Retrieval Algorithms for HES

Atmospheric Soundings under clear skies:

The algorithm for retrieving of atmospheric temperature, moisture and ozone profiles is as following:

  • Atmospheric transmittance: A fast and accurate transmittance models called Pressure Layer Optical Depth (PLOD) or Pressure layer Fast Algorithm for Atmospheric Transmittances (PFAAST) (Hannon et al. 1996) is chosen. The calculation are made at 101 pressure levels (0.01-105kPa) and take into account the satellite zenith angle, absorption by well mixed gases (including nitrogen, oxygen, and (carbon dioxide), water vapor (including the water vapor continuum), and ozone. For each layer and for each channel of radiometer, transmittances are calculated line-by-line for a variety of temperature, humidities and ozone. Polynomial functions of temperature, humidities and ozone are then fitted to the transimittance.
  • Regression retrieval: A Principle Component Regression (PCR, Huang and Antonelli 2001) serves as the first guess (or background) in the next step physical retrieval. The PCR uses the projections of the predictor variables (temperature, humidities and ozone) onto a subset of principle component of the simulated bands radiance. The Instrument noise is added before creating the regression coefficients.
  • Physical retrieval: Once a first guess is obtained, a non-linear iterative procedure is applied to the radiative transfer equation to further improve the profiles (Li and Huang 1999; Li et al. 2000). Approximately half of the channels (optimal channels) are used in the physical retrieval procedure.

Atmospheric Soundings under cloudy skies:

With its high spectral resolution and radiometric accuracy, HES will provide atmospheric vertical temperature and moisture sounding information with high vertical resolution and accuracy for Numerical Weather Prediction (NWP). Due to its relatively coarse spatial resolution (10 km for HES-DS), the chance for a HES footprint to be completely clear is small. However, ABI, with its high spatial resolution (0.5 ~ 2 km), provides cloud mask, cloud phase mask, cloud classification mask (Li et al. 2003), as well as clear radiances at several spectrally broad infrared (IR) bands with 1 km spatial resolution within many AIRS cloudy footprints. ABI can (1) provide HES sub-pixel cloud characterization (mask, amount, phase, layer information, etc.) within each HES footprint (Li et al. 2004a); (2) provide background information in variational retrieval of cloud properties such as cloud-top pressure, optical thickness, and particle size with AIRS cloudy radiances (Li et al. 2004b; 2005a); and (2) be used for HES cloud-clearing for partly cloudy AIRS footprints (Li et al 2005b). Moderate-Resolution Imaging Spectroradiometer (MODIS) and Atmospheric InfraRed Sounder (AIRS) data on EOS Aqua satellite data are used for ABI/HES cloud-clearing study.

In order to derive sounding from combined MODIS and AIRS radiance measurements from AIRS partly cloudy footprint, an optimal cloud-clearing algorithm using multispectral MODIS IR bands is developed to retrieve the AIRS clear column radiances on single footprint basis (Li et al. 2005b). Unlike using AMSU in AIRS cloud-clearing, this method uses MODIS data. Since both the imager and sounder measure radiances at the same IR spectral regions, a direct relationship between an imager IR radiance and a sounder IR radiance spectrum for a given imager spectral band provides unique advantages for imager/sounder cloud-clearing. The advantages of imager/sounder cloud-clearing include: (1) it is easy to find imager clear pixels within the sounder footprint which is critical for the N* calculation and the quality control on CCRs; (2) cloud-clearing can be achieved on a single footprint basis (hence maintaining the spatial gradient information); and (3) imager IR clear radiances provide tropospheric atmospheric information that enhances the effectiveness of cloud-clearing for IR sounder cloudy radiances. The optimal AIRS cloud-clearing method using multispectral MODIS IR bands (bands 22, 24, 25, 28, 30, 31, 32, 33, and 34) is also demonstrated to be better than the traditional single band N* cloud-clearing approach (Li et al. 2005b). The AIRS cloud-cleared radiance spectrum is convoluted to all the MODIS IR spectral bands with spectral response functions (SRFs), and the convoluted brightness temperatures (BTs) are compared with MODIS clear BT observations within all successful cloud-cleared footprints. The bias and the standard deviation between the convoluted BTs and MODIS clear BT observations is less than 0.25 K and 0.5 K, respectively, over both water and land for most MODIS IR spectral bands. It is found that more than 30% of the AIRS cloudy (partly and overcast) footprints in this study have been successfully cloud-cleared using the optimal cloud-clearing method, revealing the potential application of this method on the operational processing of hyperspectral IR sounder cloudy radiance measurements when the collocated imager IR data is available. Comparing the AIRS cloud-cleared BT spectrum and its nearby clear footprint BT spectrum also helps to evaluate the performance of cloud-clearing. The difference, accounting the effects due to scene non- uniformity, is reasonable (with standard deviation less than 0.5 K in most IR spectral regions) according to the analysis (Li et al 2005b). This work is very effective for cloud-clearing the AIRS footprints contaminated by water clouds. The approach could be employed on GOES-R with Hyperspectral Environmental Suite (HES) sounder and Advanced Baseline Imager (ABI) (Schmit et al. 2005) on the next generation of Geostationary Operational Environmental Satellite (GOES-R) (e.g., ABI 3.9, 6.15, 7.0, 7.4, 8.5, 9.73, 10.35, 11.2, 12.3 and 13.3 mm bands are critical for HES/ABI cloud-clearing).

Cloud properties:

CO2-slicing originally developed by Smith et al. (1978) and Menzel et al. (1983), and late refined by Zhang and Menzel (2002), is used for cloud-top height retrieval. Minimum Residual (MR) methodology is used to retrieve the cloud particle size (CPS) and cloud optical thickness (COT) from AIRS longwave window region (790 - 970 cm-1 or 10.31 - 12.66 µm, and 1050 - 1130 cm-1 or 8.85 - 9.52 µm) cloudy radiance measurements (Li et al. 2005a). In the MR procedure, the CTP is derived from the AIRS radiances of CO2 channels (720 - 790 cm-1) while the cloud phase information is derived from the collocated MODIS 1km phase mask. In addition, the collocated 1km MODIS cloud mask defines the AIRS cloud detection. The atmospheric temperature profile, moisture profile and surface skin temperature used in the AIRS cloud retrieval processing are those from the European Center for Medium-range Weather Forecasting (ECMWF) system. MR provides the cloud microphysical property retrievals during both the daytime and nighttime.

The AIRS MR method seeks the CPS and COT by minimizing the differences between observations and calculations using AIRS longwave channels. Three steps for retrieving cloud particle diameter and visible optical thickness are included in the MR procedure (Li et al. 2004b, Huang et al. 2004).

  1. with retrieved cloud top pressure and effective emissivity from AIRS radiances using the CO2 channels, an initial  is estimated as , where  is the effective cloud emissivity of 790 cm-1.
  2. with estimated initial , the  is retrieved with MR scheme using AIRS channels with wavenumbers between 790 and 960 cm-1.
  3. with retrieved  from step 2, the estimated  is retrieved with the MR scheme using AIRS channels with wavenumbers between 1080 and 1130 cm-1.

These three steps are iterated for the improved retrieval of CPS and COT in the implementation. A fast cloud radiative transfer model (Wei et al. 2004) is integrated in the retrieval process.  For ice clouds, the bulk single-scattering properties of ice crystals are derived by assuming aggregates for large particles (>300 mm), hexagonal geometries for moderate particles (50 – 300 mm) and droxtals for small particles (0 – 50 mm).  For water clouds, spherical water droplets are assumed, and the classical Lorenz-Mie theory is used to compute their single-scattering properties.

Cloud microphysical property retrieval is performed with AIRS data during Mixed Phase Arctic Cloud Experiment (MPACE) field campaign. October 17th 2004 is chosen as the focus day for our MPACE study. There is a nice Aqua overpass of Barrow at 22:20 UTC on this day. The cloud scene according to the lidar is a typical cirrus case. The AIRS cloud properties (CTP, CPS, and COT) are close to those from lidar measurements at ARM Barrow site.

References

  • Hannon, S. L. L. Strow, and W. W. McMillan, 1996: Atmospheric infrared fast transmittance models: A comparison of two approaches, Proceedings of SPIE, 2830, 94-105.
  • Huang, H. L., and Antonelli, 2001: Application of principle component analysis to high-resolution infrared measurement compression and retrieval, J. Appl. Meteor., 40, 365-388.
  • Huang, H. L., P. Yang, H. Wei, B. A. Baum, Y. Hu, P. Antonelli, and S. A. Ackerman,2004: Inference of ice cloud properties from high spectral resolution infrared observations, IEEE Trans on Geoscience and Remote Sensing, 42, 842 - 853.
  • Li, J. and H. L. Huang, 1999: Retrieval of atmospheric profiles from satellite sounder measurements using the discrepancy principle, Appl. Optics, 38, 916-923.
  • Li, J., and W. Wolf, W. P. Menzel, W. Zhang, H.-L. Huang, and T. H. Achtor, 2000: Global soundings of the atmosphere from ATOVS measurements: the algorithm and validation. J. Appl. Meteor., 39, 1248-1268.
  • Li J., W. P. Menzel, F. Sun, T. J. Schmit, and J. Gurka, 2004a: AIRS sub-pixel cloud characterization using MODIS cloud products, J. Appl. Meteorol. Vol. 43, 1083 - 1094.
  • Li, J., W. P. Menzel, W. Zhang, F. Sun, T. J. Schmit, J. J. Gurka, and E. Weisz, 2004b: Synergistic use of MODIS and AIRS in a variational retrieval of cloud parameters, J. Appl. Meteorol., vol. 43, 1619 - 1634, 2004.
  • Li, J., H.-L. Huang, C.-Y. Liu, P. Yang, T. J. Schmit, H. Wei, E. Weisz and W. P. Menzel, 2005a: Retrieval of cloud microphyiscal properties from MODIS and AIRS, J. Appl. Meteorol. (in press), 2005.
  • Li, J., C.-Y. Liu, H.-L. Huang, T. J. Schmit, X. Wu, W. P. Menzel, and J. J. Gurka, 2005b: Optimal cloud-clearing for AIRS radiances using MODIS, IEEE Trans. on Geoscience and Remote Sensing. 43, 1266 - 1278.
  • Menzel, W. P., W. L. Smith, and T. R. Stewart, 1983: Improved cloud motion wind vector and altitude assignment using VAS. J. Climate Appl. Meteorol., 22, 377-384.
  • Schmit, T. J., M. M. Gunshor, W. Paul Menzel, J. Li, S. Bachmeier, and J. J. Gurka, 2005: Introduction the next-generation advanced baseline imager (ABI) on Geostationary Operational Environmental Satellites (GOES)-R, Bull. American Meteorol. Society (in press).
  • Smith, W. L., and C. M. R. Platt, 1978: Intercomparison of radiosonde, ground based laser, and satellite deduced cloud heights. Journal Appl. Meteorol., 17, 1796-1802.
  • Zhang, H., and W. P. Menzel, 2002: Improvement in thin cirrus retrievals using an emissivity-adjusted CO2 slicing algorithm. J. Geophysical Research, D17, 107, 4327 - 4339.
  • Wei, H., P. Yang, J. Li, B. A. Baum, H. L. Huang, S. Platnick, and Y. X. Hu, 2004: Retrieval of ice cloud optical thickness from Atmospheric Infrared Sounder (AIRS) measurements, IEEE Trans. on Geosci. and Remote Sensing, 42, 2254 - 2267.

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