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Excess atmospheric ammonia (NH 3 ) leads to deleterious effects on biodiversity, ecosystems, air quality and health, and it is therefore essential to monitor its budget and temporal evolution. Hyperspectral infrared satellite sounders provide daily NH 3 observations at global scale for over a decade. Here we use the version 3 of the Infrared Atmospheric Sounding Interferometer (IASI) NH 3 dataset to derive global, regional and national trends from 2008 to 2018. We find a worldwide increase of 12.8 ± 1.3 % over this 11-year period, driven by large increases in east Asia (5.80 ± 0.61% increase per year), western and central Africa (2.58 ± 0.23 % yr −1 ), North America (2.40 ± 0.45 % yr −1 ) and western and southern Europe (1.90 ± 0.43 % yr −1 ). These are also seen in the Indo-Gangetic Plain, while the southwestern part of India exhibits decreasing trends. Reported national trends are analyzed in the light of changing anthropogenic and pyrogenic NH 3 emissions, meteorological conditions and the impact of sulfur and nitrogen oxides emissions, which alter the atmospheric lifetime of NH 3 . We end with a short case study dedicated to the Netherlands and the ‘Dutch Nitrogen crisis’ of 2019.

Recently, 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).

Concentrated agricultural activities and animal feeding operations in the northeastern plains of Colorado represent an important source of atmospheric ammonia (NH3). The NH3 from these sources contributes to regional fine particle formation and to nitrogen deposition to sensitive ecosystems in Rocky Mountain National Park (RMNP), located  ∼  80 km to the west. In order to better understand temporal and spatial differences in NH3 concentrations in this source region, weekly concentrations of NH3 were measured at 14 locations during the summers of 2010 to 2015 using Radiello passive NH3 samplers. Weekly (biweekly in 2015) average NH3 concentrations ranged from 2.66 to 42.7 µg m−3, with the highest concentrations near large concentrated animal feeding operations (CAFOs). The annual summertime mean NH3 concentrations were stable in this region from 2010 to 2015, providing a baseline against which concentration changes associated with future changes in regional NH3 emissions can be assessed. Vertical profiles of NH3 were also measured on the 300 m Boulder Atmospheric Observatory (BAO) tower throughout 2012. The highest NH3 concentration along the vertical profile was always observed at the 10 m height (annual average concentration of 4.63 µg m−3), decreasing toward the surface (4.35 µg m−3) and toward higher altitudes (1.93 µg m−3). The NH3 spatial distributions measured using the passive samplers are compared with NH3 columns retrieved by the Infrared Atmospheric Sounding Interferometer (IASI) satellite and concentrations simulated by the Comprehensive Air Quality Model with Extensions (CAMx). The satellite comparison adds to a growing body of evidence that IASI column retrievals of NH3 provide very useful insight into regional variability in atmospheric NH3, in this case even in a region with strong local sources and sharp spatial gradients. The CAMx comparison indicates that the model does a reasonable job simulating NH3 concentrations near sources but tends to underpredict concentrations at locations farther downwind. Excess NH3 deposition by the model is hypothesized as a possible explanation for this trend.

Tropical cyclone (TC) detection is essential to mitigate natural disasters, as TCs can cause significant damage to life, infrastructure and economy. In this study, we applied the deep learning object detection model YOLOv3 to detect TCs in the North Atlantic Basin, using data from the Thermal InfraRed (TIR) Atmospheric Sounding Interferometer (IASI) onboard the Metop satellites. IASI measures the outgoing TIR radiation of the Earth-Atmosphere. For the first time, we provide a proof of concept of the possibility of constructing images required by YOLOv3 from a TIR remote sensor that is not an imager. We constructed a dataset by selecting 50 IASI radiance channels and using them to create images, which we labeled by constructing bounding boxes around TCs using the hurricane database HURDAT2. We trained the YOLOv3 on two settings, first with three “best” selected channels, then using an autoencoder to exploit all 50 channels. We assessed its performance with the Average Precision (AP) metric at two different intersection over union (IoU) thresholds (0.1 and 0.5). The model achieved promising results with AP at IoU threshold 0.1 of 78.31%. Lower performance was achieved with IoU threshold 0.5 (31.05%), showing the model lacks precision regarding the size and position of the predicted boxes. Despite that, we show YOLOv3 demonstrates great potential for TC detection using TIR instruments data.

The Infrared Atmospheric Sounding Interferometers (IASIs) are three instruments flying on board the Metop satellites, launched in 2006 (IASI-A), 2012 (IASI-B), and 2018 (IASI-C). They measure infrared radiance from the Earth and atmosphere system, from which the atmospheric composition and temperature can be retrieved using dedicated algorithms, forming the Level 2 (L2) product. The operational near real-time processing of IASI data is conducted by the EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT). It has improved over time, but due to IASI’s large data flow, the whole dataset has not yet been reprocessed backwards. A necessary step that must be completed before initiating this reprocessing is to uniformize the IASI radiance record (Level 1C), which has also changed with time due to various instrumental and software modifications. In 2019, EUMETSAT released a reprocessed IASI-A 2007–2017 radiance dataset that is consistent with both the L1C product generated after 2017 and with IASI-B. First, this study aimed to assess the changes in radiance associated with this update by comparing the operational and reprocessed datasets. The differences in the brightness temperature ranged from 0.02 K at 700 cm−1 to 0.1 K at 2200 cm−1. Additionally, two major updates in 2010 and 2013 were seen to have the largest impact. Then, we investigated the effects on the retrieved temperatures due to successive upgrades to the Level 2 processing chain. We compared IASI L2 with ERA5 reanalysis temperatures. We found differences of ~5–10 K at the surface and between 1 and 5 K in the atmosphere. These differences decreased abruptly after the release of the IASI L2 processor version 6 in 2014. These results suggest that it is not recommended to use the IASI inhomogeneous temperature products for trend analysis, both for temperature and trace gas trends.

Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis.

In recent years, the interest has grown in satellite-derived hyperspectral radiance measurements for assessing the individual impact of climate drivers and their cascade of feedbacks on the outgoing longwave radiation (OLR). In this paper, we use 10 years (2008–2017) of reprocessed radiances from the infrared atmospheric sounding interferometer (IASI) to evaluate the linear trends in clear-sky spectrally resolved OLR (SOLR) in the range [645–2300] cm −1 . Spatial inhomogeneities are observed in most of the analyzed spectral regions. These mostly reflected the natural variability of the atmospheric temperature and composition but long-term changes in greenhouse gases concentrations are also highlighted. In particular, the increase of atmospheric CO 2 and CH 4 led to significant negative trends in the SOLR of −0.05 to −0.3% per year in the spectral region corresponding to the ν 2 and the ν 3 CO 2 and in the ν 4 CH 4 band. Most of the trends associated with the natural variability of the OLR can be related to the El Niño/Southern Oscillation activity and its teleconnections in the studied period. This is the case for the channels most affected by the temperature variations of the surface and the first layers of the atmosphere but also for the channels corresponding to the ν 2 H 2 O and the ν 3 O 3 bands.

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 studies The IASI‐derived SST data set is compared with other available data sets Climate variability and trends are shown and compared to other data sets

Through its important role in the formation of particulate matter, atmospheric ammonia affects air quality and has implications for human health and life expectancy 1 , 2 . Excess ammonia in the environment also contributes to the acidification and eutrophication of ecosystems 3 – 5 and to climate change 6 . Anthropogenic emissions dominate natural ones and mostly originate from agricultural, domestic and industrial activities 7 . However, the total ammonia budget and the attribution of emissions to specific sources remain highly uncertain across different spatial scales 7 – 9 . Here we identify, categorize and quantify the world’s ammonia emission hotspots using a high-resolution map of atmospheric ammonia obtained from almost a decade of daily IASI satellite observations. We report 248 hotspots with diameters smaller than 50 kilometres, which we associate with either a single point source or a cluster of agricultural and industrial point sources—with the exception of one hotspot, which can be traced back to a natural source. The state-of-the-art EDGAR emission inventory 10 mostly agrees with satellite-derived emission fluxes within a factor of three for larger regions. However, it does not adequately represent the majority of point sources that we identified and underestimates the emissions of two-thirds of them by at least one order of magnitude. Industrial emitters in particular are often found to be displaced or missing. Our results suggest that it is necessary to completely revisit the emission inventories of anthropogenic ammonia sources and to account for the rapid evolution of such sources over time. This will lead to better health and environmental impact assessments of atmospheric ammonia and the implementation of suitable nitrogen management strategies. Satellite observations reveal over 200 ammonia hotspots associated with agricultural and industrial point sources, which emit much larger quantities of ammonia to the atmosphere than previously thought.

The Arabian Peninsula exhibits extreme hot summers and has one of the world’s largest population growths. We use satellite observations and reanalysis as well as climate model projections to analyze morning and evening land surface temperatures (LSTs), to refer to processes at the surface, and wet bulb temperatures (WBTs) to measure human heat stress. We focus on three regions: the Persian Gulf and Gulf of Oman, the inland capital of Saudi Arabia, Riyadh and the irrigated agricultural region in Al-Jouf, Saudi Arabia. This study shows that the time of day is important when studying LST and WBT, with current and future WBT higher in the early summer evenings. It also shows that the effect of humidity brought from waterbodies or through irrigation can significantly increase heat stress. Over the coasts of the Peninsula, humidity decreases LST but increases heat stress via WBT values higher than 25 °C in the evening. Riyadh, located in the heart of the Peninsula has lower WBT of 15 °C–17.5 °C and LST reaching 42.5 °C. Irrigation in the Al-Jouf province decreases LST by up to 10° with respect to its surroundings, while it increases WBT by up to 2.5°. Climate projections over the Arabian Peninsula suggest that global efforts will determine the survivability in this region. The projected increase in LST and WBT are +6 °C and +4 °C, respectively, in the Persian Gulf and Riyadh by the end of the century, posing significant risks on human survivability in the Peninsula unless strict climate mitigation takes place.