1 / 19

IR Land Emissivity study using Radiative Transfer and Satellite Observation

July. 7, 2008 IR/MW emissivity group meeting. IR Land Emissivity study using Radiative Transfer and Satellite Observation. Sungwook Hong, Banghua Yan, and Fuzhong Weng. Objective. This study presents a physical scheme for deriving infrared land emissivity.

amanda
Download Presentation

IR Land Emissivity study using Radiative Transfer and Satellite Observation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. July. 7, 2008 IR/MW emissivity group meeting IR Land Emissivity study using Radiative Transfer and Satellite Observation Sungwook Hong, Banghua Yan, and Fuzhong Weng

  2. Objective This study presents a physical scheme for deriving infrared land emissivity. This results based on the physical algorithm are inter-compared with other emissivity retrieval algorithms.

  3. Region for Emissivity Study • Region : Northern Africa • Date : 01-31-2008 • Image: Terra, MOD09CMG, 01-31-2008

  4. GDAS: Atmospheric and Surface information • GDAS land surface model (NOAH) outputs • GFS Data Assimilation System (GDAS) used at NCEP assimilates a variety of conventional data from radiosonde, buoy, ship, and airborne, satellite radiances, -derived produces. Global analyses are generated at four synoptic hours: 0000, 0600, 1200, and 1800 UTC. As part of GDAS, land surface parameters are also produced through its land data assimilation system called NOAH. The key NOAH parameters include skin temperature, soil volumetric water content, snow depth, soil temperature, global vegetation coverage, canopy water content, land surface vegetation type, surface wind vector at 10 m height, total precipitable water, temperature and relative humidity at 2 m height, and surface pressure.

  5. Physical algorithm • A Physical algorithm used to derive the surface emissivity for window channels in the clear sky conditions using GDAS and CRTM. where : Surface emissivity : Radiance observed by an Earth-viewing satellite radiometer, : Up-welling radiance : Down-welling radiance : Transmittance : Surface radiance

  6. AVHRR brightness temperature on IASI FOVat 11 and 12 micron Cloudy area from the cold brightness temperature

  7. IASI Cloud Detection algorithm : NASA/LaRc at 10, 11 and 12 micron 12 microns BT(Averaged): tb12 11 microns BT(Averaged): tb11 10 microns BT(Averaged): tb10 - tb11m10= | tb11-tb10| - tb12m11= | tb12-tb11| Cloud detection: If tb11m10 <= 2.0 & tb12m11 <= 2.0, clear Cloudy area

  8. Physical algorithm: IASI surface Emissivity at 3.78 and 8.96 micron • Ascending node at UTC : 19.85 - 21.35 Hr

  9. Physical algorithm: AIRS Surface Emissivity at 3.78 and 8.96 micron • Descending node atUTC : 0.29 – 2.09 Hr

  10. CIMSS AIRS algorithm (Li,J.): AIRS Emissivity (01-01~08-2004) at 3.78 and 8.96 micron • The global composite of emissivity covers from Jan. 1 of 2004 to Jan. 8 of 2004 • Emissivities are retrieved only for clear pixels for each granule by using CIMSS AIRS alone algorithm. • Then the retrieved data are binned into 0.5 by 0.5 degree over land area.

  11. IASI Emissivity vs. AIRS Emissivity 3.78 micron 8.96 micron • Mean bias : 0.010 • RMSE : 0.068 • Mean bias: 0.009 • RMSE : 0.106

  12. MODIS Emissivity, 8.4-8.7 micron MODIS Emissivity, 3.66-3.84 micron MODIS Surface Emissivity at 3.66-3.84 and 8.4-8.7 micron • Dataset: MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 6km SIN Grid V005 (MOD11B1.5 MODIS product )

  13. Summary and Future Work • Emissivity from the radiative transfer model at IR is simulated a little bit lower than other algorithm results. • The results provide the reasonable emissivity variation in both IASI and AIRS cases. • More reasonable cloud detection algorithm should be applied to find clear sky cases. • Our next studies will include 1) seasonal variation of emissivity, 2) dielectric constants retrieval from the retrieved emissivity, 3) a full coupling of radiative transfer scheme among soil, vegetation and atmosphere, and 4) development of an interface of land emissivity model with Community Radiative Transfer Model (CRTM) for improved radiance assimilation for IR channels.

  14. Index of refraction (n) and dielectric constant (ε) - Index of refraction (n) and dielectric constant (ε): - Complex dielectric constant: - Index of refraction : - Relationship between n and ε: - Fresnel’s formulae: limit applicability: due to surface roughness and inhomogeneities - Emissivity: : Mixed Emissivity

  15. Complex Dielectric Constant of Water

  16. Representative physical properties of basic constituents and composites of Soil ( Liu and Li: J. Appl. Geophys., 2001)

  17. Dielectric Constant Retrieval • Procedure : a) Two inputted emissivities for two angles. b) Applying Fresnel formula to compute the surface emissivity and associated derivative to inputted dielectric constant (by Quanhua (Mark) Liu) c) Iteration : |Inputted Emissivity – Calculated Emissivity| < Threshold d) Derive Dielectric constant

  18. IASI Dielectric Constants at 3.78 and 8.96 micron(Ascending node)

  19. IASI Dielectric Constants at 3.78 and 8.96 micron(Descending node)

More Related