# Presentation: 756942

## Uncertainties assessment in the AMSR-E SWE product and validation plans

**Uncertainties assessment in the AMSR-E SWE product and validation plans**

- M. Tedesco1,2,3, R. Kelly4, J. Foster2, E. Kim2, T. Markus2, R. Reichle2, A. Loew5, J. Wang2
*City University of New York – CCNY, USA*
*NASA GSFC, USA*
*JCET – UMBC, USA*
*University of Waterloo, Canada*
*University of Munich, Germany*

- Telluride, CO
- July 14 – 16, 2008

**SWE Algorithm Refinement & Validation**
** **
- Example of algorithm refinement I: grain size
- Example of algorithm refinement III: Atmospheric correction effort
- Validation data sets
- Example of algorithm refinement III: Surface/air temperature effects (Richard Kelly)

## The sensitivity of MW Tb to grain size

- DRY SNOW:
- The main contribution to the Tb is due to the soil whose signal is reduced by the volumetric
- Scattering due to snow and to snow itself (lower)

- The sensitivity of Tb to grain size has been recognized since the beginning but few attempts have been made to incorporate a physical understanding into retrieval.
- GRAIN SIZE IS RELATED TO THE COEFFICIENT RELATING Tb DIFFERENCES AND SWE

- How do we define grain size ?
- Can we reproduce its behavior ?
- What is the relationship between optical grain size and microwave effective grain size ?

## Snow depth retrieval error when missing the ‘correct’ grain size

- Percentage error (underestimation) between the snow depth values obtained using 1 mm in grain size matching the brightness temperatures obtained considering 0.75 mm.

- % error

- HUT model

## Grain size exponential with time (Sturm, 1994)
Density exponential with time (Sturm, 1994)
Air/snow temperature as a linear combination of satellite channels (Kelly et al, 2003)
Snow depth is the only measured parameters (from ground AWS stations) as it is the parameters we want to retrieve and, hence, we want the smallest error on this

- Grain size exponential with time (Sturm, 1994)
- Density exponential with time (Sturm, 1994)
- Air/snow temperature as a linear combination of satellite channels (Kelly et al, 2003)
- Snow depth is the only measured parameters (from ground AWS stations) as it is the parameters we want to retrieve and, hence, we want the smallest error on this

- Our approach: forward modeling of Tb with grain size exponential growth

- Results obtained using the HUT electromagnetic model and AMSR-E brightness temperatures

- HUT model

- Test site # 1

- Test site # 2

## Exponential grain size vs. ‘ad-hoc’ grain size

- Ad-hoc grain size is obtained by minimizing the difference between measured and simulated Tb’s: it is the grain size that we should give as input to the model

- Ad-hoc grain size

- Ad-hoc grain size

- Exponent. growth

- Exponent. growth

## Optimal grain size vs. polarization differences

- The current SWE algorithm uses polarization differences to compute the coefficient relating SWE to Tb’s

## Atmospheric effects have been neglected but their effect on the brightness temperature gradient can become significant.

In the following, results regarding both  clear and cloudy skies are reported

- Atmospheric effects have been neglected but their effect on the brightness temperature gradient can become significant.
- In the following, results regarding both  clear and cloudy skies are reported

## Absorption by atmospheric gases`

_** **__**OXYGEN**_
- 60 GHz  Many lines
- 118.75 GHz  Single line
_** **__**WATER VAPOR**_
- 22.23  Weak line
- 183.31  Strong line
- Above 300 GHz  many lines

- ktot= kO2 +kH2O

## Attenuation by clouds

- Scattering is smaller than absorption for clouds

- Note that for _ice clouds_ ke is an _order of magnitude smaller_

## Corrected vs. uncorrected snow water equivalent

## Comparison with ground measurements

- The impact of the regional dependence of atmospheric parameters on the *SWE* estimation for an entire season near Fargo, North Dakota: (A) when Midwest atmospheric parameters were used, and (B) when Sierra-Nevada atmospheric parameters were used in the calculations

## Examples of maps for atmospheric correction

## Validation

- 1) Operational data: individual *in situ *(well-behaved) sites
- 2) Operational data: basin-wide aggregates
- 3) Experiment sites: short-lived campaigns
- 4) Inter-comparison with other sensors (SCA): e.g. MODIS

- ©R.Kelly, 2008

- 1) WMO snow depth

- 2) WMO snow depth at Basin Scale (e.g. Ob Basin)

- 3) Experiment sites

- 4) AMSRE-MODIS (MOD09) SCA comparisons

## Field work for 2008 - 2009

- 3 radiometers have been purhased by CCNY – CUNY with AMSR-E frequencies. Expected delivering time Summer ’09.
- In the meantime, 19 and 37 GHz will be given from the constructors by winter 08.
- Plus, a handheld 35 GHz operating radiometer is at CCNY and field work for covering AMSR-E EASE-grid pixel (or part of it is being planned)
- Improved estimation of snow vertical properties such as grain size and density through microscope techniques and infra-red photography
- Potential locations: Upstate NY, Michigan, Canada (Team with Canadians). Test sites and dates to be arranged

## Summary

- Modeling large spatial scale behavior of both snow and electromagnetic quantities is being performed for refining the current SWE algorithm by incorporating physically-driven information
- Grain size is a dominant factor and its modeling is supporting the uncertainties estimation and refinement of the algorithm
- Atmospheric correction has been neglected in the past. Recent studies show that this can be of significant impact on SWE retrieval and uncertainties estimation
- Validation effort is undertaken.