1. Promoting Satellite Applications in the TPE Water and Energy Cycle Studies: Chance and Challenge Kun Yang Institute of Tibetan Plateau Research Chinese Academy of Sciences 2 nd Third Pole Environment Workshop, 26-28 October 2010, Kathmandu, Nepal

2. Nearly no stations in west and > 4800 m CMA stations Lack of data in TP studies

3. TP hydro-meteorological studies need Radiation Soil moisture Land fluxes Land surface temperature Water vapor Albedo …… Need satellite products!

4. Verify satellite products before applied Satellite products: usually developed, calibrated and validated in lowlands TP represents an extreme –High elevation –Low air mass –Low aerosol –…… TP provides an opportunity to validate a satellite product’s global applicability

5. Outline Assessment of RS/DA products Development of satellite products Application of satellite products Challenge of satellite applications

6. Assessment of RS/DA products –Radiation budget: GEWEX-SRB and ISCCP-FD –Water vapor: AIRS and MODIS –Albedo: MODIS Development of satellite products Application of satellite products Challenge of satellite applications

7. Rad observations in Tibet

8. Yang et al. (2006 GRL) GEWEX-SRB V2.5 under-estimates ~50 Wm -2 Partially due to neglect of elevation effects Mean Rad Shortwave Rad: Obs. vs GEWEX-SRB v2.5

9. Mean bias in Rsw after accounting for elevation effects (Yang et al., 2008 JGR)

10. Longwave Rad: Obs. vs ISCCP-FD Yang et al. (2006 GRL)

11. Assessment of satellite water vapor JICA GPS network

12. Assessment of MODIS Precipitable Water Vapor Clear-sky Cloudy All-sky Statistics Bias=mean(MOD-GPS) Std=Standard deviation RMSE= Root mean square error NRMSE= 100*RMSE/mean(GPS) Mean=mean(MOD) MaxDiff= Max(abs(MOD-GPS)) 24 GPS Receivers (By Dr. Lv Ning )

13. For ground sites > 3000 m MOD-PWV assessment under clear-sky Before Optimization After Optimization We propose a formula to correct the large uncertainty for high-altitude regions (By Dr. Lv Ning )

14. Qin et al. JMSJ, submitted AIRS-PWV assessment under clear-sky

15. Outline Assessment of satellite products Development of satellite products –Soil moisture and land fluxes –Radiation Application of satellite products Challenge of satellite applications

16. LSM Minimization scheme F(Tb obs -Tb sim ) Tg, Tc, Wsfc Tb sim Wsfc Vegetation layer Surface Surface radiationVegetation emission RTE Tb obs Microwave TMI/AMSR/AMSR-E (6.9/10.6 and 18.7 GHz) Microwave data assimilation (Yang et al., 2007 JMSJ)

17. Validation at Tibet site (Yang et al., 2007 JMSJ)

18. Assessment of soil moisture estimate at a Mongolian site (Yang et al., 2009 JHM)

19. LDASNCEP An example: 2003 Seasonality of distributed Bowen Ratio Compared to NCEP, LDAS shows a reasonable seasonal march and regional contrast between eastern Tibet and western Tibet

20. Outline Assessment of satellite products Development of satellite products Application of satellite products –Tibet warming trend: elevation dependence –Atmospheric heating sources Challenge of satellite data applications

21. Backgrounds They concluded “ there exists a clear tendency of the surface temperature trends to increase generally as the site elevation rises “ by analyzing station data from nearly 0 m to 4800 m. This figure is adopted from Liu and Chen ’ s paper.

22. CMA stations

23. How warming rate depends on elevation? 500m increment Warming rate 200m increment Warming rate Warming rate above 5000 m ? Based on CMA data

24. Can MODIS data show the warming dependence on elevation? MODIS station Warming rate Station MODIS (dz=500 m)

25. Warming rate derived from MODIS data ? 4800m (Qin et al., 2009)

26. Outline Assessment of satellite products Development of satellite products Application of satellite products Challenge of satellite applications –Validation issue: Scale match –Application issue: Accuracy

27. Soil moisture validation: Scale-match validation

28. Naqu 4500-4700 m Cal/Val central Tibet site of SMOS and SMAP soil moisture 39 sets, starting on 30 July 2010 Each SMTMS station: 4 levels 0-3 cm, 20 cm, 40 cm, 100m

29. Radiation accuracy for glacier and snow surfaces Palong No.4 1.27m / month SE-Tibet mass and energy balance station

30. (Lu et al., 2010 JGR) Under-estimated by 100 Wm -2 (from 240 to 140), due to the difficulty to discriminate cloud and snow surface RS-estimated downward solar radiation

31. Summary Satellite data are very helpful for understanding the status, processes, and modeling in this region Need to improve the accuracy of satellite products and to develop new products for this region Thank you for your attention ！