Copernicus Atmosphere Monitoring Service Research Articles

Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran

Abstract. Tropospheric ozone time series consist of the effects of various scales of motion, from meso-scales to large timescales, which are often challenging for global models to capture. This study uses two global datasets, namely the reanalysis and the daily forecast of the Copernicus Atmosphere Monitoring Service (CAMS), to assess the capability of these products in presenting ozone's features on regional scales. We obtained 16 relevant meteorological and several pollutant species, such as O3, CO, NOx, etc., from CAMS. Furthermore, we employed a comprehensive set of in situ measurements of ozone at 27 urban stations in Iran for the year 2020. We decomposed the time series into three spectral components, i.e., short (S), medium (M), and long (L) terms. To cope with the scaling issue between the measured data and the CAMS' products, we developed a downscaling approach based on a long short-term memory (LSTM) neural network method which, apart from modeled ozone, also assimilated meteorological quantities as well as lagged O3 observations. Results show the benefit of applying the LSTM method instead of using the original CAMS products for providing O3 over Iran. It is found that lagged O3 observation has a larger contribution than other predictors in improving the LSTM. Compared with the S, the M component shows more associations with observations, e.g., correlation coefficients larger than 0.7 for the S and about 0.95 for the M in both models. The performance of the models varies across cities; for example, the highest error is for areas with high emissions of O3 precursors. The robustness of the results is confirmed by performing an additional downscaling method. This study demonstrates that coarse-scale global model data, such as CAMS, need to be downscaled for regulatory purposes or policy applications at local scales. Our method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.

Forecasting the Exceedances of PM2.5 in an Urban Area

Forecasting the Exceedances of PM2.5 in an Urban Area

Global anthropogenic emissions (CAMS-GLOB-ANT) for the Copernicus Atmosphere Monitoring Service simulations of air quality forecasts and reanalyses

Abstract. Anthropogenic emissions are the result of many different economic sectors, including transportation, power generation, industrial, residential and commercial activities, waste treatment and agricultural practices. Air quality models are used to forecast the atmospheric composition, analyze observations and reconstruct the chemical composition of the atmosphere during the previous decades. In order to drive these models, gridded emissions of all compounds need to be provided. This paper describes a new global inventory of emissions called CAMS-GLOB-ANT, developed as part of the Copernicus Atmosphere Monitoring Service (CAMS; https://doi.org/10.24380/eets-qd81, Soulie et al., 2023). The inventory provides monthly averages of the global emissions of 36 compounds, including the main air pollutants and greenhouse gases, at a spatial resolution of 0.1° × 0.1° in latitude and longitude, for 17 emission sectors. The methodology to generate the emissions for the 2000–2023 period is explained, and the datasets are analyzed and compared with publicly available global and regional inventories for selected world regions. Depending on the species and regions, good agreements as well as significant differences are highlighted, which can be further explained through an analysis of different sectors as shown in the figures in the Supplement.

Moving beyond the Aerosol Climatology of WRF-Solar: A Case Study over the North China Plain

Abstract Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%. Significance Statement Numerical weather prediction (NWP) is the “go-to” approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP.

Potential of low-cost PM monitoring sensors to fill monitoring gaps in areas of Sub-Saharan Africa

Recent advances in low-cost (LC) sensor technology fostered their deployment in low-income and undersampled countries such as Sub-Saharan Africa (SSA) regions, affected by the highest particulate matter (PM) concentrations and population exposure. The present study is the first addressed in Niamey, Niger, and focuses on assessing LC sensor data and global reanalysis products. Three LC PM2.5 and PM10 monitoring stations were deployed and successfully operated across ∼8 months at different (urban, suburban and rural) locations.Observed PM2.5 and PM10 concentrations revealed consistent patterns, higher during the dry Harmattan season, while appreciably lower during the humid monsoon season. In Niamey, PM2.5 mean concentrations (6.1–20.1 μg/m3) were similar to those observed over higher-income countries, confirming the hypothesis of strictly depending on urbanisation, and thus on anthropogenic activities. Conversely, PM10 concentrations (55.3–142.8 μg/m3) were remarkably higher than most of those measured elsewhere worldwide, and predominantly constituted (81–89%) by coarse fraction. PM10 origin, inferred by backtrajectory analysis, was mainly natural (Saharan dust) during the Harmattan season, and both natural and anthropogenic during the monsoon season.Low-resolution gridded estimations by the Copernicus Atmosphere Monitoring Service (CAMS) were not capable of adequately resolving the spatial variability of PM2.5 and PM10 observations in Niamey, further highlighting the importance of sensor network data to improve air quality knowledge. To tackle the harmful effects of Saharan dust on population, and create robust datasets integrated with gridded products in this challenging region, effort should be put toward creation of trans-national integrated monitoring networks based on LC sensors across SSA.

Quantifying CH4 emissions from coal mine aggregation areas in Shanxi, China, using TROPOMI observations and the wind-assigned anomaly method

Abstract. China stands out as a major contributor to anthropogenic methane (CH4) emissions, with coal mine methane (CMM) playing a crucial role. To control and reduce CH4 emissions, China has made a dedicated commitment and formulated an ambitious mitigation plan. To verify the progress made, the consistent acquisition of independent CH4 emission data is required. This paper aims to implement a wind-assigned anomaly method for the precise determination of regional-scale CMM emissions within the coal-rich Shanxi province. We use the TROPOspheric Monitoring Instrument (TROPOMI) CH4 observations from May 2018 to May 2023, coupled with ERA5 wind and a bottom-up inventory dataset based on the IPCC (Intergovernmental Panel on Climate Change) Tier 2 approach covering the Changzhi, Jincheng, and Yangquan regions of the Shanxi province. The derived emission strengths are 8.4 × 1026 molec. s−1 (0.706 Tg yr−1, ±25 %), 1.4 × 1027 molec. s−1 (1.176 Tg yr−1, ±20 %), and 4.9 × 1026 molec. s−1 (0.412 Tg yr−1, ±21 %), respectively. Our results exhibit biases of −18 %, 8 %, and 14 %, respectively, when compared to the IPCC Tier 2 bottom-up inventory. Larger discrepancies are found when comparing the estimates to the Copernicus Atmosphere Monitoring Service global anthropogenic emissions (CAMS-GLOB-ANT) and Emissions Database for Global Atmospheric Research (EDGARv7.0) inventories (64 %–176 %), suggesting that the two inventories may be overestimating CH4 emissions from the studied coal mining regions. Our estimates provide a comprehensive characterization of the regions within the Shanxi province, contribute to the validation of emission inventories, and provide additional insights into CMM emission mitigation.

Estimation of maximum photovoltaic cover ratios in greenhouses based on global irradiance data

In this study, a method for estimating the maximum PV (photovoltaic) cover ratio for plastic greenhouses based on various years of global horizontal irradiance (GHI) data is presented and illustrated with an exemplary site in southeastern Spain. CAMS (Copernicus Atmosphere Monitoring Service) GHI data from 2005 to 2023 were analyzed to estimate the DLI (daily light integral) inside the greenhouses for various PV coverage ratios with East-West or North-South orientation. The conversion from GHI to photosynthetically active radiation is performed with the usage of a regression model from literature based on satellite and measurement data. The shading effect of the PV cover is estimated with a regression model from literature based on radiation distribution simulations in different greenhouse types. The maximum PV cover ratio was derived for different minimal DLI thresholds, corresponding to different crops. The proposed methodology has been tested for the Almería region in southeastern Spain which is characterized by high solar irradiance and can be applied also to other regions with similar climatic conditions. With a required DLI of at least 12 mol/m2/day, a theoretical maximum PV coverage of about 44% is acceptable even in December at the studied site for East-West orientation, while it reaches up to 100% (June) during the year. Further, the maximum PV cover ratios for a DLI threshold range have been calculated and compared with experimental results for plastic greenhouses from literature. In 87.2% of the case studies analyzed from literature, the proposed method showed an agreement in the estimation of the effect of PV shading ratios on marketable crop yields. The study indicates that significant PV cover ratios are theoretically possible even for light demanding crops considering DLI thresholds only and can help to select a useful PV cover ratio in PV greenhouses.

Application of Satellite Observation and GIS in Mapping Greenhouse Gases in Part Delta State, Nigeria

The present study aimed to apply satellite observation and Geographic Information System (GIS) to map GHGs in Delta State, Nigeria. Carbon monoxide (CO), nitrous oxide (N O), and sulfur dioxide (SO ) constituted the main GHGs studied. The2 2 datasets covering January 2019 to December 2022 were sourced from the archive of Copernicus Atmosphere Monitoring Service Emissions of Atmospheric Compound and Compilation of Ancillary Data. The Google Earth Engine framework, the inverse distance weighting interpolation algorithm in ArcGIS 10.8 software, and Microsoft Excel were deployed in data analyses. In general, the results showed remarkable seasonal oscillation in the mean concentration of CO, with peak values in January and February, while the lowest mean concentration was observed in October. Higher values of NO were observed2 from November to February and the lowest values in July. July also recorded the highest SO values, with the lowest mean2 observable atmospheric concentrations in August, in addition to remarkable insignificant values in 2020–2022. Besides, the 12 geospatial models (maps) also depicted the spatio-temporal distribution of CO, NO , and SO concentrations from 2019 to2 2 2022 for easy visualization and facilitation of workable policies towards global warming mitigation. The paper recommended the establishment of well-equipped and functional ground-based GHG observatories with early warning systems across the state. This will facilitate real-time GHG monitoring, early warning forecasts, and stimulate scholarship on global warming and climate change.

Effects of clouds and aerosols on downwelling surface solar irradiance nowcasting and short-term forecasting

Abstract. Solar irradiance nowcasting and short-term forecasting are important tools for the integration of solar plants into the electricity grid. Understanding the role of clouds and aerosols in those techniques is essential for improving their accuracy. In this study, we introduce improvements in the existing nowcasting and short-term forecasting operational systems SENSE (Solar Energy Nowcasting System) and NextSENSE achieved by using a new configuration and by upgrading cloud and aerosol inputs, and we also investigate the limitations of evaluating such models using surface-based sensors due to cloud effects. We assess the real-time estimates of surface global horizontal irradiance (GHI) produced by the improved SENSE2 operational system at high spatial and temporal resolution (∼ 5 km, 15 min) for a domain including Europe and the Middle East–North Africa (MENA) region and the short-term forecasts of GHI (up to 3 h ahead) produced by the NextSENSE2 system against ground-based measurements from 10 stations across the models' domain for a whole year (2017). Results for instantaneous (every 15 min) comparisons show that the GHI estimates are within ±50 W m−2 (or ±10 %) of the measured GHI for 61 % of the cases after the implementation of the new model configuration and a proposed bias correction. The bias ranges from −12 to 23 W m−2 (or from −2 % to 6.1 %) with a mean value of 11.3 W m−2 (2.3 %). The correlation coefficient is between 0.83 and 0.96 and has a mean value of 0.93. Statistics are significantly improved when integrating on daily and monthly scales (the mean bias is 3.3 and 2.7 W m−2, respectively). We demonstrate that the main overestimation of the SENSE2 GHI is linked with the uncertainties of the cloud-related information within the satellite pixel, while relatively low underestimation, linked with aerosol optical depth (AOD) forecasts (derived from the Copernicus Atmospheric Monitoring Service – CAMS), is reported for cloudless-sky GHI. The highest deviations for instantaneous comparisons are associated with cloudy atmospheric conditions, when clouds obscure the sun over the ground-based station. Thus, they are much more closely linked with satellite vs. ground-based comparison limitations than the actual model performance. The NextSENSE2 GHI forecasts based on the cloud motion vector (CMV) model outperform the persistence forecasting method, which assumes the same cloud conditions for future time steps. The forecasting skill (FS) of the CMV-based model compared to the persistence approach increases with cloudiness (FS is up to ∼ 20 %), which is linked mostly to periods with changes in cloudiness (which persistence, by definition, fails to predict). Our results could be useful for further studies on satellite-based solar model evaluations and, in general, for the operational implementation of solar energy nowcasting and short-term forecasting, supporting solar energy production and management.

Assessing the impact of global carbon dioxide changes on atmospheric fluctuations in Iran through satellite data analysis

Abstract This study aimed to examine how global CO2 changes affect atmospheric CO2 (XCO2) concentrations in Iran from 2015 to 2020. XCO2 data from the Orbiting Carbon Observatory-2 (OCO-2) satellite and CO2 surface flux data from the Copernicus Atmosphere Monitoring Service were analyzed. Monthly and annual XCO2 and surface flux values were compared. Over the 6 years, XCO2 in Iran increased steadily by 12.66 ppm, mirroring global rises. However, Iran's CO2 surface flux decreased, with slight increases in anthropogenic emissions but decreased natural and total fluxes. Monthly patterns of XCO2 and surface flux exhibited variations, with XCO2 reaching its zenith in spring and dipping to its lowest point during summer, while surface flux attained its peak during the summer months. The results reveal a significant discrepancy between Iran's surface CO2 flux and atmospheric XCO2 trends. While Iran's anthropogenic emissions increased barely from 2015–2020, its natural and total CO2 fluxes decreased. However, XCO2 increased steadily over this period, indicating the dominant impact of global rather than local factors on Iran's CO2 levels. The research emphasizes the critical need for a coordinated international effort, utilizing satellite monitoring data, to implement science-based policies that mitigate escalating global CO2 emissions. Curbing worldwide greenhouse gas.

Interpreting hourly mass concentrations of PM2.5 chemical components with an optimal deep-learning model

PM2.5 constitutes a complex and diverse mixture that significantly impacts the environment, human health, and climate change. However, existing observation and numerical simulation techniques have limitations, such as a lack of data, high acquisition costs, and multiple uncertainties. These limitations hinder the acquisition of comprehensive information on PM2.5 chemical composition and effectively implement refined air pollution protection and control strategies. In this study, we developed an optimal deep learning model to acquire hourly mass concentrations of key PM2.5 chemical components without complex chemical analysis. The model was trained using a randomly partitioned multivariate dataset arranged in chronological order, including atmospheric state indicators, which previous studies did not consider. Our results showed that the correlation coefficients of key chemical components were no less than 0.96, and the root mean square errors ranged from 0.20 to 2.11 µg/m3 for the entire process (training and testing combined). The model accurately captured the temporal characteristics of key chemical components, outperforming typical machine-learning models, previous studies, and global reanalysis datasets (such as Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service ReAnalysis (CAMSRA)). We also quantified the feature importance using the random forest model, which showed that PM2.5, PM1, visibility, and temperature were the most influential variables for key chemical components. In conclusion, this study presents a practical approach to accurately obtain chemical composition information that can contribute to filling missing data, improved air pollution monitoring and source identification. This approach has the potential to enhance air pollution control strategies and promote public health and environmental sustainability.

Urban ozone variability using automated machine learning: inference from different feature importance schemes.

Tropospheric ozone is an air pollutant at the ground level and a greenhouse gas which significantly contributes to the global warming. Strong anthropogenic emissions in and around urban environments enhance surface ozone pollution impacting the human health and vegetation adversely. However, observations are often scarce and the factors driving ozone variability remain uncertain in the developing regions of the world. In this regard, here, we conducted machine learning (ML) simulations of ozone variability and comprehensively examined the governing factors over a major urban environment (Ahmedabad) in western India. Ozone precursors (NO2, NO, CO, C5H8 and CH2O) from the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis and meteorological parameters from the ERA5 (European Centre for Medium-Range Weather Forecast's (ECMWF) fifth-generation reanalysis) were included as features in the ML models. Automated ML (AutoML) fitted the deep learning model optimally and simulated the daily ozone with root mean square error (RMSE) of ~2 ppbv reproducing 84-88% of variability. The model performance achieved here is comparable to widely used ML models (RF-Random Forest and XGBoost-eXtreme Gradient Boosting). Explainability of the models is discussed through different schemes of feature importance, including SAGE (Shapley Additive Global importancE) and permutation importance. The leading features are found to be different from different feature importance schemes. We show that urban ozone could be simulated well (RMSE = 2.5 ppbv and R2 = 0.78) by considering first four leading features, from different schemes, which are consistent with ozone photochemistry. Our study underscores the need to conduct science-informed analysis of feature importance from multiple schemes to infer the roles of input variables in ozone variability. AutoML-based studies, exploiting potentials of long-term observations, can strongly complement the conventional chemistry-transport modelling and can also help in accurate simulation and forecast of urban ozone.

Comparative analysis of CAMS aerosol optical depth data and AERONET observations in the Eastern Mediterranean over 19 years

Aerosol optical depth (AOD) is an essential metric for evaluating the atmospheric aerosol load and its impacts on climate, air quality, and public health. In this study, the AOD data from the Copernicus Atmosphere Monitoring Service (CAMS) were validated against ground-based measurements from the Aerosol Robotic Network (AERONET) throughout the Eastern Mediterranean, a region characterized by diverse aerosol types and sources. A comparative analysis was performed on 3-hourly CAMS AOD values at 550 nm against observations from 20 AERONET stations across Cyprus, Greece, Israel, Egypt, and Turkey from 2003 to 2021. The CAMS AOD data exhibited a good overall agreement with AERONET AOD data, demonstrated by a Pearson correlation coefficient of 0.77, a mean absolute error (MAE) of 0.08, and a root mean square error (RMSE) of 0.11. Nonetheless, spatial and temporal variations were observed in the CAMS AOD data performance, with site-specific correlation coefficients ranging from 0.57 to 0.85, the lowest correlations occurring in Egypt and the highest in Greece. An underestimation of CAMS AOD was noted at inland sites with high AOD levels, while a better agreement was observed at coastal sites with lower AOD levels. The diurnal variation analysis indicated improved CAMS reanalysis performance during the afternoon and evening hours. Seasonally, CAMS reanalysis showed better agreement with AERONET AODs in spring and autumn, with lower correlation coefficients noted in summer and winter. This study marks the first comprehensive validation of CAMS AOD performance in the Eastern Mediterranean, offering significant enhancements for regional air quality and climate modeling, and underscores the essential role of consistent validation in refining aerosol estimations within this complex and dynamic geographic setting.

Optical and physical characteristics of aerosols over Asia: AERONET, MERRA-2 and CAMS

A comprehensive study on the regional and spatial distributions of aerosol columnar optical and physical characteristics utilizing high-quality Aerosol Robotic Network (AERONET) datasets over Asia (Central, South, South-East, and East Asia), along with spatiotemporal collocated validation of two highly-spatially resolved models (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) simulated aerosol optical depth (AOD) and Ångström exponent (AE) on annual and seasonal scales, is conducted for the first time. AOD is the highest over South Asia in each season, followed by South-East, East, and Central Asia. Annual regional average AOD at 0.50 μm over South Asia is 0.61. Combined influence of both fine mode anthropogenic aerosol emissions from fossil fuel combustion and biomass burning, and coarse mode dust aerosols from seasonal transport lead to higher AOD and total volume concentration (TVC), and exhibit significant spatiotemporal variations. Coarse volume fraction (CVF) and effective radius (Reff) are higher over Central Asia due to the dominance of coarse dust aerosols. East and South-East Asia are dominated by fine mode aerosols and result in higher fine volume concentration (FVC), AE, fine mode fraction (FMF), and lower Reff. Compared to the other regions of the globe (except Africa), AOD is higher over Asia, with higher spatiotemporal variabilities in AOD, AE, FMF, and TVC. Over Asia, the agreement between AERONET and CAMS AOD is better than that of AERONET and MERRA-2 AOD. For high AOD conditions, underestimation in model AODs is higher, and lower fraction of model AODs satisfy the Global Climate Observing System (GCOS) requirement over all regions, and these are more pronounced over Asia. Biases in model AODs are higher over Asia compared to the other regions of the globe, and lower over North America, Europe, and Australia. Higher bias in model AEs compared to AODs over all the regions shows substantial challenges in simulating spectral AOD and appropriate contributions of fine and coarse mode aerosols. These findings over a global aerosol hotspot region, Asia, along with other regions of the globe are crucial for accurate simulation and fine-tuning of aerosol characteristics by regional and global models, and for reducing uncertainties in the assessment of radiative and climate impact of aerosols.

Variability of nocturnal aerosol optical properties in China and correlations with meteorological variables during 2003–2022

Aerosol optical properties in daytime have been widely explored based on the ground-based and satellite-based passive remote sensing. However, few studies have investigated long-term variability of nocturnal aerosol optical properties and its impact factors mainly due to scarce observations. Nocturnal aerosol optical depth (NAOD) and Ångström exponent (NAE) derived from Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) from 2003 to 2022 are analyzed to investigate the spatiotemporal variability of nocturnal aerosol optical properties and their meteorological impact factors in China. The high NAOD ranging from 0.8 to 1.4 was mainly found over inland of southeastern China, especially in the Sichuan basin and North and Central China plain, corresponding with heavily populated, economically developed and low-lying regions, but observably decreased since 2018. The NAE was less than 0.5 in arid and semiarid regions of the northwest China, while more than 0.7 in monsoon-influenced eastern China. The NAOD differs seasonally, and is generally higher in spring and summer. Reversely, the seasonal mean NAE increased from spring to winter. The monthly mean NAOD of China, which was high in February to October of 2006–2016, ranged from 0.9 to 1.1 related to the rapid urbanization and industrialization. The low NAE ranging from 0.7 to 0.8 mainly occurred in April, May and June due to the dust transportation from northwestern China in spring. The correlation between NAOD and potential evapotranspiration (PE), surface pressure (SP) and relative humidity (RH) was negative in northern China, while positive in southern China. The NAOD in most China was correlated positively to total precipitation (TP), but negatively to 10m u-component of wind (U10). The correlation between NAOD and 10m v-component of wind (V10) was positive in northeastern China, while negative in northwestern China. NAE in most China was correlated positively to PE and SP, while negatively to TP and T. The correlation between NAE and RH/V10 was positive in northwestern China, while negative in eastern China. The correlation between NAE and U10 was positive in northeastern and northwestern China, while negative in North and Central China. The findings in the study enable more comprehensive understanding of spatiotemporal variability of nocturnal aerosols optical properties and its impact factors in China.

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China

Can the aerosol pollution extreme events be revealed by global reanalysis products?

Extreme aerosol pollution poses significant risks to the climate, environment, and human health. To investigate the formation and impacts of aerosol pollution extreme events (APEE), the reanalysis product presents meticulous spatiotemporal information on the three-dimensional distribution of aerosols. However, there is a lack of comprehensive evaluation and information regarding the data quality of reanalysis products employed in APEE research, as well as limited understanding of their spatial and temporal distribution, variation, and long-term trends. To address this scientific gap, we conducted a global study for distribution and variation patterns of APEE using two widely-used reanalysis products, MERRA-2 (Modern-Era Retrospective Analysis for Research-2) and CAMS (Copernicus Atmospheric Monitoring Service). The APEE was defined here as a day when the daily aerosol optical depth (AOD) exceeding its 90th percentile for a given station and month. Eleven distinct land regions worldwide were selected for evaluation by comparing both reanalysis products with MODIS satellite products and ground-based observations in terms of frequency, intensity, and temporal trends of APEE. The analysis indicates that MERRA-2 and CAMS exhibit high matching rates (70 % and 80 %, respectively) in terms of occurrence timeline for APEE at monthly and seasonal scales, while also exhibiting strong monthly correlation coefficients (>0.65) with ground-based observations over selected regions. The total AOD (−0.002 ∼ −0.123 decade−1), APEE AOD (−0.004 ∼ −0.293 decade−1), and APEE frequency (−0.264 ∼ −1.769 day month−1 decade−1) of both observations and reanalysis products in most regions showed a decreasing trend with various magnitude, except for some regions such as South Asia where the trend is increasing. Based on the aforementioned evaluation, it is evident that reanalysis products are effective and useful in identifying the temporal trends associated with APEE.

Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia

Methane (CH4) is the second most abundant greenhouse gas after CO2, which plays the most important role in global and regional climate change. To explore the long-term spatiotemporal variations of near-surface CH4, datasets were extracted from Greenhouse gases Observing SATellite (GOSAT), and the Copernicus Atmospheric Monitoring Service (CAMS) reanalyzed datasets from June 2009 to September 2020 over South, East, and Southeast Asia. The accuracy of near-surface CH4 from GOSAT and CAMS was verified against surface observatory stations available in the study region to confirm both dataset applicability and results showed significant correlations. Temporal plots revealed continuous inflation in the near-surface CH4 with a significant seasonal and monthly variation in the study region. To explore the factors affecting near-surface CH4 distribution, near-surface CH4 relationship with anthropogenic emission, NDVI data, wind speed, temperature, precipitation, soil moisture, and relative humidity were investigated. The results showed a significant contribution of anthropogenic emissions with near-surface CH4. Regression and correlation analysis showed a significant positive correlation between NDVI data and near-surface CH4 from GOSAT and CAMS, while a significant negative correlation was found between wind and near-surface CH4. In the case of temperature, soil moisture, and near-surface CH4 from GOSAT and CAMS over high CH4 regions of the study area showed a significant positive correlation. However significant negative correlations were found between precipitation and relative humidity with GOSAT and CAMS datasets over high CH4 regions in South, East, and Southeast Asia. Moreover, these climatic factors showed no significant correlation within the low near-surface CH4 areas in our study region. Our study results showed that anthropogenic emissions, NDVI data, wind speed, temperature, precipitation, soil moisture, and humidity could significantly affect the near-surface CH4 over South, East, and Southeast Asia.

Characterization of the major aerosol species over Egypt based on 10 years of CAMS reanalysis data

This study investigates the spatiotemporal distribution of the major aerosol species (sulfate, organic matter, black carbon, dust, and sea salt) and their contributions to the total aerosol optical depth (AOD) over Egypt from 2010 to 2019 by using the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis data. Dust was the major aerosol species, with an average contribution of 48% to the total AOD. The highest concentrations of dust were found in spring because of the significant impact of the Khamsin wind in carrying sand aerosols from deserts. The dispersion of dust aerosols is greatly influenced by topography. While lowlands, such as the Qattara Depression, accumulate sand aerosols from the surroundings, the mountainous highlands disrupt horizontal diffusion. El-Farafra and Siwa Oasis, which are close to the Qattara depression, had significant dust contributions to the total AOD. Because the Nile Delta, Suez Canal, and Nile River regions are densely populated and industrialized, organic matter and sulfate showed considerable contributions of 24% and 23%, respectively, to the total AOD. The highest concentrations of organic matter and sulfate were found in summer because of the high temperatures that accelerate photochemical reactions. While fossil fuels and biomass burning increase carbon emissions in cities such as Cairo and Tanta, shipping in the Suez Canal and natural gas flares in the eastern Mediterranean increase sulfate aerosols in cities such as Port Said, Suez, and Arish. Black carbon and sea salt were the minor aerosol species, with average contributions of 3% and 2%, respectively, to the total AOD.

Technical note: Accurate, reliable, and high-resolution air quality predictions by improving the Copernicus Atmosphere Monitoring Service using a novel statistical post-processing method

Abstract. Starting from the regional air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS), we propose a novel post-processing approach to improve and downscale results on a finer scale. Our approach is based on the combination of ensemble model output statistics (EMOS) with a spatio-temporal interpolation process performed through the stochastic partial differential equation–integrated nested laplace approximation (SPDE-INLA). Our interpolation approach includes several spatial and spatio-temporal predictors, including meteorological variables. A use case is provided that scales down the CAMS forecasts on the Italian peninsula. The calibration is focused on the concentrations of several air quality pollutants (PM10, PM2.5, NO2, and O3) at a daily resolution from a set of 750 monitoring sites, distributed throughout the Italian country. Our results show the key role that conditioning variables play in improving the forecast capabilities of ensemble predictions, thus allowing for a net improvement in the calibration with respect to ordinary EMOS strategies. From a deterministic point of view, the performance of the predictive model shows a significant improvement in the performance of the raw ensemble forecast, with an almost-zero bias, significantly reduced root mean square errors, and correlations that are almost always higher than 0.9 for each pollutant; moreover, the post-processing approach is able to significantly improve the prediction of exceedances, even for very low thresholds, such as those recently recommended by the World Health Organisation. This is particularly significant if a forecasting approach is used to predict air quality conditions and plan adequate human health protection measures, even for low alert thresholds. From a probabilistic point of view, the quality of the forecast was verified in terms of reliability and credible intervals. After post-processing, the predictive probability density functions were sharp and much better calibrated than the raw ensemble forecast. Finally, we present some additional results based on a set of gridded (4 km × 4 km) maps covering the entire Italian country for the detection of areas where pollution peaks occur (exceedances of the current and/or proposed regulatory thresholds).