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Satellite measurements play an increasingly important role in the study of atmospheric ammonia (NH3). Here, we present version 4 of the Artificial Neural Network for IASI (ANNI; IASI: Infrared Atmospheric Sounding Interferometer) retrieval of NH3. The main change is the introduction of total column averaging kernels (AVKs), which can be used to undo the effect of the vertical profile shape assumption of the retrieval. While the main equations can be matched term for term with analogous ones used in UV/Vis retrievals for other minor absorbers, we derive the formalism from the ground up, as its applicability to thermal infrared measurements is non-trivial. A large number of other smaller changes were introduced in ANNI v4, most of which improve the consistency of the measurements across time and across the series of IASI instruments. This includes a more robust way of calculating the hyperspectral range index (HRI), explicitly accounting for long-term changes in CO2 in the HRI calculation and the use of a reprocessed cloud product that was specifically developed for climate applications. The NH3 distributions derived with ANNI v4 are very similar to the ones derived with v3, although values are about 10 %–20 % larger due to the improved setup of the HRI. We exclude further large biases of the same nature by showing the consistency between ANNI v4 derived NH3 columns with columns obtained with an optimal estimation approach. Finally, with v4, we revised the uncertainty budget and now report systematic uncertainty estimates alongside random uncertainties, allowing realistic mean uncertainties to be estimated. [ABSTRACT FROM AUTHOR]

Satellite measurements play an increasingly important role in the study of atmospheric ammonia (NH3). Here, we present version 4 of the Artificial Neural Network for IASI (ANNI) retrieval of NH3. The main change is the introduction of total column averaging kernels (AVKs), which can be used to undo the effect of the vertical profile shape assumption of the retrieval. While the main equations can be matched term for term with analogous ones used in UV/Vis retrievals for other minor absorbers, we derive the formalism from the ground up, as its applicability to thermal infrared measurements is nontrivial. A large number of other smaller changes were introduced in ANNI v4, most of which improve the consistency of the measurements, across time and across the series of IASI instruments. This includes a more robust way of calculating the hyperspectral range index (HRI), explicitly accounting for long-term changes in CO2 in the HRI calculation and the use of a reprocessed cloud product that was specifically developed for climate applications. The NH3 distributions derived with ANNI v4 are very similar to the ones derived with v3, although values are about 15–20 % larger due to the improved setup of the HRI. We exclude further large biases of the same nature, by showing the consistency between ANNI v4 derived NH3 columns with columns obtained with an optimal estimation approach. Finally, with v4, we revised the uncertainty budget and now report systematic uncertainty estimates alongside random uncertainties, allowing realistic mean uncertainties to be estimated. [ABSTRACT FROM AUTHOR]

With more than 15 years of continuous and consistent measurements, the Infrared Atmospheric Sounding Interferometer (IASI) radiance dataset is becoming a reference climate data record. To be exploited to its full potential, it requires a cloud filter that is accurate, unbiased over the full IASI life span and strict enough to be used in satellite data retrieval schemes. Here, we present a new cloud detection algorithm which combines (1) a high sensitivity, (2) a good consistency over the whole IASI time series and between the different copies of the instrument flying on board the suite of Metop satellites, and (3) simplicity in its parametrization. The method is based on a supervised neural network (NN) and relies, as input parameters, on the IASI radiance measurements only. The robustness of the cloud mask over time is ensured in particular by avoiding the IASI channels that are influenced by CO 2 , N 2 O, CH 4 , CFC-11 and CFC-12 absorption lines and those corresponding to the ν2 H 2 O absorption band. As a reference dataset for the training, version 6.5 of the operational IASI Level 2 (L2) cloud product is used. We provide different illustrations of the NN cloud product, including comparisons with other existing products. We find very good agreement overall with version 6.5 of the operational IASI L2 with an identical mean annual cloud amount and a pixel-by-pixel correspondence of about 87 %. The comparison with the other cloud products shows a good correspondence in the main cloud regimes but with sometimes large differences in the mean cloud amount (up to 10 %) due to the specificities of each of the different products. We also show the good capability of the NN product to differentiate clouds from dust plumes. [ABSTRACT FROM AUTHOR]

The presence of a weekly cycle in the abundance of an atmospheric constituent is a typical fingerprint for the anthropogenic nature of its emission sources. However, while ammonia is mainly emitted as a consequence of human activities, a weekly cycle has never been detected in its abundances at large scale. We expose here for the first time the presence of a weekend effect in the NH3 total columns measured by the IASI satellite sounder over the main agricultural source regions in Europe: northwestern Europe (Belgium-the Netherlands-northwest Germany), the Po Valley, Brittany, and, to a lesser extent, the Ebro Valley. A decrease of 15% relative to the weekly mean is seen on Sunday–Monday observations in northwestern Europe, as a result of reduced NH3 emissions over the weekend. This is confirmed by in situ NH3 concentration data from the National Air Quality Monitoring Network in the Netherlands, where an average reduction of 10% is found around midnight on Sunday. The identified weekend effect presents a strong seasonal variability, with two peaks, one in spring and one in summer, coinciding with the two main (manure) fertilization periods. In spring, a reduction on Sunday–Monday up to 53 and 26% is found in the NH3 satellite columns and in situ concentrations, respectively, as fertilization largely drives atmospheric NH3 abundances at this time of the year. [ABSTRACT FROM AUTHOR]

With more than 15 years of continuous and consistent measurements, the Infrared Atmospheric Sounding Interferometer (IASI) radiance dataset is becoming a reference climate data record. To be exploited to its full potential, it requires a cloud filter that is both accurate, unbiased over the full IASI lifespan, and strict enough to be used in satellite data retrieval schemes. Here, we present a new cloud detection algorithm which combines (1) a high sensitivity, (2) a good consistency over the whole IASI time series and between the different copies of the instrument flying on board the suite of Metop satellites and (3) simplicity in its parametrization. The method is based on a supervised neural network (NN) and relies, as input parameters, on the IASI radiance measurements only. The robustness of the cloud mask over time is ensured in particular by avoiding the IASI channels that are influenced by CO2, N2O, CH4, CFC-11 and CFC-12 absorption lines and those corresponding to the X2 H2O absorption band. As a reference dataset for the training, the latest version of the operational IASI Level 2 (L2) cloud product is used. We provide different illustrations of the NN cloud product, including comparisons with other existing products. We find a very good agreement overall with the last version of the operational IASI L2 with an identical mean annual cloud amount and a pixel-by-pixel correspondence of about 87%. The comparison with the other cloud products shows a good correspondence in the main cloud regimes but with sometimes large differences in the mean cloud amount (up to 10%) due to the specificities of each of the different products. We also show the good capability of the NN product to differentiate clouds from dust plumes. [ABSTRACT FROM AUTHOR]

Limiting excess atmospheric ammonia (NH3) is one of the great challenges for humanity in the 21st century but can only be achieved with adequate monitoring means in place. Here, we report the first NH3 measurements from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Chinese FY‐4A satellite. The instrument scans almost all of Asia 10 times per day. We show that GIIRS′ unprecedented temporal sampling can be exploited to measure diurnal and nocturnal variations of NH3 and demonstrate this on two case studies over Punjab and the North China Plain. Day–night variations are found to be almost absent in winter, but can reach a factor 2–3 in the warmer months. These case studies are very promising given the future landscape of geostationary sounders, but also show that improved knowledge on lower tropospheric air temperatures and the vertical profile of NH3 is key to better exploit their measurements. Plain Language Summary: To reduce the worldwide concentrations of ammonia (NH3) in the air is one of the great challenges for humanity in the 21st century, but this can only be achieved with stringent policies in place, and with the support of accurate and frequent measurements. In this paper, we report the first ammonia measurements from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Chinese FY‐4A satellite. The satellite is positioned above Indonesia and measures almost all of Asia 10 times per day. We show that GIIRS' measurements can be used to measure ammonia both during day and night. We look into more detail at two locations, over Punjab (Pakistan) and the North China Plain. Day‐night variations are found to be almost absent in the colder months, but can reach a factor 2–3 in the warmer months. These case studies are very promising given the future landscape of geostationary sounders, but demonstrate also that improved knowledge on the air temperature at the surface and the behavior of ammonia at higher altitudes will result in more accurate ammonia measurements. Key Points: We report the first observations of atmospheric NH3 from the geostationary satellite FY‐4A GIIRS over AsiaThe potential of GIIRS to reveal the diurnal and nocturnal variations of NH3 total columns is demonstrated over two sitesTo better exploit geostationary data, improved temperature profiles and knowledge on the vertical NH3 distribution is needed [ABSTRACT FROM AUTHOR]

In recent years, major progress has been made in measuring weakly absorbing atmospheric trace gases from high spectral resolution space observations. In this paper, we apply the so‐called whitening transformation on spectra of the Infrared Atmospheric Sounding Interferometer, and show that it allows removing most of the climatological background from spectra, leaving a residual that contains those spectral signatures that depart from normality. These can subsequently be attributed to changes in the abundance of trace species. This is illustrated for two diverging cases: (1) a biomass burning plume from the 2019/2020 Australian bushfires, leading to the unambiguous identification of nine reactive trace gases, including a first observation of glycolaldehyde; (2) spectra observed a decade apart, from which changes in eight long‐lived halogenated substances are identified; three of them never observed before by a nadir sounder. Plain Language Summary: In recent years, several techniques have been developed for the detection of gases present in very small quantities in the atmosphere, which has significantly improved our knowledge on atmospheric composition and chemistry. In this paper, we describe a powerful complementary technique that transforms atmospheric spectra to highlight and attribute the spectral signatures of different species. We apply it on spectra measured by the spaceborne Infrared Atmospheric Sounding Interferometer for two different cases: (1) a plume from the 2019/2020 Australian bushfires leading to the clear identification of nine rare gases including a first observation from space of glycolaldehyde; (2) spectra observed a decade apart, of which eight long‐lived halogenated substances were identified; three of them never observed before by a nadir sounder. The detection of these reactive and long‐lived trace gases is very promising for the monitoring of their temporal evolution and the attribution of their sources. Key Points: The whitening transformation allows analyzing anomalies in hyperspectral infrared spectraIn a biomass burning plume we identify nine compounds including a first observation of glycolaldehydeOn spectra observed a decade apart we identify atmospheric changes in eight long‐lived halocarbons [ABSTRACT FROM AUTHOR]

Atmospheric emissions of chlorofluorocarbons (CFCs), their hydrogenated derivatives (HCFCs, HFCs) and other long-lived halogen-containing substances perturb the chemical and radiative equilibrium of our atmosphere. A global network of ground-based stations (AGAGE) monitors the concentrations of most of these species. Supplementing these, upper tropospheric and stratospheric concentrations are available from satellite measurements of the ACE-FTS solar occultation sounder. Measurements derived from nadir viewing infrared sounders can potentially complement both observational datasets, offering extensive spatial coverage and temporal sampling. With a preferential sensitivity to the middle troposphere, they also cover the vertical altitude range that is not covered by other means. However, fluctuations in surface temperature and the concentration of interfering atmospheric constituents render the retrieval of halogenated species particularly challenging. Relying on previous work on spectral whitening, we present an unconstrained generalized least squares estimation retrieval methodology, which largely allows to overcome the problem of interference. We demonstrate that it can be used to retrieve monthly anomalies of all halogenated species previously observed in spectra from the Infrared Atmospheric Sounding Interferometer (IASI/Metop). Focussing on northern mid-latitudes, we derive the monthly concentrations of CFC-11, CFC-12, HCFC-22, HCFC-142b, HFC-134a, CF 4 , SF 6 and CCl 4 between 2008 and 2022. Trends are compared to the observations from AGAGE and ACE-FTS. A good match is obtained with both, with especially remarkable agreement in the linear trends for CF 4 , SF 6 and HFC-134a, and in the non-linear trends of CFC-11 and HCFC-22. Large discrepancies and unexplained variations are seen in the time series of HCFC-142b, CFC-12 and CCl 4 , necessitating further optimization of the retrieval technique. The results demonstrate the potential of IASI and follow-up missions for establishing a unique long-term time series of the most important long-lived halogenated species. • A retrieval algorithm is presented for halogenated gases using IR nadir observations. • Time series of 8 long-lived halogenated gases are retrieved from IASI measurements. • Trends of CFC-11, CFC-22, HCFC-22, HCFC-142b, HFC-134a, CF 4 , SF 6 , CCl 4 are derived. • The data is compared with AGAGE and ACE-FTS observations. [ABSTRACT FROM AUTHOR]

Space-based measurements of the outgoing longwave radiation (OLR) are essential for the study of Earth’s climate system. While the CERES instrument provides accurate measurements of this quantity, its measurements are not spectrally resolved. Here we present a high-resolution OLR product (sampled at 0.25 cm−1), derived from measurements of the IASI satellite sounder. The applied methodology relies on precalculated angular distribution models (ADMs). These are usually calculated for tens to hundreds of different scene types (characterized by surface and atmosphere parameters). To guarantee accurate results in the range 645–2300 cm−1 covered by IASI, we constructed ADMs for over 140 000 scenes. These were selected from one year of CAMS reanalysis data. A dissimilarity-based selection algorithm was applied to choose scenes as different from each other as possible, thereby maximizing the performance on real data, while keeping the number of scenes manageable. A comparison of the IASI OLR integrated over the 645–2300 cm−1 range was performed with the longwave broadband OLR products from CERES and the AIRS instrument. The latter are systematically higher due to the contribution of the far infrared to the total IR spectral range, but as expected exhibit generally high spatial correlations with the IASI OLR, except for some areas in the tropical region. We also compared the IASI OLR against the spectrally resolved OLR derived from AIRS. A good agreement was found above 1200 cm−1 while AIRS OLR appeared to be systematically higher in the atmospheric window region, likely related to differences in overpass time or to the use of a different cloud detection algorithm. [ABSTRACT FROM AUTHOR]

Bogoslof volcano, Alaska, experienced at least 70 explosive eruptions between 12 December 2016 and 31 August 2017. Due to its remote location and limited local monitoring network, this eruption was monitored and characterized primarily using remote geophysical and satellite techniques. SO2 emissions from Bogoslof were persistently detected by the Infrared Atmospheric Sounding Interferometer (IASI) satellite sensors. Of Bogoslof's 70 explosive events, 50% produced measurable SO2 masses ranging from 0.1 to 21.5 kt, with a median and standard deviation of 0.7 ± 4.0 kt SO2, respectively. Here, we compare IASI-derived SO2 masses from Bogoslof events to complementary geophysical datasets to provide insights into eruption source processes, namely the degree of seawater scrubbing of water-soluble SO2 and variations in magma flux. Correlations with the number of lightning strokes and infrasound energy are expected to indicate magma-flux as a controlling process, while correlations with infrasound frequency index are expected to indicate variations in vent-water content as a controlling factor. These comparisons suggest that the measured SO2 masses are primarily a function of eruption magnitude (degassed magma mass) and that scrubbing of SO2 emissions by vent seawater may have exerted a minor effect on the observed SO2 masses. SO2 masses were combined with petrologic constraints on melt inclusion and matrix glass S concentrations to calculate degassed magma masses and volumes. The cumulative SO2-derived degassed magma mass and estimated volume (dense-rock equivalent) for the full Bogoslof eruption were found to be 2.8 × 1010 kg and 9.3 × 106 m3, respectively. When individual event masses are compared against event masses calculated using an empirical plume-height method, a strong correlation is found (R2 = 0.83), with better than order-of-magnitude agreement in most cases. These estimates of eruption masses provide useful information on the magnitude, behavior, and associated hazards of the 2016–2017 eruption, and potentially future unrest at Bogoslof volcano. [ABSTRACT FROM AUTHOR]

As a precursor of atmospheric aerosols, ammonia (NH3) is one of the primary gaseous air pollutants. Given its short atmospheric lifetime, ambient NH3 concentrations are dominated by local sources. In a recent study, have highlighted the importance of NH3 point sources, especially those associated with feedlots and industrial ammonia production. Their emissions were shown to be largely underestimated in bottom-up emission inventories. The discovery was made possible thanks to the use of oversampling techniques applied to 9 years of global daily IASI NH3 satellite measurements. Oversampling allows one to increase the spatial resolution of averaged satellite data beyond what the satellites natively offer. Here we apply for the first time superresolution techniques, which are commonplace in many fields that rely on imaging, to measurements of an atmospheric sounder, whose images consist of just single pixels. We demonstrate the principle on synthetic data and on IASI measurements of a surface parameter. Superresolution is a priori less suitable to be applied on measurements of variable atmospheric constituents, in particular those affected by transport. However, by first applying the wind-rotation technique, which was introduced in the study of other primary pollutants, superresolution becomes highly effective in mapping NH3 at a very high spatial resolution. We show that plume transport can be revealed in greater detail than what was previously thought to be possible. Next, using this wind-adjusted superresolution technique, we introduce a new type of NH3 map that allows tracking down point sources more easily than the regular oversampled average. On a subset of known emitters, the source could be located within a median distance of 1.5 km. We subsequently present a new global point-source catalog consisting of more than 500 localized and categorized point sources. Compared to our previous catalog, the number of identified sources more than doubled. In addition, we refined the classification of industries into five categories – fertilizer, coking, soda ash, geothermal and explosives industries – and introduced a new urban category for isolated NH3 hotspots over cities. The latter mainly consists of African megacities, as clear isolation of such urban hotspots is almost never possible elsewhere due to the presence of a diffuse background with higher concentrations. The techniques presented in this paper can most likely be exploited in the study of point sources of other short-lived atmospheric pollutants such as SO2 and NO2. [ABSTRACT FROM AUTHOR]

As a precursor of atmospheric aerosols, ammonia (NH3) is one the primary gaseous air pollutants. Given its short atmospheric lifetime, ambient NH3 concentrations are dominated by local sources. In a recent study, Van Damme et al. (2018) have highlighted the importance of NH3 point sources, especially those associated with feedlots and industrial ammonia production. Their emissions were shown to be largely underestimated in bottom-up emission inventories. The discovery was made possible thanks to the use of oversampling techniques applied on 9 years of global daily IASI NH3 satellite measurements. Oversampling allows to increase the spatial resolution of averaged satellite data, beyond what the satellites natively offer. Here, we apply for the first time the so-called superresolution techniques, which are commonplace in many fields that rely on imaging, to measurements of an atmospheric sounder, whose images consist of just single pixels. We demonstrate the principle on synthetic data and on IASI measurements of a surface parameter. Superresolution is a priori less suitable to be applied on measurements of variable atmospheric constituents, in particular those affected by transport. However, by first applying the so-called wind-rotation technique, which was introduced in the study of other primary pollutants, superresolution becomes highly effective to map NH3 at very high spatial resolution. We show in particular that it allows revealing plume transport in much greater detail than what was previously thought to be possible. Next, using this wind-adjusted superresolution technique, we introduce a new type of NH3 map that allows to track down point sources much more easily than the regular oversampled average. On a subset of known emitters, it allows to locate the source within a median distance of 1.5 km. We subsequently present a new global point source catalog consisting of more than 500 localized and categorized point sources. Compared to our previous catalog, the number of identified sources more than doubled. In addition, we refined the classification of industries into five categories: fertilizer, coking, soda ash, geothermal and explosive industry; and introduced a new urban category for isolated NH3 hotspots over cities. The latter mainly consists of African megacities, as clear isolation of such urban hotspots is almost never possible elsewhere due to the presence of a larger diffuse background. The techniques presented in this paper can most likely be exploited in the study of point sources of other short-lived atmospheric pollutants such as SO2 and NO2. [ABSTRACT FROM AUTHOR]

Atmospheric emissions of reactive nitrogen in the form of nitrogen dioxide (NO 2) and ammonia (NH 3) worsen air quality and upon deposition, dramatically affect the environment. Recent infrared satellite measurements have revealed that NH 3 emitted by industries are an important and underestimated emission source. Yet, to assess these emissions, current satellite sounders are severely limited by their spatial resolution. In this paper, we analyse measurement data recorded in a series of imaging surveys that were conducted over industries in the Greater Berlin area (Germany). On board the aircraft were the Telops Hyper-Cam LW, targeting NH 3 measurements in the longwave infrared at a resolution of 4 m and the SWING+ spectrometer targeting NO 2 measurements in the UV–Vis at a resolution of 180 m. Two flights were carried out over German's largest production facility of synthetic NH 3 , urea and other fertilizers. In both cases, a large NH 3 plume was observed originating from the factory. Using a Gaussian plume model to take into account plume rise and dispersion, coupled with well-established radiative transfer and inverse methods, we retrieve vertical column densities. From these, we calculate NH 3 emission fluxes using the integrated mass enhancement and cross-sectional flux methods, yielding consistent emissions of the order of 2200 t yr−1 for both flights, assuming constant fluxes across the year. These estimates are about five times larger than those reported in the European Pollutant Release and Transfer Register (E-PRTR) for this plant. In the second campaign, a co-emitted NO 2 plume was measured, likely related to the production of nitric acid at the plant. A third flight was carried out over an area comprising the cities of Staßfurt and Bernburg. Several small NH 3 plumes were seen, one over a production facility of mineral wool insulation, one over a sugar factory and two over the soda ash plants in Staßfurt and Bernburg. A fifth and much larger plume was seen to originate from the sedimentation basins associated with the soda ash plant in Staßfurt, indicating rapid volatilization of ammonium rich effluents. We use the different measurement campaigns to simulate measurements of Nitrosat, a potential future satellite sounder dedicated to the sounding of reactive nitrogen at a resolution of 500 m. We demonstrate that such measurements would allow accurately constraining emissions in a single overpass, overcoming a number of important drawbacks of current satellite sounders. • Industrial plumes of ammonia are observed using airborne infrared measurements. • Ammonia emitted by a chemical fertilizer plant is estimated using different methods. • The plant is seen to observe simultaneously ammonia and nitrogen dioxide. • Sedimentation basins associated with soda ash production are seen to emit ammonia. • Results are analysed in the light of a potential future Nitrosat satellite mission. [ABSTRACT FROM AUTHOR]

Separating concentrations of carbon monoxide (CO) in the boundary layer from the rest of the atmosphere with nadir satellite measurements is of particular importance to differentiate emission from transport. Although thermal infrared (TIR) satellite sounders are considered to have limited sensitivity to the composition of the near-surface atmosphere, previous studies show that they can provide information on CO close to the ground in case of high thermal contrast. In this work we investigate the capability of IASI (Infrared Atmospheric Sounding Interferometer) to retrieve near-surface CO concentrations, and we quantitatively assess the influence of thermal contrast on such retrievals. We present a 3-part analysis, which relies on both theoretical forward simulations and retrievals on real data, performed for a large range of negative and positive thermal contrast situations. First, we derive theoretically the IASI detection threshold of CO enhancement in the boundary layer, and we assess its dependence on thermal contrast. Then, using the optimal estimation formalism, we quantify the role of thermal contrast on the error budget and information content of near-surface CO retrievals. We demonstrate that, contrary to what is usually accepted, large negative thermal contrast values (ground cooler than air) lead to a better decorrelation between CO concentrations in the low and the high troposphere than large positive thermal contrast (ground warmer than the air). In the last part of the paper we use Mexico City and Barrow as test cases to contrast our theoretical predictions with real retrievals, and to assess the accuracy of IASI surface CO retrievals through comparisons to ground-based in-situ measurements. [ABSTRACT FROM AUTHOR]

Abstract: Relating to a conjecture on the decomposability of positive maps in , we solve the open problem of the existence of and PPT edge states. [Copyright &y& Elsevier]

To mitigate the impact of greenhouse gas (GHG) and air pollutant emissions, it is of utmost importance to understand where emissions occur. In the real world, atmospheric pollutants are produced by various human activities from point sources (e.g. power plants and industrial facilities) but also from diffuse sources (e.g. residential activities and agriculture). However, as tracking all these single sources of emissions is practically impossible, emission inventories are typically compiled using national-level statistics by sector, which are then downscaled at the grid-cell level using spatial information. In this work, we develop high-spatial-resolution proxies for use in downscaling the national emission totals for all world countries provided by the Emissions Database for Global Atmospheric Research (EDGAR). In particular, in this paper, we present the latest EDGAR v8.0 GHG, which provides readily available emission data at different levels of spatial granularity, obtained from a consistently developed GHG emission database. This has been achieved through the improvement and development of high-resolution spatial proxies that allow for a more precise allocation of emissions over the globe. A key novelty of this work is the potential to analyse subnational GHG emissions over the European territory and also over the United States, China, India, and other high-emitting countries. These data not only meet the needs of atmospheric modellers but can also inform policymakers working in the field of climate change mitigation. For example, the EDGAR GHG emissions at the NUTS 2 level (Nomenclature of Territorial Units for Statistics level 2) over Europe contribute to the development of EU cohesion policies, identifying the progress of each region towards achieving the carbon neutrality target and providing insights into the highest-emitting sectors. The data can be accessed at 10.2905/b54d8149-2864-4fb9-96b9-5fd3a020c224 specifically for EDGAR v8.0 (Crippa et al., 2023a) and 10.2905/D67EEDA8-C03E-4421-95D0-0ADC460B9658 for the subnational dataset (Crippa et al., 2023b). [ABSTRACT FROM AUTHOR]

Abstract: We define the disentangling power of a unitary operator in a similar way as the entangling power defined by Zanardi et al. [P. Zanardi, C. Zalka, L. Faoro, Phys. Rev. A 62 (2000) 030301(R), quant-ph/0005031]. A general formula is derived and it is shown that both quantities are directly proportional. All results concerning the entangling power can simply be translated into similar statements for the disentangling power. In particular, the disentangling power is maximal for certain permutations derived from orthogonal Latin squares. These permutations can therefore be interpreted as those that distort entanglement in a maximal way. [Copyright &y& Elsevier]

Formic acid (HCOOH) and acetic acid (CH3COOH) are ubiquitous atmospheric trace gases and the most abundant carboxylic acids in the global troposphere. They have a substantial impact on the atmospheric aqueous-phase chemistry and are major sources of cloud and precipitation acidity. Together, they account for > 60% of the rainwater acidity in remote regions (e.g., over tropical and boreal forests). Sources of formic and acetic acids include direct emissions (e.g., from biomass burning, fossil fuel, plants) and secondary production from sunlight-induced degradation of a suite of other volatile organic compounds. However, several investigations have pointed to large inconsistencies between measurements and model simulations, suggesting key gaps in our understanding of their sources and the likely existence of so far unidentified sources.Here we use a neural network-based approach to retrieve total columns of formic and acetic acids from the IASI (Infrared Atmospheric Sounding Interferometer) satellite observations, and to produce daily global and regional pictures of both species. With this dataset we characterize their respective spatial distributions, seasonal variability as well as their transport patterns from emission sources. We focus on their emission regions to study the variability and evolution of the HCOOH-to-CH3COOH ratios. Their respective enhancement ratios with respect to CO total columns from IASI are also investigated in fire plumes. This study contributes to improving our understanding of the emission sources of these dominant carboxylic acids. [ABSTRACT FROM AUTHOR]

Oxygenated volatile organic compounds (OVOCs) have a significant impact on the atmospheric oxidative capacity and hence on methane lifetime. However, large uncertainties are associated with their current atmospheric budget estimates, mainly because of the paucity of global consistent measurements to constrain the distribution and magnitude of their various primary and/or secondary sources (e.g., terrestrial vegetation, anthropogenic emissions, biomass burning, oceans and the oxidation of precursor hydrocarbons). The global, spatially dense observations from nadir-viewing infrared sensors onboard meteorological satellites can help reducing these uncertainties but retrieving concentrations of atmospheric OVOCs from spaceborne infrared radiance spectra is challenging due to their weak, sometimes broadband spectral absorptions.Here we present a general framework for fast retrieval of OVOC total columns from the Metop/IASI (Infrared Atmospheric Sounding Interferometer) observations. Initially developed for the retrieval of ammonia, the ANNI (Artificial Neural Network for IASI) method relies on a hyperspectral range index (HRI) for the quantification of the gas spectral signature and on an artificial neural network to convert the HRI into a gas total column. This method reveals particularly robust and sensitive to the detection of weak absorbers in IASI spectra and is used to retrieve twice-daily global total column distributions of five major atmospheric OVOCs: methanol, formic acid, PAN, acetone and acetic acid. Furthermore, its computing efficiency allows processing the decadal time series (from 2007) of IASI observations.The comparison between the OVOC columns retrieved via this approach and those obtained using a physical inversion (optimal estimation method) shows an overall good agreement and is consistent with the conceptual differences between the two approaches. Independent OVOC measurements from aircraft campaigns and ground-based FTIR observations are also used to validate the IASI data. These new satellite products offer the opportunity to further investigate the distribution, sources, transport and seasonal and inter-annual variability of these dominant OVOCs. A 10-year climatology is presented for each species. Moreover, the retrieved IASI data allow the production of daily global pictures and regional quick-looks of OVOC columns, which we use to illustrate the spatial and temporal heterogeneity of these species as well as their dominant transport patterns, and to study specific events (e.g., fires and biomass burning plumes). [ABSTRACT FROM AUTHOR]

Particulate nitrate is a major component of fine particulate matter (PM2.5). Its formation may be varyingly sensitive to emissions of ammonia (NH3), nitrogen oxides (NOx ≡ NO + NO2), and volatile organic compounds (VOCs), depending on local conditions. Diagnosing these sensitivities is critical for successful air quality management. Here, we show that satellite measurements of tropospheric NH3 and NO2 columns can be used as a quick indicator of the dominant sensitivity regime through the NH3/NO2 column ratio together with the NO2 column. We demonstrate the effectiveness of this indicator with the GEOS‐Chem chemical transport model and define thresholds to separate the different sensitivity regimes. Applying the method to wintertime IASI and OMI observations in East Asia reveals that surface nitrate is dominantly VOC‐sensitive in the southern North China Plain (NCP), NOx‐sensitive in most of the East China Plain, and NH3‐sensitive in the northern NCP, southern China, and Korea. Plain Language Summary: We present a novel application of satellite remote sensing to better understand the causes of particulate nitrate pollution. Particulate nitrate is a major air pollutant throughout the urbanized world. It is produced by atmospheric oxidation of emitted nitrogen oxides (NOx) but may be more sensitive to emissions of ammonia (NH3) or volatile organic compounds (VOCs). Understanding which of NH3, NOx, or VOC emissions is most important in driving nitrate formation is critical for air quality management. We show that satellite measurements of the NH3/NO2 column ratio along with NO2 columns is an effective indicator to determine the dominant sensitivity regime (NH3−,NOx−,orVOC−sensitive ${\text{NH}}_{3}-,{\text{NO}}_{\mathrm{x}}-,\text{or}\,\text{VOC}-\text{sensitive}$). We develop this approach using an atmospheric chemistry model and apply it to wintertime satellite observations in East Asia. The approach should be applicable to other continents, seasons, and a broader range of satellite instruments, providing valuable insights for particulate nitrate reduction strategies. Key Points: Reducing particulate nitrate pollution requires understanding its local sensitivities to NH3, NOx, and volatile organic compound emissionsSatellite observation of the NH3/NO2 column ratio is an effective indicator for diagnosing these sensitivitiesIASI NH3 and OMI NO2 observations reveal varying regimes of nitrate sensitivity across wintertime East Asia [ABSTRACT FROM AUTHOR]

Knowing where emissions occur is essential for planning effective emission reduction measures and for atmospheric modelling. Emission inventories are typically compiled at national level and provide sector-specific emission estimates. Disaggregating national emissions on high-resolution grids requires spatial proxies that contain information on the location of different emission sources (e.g. point sources, linear and area sources). Knowing the correct allocation of emissions from point sources is essential to avoid the misallocating high emission levels. However, gathering information on point sources covering the entire globe and a wide temporal domain (1970 to present) is challenging due to limited data availability, accuracy of the reporting and completeness of data. The latest spatial proxies developed as part of the Emissions Database for Global Atmospheric Research (EDGARv8.0) provide the user with the possibility to work with different geographical details using a consistently developed GHG emissions database. A key novelty of EDGARv8.0 is the possibility to analyse sub-national GHG emissions over the European domain, but also over the US, China, India and main world countries. The relevance of using updated spatial information is assessed on the basis of regional case studies. [ABSTRACT FROM AUTHOR]

Atmospheric ammonia (NH3) plays an important role in the formation of fine particulate matter, leading to severe environmental degradation and human health issues. In this work, ground-based FTIR observations are used to obtain the total columns and vertical profiles of atmospheric NH3 at a measurement site in Hefei, China, from December 2016 to November 2018. The spatial distribution and temporal variation, seasonal trend, emission sources and potential sources of NH3 are analyzed. The time series of ammonia columns show that FTIR observations captured the seasonal cycle of NH3 over the two years of measurement, with a 22.14 % yr-1 annual increase rate over the Hefei site. We used IASI satellite data to compare with the FTIR data, and the correlation coefficients (R) between the two datasets are 0.86 and 0.78 for IASI-A and IASI-B, respectively. The results demonstrate the IASI data are in broad agreement with our FTIR data. To examine the contribution of traffic to NH3 columns, we analyze the relationship of NH3 columns with CO surface concentrations. NH3 columns show high correlation (R = 0.77) with CO concentrations in summer, indicating that the elevated NH3 columns are partly caused by urban emissions from vehicles. Further, high correlation of NH3 columns with air temperature is obvious from their diurnal variation during the observation period. In addition, the clear correlation between NH3 columns and air temperature in spring and autumn over Hefei, suggests that agriculture was indeed the main source of ammonia in spring and autumn. Furthermore, the back trajectories of air masses calculated by the HYSPLIT model confirmed that agriculture was the dominant source of ammonia in spring, autumn and winter, while urban anthropogenic emissions contributed to the high level of NH3 in summer over the Hefei site. The potential source areas influencing the NH3 columns were distributed in the local area of Hefei, the northern part of Anhui province, as well as Shangdong, Jiangsu and Henan provinces. This study helps to identify the emission sources and potential sources that contribute to NH3 columns over Hefei, a highly populated and polluted area. This is the first time that ground-based FTIR remote sensing of NH3 columns and comparison with satellite data are reported in China. [ABSTRACT FROM AUTHOR]

Atmospheric ammonia (NH 3) is a reactive nitrogen compound that pollutes our environment and threatens public health. Monitoring the spatial and temporal variations is important for quantifying its emissions and depositions and evaluating the strategies for managing anthropogenic sources of NH 3. In this study, we present an NH 3 retrieval algorithm based on the optimal estimation method for the Geostationary Interferometric Infrared Sounder (GIIRS) on board China's FengYun-4B satellite (FY-4B/GIIRS). In particular, we examine the information content based on the degree of freedom for signal (DOFS) in retrieving the diurnal NH 3 in East Asia, with a focus on two source regions including the North China Plain and North India. Our retrieval is based on the FengYun Geostationary satellite Atmospheric Infrared Retrieval (FY-GeoAIR) algorithm and exploits the strong NH 3 absorption window of 955–975 cm -1. Retrieval results using FY-4B/GIIRS spectra from July to December 2022 show that the DOFS for the majority ranges from 0 to 1.0, mainly depending on the thermal contrast (TC) defined as the temperature difference between the surface and the lowest atmospheric layer. Consistent with retrievals from low-Earth-orbit (LEO) infrared sounders, the detection sensitivity, as quantified by the averaging kernel (AK) matrix, peaks in the lowest 2 km atmospheric layers. The DOFS and TC are highly correlated, resulting in a typical "butterfly" shape. That is, the DOFS increases when TC becomes either more positive or more negative. The NH 3 columns from FY-4B/GIIRS exhibit significant diurnal cycles that are consistent with the day–night gradient from the collocated IASI retrievals in the North China Plain and North India for the averages in July–August, September–October, and November–December, respectively. A collocated point-by-point intercomparison with the IASI NH 3 dataset shows generally good agreement with a small systematic difference in the summer months that may be attributed to the slight difference in a priori profiles. This study demonstrates the capability of FY-4B/GIIRS in capturing the diurnal NH 3 changes in East Asia, which will have the potential to improve regional and global air quality and climate research. [ABSTRACT FROM AUTHOR]

Recently, Whitburn et al. (2016) presented a neural-network-based algorithm for retrieving atmospheric ammonia (NH3) columns from Infrared Atmospheric Sounding Interferometer (IASI) satellite observations. In the past year, several improvements have been introduced, and the resulting new baseline version, Artificial Neural Network for IASI (ANNI)-NH3-v2.1, is documented here. One of the main changes to the algorithm is that separate neural networks were trained for land and sea observations, resulting in a better training performance for both groups. By reducing and transforming the input parameter space, performance is now also better for observations associated with favourable sounding conditions (i.e. enhanced thermal contrasts). Other changes relate to the introduction of a bias correction over land and sea and the treatment of the satellite zenith angle. In addition to these algorithmic changes, new recommendations for post-filtering the data and for averaging data in time or space are formulated. We also introduce a second dataset (ANNI-NH3-v2.1R-I) which relies on ERA-Interim ECMWF meteorological input data, along with surface temperature retrieved from a dedicated network, rather than the operationally provided Eumetsat IASI Level 2 (L2) data used for the standard near-real-time version. The need for such a dataset emerged after a series of sharp discontinuities were identified in the NH3 time series, which could be traced back to incremental changes in the IASI L2 algorithms for temperature and clouds. The reanalysed dataset is coherent in time and can therefore be used to study trends. Furthermore, both datasets agree reasonably well in the mean on recent data, after the date when the IASI meteorological L2 version 6 became operational (30 September 2014). [ABSTRACT FROM AUTHOR]

Recently, Whitburn et al. (2016) presented a neural network-based algorithm for retrieving atmospheric ammonia (NH3) columns from IASI satellite observations. In the past year, several improvements have been introduced and the resulting new baseline version, ANNI-NH3-v2, is documented here. One of the main changes to the algorithm is that separate neural networks were trained for land and sea observations, resulting in a better training performance for both groups. By reducing and transforming the input parameter space, performance is now also better for observations associated with favourable sounding conditions (i.e. enhanced thermal contrasts). Other changes relate to the introduction of a bias correction over sea and the treatment of the satellite zenith angle. In addition to these algorithmic changes, new recommendations for post-filtering the data and for averaging data in time or space are formulated. We also introduce a second dataset (ANNI-NH3-v2R-I) which relies on ERA-Interim ECMWF meteorological input data, along with built-in surface temperature, rather than the operationally provided Eumetsat IASI L2 data used for the standard near-real time version. The need for such a dataset emerged after a series of sharp discontinuities were identified in the NH3 timeseries, which could be traced back to incremental changes in the IASI L2 algorithms for temperature and clouds. The reanalysed dataset is coherent in time and can therefore be used to study trends. Furthermore, both datasets agree reasonably well in the mean on recent data, after the date when the IASI meteorological L2 version 6 became operational (30 September 2014). [ABSTRACT FROM AUTHOR]

The three Infrared Atmospheric Sounding Interferometer (IASI) instruments on board the Metop family of satellites have been sounding the atmospheric composition since 2006. More than 30 atmospheric gases can be measured from the IASI radiance spectra, allowing the improvement of weather forecasting and the monitoring of atmospheric chemistry and climate variables. The early detection of extreme events such as fires, pollution episodes, volcanic eruptions, or industrial releases is key to take safety measures to protect the inhabitants and the environment in the impacted areas. With its near-real-time observations and good horizontal coverage, IASI can contribute to the series of monitoring systems for the systematic and continuous detection of exceptional atmospheric events in order to support operational decisions. In this paper, we describe a new approach to the near-real-time detection and characterization of unexpected events, which relies on the principal component analysis (PCA) of IASI radiance spectra. By analyzing both the IASI raw and compressed spectra, we applied a PCA-granule-based method on various past, well-documented extreme events such as volcanic eruptions, fires, anthropogenic pollution, and industrial accidents. We demonstrate that the method is well suited to the detection of spectral signatures for reactive and weakly absorbing gases, even for sporadic events. Consistent long-term records are also generated for fire and volcanic events from the available IASI/Metop-B data record. The method is running continuously, delivering email alerts on a routine basis, using the near-real-time IASI L1C radiance data. It is planned to be used as an online tool for the early and automatic detection of extreme events, which was not done before. [ABSTRACT FROM AUTHOR]

Ammonia (NH3) is an important atmospheric constituent. It plays a role in air quality and climate through the formation of ammonium sulfate and ammonium nitrate particles. It has also an impact on ecosystems through deposition processes. About 85 % of NH3 global anthropogenic emissions are related to food and feed production and, in particular, to the use of mineral fertilizers and manure management. Most global chemistry transport models (CTMs) rely on bottom-up emission inventories, which are subject to significant uncertainties. In this study, we estimate emissions from livestock by developing a new module to calculate ammonia emissions from the whole agricultural sector (from housing and storage to grazing and fertilizer application) within the ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems) global land surface model. We detail the approach used for quantifying livestock feed management, manure application, and indoor and soil emissions and subsequently evaluate the model performance. Our results reflect China, India, Africa, Latin America, the USA, and Europe as the main contributors to global NH3 emissions, accounting for 80 % of the total budget. The global calculated emissions reach 44 TgNyr-1 over the 2005–2015 period, which is within the range estimated by previous work. Key parameters (e.g., the pH of the manure, timing of N application, and atmospheric NH3 surface concentration) that drive the soil emissions have also been tested in order to assess the sensitivity of our model. Manure pH is the parameter to which modeled emissions are the most sensitive, with a 10 % change in emissions per percent change in pH. Even though we found an underestimation in our emissions over Europe (-26 %) and an overestimation in the USA (+56 %) compared with previous work, other hot spot regions are consistent. The calculated emission seasonality is in very good agreement with satellite-based emissions. These encouraging results prove the potential of coupling ORCHIDEE land-based emissions to CTMs, which are currently forced by bottom-up anthropogenic-centered inventories such as the CEDS (Community Emissions Data System). [ABSTRACT FROM AUTHOR]

Volcanic eruptions can emit large amounts of ash into the atmosphere, which can have significant impacts on infrastructure, human health, agriculture and air traffic. Remote sensing instruments can efficiently detect airborne ash plumes, and the measured spectra can be exploited to obtain information on the physical characteristics of ash (grain size distribution, concentration, optical depth). The key parameter on which all such satellite retrievals depend is the complex refractive index (CRI) which remains one of the largest sources of uncertainty in the retrieval process. Here we present a complementary dataset of refractive indices of volcanic ash to that published by Deguine et al. (2020), to cover a part of the major explosive eruptions occurred during the past 50 years. These CRIs were obtained using an innovative experimental methodology which consists in measuring simultaneously the extinction spectra in the IR and UV/visible domain and the size distribution of ash in suspension in a nitrogen flow. These experimental data are the main input to the retrieval process of CRI. The numerical routine uses Mie theory coupled with Kramers–Kronig relationship to retrieve the imaginary and the real part of the complex refractive index. This methodology has been successfully applied on samples collected from various eruptions and deposits in Indonesia (Kelud), Chile (Chaitén), Italy (Stromboli), Russia (Karymsky), Tanzania (Rungwe, Mount Meru), Ethiopia (Corbetti), Philippines (Taal, Pinatubo) and USA (Mount St. Helens). Significant variations of the real and imaginary part of the CRI are observed according to the chemical composition of the samples. Moreover, the sensitivity of the CRI to chemical composition and mineralogical structure (amorphous/crystalline fraction) has been investigated and shows a strong dependence of the CRI on these parameters. • Large dataset of complex refractive indices are provided from 10 volcanic ash samples. • Complex refractive indices vary according to the chemical composition of ash samples. • Crystalline/amorphous fraction influence the optical properties of volcanic ashes. [ABSTRACT FROM AUTHOR]

Knowledge of sulfur dioxide layer height (SO 2 LH) is important to understand volcanic eruption processes, the climate impact of SO 2 emissions and to mitigate volcanic risk for civil aviation. However, the estimation of SO 2 LH from ground-based instruments is challenging in particular for rapidly evolving and sustained eruptions. Satellite wide-swath nadir observations have the advantage to cover large-scale plumes and the potential to provide key information on SO 2 LH. In the ultraviolet, SO 2 LH retrievals leverage the fact that, for large SO 2 columns, the light path and its associated air mass factor (AMF) depends on the SO 2 absorption (and therefore on the vertical distribution of SO 2), and SO 2 LH information can be obtained from the analysis of measured back-scattered radiances coupled with radiative transfer simulations. However, existing algorithms are mainly sensitive to SO 2 LH for SO 2 vertical columns of at least 20 DU. Here we develop a new SO 2 LH algorithm and apply it to observations from the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI). It is based on an SO 2 optical depth look-up table and an iterative approach. The strength of this scheme lies in the fact that it is a Covariance-Based Retrieval Algorithm (COBRA; Theys et al., 2021). This means that the SO 2 -free contribution of the measured optical depth is treated in an optimal way, resulting in an improvement of the SO 2 LH sensitivity to SO 2 columns as low as 5 DU, with a precision better than 2 km. We demonstrate the value of this new data through a number of examples and comparison with satellite plume height estimates (from IASI and CALIOP), and back-trajectory analyses. The comparisons indicate an SO 2 LH accuracy of 1–2 km, except for some difficult observation conditions, in particular for optically thick ash plumes or partially SO 2 -filled scenes. [ABSTRACT FROM AUTHOR]

Atmospheric ammonia (NH 3) plays an important role in the formation of fine particulate matter, leading to severe environmental degradation and human health issues. In this work, ground-based Fourier transform infrared spectrometry (FTIR) observations are used to obtain the total columns of atmospheric NH 3 at Hefei, China, from December 2016 to December 2020. After the presentation of the retrieval algorithm and uncertainty budget, we perform a spatio-temporal analysis of the dataset. Over the four years, NH 3 columns have been increasing by 15.82% (2017–2018), 3.83% (2018–2019) and 3.68% (2019–2020). A clear seasonal cycle is observed, with the largest surface concentrations (12.93 ± 6.40 ppb) observed in June to August, and the lowest (4.08 ± 2.66 ppb) in November to January. The diurnal cycles of NH 3 exhibit increased morning and afternoon concentrations. Interpretation of the diurnal cycles is difficult, however, the absence of a peak during rush hours, and the absence of correlation with CO and NO 2 suggest that agriculture and not traffic is the main source of NH 3 at Hefei. The polar plots of NH 3 columns with wind and back trajectories of air masses calculated by the HYSPLIT model confirmed that agriculture was the dominant source of ammonia in four seasons, while urban anthropogenic emissions contributed to the high level of NH 3 in summer over the Hefei site. We end this paper with a short validation exercise of NH 3 columns retrieved from measurements of the IASI satellite data with the FTIR measurements over Hefei. Correlation coefficients (R) between the two datasets are 0.79 and 0.75 for IASI-A and IASI-B, with the slope of 0.96 and 1.10, respectively. The mean difference is −3.44 × 1015 and −3.96 × 1015 molec cm−2, with standard deviation of 7.16 × 1015 and 8.10 × 1015 molec cm−2, respectively. These results demonstrate the IASI and FTIR data, over Hefei, are in broad agreement. In order to know about the temporal and spatial distribution of atmospheric ammonia, and what influence ammonia columns over Hefei, China, we observed the total columns of atmospheric ammonia by ground-based FTIR remote sensing from December 2016 to December 2020. The time series of the ammonia column observed at the Hefei site are plotted. The green dots are the individual measurements of NH 3 ; the red diamonds represent the daily averaged NH 3 ; the blue line is the fitting curve of the individual NH3 columns. We discussed the seasonal trend, annual variability, diurnal variation of NH 3 , and the main factors that influence gaseous NH 3 concentrations in Hefei. [Display omitted] • FTIR measurements of NH 3 column and comparison with satellite data are reported. • FTIR observations captured the spatio-temporal variation of NH 3 columns. • Agriculture was the dominant source of ammonia in four seasons over Hefei. • IASI satellite data are in broad agreement with FTIR data over Hefei. [ABSTRACT FROM AUTHOR]

Ammonia (NH3) is an important atmospheric constituent. It plays a role in air quality and climate through the formation of ammonium sulfate and ammonium nitrate particles. It has also an impact on ecosystems through deposition processes. About 85% of NH3 global anthropogenic emissions are related to food and feed production and, in particular, to the use of mineral fertilizers and manure management. Most global chemistry transport models rely on bottom-up emission inventories subject to significant uncertainties. In this study, we estimate emissions from livestock by developing a new module to calculate ammonia emissions coming from the whole agricultural sector (from housing and storage to grazing and fertilizer applications) within the global land surface model ORCHIDEE.We detail the approach used for quantifying livestock feeding management, manure applications and indoor and soil emissions and evaluate the model performance. Our results reflect China, India, Africa, Latin America, the USA, and Europe as the main contributors to the global NH3 emissions accounting for 80 % of the total budget. The global calculated emissions reach 44TgNyr-1 over the 2005-2015 period, which is within the range estimated by previous work. Key parameters (pH of the manure, timing of the N application, atmospheric NH3 surface concentration, etc . . . ) which drive the soil emissions have also been tested in order to assess the sensibility of our model. Manure pH is the parameter to which modeled emissions are the most sensitive to with a 10% change in emissions per % change in pH. Even though we found an under-estimation in our emissions over Europe (-26%) and an over-estimation in the USA (+56%) compared to previous work, other hot-spot regions are consistent. The calculated emissions seasonality is in very good agreement with satellite based emissions. These encouraging results prove the potential of coupling ORCHIDEE land-based emissions to CTMs, which are currently forced by bottom-up anthropogenic-centered inventories such as CEDS. [ABSTRACT FROM AUTHOR]

The objective of this study is to assess and understand the NH3 recent trends and to identify the key components driving its concentrations. We have simulated the seasonal cycle, the interannual variability, and the trends in NH3 vertical column densities (VCD) from 2008 to 2015 over Europe, with the CHIMERE regional chemistry–transport model. We have also confronted the simulations against the Infrared Atmospheric Sounding Interferometer (IASI) satellite observations. IASI often shows a strong maximum in summer in addition to the spring peak, whereas CHIMERE only shows a slight peak in summer some years. This result could point to a misrepresentation of the temporal profile of the NH3 emissions, i.e., to missing emission sources during summertime either due to more than expected fertilizer use or to increased volatilization under warmer conditions. The simulated NH3 VCDs present an increasing trend over continental Europe (+2.7 ± 1.0 %/yr) but also at the national scale for Spain, Germany, UK, France, and Poland. Sensitivity tests indicate that these simulated positive trends are mainly due to (i) the trends in NH3 emissions, found heterogeneous in the EMEP NH3 emissions with strong disparities depending on the country, and (ii) the negative trends in NOx and SOx emissions. The impact of reductions in NO2 and SO2 emissions on NH3 concentrations should therefore be taken into account in future policies. This simulated NH3 VCD increase at the European scale is confirmed by IASI-v3R satellite observations in spring and summer, when ammonia emissions strongly contribute to the annual budget in accordance with crop requirements. Nevertheless, there are remaining differences about the significance and magnitude between the simulated and observed trends at the national scale, and it warrants further investigation. [ABSTRACT FROM AUTHOR]

The three infrared atmospheric sounding interferometers (IASIs), launched in 2006, 2012, and 2018, are key instruments to weather forecasting, and most meteorological centres assimilate IASI nadir radiance data into atmospheric models to feed their forecasts. The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) recently released a reprocessed homogeneous radiance record for the whole IASI observation period, from which 13 years (2008–2020) of temperature profiles can be obtained. In this work, atmospheric temperatures at different altitudes are retrieved from IASI radiances measured in the carbon dioxide absorption bands (654–800 and 2250–2400 cm -1) by selecting the channels that are the most sensitive to the temperature at different altitudes. We rely on an artificial neural network (ANN) to retrieve atmospheric temperatures from a selected set of IASI radiances. We trained the ANN with IASI radiances as input and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) as output. The retrieved temperatures were validated with ERA5, with in situ radiosonde temperatures from the Analysed RadioSoundings Archive (ARSA) network and with EUMETSAT temperatures retrieved from IASI radiances using a different method. Between 750 and 7 hPa, where IASI is most sensitive to temperature, a good agreement is observed between the three datasets: the differences between IASI on one hand and ERA5, ARSA, or EUMETSAT on the other hand are usually less than 0.5 K at these altitudes. At 2 hPa, as the IASI sensitivity decreases, we found differences up to 2 K between IASI and the three validation datasets. We then computed atmospheric temperature linear trends from atmospheric temperatures between 750 and 2 hPa. We found that in the past 13 years, there is a general warming trend of the troposphere that is more important at the poles and at mid-latitudes (0.5 K/decade at mid-latitudes, 1 K/decade at the North Pole). The stratosphere is globally cooling on average, except at the South Pole as a result of the ozone layer recovery and a sudden stratospheric warming in 2019. The cooling is most pronounced in the equatorial upper stratosphere (- 1 K/decade). This work shows that ANN can be a powerful and simple tool to retrieve IASI temperatures at different altitudes in the upper troposphere and in the stratosphere, allowing us to construct a homogeneous and consistent temperature data record adapted to trend analysis. [ABSTRACT FROM AUTHOR]

An updated and expanded representation of organics in the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) has been evaluated. First, the comprehensive Mainz Organic Mechanism (MOM) in the submodel MECCA (Module Efficiently Calculating the Chemistry of the Atmosphere) was activated with explicit degradation of organic species up to five carbon atoms and a simplified mechanism for larger molecules. Second, the ORACLE submodel (version 1.0) now considers condensation on aerosols for all organics in the mechanism. Parameterizations for aerosol yields are used only for the lumped species that are not included in the explicit mechanism. The simultaneous usage of MOM and ORACLE allows an efficient estimation of not only the chemical degradation of the simulated volatile organic compounds but also the contribution of organics to the growth and fate of (organic) aerosol, with the complexity of the mechanism largely increased compared to EMAC simulations with more simplified chemistry. The model evaluation presented here reveals that the OH concentration is reproduced well globally, whereas significant biases for observed oxygenated organics are present. We also investigate the general properties of the aerosols and their composition, showing that the more sophisticated and process-oriented secondary aerosol formation does not degrade the good agreement of previous model configurations with observations at the surface, allowing further research in the field of gas–aerosol interactions. [ABSTRACT FROM AUTHOR]

The three IASI instruments, launched in 2006, 2012, and 2018, are key instruments to weather forecasting, and most meteorological centers assimilate IASI nadir radiance data into atmospheric models to feed their forecasts. The EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT) recently released a reprocessed homogeneous radiance record for the whole IASI observation period, from which thirteen years (2008-2020) of temperature profiles can be obtained. In this work, atmospheric temperatures at different altitudes are retrieved from IASI radiances measured in the carbon dioxide absorption bands (654-800 cm-1 and 2250-2400 cm-1) by selecting the channels that are the most sensitive to the temperature at different altitudes. We rely on an Artificial Neural Network (ANN) to retrieve atmospheric temperatures from a selected set of IASI radiances. We trained the ANN with IASI radiances as input and the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) as output. The retrieved temperatures were validated with ERA5, with in-situ radiosonde temperatures from the Analysed RadioSoundings Archive (ARSA) network and with EUMETSAT temperatures retrieved from IASI radiances using a different method. Between 750 and 7 hPa, where IASI is most sensitive to temperature, a good agreement is observed between the three datasets: the differences between IASI on one hand, and ERA5, ARSA or EUMETSAT on the other hand are usually less than 0.5 K at these altitudes. At 2 hPa, as the IASI sensitivity decreases, we found differences up to 2 K between IASI and the three validation datasets. We then computed atmospheric temperature linear trends from atmospheric temperatures between 750 and 2 hPa. We found that in the past thirteen years, there is a general warming trend of the troposphere, that is more important at the poles than at the equator (0.7 K/decade at the equator, 1 K/decade at the North Pole). The stratosphere is globally cooling on average, except at the South Pole as a result of the ozone layer recovery. The cooling is most pronounced in the equatorial upper stratosphere (-1 K/decade). This work shows that ANN can be a powerful and simple tool to retrieve IASI temperatures at different altitudes in the upper troposphere and in the stratosphere, allowing us to construct a homogeneous and consistent temperature data record adapted to trend analyis. [ABSTRACT FROM AUTHOR]

An updated and expanded representation of organics in the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) has been evaluated. First, the comprehensive Mainz Organic Mechanism (MOM) in the submodel MECCA (Module Efficiently Calculating the Chemistry of the Atmosphere) was activated with explicit degradation of organic species up to five carbon atoms and a simplified mechanism for larger molecules. Second, the ORACLE submodel (version 1.0) considers now condensation on aerosols for all organics in the mechanism. Parameterizations for aerosol yields are used only for the lumped species that are not included in the explicit mechanism. The simultaneous usage of MOM and ORACLE allows an efficient estimation, not only of the chemical degradation of the simulated volatile organic compounds, but also of the contribution of organics to the growth and fate of (organic) aerosol, with a complexity of the mechanism largely increased compared to EMAC simulations with more simplified chemistry. The model evaluation presented here reveals that the OH concentration is well reproduced globally, while significant biases for observed oxygenated organics are present. We also investigate the general properties of the aerosols and their composition, showing that the more sophisticated and process-oriented secondary aerosol formation does not degrade the good agreement of previous model configurations with observations at the surface, allowing further research in the field of gas-aerosol interactions. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) is an essential climate variable, that is directly used in climate monitoring. Although satellite measurements can offer continuous global coverage, obtaining a long‐term homogeneous satellite‐derived SST data set suitable for climate studies based on a single instrument is still a challenge. In this work, we assess a homogeneous SST data set derived from reprocessed Infrared Atmospheric Sounding Interferometer (IASI) level‐1 (L1C) radiance data. The SST is computed using Planck's Law and simple atmospheric corrections. We assess the data set using the ERA5 reanalysis and the EUMETSAT‐released IASI level‐2 SST product. Over the entire period, the reprocessed IASI SST shows a mean global difference with ERA5 close to zero, a mean absolute bias under 0.5°C, with a SD of difference around 0.3°C and a correlation coefficient over 0.99. In addition, the reprocessed data set shows a stable bias and SD, which is an advantage for climate studies. The interannual variability and trends were compared with other SST data sets: ERA5, Hadley Centre's SST (HadISST), and NOAA's Optimal Interpolation SST Analysis (OISSTv2). We found that the reprocessed SST data set is able to capture the patterns of interannual variability well, showing the same areas of high interannual variability (>1.5°C), including over the tropical Pacific in January corresponding to the El Niño Southern Oscillation. Although the period studied is relatively short, we demonstrate that the IASI data set reproduces the same trend patterns found in the other data sets (i.e., cooling trend in the North Atlantic, warming trend over the Mediterranean). Plain Language Summary: Sea surface temperature (SST) is an essential variable for monitoring climate, as defined by the Global Climate Observing System (GCOS; https://gcos.wmo.int/en/essential-climate-variables/sst). Satellite measurements can offer global continuous SST measurements, but their stability over the time needs to be assured. In this work, we present a new data set derived from the Infrared Atmospheric Sounding Interferometer, IASI (flying aboard the Metop satellites), and compare it with other available data sets. This comparison shows that our data set produces similar means, variability and trends as other data sets, with the advantage that it is derived with a single algorithm from a single well‐calibrated instrument. This assures there are no substantial changes to the instrument characteristics over time that might result in artificial trends. Key Points: First IASI algorithm focused on sea surface temperature (SST) suitable for climate studiesThe IASI‐derived SST data set is compared with other available data setsClimate variability and trends are shown and compared to other data sets [ABSTRACT FROM AUTHOR]

Monthly, high‐resolution (∼2 km) ammonia (NH3) column maps from the Infrared Atmospheric Sounding Interferometer (IASI) were developed across the contiguous United States and adjacent areas. Ammonia hotspots (95th percentile of the column distribution) were highly localized with a characteristic length scale of 12 km and median area of 152 km2. Five seasonality clusters were identified with k‐means++ clustering. The Midwest and eastern United States had a broad, spring maximum of NH3 (67% of hotspots in this cluster). The western United States, in contrast, showed a narrower midsummer peak (32% of hotspots). IASI spatiotemporal clustering was consistent with those from the Ammonia Monitoring Network. CMAQ and GFDL‐AM3 modeled NH3 columns have some success replicating the seasonal patterns but did not capture the regional differences. The high spatial‐resolution monthly NH3 maps serve as a constraint for model simulations and as a guide for the placement of future, ground‐based network sites. Plain Language Summary: Ammonia (NH3) contributes to the formation of particulate matter, which is known to degrade air quality and human health. The major source of NH3 is from agricultural activities, yet observational constraints on NH3 are limited, particularly at both monthly resolution and high spatial resolution. We have developed high spatial resolution (2 km) satellite maps of NH3 on a monthly scale in the United States. Areas with the highest NH3 are generally very localized with typical length scales of ∼12 km. The seasonal patterns varied dramatically based upon the underlying agricultural activities. These high‐resolution satellite maps can be used as observational constraints on the seasonalities and spatial patterns for modeling of atmospheric NH3. Key Points: High spatial resolution (2 km) maps of NH3 show that hotspots are highly localized with characteristic length scales of ∼12 kmLarge monthly variations of NH3 columns are observed with different seasonality patterns by region and type of agricultural activitiesSatellite NH3 maps provide insights for future ground‐based observational networks and constraints for model NH3 spatiotemporal patterns [ABSTRACT FROM AUTHOR]

Ammonia (NH 3) is a major source of nitrates in the atmosphere and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH 3 and its spatial and temporal variability. In this study, long-term measurements of NH 3 derived from multiscale datasets are examined. These NH 3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, 3 years of surface in situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH 3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the representativeness of the FTIR measurements. All three time series showed positive trends in NH 3 over Toronto: 3.34 ± 0.89 %/yr from 2002 to 2018 in the FTIR columns, 8.88 ± 5.08 %/yr from 2013 to 2017 in the surface in situ data, and 8.38 ± 1.54 %/yr from 2008 to 2018 in the IASI columns. To assess the representative scale of the FTIR NH 3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤25 km and ≤20 min, with r=0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in situ measurements were standardized and correlated. Comparison of 24 d averages and monthly averages resulted in correlation coefficients of r=0.72 and r=0.75 , respectively, although correlation without averaging to reduce high-frequency variability led to a poorer correlation, with r=0.39. The GEOS-Chem model, run at 2 ∘ × 2.5 ∘ resolution, was compared to FTIR and IASI to assess model performance and investigate the correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35 to 53 ∘ N and 93.75 to 63.75 ∘ W) resulted in r=0.57 and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r2=0.33 , but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r2=0.13 , indicating that a finer spatial resolution is needed for modeling NH 3. [ABSTRACT FROM AUTHOR]

We present a multi-sensor archive collecting spatial and temporal information about volcanic SO2 clouds generated by the 11 largest eruptions of this century. The detection and monitoring of volcanic clouds are an important topic for aviation management, climate issues and weather forecasts. Several studies focusing on single eruptive events exist, but no archive available at the moment combines quantitative data from as many instruments. We archived and collocated the SO2 vertical column density estimations from three different satellite instruments (AIRS, IASI and GOME-2), atmospheric parameters as vertical profiles from the Global Navigation Satellite Systems (GNSS) Radio Occultations (RO), and the cloud-top height and aerosol type from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). Additionally, we provide information about the cloud-top height from three different algorithms and the atmospheric anomaly due to the presence of the cloud. The dataset is gathering 206 d of SO2 data, collocated with 44 180 backscatter profiles and 64 764 radio occultation profiles. The modular structure of the archive allows an easy collocation of the datasets according to the users' needs, and the cross-comparison of the datasets shows different consistency of the parameters estimated with different sensors and algorithms, according to the sensitivity and resolution of the instruments. The data described here are published with a DOI at 10.5880/fidgeo.2020.016 (Tournigand et al., 2020a). [ABSTRACT FROM AUTHOR]

Ammonia (NH3) is a major source of nitrates in the atmosphere, and a major source of fine particulate matter. As such, there have been increasing efforts to measure the atmospheric abundance of NH3 and its spatial and temporal variability. In this study, long-term measurements of NH3 derived from multiscale datasets are examined. These NH3 datasets include 16 years of total column measurements using Fourier transform infrared (FTIR) spectroscopy, three years of surface in-situ measurements, and 10 years of total column measurements from the Infrared Atmospheric Sounding Interferometer (IASI). The datasets were used to quantify NH3 temporal variability over Toronto, Canada. The multiscale datasets were also compared to assess the observational footprint of the FTIR measurements. All three time series showed positive trends in NH3 over Toronto: 3.34 ± 0.46%/year from 2002 to 2018 in the FTIR columns, 8.88 ± 2.83%/year from 2013 to 2017 in the surface in-situ data, and 8.38 ± 0.77%/year from 2008 to 2018 in the IASI columns. To assess the observational footprint of the FTIR NH3 columns, correlations between the datasets were examined. The best correlation between FTIR and IASI was obtained with coincidence criteria of ≤ 25 km and ≤ 20 minutes, with r = 0.73 and a slope of 1.14 ± 0.06. Additionally, FTIR column and in-situ measurements were standardized and correlated. Comparison of 24-day averages and monthly averages resulted in correlation coefficients of r = 0.72 and r = 0.75, respectively, although correlation without resampling to reduce high-frequency variability led to a poorer correlation, with r = 0.39. The GEOS-Chem model, run at 2° × 2.5° resolution, was compared against FTIR and IASI to assess model performance and investigate correlation of observational data and model output, both with local column measurements (FTIR) and measurements on a regional scale (IASI). Comparisons on a regional scale (a domain spanning 35° N to 53° N, and 93.75° W to 63.75° W) resulted in r = 0.57, and thus a coefficient of determination, which is indicative of the predictive capacity of the model, of r² = 0.33, but comparing a single model grid point against the FTIR resulted in a poorer correlation, with r² = 0.13, indicating that a finer spatial resolution is needed for modeling NH3. [ABSTRACT FROM AUTHOR]

We present a multi-sensor archive collecting spatial and temporal information about volcanic SO2 clouds generated by the eleven largest eruptions of this century. The detection and monitoring of volcanic clouds is an important topic for aviation management, climate issues and weather forecast. Several papers have been published focusing on single events, but not any archive is available at the moment to be used as background for future studies. We archived and collocated the SO2 vertical column density estimations from three different instruments (AIRS, IASI and GOME-2), the atmospheric parameters vertical profiles from the Global Navigation Satellite Systems (GNSS) Radio Occultations (RO) and the vertical backscatter from the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). We additionally provide information about the cloud top height from three different algorithms and the atmospheric anomaly due to the presence of the cloud. The dataset consists of 223 days monitored with SO2 clouds, collocated with 56675 backscatter profiles and 70126 radio occultation profiles. The modular structure of the archive allows an easy collocation of the different datasets according to the users' needs and the cross-comparison of the datasets shows the high consistency of the parameters estimated with different sensors and algorithms. The data described here will be published with a DOI after final acceptance of this manuscript (Tournigand et al., 2020, http://doi.org/10.5880/fidgeo.2020.016). During the discussion period, the data are accessible via this temporary link: http://pmd.gfz-potsdam.de/panmetaworks/review/ 0f85d699707efcdc567765bd0dafaaadf94b6df5a531f310167f7e974ea803bf [ABSTRACT FROM AUTHOR]

The accurate determination of the location, height, and loading of sulfur dioxide (SO2) plumes emitted by volcanic eruptions is essential for aviation safety. The SO2 layer height is also one of the most critical parameters with respect to determining the impact on the climate. Retrievals of SO2 plume height have been carried out using satellite UV backscatter measurements, but, until now, such algorithms are very time-consuming. We have developed an extremely fast yet accurate SO2 layer height retrieval using the Full-Physics Inverse Learning Machine (FP_ILM) algorithm. This is the first time the algorithm has been applied to measurements from the TROPOMI instrument onboard the Sentinel-5 Precursor platform. In this paper, we demonstrate the ability of the FP_ILM algorithm to retrieve SO2 plume layer heights in near-real-time applications with an accuracy of better than 2 km for SO2 total columns larger than 20 DU. We present SO2 layer height results for the volcanic eruptions of Sinabung in February 2018, Sierra Negra in June 2018, and Raikoke in June 2019, observed by TROPOMI. [ABSTRACT FROM AUTHOR]

While long-term temperature time series mostly rely on weather stations, only satellite data are able to provide systematic global temperature data, from pole to pole on a regular basis, over both land and sea. Satellites measure the "skin" temperature derived from upwelling radiation at the Earth's land surface. The evolution of skin temperature is not yet fully exploited as its measurement is fairly recent.One of the IASI-Flux and Temperature ERC project tasks aims at providing new climate benchmarks by using skin temperature observations from the calibrated radiances measured twice a day at any location by the IASI thermal infrared instrument on the suite of Metop satellites (2006-2025). The uniqueness of this project is that the IASI-data record will be completely independent from third party information, with no other data from observations or models used, and can therefore serve as an independent reference to e.g. reanalysis, or other climate data records. In this presentation, we first describe our iterative method based on entropy reduction combined with artificial neural networks to derive an independent record of IASI temperature, we next compare and validate our novel method with different datasets (e.g. EUMETSAT, ECMWF reanalysis, SEVIRI satellite products and ground measurements). We then show our results of global skin temperature over land and sea and in different regions in the world over the period [2008- present]. The observed trends are analyzed at seasonal and regional scales in order to disentangle natural (weather/dynamical) variability and human-induced climate forcing. Finally, we show how expanding cities are hotspots for skin temperature reflecting the usefulness of skin temperature as a tracer for human-induced land use and climate change. [ABSTRACT FROM AUTHOR]

During a volcanic eruption, a huge amount of aerosols are emitted into the atmosphere which can be transported over long distances. By absorbing and scattering radiation, volcanic ashes influence strongly the Earth radiative budget. These particles may also affect human health and for some intense events may perturb or interrupt air traffic. These interruptions are mainly due to a poor knowledge of optical, chemical and physical properties of volcanic ashes. Recent work1 showed the potential of using hyperspectral infrared spectrometer to detect and quantify volcanic ashes contained in the plume especially using IASI (Infrared Atmospheric Sounding Interferometer). However, in order to fully exploit IASI's spectra, a perfect knowledge of complex refractive index (CRI) of ashes is required.In that purpose, a new methodology2,3 based on laboratory measurements of volcanic ashes in suspension coupled with an optimal estimation method has been developed. This approach allow to get access to CRI of several volcanic ash samples with various chemical compositions. These specific CRI has been successfully used to retrieve physical parameters (effective radius and concentration) from two intense volcanic eruptions: Puyehue Cordon Caulle (Chile) and Grimsvötn (Iceland). A comparison with other studies4,5, using other CRI, has been performed and show a significant impact of CRI on physical parameters retrieved. 1 Clarisse, L., Prata, F., Lacour, J. L., Hurtmans, D., Clerbaux, C., & Coheur, P. F. (2010). A correlation method for volcanic ash detection using hyperspectral infrared measurements. Geophysical research letters, 37(19).2. Herbin, H., Pujol, O., Hubert, P., & Petitprez, D. (2017). New approach for the determination of aerosol refractive indices–Part I: Theoretical bases and numerical methodology. Journal of Quantitative Spectroscopy and Radiative Transfer, 200, 311-319.3. Hubert, P., Herbin, H., Visez, N., Pujol, O., & Petitprez, D. (2017). New approach for the determination of aerosol refractive indices–Part II: Experimental set-up and application to amorphous silica particles. Journal of Quantitative Spectroscopy and Radiative Transfer, 200, 320-327.4 Pollack, J. B., Toon, O. B., & Khare, B. N. (1973). Optical properties of some terrestrial rocks and glasses. Icarus, 19(3), 372-389.5 Reed, Benjamin & M. Peters, Daniel & McPheat, Robert & Grainger, Roy. (2018). The Complex Refractive Index of Volcanic Ash Aerosol Retrieved From Spectral Mass Extinction. Journal of Geophysical Research: Atmospheres. 10.1002/2017JD027362. [ABSTRACT FROM AUTHOR]

Skin temperature (Tskin) derived from infrared sensors on board satellites provides a continuous view of Earth’s surface day and night and allows for the monitoring of global temperature changes relevant for climate trends. Tskin from the Infrared Atmospheric Sounding Interferometer (IASI) has not been properly exploited to date to assess its long-term spatio-temporal variability and no current homogenous Tskin record from IASI exists. In this study, we present a fast retrieval method of Tskin based on an artificial neural network from a set of IASI channels selected using the information theory/entropy reduction technique. We compare and validate our IASI Tskin product with that from EUMETSAT Level 2, ECMWF Reanalysis ERA5, SEVIRI land-surface temperature products, as well as ground measurements. Our results show good correlation between the IASI neural network product and the datasets used for validation, with a standard deviation between 1 and 4 °C. This method can be applied to other infrared measurements, and allows for the construction of a robust Tskin dataset, making it suitable for trend analysis. [ABSTRACT FROM AUTHOR]