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. Improvements to climate model results in polar regions require improvedknowledge of cloud properties. Surface-based infrared (IR) radiancespectrometers have been used to retrieve cloud properties in polar regions,but measurements are sparse. Reductions in cost and power requirements toallow more widespread measurements could be aided by reducing instrumentresolution. Here we explore the effects of errors and instrument resolutionon cloud property retrievals from downwelling IR radiances for resolutionsof 0.1 to 20 cm −1 . Retrievals are tested on 336 radiance simulationscharacteristic of the Arctic, including mixed-phase, verticallyinhomogeneous, and liquid-topped clouds and a variety of ice habits.Retrieval accuracy is found to be unaffected by resolution from 0.1 to 4 cm −1 , after which it decreases slightly. When cloud heights areretrieved, errors in retrieved cloud optical depth (COD) and ice fractionare considerably smaller for clouds with bases below 2 km than for higherclouds. For example, at a resolution of 4 cm −1 , with errors imposed(noise and radiation bias of 0.2 mW/(m 2  sr cm −1 ) and biases intemperature of 0.2 K and in water vapor of −3  %), using retrieved cloudheights, root-mean-square errors decrease from 1.1 to 0.15 for COD, 0.3 to0.18 for ice fraction ( fice ), and 10 to 7  µ m for iceeffective radius (errors remain at 2  µ m for liquid effective radius).These results indicate that a moderately low-resolution, surface-based IRspectrometer could provide cloud property retrievals with accuracycomparable to existing higher-resolution instruments and that such aninstrument would be particularly useful for low-level clouds.

Toward autonomous surface-based infrared remote sensing of polar clouds: retrievals of cloud optical and microphysical properties