Infrared Atmospheric Sounding Interferometer Research Articles

Pyrogenic HONO seen from space: insights from global IASI observations

Abstract. Nitrous acid (HONO) is a key atmospheric component, acting as a major source of the hydroxyl radical (OH), the primary oxidant in the Earth's atmosphere. However, understanding its spatial and temporal variability remains a significant challenge. Recent TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P) ultraviolet–visible (UV–Vis) measurements of fresh fire plumes shed light on the impact of global pyrogenic HONO emissions. Here, we leverage Infrared Atmospheric Sounding Interferometer (IASI)/MetOp's global infrared satellite measurements, complementing midday TROPOMI observations with morning and evening overpasses, to detect and retrieve pyrogenic HONO in 2007–2023. Employing a sensitive detection method, we identify HONO enhancements within concentrated fire plumes worldwide. Most detections are in the Northern Hemisphere (NH) mid- and high latitudes, where intense wildfires and high injection heights favour HONO detection. IASI's nighttime measurements yield 10-fold more HONO detections than daytime measurements, emphasizing HONO's extended lifetime in the absence of photolysis during the night. The annual detection count increases by at least 3–4 times throughout the IASI time series, mirroring the recent surge in intense wildfires at these latitudes. Additionally, we employ a neural-network-based algorithm for retrieving pyrogenic HONO total columns from IASI and compare them with TROPOMI in the same fire plumes. The results demonstrate TROPOMI's efficacy in capturing HONO enhancements in smaller fire plumes and in proximity to fire sources, while IASI's morning and evening overpasses enable HONO measurements further downwind, highlighting the survival of HONO or its secondary formation along long-range transport in smoke plumes.

Volcanic clouds detection applying machine learning techniques to GNSS radio occultations

AbstractVolcanic clouds detection is a challenge especially when meteorological clouds are present in the same area. Several algorithms have been developed to detect and monitor volcanic clouds by using satellite instruments based on different remote sensing techniques. This work aims at classifying volcanic clouds based on atmospheric profiles retrieved by the GNSS (Global Navigation Satellite Systems) radio occultation technique. We collocated the radio occultations with the volcanic cloud detection from AIRS (Atmospheric InfraRed Sounder) and IASI (Infrared Atmospheric Sounding Interferometer) for 11 big eruptions happening in the period 2008–2015 resulting in about 15000 profiles. We created an archive with the collocations and a corresponding number of profiles in “non-volcanic” environment in the same area and on the same period of the year. A support vector machine algorithm was applied to the archive in order to classify the clouds and to distinguish the volcanic clouds from the other types. The model performances are promising: the GNSS radio occultations are able to distinguish the volcanic clouds with an accuracy higher than 80% when the eruption occurs at high latitudes. The performances of the model are affected by the number of collocations used for the training. Nowadays, the number of radio occultations is higher than in the period considered in this research, making this work a pioneering study for a future operational product.

Long-term spatiotemporal variations of ammonia in the Yangtze River Delta region of China and its driving factors

This study focuses on the spatiotemporal distribution, urban-rural variations, and driving factors of ammonia Vertical Column Densities (VCDs) in China's Yangtze River Delta region (YRD) from 2008 to 2020. Utilizing data from the Infrared Atmospheric Sounding Interferometer (IASI), Generalized Additive Models (GAM), and the GEOS-Chem chemical transport model, we observed a significant increase of NH3 VCDs in the YRD between 2014 and 2020. The spatial distribution analysis revealed higher NH3 concentrations in the northern part of the YRD region, primarily due to lower precipitation, alkaline soil, and intensive agricultural activities. NH3 VCDs in the YRD region increased significantly (65.18%) from 2008 to 2020. The highest growth rate occurs in the summer, with an annual average growth rate of 7.2% during the period from 2014 to 2020. Agricultural emissions dominated NH3 VCDs during spring and summer, with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones. Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH3 VCDs. The rise in NH3 VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO2, as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model. The concentration changes of acidic gases contribute to over 80% of the interannual variations in NH3 VCDs. This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases. Effective emission control is urgent to mitigate environmental hazards and secondary particulate matter, especially in the northern YRD.

NH3 Emissions and Lifetime Estimated by Satellite Observations with Differential Evolution Algorithm

As an important irritant trace gas in the atmosphere, ammonia (NH3) significantly impacts human health and environment. Bottom-up emission inventories are widely used to estimate ammonia emissions and their geographical distributions over China. However, high uncertainties are still associated with emission inventories due to inaccurate emission factors used. The Differential Evolution (DE) algorithm is a population-based stochastic optimization algorithm used to solve complicated optimization problems. We quantify NH3 emissions and lifetime from Infrared Atmospheric Sounding Interferometer (IASI) NH3 observations together with MERRA-2 wind fields based on the DE algorithm. Two inland cities, Urumchi and Golmud in China, are chosen to study of the NH3 emissions based on the distributions of NH3 total columns and wind fields. The NH3 emissions rate estimated is about 5.84 × 10−11 and 4.19 × 10−11 kg·m−2s−1 in Urumchi and in the Golmud area from May to September from 2008 to 2023, respectively. The lifetime of NH3 estimated in the two areas is 4.31 and 9.19 h, respectively. We compare the NH3 emissions and lifetime estimated in this study with the values in other studies, and the results show the reliability of the method used. This work is one of few quantitative studies of NH3 emissions from cities using satellite observations in China.

How well are aerosol–cloud interactions represented in climate models? – Part 1: Understanding the sulfate aerosol production from the 2014–15 Holuhraun eruption

Abstract. For over 6 months, the 2014–2015 effusive eruption at Holuhraun, Iceland, injected considerable amounts of sulfur dioxide (SO2) into the lower troposphere with a daily rate of up to one-third of the global emission rate, causing extensive air pollution across Europe. The large injection of SO2, which oxidises to form sulfate aerosol (SO42-), provides a natural experiment offering an ideal opportunity to scrutinise state-of-the-art general circulation models' (GCMs) representation of aerosol–cloud interactions (ACIs). Here we present Part 1 of a two-part model inter-comparison using the Holuhraun eruption as a framework to analyse ACIs. We use SO2 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) instrument and ground-based measurements of SO2 and SO42- mass concentrations across Europe, in conjunction with a trajectory analysis using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, to assess the spatial and chemical evolution of the volcanic plume as simulated by five GCMs and a chemical transport model (CTM). IASI retrievals of plume altitude and SO2 column load reveal that the volcanic perturbation is largely contained within the lower troposphere. Compared to the satellite observations, the models capture the spatial evolution and vertical variability of the plume reasonably well, although the models often overestimate the plume altitude. HYSPLIT trajectories are used to attribute to Holuhraun emissions 111 instances of elevated sulfurous surface mass concentrations recorded at European Monitoring and Evaluation Programme (EMEP) stations during September and October 2014. Comparisons with the simulated concentrations show that the modelled ratio of SO2 to SO42- during these pollution episodes is often underestimated and overestimated for the young and mature plume, respectively. Models with finer vertical resolutions near the surface are found to better capture these elevated sulfurous ground-level concentrations. Using an exponential function to describe the decay of observed surface mass concentration ratios of SO2 to SO42- with plume age, the in-plume oxidation rate constant is estimated as 0.032 ± 0.002 h−1 (1.30 ± 0.08 d e-folding time), with a near-vent ratio of 25 ± 5 (µg m−3 of SO2 / µg m−3 of SO42-). The majority of the corresponding derived modelled oxidation rate constants are lower than the observed estimate. This suggests that the representation of the oxidation pathway/s in the simulated plumes is too slow. Overall, despite their coarse spatial resolutions, the six models show reasonable skill in capturing the spatial and chemical evolution of the Holuhraun plume. This capable representation of the underlying aerosol perturbation is essential to enable the investigation of the eruption's impact on ACIs in the second part of this study.

Spectral Fingerprinting of Methane from Hyper-Spectral Sounder Measurements Using Machine Learning and Radiative Kernel-Based Inversion

Satellite-based hyper-spectral infrared (IR) sensors such as the Atmospheric Infrared Sounder (AIRS), the Cross-track Infrared Sounder (CrIS), and the Infrared Atmospheric Sounding Interferometer (IASI) cover many methane (CH4) spectral features, including the ν1 vibrational band near 1300 cm−1 (7.7 μm); therefore, they can be used to monitor CH4 concentrations in the atmosphere. However, retrieving CH4 remains a challenge due to the limited spectral information provided by IR sounder measurements. The information required to resolve the weak absorption lines of CH4 is often obscured by interferences from signals originating from other trace gases, clouds, and surface emissions within the overlapping spectral region. Consequently, currently available CH4 data product derived from IR sounder measurements still have large errors and uncertainties that limit their application scope for high-accuracy climate and environment monitoring applications. In this paper, we describe the retrieval of atmospheric CH4 profiles using a novel spectral fingerprinting methodology and our evaluation of performance using measurements from the CrIS sensor aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. The spectral fingerprinting methodology uses optimized CrIS radiances to enhance the CH4 signal and a machine learning classifier to constrain the physical inversion scheme. We validated our results using the atmospheric composition reanalysis results and data from airborne in situ measurements. An inter-comparison study revealed that the spectral fingerprinting results can capture the vertical variation characteristics of CH4 profiles that operational sounder products may not provide. The latitudinal variations in CH4 concentration in these results appear more realistic than those shown in existing sounder products. The methodology presented herein could enhance the utilization of satellite data to comprehend methane’s role as a greenhouse gas and facilitate the tracking of methane sources and sinks with increased reliability.

On the use of routine airborne observations for evaluation and monitoring of satellite observations of thermodynamic profiles

Abstract. Satellite-based observations require independent sources of data to monitor and evaluate their precision and accuracy. For the temperature and water vapor profiles produced by satellite-based sounders, this typically results in comparisons to operational radiosonde observations. However, polar-orbiting satellite overpasses are frequently misaligned with the global synoptic launch times. The routine airborne in situ observations of temperature and water vapor from the Airborne Meteorological Data Relay (AMDAR) program and the Water Vapor Sensing System-II (WVSS-II) instrument greatly enhance opportunities to make precise matchups due to the far greater temporal frequency and spatial density of aircraft flights. The potential for the use of aircraft-based observations as a source of evaluation of tropospheric satellite sounder profiles is explored through a year-long intercomparison with the Infrared Atmospheric Sounding Interferometer (IASI) level-2 profiles produced from both the Metop-A and Metop-B satellites. Results using 1 h and 50 km match criteria indicating good agreement between the satellites and the aircraft-based observations with temperature, specific humidity, and relative humidity biases generally less than 0.5 K, 0.8 g kg−1, and 5 %, respectively; both IASI instruments perform nearly identically. While the intercomparisons are generally limited to the troposphere as aircraft typically reach their maximum height at the tropopause, the substantially larger number of intercomparison points enable characterization as a function of season, scan angle, and other characteristics heretofore unexplored due to a lack of sufficient validation data.

Fingerprints of the COVID-19 economic downturn and recovery on ozone anomalies at high-elevation sites in North America and western Europe

Abstract. With a few exceptions, most studies on tropospheric ozone (O3) variability during and following the COrona VIrus Disease (COVID-19) economic downturn focused on high-emission regions or urban environments. In this work, we investigated the impact of the societal restriction measures during the COVID-19 pandemic on surface O3 at several high-elevation sites across North America and western Europe. Monthly O3 anomalies were calculated for 2020 and 2021, with respect to the baseline period 2000–2019, to explore the impact of the economic downturn initiated in 2020 and its recovery in 2021. In total, 41 high-elevation sites were analyzed: 5 rural or mountaintop stations in western Europe, 19 rural sites in the western US, 4 sites in the western US downwind of highly polluted source regions, and 4 rural sites in the eastern US, plus 9 mountaintop or high-elevation sites outside Europe and the United States to provide a “global” reference. In 2020, the European high-elevation sites showed persistent negative surface O3 anomalies during spring (March–May, i.e., MAM) and summer (June–August, i.e., JJA), except for April. The pattern was similar in 2021, except for June. The rural sites in the western US showed similar behavior, with negative anomalies in MAM and JJA 2020 (except for August) and MAM 2021. The JJA 2021 seasonal mean was influenced by strong positive anomalies in July due to large and widespread wildfires across the western US. The polluted sites in the western US showed negative O3 anomalies during MAM 2020 and a slight recovery in 2021, resulting in a positive mean anomaly for MAM 2021 and a pronounced month-to-month variability in JJA 2021 anomalies. The eastern US sites were also characterized by below-mean O3 for both MAM and JJA 2020, while in 2021 the negative values exhibited an opposite structure compared to the western US sites, which were influenced by wildfires. Concerning the rest of the world, a global picture could not be drawn, as the sites, spanning a range of different environments, did not show consistent anomalies, with a few sites not experiencing any notable variation. Moreover, we also compared our surface anomalies to the variability of mid-tropospheric O3 detected by the IASI (Infrared Atmospheric Sounding Interferometer) satellite instrument. Negative anomalies were observed by IASI, consistent with published satellite and modeling studies, suggesting that the anomalies can be largely attributed to the reduction of O3 precursor emissions in 2020.

A satellite chronology of plumes from the April 2021 eruption of La Soufrière, St Vincent

Abstract. Satellite instruments play a valuable role in detecting, monitoring and characterising emissions of ash and gas into the atmosphere during volcanic eruptions. This study uses two satellite instruments, the Infrared Atmospheric Sounding Interferometer (IASI) and the Advanced Baseline Imager (ABI), to examine the plumes of ash and sulfur dioxide (SO2) from the April 2021 eruption of La Soufrière, St Vincent. The frequent ABI data have been used to construct a 14 d chronology of a series of explosive events at La Soufrière, which is then complemented by measurements of SO2 from IASI, which is able to track the plume as it is transported around the globe. A minimum of 35 eruptive events were identified using true, false and brightness temperature difference maps produced with the ABI data. The high temporal resolution images were used to identify the approximate start and end times, as well as the duration and characteristics of each event. From this analysis, four distinct phases within the 14 d eruption have been defined, each consisting of multiple explosive events with similar characteristics: (1) an initial explosive event, (2) a sustained event lasting over 9 h, (3) a pulsatory phase with 25 explosive events in a 65.3 h period and (4) a waning sequence of explosive events. It is likely that the multiple explosive events during the April 2021 eruption contributed to the highly complex plume structure that can be seen in the IASI measurements of the SO2 column amounts and heights. The bulk of the SO2 from the first three phases of the eruption was transported eastwards, which based on the wind direction at the volcano implies that the SO2 was largely in the upper troposphere. Some of the SO2 was carried to the south and west of the volcano, suggesting a smaller emission of the gas into the stratosphere, there being a shift in wind direction around the height of the tropopause. The retrieved SO2 heights show that the plume had multiple layers but was largely concentrated between 13 and 19 km, with the majority of the SO2 being located in the upper troposphere and around the height of the tropopause, with some emission into the stratosphere. An average e-folding time of 6.07±4.74 d was computed based on the IASI SO2 results: similar to other tropical eruptions of this magnitude and height. The SO2 was trackable for several weeks after the eruption and is shown to have circulated the globe, with parts of it reaching as far as 45∘ S and 45∘ N. Using the IASI SO2 measurements, a time series of the total SO2 mass loading was produced, with this peaking on 13 April (descending orbits) at 0.31±0.09 Tg. Converting these mass values to a temporally varying SO2 flux demonstrated that the greatest emission occurred on 10 April with that measurement incorporating SO2 from the second phase of the eruption (sustained emission) and the beginning of the pulsatory phase. The SO2 flux is then shown to fall during the later stages of the eruption: suggesting a reduction in eruptive energy, something also reflected in ash height estimates obtained with the ABI instrument. A total SO2 emission of 0.63±0.5 Tg of SO2 has been derived, although due to limitations associated with the retrieval, particularly in the first few days after the eruption began, this, the retrieved column amounts and the total SO2 mass on each day should be considered minimum estimates. There are a number of similarities between the 1979 and 2021 eruptions at La Soufrière, with both eruptions consisting of a series of explosive events with varied heights and including some emission into the stratosphere. These similarities highlight the importance of in-depth investigations into eruptions and the valuable contribution of satellite data for this purpose; as these studies aid in learning about a volcano's behaviour, which may allow for better preparation for future eruptive activity.

Interannual variability of atmospheric ammonia over the Sichuan Basin, southwestern China: Trend, sources, and implications on particle matter control

Atmospheric ammonia (NH3) is an important constituent of biogeochemical cycle and excessive NH3 in the environment results in detrimental effects on ecosystems, air quality, and human health. However, interannual variability and emission changes of NH3 remain highly uncertain, particularly for NH3 hotspot areas. Here, we identify the emission hotspots of NH3 and reveal the trend of NH3 emissions over the Sichuan Basin (SCB) based on a high-resolution map from infrared Atmospheric Sounding Interferometer (IASI) satellite observations in combination with the MEIC inventory from 2008 to 2019. IASI observations indicate that NH3 columns persistent increase over time while MEIC inventory suggests that the past increase in NH3 emissions has indeed ended in 2017 and exhibit decreasing trend afterwards. The NH3 hotspots derived from IASI mainly concentrate in the Chengdu Plain and southern SCB city cluster, which mostly agrees with MEIC emission inventory. CMAQ model simulations well reproduce PM2.5 and ammonia loading and capture observed patterns of NH3. We find that ammonium makes up 15–35% of inorganic fine PM2.5 mass in winter over the SCB. Regulation efforts focused on joint reducing 30% of NOx, SO2, and NH3 emissions could lead to basin-wide wintertime PM2.5 reduce by 10.0 μg/m3, while cutting 30% emissions of NH3 or NOx alone only yield PM2.5 reduced by 0.8 and 5.0 μg/m3, respectively. This work contributes insights on the trend and spatial pattern of NH3 source over the SCB and reveals the critical role of regulating NH3 emissions in mitigating PM2.5 pollution, shedding insights on nitrogen management strategies

A decadal atmospheric ammonia reanalysis product in China

Atmospheric ammonia has great environmental implications due to its important role in ecosystem and nitrogen cycle, as well as contribution to formation of secondary particles. China is recognized as a hotspot of NH3 pollution owing to agricultural and livestock intensification. In the quest to achieve a comprehensive understanding of atmospheric ammonia load and to quantify its environmental impacts in China, relying solely either on existing measurements or on model simulations falls short. Their limitations, either in spatial coverage and integrity or in data quality, fails to meet the needs. Available reanalysis products exhibit a marked deficiency in ammonia data. We therefore aim to propose an integrated ammonia reanalysis product in China, adeptly melding satellite observations from the Infrared Atmospheric Sounding Interferometer (IASI) NH3 retrievals with chemical transport model simulation, capitalizing on the robust Ensemble Kalman Filter (EnKF) data assimilation methodology. The product is validated in high quality via the comparison against independent measurements from ground monitoring stations. Spanning a decade from 2013 to 2022, our reanalysis uncovers not just the spatial intricacies of NH3 concentrations but also their temporal dynamics. Our findings pinpointed the spatial disparities in atmospheric ammonia intensities, highlighting regional hotspots in the NCP, SCB, and Northeast China, and identified annual and seasonal patterns. Our research provides crucial insights for shaping future NH3 pollution prevention and control strategies in China.

Data Gap: Air Quality Networks Miss Air Pollution from Concentrated Animal Feeding Operations.

In the U.S., the agricultural sector is the largest controllable source of several air pollutants, including ammonia (NH3), which is a key precursor to PM2.5 formation. Livestock waste is the dominant contributor to ammonia emissions. In contrast to most controllable air pollutants, satellite records show ammonia mixing ratios are rising. The number of confined animal feeding operations (CAFOs) that generate considerable livestock waste is also increasing. Spatial and temporal trends in USDA-reported animal numbers normalized by county area at medium and large CAFOs provide plausible explanations for patterns in satellite-derived NH3 over the contiguous U.S. (CONUS). The correlation between summertime ammonia derived from the European Space Agency's (ESA) Infrared Atmospheric Sounding Interferometer (IASI) and CAFO animal unit density in 2017 is positive and significant (r = 0.642; p ≈ 0). The temporal changes from 2002 to 2017 in animal unit density and NH3 derived from NASA's Atmospheric Infrared Sounder (AIRS) are spatially similar. Trends and ambient concentrations of PM2.5 mass in agricultural regions are difficult to assess relative to those of urban population centers given the sparseness of rural monitors in regulatory surface networks. Results suggest that in agricultural areas where ammonia concentrations and animal density are highest, air quality improvement lags behind the national average.

Retrieving land surface temperatures from IASI hyperspectral thermal infrared data using an AFNO-transformer model.

An adaptive Fourier neural operator (AFNO)-transformer model was developed to retrieve land surface temperature (LST) data from infrared atmospheric sounding interferometer (IASI) observations. A weight selection scheme based on linearization of the radiative transfer equation was proposed to solve the hyperspectral data channel redundancy problem. The IASI brightness temperatures and Advanced Very High Resolution Radiometer onboard MetOp (AVHRR/MetOp) LST product were selected to construct the training and test datasets. The AFNO-transformer performed effective token mixing through self-attention and effectively solved the global convolution problem in the Fourier domain, which can better learn complex nonlinear equations and achieve time-series forecasting. The root mean square error indicated that the LST in Eastern Spain and North Africa could be retrieved with an error of less than 2.5 K compared with the AVHRR/MetOp LST product. Moreover, the validation results from other time period data showed that the retrieval accuracy of this model can be less than 3 K. The proposed model provides a novel approach for hyperspectral LST retrieval.

The IASI NH3 version 4 product: averaging kernels and improved consistency

Abstract. 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.

The new σ-IASI code for all sky radiative transfer calculations in the spectral range 10 to 2760 cm-1: σ-IASI/F2N

The paper describes a recently developed forward model (σ-IASI/F2N) to produce spectral radiances from the far to near-infrared spectrum (100 to 2760 cm-1). The model is a pseudo-monochromatic radiative transfer tool that exploits lookup tables to compute the optical depths of atmospheric gas and clouds. Multiple scattering effects are accurately included in both the Far IR and Thermal IR by using a scaling method for cloud and aerosol radiative properties parameterized in terms of their effective radius, which allows them to be handled adopting the same formalism used in the clear sky. In this paper we apply a novel approach to a classical scaling method in the thermal IR relying on our improved parametrization of backscattering parameter over that used by Chou (Martinazzo et al., 2021), while in the Far IR a corrective term is introduced. The code is written in Fortran and runs on Unix-based (Linux and macOS) or MS Windows operating systems. σ-IASI/F2N can be used to develop custom versions of fast-forward modules for satellite instruments working in the infrared spectral range, such as the Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) and the Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) missions. We discuss the σ-IASI/F2N performance in simulating a set of ECMWF analyses at the global scale. For this purpose, we compare observations from the Infrared Atmospheric Sounding Interferometer (IASI) flying on MetOp B, and C. Results show that σ-IASI/F2N can easily ingest ECMWF analyses data and accurately reproduce cloud patterns. We also show that the difference between σ-IASI/F2N simulations and corresponding IASI observations is below 1 K in the 8–12 um window region, which is mainly affected by the water vapor continuum absorption and weak lines, while for night-time clear sky, the differences are below 0.3 K. again within the same window region.

Simulation of Thermal Infrared Brightness Temperatures from an Ocean Color and Temperature Scanner Onboard a New Generation Chinese Ocean Color Observation Satellite

Since 2002, China has launched four Haiyang-1 (HY-1) satellites equipped with the Chinese Ocean Color and Temperature Scanner (COCTS), which can observe the sea surface temperature (SST). The planned new generation ocean color observation satellites also carry a sensor for observing the SST represented by the payload in this paper. We analyze the spectral brightness temperature (BT) difference between the payload and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra for the thermal infrared channels (11 and 12 µm) based on atmospheric radiative transfer simulation. The bias and standard deviation (SD) of spectral BT difference for the 11 µm channel are −0.12 K and 0.15 K, respectively, and those for the 12 µm channel are −0.10 K and 0.03 K, respectively. When the total column water vapor (TCWV) decreases from the oceans near the equator to high-latitude oceans, the spectral BT difference of the 11 µm channel varies from a positive deviation to a negative deviation, and that of the 12 µm channel basically remains stable. By correcting the MODIS BT observation using the spectral BT differences, we produce the simulated BT data for the thermal infrared channels of the payload, and then validate it using the Infrared Atmospheric Sounding Interferometer (IASI) carried on METOP-B. The validation results show that the bias of BT difference between the payload and IASI is −0.22 K for the 11 µm channel, while it is −0.05 K for the 12 µm channel. The SD of both channels is 0.13 K. In this study, we provide the simulated BT dataset for the 11 and 12 µm channels of a payload for the retrieval of SST. The simulated BT dataset corrected may be used to develop SST-retrieval algorithms.

Bridging the spatial gaps of the Ammonia Monitoring Network using satellite ammonia measurements

Abstract. Ammonia (NH3) is a key precursor to fine particulate matter (PM2.5) and a primary form of reactive nitrogen. The limited number of NH3 observations hinders the further understanding of its impacts on air quality, climate, and biodiversity. Currently, NH3 ground monitoring networks are few and sparse across most of the globe, and even in the most established networks, large spatial gaps exist between sites and only a few sites have records that span longer than a decade. Satellite NH3 observations can be used to discern trends and fill spatial gaps in networks, but many factors influence the syntheses of the vastly different spatiotemporal scales between surface network and satellite measurements. To this end, we intercompared surface NH3 data from the Ammonia Monitoring Network (AMoN) and satellite NH3 total columns from the Infrared Atmospheric Sounding Interferometer (IASI) in the contiguous United States (CONUS) and then performed trend analyses using both datasets. We explored the sensitivity of correlations between the two datasets to factors such as satellite data availability and distribution over the surface measurement period, as well as agreement within selected spatial and temporal windows. Given the short lifetime of atmospheric ammonia and consequently sharp gradients, smaller spatial windows show better agreement than larger ones except in areas of relatively uniform, low concentrations where large windows and more satellite measurements improve the signal-to-noise ratio. A critical factor in the comparison is having satellite measurements across most of the measurement period of the monitoring site. When IASI data are available for at least 80 % of the days of AMoN's 2-week sampling period within a 25 km spatial window of a given site, IASI NH3 column concentrations and the AMoN NH3 surface concentrations have a correlation of 0.74, demonstrating the feasibility of using satellite NH3 columns to bridge the spatial gaps existing in the surface network NH3 concentrations. Both IASI and AMoN show increasing NH3 concentrations across the CONUS (median: 6.8 % yr−1 versus 6.7 % yr−1) in the last decade (2008–2018), suggesting the NH3 will become a greater contributor to nitrogen deposition. NH3 trends at AMoN sites are correlated with IASI NH3 trends (r = 0.66) and show similar spatial patterns, with the highest increases in the Midwest and eastern US. In spring and summer, increases in NH3 were larger than 10 % yr−1 in the eastern US and Midwest (cropland dominated) and the western US (pastureland dominated), respectively. NH3 hotspots are defined as regions where the IASI NH3 column is larger than the 95th percentile of the 11-year CONUS map (6.7 × 1015 molec.cm-2), they also experience increasing concentrations over time, with a median of NH3 trend of 4.7 % yr−1. IASI data show large NH3 increases in urban areas (8.1 % yr−1), including 8 of the top 10 most populous regions in the CONUS, where AMoN sites are sparse. A comparison between IASI NH3 concentration trends and state-level NH3 emission trends is then performed to reveal that positive correlations exist in states with strong agricultural NH3 emissions, while there are negative correlations in states with low NH3 emissions and large NOx emissions, suggesting the different roles of emission and partitioning in NH3 increases. The increases in NH3 could have detrimental effects on nearby eco-sensitive regions through nitrogen deposition and on aerosol chemistry in the densely populated urban areas, and therefore they should be carefully monitored and studied.

Investigation of the summer 2018 European ozone air pollution episodes using novel satellite data and modelling

Abstract. In the summer of 2018, Europe experienced an intense heatwave which coincided with several persistent large-scale ozone (O3) pollution episodes. Novel satellite data of lower-tropospheric column O3 from the Global Ozone Monitoring Experiment-2 (GOME-2) and Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp satellite showed substantial enhancements in 2018 relative to other years since 2012. Surface observations also showed ozone enhancements across large regions of continental Europe in summer 2018 compared to 2017. Enhancements to surface temperature and the O3 precursor gases carbon monoxide and methanol in 2018 were co-retrieved from MetOp observations by the same scheme. This analysis was supported by the TOMCAT chemistry transport model (CTM) to investigate processes driving the observed O3 enhancements. Through several targeted sensitivity experiments we show that meteorological processes, and emissions to a secondary order, were important for controlling the elevated O3 concentrations at the surface. However, mid-tropospheric (∼ 500 hPa) O3 enhancements were dominated by meteorological processes. We find that contributions from stratospheric O3 intrusions ranged between 15 %–40 %. Analysis of back trajectories indicates that the import of O3-enriched air masses into Europe originated over the North Atlantic, substantially increasing O3 in the 500 hPa layer during summer 2018.

A roadmap to estimating agricultural ammonia volatilization over Europe using satellite observations and simulation data

Abstract. Ammonia (NH3) is one of the most important gases emitted from agricultural practices. It affects air quality and the overall climate and is in turn influenced by long-term climate trends as well as by short-term fluctuations in local and regional meteorology. Previous studies have established the capability of the Infrared Atmospheric Sounding Interferometer (IASI) series of instruments, aboard the Metop satellites, to measure ammonia from space since 2007. In this study, we explore the interactions between atmospheric ammonia, land and meteorological variability, and long-term climate trends in Europe. We investigate the emission potential (Γsoil) of ammonia from the soil, which describes the soil–atmosphere ammonia exchange. Γsoil is generally calculated in-field or in laboratory experiments; here, and for the first time, we investigate a method which assesses it remotely using satellite data, reanalysis data products, and model simulations. We focus on ammonia emission potential in March 2011, which marks the start of growing season in Europe. Our results show that Γsoil ranges from 2 × 103 to 9.5 × 104 (dimensionless) in fertilized cropland, such as in the North European Plain, and is of the order of 10–102 in a non-fertilized soil (e.g., forest and grassland). These results agree with in-field measurements from the literature, suggesting that our method can be used in other seasons and regions in the world. However, some improvements are needed in the determination of mass transfer coefficient k (m s−1), which is a crucial parameter to derive Γsoil. Using a climate model, we estimate the expected increase in ammonia columns by the end of the century based on the increase in skin temperature (Tskin), under two different climate scenarios. Ammonia columns are projected to increase by up to 50 %, particularly in eastern Europe, under the SSP2-4.5 scenario and might even double (increase of 100 %) under the SSP5-8.5 scenario. The increase in skin temperature is responsible for a formation of new hotspots of ammonia in Belarus, Ukraine, Hungary, Moldova, parts of Romania, and Switzerland.

3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective

In this work, we present an efficient optic flow algorithm for the extraction of vertically resolved 3D atmospheric motion vector (AMV) fields from incomplete hyperspectral image data measures by infrared sounders. The model at the heart of the energy to be minimized is consistent with atmospheric dynamics, incorporating ingredients of thermodynamics, hydrostatic equilibrium and statistical turbulence. Modern optimization techniques are deployed to design a low-complexity solver for the energy minimization problem, which is non-convex, non-differentiable, high-dimensional and subject to physical constraints. In particular, taking advantage of the alternate direction of multipliers methods (ADMM), we show how to split the original high-dimensional problem into a recursion involving a set of standard and tractable optic-flow sub-problems. By comparing with the ground truth provided by the operational numerical simulation of the European Centre for Medium-Range Weather Forecasts (ECMWF), we show that the performance of the proposed method is superior to state-of-the-art optical flow algorithms in the context of real infrared atmospheric sounding interferometer (IASI) observations.