Abstract
Abstract. Cloud microphysical and macrophysical properties are critical for understanding the role of clouds in climate. These properties are commonly retrieved from ground-based and satellite-based infrared remote sensing instruments. However, retrieval uncertainties are difficult to quantify without a standard for comparison. This is particularly true over the polar regions, where surface-based data for a cloud climatology are sparse, yet clouds represent a major source of uncertainty in weather and climate models. We describe a synthetic high-spectral-resolution infrared data set that is designed to facilitate validation and development of cloud retrieval algorithms for surface- and satellite-based remote sensing instruments. Since the data set is calculated using pre-defined cloudy atmospheres, the properties of the cloud and atmospheric state are known a priori. The atmospheric state used for the simulations is drawn from radiosonde measurements made at the North Slope of Alaska (NSA) Atmospheric Radiation Measurement (ARM) site at Barrow, Alaska (71.325° N, 156.615° W), a location that is generally representative of the western Arctic. The cloud properties for each simulation are selected from statistical distributions derived from past field measurements. Upwelling (at 60 km) and downwelling (at the surface) infrared spectra are simulated for 260 cloudy cases from 50 to 3000 cm−1 (3.3 to 200 µm) at monochromatic (line-by-line) resolution at a spacing of ∼ 0.01 cm−1 using the Line-by-line Radiative Transfer Model (LBLRTM) and the discrete-ordinate-method radiative transfer code (DISORT). These spectra are freely available for interested researchers from the NSF Arctic Data Center data repository (doi:10.5065/D61J97TT).
Highlights
Cloud properties, including height, temperature, particle size, and thermodynamic phase modulate precipitation development, cloud lifetime, and cloud radiative forcing
We describe a simulated data set that can be used to represent a cloud climatology for the Arctic as viewed from the surface or space by passive infrared sensors
Because clear- and cloudy-sky profiles may differ, only profiles likely to represent cloudy times were selected. These were identified by the presence of one or more layers where the relative humidity with respect to water was greater than 95 % between the surface and 8 km, where Arctic clouds are typically found (Shupe et al, 2011). (Throughout this manuscript, “relative humidity” is defined as being with respect to water.) This threshold was chosen because humidity sensors are typically biased low in the dry polar atmosphere (Miloshevich et al, 2006; and Vömel et al, 2007; Rowe et al, 2008); relative humidities at model cloud heights were subsequently set to 100 %, as described below
Summary
Cloud properties, including height, temperature, particle size, and thermodynamic phase modulate precipitation development, cloud lifetime, and cloud radiative forcing. The gaseous optical depth profiles are used together with cloud properties as input to a program for calculating discrete-ordinate-method radiative transfer in scattering and emitting layered media (DISORT) (Stamnes et al, 1988, 2000) to simulate cloudy-sky spectra. For this data set, only single-layered clouds were calculated. A cloud with a set of properties that satisfy the criteria of the study
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