A multi-year dataset of detailed ARM ice cloud measurements allows scientists to determine the optimal satellite channels for gathering global ice cloud properties.
Accurate representations of ice clouds are necessary to accurately constrain atmospheric processes in Earth system models. The manner in which clouds interact with solar and terrestrial radiation depends on their macrophysical and microphysical properties, including water path, particle size, geometric thickness, and height. For this reason, much effort has been devoted to retrieving these parameters from a variety of satellite remote sensors, as well as surface-based observations and aircraft in situ sampling. Algorithms to retrieve cloud properties from multiple channels of satellite observations can be computationally expensive, therefore there is a need for objectively selecting optimal channel sets for cloud retrievals that maximize information while minimizing computational time.
In this study, scientists apply a formal channel selection process to a variety of ice clouds objectively extracted using a clustering algorithm on a database of cloud retrievals from observations of ground radar/lidars deployed by the U. S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility. This diverse collection of cloud states spans a wide range of optical and geometrical thicknesses, particle size distributions, and heights, allowing optimal channels to be selected for a range of scenes appropriate for retrievals over the tropics. When applied to the Atmospheric Infrared Sounder (AIRS) hyperspectral sensor, the results suggest that satellite channels near wavelengths of 14, 10.4, 4.2, and 3.8 microns contain the most information. This study demonstrates the utility of a formal information content approach for channel selection and analysis that can be applied to current and future satellite sensors. This approach provides a more robust assessment of optimal retrieval channels than simpler sensitivity analyses because it considers multiple sources of forward model uncertainties and prior knowledge of retrieved variables.
Hyperspectral instruments such as AIRS have spectrally dense observations effective for ice cloud retrievals. However, due to the large number of channels, only a small subset is typically used. It is crucial that this subset of channels be chosen to contain the maximum possible information about the retrieved variables. This study describes an information content analysis designed to select optimal channels for ice cloud retrievals. To account for variations in ice cloud properties, we perform channel selection over an ensemble of cloud regimes, extracted with a clustering algorithm, from a multiyear database at a tropical Atmospheric Radiation Measurement (ARM) site. Multiple satellite viewing angles over land and ocean surfaces are considered to simulate the variations in observation scenarios. The results suggest that AIRS channels near wavelengths of 14, 10.4, 4.2, and 3.8 microns contain the most information. With an eye toward developing a joint AIRS-MODIS (Moderate Resolution Imaging Spectroradiometer) retrieval, the analysis is also applied to combined measurements from both instruments. While application of this method to MODIS yields results consistent with previous channel sensitivity studies, the analysis shows that this combination may yield substantial improvement in cloud retrievals. MODIS provides most information on optical thickness and particle size, aided by a better constraint on cloud vertical placement from AIRS. An alternate scenario where cloud top boundaries are supplied by the active sensors in the A-train is also explored. The more robust cloud placement afforded by active sensors shifts the optimal channels toward the window region and shortwave infrared, further constraining optical thickness and particle size.
ARM Program Manager
Department of Atmospheric and Oceanic Sciences
University of Wisconsin-Madison
Data were obtained from the Atmospheric Radiation Measurement (ARM) Climate Research Facility, a U.S. Department of Energy Office of Science user facility sponsored by the Office of Biological and Environmental Research. A portion of this research was carried out at the Jet Propulsion Laboratory (JPL), California Institute of Technology, under a contract with the National Aeronautics and Space Administration. The authors were supported by the NASA Science of Terra and Aqua program under grant NNN13D455T.
Chang, K.-W., T. S. L'Ecuyer, B. H. Kahn, and V. Natraj (2017), Information content of visible and midinfrared radiances for retrieving tropical ice cloud properties, J. Geophys. Res. Atmos., 122, 4944-4966, [doi:10.1002/2016JD026357]. (Reference link)
SC-23.1 Climate and Environmental Sciences Division, BER
BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER
Aug 24, 2019
New Approach for Studying How Microbes Influence Their Environment
A diverse group of scientists suggests a common framework and targeting of known microbial processes [more...]
Aug 08, 2019
Nutrient-Hungry Peatland Microbes Reduce Carbon Loss Under Warmer Conditions
Enzyme production in peatlands reduces carbon lost to respiration under future high temperatures. [more...]
Aug 05, 2019
Amazon Forest Response to CO2 Fertilization Dependent on Plant Phosphorus Acquisition
AmazonFACE Model Intercomparison. The Science Plant growth is dependent on the availabi [more...]
Jul 29, 2019
A Slippery Slope: Soil Carbon Destabilization
Carbon gain or loss depends on the balance between competing biological, chemical, and physical reac [more...]
Jul 15, 2019
Field Evaluation of Gas Analyzers for Measuring Ecosystem Fluxes
How gas analyzer type and correction method impact measured fluxes. The Science A side- [more...]
List all highlights (possible long download time)