Toward autonomous surface-based infrared remote sensing of polar clouds: cloud-height retrievals

Abstract

Abstract. Polar regions are characterized by their remoteness, making measurements challenging, but an improved knowledge of clouds and radiation is necessary to understand polar climate change. Infrared radiance spectrometers can operate continuously from the surface and have low power requirements relative to active sensors. Here we explore the feasibility of retrieving cloud height with an infrared spectrometer that would be designed for use in remote polar locations. Using a wide variety of simulated spectra of mixed-phase polar clouds at varying instrument resolutions, retrieval accuracy is explored using the CO 2 slicing/sorting and the minimum local emissivity variance (MLEV) methods. In the absence of imposed errors and for clouds with optical depths greater than ∼ 0.3, cloud-height retrievals from simulated spectra using CO 2 slicing/sorting and MLEV are found to have roughly equivalent high accuracies: at an instrument resolution of 0.5 cm −1 , mean biases are found to be ∼ 0.2 km for clouds with bases below 2 and −0.2 km for higher clouds. Accuracy is found to decrease with coarsening resolution and become worse overall for MLEV than for CO 2 slicing/sorting; however, the two methods have differing sensitivity to different sources of error, suggesting an approach that combines them. For expected errors in the atmospheric state as well as both instrument noise and bias of 0.2 mW/(m 2 sr cm −1 ), at a resolution of 4 cm −1 , average retrieval errors are found to be less than ∼ 0.5 km for cloud bases within 1 km of the surface, increasing to ∼ 1.5 km at 4 km. This sensitivity indicates that a portable, surface-based infrared radiance spectrometer could provide an important complement in remote locations to satellite-based measurements, for which retrievals of low-level cloud are challenging.

Toward autonomous surface-based infrared remote sensing of polar clouds: Retrievals of cloud microphysical properties

“…The set of simulated downwelling radiances used to test the retrieval is described in detail by Cox et al (2016) and by Rowe et al (2016), and are thus described only briefly here. The simulations are based on observed Arctic atmospheric profiles and cloud properties meant to represent a typical Arctic year, based on statistics from field observations (Cox et al 2016 and 85 references therein; although designed for the Arctic, significant overlap is expected for typical Antarctic atmospheric states, except perhaps in winter in the interior, when the atmosphere is colder and drier). All clouds were modeled as plane-parallel, single-layer clouds.…”

“…Additional examples at 0.5 cm -1 , as well as at 4.0 cm -1 , are given in Fig. 2 of Rowe et al 2016. 3 CLoud and Atmospheric Radiation Retrieval Algorithm (CLARRA) CLARRA retrieves cloud macrophysical properties (cloud height and temperature) and microphysical properties, (COD, ice fraction, effective radius of liquid droplets, and effective radius of ice droplets) from downwelling infrared radiances, given knowledge of the atmospheric state.…”

“…As the first step in the retrieval, cloud heights are retrieved by CLARRA using the methods described in Rowe et al 2016 (see also references therein). Alternatively, cloud heights can be input into CLARRA (e.g from other instrumentation, such as lidar, or from reanalysis models).…”

“…Using the above and the temperature profile, calculate terms related to emission and transmission by gases at the effective instrument resolution. Rowe et al 2016), or alternatively, input the cloud height from another source. 140 4.…”

“…The cloud-height retrieval (Rowe et al 2016) requires calculations of the gaseous radiance from the surface up to each 195 possible cloud layer (R c ), the clear-sky radiance (R clr ), and the Planck function at the temperature of each possible cloud times the transmittance of the atmosphere below the cloud (B c t c ). To calculate R c and R clr , first gaseous layer optical depths Δ !…”