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Aerosol monitoring is the emerging application field of satellite remote sensing. As a satellite-based indicator of aerosol concentration, aerosol optical depth (AOD) can aid in assessing the crucial effects of aerosols on the global environment. Among various satellite-based aerosol product, Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6), Multiangle Implementation of Atmospheric Correction (MAIAC) aerosol product (1 km resolution) has still untapped potential in Indian regions. Considering the importance of regional validation of such high-resolution aerosol product, the present study attempts to fill this gap by validating MAIAC aerosol estimates (AODMAIAC) in highly polluted districts (Faridabad, Ghaziabad, Gautam Budh Nagar, Gurugram) of National Capital Region (NCR) with heavy aerosol loading using limited AErosol RObotic NETwork (AERONET) observations obtained from AERONET sites at Amity University (AU) and Gual Pahari (GP). Such evaluation of satellite-retrieved aerosol product with ground data confirms its practicality based on retrieval errors (Expected Error (EE) values (EE = 0.05 + 15 %*AOD) (EE: 78.85 % at AU, 73.58 % at GP), root mean square error (RMSE) values (RMSE: 0.15 at AU, 0.24 at GP), and correlation coefficient (R) values (R: 0.86 at AU, 0.73 at GP). The seasonal variation in AOD over the study area from 2010-2019 reveals increasing trend of AOD in the monsoon and post-monsoon season due to natural and anthropogenic factors. In addition to contributing to a holistic assessment of MAIAC aerosol estimates as a recent, high-resolution aerosol product, present results provide a basis for further research into NCR aerosols.

One of the largest uncertainties in estimating the anthropogenic radiative forcing is related to the impact of atmospheric aerosols on cloud properties. This uncertainty originates mainly from the complicated nature of aerosol-cloud interaction as it is much stronger and more difficult to observe than the aerosol-radiation interaction. The estimates of radiative forcing due to changes in cloud properties vary significantly between different global climate models, highlighting the need for constraining this forcing by using observations. Moreover, it is challenging to determine the impact of aerosols on clouds from satellite observations only. In this study, we aim to improve that by combining the in-situ observations with satellite retrievals, in order to reduce uncertainties in the anthropogenic impact on clouds and the climate. The study is performed for the low-level liquid clouds over the Southern Great Plains (SGP), Oklahoma. The in-situ data on particle number concentration, large enough to act as cloud condensation nuclei (CCN), with diameter larger than 100 nm (N100) were collected for a 5-year period from the Atmospheric Radiation Measurement (ARM) observatory at SGP site. In this analysis, the level-2 collection-6 MODIS cloud property dataset (MYD06_L2) with a 1x1 km resolution was used, where only liquid, single-layer clouds with a cloud top warmer than 268 K were included. It was observed that both CER and COT increased with increasing CWP. However, at a same level of CWP, the CER was smaller, and COT was larger at high N100 concentrations, as compared to the lower N100 concentrations. This result is consistent with the hypothesis of enhanced aerosol load increasing the number concentration of CCN, which in turn leads to smaller cloud droplets.

To be able to meet global grand challenges (climate change; biodiversity loss; environmental pollution; scarcity of water, food and energy supplies; acidification; deforestation; chemicalization; pandemics), which all are closely interlinked with each other, we need comprehensive open data with proper metadata. The large data sets from ground-base in situ observations, ground and satellite remote sensing and multiscale modelling need to be utilized seamlessly. In this opinion paper, we describe the SMEAR (Station for Measuring Earth surface – Atmosphere Relations) concept. We also demonstrate its power via several examples, such as detection of new particle formation and their subsequent growth, quantifying atmosphere-ecosystem feedback loops, combining comprehensive observations with emergency science and services, as well as studying the effect of COVID restrictions on different air quality and climate variables. The future needs and the potential of comprehensive observations of the environment are summarized.

Being part of the Copernicus Atmosphere Monitoring Service (CAMS), the McClear service provides estimates of the downwelling shortwave irradiance and its direct and diffuse components received at ground level in cloud-free conditions, with inputs on ozone, water vapor and aerosol properties from CAMS. McClear estimates have been validated over several parts of the world by various authors. This article makes a step forward by comparing McClear estimates to measurements performed at 44 ground-based stations located in Sub-Saharan Africa and the Maldives Archipelago in the Indian Ocean. The global irradiance received on a horizontal surface (G) and its direct component received at normal incidence (BN) provided by the McClear-v3 service were compared to 1 min measurements made in cloud-free conditions at the stations. The correlation coefficient is greater than 0.96 for G, whereas it is greater than 0.70 at all stations but five for BN. The mean of G is accurately estimated at stations located in arid climates (BSh, BWh, BSk, BWk) and temperate climates without a dry season and a hot or warm summer (Cfa, Cfb) or with a dry and hot summer (Csa) with a relative bias in the range [−1.5, 1.5] % with respect to the means of the measurements at each station. It is underestimated in tropical climates of monsoon type (Am) and overestimated in tropical climates of savannah type (Aw) and temperate climates with a dry winter and hot (Cwa) or warm (Cwb) summer. The McClear service tends to overestimate the mean of BN. The standard deviation of errors for G ranges between 13 W m−2 (1.3 %) and 31 W m−2 (3.7 %) and that for BN ranges between 31 W m−2 (3.0 %), and 70 W m−2 (7.9 %). Both offer small variations in time and space. A review of previous works reveals no significant difference between their results and ours. This work establishes a general overview of the performances of the McClear service.

Being part of the Copernicus Atmosphere Monitoring Service (CAMS), the McClear service provides estimates of the downwelling shortwave irradiance and its direct and diffuse components received at ground level in cloud-free conditions, with inputs on ozone, water vapor and aerosol properties from CAMS. McClear estimates have been validated over several parts of the world by various authors. This article makes a step forward by comparing McClear estimates to measurements performed at 44 ground-based stations located in Sub-Saharan Africa and the Maldives Archipelago in the Indian Ocean. The global irradiance received on a horizontal surface (G) and its direct component received at normal incidence (BN) provided by the McClear-v3 service were compared to 1 min measurements made in cloud-free conditions at the stations. The correlation coefficient is greater than 0.96 for G, whereas it is greater than 0.70 at all stations but five for BN. The mean of G is accurately estimated at stations located in arid climates (BSh, BWh, BSk, BWk) and temperate climates without a dry season and a hot or warm summer (Cfa, Cfb) or with a dry and hot summer (Csa) with a relative bias in the range [−1.5, 1.5] % with respect to the means of the measurements at each station. It is underestimated in tropical climates of monsoon type (Am) and overestimated in tropical climates of savannah type (Aw) and temperate climates with a dry winter and hot (Cwa) or warm (Cwb) summer. The McClear service tends to overestimate the mean of BN. The standard deviation of errors for G ranges between 13 W m−2 (1.3 %) and 31 W m−2 (3.7 %) and that for BN ranges between 31 W m−2 (3.0 %), and 70 W m−2 (7.9 %). Both offer small variations in time and space. A review of previous works reveals no significant difference between their results and ours. This work establishes a general overview of the performances of the McClear service.

A quantitative comparison study for Raman lidar and ceilometer observations, and for model simulations of mass concentration estimates of smoke particles is presented. Layers of biomass burning aerosol particles were observed in the lower troposphere, at 2 to 5 km height on 4 to 6 June 2019, over Kuopio, Finland. These long-range-transported smoke particles originated from a Canadian wildfire event. The most pronounced smoke plume detected on 5 June was intensively investigated. Optical properties were retrieved from the multi-wavelength Raman polarization lidar PollyXT. Particle linear depolarization ratios (PDRs) of this plume were measured to be 0.08±0.02 at 355 nm and 0.05±0.01 at 532 nm, suggesting the presence of partly coated soot particles or particles that have mixed with a small amount of dust or other non-spherical aerosol type. The layer-mean PDR at 355 nm (532 nm) decreased during the day from ∼ 0.11 (0.06) in the morning to ∼ 0.05 (0.04) in the evening; this decrease with time could be linked to the particle aging and related changes in the smoke particle shape properties. Lidar ratios were derived as 47±5 sr at 355 nm and 71±5 sr at 532 nm. A complete ceilometer data processing for a Vaisala CL51 ceilometer is presented from a sensor-provided attenuated backscatter coefficient to particle mass concentration (including the water vapor correction for high latitude for the first time). Aerosol backscatter coefficients (BSCs) were measured at four wavelengths (355, 532, 1064 nm from PollyXT and 910 nm from CL51). Two methods, based on a combined lidar and sun-photometer approach, are applied for mass concentration estimations from both PollyXT and the ceilometer CL51 observations. In the first method, no. 1, we used converted BSCs at 532 nm (from measured BSCs) by corresponding measured backscatter-related Ångström exponents, whereas in the second method, no. 2, we used measured BSCs at each wavelength independently. A difference of ∼ 12 % or ∼ 36 % was found between PollyXT and CL51 estimated mass concentrations using method no. 1 or no. 2, showing the potential of mass concentration estimates from a ceilometer. Ceilometer estimations have an uncertainty of ∼ 50 % in the mass retrieval, but the potential of the data lies in the great spatial coverage of these instruments. The mass retrievals were compared with the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) meteorological and aerosol reanalysis. The inclusion of dust (as indicated by MERRA-2 data) in the retrieved mass concentration is negligible considering the uncertainties, which also shows that ceilometer observations for mass retrievals can be used even without exact knowledge of the composition of the smoke-dominated aerosol plume in the troposphere.

Satellite estimates of surface UV irradiance have been available since 1978 from the TOMS UV spectrometer and have continued with significantly improved ground resolution using the Ozone Monitoring Instrument (OMI 2004–current) and Sentinel 5 Precursor (S5P 2017–current). The surface UV retrieval algorithm remains essentially the same: it first estimates the clear-sky UV irradiance based on measured ozone and then accounts for the attenuation by clouds and aerosols, applying two consecutive correction factors. When estimating the total aerosol effect in surface UV irradiance, there are two major classes of aerosols to be considered: (1) aerosols that only scatter UV radiation and (2) aerosols that both scatter and absorb UV radiation. The former effect is implicitly included in the measured effective Lambertian-equivalent scene reflectivity (LER), so the scattering aerosol influence is estimated through cloud correction factor. Aerosols that absorb UV radiation attenuate the surface UV radiation more strongly than non-absorbing aerosols of the same extinction optical depth. Moreover, since these aerosols also attenuate the outgoing satellite-measured radiance, the cloud correction factor that treats these aerosols as purely scattering underestimates their aerosol optical depth (AOD), causing underestimation of LER and overestimation of surface UV irradiance. Therefore, for correction of aerosol absorption, additional information is needed, such as a model-based monthly climatology of aerosol absorption optical depth (AAOD). A correction for absorbing aerosols was proposed almost a decade ago and later implemented in the operational OMI and TROPOMI UV algorithms. In this study, however, we show that there is still room for improvement to better account for the solar zenith angle (SZA) dependence and nonlinearity in the absorbing aerosol attenuation, and as a result we propose an improved correction scheme. There are two main differences between the new proposed correction and the one that is currently operational in OMI and TROPOMI UV algorithms. First, the currently operational correction for absorbing aerosols is a function of AAOD only, while the new correction additionally takes the solar zenith angle dependence into account. Second, the second-order polynomial of the new correction takes the nonlinearity in the correction as a function of AAOD better into account, if compared to the currently operational one, and thus better describes the effect by absorbing aerosols over a larger range of AAOD. To illustrate the potential impact of the new correction in the global UV estimates, we applied the current and new proposed correction for global fields of AAOD from the aerosol climatology currently used in OMI UV algorithm, showing a typical differences of ±5 %. This new correction is easy to implement operationally using information of solar zenith angle and existing AAOD climatology.

Satellite-based aerosol retrievals provide a timely view of atmospheric aerosol properties, having a crucial role in the subsequent estimation of air quality indicators, atmospherically corrected satellite data products, and climate applications. However, current aerosol data products based on satellite data often have relatively large biases compared to accurate ground-based measurements and distinct uncertainty levels associated with them. These biases and uncertainties are often caused by oversimplified assumptions and approximations used in the retrieval algorithms due to unknown surface reflectance or fixed aerosol models. Moreover, the retrieval algorithms do not usually take advantage of all the possible observational data collected by the satellite instruments and may, for example, leave some spectral bands unused. The improvement and the re-processing of the past and current operational satellite data retrieval algorithms would become tedious and computationally expensive. To overcome this burden, we have developed a model-enforced post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite aerosol retrievals and a post-processing step carried out with a machine-learning-based correction model for the approximation error in the retrieval. The developed approach allows for the utilization of auxiliary data sources, such as meteorological information, or additional observations such as spectral bands unused by the original retrieval algorithm. The post-process correction model can learn to correct for the biases and uncertainties in the original retrieval algorithms. As the correction is carried out as a post-processing step, it allows for computationally efficient re-processing of existing satellite aerosol datasets without fully re-processing the much larger original radiance data. We demonstrate with over-land aerosol optical depth (AOD) and Ångström exponent (AE) data from the Moderate Imaging Spectroradiometer (MODIS) of the Aqua satellite that our approach can significantly improve the accuracy of the satellite aerosol data products and reduce the associated uncertainties. For instance, in our evaluation, the number of AOD samples within the MODIS Dark Target expected error envelope increased from 63 % to 85 % when the post-process correction was applied. In addition to method description and accuracy results, we also give recommendations for validating machine-learning-based satellite data products.

The new platforms for Earth observation from space are characterized by measurements made at great spatial and temporal resolutions. While this abundance of information makes it possible to detect and study localized phenomena, it may be difficult to manage this large amount of data for the study of global and large-scale phenomena. A particularly significant example is the use by assimilation systems of Level 2 products that represent gas profiles in the atmosphere. The models on which assimilation systems are based are discretized on spatial grids with horizontal dimensions of the order of tens of kilometres in which tens or hundreds of measurements may fall in the future. A simple procedure to overcome this problem is to extract a subset of the original measurements, but this involves a loss of information. Another option is the use of simple averages of the profiles, but this approach also has some limitations that we will discuss in the paper. A more advanced solution is to resort to the so-called fusion algorithms, capable of compressing the size of the dataset while limiting the information loss. A novel data fusion method, the Complete Data Fusion algorithm, was recently developed to merge a set of retrieved products in a single product a posteriori. In the present paper, we apply the Complete Data Fusion method to ozone profile measurements simulated in the thermal infrared and ultraviolet bands in a realistic scenario. Following this, the fused products are compared with the input profiles; comparisons show that the output products of data fusion have smaller total errors and higher information contents in general. The comparisons of the fused products with the fusing products are presented both at single fusion grid box scale and with a statistical analysis of the results obtained on large sets of fusion grid boxes of the same size. We also evaluate the grid box size impact, showing that the Complete Data Fusion method can be used with different grid box sizes even if this possibility is connected to the natural variability of the considered atmospheric molecule.

A novel method has been developed to estimate aerosol optical depth (AOD) from sunshine duration (SD) measurements under cloud-free conditions. It is a physically-based method serving for the reconstruction of the historical evolution of AOD during the last century. In addition to sunshine duration data, it requires daily water vapor and ozone products as inputs taken from the ECMWF twentieth century reanalysis ERA-20C, available at global scale over the period 1900–2010. Surface synoptic cloud observations are used to identify cloud-free days. For sixteen sites over Europe, the accuracy of the estimated daily AOD, and its seasonal variability, is similar or better than those from two earlier methods when compared to AErosol RObotic NETwork measurements. In addition, it also improves the detection of the signal from massive aerosol events such as important volcanic eruptions (e.g., Arenal and Fernandina Island in 1968, Chichón in 1982 and Pinatubo in 1992). Finally, the reconstructed AOD time series are in good agreement with the dimming/brightening phenomenon and also provides preliminary evidence of the early-brightening phenomenon.

Satellite-based aerosol retrievals provide global spatially distributed estimates of atmospheric aerosol parameters that are commonly needed in applications such as estimation of atmospherically corrected satellite data products, climate modelling and air quality monitoring. However, a common feature of the conventional satellite aerosol retrievals is that they have reasonably low spatial resolution and poor accuracy caused by uncertainty in auxiliary model parameters, such as fixed aerosol model parameters, and the approximate forward radiative transfer models utilized to keep the computational complexity feasible. As a result, the improvement and reprocessing of the operational satellite data retrieval algorithms would become a tedious and computationally excessive problem. To overcome these problems, we have developed a machine-learning-based post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite retrieval data and a post-processing step where a machine learning algorithm is utilized to predict the approximation error in the conventional retrieval. With approximation error, we refer to the discrepancy between the true aerosol parameters and the ones retrieved using the satellite data. Our hypothesis is that the prediction of the approximation error with a finite training dataset is a less complex and easier task than the direct, fully learned machine-learning-based prediction in which the aerosol parameters are directly predicted given the satellite observations and measurement geometry. Our approach does not require reprocessing of the satellite retrieval products; it requires only a computationally fast machine-learning-based post-processing step of the existing retrieval product. Our approach is based on neural networks trained based on collocated satellite data and accurate ground-based Aerosol Robotic Network (AERONET) aerosol data. Based on our post-processing approach, we propose a post-process-corrected high-resolution Sentinel-3 Synergy aerosol product, which gives a spectral estimate of the aerosol optical depth at five different wavelengths with a high spatial resolution equivalent to the native resolution of the Sentinel-3 Level-1 data (300 m at nadir). With aerosol data from Sentinel-3A and 3B satellites, we demonstrate that our approach produces high-resolution aerosol data with clearly better accuracy than the operational Sentinel-3 Level-2 Synergy aerosol product, and it also results in slightly better accuracy than the conventional fully learned machine learning approach. We also demonstrate better generalization capabilities of the post-process correction approach over the fully learned approach.

During the COVID-19 lockdown, the dramatic reduction of anthropogenic emissions provided a unique opportunity to investigate the effects of reduced anthropogenic activity and primary emissions on atmospheric chemical processes and the consequent formation of secondary pollutants. Here, we utilize comprehensive observations to examine the response of atmospheric new particle formation (NPF) to the changes in the atmospheric chemical cocktail. We find that the main clustering process was unaffected by the drastically reduced traffic emissions, and the formation rate of 1.5 nm particles remained unaltered. However, particle survival probability was enhanced due to an increased particle growth rate (GR) during the lockdown period, explaining the enhanced NPF activity in earlier studies. For GR at 1.5–3 nm, sulfuric acid (SA) was the main contributor at high temperatures, whilst there were unaccounted contributing vapors at low temperatures. For GR at 3–7 and 7–15 nm, oxygenated organic molecules (OOMs) played a major role. Surprisingly, OOM composition and volatility were insensitive to the large change of atmospheric NOx concentration; instead the associated high particle growth rates and high OOM concentration during the lockdown period were mostly caused by the enhanced atmospheric oxidative capacity. Overall, our findings suggest a limited role of traffic emissions in NPF.

As part of the UV-Indien project, a station for measuring ultraviolet radiation and the cloud fraction was installed in December 2019 in Moroni, the capital of the Comoros, situated on the west coast of the island of Ngazidja. A ground measurement campaign was also carried out on 12 January 2020 during the ascent of Mount Karthala, located in the center of the island of Ngazidja. In addition, satellite estimates (Ozone Monitoring Instrument and TROPOspheric Monitoring Instrument) and model outputs (Copernicus Atmospheric Monitoring Service and Tropospheric Ultraviolet Model) were combined for this same region. On the one hand, these different measurements and estimates make it possible to quantify, evaluate, and monitor the health risk linked to exposure to ultraviolet radiation in this region, and, on the other, they help to understand how cloud cover influences the variability of UV-radiation on the ground. The measurements of the Ozone Monitoring Instrument onboard the EOS-AURA satellite, being the longest timeseries of ultraviolet measurements available in this region, make it possible to quantify the meteorological conditions in Moroni and to show that more than 80% of the ultraviolet indices are classified as high and that 60% of these are classified as extreme. The cloud cover measured in Moroni by an All Sky Camera was used to distinguish between the cases of UV index measurements taken under clear or cloudy sky conditions. The ground-based measurements thus made it possible to describe the variability of the diurnal cycle of the UV index and the influence of cloud cover on this parameter. They also permitted the satellite measurements and the results of the simulations to be validated. In clear sky conditions, a relative difference of between 6 and 11% was obtained between satellite or model estimates and ground measurements. The ultraviolet index measurement campaign on Mount Karthala showed maximum one-minute standard erythemal doses at 0.3 SED and very high daily cumulative erythemal doses at more than 80 SED. These very high levels are also observed throughout the year and all skin phototypes can exceed the daily erythemal dose threshold at more than 20 SED.

We present here an aerosol model selection based statistical method in Bayesian framework for retrieving atmospheric aerosol optical depth (AOD) and pixel-level uncertainty. Especially, we focus on to provide more realistic uncertainty estimate by taking into account a model selection problem when searching for the solution by fitting look-up table (LUT) models to a satellite measured top-of-atmosphere reflectance. By means of Bayesian model averaging over the best-fitting aerosol models we take into account an aerosol model selection uncertainty and get also a shared inference about AOD. Moreover, we acknowledge model discrepancy, i.e. forward model error, arising from approximations and assumptions done in forward model simulations. We have estimated the model discrepancy empirically by a statistical approach utilizing residuals of model fits. We use the measurements from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor in ultraviolet and visible bands, and in one wavelength band 675 nm in near-infrared, in order to study the functioning of the retrieval in a broad wavelength range. We exploit a fundamental classification of the aerosol models as weakly absorbing, biomass burning and desert dust aerosols. For experimental purpose we have included some dust type of aerosols having non-spherical particle shapes. For this study we have created the aerosol model LUTs with radiative transfer simulations using the libRadtran software package. It is reasonably straightforward to experiment with different aerosol types and evaluate the most probable aerosol type by the model selection method. We demonstrate the method in wildfire and dust events in a pixel level. In addition, we have evaluated in detail the results against ground-based remote sensing data from the AErosol RObotic NETwork (AERONET). Based on the case studies the method has ability to identify the appropriate aerosol types, but in some wildfire cases the AOD is overestimated compared to the AERONET result. The resulting uncertainty when accounting for the model selection problem and the imperfect forward modelling is higher compared to uncertainty when only measurement error is included in an observation model, as can be expected.

To investigate near UV aerosol absorbers, such as iron in mineral dust and brown carbon from biomass burning, AERONET spectral imaginary refractive index and size distributions retrievals were performed with the input of additional wavelength data. This includes adding both measured spectral AOD and sky radiances at 380 nm and 500 nm to the four channels (440, 675, 870 and 1020 nm) that are used in the standard retrieval data product. The vector radiative transfer code in the AERONET Version 3 retrievals enabled more accurate computations in the ultraviolet region (380 nm). Calibration of the 380 nm channel radiances was done by the vicarious method since the integrating sphere radiance source that is used for all other wavelengths does not produce sufficient energy at 380 nm. Spectral single scattering albedo, imaginary refractive indices and aerosol size distributions from these six wavelength retrievals are compared to the standard four wavelength retrieval values.

Layers of biomass burning aerosol particles were observed in the lower troposphere, at 2 to 5 km height on 4 to 6 June 2019, over Kuopio, Finland. These long-range-transported smoke particles originated from a Canadian wildfire event. The most pronounced smoke plume detected on 5 June was intensively investigated. Optical properties were retrieved from the multi-wavelength Raman polarization lidar PollyXT. Particle linear depolarization ratios of this plume were measured to be 0.08 ± 0.02 at 355 nm and 0.05 ± 0.01 at 532 nm which were slightly higher than the values given in the literature. Non-spherical shaped aged smoke particles and/or the mixing with a small amount of fine dust particles could cause the observed increase in the particle linear depolarization ratios. Lidar ratios were derived as 47 ± 5 sr at 355 nm and 71 ± 5 sr at 532 nm. A complete ceilometer data processing for a Vaisala CL51 is presented, including the water vapor correction for high latitude for the first time, from sensor provided attenuated backscatter coefficient to particle mass concentration. A combined lidar and sun-photometer approach (based on AERONET products) is applied for mass concentration estimations. Mass concentrations were estimated from both PollyXT and the ceilometer CL51 observations, which were of the order of ~ 30 µg m−3 in the morning and decreased to ~ 20 µg m−3 in the night. A difference of ~ 30% was found between PollyXT and CL51 estimated mass concentrations. The mass retrievals were discussed and compared with the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) meteorological and aerosol reanalysis. The inclusion of dust in the retrieved mass concentration slightly improved the correspondence between the observations and the MERRA-2 simulations.

The Pan-Eurasian Experiment Program (PEEX) is an interdisciplinary scientific program bringing together ground-based in situ and remote sensing observations, satellite measurements and modeling tools aiming to improve the understanding of land-water-atmosphere interactions, feedback mechanisms and their effects on the ecosystem, climate and society in northern Eurasia, Russia and China. In a view of the large area covering thousands of kilometres, large gaps will remain where no or little ground-based observational information will be available. The gap can partly be filled by satellite remote sensing of relevant parameters as regards atmospheric composition.Biomass burning is a violent source of atmospheric pollutants. Fires and corresponding emissions to the atmosphere dramatically change the atmospheric composition in case of long-lasting fire events, which might cover extended areas. In the burned areas, CO2 exchange, as well as emissions of different compounds are getting to higher levels, which might contribute to climate change by changing the radiative budget through the aerosol-cloud interaction and cloud formation. In the boreal forest, after CO2, CO and CH4, the largest emission factors for individual species were formaldehyde, followed by methanol and NO2 (Simpson et al., ACP, 2011). The emitted long-life components, e.g., black carbon, might further be transported to the distant areas and measured at the surface far from the burned areas.In the boreal forest region, fires are very common, very large and produce a lot of smoke. Boreal areas have been burning regularly for thousands of years and is adapted to fires, which happen most often between May and October. In boreal ecosystems, future increases in air temperature may lengthen the fire season and increase the probability of fires, leading some to hypothesize a positive feedback between warming, fire activity, carbon loss, and future climate change (Kasischke et al., 2000). During the last few decades, several burning episodes have been observed over PEEX area by satellites (as fire counts), specifically over Siberia and central Russia. The following information available from satellites will be utilized to reveal a connection between Fire activity and atmospheric composition for the period 2002-2020 over the PEEX area:- Fire count, FRP and burned areas from MODIS - Absorbing Aerosol Index (AAI), multi-instrument (GOME-2, OMI, TOMS) product - CO from MOPPIT - HCHO and NO2 from OMI Monthly temperature and humidity fields from ERA5 re-analysis will be also utilized to reveal if a connection exist between climate variables and occurrence and intensity of the forest fires.Kasischke, B. J. Stocks: Fire, Climate Change, and Carbon Cycling in the Boreal Forest. M. M. Cadwellet al.,Eds., Ecological Studies (Springer, New York, 2000)Simpson, I. J., Akagi, S. K., Barletta, B., Blake, N. J., Choi, Y., Diskin, G. S., Fried, A., Fuelberg, H. E., Meinardi, S., Rowland, F. S., Vay, S. A., Weinheimer, A. J., Wennberg, P. O., Wiebring, P., Wisthaler, A., Yang, M., Yokelson, R. J., and Blake, D. R.: Boreal forest fire emissions in fresh Canadian smoke plumes: C1-C10 volatile organic compounds (VOCs), CO2, CO, NO2, NO, HCN and CH3CN, Atmos. Chem. Phys., 11, 6445–6463, https://doi.org/10.5194/acp-11-6445-2011, 2011.

Solar ultraviolet (UV) radiation has a broad range of effects concerning life on Earth. Because of its high photon energy, UV radiation influences human health, terrestrial and aquatic ecosystems, air quality, and materials in various ways. The Sentinel 5 Precursor (S5P) mission on a sun-synchronous orbit with an ascending node equatorial crossing at 13:30, which in conjunction with a wide swath of 2600 km provides near-global daily coverage. S5P’s TROPOMI instrument measures radiation backscattered from the Earth–atmosphere system and provides observations of atmospheric composition with the best spatial resolution presently. Among other things, TROPOMI measurements are used for calculating the UV radiation reaching the Earth's surface over the sunlit part of the globe. This UV-radiation product is processed at the Finnish Meteorological Institute Copernicus Collaborative Ground segment. The product was released via FinHUB in summer 2020. The TROPOMI L2 UV product contains 36 UV parameters in total, including irradiances at four different wavelengths and dose rates for erythemal and vitamin D synthesis action spectra. All parameters are calculated for overpass time, for solar noon time, and for theoretical clear-sky conditions with no clouds or aerosols. Daily doses and accumulated irradiances are also calculated by integrating over the sunlit part of the day. In addition to UV parameters, quality flags related to the UV product and processing are generated.Validation with ground based instruments have shown that the agreement is very good, typically within 10%. The S5P is the first Copernicus mission dedicated to atmospheric observations, and it will be complemented by Sentinel 4 with geostationary orbit and Sentinel 5 on Sun-synchronous morning orbit with planned launches in the coming years. It is expected that surface UV-radiation products from these instruments will continue the present time series. The TROPOMI surface UV radiation product responds to the increasing need for information regarding the tropospheric chemistry and biologically active wavelengths of the solar spectrum reaching the surface. In this presentation we introduce the TROPOMI UV radiation product and future developments, discuss about the quality of the product and demonstrate the usefulness of the satellite UV-data by showing resent applications including among others the exceptionally high UV-radiation conditions in mid latitudes due to persistent Antarctic ozone hole in December 2020 and modeling of seasonal cycle of COVID-19. By combining the TROPOMI UV data with observations of trace gases from the same instrument, there is also a potential for new kind of applications, where satellite data can be used in novel ways to study photochemical processes in the troposphere.

Biogenic secondary organic aerosol (BSOA) constitutes a major fraction of aerosol over boreal forests. As the emissions of BSOA precursors are temperature dependent, changes in temperature have potentially important implications on regional aerosol radiative forcing. Here, we have used long-term aerosol composition and temperature data measurements from a boreal forest site together with remote sensing observations of aerosol and cloud properties to investigate the effect of increasing temperature on organic aerosol mass loadings, and further on aerosol direct and indirect radiative effects. The analysis was based on 7 years of measurements done at Hyytiälä, Southern Finland, and they cover the summer months (July-August) between 2012-2018. We limited the analyses to these summer months to isolate the temperature dependence of the organic mass loadings from the seasonal effects arising from the vegetation growth cycle. Our analysis showed that organic aerosol loadings and cloud condensation nuclei concentrations increased in concert with surface temperature. Furthermore, we found that cloud reflectivity increased when the organic aerosol loadings increased. This research presents the first direct observational evidence on the effect of BSOA on cloud properties and their climatic significance.

As part of the UV-Indien Network, 9 ground-based stations have been equipped with one spectroradiometer, radiometers and all-sky cameras. These stations are homogeneously distributed in 5 countries of the Western Indian Ocean region (Comoros, France, Madagascar, Mauritius and Seychelles), a part of the world where almost no measurements have been made so far. The main scientific objectives of this network are to study the annual and inter-annual variability of the ultraviolet (UV) radiation in this area, to validate the output of numerical models and satellite estimates of ground-based UV measurements, and to monitor UV radiation in the context of climate change and projected ozone depletion in this region. The first results are presented here for the oldest stations (Antananarivo, Anse Quitor, Mahé and Saint-Denis). Ground-based measurements of UV index (UVI) are compared against satellite estimates (Ozone Monitoring Instrument (OMI), the TROPOspheric Monitoring Instrument (TROPOMI), the Global Ozone Monitoring Experiment (GOME) and model forecasts of UVI (Tropospheric Emission Monitoring Internet Service (TEMIS) and Copernicus Atmospheric Monitoring Service (CAMS). The median relative differences between satellite or model estimates and ground-based measurements of clear-sky UVI range between −34.5 % and 15.8 %. Under clear skies, the smallest UVI median difference between the satellites or model estimates and the measurements of ground-based instruments is found to be 0.02 (TROPOMI), 0.04 (OMI), −0.1 (CAMS) and −0.4 (CAMS) at St-Denis, Antananarivo, Anse Quitor and Mahé respectively. The cloud fraction and UVI diurnal profile are calculated for these four stations. The mean UVI values at local solar noon range between 10 (Antananarivo, Anse Quitor and Saint-Denis) and 14 at Mahé. The mean UVIs in clear-sky conditions are higher than mean UVI in all-sky conditions, although it can still be noted that UVI maxima are higher for all-sky conditions than for clear sky conditions. This is the result of UVI enhancement induced by clouds, observed at these four stations. The greatest increase in UV radiation under cloudy conditions was observed at the Mahé station, with increases of more than 4. The data used in this study is available at https://doi.org/10.5281/zenodo.4572026 (Lamy and Portafaix, 2021).

Satellite estimates of surface UV irradiance have been available since 1978 from TOMS UV spectrometer and continued with significantly improved ground resolution using Ozone Monitoring Instrument (OMI 2004-current) and Sentinel 5 Precursor (S5P 2017-current). The surface UV retrieval algorithm remains essentially the same: it first estimates the clear-sky UV irradiance based on measured ozone and then accounts for the attenuation by clouds and aerosols applying two consecutive correction factors. When estimating the total aerosol effect in surface UV irradiance, there are two major classes of aerosols to be considered: 1) aerosols that only scatter UV radiation and 2) aerosols that both scatter and absorb UV radiation. The former effect is implicitly included in the measured effective Lambertian Equivalent scene reflectivity (LER), so the scattering aerosol influence is estimated through cloud correction factor. Aerosols that absorb UV radiation attenuate the surface UV radiation more strongly than non-absorbing aerosols of the same extinction optical depth (AOD). Moreover, since these aerosols also attenuate the outgoing satellite-measured radiance, the cloud correction factor that treats these aerosols as purely scattering underestimates their AOD causing underestimation of LER and overestimation of surface UV irradiance. Therefore, for correction of aerosol absorption additional information is needed, such as the UV absorbing Aerosol Index (UVAI) or a model-based monthly climatology of aerosol absorption optical depth (AAOD). A correction for absorbing aerosols was proposed almost a decade ago and later implemented in the operational OMI and TROPOMI UV algorithms. In this study, however, we show that there is still room for an improvement to better account for the solar zenith angle dependence and non-linearity in the absorbing aerosol attenuation and as a result we propose an improved correction scheme. There are two main differences between the new proposed correction and the one that is currently operational in OMI and TROPOMI UV-algorithms. First, the currently operational correction for absorbing aerosols is a function of AAOD only, while the new correction takes additionally the solar zenith angle dependence into account. Second, the 2nd order polynomial of the new correction takes better into account the non-linearity in the correction as a function of AAOD, if compared to the currently operational one, and thus better describes the effect by absorbing aerosols over larger range of AAOD. To illustrate the potential impact of the new correction in the global UV estimates, we applied the current and new proposed correction for global fields of AAOD from the aerosol climatology currently used in OMI UV algorithm, showing a typical differences of ±5 %. This new correction is easy to implement operationally using information of solar zenith angle and existing AAOD climatology.

A quantitative comparison study for Raman lidar and ceilometer observations, and for model simulations of mass concentration estimates of smoke particles is presented. Layers of biomass burning aerosol particles were observed in the lower troposphere, at 2 to 5km height on 4 to 6 June 2019, over Kuopio, Finland. These long-range-transported smoke particles originated from a Canadian wildfire event. The most pronounced smoke plume detected on 5 June was intensively investigated. Optical properties were retrieved from the multi-wavelength Raman polarization lidar PollyXT. Particle linear depolarization ratios (PDRs) of this plume were measured to be 0.08 +/- 0.02 at 355nm and 0.05 +/- 0.01 at 532nm, suggesting the presence of partly coated soot particles or particles that have mixed with a small amount of dust or other non-spherical aerosol type. The layer-mean PDR at 355nm (532nm) decreased during the day from similar to 0.11 (0.06) in the morning to similar to 0.05 (0.04) in the evening; this decrease with time could be linked to the particle aging and related changes in the smoke particle shape properties. Lidar ratios were derived as 47 +/- 5sr at 355nm and 71 +/- 5sr at 532nm. A complete ceilometer data processing for a Vaisala CL51 ceilometer is presented from a sensor-provided attenuated backscatter coefficient to particle mass concentration (including the water vapor correction for high latitude for the first time). Aerosol backscatter coefficients (BSCs) were measured at four wavelengths (355, 532, 1064nm from PollyXT and 910nm from CL51). Two methods, based on a combined lidar and sun-photometer approach, are applied for mass concentration estimations from both PollyXT and the ceilometer CL51 observations. In the first method, no. 1, we used converted BSCs at 532nm (from measured BSCs) by corresponding measured backscatter-related angstrom ngstrom exponents, whereas in the second method, no. 2, we used measured BSCs at each wavelength independently. A difference of similar to 12% or similar to 36% was found between PollyXT and CL51 estimated mass concentrations using method no. 1 or no. 2, showing the potential of mass concentration estimates from a ceilometer. Ceilometer estimations have an uncertainty of similar to 50% in the mass retrieval, but the potential of the data lies in the great spatial coverage of these instruments. The mass retrievals were compared with the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) meteorological and aerosol reanalysis. The inclusion of dust (as indicated by MERRA-2 data) in the retrieved mass concentration is negligible considering the uncertainties, which also shows that ceilometer observations for mass retrievals can be used even without exact knowledge of the composition of the smoke-dominated aerosol plume in the troposphere.

International audience; Natural daylight is recognized as an important variable in the energy performance of buildings. A method that estimates the global illuminance received on a horizontal surface at ground level and its direct component at normal incidence under cloudless conditions is presented. The method uses the k-distribution method and the correlated-k approximation to compute a set of clearness indices integrated over 13 spectral bands covering the range 380–780 nm. A spectral resampling technique, including a spectral disaggregation and a spectral linear interpolation, is applied to these indices for providing a detailed set of solar irradiances at 1 nm in spectral resolution over the whole range. Then, these are weighted by the standardized CIE action spectrum for human eye for assessing the illuminance. Inputs to the method include the total column contents of ozone and water vapor as well as aerosol optical properties produced by the Copernicus Atmosphere Monitoring Service. Estimates of illuminance were compared to high-quality 1 min measurements of illuminance that were collected from two experimental sites located in two different climatic zones. A slight overestimation is observed for the global illuminance: the bias is between +1 klx and +3 klx, i.e., between +1% and +4% in relative value. The root mean square error varies between 5 klx (8%) and 6 klx (9%). The squared correlation coefficient ranges between 0.95 and 0.97. At the site providing the direct illuminance at normal incidence, the performance of the method is lower compared to global illuminance with a lower squared correlation coefficient of 0.53. The bias, relative bias, RMSE, and rRMSE are +7 klx, +9%, 12 klx, and 15%, respectively. The uncertainty of the method is of the order of the uncertainty of the measurements. The method offers accurate estimates of illuminance in cloudless conditions at high spatial and temporal resolutions useful for construction industries and operators as well as thermal simulation tools for optimal building design strategies.

Remote sensing of the atmospheric composition from current and future satellites, such as the Sentinel missions of the Copernicus programme, yields an unprecedented amount of data to monitor air quality, ozone, UV radiation and other climate variables. Hence, full exploitation of the growing wealth of information delivered by spaceborne observing systems requires addressing the technological challenges for developing new strategies and tools that are capable to deal with these huge data volumes. The H2020 AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) project investigated a novel approach for synergistic use of ozone profile measurements acquired at different frequencies (ultraviolet, visible, thermal infrared) by sensors onboard Geostationary Equatorial Orbit (GEO) and Low Earth Orbit (LEO) satellites in the framework of the Copernicus Sentinel-4 and Sentinel-5 missions. This paper outlines the main features of the technological infrastructure, designed and developed to support the AURORA data processing chain as a distributed data processing and describes in detail the key components of the infrastructure and the software prototype. The latter demonstrates the technical feasibility of the automatic execution of the full processing chain with simulated data. The Data Processing Chain (DPC) presented in this work thus replicates a processing system that, starting from the operational satellite retrievals, carries out their fusion and results in the assimilation of the fused products. These consist in ozone vertical profiles from which further modules of the chain deliver tropospheric ozone and UV radiation at the Earth&rsquo, s surface. The conclusions highlight the relevance of this novel approach to the synergistic use of operational satellite data and underline that the infrastructure uses general-purpose technologies and is open for applications in different contexts.

Satellite-based aerosol retrievals provide a timely global view of atmospheric aerosol properties for air quality, atmospheric characterization, and correction of satellite data products and climate applications. Current aerosol data products based on satellite data, however, often have relatively large biases relative to accurate ground-based measurements and distinct levels of uncertainty associated with them. These biases and uncertainties are often caused by oversimplified assumptions and approximations used in the retrieval algorithms due to unknown surface reflectance or fixed aerosol models. Moreover, the retrieval algorithms do not usually take advantage of all the possible observational data collected by the satellite instruments and may, for example, leave some spectral bands unused. The improvement and the re-processing of the past and current operational satellite data retrieval algorithms would become a tedious and computationally expensive task. To overcome this burden, we have developed a model enforced post-process correction approach that can be used to correct the existing and operational satellite aerosol data products. Our approach combines the existing satellite aerosol retrievals and a post-processing step carried out with a machine learning based correction model for the approximation error in the retrieval. The developed approach allows for the utilization of auxiliary data sources, such as meteorological information, or additional observations such as spectral bands unused by the original retrieval algorithm. The post-process correction model can learn to correct for the biases and uncertainties in the original retrieval algorithms. As the correction is carried out as a post-processing step, it allows for computationally efficient re-processing of existing satellite aerosol datasets with no need to fully reprocess the much larger original radiance data. We demonstrate with over land aerosol optical depth (AOD) and Angstrom exponent (AE) data from the Moderate Imaging Spectroradiometer (MODIS) of Aqua satellite that our approach can significantly improve the accuracy of the satellite aerosol data products and reduce the associated uncertainties. We also give recommendations for the validation of satellite data products that are constructed using machine learning based models.