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

Abstract

Abstract. Improvements to climate model results in polar regions require improved knowledge of cloud properties. Surface-based infrared (IR) radiance spectrometers have been used to retrieve cloud properties in polar regions, but measurements are sparse. Reductions in cost and power requirements to allow more widespread measurements could be aided by reducing instrument resolution. Here we explore the effects of errors and instrument resolution on cloud property retrievals from downwelling IR radiances for resolutions of 0.1 to 20 cm−1. Retrievals are tested on 336 radiance simulations characteristic of the Arctic, including mixed-phase, vertically inhomogeneous, 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 are retrieved, errors in retrieved cloud optical depth (COD) and ice fraction are considerably smaller for clouds with bases below 2 km than for higher clouds. For example, at a resolution of 4 cm−1, with errors imposed (noise and radiation bias of 0.2 mW/(m2 sr cm−1) and biases in temperature of 0.2 K and in water vapor of −3 %), using retrieved cloud heights, root-mean-square errors decrease from 1.1 to 0.15 for COD, 0.3 to 0.18 for ice fraction (fice), and 10 to 7 µm for ice effective radius (errors remain at 2 µm for liquid effective radius). These results indicate that a moderately low-resolution, surface-based IR spectrometer could provide cloud property retrievals with accuracy comparable to existing higher-resolution instruments and that such an instrument would be particularly useful for low-level clouds.

AWARE: The Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment

“…The magnitude and spectral shape of the downwelling longwave radiance measured by the AERI from 8 to 25 µm is sensitive to cloud temperature, optical depth, thermodynamic phase, and particle effective radius. Cloud properties retrieved from AERI measurements using the Cloud and Atmospheric Radiation Retrieval Algorithm (CLARRA; Rowe et al 2019) are shown in Figs. 11g-n.…”

Retrieval of microphysical cloud parameters from EM-FTIR spectra measured in Arctic summer 2017

“…5.2 TCWret vs. CLARRA Figure (10) shows the comparison of the results from TCWret and CLARRA for the cloud water optical depth τ cw , the total effective droplet radius r total and the condensed water path CW P . Although both retrievals use different coupling algorithms between LBLRTM and DISORT with different uncertainties in the spectral radiances (Rowe et al, 2019) and TCWret does not use the microwindows below 770.9 cm −1 , all retrieved quantities show a high agreement between both retrievals. Thus, retrieval results of TCWret are consistent with those from CLARRA.…”

“…Total Cloud Water retrieval (TCWret) is a retrieval for microphysical cloud parameters from FTIR spectra. It is inspired by MIXCRA (Turner, 2005), CLARRA (Rowe et al, 2019) and XTRA (Rathke and Fischer, 2000) and uses an optimal estimation approach (Rodgers, 2000) to invert the measured spectral radiances for retrieving microphysical cloud parameters. TCWret is a new software developed at the Institute of Environmental Physics (University of Bremen) and is originally designed to be applied to the dataset acquired during the PS106 and PS107.…”

“…Spectral radiances are divided into microwindows. Microwindows shown in table (1) are chosen according to Rowe et al (2019) and modified. Cloud parameters are retrieved from the offset, slope and curvature of the spectral radiance in the spectrum, so the emision lines of the trace gases distort the retrieval, if the amount of the trace gases is unknown.…”

“…The aim of TCWret is to retrieve the cloud water optical depth τ cw = τ liquid + τ ice , the total effective droplet radius r total and the condensed water path CW P = LW P + IW P (IW P : Ice Water Path) of Arctic mixed-phase clouds using an optimal estimation algorithm (Rodgers, 2000). The principle of this retrieval technique has been proven already for mixed-phase clouds using the far infrared and thermal spectral range (mixed-phase cloud property retrieval algorithm MIXCRA by Turner (2005) and CLoud and Atmospheric Radiation Retrieval Algorithm CLARRA by Rowe et al (2019)) and for singlephase liquid clouds using the thermal infrared spectral range (extended line-by-line atmospheric transmittance and radiance https://doi.org/10.5194/amt-2020-266 Preprint. Discussion started: 17 July 2020 c Author(s) 2020.…”