1. 1 Snow cover mapping using multi-temporal Meteosat-8 data Martijn de Ruyter de Wildt Jean-Marie Bettems* Gabriela Seiz** Armin Grün Institute of Geodesy and Photogrammetry, ETH Zürich, Switzerland * MeteoSwiss, Zürich, Switzerland ** now at: ESA-ESRIN, Directorate of Earth Observation, Rome, Italy A fellowship, in cooperation with

2. 2 Introduction Objective: to obtain accurate snow cover maps for the numerical weather prediction model of MeteoSwiss (aLpine Model, aLMo). Main problem: discrimination between ice clouds and snow. Use high temporal frequency of MSG (15 minutes) in addition to spectral capabilities (12 channels) to improve separation of clouds and snow in real-time, fully automatic usable over alpine terrain

3. 3 Data Areas of interest: model domains of aLMo (western and central Europe). Resolution: 7 and 2.2 km. Training and validation periods: 8 - 10 March, 2004 23 - 24 February, 2005 (only day-time images) 8+1 spectral bands used: 1 VIS 0.635  m 2 VIS 0.81  m 3 NIR 1.64  m 4 IR 3.92  m 7 IR 8.70  m 9 IR10.80  m 10 IR12.00  m 11 IR13.40  m 12 HR-VIS 0.70  m

4. 4 r 1.6 BT 3.9 - BT 10.8 BT 10.8 Spectral image classification: “traditional” features (10-3-2004, 12:12 UTC) r 0.81 snow ice cloud snow ice cloud

5. 5 BT 3.9 - BT 10.8 BT 3.9 - BT 13.4 Improved spectral classification II BT 3.9 - BT 10.8 : snow is as dark as or darker than ice clouds; BT 3.9 - BT 13.4 : snow is as dark as or brighter than ice clouds; => the following feature should enhance the contrast between snow and ice clouds: (BT 3.9 - BT 10.8 ) / (BT 3.9 - BT 13.4 ) snow ice cloud snow ice cloud snow ice cloud

6. 6 Spectral classification classification result: white: snow dark gray: clouds light gray: snow-free land black: sea UTC:200403101212 clouds snow

7. 7 Temporal classification Temporal test snow

8. 8 Temporal classification Temporal variability can be quantified for each channel m with: where more ice more water

9. 9 Temporal classification The temporal standard deviations of the 9 used channels form a 9-dimensional parameter space, where some of the parameters are correlated with each-other. Reduce data redundancy: principal components analysis (PCI); when applied to the difference between two images, the change information is concentrated into fewer dimensions (Gong, 1993). Here: - standardised PCI (applicable to data with variables at different scales) - applied to the 9 temporal standard deviations Normalised eigenvalues of the 9 new components, averaged over all training data: 10.587 20.288 30.079 40.024 50.013 60.006 70.002 80.001 90.000 Change information noise

10. 10 First principal component of the temporal standard deviation (10-3-2004, 12:12 UTC): Second and third components are also useful for detecting clouds. more ice more water clouds snow

11. 11 white: snow dark gray: clouds light gray: snow-free land black: sea UTC:200403101212 temporal spectral temporal cloudmask is ‘liberal’, only used to check snowy pixels for misclassifications: spectral/temporal Spectral and temporal classification UTC:200403101212

12. 12 Composite snow map, March 10 th, 2004, 07:00 - 12:00 UTC March 10 th, 2004, 12:12 UTC white: snow dark gray: cloudslight gray: snow-free land black:sea spectral/temporal UTC:200403101212 Composite snow map, March 8 th - March 10 th spectral/temporal Composite snow maps

13. 13 Composite snow maps: spectral vs. spectral/temporal March 10 th, 2004, 07:00 - 12:00 UTC white: snow dark gray: cloudslight gray: snow-free land black:sea spectral spectral/temporal

14. 14 High resolution visible (hrv) channel RGB image, red= r hrv, green= r 1.6 (low res.), blue= (low res.) red pixels: surface snow OR ice clouds

15. 15 Classification of hrv channel Use low resolution cloud mask and temporal variability in hrv channel to detect clouds. Composite snow map, March 10 th, 2004, 07:00 - 12:00 UTC

16. 16 Conclusions: new spectral feature detects more clouds than BT 3.9 - BT 10.8 alone and is less influenced by the solar zenith angle spectral classification separates snow and clouds reasonably well, but: some clouds have the same spectral signature as snow using temporal information, most of these clouds can be detected temporal classification classifies snow in a conservative way (somewhat too little snow detected, but with high certainty) high frequency strongly reduces cloud obscurance snow mapping also possible in hrv channel start of implementation at MeteoSwiss this winter