We describe studies undertaken in support of the Far‐infrared Outgoing Radiation Understanding and Monitoring mission, European Space Agency's ninth Earth Explorer, designed to investigate whether airborne observations of far‐infrared radiances can provide beneficial information on mid and upper tropospheric water vapor concentrations. Initially we perform a joint temperature and water vapor retrieval and show that the water vapor retrieval exploiting far‐infrared measurements from the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) shows improvement over the a‐priori Unified Model global forecast when compared to in situ dropsonde measurements. For this case the improvement is particularly noticeable in the mid‐upper troposphere. Equivalent retrievals using mid‐infrared radiances measured by the Airborne Research Interferometer Evaluation System (ARIES) show much reduced performance, with the degrees of freedom for signal (DFS), reduced by a factor of almost 2. Further sensitivity studies show that this advantage is decreased, but still present when the spectral resolution of the TAFTS measurements is reduced to match that of ARIES. The beneficial role of the far infrared for this case is further confirmed by performing water vapor only retrievals using ARIES and TAFTS individually, and then in combination. We find that the combined retrieval has a DFS value of 6.7 for water vapor, marginally larger than that obtained for the TAFTS retrieval and almost twice as large as that obtained for ARIES. These results provide observational support of theoretical studies highlighting the potential improvement that far‐infrared observations could bring for the retrieval of tropospheric water vapor.

The spectrum of reflected solar radiation emerging at the top of the atmosphere is rich with Earth system information. To identify spectral signatures in the reflected solar radiation and directly relate them to the underlying physical properties controlling their structure, over 90 000 solar reflectance spectra are computed over West Africa in 2010 using a fast radiation code employing the spectral characteristics of the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY). Cluster analysis applied to the computed spectra reveals spectral signatures related to distinct surface properties, and cloud regimes distinguished by their spectral shortwave cloud radiative effect (SWCRE). The cloud regimes exhibit a diverse variety of mean broadband SWCREs, and offer an alternative approach to define cloud type for SWCRE applications that does not require any prior assumptions. The direct link between spectral signatures and distinct physical properties extracted from clustering remains robust between spatial scales of 1, 20, and 240 km, and presents an excellent opportunity to understand the underlying properties controlling real spectral reflectance observations. Observed SCIAMACHY spectra are assigned to the calculated spectral clusters, showing that cloud regimes are most frequent during the active West African monsoon season of June–October in 2010, and all cloud regimes have a higher frequency of occurrence during the active monsoon season of 2003 compared with the inactive monsoon season of 2004. Overall, the distinct underlying physical properties controlling spectral signatures show great promise for monitoring evolution of the Earth system directly from solar spectral reflectance observations.

This article presents a generic flexible framework for an End-to-end Instrument Performance Simulation System (EIPS) for satellite atmospheric remote sensing instruments. A systematic process for developing an end-to-end simulation system based on Rodgers’ atmospheric observing system design process has been visualised. The EIPS has been developed to support the quantitative evaluation of new satellite instrument concepts in terms of performance simulations, design optimisation, and trade-off analysis. Important features of this framework include: fast radiative transfer simulation capabilities (fast computation and line-by-line like simulations), applicability across the whole electromagnetic (EM) spectrum and a number of integrated retrieval diagnostics. Because of its applicability across the whole EM spectrum, the framework can be usefully applied to synergistic atmospheric retrieval studies. The framework is continually developing and evolving, and finding applications to support and evaluate emerging instrument and mission concepts. To demonstrate the framework’s flexibility in relation to advanced sensor technologies in the microwave range, a novel superconducting transition edge sensor (TES) -based multi-spectral microwave instrument has been presented as an example. As a case study, the performance of existing multi-spectral-type microwave instruments and a TES-technology-based multi-spectral microwave instrument has been simulated and compared using the developed end-to-end simulation framework.

The HT-FRTC can be used across the electromagnetic spectrum from the microwave through to the ultraviolet to calculate transmittance, radiance and flux spectra, which are represented by their principal components. The code uses monochromatic calculations at a small number of frequencies, which are selected by k-means clustering, to predict the principal component scores. It has been found that kernel regression yields more robust predictions than a linear regression. The principal components cover the spectrum at a very high spectral resolution, similar to that of conventional line-by-line models such that the individual spectral lines are resolved. This approach allows very fast line-by-line-like, hyperspectral and broadband simulations for satellite-based, airborne and ground-based sensors. The principal components are derived during a code training phase from monochromatic simulations for a diverse set of atmospheres and surfaces. They are sensor independent and no extra training is required for additional sensors. The HT-FRTC has been trained with all of the trace gases in the High-resolution TRANsmission molecular absorption database (HITRAN) and on a large variety of surface emissivity/reflectance spectra. It can be run for any Lambertian or specular surface. The HT-FRTC is a plane-parallel one-dimensional model but some effects of the Earth's sphericity are accounted for. Solar, lunar and cosmic microwave background sources have been included. Scattering by frozen and liquid cloud, precipitation particles and twenty different aerosol species has been included, as well as Rayleigh scattering which is significant in the short-wave. The scattering phase function can be fully accounted for by an integrated monochromatic version of the Edwards-Slingo spherical harmonics radiation code or approximately by a modification to the extinction (Chou scaling). Crown Copyright (C) 2018 Published by Elsevier Ltd. All rights reserved.

Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud-free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium-Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900cm(-1) have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12K. Results at 2,616cm(-1) at night are reasonably consistent with results at 900cm(-1). Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616cm(-1) are inferior to those at 900cm(-1) for daytime calculations.

This article reports the results of a preliminary mission study to assess the potential of space‐borne laser heterodyne radiometry (LHR) for the remote sensing of temperature for assimilation in a numerical weather prediction (NWP) model. The LHR instruments are low cost and small in size, lending themselves to a wide variety of satellite platforms. The impact of different configurations of an idealized LHR instrument is assessed against the Infrared Atmospheric Sounding Interferometer (IASI), via single‐column linear information content analysis, using inputs consistent with the background errors of the Met Office 4D‐Var assimilation system. Multiplexed configurations give promising results, in particular for sounding of upper‐atmospheric temperatures.

In context of numerical weather prediction (NWP), increased usage of satellites radiance observations from passive microwave sensors have brought significant improvements in the forecast skills. In the infrared spectral region, hyperspectral sounder instruments such as IASI have already benefitted the NWP assimilation systems, but they are useful only under clear sky conditions. Currently, microwave instruments are providing wealth of information on clouds, precipitation and surface etc., but only with limited number of channels. Furthermore, due to limited number of channels and with poor signal-to-noise ratio, existing passive microwave sensors have very poor resolution and accuracy. We are currently developing a new microwave instrument concept, based on superconducting filterbank spectrometers, which will enable high spectral resolution observations of atmospheric temperature and humidity profiles across the microwave/sub-millimeter wavelength region with photon-noise-limited sensitivity. This study aims at investigating the information content on temperature and water-vapour that could be provided by such a hyperspectral microwave instrument under clear sky-conditions. Here, we present a new concept of Transition Edge Sensors (TESs)-based hyperspectral microwave instrument for atmospheric sounding applications. In this study, for assessing the impact of hyperspectral sampling in microwave spectral region in clear sky-conditions, we have estimated the information content as standard figure of merit called as degrees of freedom for signal (DFS). The DFS for a set of temperature and humidity sounding channels (50-60 GHz, 118GHz and 183 GHz) have been analyzed under the linear optimal estimation theory framework.

Typically referred to as brownout or whiteout, degraded visual environments (DVE) more accurately includes all forms of materially reduced visibility where pilots and operators may become disorientated. These conditions include very low night illumination levels, adverse meteorological conditions (clouds and precipitation) and obscurant particulates (dust storms and sea spray), which can all reduce photopic and thermal target contrasts with respect to their background environment. DVE operations are a significant and persistent safety concern for autonomous or piloted platforms at all points of their mission cycle including launch, transit, execution, egress and recovery. Impacted platforms of all sizes can include aircraft (from drones to hybrid airships), ground vehicles and maritime vessels (surface or submerged).This paper seeks to highlight the capabilities of the Met Office over both land and sea in accurately forecasting the presence of DVE through Numerical Weather Prediction (NWP). These forecasts can then be furthermore leveraged with Met Office expertise in environmental impacts to predict the likely effects on sensor acquisition ranges. These sensor impacts are robustly modelled through Tactical Decision Aids (TDAs) that can take account of platform range, elevation, sensor waveband, target characteristics and background environment, in addition to many other pertinent parameters.The Met Office is the United Kingdom's National Meteorological Service (NMS) tasked to provide weather information and weather related warnings. Within the realm of defence engagement, the Met Office has provided key elements of geospatial intelligence capability for multinational operations worldwide for over a century. The Met Office supports its defence stakeholders, such as NATO and the USAF, by assimilating over 10 million daily global observations into its Unified Model (UM) for Numerical Weather Predication (NWP). The UM provides the input for the Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) to predict atmospheric impacts on sensor performance within the context of Tactical Decision Aids (TDAs), such as Neon for thermal contrast and MONIM for night illumination levels.This global UM forecast data, with high temporal-spatial resolution regional sub-models, enables further environmental impact prediction for atmospheric dispersion events such as volcanic ash and radiological incidents through the NAME capability (Numerical Atmospheric-dispersion Modelling Environment). In addition to capabilities in Space Weather, the Met Office also specializes in operational ocean monitoring and forecasting services to support safe operations in the marine environment, but have also evolved to cater for, amongst others, marine security, commercial operations, licensing for marine operations and environmental monitoring.

The Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) is a principal component based fast radiative transfer code that can be used across the whole electromagnetic spectrum to calculate transmittance, radiance and flux spectra. The principal components cover the spectrum at a very high spectral resolution, which allows very fast line-by-line, hyperspectral and broadband simulations for satellite-based, airborne and ground-based sensors. The principal components are derived during a code training phase from line-by-line simulations for a diverse set of atmospheric and surface conditions. The derived principal components are sensor independent, i.e. no extra training is required to include additional sensors. During the training phase, predictors that are required by the fast radiative transfer code to determine the principal component scores from the monochromatic radiances (or fluxes, transmittances) are also derived. These predictors are calculated for each training profile at a small number of frequencies, which are selected by a k-means cluster algorithm during the training phase. The predictors are calculated using Gaussian Processes, which is more accurate than a linear regression algorithm. The HT-FRTC code is an integral part of the Met Office’s Tactical Decision aids such as Neon and IRVIS and as such plays an important part in the prediction of environmental impacts on various sensors such as IR cameras and night-vision goggles. Moreover, the HT-FRTC has been incorporated into a onedimensional variation (1D-Var) retrieval system that also works solely in principal component space. This keeps the dimensions of the matrices involved small which is important for computational efficiency.

The Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) is a fast radiative transfer model based on Principal Components. Scattering has been incorporated into HT-FRTC which allows simulations of aerosol as well as clear-sky atmospheres. This work evaluates the scattering scheme in HT-FRTC and investigates dust-affected brightness temperatures using in-situ observations from Ice in Clouds Experiment – Dust (ICE-D) campaign. The ICE-D campaign occurred during August 2015 and was based from Cape Verde. The ICE-D campaign is a multidisciplinary project which achieved measurements of in-situ mineral dust properties of the dust advected from the Sahara, and on the aerosol-cloud interactions using the FAAM BAe-146 research aircraft.

Scattering by atmospheric ice in the mm-wave and sub-mm-wave spectral regions presents a challenge to the cloud microphysics and light scattering communities. This is because scattering by atmospheric ice, at these frequencies, not only depends on ice crystal shape, orientation and size, but also on how its mass or density evolves with size, and on the shape of the size spectrum. As there is no universal density-size relationship or representation of the size spectrum this makes interpretation of mm-wave and sub-mm-wave observations problematic. In this talk, the methodologies proposed in [1,2] will be applied to state-of-the-art mm-wave and sub-mm-wave airborne observations of ice cloud that occurred around the United Kingdom [3]. Furthermore, these observations also consisted of state-of-the-art detailed microphysics measurements. From the latter observations, and using the geometric optics approximation to estimate the volume extinction coefficient, ice mass-extinction relationships were previously derived by [4]. The three-component models presented in [2] are applied to predict the geometricbased relationships and these models are then used to simulate the observations in the microwave using a generalized state-of-the-art line-by-line radiative transfer model [5]. The results of these analyzes will be presented and discussed in relation to the scattering challenge.

The Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) represents transmittances, radiances and fluxes by principal components that cover the spectra at very high resolution, allowing fast highly-resolved pseudo line-by-line, hyperspectral and broadband simulations across the electromagnetic spectrum form the microwave to the ultraviolet for satellite-based, airborne and ground-based sensors. HT-FRTC models clear atmospheres and those containing clouds and aerosols, as well as any surface (land/sea/man-made). The HT-FRTC has been used operationally in the NEON Tactical Decision Aid (TDA) since 2008. The TDA combines the HT-FRTC with a thermal contrast model and an NWP model forecast data feed to predict the apparent thermal contrast between different surfaces and ground-based targets in the thermal and short-wave IR. The new objective here is to predict the optical contrast of air-borne targets under realistic night-time scenarios in the Photopic and NVG parts of the spectrum. This requires the inclusion of all the relevant radiation sources, which include twilight, moonlight, starlight, airglow and cultural light. A completely new exact scattering code has been developed which allows the straight-forward addition of any number of direct and diffuse sources anywhere in the atmosphere. The new code solves the radiative transfer equation iteratively and is faster than the previous solution. Simulations of scenarios with different light levels, from situations during a full moon to a moonless night with very low light levels and a situation with cultural light from a town are presented. The impact of surface reflectance and target reflectance is investigated.

ABSTRACTVolcanic eruptions are natural hazards with dire consequences to life and economy. As most volcanoes are in remote areas, satellites play a vital role in providing observations and input to models used for forecasting volcanic plume evolution. Radiances from the Infrared Atmospheric Sounding Interferometer (IASI) on board the MetOp polar orbiting meteorological satellites were used to detect sulphur dioxide (SO2) from three volcanic eruptions in 2014, in different meteorological situations. Two of these eruption cases, Mount Sinabung in January and Kelut in February, are in the tropics, whereas the case in October from Bárðarbunga, is at a higher latitude. The SO2 plumes from these volcanic eruptions were identified easily and tracked using established methods from the literature, that are based on the principle that areas of increased SO2 from the volcano produce a reduction in the spectrum of the observed radiances (in the relevant absorption bands of IASI), whereas areas outside the volcanic plume do not. An estimate of the plume height was obtained in the first two cases by examination of the winds from an NWP model at different heights, and in the third, where the meteorological pattern is more complicated, by using trajectories from a Lagrangian model and matching their position with satellite observations of the plume at different times. The importance of meteorology in the detection and evolution of volcanic plumes, especially at low levels, is especially well demonstrated by the Bárðarbunga eruption. Estimates of the plume concentrations were obtained from explicit line‐by‐line calculations.

The HT-FRTC is a principal component based fast radiative transfer code that can be used across the electromagnetic spectrum from the microwave through to the ultraviolet to calculate transmittance, radiance and flux spectra. The principal components cover the spectrum at a very high spectral resolution, which allows very fast line-by-line, hyperspectral and broadband simulations for satellite-based, airborne and ground-based sensors. The principal components are derived during a code training phase from line-by-line simulations for a diverse set of atmosphere and surface conditions. The derived principal components are sensor independent, i.e. no extra training is required to include additional sensors. During the training phase we also derive the predictors which are required by the fast radiative transfer code to determine the principal component scores from the monochromatic radiances (or fluxes, transmittances). These predictors are calculated for each training profile at a small number of frequencies, which are selected by a k-means cluster algorithm during the training phase. Until recently the predictors were calculated using a linear regression. However, during a recent rewrite of the code the linear regression was replaced by a kernel regression which resulted in a significant increase in accuracy when compared to the linear regression. The HT-FRTC has been trained with a large variety of gases, surface properties and scatteres. Rayleigh scattering as well as scattering by frozen/liquid clouds, hydrometeors and aerosols have all been included. The scattering phase function can be fully accounted for by an integrated line-by-line version of the Edwards-Slingo spherical harmonics radiation code or approximately by a modification to the extinction (Chou scaling). Typically the simulation of a whole clear-sky radiance spectrum (3600000 monochromatic frequencies) takes less than one millisecond.

We have developed a new algorithm for the simultaneous retrieval of the atmospheric profiles (temperature, humidity, ozone and aerosol) and the surface reflectance from hyperspectral radiance measurements obtained from air/space-borne, hyperspectral imagers such as Hyperion EO-1. The new scheme, proposed here, consists of a fast radiative transfer code, based on empirical orthogonal functions (EOFs), in conjunction with a 1D-Var retrieval scheme. The inclusion of an 'exact' scattering code based on spherical harmonics, allows for an accurate treatment of Rayleigh scattering and scattering by aerosols, water droplets and ice-crystals, thus making it possible to also retrieve cloud and aerosol optical properties, although here we will concentrate on non-cloudy scenes. We successfully tested this new approach using hyperspectral images taken by Hyperion EO-1, an experimental pushbroom imaging spectrometer operated by NASA

The Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC) is a component of the Met Office NEON Tactical Decision Aid (TDA). Within NEON, the HT-FRTC has for a number of years been used to predict the IR apparent thermal contrasts between different surface types as observed by an airborne sensor. To achieve this, the HT-FRTC is supplied with the inherent temperatures and spectral properties of these surfaces (i.e. ground target(s) and background).A key strength of the HT-FRTC is its ability to take into account the detailed properties of the atmosphere, which in the context of NEON tend to be provided by a Numerical Weather Prediction (NWP) forecast model. While water vapour and ozone are generally the most important gases, additional trace gases are now being incorporated into the HT-FRTC.The HT-FRTC also includes an exact treatment of atmospheric scattering based on spherical harmonics. This allows the treatment of several different aerosol species and of liquid and ice clouds. Recent developments can even account for rain and falling snow. The HT-FRTC works in Principal Component (PC) space and is trained on a wide variety of atmospheric and surface conditions, which significantly reduces the computational requirements regarding memory and time. One clear-sky simulation takes approximately one millisecond.Recent developments allow the training to be completely general and sensor independent. This is significant as the user of the code can add new sensors and new surfaces/targets by simply supplying extra files which contain their (possibly classified) spectral properties.The HT-FRTC has been extended to cover the spectral range of Photopic and NVG sensors. One aim here is to give guidance on the expected, directionally resolved sky brightness, especially at night, again taking the actual or forecast atmospheric conditions into account. Recent developments include light level predictions during the period of twilight.