensemble Methods 1. ENSEMBLE OF METEOROLOGICAL FORCING 2 nd step SW LW Tair Wind Snow fall New Ensemble of meteorological forcing considered

22 Ensemble Methods 2. ENSEMBLE SIMULATIONS SW LW Tair Wind Snow fall New Ensemble of meteorological forcing considered Assimilatio n scheme Ensemble simulations Relationship between variables Errors distribution

23 Ensemble Methods 2. ENSEMBLE SIMULATIONS Ensemble Tair Wind SW LW Snowfall Col du Lautaret, French Alps 2010/2011 Altitude: 2100m one point simulation Slope: members MODIS Band 1: 640nm

24 Ensemble Methods 2. ENSEMBLE SIMULATIONS Non - linearity Non - Gaussian errors Assimilation tests with Particle Filter Ensemble

25 Particle Filter PARTICLE FILTER WITH RESAMPLING (SIR) Sequential simulation based on Monte-Carlo method members Size of bullets = weight of each particle Particle Filter Represent the model pdf by a number of randoms draws (ensemble members) Each member (model state) is propagated forward in time [Vanleeuwen et al., 2009]

26 Particle Filter PARTICLE FILTER WITH RESAMPLING (SIR) Size of bullets = weight of each particle members Resampling SIR Low weights are abandoned Copies of particles with high weight Nb of particles N conserved [Vanleeuwen et al., 2009]

27 Particle Filter PARTICLE FILTER WITH RESAMPLING (SIR) Simulated obs.

28 Conclusion CONCLUSION & PERSPECTIVES Error accumulation into snowpack simulations No corrections using observations! Ensemble methods Impact of meteorological forcing variables Tair, SW, LW, Wind, Snowfall, Impurity Complex relationship with spectral albedo Snowpack model sensitivity Non linearity Non Gaussian error Diagnostic to check our assumptions Quality control of MODIS data Impact tests of data assimilation One simulation point Mountain range

29 Conclusion Thank you for your attention

30 Particle Filter PARTICLE FILTER WITH RESAMPLING (SIR) MODIS data