ABI/HES Synergism

The Advanced Baseline Imager (ABI) and the Hyperspectral Environmental Suite (HES) on GOES-R and beyond will enable improved monitoring of the distribution and evolution of atmospheric thermodynamics and clouds. The HES will be able to provide hourly atmospheric soundings with spatial resolution of 4 ~ 10 km with high accuracy using its high spectral resolution measurements. However, presence of clouds affects the sounding retrieval and needs to be dealt with properly. The ABI is able to provide at high spatial resolution (0.5 ~ 2km) a cloud mask, surface and cloud types, cloud phase mask etc, cloud top pressure (CTP), cloud particle size (CPS), and cloud optical thickness (COT). The combined ABI/HES system offers the opportunity for atmospheric and cloud products improved over those possible from either system alone. The key step for synergistic use of ABI/HES radiance measurements is the collocation in space and time. ABI can (1) provide cloud characterization (amount, phase, layer information, etc.) within the HES footprint; (2) be used for HES cloud-clearing for partly cloudy HES footprints; (3) provide background information in variational retrieval of cloud properties with HES cloudy radiances. The Moderate-Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) measurements from the Earth Observing System’s (EOS) Aqua satellite provide the opportunity to study the synergistic use of advanced imager and sounder measurements. The combined MODIS and AIRS data for various scenes are analyzed to study the utility of synergistic use of ABI products and HES radiances for better retrieving atmospheric soundings and cloud properties.

Note: click on image to enlarge.

1. HES sub-pixel cloud characterization using ABI data

HES sub-pixel cloud characterization with ABI data is very useful because: (a) collocated ABI 2km cloud phase mask indicates whether a HES sub-pixel contains water clouds, ice clouds, or mixed phase clouds which is required in the cloud microphysical property retrieval; (b) collocated ABI 2km classification mask helps to determine whether a HES sub-pixel is partly cloudy or overcast, and whether it is characterized by single-layer clouds or multi-layer clouds.

Figure 1. BT of an AIRS window channel on 06 September 2002

Figure 2. 1km MODIS 11µm BT superimposed to the small area A1 indicated by Figure 1.

Figure 3. 1km MODIS phase mask and classification mask superimposed to the small area A1.

2. HES cloud-clearing with ABI data

Figure 4. BT of an AIRS window channel on 18 February 2004.

Figure 5. AIRS cloud detection from 1km MODIS cloud mask. Cloud-clearing approach using MODIS/AIRS data can be applied to the partly AIRS cloudy footprints.

Figure 6. 1km MODIS classification mask superimposed to the small area indicated in Figure 4

Figure 7. AIRS longwave cloudy (red) radiance observation, clear sky neighbor (black) and cloud-cleared (green) radiances for the FOV indicated by Figure 6 (line 74, column 55).

Figure 8. AIRS single FOV profile retrievals from the radiances indicated by Figure 7 versus ECMWF analysis

Figure 9. Temperature RMS difference between AIRS and ECMWF~ 250 thin cloudy FOVs.

3. ABI/HES synergism for cloud property retrieval

Imager products such as cloud-top pressure (CTP), cloud particle size (CPS) in diameter, cloud optical thickness (COT) can serve as background information and first guess in a variational retrieval of cloud property using hyperspectral radiances. Study (Li et. At. 2004b) shows that cloud property retrievals from synergistic use of MODIS and AIRS are better than those from either MODIS alone or AIRS alone.

Figure 10. The study area (see A3 in Figure 1) of the MODIS 1km classification mask shading to the AIRS footprints.

Figure 11. The AIRS longwave clear BT calculation from the ECMWF forecast model analysis (yellow line), the cloudy BT calculation with the MODIS CTP and effective cloud amount (ECA) (green line), the BT calculation from the AIRS retrieved CTP and ECA, and the BT calculation with AIRS retrieved CTP as well as CPS and COT (redline), along with the cloudy BT observation (black line) spectra for footprint indicated by Figure 8; the lower panel shows the corresponding BT difference between observation and calculation.

Summary

(1) ABI helps HES in sub-pixel cloud detection, classification and characterization.
(2) ABI helps HES in cloud-clearing on the single FOV basis.
(3) ABI can be used together with HES for cloud property retrievals.

References

Li, J., W. P. Menzel, F. Sun, T. J. Schmit, and J. Gurka, 2004: AIRS sub-pixel cloud characterization using MODIS cloud products, J. Appl. Meteorol. (in press)
Li, J., W. P. Menzel, W. Zhang, F. Sun, T. J. Schmit, J. Gurka, and E. Weisz, 2004: Synergistic use of MODIS and AIRS in a variational retrieval of cloud parameters, J. Appl. Meteorol. (accepted)

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