Richard Kelly Department of Geography University of Waterloo Ontario, Canada

1. The impact of physical temperature on brightness temperature observations over snow for NASA’s AMSR-E Richard Kelly Department of Geography University of Waterloo Ontario, Canada Marco TedescoCity College of New York - CUNYNew York, USA Thorsten Markus & James FosterNASA/GSFC, USA

2. 2. Add dynamism to SD retrievalDMRT: Grain Radius (x-axis) vs. 36 GHz Pol. (y-axis) for different SD (10-100 cm) Probable volume scattering at 18 GHz reduces sensitivity of 18-36 difference More pronounced for for H-Polarization Observation There are high temporal frequency variations in the brightness temperatures (and therefore retrievals) at 36, 18 and 10 GHz. Question What controls/causes high frequency (day to a few days) changes? 57.07N, 86.22E

3. Outline • Simple theory • Met station measurements • AMSR-E observations • Summary & further work

4. Simple theoretical standpoint What controls the brightness temperature (Tb) variation from a snow-covered scene as observed by a spaceborne microwave radiometer? (1) Tbs is snow Brightness Temperature Tbv is vegetation (tree canopy) Brightness Temperature Gtis a atmospheric transmissivity Tbatm atmospheric brightness temp (up & down) (assume negligible in this case) NB Tb responses are frequency dependent.

5. What controls the brightness temperature (Tb) variation from a snow-covered scene as observed by a spaceborne microwave radiometer? Deconstructing previous expression: (2) Ts is snow physical temperature: Air temperature is the driver here and changes through time: the snowpack thermal gradient is constantly adjusting. Sub-nivean temperature probably stable es is snow emissivity and related to bulk snow properties: grain size, snow crystal packing, number of scatters in the path length [SWE], water content [free or bounding] probably (?) buried vegetation effects too Tv is vegetation physical temperature ev is vegetation emissivity

6. What is the role of Tvor Ts ? In the models, Tvand Ts are often equated or combined as the effective temperature, T0, where: (3) T0 is also computed through (e.g.) (4) where Tair is the air temperature and Tsis the snow temperature. But, are there overlooked implications to these assumptions ?

7. What do physical temperature measurements suggest?

8. CLPX Experiment Data Colorado: 19-24 Feb. 2003 3 MSAs (25x25km) Each MSA had 3 ISAs (1x1km): Fraser ISA: moderate snow accumulations & denser forest fraction Rabbit Ears: deep snow accumulations & less dense forest fraction

9. Fraser Experimental Catchment MSA St. Louis Creek ISAs (forest and moderate snow) and LSOS site. SWEmean 189mm SWEs55 mm Depthmean 80 cm Depths 20 cm

10. Rabbit Ears MSA Walton Creek ISA (moderate forest and deep snow) SWEmean 580 mm SWEs115 mm Depthmean 189 cm Depths 55 cm

11. In situ measurements: dense pine at CLPX LSOS

12. In situ measurements: Rabbit Earsless dense forest

13. Summary of in situ measurements • Scene Tb’s are sensitive to (constituent surface) physical temperature. • (Tv) Vegetation canopy temperature is likely affected by air temperature • overall large fluctuations • (Ts) Snow temperature at the near air-snow interface varies more than at near basal snow temperature. • overall small fluctuations

14. How might Tphys affect PM SWE retrievals? AMSR-E Observations

15. Retrieval approaches based on R-T theory (Chang et al., 1987 & 1996): where a is a calibration coefficient and ff the forest fraction. If this is deconstructed further: where es18 and es36are snow emissivities at 18 and 36 GHz respectively. Is SWE a function of To / Tv / Ts ?

16. AMSR-E Tbs

17. Tbs at adjacent CLPX ISA sites (separated by ~8km)

18. Fraser: St. Louis data Variations of surface temperature (Tair) and Tbs at 18V & 36V

19. Variations of surface temperature (Tair) & Tb18V-Tb36V [K] But which of these channels contributes most to the variations?

20. Variations of Tair match Tb variations (somewhat) at low frequencies but less at 36 GHz …… Fraser (St. Louis Creek), Colorado - dense tree cover. 10V GHz 18V GHz 36V GHz

21. Again, variations of Tair match well variations at low frequencies and to some extent the 36 GHz …… Rabbit Ears, (Walton Creek), Colorado - dense tree cover. 10V GHz 18V GHz 36V GHz

22. Summary What causes apparent fluctuations in the SWE estimates or Tb18-Tb36? Contribution of Tair to Tbs at lower frequencies is greater than higher frequencies; ‘Surface’ temperature-related effects (driven by air temps) are a likely cause of Tb fluctuations; Vegetation temperatures are likely to change with air temperature; Vegetation emissivity changes are small (excepting snow in the canopy); Snowpack temperature variations Ts are not a likely cause; Ground temperature/emissivity variations are not a likely cause; Snow emissivity changes in response to punctuated snowfall events and seasonal snowpack evolution but not at the time scale under consideration.

23. Conclusions & Further Work We are looking at correcting for Ts & Tv in the retrievals. Can we estimate Tair from AMSR-E? (synergy w/ John Kimball). If achievable, Tair could be used to help drive a snowpack stratigraphy model (information needed in retrieval parameterization). Other sites under test (Canada: tundra and Boreal forest; Russia). A simple fix could be to ratio Tb18/Tb36 rather than subtract Tb18-Tb36 Validation of current version is in progress for Sept 2008 - refinement activity will follow.

25. Rationale • Retrieval approach is often snapshot in scope • Algorithms generate coarse-resolution SWE estimates at ~25 x 25 km • Uncertainties in the estimates are related to algorithms and spatial resolution Monthly average

26. In situ measurements:open pine at CLPX LSOS