FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) has been approved to be the ninth Earth Explorer mission of the European Space Agency. The mission is scheduled for launch on a Polar satellite in the 2025–2026 time frame. The core FORUM instrument is a Fourier Transform Spectrometer measuring, with very high accuracy, the upwelling spectral radiance, from 100 to 1600 cm − 1 (from 100 to 6.25 microns in wavelength), thus covering the Far-Infrared (FIR), and a Mid-Infrared (MIR) portion of the spectrum emitted by the Earth. FORUM will fly in loose formation with the MetOp-SG-1A satellite, hosting the Infrared Atmospheric Sounding Interferometer – New Generation (IASI-NG). IASI-NG will measure only the MIR part of the upwelling atmospheric spectrum, from 645 to 2760 cm − 1 (from 15.5 to 3.62 microns in wavelength), thus, the matching FORUM measurements will supply the missing FIR complement. Together, the two missions will provide, for the first time, a spectrally resolved measurement of the full Earth emitted thermal spectrum. The calibrated spectral radiance will be, on its own, the main product of the FORUM mission, however, the radiances will also be processed up to Level 2, to determine the vertical profile of water vapour, surface spectral emissivity and cloud parameters in the case of cloudy atmospheres. In this paper we assess the performance of the FORUM Level 2 products based on clear-sky simulated retrievals and we study how the FORUM and IASI-NG matching measurements can be fused in a synergistic retrieval scheme, to provide improved Level 2 products. Considering only the measurement noise and the systematic calibration error components, we find the following figures for the synergistic FORUM and IASI-NG retrieval products. In the upper troposphere/lower stratosphere region, individual water vapour profiles can be retrieved with 1 km vertical sampling and an error ranging from 10% to 15%. In the range from 300 to 600 cm − 1 , surface spectral emissivity can be retrieved with an absolute error as small as 0.001 in dry Polar atmospheres. Ice cloud parameters such as ice water path and cloud top height can be retrieved with errors smaller than 10% and 1 km, respectively, for ice water path values ranging from 0.2 to 60 g/m 2 .

Abstract. Total column water vapour (TCWV) is a key atmospheric variable which is generally evaluated on global scales through the use of satellite data. Recently a new algorithm, called AIRWAVE (Advanced Infra-Red WAter Vapour Estimator), has been developed for the retrieval of the TCWV from the Along-Track Scanning Radiometer (ATSR) instrument series. The AIRWAVE algorithm retrieves TCWV by exploiting the dual view of the ATSR instruments using the infrared channels at 10.8 and 12 µm and nadir and forward observation geometries. The algorithm was used to produce a TCWV database over sea from the whole ATSR mission. When compared to independent TCWV products, the AIRWAVE version 1 (AIRWAVEv1) database shows very good agreement with an overall bias of 3 % all over the ATSR missions. A large contribution to this bias comes from the polar and the coastal regions, where AIRWAVE underestimates the TCWV amount. In this paper we describe an updated version of the algorithm, specifically developed to reduce the bias in these regions. The AIRWAVE version 2 (AIRWAVEv2) accounts for the atmospheric variability at different latitudes and the associated seasonality. In addition, the dependency of the retrieval parameters on satellite across-track viewing angles is now explicitly handled. With the new algorithm we produced a second version of the AIRWAVE dataset. As for AIRWAVEv1, the quality of the AIRWAVEv2 dataset is assessed through the comparison with the Special Sensor Microwave/Imager (SSM/I) and with the Analyzed RadioSounding Archive (ARSA) TCWV data. Results show significant improvements in both biases (from 0.72 to 0.02 kg m−2) and standard deviations (from 5.75 to 4.69 kg m−2), especially in polar and coastal regions. A qualitative and quantitative estimate of the main error sources affecting the AIRWAVEv2 TCWV dataset is also given. The new dataset has also been used to estimate the water vapour climatology from the 1991–2012 time series.

In recent years, technology advancement has led to an enormous increase in the amount of satellite data. The availability of huge datasets of remote sensing measurements to be processed, and the increasing need for near-real-time data analysis for operational uses, has fostered the development of fast, efficient-retrieval algorithms. Deep learning techniques were recently applied to satellite data for retrievals of target quantities. Forward models (FM) are a fundamental part of retrieval code development and mission design, as well. Despite this, the application of deep learning techniques to radiative transfer simulations is still underexplored. The DeepLIM project, described in this work, aimed at testing the feasibility of the application of deep learning techniques at the design of the retrieval chain of an upcoming satellite mission. The Land Surface Temperature Mission (LSTM) is a candidate for Sentinel 9 and has, as the main target, the need, for the agricultural community, to improve sustainable productivity. To do this, the mission will carry a thermal infrared sensor to retrieve land-surface temperature and evapotranspiration rate. The LSTM land-surface temperature retrieval chain is used as a benchmark to test the deep learning performances when applied to Earth observation studies. Starting from aircraft campaign data and state-of-the-art FM simulations with the DART model, deep learning techniques are used to generate new spectral features. Their statistical behavior is compared to the original technique to test the generation performances. Then, the high spectral resolution simulations are convolved with LSTM spectral response functions to obtain the radiance in the LSTM spectral channels. Simulated observations are analyzed using two state-of-the-art retrieval codes and deep learning-based algorithms. The performances of deep learning algorithms show promising results for both the production of simulated spectra and target parameters retrievals, one of the main advances being the reduction in computational costs.