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info:eu-repo/semantics/nonPublished

info:eu-repo/semantics/nonPublished

info:eu-repo/semantics/nonPublished

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., info:eu-repo/semantics/published

info:eu-repo/semantics/nonPublished

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., SCOPUS: ar.j, info:eu-repo/semantics/published

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., SCOPUS: ar.j, info:eu-repo/semantics/published

The Infrared Atmospheric Sounding Interferometer (IASI), flying on board the suite of Metop satellites for more than 15 years, has shown an excellent stability over its entire lifespan and a very good consistency between the three instruments (on board Metop-A, -B and -C). This makes the IASI dataset an excellent climate data record. For the detection and the characterization of clouds, the current IASI operational Level 2 product is highly performant. However, since it was first released in 2007, the L2 cloud data have undergone a series of updates which have not yet been reprocessed back in time. This leads to discontinuities in the data record which makes it very difficult for use in long-term studies. Even in the event of a complete reprocessing of the L2, there would also be no guarantee on the homogeneity of the futures versions. Other cloud products exist (e.g. the AVHRR-L1C, the cloud_cci, the CIRS-LMD) but those are usually either less accurate or sensitive to cloud detection or are not available in near-real-time. These limitations in the existing products triggered the development of a sensitive and coherent IASI cloud detection dataset. Here we present a new cloud detection algorithm for the IASI measurements based on a Neural Network (NN). The input data consists of a set of 45 IASI channels. Those were selected outside the regions affected by CO2, CFC-11 and CFC-12 absorptions to avoid any long-term bias in the detection as their concentrations are evolving over time in the atmosphere. As a reference dataset, we use the current version (v6.6) of the IASI L2 cloud product. The IASI-derived NN cloud product appears to be both accurate in the cloud detection and coherent over the whole IASI period and between the three versions of the instrument. To illustrate this, we show global distributions and time series of the cloud fractions and we assess the quality of the cloud mask by comparing the NN product against several other cloud products. We also evalua, info:eu-repo/semantics/nonPublished

info:eu-repo/semantics/nonPublished

The emissions of halocarbons have profoundly modified the chemical and radiative equilibrium of our atmosphere. These halogenated compounds are known to be powerful greenhouse gases and contribute, for chlorinated and brominated compounds, to the depletion of stratospheric ozone and to the development of the ozone hole. Their monitoring is therefore essential. The aim of this work is to assess the potential of infrared satellite sounders operating in the nadir geometry, to contribute to this monitoring and thereby to complement existing surface measurement networks. This work is centered on the exploitation of the measurements from the infrared satellite sounder IASI. The instrument stability and the consistency between the different instruments on the successive Metop platforms (A, B and C) is remarkable and makes it a reference for climate monitoring. Among other things, IASI offers the potential to investigate trends in the atmospheric abundance of various species better than with any other hyperspectral IR sounder. The low noise of the IASI radiances is also such that even weakly absorbing halocarbons can be identified. Recently, we managed to detect the spectral signatures of eight halocarbons: CFC-11, CFC-12, HCFC-22, HCFC-142b, HFC-134a, CF4, SF6 and CCl4. In this work we exploit the 15 years record of continuous IASI measurements to give a first assessment of the trend evolution of these species. This is done by targeting various geographical areas on the globe and examining the remote oceanic and continental source regions separately. The trend evolution in the different chemical species, either negative or positive, is validated against what is observed with ground-based measurement networks and other remote sensors. We conclude by assessing the usefulness of IASI and follow-on missions to contribute to the global monitoring of halocarbons., info:eu-repo/semantics/nonPublished

The emissions of halocarbons have profoundly modified the chemical and radiative equilibrium of our atmosphere. These halogenated compounds are known to be powerful greenhouse gases and contribute, for chlorinated and brominated compounds, to the depletion of stratospheric ozone and to the development of the ozone hole. Their monitoring is therefore essential. The aim of this work is to assess the potential of infrared satellite sounders operating in the nadir geometry, to contribute to this monitoring and thereby to complement existing surface measurement networks. This work is centered on the exploitation of the measurements from the infrared satellite sounder IASI. The instrument stability and the consistency between the different instruments on the successive Metop platforms (A, B and C) is remarkable and makes it a reference for climate monitoring. Among other things, IASI offers the potential to investigate trends in the atmospheric abundance of various species better than with any other hyperspectral IR sounder. The low noise of the IASI radiances is also such that even weakly absorbing halocarbons can be identified. Recently, we reported the detection of the spectral signatures of eight halocarbons: CFC-11, CFC-12, HCFC-22, HCFC-142b, HFC-134a, CF4, SF6 and CCl4. In this work we exploit the 15 years record of continuous IASI measurements to analyze the trend of these species. This is done by targeting various geographical areas on the globe and examining the remote oceanic and continental source regions separately. The trend evolution in the different chemical species, either negative or positive, is validated against what is observed with ground-based measurement networks and other remote sensors. We conclude by assessing the usefulness of IASI and follow-on missions to contribute to the global monitoring of halocarbons., info:eu-repo/semantics/nonPublished

info:eu-repo/semantics/nonPublished

info:eu-repo/semantics/nonPublished

Clouds are an essential component in our Earth system because of their importance for the weather, the water cycle and the Earth radiation budget. To better understand the climate, its past and future evolution, the development of long coherent time series of cloud properties is needed. In addition, as the clouds strongly impact the radiance at the top of the atmosphere, the detection of clear-sky scenes is a major preprocessing step for most climate and atmospheric satellite applications, such as trace gas retrieval or to derive the Earth Outgoing Longwave Radiation (OLR) budget. The Infrared Atmospheric Sounding Interferometer (IASI), flying on board the suite of Metop satellites for more than 15 years, has shown an excellent stability over its entire lifespan and a very good consistency between the three instruments (on board Metop-A, -B and -C). This makes the IASI dataset an excellent climate data record. For the detection and the characterization of clouds, the current IASI operational Level 2 product is highly performant. However, since it was first released in 2007, the L2 cloud data have undergone a series of updates which have not yet been reprocessed back in time. This leads to discontinuities in the data record which makes it very difficult for use in long-term studies. Even in the event of a complete reprocessing of the L2, there would also be no guarantee on the homogeneity of the futures versions. Other cloud products exist (e.g. the AVHRR-L1C, the cloud_cci, the CIRS-LMD) but those are usually either less accurate or sensitive to cloud detection or are not available in near-real-time. These limitations in the existing products triggered the development of a sensitive and coherent IASI cloud detection dataset, using the IASI radiances. Here we present a new cloud detection algorithm for the IASI measurements based on a Neural Network (NN). The input data consists of a set of 45 IASI channels. Those were selected outside the regions affected by CO2, CH, info:eu-repo/semantics/nonPublished

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 2250-2400gcm-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 7ghPa, 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 0.5gK at these altitudes. At 2ghPa, as the IASI sensitivity decreases, we found differences up to 2gK between IASI and the three validation datasets. We then computed atmospheric temperature linear trends from atmospheric temperatures between 750 and 2ghPa. 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 (0.5gK/decade at mid-latitudes, 1gK/decade at the North Pole). Th, SCOPUS: ar.j, info:eu-repo/semantics/published

info:eu-repo/semantics/published

Excess atmospheric ammonia (NH3) 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 NH3 observations at global scale for over a decade. Here we use the version 3 of the Infrared Atmospheric Sounding Interferometer (IASI) NH3 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 NH3 emissions, meteorological conditions and the impact of sulfur and nitrogen oxides emissions, which alter the atmospheric lifetime of NH3. We end with a short case study dedicated to the Netherlands and the "Dutch Nitrogen crisis" of 2019.

info:eu-repo/semantics/nonPublished

info:eu-repo/semantics/nonPublished

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)., SCOPUS: ar.j, info:eu-repo/semantics/published

info:eu-repo/semantics/nonPublished

info:eu-repo/semantics/published

info:eu-repo/semantics/published

The Earth’s Outgoing Longwave Radiation (OLR) is a key component in the study of climate. As part of the Earth’s radiation budget, it reflects how the Earth-atmosphere system compensates the incoming solar radiation at the top of the atmosphere. At equilibrium, incoming and outgoing radiation compensate each other on average. Any perturbation of this balance through, for example, a variation of the climate drivers (e.g. rise in greenhouse gases concentration) causes a climate response (e.g. surface temperature increase) which brings the radiation budget back to equilibrium. OLR estimates from dedicated broadband instruments are available since the mid-1970s and have considerably improved our understanding of the Earth-atmosphere system and of its long-term changes. However, such instruments only provide an integrated OLR over a broad spectral range and are therefore not well suited for tracking separately the impact of the different parameters affecting the OLR, making it difficult to identify compensating biases and errors in climate models. Better constraints can be obtained from spectrally resolved OLR (i.e. the integrand of broadband OLR, in units of W m-2 (cm-1)-1) derived from infrared hyperspectral sounders. Recently, we developed an algorithm to derive clear-sky spectrally resolved OLR from measurements made by the IASI sounder on board the Metop satellites at the 0.25 cm-1 native spectral sampling of the L1C spectra (Whitburn et al. 2020). It is based on a set of spectrally resolved angular distribution models (ADMs) developed from synthetic spectral for a large selection of scene types associated with different states of the atmosphere and the surface. Here, we present the retrieval algorithm and evaluate how the derived-OLR compares with other known and widely used datasets such as the CERES and the AIRS integrated and spectral OLR. We then analyze the changes in 10 years (2008-2017) of the spectrally resolved OLR (at the 0.25 cm-1 spectral sampling) and, info:eu-repo/semantics/nonPublished

The first Infrared Atmospheric Sounding Instrument (IASI) on the Metop satellites suite has achieved more than 13 years of continuous operation. The instrument stability and the consistency between the different instruments on the successive Metop (A, B and C) is remarkable and offer the potential to investigate trends in the concentration of various species better than with any other previous or current hyperspectral IR sounder. The low noise of IASI radiances is also such that even weakly absorbing species can be identified, on single or at least on averaged spectra. In this work we exploit the first decadal record of IASI measurements to (1) detect and monitor halogenated compounds regulated by the Montreal protocol (CFCs) or used as substitutes (HCFCs, HFCs), as well as fluorinated compounds (CF4, SF6) and potentially short lived chlorine species, for which substantial emissions are suspected (2) give a first assessment of the trend evolution of these species over the 2008-2017 period covered by IASI on Metop-A. This is done by targeting various geographical areas on the globe and examining the remote oceanic and continental source regions separately. The trend evolution in the different chemical species, either negative or positive, is validated against what is observed from ground-based measurement networks. We will conclude by assessing the usefulness of IASI and follow-on mission to contribute to global measurements of ozone depleting substances., info:eu-repo/semantics/nonPublished