A novel Spectral Gamma Function is introduced to address the limitations associated with the “correlated” spectrum assumption in radiative transfer modeling in non-uniform gaseous media. The Spectral Gamma Function combines high-resolution gas absorption spectra with their distribution functions, capturing the “correlated” properties of absorption spectra at different thermodynamic states. This concept is applicable both to narrow bands and the full spectrum, enabling the construction of truly “correlated” models and the calculation of statistical relationships between absorption spectra. The paper presents the definition and properties of the Spectral Gamma Function. The Function 1) allows the construction of sets of truly “correlated” high-resolution spectra from which the “distance” to a real set of gas spectra can be characterized; 2) provides a simple way to evaluate Spearman's rank correlation between spectra in distinct states yielding useful information about the statistical relationship between these spectra; and 3) allows for the approximation of a real set of Line-by-Line (LBL) data as a non-linear combination of “correlated” models called MetaLBL modeling. Additionally, the function may be used to demonstrate the optimality of the Rank Correlated Spectral Line Weighted-sum-of-gray-gases (RC-SLW) Model in the space of “correlated” models, thus demonstrating that the RC-SLW Model represents the limit of accuracy in the family of correlated models. This work represents the first proposal of such a development extending modeling approaches beyond those inherent in the assumption of correlated spectra in the field of gas radiation modeling, offering promising insights for practical applications. Moreover, the MetaLBL modeling strategy is applied in a test case from atmospheric sciences for which models with a user-specified accuracy are constructed.

Aerosols have significant effects on Earth, which vary according to the type of these atmospheric particles. Different observing systems exist today to monitor aerosols, mainly through the retrieval of aerosol optical depth (AOD), among which meteorological satellites in geostationary orbit provide unique information thanks to their acquisition of several Earth's images per hour. The third generation of European geostationary satellites, Meteosat Third Generation-Imager with the onboard Flexible Combined Imager (FCI) operational since December 2024, brings new possibilities for aerosol remote sensing compared to its predecessor, Meteosat Second Generation, with the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board. This article assesses the improvements in aerosol characterization that will be made possible thanks to FCI, based on realistically generated synthetic data that are processed by optimal estimation methods to quantify aerosol information content and to retrieve relevant aerosol properties. Two case studies corresponding to challenging aerosol retrieval situations are simulated, a dust outbreak in North Africa and the wildfire season in South West Africa. First, synthetic data are used to study the potential for AOD retrieval of new FCI spectral channels in comparison to SEVIRI's. Results prove that channel VIS04 (centered at 444 nm) is the best suited for this task, with a significant decrease in retrieval error (root square mean error by 23 % and mean bias error by 65 %) in comparison to AOD estimated from the SEVIRI-heritage channel VIS06 (centered at 640 nm). Second, the FCI capabilities to further characterize aerosol particles are investigated, with the joint retrieval of AOD and fine mode fraction (FMF), which is linked to particle size distribution and therefore aerosol type. This is achieved by exploiting near-infrared channel NIR22 (centered at 2250 nm, and found to be sensitive to coarse particles only in the first part of the study) in addition to channel VIS04. Experiments show that, except under certain unfavorable conditions, the joint retrieval of AOD and FMF is possible, even if fast but less accurate radiative transfer models are used, which could be employed in an operational setting. This article demonstrates the possibility of obtaining advanced high temporal frequency aerosol observations from FCI and opens pathways for the future study of aerosol diurnal variations from space.

Surface solar irradiance (SSI) is a fundamental parameter whose components (direct and diffuse) and variabilities are highly influenced by changes in atmospheric content and scene parameters. The respective importance of cloudy-sky conditions and atmospheric aerosols on SSI evolutions is region dependent and only partially quantified. Here we provide a comprehensive analysis of SSI variabilities recorded in northern France, a region with extensive variability in sky conditions and aerosol loads. Through the application of automatic filtering methods to 1 min resolution SSI ground-based measurements over Lille, sky conditions are classified as clear-sky, 11 %; clear-sun-with-cloud, 22 %; and cloudy-sun situations, 67 % from 2010 to 2022, for which we analyze the statistics and variabilities in the global horizontal irradiance (GHI), beam horizontal irradiance (BHI), and diffuse horizontal irradiance (DHI). Coincident photometric measurements of aerosol properties and radiative-transfer simulations provide the means to conduct a multivariate analysis of the SSI observed trends and year-to-year evolutions and to estimate aerosol and cloud forcings under clear-sun conditions. The analysis of the record value of all-sky GHI in spring 2020 attributes 89 % of the changes to the exceptional sunlight conditions (57 % of clear-sun situations). It highlights also for that season the importance of solar zenith-angle changes, whose positive effects on clear-sun conditions surpass those due to aerosols. Our results show all-sky GHI and BHI positive trends of around +4.0 and +4.4 Wm-2yr-1, respectively, in both spring and summer, which are explained by more than 60 % by an increase in clear-sun occurrences of +1 % yr−1. Additional significant BHI increases under clear-sun conditions are mainly explained in spring by the negative trend in aerosol optical depth (−0.011 yr−1) and partly by angular effects in summer. Moreover, we find that clear-sun-with-cloud situations are frequently marked by irradiance enhancement due to clouds, with 13 % more GHI on a monthly average and 10 % additional diffuse proportion than in clear-sky situations. Under such conditions, clouds add on average 25 W m−2 of diffuse irradiance that sets the GHI at the remarkable level of pristine (aerosol-and-cloud-free) conditions or even higher, by more than +10 W m−2 in summer and for low aerosol loads. Overall, our results highlight the dominant and complex influence of cloudy conditions on SSI, which precedes or combines with that of aerosols and geometrical effects, and leads to a remarkable global level of SSI in clear-sun-with-cloud situations.

This article presents a realistic and novel method to estimate the solar radiant flux collected by the receiver of a solar power tower (SPT) system, taking into account the detailed atmospheric radiative transfer. It describes how an atmospheric radiative transfer Monte Carlo code is modified to solve the radiative transfer both in the atmosphere and within the concentrating system consisting of the heliostat field and the receiver. To validate the geometric modeling of a complete SPT (624 heliostats with 24 facets) as well as the estimation of its optical efficiency (both independent of the atmosphere), a comparison with the reference ray-tracing code “Solstice” is presented for two times of the day, two solar disk half-angles, and two heliostat surface slope errors. This new model allows the estimation of not only the optical losses but also, as in Moulana (2019), the gains due to atmospheric and environmental contributions i.e., radiant flux from circumsolar, aerosol scattering, ground reflection, etc. Annual average results (with a numerical uncertainty less than 0.01%) under clear sky conditions (without clouds) show that the gains are not negligible and could reach up to 0.414 MW (1.08% of the radiant flux collected by the receiver) for a relatively small SPT located in a desert area.

The EPIC/DSCOVR observations of the Earth’s surface lit by the Sun made from the first Lagrange point several times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean photosynthetically available radiation (PAR) at the ice-free ocean surface. The PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known), the irradiance reflected to space (estimated from the EPIC Level 1b radiance data), taking account of atmospheric transmission and surface albedo (modeled). Clear and cloudy regions within a pixel do not need to be distinguished, which dismisses the need for often-arbitrary assumptions about cloudiness distribution within a pixel and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness changes during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NCEP Reanalysis 2 data, aerosol optical thickness and Angström coefficient from MERRA-2 data, and ozone amount from EPIC Level 2 data. Areas contaminated by Sun glint are excluded using a threshold on Sun glint reflectance calculated using wind data. Ice masking is based on NSIDC near-real-time ice fraction data. The product is evaluated against in situ measurements at various locations and compared with estimates from sensors in polar and geostationary orbits (MODIS, AHI). Unlike with MODIS, the EPIC PAR product does not exhibit gaps at low and middle latitudes. Accuracy is satisfactory for long-term studies of aquatic photosynthesis, especially given the much larger uncertainties on the fraction of PAR absorbed by live algae and the quantum yield of carbon fixation. The EPIC daily mean PAR product is generated operationally on a Plate Carrée (equal-angle) grid with 18.4 km resolution at the equator and on an 18.4 km equal-area grid, i.e., it is fully compatible with the NASA Greenbelt OBPG ocean-color products. Data are available since the beginning of the DSCOVR mission (i.e., June 2015) from the NASA Langley ASDC website.

Besides their strong contribution to weather forecast improvement through data assimilation in clear-sky conditions, thermal infrared sounders on board polar orbiting platforms are now playing a key role in monitoring changes in atmospheric composition. However, it is known that clear sky observations are only a small part of the entire set of measurements, the remaining part is only slightly used as they are contaminated by either aerosols and/or clouds. Moreover, ice or liquid cloud retrieval of column and profile properties from passive and active measurements respectively help us in reaching a better understanding of climate processes. If the information provided by the latter has allowed a significant advance in our knowledge of the vertical distribution of condensed water, it suffers from spatial coverage compared to passive measurements. It is therefore fundamental to better characterize cloud properties from passive measurements by using, for example, high spectral resolution instruments such as IASI and the future IASI-NG. An information content analysis based on Shannon's formalism has been used to determine the level and the spectral repartition of the information about the ice cloud properties in the IASI and IASI-NG spectra. Based on this analysis, we have developped and tested an algorithm which allows to retrieve from an optimal estimation approach the cloud integrated ice water content together with the cloud layer altitude. We have taken into account the Signal-to-Noise ratio of each specific instrument and the uncertainties due to the non-retrieved atmospheric and surface parameters. The forward model is the fast radiative transfer model RTTOV which has been developped for satellite data assimilation in Numerical Weather Prediction (NWP) models. The ice cloud microphysical model is based on the ensemble model of Baran and Labonnote (2007), where the bulk ice optical properties have been parametrized as a function of the ice water content (expressed in g/m³) and in cloud temperature. The present study aims to quantify the potential and limits of thermal infrared sounders such as IASI or IASI-NG to retrieve ice cloud properties by using a representative dataset from the global operational short range forecast of the european center of medium-range weather forecast.

The present study aims to quantify the potential of hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the future IASI next generation (IASI-NG) for retrieving the ice cloud layer altitude and thickness together with the ice water path. We employed the radiative transfer model Radiative Transfer for TOVS (RTTOV) to simulate cloudy radiances using parameterized ice cloud optical properties. The radiances have been computed from an ice cloud profile database coming from global operational short-range forecasts at the European Center for Medium-range Weather Forecasts (ECMWF) which encloses the normal conditions, typical variability, and extremes of the atmospheric properties over one year (Eresmaa and McNally (2014)). We performed an information content analysis based on Shannon’s formalism to determine the amount and spectral distribution of the information about ice cloud properties. Based on this analysis, a retrieval algorithm has been developed and tested on the profile database. We considered the signal-to-noise ratio of each specific instrument and the non-retrieved atmospheric and surface parameter errors. This study brings evidence that the observing system provides information on the ice water path (IWP) as well as on the layer altitude and thickness with a convergence rate up to 95% and expected errors that decrease with cloud opacity until the signal saturation is reached (satisfying retrievals are achieved for clouds whose IWP is between about 1 and 300 g/m2).

The operational MEdium Resolution Imaging Spectrometer (MERIS) daily mean photosynthetically available radiation (PAR) product generated by the NASA Ocean Biology Processing Group (OBPG) was evaluated in clear sky conditions against in-situ measurements at various sites in the northwestern Mediterranean Sea (BOUSSOLE buoy), the northwestern Pacific (CCE-1 and -2 moorings), and the northeastern Atlantic (COVE platform). The measurements were first checked and corrected for calibration errors and uncertainties in data processing by comparing daily means for clear days (i.e., no clouds from sunrise to sunset and low aerosol abundance) with theoretical values from an accurate Monte Carlo radiative transfer code. The OBPG algorithm performed well when sky was completely cloudless during daytime, with a bias of 0.26 E/m2/d (0.6%) and a RMS difference of 1.7 E/m2/d (4.0%). Using satellite-derived aerosol optical thickness (AOT) and Angström coefficient instead of climatology slightly degraded the results, which was likely due to uncertainties in the aerosol retrievals. A sensitivity study to aerosol properties indicated that climatology may not work in some situations (e.g., episodic dust, pollution, or biomass burning events), suggesting that it is best to use actual aerosol estimates in clear sky conditions. The analysis also revealed that specifying aerosol properties, therefore atmospheric transmittance, from AOT and Angström coefficient, even retrieved from the satellite imagery, may not be sufficient in the presence of absorbing aerosols, especially when loadings are important. Performance was degraded when including situations of clear sky at the time of the MERIS observation but cloudy sky before and/or after overpass, resulting in a bias (overestimation) of 2.8 E/m2/d (7.3%) and a RMS difference of 6.0 E/m2/d (15.8%). The relatively large overestimation was due to the inability of the OBPG PAR algorithm to detect cloudiness at times other than the time of satellite overpass. The key to improving the daily mean PAR estimates in such situations does not reside so much in improving the radiative transfer treatment or specifying more accurately aerosol properties, but rather in accounting properly for the diurnal variability of cloudiness. To this end, a methodology that utilized Modern Era Retrospective Reanalysis for Research and Applications, Version 2 (MERRA-2) hourly cloud data (fractional coverage, optical thickness) was proposed and tested, reducing the bias to 1.6 E/m2/d (4.2%). Improvement was not sufficient in some situations, due to the coarse resolution and uncertainties of the MERRA-2 products, which could not describe properly the cloud properties at the local scale (MERIS pixel). The treatment is applicable to any cloud situation and should be considered in a future version of the of OBPG PAR algorithm. This would require, however, refreshing the standard OBPG PAR products generated as part of the ocean-color processing line according to MERRA-2 data availability.

SMART-G (Speed-Up Monte-Carlo Advanced Radiative Transfer code with GPU) is a radiative transfer solver for the coupled ocean-atmosphere system with a wavy interface. It is based on the Monte-Carlo technique, works in either plane-parallel or spherical-shell geometry, and accounts for polarization. The vector code is written in CUDA (Compute Unified Device Architecture) and runs on GPUs (Graphic Processing Units). For typical simulations, the GPU-based code running on the Nvidia GTX 1070 card is shown to be 100 time faster than a state of the art CPU-based code running on an AMD Phenomllx4 965 at 3.4GHz. This makes SMART-G competitive, in terms of computational efficiency, with codes based on other techniques (e.g., discrete ordinate, doubling-adding, matrix-operator, and successive-orders-ofscattering), while allowing maximum flexibility regarding the scope of the simulations. The monochromatic version of the code without trans-spectral processes (Raman scattering, fluorescence) is described, including the treatment of photon propagation and interactive processes (elastic scattering, absorption, reflection, refraction, but not thermal emission) and variance reduction (local estimate). Benchmark values are accurately reproduced for clear and cloudy atmospheres over a wavy reflecting surface and a black ocean. Results obtained for a diffusely reflecting ocean agree with those from a discrete ordinate code. SMART-G may be used, not only as a reference code, but also to simulate the signal/imagery observed/produced by optical sensors, create accurate look-up tables, and investigate new remote sensing techniques. (C) 2018 Elsevier Ltd. All rights reserved.

Knowing the spatial and temporal distribution of the underwater light field, i.e., the spectral and angular structure of the radiant intensity at any point in the water column, is essential to understanding the biogeochemical processes that control the composition and evolution of aquatic ecosystems and their impact on climate and reaction to climate change. At present, only a few properties are reliably retrieved from space, either directly or via water-leaving radiance. Existing satellite products are limited to planar photosynthetically available radiation (PAR) and ultraviolet (UV) irradiance above the surface and diffuse attenuation coefficient. Examples of operational products are provided, and their advantages and drawbacks are examined. The usefulness and convenience of these products notwithstanding, there is a need, as expressed by the user community, for other products, i.e., sub-surface planar and scalar fluxes, average cosine, spectral fluxes (UV to visible), diurnal fluxes, absorbed fraction of PAR by live algae (APAR), surface albedo, vertical attenuation, and heating rate, and for associating uncertainties to any product on a pixel-by-pixel basis. Methodologies to obtain the new products are qualitatively discussed in view of most recent scientific knowledge and current and future satellite missions, and specific algorithms are presented for some new products, namely sub-surface fluxes and average cosine. A strategy and roadmap (short, medium, and long term) for usage and development priorities is provided, taking into account needs and readiness level. Combining observations from satellites overpassing at different times and geostationary satellites should be pursued to improve the quality of daily-integrated radiation fields, and products should be generated without gaps to provide boundary conditions for general circulation and biogeochemical models. Examples of new products, i.e., daily scalar PAR below the surface, daily average cosine for PAR, and sub-surface spectral scalar fluxes are presented. A procedure to estimate algorithm uncertainties in the total uncertainty budget for above-surface daily PAR, based on radiative simulations for expected situations, is described. In the future, space-borne lidars with ocean profiling capability offer the best hope for improving our knowledge of sub-surface fields. To maximize temporal coverage, space agencies should consider placing ocean-color instruments in L1 orbit, where the sunlit part of the Earth can be frequently observed.

Satellite ocean-color project offices routinely generate Level 2 and Level 3 daily Photo-synthetically Available Radiation (PAR) products. Accuracy is currently evaluated against in-situ measurements from buoys and fixed platfouns at a few locations, but specifying algorithm (and other) uncertainties on a pixel-by-pixel basis is needed to assess product quality. Expressing uncertainties requires modeling the measurement, identifying all possible error sources (e.g., noise in the input variables, imperfect/incomplete mathematical model), and deteunining the combined uncertainty. In the present study, algorithm uncertainties associated with PAR products are considered, i.e., those due to model approximations and parameter errors (e.g., decoupling effects of clouds and clear atmosphere, neglecting diurnal variability of clouds, using aerosol climatology) assuming that the input variables (TOA reflectance at wavelengths in the PAR spectral range) are known perfectly. A procedure is provided to estimate and provide, for each pixel of a product, this uncertainty component of the total uncertainty budget, which is expected to dominate. The bias and standard deviation of the daily PAR estimates are calculated as a function of clear sky PAR and cloud factor (i.e., the effect of clouds on daily PAR). The uncertainty characterization is accomplished using an extended simulation dataset covering the 2003-2012 time period using hourly MERRA-2 input data. The large number of data points allows one to sample well atmospheric variability and in particular many variations of daytime cloudiness, for all latitudes. Selected maps of global daily and monthly PAR and associated uncertainties (bias, standard deviation), obtained from MERIS data, are analyzed. Comparisons with match-up data at the COVE calibration/evaluation site reveal that experimental uncertainties are similar to the theoretical uncertainties obtained from simulated data.

Aims. We present the first public release of high-quality data products (DR1) from Hi-GAL, the Herschel infrared Galactic Plane Survey. Hi-GAL is the keystone of a suite of continuum Galactic plane surveys from the near-IR to the radio and covers five wavebands at 70, 160, 250, 350 and 500 mu m, encompassing the peak of the spectral energy distribution of cold dust for 8 less than or similar to T less than or similar to 50 K. This first Hi-GAL data release covers the inner Milky Way in the longitude range 68 degrees greater than or similar to l greater than or similar to -70 degrees in a vertical bar b vertical bar extless= 1 degrees latitude strip. Methods. Photometric maps have been produced with the ROMAGAL pipeline, which optimally capitalizes on the excellent sensitivity and stability of the bolometer arrays of the Herschel PACS and SPIRE photometric cameras. It delivers images of exquisite quality and dynamical range, absolutely calibrated with Planck and IRAS, and recovers extended emission at all wavelengths and all spatial scales, from the point-spread function to the size of an entire 2 degrees x 2 degrees “tile” that is the unit observing block of the survey. The compact source catalogues were generated with the CuTEx algorithm, which was specifically developed to optimise source detection and extraction in the extreme conditions of intense and spatially varying background that are found in the Galactic plane in the thermal infrared. Results. Hi-GAL DR1 images are cirrus noise limited and reach the 1 sigma-rms predicted by the Herschel Time Estimators for parallel-mode observations at 60 `' s(-1) scanning speed in relatively low cirrus emission regions. Hi-GAL DR1 images will be accessible through a dedicated web-based image cutout service. The DR1 Compact Source Catalogues are delivered as single-band photometric lists containing, in addition to source position, peak, and integrated flux and source sizes, a variety of parameters useful to assess the quality and reliability of the extracted sources. Caveats and hints to help in this assessment are provided. Flux completeness limits in all bands are determined from extensive synthetic source experiments and greatly depend on the specific line of sight along the Galactic plane because the background strongly varies as a function of Galactic longitude. Hi-GAL DR1 catalogues contain 123210, 308509, 280685, 160972, and 85460 compact sources in the five bands.