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Surface Skin Temperatures Observed from IR and Microwave Satellite Measurements

Surface Skin Temperatures Observed from IR and Microwave Satellite Measurements Catherine Prigent, CNRS, LERMA, Observatoire de Paris, France Filipe Aires, CNRS, LMD, University Paris VI, France Bill Rossow, NOAA-CREST, USA. I - The Problem II - Analysis of Ts diurnal cycle

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Surface Skin Temperatures Observed from IR and Microwave Satellite Measurements

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  1. Surface Skin Temperatures Observed from IR and Microwave Satellite Measurements Catherine Prigent, CNRS, LERMA, Observatoire de Paris, France Filipe Aires, CNRS, LMD, University Paris VI, France Bill Rossow, NOAA-CREST, USA

  2. I - The Problem II - Analysis of Ts diurnal cycle III - Ts estimation under cloudy conditions VI - Conclusion

  3. I - The Problem: • How to obtain reliable estimates of Ts, covering the full diurnal cycle, all types of environments, and under all atmospheric conditions (clear and cloudy skies)? • Key parameter in the turbulent flux estimates: • sensible heat flux proportional to (Ts-Tair) • latent heat flux estimates from Ts diurnal cycle

  4. Sources of Ts estimates (besides models…) • No routine in situ measurements of Ts • Surface skin temperature traditionally measured from ThermalIR: • the most direct estimate • Tb ~ eIR T4 • with eIR close to 1, varying on a rather limited range, but still uncertainties: emissivity reduction of 0.05 can reduce Ts by ~ 4 K) • but, impossible measurements belowclouds • AVHRR, GOES, MODIS… ISCCP (combination of AVHRR and geo observations, 30km resolution, 3h, since 1982) • high temporal and spatial resolution • Passive microwave in window channels can penetrate clouds (to some extent) • more sensitivity to the emissivity eMW, that varies with vegetation, soil moisture… • Tb ~ eMW T • high frequencies more affected by the atmospheric contribution • lower frequencies have larger penetration depth (in that case, it is not the skin temperature that is measured but the temperature somewhere below the surface) • SMMR, SSM/I, AMSR, SSM/IS… polar orbitors only

  5. Large differences between the Ts estimates Even when based on similar techniques… An example: comparisons ISCCP vs. MODIS (clear only) Mean = 2.41 Std = 2.55 Mean = 4.81 Std = 6.05 from Moncet et al., AER Day (July 2003) Night (July 2003) Large discrepancies during day time Better agreement at night

  6. Large differences between the Ts estimates An example: comparisons AGRMET vs. MODIS (clear only) Mean = -2.64 Std = 2.40 Mean = -0.32 Std = 5.56 from Moncet et al., AER Day (July 2003) Night (July 2003)

  7. Large differences between the Ts estimates An example: comparisons LDAS vs. GOES from Meng and Mitchel, NOAA Monthly mean 18Z February 2006

  8. II - Analysis of Ts diurnal cycle: • How to estimate the diurnal cycle from incomplete time series of Ts? • Lack of data due to • satellite mode (polar orbitors) • clouds

  9. Estimation of the Ts diurnal cycle from satellite A method to estimate the diurnal cycle derived from ISCCP estimates at 30 km resolution, every 3 hours • includes observations from polar and geostationary satellites =>AVHRR onboard polar orbitors => GOES, Meteosat onboard geostationary satellites • statistical analysis only => no model contributions to be independent from them for potential evaluation of these models Principal Component Analysis to extract the main features. • 1st component: the amplitude • 2d component: the phase • 3rd component: the with (duration of the daytime portion) => 97% of the variability Coupling of the PCA with an iterative optimization technique to reconstruct the diurnal cycle, even when limited observations available during the day. (Aires et al., JGR, 2004)

  10. Estimation of the Ts diurnal cycle from satellite Realistic diurnal cycles: relationship with the vegetation type 40N-45N January 1999 40N-45N July 1999

  11. Estimation of the Ts diurnal cycle from satellite Realistic diurnal cycles: relationship with the annual cycle 40N-45N shrubland 1999

  12. Estimation of the Ts diurnal cycle from satellite Amplitude of the diurnal cycles July 1999

  13. III - Ts estimation under cloudy conditions • Use of passive microwave observations, with a priori estimates of microwave land surface emissivities and IR-derived temperatures • Evaluation by comparisons with Ts air

  14. Multi-variable retrieval over land from SSM/I Simultaneous inversion of surface temperature, emissivity, atmospheric water content, and cloud liquid water: a neural network inversion that uses first guess estimates Learning data base Learning phase (Aires et al., JGR, 2001) Operational phase

  15. Multi-variable retrieval over land from SSM/I Pre-calculated monthly-mean microwave emissivities used as first guess (http://geo.obspm.fr) (Prigent et al., JGR, 1997, BAMS, 2006)

  16. Multi-variable retrieval over land from SSM/I Development of specific tools to analyze the neural network retrieval: calculation of neural Jacobians For different emissivity ranges, contribution of the different inputs to the Ts retrieval (Aires et al., JGR, 2001)

  17. Multi-variable retrieval over land from SSM/I Systematic calculation of surface temperature from combined SSM/I and IR, for an all weather time record (Aires et al., JGR, 2001) Ts theoretical errors calculated on the data base - no bias related to cloud cover - significant improvement in rms compared to the first guess (4K) - no bias related to surface emissivities or cloud amount

  18. Evaluation of the retrieved Ts: comparisons with Tair One year comparison between the Tair routine measurements and the microwave retrievals Locations of the Tair in situ measurements • Differences per scene type • under clear sky: positive during the day, negative at night • during the day: less difference when cloudy • (ascending time 21:00 for F11 and 18.15 for F10) (Prigent et al., JAM, 2003; JGR, 2004)

  19. Evaluation of the retrieved Ts: comparisons with Tair One year comparison between the Tair routine measurements and the microwave retrievals: the expected behaviors are observed Ts-Tair variation with local time, vegetation type, and cloudiness: - during the day, clouds limit the amount of solar flux, increasingly with increasing optical thickness - at night warmer clouds tend to prevent long-wave radiation escape more than cold clouds Ts-Tair variation with solar zenith angle and vegetation: - increases with solar zenith angle - increases with vegetation

  20. IV - Perspectives Development of a data base of combined IR, MW Ts satellite products and in situ Tair, including diurnal cycle information => Application to the turbulent fluxes calculation, along with additional satellite information about other surface parameters => Application to the analysis of the inter-annual variability of the surface temperatures (both Ts and Tair) and its relation to surface hydrology

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