[1] The paper presents a review of the far-infrared (FIR) properties of the Earth's atmosphere and their role in climate. These properties have been relatively poorly understood, and it is one of the purposes of this review to demonstrate that in recent years we have made great strides in improving this understanding. Seen from space, the Earth is a cool object, with an effective emitting temperature of about 255 K. This contrasts with a global mean surface temperature of $288 K and is due primarily to strong absorption of outgoing longwave energy by water vapor, carbon dioxide, and clouds (especially ice). A large fraction of this absorption occurs in the FIR, and so the Earth is effectively a FIR planet. The FIR is important in a number of key climate processes, for example, the water vapor and cloud feedbacks (especially ice clouds). The FIR is also a spectral region which can be used to remotely sense and retrieve atmospheric composition in the presence of ice clouds. Recent developments in instrumentation have allowed progress in each of these areas, which are described, and proposals for a spaceborne FIR instrument are being formulated. It is timely to review the FIR properties of the clear and cloudy atmosphere, the role of FIR processes in climate, and its use in observing our planet from space.

[1] This paper presents the project Earth Cooling by Water Vapor Radiation, an observational programme, which aims at developing a database of spectrally resolved far infrared observations, in atmospheric dry conditions, in order to validate radiative transfer models and test the quality of water vapor continuum and line parameters. The project provides the very first set of far-infrared spectral downwelling radiance measurements, in dry atmospheric conditions, which are complemented with Raman Lidar-derived temperature and water vapor profiles. Citation: Bhawar, R., et al. (2008), Spectrally resolved observations of atmospheric emitted radiance in the H 2 O rotation band, Geophys. Res. Lett., 35, L04812,

Capsule summary The Far-infrared Outgoing Radiation Understanding and Monitoring mission will observe the Earth’s emitted outgoing radiation spectrum across the far-infrared with high spectral resolution and accuracy from space for the first time.

Abstract. A new cloud identification and classification algorithm named CIC is presented. CIC is a machine learning algorithm, based on principal component analysis, able to perform a cloud detection and scene classification using a univariate distribution of a similarity index that defines the level of closeness between the analysed spectra and the elements of each training dataset. CIC is tested on a widespread synthetic dataset of high spectral resolution radiances in the far- and mid-infrared part of the spectrum, simulating measurements from the Fast Track 9 mission FORUM (Far-Infrared Outgoing Radiation Understanding and Monitoring), competing for the ESA Earth Explorer programme, which is currently (2018 and 2019) undergoing industrial and scientific Phase A studies. Simulated spectra are representatives of many diverse climatic areas, ranging from the tropical to polar regions. Application of the algorithm to the synthetic dataset provides high scores for clear or cloud identification, especially when optimisation processes are performed. One of the main results consists of pointing out the high information content of spectral radiance in the far-infrared region of the electromagnetic spectrum to identify cloudy scenes, specifically thin cirrus clouds. In particular, it is shown that hit scores for clear and cloudy spectra increase from about 70 % to 90 % when far-infrared channels are accounted for in the classification of the synthetic dataset for tropical regions.

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