ARM Measurements Validate New Satellite Multilayer Cloud Remote Sensing Method
Minnis, P., NASA - Langley Research Center
Huang, J., P. Minnis, B. Lin, Y. Yi, T.-F. Fan, S. Sun-Mack, and J. K. Ayers, 2006: Determination of ice water path in ice-over-water cloud systems using combined MODIS and AMSR-E measurements. Geophys. Res. Lett., 33, L21801, 10.1029/2006GL027038.
Minnis, P., J. Huang, B. Lin, Y. Yi, R. F. Arduini, T.-F. Fan, J. K. Ayers, and G. G. Mace, 2007: Ice cloud properties in ice-over-water cloud systems using TRMM VIRS and TMI data. J. Geophys. Res., 112, D06206, doi:10.1029/2006JD007626.
One of the most perplexing problems for retrieving cloud properties from global satellite measurements is the existence of multilayered clouds. Accurate assessment of the global distribution of cloud ice water is affected significantly by the frequent occurrence of ice clouds over liquid water clouds. A new method has been developed to determine the ice water path over ocean for both single and multilayered clouds by combining visible, infrared, near-infrared and microwave satellite data. Objective "ground truth" data to verify this new method have been scarce except at ARM Climate Research Facility sites. Data taken at the Southern Great Plains (SGP) and Tropical Western Pacific (TWP) sites and analyzed by ARM researchers have been used to demonstrate that the method is accurate, giving us the most accurate estimates of cloud ice water path to date.
The method, used over oceans, employs imager data from the Aqua moderate-resolution imaging spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) visible infrared scanning radiometer (VIRS) combined with the Aqua advanced microwave scanning radiometer (AMSR-E) and TRMM microwave imager (TMI) microwave data, respectively. The Aqua retrievals were compared with data taken by the Millimeter Wave Cloud Radar (MMCR) over the TWP site (Huang et al., 2006). The MMCR data were analyzed together with the microwave radiometer data to determine the ice water path (IWP) for multilayered cases. Figure 1 shows that the IWP is overestimated when derived using the single-layer retrieval method (VISST) applied to Aqua data. However, the new Multilayer Cloud Retrieval System (MCRS) yields IWP values that are in close agreement with the ARM retrievals. Currently, it is not possible to apply the MCRS over land using only satellite data because the microwave retrievals are too sensitive to moisture changes in the land surface. However, the method was applied over the ACRF SGP site by using the ARM microwave retrieval of liquid water path (LWP) as a substitute for the satellite microwave retrieval. Figure 2 shows the results of applying the MCRS method to a combination of Goddard Earth Observing System (GOES) and ARM LWP to provide an estimate of IWP for multilayered clouds over the SGP site (Minnis et al., 2007). The results confirm the TWP findings indicating the MCRS is a robust technique. Globally, when the multilayered clouds are taken into account the average IWP is dramatically decreased. Figure 3 shows the histograms of IWP derived from VIRS and TMI data over the Tropics for multilayered and single-layered clouds. The VISST method yields average IWP values for multilayered clouds that are 65% greater than those from the MCRS. The MCRS also yields smaller values than an earlier version of the microwave, visible, and infrared (MVI) technique that did not explicitly account for the multilayer radiative transfer. The histograms show that true single-layer clouds have approximately the same values of IWP as the ice clouds that occur in multilayer clouds.
The ARM data confirm that single-layer retrievals overestimate the IWP in multilayer clouds and show that the MCRS retrievals are quite accurate. The global retrievals using the Aqua and TRMM data indicate that standard estimates of IWP from satellite imagers (e.g., those from the International Satellite Cloud Climatology Program) overestimate the IWP whenever clouds are overlapped, ice over water. These results have significant implications for global climate model validation and formulation.