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Statistically predicting land surface emissivity What: AMSR-E retrieved emissivity from CSU

Statistically predicting land surface emissivity What: AMSR-E retrieved emissivity from CSU Where: SGP (5 x 5 deg area, 0.25-deg resolution) When: 8/1/2002 - 7/31/2011 (9 years) How: Using MPDI 10GHz as sole predictor. H-pol V-pol.

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Statistically predicting land surface emissivity What: AMSR-E retrieved emissivity from CSU

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  1. Statistically predicting land surface emissivity What: AMSR-E retrieved emissivity from CSU Where: SGP (5 x 5 deg area, 0.25-deg resolution) When: 8/1/2002 - 7/31/2011 (9 years) How: Using MPDI 10GHz as sole predictor

  2. H-pol V-pol • Emissivities show strong, regular dependence on mpdi • When fitted with e = a + b x mpdi + c x mpdi^2 rmse error 0.01~0.03

  3. H-pol V-pol Predicting emissivity with e = a + b x mpdi + c x mpdi^2 • Fit model with historical data • Predict new emis • Validate against obs rmse error 0.01~0.03

  4. Regression parameters (a, b, c in e = a + b x mpdi + c x mpdi^2 ) a b c

  5. Prediction errors rmse

  6. Advantage of mpdi-based prediction: Real-time availability of MPDI from 10GHz Tb Tb-based MPDI: Emissivity-based: Emissivity-based mpdi

  7. Statistically predicting land surface emissivity Summary: approach is promising Strength: 1. simple 2. predictor (mpdi) available in real-time (10G on GMI) 3. very low errors 4. simple GPROF S2 implementation Weakness: 1. no physics 2. depends on quality of historical data for regression

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