Satellite Observations Research Articles

Deep learning-based gap filling for near real-time seamless daily global sea surface salinity using satellite observations

Sea surface salinity (SSS) provides crucial information about ocean environments, influencing global hydrological cycles, thermohaline circulation, and climate change. Although L-band passive microwave radiometers have provided satellite-based SSS data, there are gaps due to the limited daily coverage of the sensors. This study proposes a U-Net-based spatial gap-filling model for global SSS using Soil Moisture Active Passive (SMAP) satellite data. The proposed model utilizes SSS swath data from the target and past days to generate daily global SSS maps with full coverage, incorporating only past temporal information. Additionally, bias-corrected data using gradient-boosted regression trees (GBRT) are employed to reduce inherent errors in the SMAP SSS data. We designed 24 schemes using data from the past 3, 5, and 7 days for both GBRT-corrected and original SMAP SSS data, with one to four times oversampling for low-salinity water, where the number of samples is significantly small. Validation results using masked-out pixels indicate that the gap-filling models with GBRT-corrected SSS data from the past 3 and 5 days and four times oversampling yielded the best performance, with root mean square errors (RMSEs) of 0.388 and 0.413 psu, respectively. Compared with the in situ Argo data for 2020, the RMSEs were 0.237 and 0.241 psu for the two models, respectively, significantly outperforming the SMAP Level 3 8-day SSS, which requires future data (RMSE of 0.456 psu). Notably, these models successfully filled gaps over coastal areas where low SSS (<30 psu) fluctuated due to freshwater discharge from inland areas. This study proposes an effective, novel gap-filling method for generating bias-corrected global daily SSS without delay, meeting operational needs. Moreover, the proposed gap-filling framework can be applied to other environmental swaths or even grid datasets.

Aggravated surface O3 pollution primarily driven by meteorological variations in China during the 2020 COVID-19 pandemic lockdown period

Abstract. Due to the lockdown during the COVID-19 pandemic in China from late January to early April in 2020, a significant reduction in primary air pollutants, as compared to the same time period in 2019, has been identified by satellite and ground observations. However, this reduction is in contrast with the increase of surface ozone (O3) concentration in many parts of China during the same period from 2019 to 2020. The reasons for this contrast are studied here from two perspectives: emission changes and inter-annual meteorological variations. Based on top-down constraints of nitrogen oxide (NOx) emissions from TROPOMI measurements and GEOS-Chem model simulations, our analysis reveals that NOx and volatile organic compound (VOC) emission reductions as well as meteorological variations lead to 8 %, −3 % and 1 % changes in O3 over North China, respectively. In South China, however, we find that meteorological variations cause ∼ 30 % increases in O3, which is much larger than −1 % and 2 % changes due to VOC and NOx emission reductions, respectively, and the overall O3 increase in the simulations is consistent with the surface observations. The higher temperature associated with the increase in solar radiation and the decreased relative humidity are the main reasons that led to the surface O3 increase in South China. Collectively, inter-annual meteorological variations had a larger impact than emission reductions on the aggravated surface O3 pollution in China during the lockdown period of the COVID-19 pandemic.

Characterizing the Regional Differences in Carbon Dioxide Concentration Based on Satellite Observations in the Beijing-Tianjin-Hebei Region during 2015–2021

The regional differences in carbon dioxide (CO2) variations from the Orbiting Carbon Observatory-2 (OCO-2) over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 2015 to 2021 are analyzed in this study. This study shows an annual increase and a seasonal cycle; the CO2 annual growth rate was about 2.63 ppm year−1, with the highest value being in spring and the lowest in summer. The spatial distribution is unbalanced, regional differences are prominent, and the CO2 concentration is lower in the north of the Jing-Jin-Ji region (like Zhangjiakou, Chengde, and Qinhuangdao). Land-type structures and population economy distributions are the key factors affecting CO2 concentration. By analyzing the land-type structures over Jing-Jin-Ji in 2020, we find that cropland, woodland, and grassland (CWG) are the main land cover types in Jing-Jin-Ji; the proportion of these three types is about 83.3%. The woodland areas in Zhangjiakou, Chengde, and Qinhuangdao account for about 65% of the total woodland areas in Jing-Jin-Ji; meanwhile, the grassland areas in these three regions account for 62% of the total grassland areas in Jing-Jin-Ji. CO2 concentration variation shows a high negative correlation with CWG land areas (coefficient of determination (R2) &gt; 0.76). The regions with lower population and GDP secondary industry (SI) density also have lower CO2 concentration (like Zhangjiakou, Chengde, and Qinhuangdao), and the regions with higher population and GDP SI density also have higher CO2 concentration (like the southeast of Jing-Jin-Jin).

Characterizing the Regional Differences in Carbon Dioxide Concentration Based on Satellite Observations in the Beijing-Tianjin-Hebei Region during 2015–2021

The regional differences in carbon dioxide (CO2) variations from the Orbiting Carbon Observatory-2 (OCO-2) over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 2015 to 2021 are analyzed in this study. This study shows an annual increase and a seasonal cycle; the CO2 annual growth rate was about 2.63 ppm year−1, with the highest value being in spring and the lowest in summer. The spatial distribution is unbalanced, regional differences are prominent, and the CO2 concentration is lower in the north of the Jing-Jin-Ji region (like Zhangjiakou, Chengde, and Qinhuangdao). Land-type structures and population economy distributions are the key factors affecting CO2 concentration. By analyzing the land-type structures over Jing-Jin-Ji in 2020, we find that cropland, woodland, and grassland (CWG) are the main land cover types in Jing-Jin-Ji; the proportion of these three types is about 83.3%. The woodland areas in Zhangjiakou, Chengde, and Qinhuangdao account for about 65% of the total woodland areas in Jing-Jin-Ji; meanwhile, the grassland areas in these three regions account for 62% of the total grassland areas in Jing-Jin-Ji. CO2 concentration variation shows a high negative correlation with CWG land areas (coefficient of determination (R2) &gt; 0.76). The regions with lower population and GDP secondary industry (SI) density also have lower CO2 concentration (like Zhangjiakou, Chengde, and Qinhuangdao), and the regions with higher population and GDP SI density also have higher CO2 concentration (like the southeast of Jing-Jin-Jin).

Random Forest Model-Based Inversion of Aerosol Vertical Profiles in China Using Orbiting Carbon Observatory-2 Oxygen A-Band Observations

Aerosol research is important for the protection of the ecological environment, the improvement of air quality, and as a response to climate change. In this study, a random forest (RF) estimation model of aerosol optical depth (AOD) and extinction coefficient vertical profiles was, respectively, established using Orbiting Carbon Observatory-2 (OCO-2) oxygen-A band (O2 A-band) data from China and its surrounding areas in 2016, combined with geographical information (longitude, latitude, and elevation) and viewing angle data. To address the high number of OCO-2 O2 A-band channels, principal component analysis (PCA) was employed for dimensionality reduction. The model was then applied to estimate the aerosol extinction coefficients for the region in 2017, and its validity was verified by comparing the estimated values with the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 extinction coefficients. In the comprehensive analysis of overall performance, an AOD model was initially constructed using variables, achieving a correlation coefficient (R) of 0.676. Subsequently, predictions for aerosol extinction coefficients were generated, revealing a satisfactory agreement between the predicted and the actual values in the vertical direction, with an R of 0.535 and a root mean square error (RMSE) of 0.107 km−1. Of the four seasons of the year, the model performs best in autumn (R = 0.557), while its performance was relatively lower in summer (R = 0.442). Height had a significant effect on the model, with both R and RMSE decreasing as height increased. Furthermore, the accuracy of aerosol profile inversion shows a dependence on AOD, with a better accuracy when AOD is less than 0.3 and RMSE can be less than 0.06 km−1.

Studying the Aerosol Effect on Deep Convective Clouds over the Global Oceans by Applying Machine Learning Techniques on Long-Term Satellite Observation

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models.

Seamless reconstruction and spatiotemporal analysis of satellite-based XCO2 incorporating temporal characteristics: A case study in China during 2015–2020

Carbon dioxide (CO2) is a crucial greenhouse gas, and its concentration and spatiotemporal characteristics are among the principal sources of uncertainty in global warming assessments. Satellite remote sensing is a widely adopted, high-accuracy approach for monitoring atmospheric CO2. However, limited swath width and cloud cover significantly reduce satellite observation coverage. This study addresses the temporal changes in CO2 concentration and utilizes a machine learning-based fusion of multiple data sources to generate daily, full-coverage, 0.05° spatial resolution column-averaged CO2 concentration data for China from 2015 to 2020. Ten-fold cross-validation yielded a determination coefficient R2 of 0.97, root mean square error of 0.92 ppm, and mean absolute error of 0.59 ppm. Compared to other datasets, this study’s dataset exhibits superior accuracy and spatiotemporal detail. Using the produced CO2 concentration in this study, we conducted a spatiotemporal analysis of CO2 concentrations in China. The results indicate that, in general, the Western region exhibits a higher growth rate in CO2 concentration than the Eastern and Central regions, with areas of lower CO2 concentration experiencing higher growth rates while regions with higher CO2 concentration have lower growth rates. Moreover, the highest increase in CO2 concentration occurred in 2016, with a substantial decrease in CO2 concentration growth observed in 2018. Notably, the reduction in CO2 concentration in the Qinghai-Tibet Plateau region during the summer is considerably smaller than in other regions, possibly due to atmospheric transport from the Indian Peninsula.

A dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR

Due to the influence of environmental factors (i.e., terrain and surface coverage) around the GPS receivers, the snow depth retrieval results obtained by the existing global positioning system interferometric reflection (GPS-IR) method show significant variability. The resulting loss of reliability and accuracy limits the broad application of this technology. Therefore, this paper proposes a dynamic snow depth retrieval model based on time-series clustering optimization for GPS-IR to fully leverage multi-source satellite observation data for automatic and high-precision snow depth retrieval. The model employs Dynamic Time Warping distance measurement combined with the K-Medoids clustering algorithm to categorize frequency sequences obtained from various satellite trajectories, facilitating effective integration of multi-constellation data and acquisition of optimal datasets. Additionally, Long Short-Term Memory networks are integrated to capture and process the long-term dependencies in snow depth data, enhancing the model’s adaptability in handling time-series data. Validated against SNOTEL measured data and standard machine learning algorithms (such as BP Neural Networks, RBF, and SVM), the model’s retrieval capability is confirmed. For P351 and AB39 sites, the correlation coefficients for L1 band data retrieval were both 0.996, with RMSEs of 0.051 and 0.018 m, respectively. The experiment results show that the proposed model demonstrates superior precision and robustness in snow depth retrieval compared to the previous method. Then, we analyze the accuracy loss caused by sudden snowfall events. The proposed model and methodology offer new insights into the in-depth study of snow depth monitoring.

Atmospheric water demand dominates terrestrial ecosystem productivity in China

Vegetation production is critical for carbon sequestration in terrestrial ecosystems. However, the relative contribution of soil moisture (SM) and vapor pressure deficit (VPD) to vegetation production in ecosystems remains debated. This study combined SM and VPD data with satellite observations of solar-induced fluorescence (SIF) from 2003 to 2018 to examine the contribution of SM and VPD to long-term variations in vegetation production using binning approach. Constraint lines based on restricted cubic splines were established to investigate the threshold for the responses of vegetation production potential to VPD and SM. The results revealed that VPD had greater impacts on vegetation production in Chinese terrestrial ecosystems than SM, with VPD-dominated and SM-dominated areas accounting for approximately 84 % and 16 % of the total national area, respectively. The mean values of SIF changes under the independent influence of SM and VPD were 0.034 mWm−2nm−1sr−1 and 0.130 mWm−2nm−1sr−1, respectively, indicating higher sensitivity of SIF to VPD. The effects of SM and VPD on ecosystem production varied across aridity gradients, with VPD making a significant contribution to forest ecosystem production. However, determining the relative effects of SM and VPD in ecosystems with low vegetation cover was difficult. Further exploration revealed that the maximum potential for terrestrial ecosystem production increased with VPD, peaking at a VPD of about 1.275 ± 0.068 kPa. Beyond this range, the maximum production potential declined. Our study confirms that VPD has a stronger and more extensive influence on the trend and variability of ecosystem production in China than SM.

Assessment of the Solar Energy Potential over South America Estimated by CMIP6 Models in the Present and Future Climate

Developing the renewable energy matrix of South America (SA) is fundamental for sustainable socioeconomic growth and mitigating climate change's adverse effects. Thus, this study estimates changes in SA's solar irradiance and solar power potential using data from eight global climate models (GCMs) belonging to the Coupled Model Intercomparison Project—Phase 6 (CMIP6). Applying statistical downscaling and bias correction with the Quantile Delta Mapping (QDM) technique, we evaluate projected changes in the Concentrated Solar Power (CSP) and Photovoltaic Power (PVP) outputs under different future climate scenarios (SSP2-4.5 and SSP5-8.5). Historical simulations (1995–2014) are validated using ERA5 reanalysis and CLARA-A3 satellite observations. The QDM method reduces the models' systematic biases, decreasing the ensemble's errors by 50% across SA throughout the year. Regarding future decades (2020–2099), the CMIP6 ensemble shows spatial and seasonal variability in solar generation. For CSP, estimates suggest that regions traditionally favorable to solar energy generation (such as the Brazilian Northeast and portions of Chile) will maintain their suitable conditions during the 21st century, projecting a potential 1–6% increase (particularly under the SSP5-8.5 scenario in southern Chile and most of Brazil). Concerning PVP generation, the CMIP6 ensemble projects a rise of 1–4% (mainly under the SSP5-8.5 scenario in the Amazonia, Midwest, and Southeast Brazilian sectors). Moreover, trend analyses projected individually by the CMIP6 GCMs converge on an increasing PVP, mainly in Brazil's Amazonia and Midwest regions. In contrast, for South Brazil, approximately 84% of the projections show a negative trend (or no trend), evidencing unfavorable or uncertain conditions for solar generation development in the region. Despite the data and processes' inherent limitations, this study yields a first analysis of statistically downscaled projections from CMIP6 for solar power generation in South America, providing valuable information for energy sector decision-makers.

Technical note: Retrieval of the supercooled liquid fraction in mixed-phase clouds from Himawari-8 observations

Abstract. The supercooled liquid fraction (SLF) in mixed-phase clouds (MPCs) is an essential variable of cloud microphysical processes and climate sensitivity. However, the SLF is currently calculated in spaceborne remote sensing only as the cloud phase–frequency ratio of adjacent pixels, which results in a loss of the original resolution in observations of cloud liquid or ice content within MPCs. Here, we present a novel method for retrieving the SLF in MPCs based on the differences in radiative properties of supercooled liquid droplets and ice particles at visible (VIS) and shortwave infrared (SWI) channels of the geostationary Himawari-8. Liquid and ice water paths are inferred by assuming that clouds are composed of only liquid or ice, with the real cloud water path (CWP) expressed as a combination of these two water paths (SLF and 1-SLF as coefficients), and the SLF is determined by referring to the CWP from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The statistically relatively small cloud phase spatial inhomogeneity at a Himawari-8 pixel level indicates an optimal scene for cloud retrieval. The SLF results are comparable to global SLF distributions observed by active instruments, particularly for single-layered cloud systems. While accessing the method's feasibility, SLF averages are estimated between 74 % and 78 % in Southern Ocean (SO) stratocumulus across seasons, contrasting with a range of 29 % to 32 % in northeastern Asia. The former exhibits a minimum SLF around midday in summer and a maximum in winter, while the latter trend differs. This novel algorithm will be valuable for research to track the evolution of MPCs and constrain the related climate impact.

Monitoring European anthropogenic NOx emissions from space

Abstract. Since the launch of TROPOMI on the Sentinel-5 Precursor (S5P) satellite, NO2 observations have become available with a resolution of 3.5× 5 km, which makes monitoring NOx emissions possible at the scale of city districts and industrial facilities. For Europe, emissions are reported on an annual basis for country totals and large industrial facilities and made publicly available via the European Environment Agency (EEA). Satellite observations can provide independent and more timely information on NOx emissions. A new version of the inversion algorithm DECSO (Daily Emissions Constrained by Satellite Observations) has been developed for deriving emissions for Europe on a daily basis, averaged to monthly mean maps. The estimated precision of these monthly emissions is about 25 % for individual grid cells. These satellite-derived emissions from DECSO have been compared to the officially reported European emissions and spatial–temporal disaggregated emission inventories. The country total DECSO NOx emissions are close to the reported emissions and the emissions compiled by the Copernicus Atmosphere Monitoring Service (CAMS). Comparison of the spatially distributed NOx emissions of DECSO and CAMS showed that the satellite-derived emissions are often higher in cities, while they are similar for large power plants and slightly lower in rural areas.

Assessing drought in Turkish basins through satellite observations

AbstractDrought occurs when there is a sustained decrease in rainfall over an extended period, impacting the socio‐cultural and environmental aspects of humans and other living beings. The geographic distribution and timing of droughts play a crucial role in drought management and mitigation strategies. Identifying and predicting the onset of droughts in specific regions, especially in watershed areas, is a primary concern in the field of hydrology. This study focuses on how the spatiotemporal patterns of drought are developing in Turkish Basins using detailed data on Terrestrial Water Storage (TWS), precipitation, and temperature at the pixel level. GRACE (Gravity Recovery and Climate Experiment), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and WorldClim (World Climate) data sets are employed to assess long‐term changes of drought on a basin‐scale. Spatial analyses are conducted in a Geographic Information System (GIS) environment for the derivation of basinal monthly mean, minimum, and maximum statistics of TWS, precipitation, and temperature anomalies within Turkish Basins. Time series analyses are implemented to investigate the temporal evolution of droughts in these basins, for the basinal monthly mean, minimum, and maximum statistics obtained. The Mann–Kendall trend test and Pettitt change point detection tests are used to assess the statistical significance of the calculated trends and to expose the existence of any change point therein, respectively. The findings of the study indicate that Turkiye faces a significant risk of drought development in nearly all its basins, particularly after 2016. The GRACE dataset provides realistic insights into the temporal behaviour of hydrological droughts. PERSIANN is effective in identifying years with extreme meteorological conditions, and the standardized precipitation index (SPI) shows similar effectiveness, while they are ineffective in exposing significant trends due to the nature of the precipitation data. WorldClim data proves insufficient for modelling the temporal behaviour of droughts in these basins.

Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization

Abstract. The Los Alamos Community Ice CodE (CICE) sea ice model is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction, where it is driven by atmospheric forcings from the NCEP Climate Forecast System Reanalysis (CFSR) and a built-in mixed-layer ocean model in CICE. The initial conditions for the sea ice and mixed-layer ocean are also from CFSR in the control experiments. The simulated sea ice extent agrees well with observations during the warm season at all lead times up to 12 months, in both the Arctic and the Antarctic. This suggests that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the model's initial conditions have ice that is too thick in the Beaufort Sea, resulting in excessive ice extent in the Arctic at 6-month lead forecasts and errors in ice volume at all lead times when compared to available observations. To address this limitation, additional CS2_IC experiments were conducted, where the Arctic ice thickness was initialized using CryoSat-2 satellite observations while keeping all other initial fields the same as in the control experiments. This reduced the positive bias in the ice thickness in the initial conditions, leading to improvements in both the simulated ice edge and the ice thickness at the seasonal timescale. This indicates that CICE has the potential to improve its seasonal forecast skill and provide more accurate predictions of sea ice extent and thickness when initialized with a more realistic sea ice thickness. This study highlights that the suitability of CICE for seasonal prediction depends on various factors, including initial conditions such as sea ice thickness, in addition to sea ice coverage, as well as oceanic and atmospheric conditions.

How we know Antarctica is rapidly losing more ice

ABSTRACT Satellite observations of the Antarctic surface since the 1990s, coupled with geophysical surveys of the flow and form of its giant ice sheet, have revealed accelerating loss of ice to the oceans. This, coupled with a reduction in floating sea ice surrounding the continent and the occurrence of unprecedented heatwaves, point to a continent being changed irreversibly by fossil-fuel burning and the global heating that results. A changing Antarctic continent will have global consequences, including sea level rise of possibly more than 1 meter in this century (and much more afterward) and by a reduced ability to naturally reflect incoming solar energy—which would lead to further heating by absorption on land and at sea as the ice pack retreats.

Multi-Instrument Observations of the Ionospheric Response Caused by the 8 April 2024 Total Solar Eclipse

This paper investigates ionospheric response characteristics from multiple perspectives based on globally distributed GNSS data and products, ionosonde data, FORMOSAT-7/COSMIC-2 occultation data, and Swarm satellite observations caused by the total solar eclipse of 8 April 2024 across North and Central America. The results show that both GNSS-derived TEC products have detected the ionospheric TEC degradation triggered by the total solar eclipse, with the maximum degradation exceeding 10 TECU. The TEC data from nine GNSS stations in the path of the maximum eclipse reveal that the intensity of ionospheric TEC degradation is related to the spatial location, with the maximum degradation value of the ionospheric TEC being about 14~23 min behind the moment of the maximum eclipse. Additionally, a negative anomaly of foF2 with a maximum of more than 2.7 MHz is detected by ionosonde. In the eclipse region, NmF2 and hmF2 show trends of decrease and increase, with percentages of variation of 40~70% and 4~16%, respectively. The Ne profile of the Swarm-A satellite is significantly lower than the reference value during the eclipse period, with the maximum negative anomaly value reaching 11.2 × 105 el/cm3, and it failed to show the equatorial ionization anomaly.

Characteristics of Marine Heatwaves in the Southeastern Baltic Sea Based on Long-Term In Situ and Satellite Observations

Marine heatwaves (MHWs) are known to pose a threat to aquatic ecosystems and coastal communities and, as a result, they receive significant attention nowadays, thus motivating our scientific interest in better understanding the regional patterns of these events. In this study, we analyze MHWs in the SE part of the Baltic Sea, defining them as anomalously warm water events, where the water temperature exceeds the 90th percentile threshold of the corresponding calendar day for at least five or more consecutive days. Our study is based on a combination of long-term (1993–2023) in situ data, field measurements, and satellite-derived sea surface temperature data during the warm (May–August) period. Study results suggest that although short-lived (5–9 days) MHW events typically dominate throughout the analyzed period, the occurrences of longer (more than three weeks) and more intense MHWs have increased in our study region in the recent decade. The heatwaves are observed both in coastal and open waters, with SST anomalies up to around 5–6 °C above the 90th percentile threshold during extreme events, extending thermal influence as deep as 20 m during prolonged and more intense events. We believe that the results of this study contribute to a better understanding of MHW patterns in the study region, which is important from an ecological and socio-economic point of view, providing valuable insights for human health aspects as well.

Quantifying Drivers of Seasonal and Interannual Variability of Dissolved Oxygen in the Canada Basin Mixed Layer

AbstractAnalysis of dissolved oxygen (O2) in the Arctic's surface ocean provides insights into gas transfer between the atmosphere‐ice‐ocean system, water mass dynamics, and biogeochemical processes. In the Arctic Ocean's Canada Basin mixed layer, higher O2 concentrations are generally observed under sea ice compared to open water regions. Annual cycles of O2 and O2 saturation, increasing from summer through spring and then sharply declining to late summer, are tightly linked to sea ice cover. The primary fluxes that influence seasonal variability of O2 are modeled and compared to Ice‐Tethered Profiler O2 observations to understand the relative role of each flux in the annual cycle. Findings suggest that sea ice melt/growth dominates seasonal variations in mixed layer O2, with minor contributions from vertical entrainment and atmospheric exchange. While the influence of biological activity on O2 variability cannot be directly assessed, indirect evidence suggests relatively minor contributions, although with significant uncertainty. Past studies show that O2 molecules are expelled from sea ice during brine rejection; sea ice cover can then inhibit air‐sea gas exchange resulting in winter mixed layers that are super‐saturated. Decreasing mixed layer O2 concentrations and saturation levels are observed during winter months between 2007 and 2019 in the Canada Basin. Only a minor portion of the decreasing trend in wintertime O2 can be attributed to decreased solubility. This suggests the O2 decline may be linked to more efficient air‐sea exchange associated with increased open water areas in the winter sea ice pack that are not necessarily detectable via satellite observations.

Improved Energy Resolution Measurements of Electron Precipitation Observed During an IPDP‐Type EMIC Event

AbstractHigh energy resolution DEMETER satellite observations from the Instrument for the Detection of Particle (IDP) are analyzed during an electromagnetic ion cyclotron (EMIC)‐induced electron precipitation event. Analysis of an Interval Pulsation with Diminishing Periods (IPDP)‐type EMIC wave event, using combined satellite observations to correct for incident proton contamination, detected an energy precipitation spectrum ranging from ∼150 keV to ∼1.5 MeV. While inconsistent with many theoretical predictions of &gt;1 MeV EMIC‐induced electron precipitation, the finding is consistent with an increasing number of experimentally observed events detected using lower resolution integral channel measurements on the POES, FIREBIRD, and ELFIN satellites. Revised and improved DEMETER differential energy fluxes, after correction for incident proton contamination shows that they agree to within 40% in peak flux magnitude, and 85 keV (within 40%) for the energy at which the peak occurred as calculated from POES integral channel electron precipitation measurements. This work shows that a subset of EMIC waves found close to the plasmapause, that is, IPDP‐type rising tone events, can produce electron precipitation with peak energies substantially below 1 MeV. The rising tone features of IPDP EMIC waves, along with the association with the high cold plasma density regime, and the rapidly varying electron density gradients of the plasmapause may be an important factor in the generation of such low energy precipitation, co‐incident with a high energy tail. Our work highlights the importance of undertaking proton contamination correction when using the high‐resolution DEMETER particle measurements to investigate EMIC‐driven electron precipitation.

Respiratory health assessment of outdoor workers exposed to urban air pollution based on satellite observations

Human health impact assessment due to air pollution is critical for evaluating environmental hazards. In this research, levels of three air pollutants (CO, NO2, and SO2) were estimated based on satellite data (sentinel-5p) to assess the air quality over three areas (Al-Mahaad Al-Dinni, Bus Stop, and Oil Company areas) at Banha city, Qalyobia in addition to evaluating the human health risks resulting from exposure to these air pollutants. Unlike the conventional approaches for human health risk assessment, which depend on in-situ air pollution measurements, in this study, the spatial distribution of the pollutants was acquired from satellite remote sensing for the period 2021–2022. The respiratory health assessment of 151 outdoor workers, exposed to air pollution in Banha City was conducted. Results showed that the highest concentration of the pollutants was found in areas of Bus Stop and Oil Company. Consequently, the percentage of predicted pulmonary function tests was significantly lower among those working in the Oil company area than those working in the Bus Stop and Al-Mahaad Al-Dinni areas. Additionally, 47.6 % of the studied group complained of nasal congestion and 67.5 % of them complained of wheezing, 30.5% of them had obstructive airway diseases. In conclusion, remotely sensed air quality data proved its efficiency in health impact studies which could be carried out in different regions globally with less cost than traditional measurement techniques. Urban pollution can influence outdoor workers with varied percentages according to their exposure rate to pollutants and work-related factors.