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Poor air quality poses a great threat to human health. Accurate high‐resolution satellite remote sensing of atmospheric aerosols would highly benefit satellite‐based air quality estimates. We have developed and validated a post‐process correction and downscaling approach for satellite remote sensing of aerosols. We use NASA's Moderate Resolution Imaging Spectroradiometer aerosol optical depth (AOD) over the Washington D.C.—Baltimore area during the Distributed Regional Aerosol Gridded Observation Networks campaign in 2011 to evaluate our approach. We derive and evaluate the AOD fields at high 250 m resolution. The results show that the post‐process correction approach is suitable for deriving downscaled, high‐resolution AOD estimates and significantly improves the accuracy of the AOD retrievals. Plain Language Summary: Satellites collect information about our atmosphere. We often use satellite data to monitor, for example, atmospheric aerosols, which are small solid and liquid particles in the air. However, the massive amount of satellite data and partially unknown atmospheric processes and land surface properties force us to use simplified computations in aerosol monitoring. Unfortunately, the simplified computations lead to sub‐optimal accuracy and low resolution in aerosol data. In this work, we have developed a new machine‐learning‐based algorithm that is used together with conventional satellite data processing to improve the data. Our algorithm takes advantage of accurate ground‐based measurements. As a result, it significantly improves the accuracy and resolution of the aerosol data. We improved the resolution of satellite‐based aerosol data from 3 km to 250 m. New high‐resolution data may allow some new applications for the satellite data, such as street‐level air quality monitoring. Key Points: Satellites produce global aerosol data, however, these data often suffer from low accuracy and spatial resolution due to data aggregationMachine‐learning‐based post‐process correction leads to a significant improvement in the aerosol optical depth (AOD) accuracy over the conventional retrievalsPost‐process correction approach is also efficient for spatial downscaling of satellite aerosol data leading to high resolution AOD [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 modeling 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 re-processing 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 data set 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. With our approach, there is no need to re-run the existing retrieval algorithms and only a computationally feasible post-processing step is needed. Our approach is based on neural networks trained based on collocated satellite data and accurate ground based 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 meters at nadir). With aerosol data from Sentinel-3A and 3B satellites, we demonstrate that our approach produces high-resolution aerosol data with better accuracy than the operational Sentinel-3 level-2 Synergy aerosol product or a conventional fully learned machine learning approach. [ABSTRACT FROM AUTHOR]

Within the framework of the UV-Indien network, nine ground stations have been equipped with ultraviolet broadband radiometers, five of them have also been equipped with an all-sky camera, and the main station in Saint-Denis de la Réunion is also equipped with a spectroradiometer. These stations are spatially distributed to cover a wide range of latitudes, longitudes, altitudes, and environmental conditions in five 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 distribution of the stations is based on the scientific interest of studying ultraviolet radiation not only in relation to atmospheric processes but also in order to provide data relevant to fields such as biology, health (prevention of skin cancer), and agriculture. The main scientific objectives of this network are to study the annual and inter-annual variability in 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. A calibration procedure including three types of calibrations responding to the various constraints of sustaining the network has been put in place, and a data processing chain has been set up to control the quality and the format of the files sent to the various data centres. A method of clear-sky filtering of the data is also applied. Here, we present an intercomparison with other datasets, as well as several daily or monthly representations of the UV index (UVI) and cloud fraction data, to discuss the quality of the data and their range of values for the older stations (Antananarivo, Anse Quitor, Mahé, and Saint-Denis). Ground-based measurements of the UVI are used to validate satellite estimates – Ozone Monitoring Instrument (OMI), the TROPOspheric Monitoring Instrument (TROPOMI), and 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 satellite or model estimates and the measurements made by ground-based instruments is found to be 0.02 (TROPOMI), 0.04 (OMI), - 0.1 (CAMS), and - 0.4 (CAMS) at Saint-Denis, Antananarivo, Anse Quitor, and Mahé, respectively. The diurnal variability in UVI and cloud fraction, as well as the monthly variability in UVI, is evaluated to ensure the quality of the dataset. The data used in this study are available at 10.5281/zenodo.4811488. [ABSTRACT FROM AUTHOR]

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 Polly XT. 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 Polly XT and 910 nm from CL51). Two methods, based on a combined lidar and sun-photometer approach, are applied for mass concentration estimations from both Polly XT 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 Polly XT 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. [ABSTRACT FROM AUTHOR]

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. Nonspherical 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 scienti?c 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 ?rst 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 pro?le 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). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Fine Mode Fraction (FMF) of atmospheric aerosol is very important for environment and climate studies. Attempts have been made to retrieve the FMF from satellite data with varying success. In this work, the development of an artificial Neural Network for AEROsol retrieval (NNAero) is presented. NNAero uses data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) flying on the NASA Terra and Aqua satellites. The MODIS-derived spectral reflectances of solar radiation at the top of the atmosphere (TOA) and at the surface were used together with ground-based Aerosol Robotic Network (AERONET) measurements of Aerosol Optical Depth (AOD) and FMF to train a Convolutional Neural Network (CNN) for the joint retrieval of FMF and AOD. The NNAero results over northern and eastern China were validated against an independent reference AERONET dataset (i.e. not used in training the CNN). The results show that 68% of the NNAero AOD values are within the MODIS expected error (EE) envelope over land of ±(0.05 + 15%), which is similar to the results from the MODIS Deep Blue (DB) algorithm (63% within EE), and both are better than the Dark Target (DT) algorithm (31% within EE). The validation of the NNAero FMF vs AERONET data shows a significant improvement with respect to the DT FMF, with Root Mean Squared Prediction Errors (RMSE) of 0.1567 (NNAero) and 0.34 (DT). The NNAero method shows the potential of improved retrieval of the FMF. Unlabelled Image • Development of an improved FMF and AOD retrieval method with high accuracy • Terra and Aqua MODIS data and ground-based AERONET data are jointly used • Highest spatial resolution of neural network retrieved FMF/AOD could be 0.5 km • The MODIS FMF and AOD retrieved with no restriction to dark targets • Impact of aerosol absorption in the FMF bias was discussed [ABSTRACT FROM AUTHOR]

The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S5P) satellite was launched on 13 October 2017 to provide the atmospheric composition for atmosphere and climate research. The S5P is a Sun-synchronous polar-orbiting satellite providing global daily coverage. The TROPOMI swath is 2600 km wide, and the ground resolution for most data products is 7.2×3.5 km 2 (5.6×3.5 km 2 since 6 August 2019) at nadir. The Finnish Meteorological Institute (FMI) is responsible for the development of the TROPOMI UV algorithm and the processing of the TROPOMI surface ultraviolet (UV) radiation product which includes 36 UV parameters in total. Ground-based data from 25 sites located in arctic, subarctic, temperate, equatorial and Antarctic areas were used for validation of the TROPOMI overpass irradiance at 305, 310, 324 and 380 nm, overpass erythemally weighted dose rate/UV index, and erythemally weighted daily dose for the period from 1 January 2018 to 31 August 2019. The validation results showed that for most sites 60 %–80 % of TROPOMI data was within ±20 % of ground-based data for snow-free surface conditions. The median relative differences to ground-based measurements of TROPOMI snow-free surface daily doses were within ±10 % and ±5 % at two-thirds and at half of the sites, respectively. At several sites more than 90 % of cloud-free TROPOMI data was within ±20 % of ground-based measurements. Generally median relative differences between TROPOMI data and ground-based measurements were a little biased towards negative values (i.e. satellite data < ground-based measurement), but at high latitudes where non-homogeneous topography and albedo or snow conditions occurred, the negative bias was exceptionally high: from -30 % to -65 %. Positive biases of 10 %–15 % were also found for mountainous sites due to challenging topography. The TROPOMI surface UV radiation product includes quality flags to detect increased uncertainties in the data due to heterogeneous surface albedo and rough terrain, which can be used to filter the data retrieved under challenging conditions. [ABSTRACT FROM AUTHOR]

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 20th century reanalysis ERA-20C, available at the global scale over the period 1900–2010. Surface synoptic cloud observations are used to identify cloud-free days. For 16 sites over Europe, the accuracy of the estimated daily AOD, and its seasonal variability, is similar to 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, El Chichón in 1982 and Pinatubo in 1992). Finally, the reconstructed AOD time series are in good agreement with the dimming/brightening phenomenon and also provide preliminary evidence of the early-brightening phenomenon. [ABSTRACT FROM AUTHOR]

A ground-based ultraviolet (GUV) multi-filter radiometer was set up at Marambio, 64 ∘ S, 56 ∘ W, Antarctica, in 2017. The instrument continuously measures ultraviolet (UV) radiation, visible (VIS) radiation and photosynthetically active radiation (PAR). The measurements are designed for providing high-quality long-term time series that can be used to assess the impact of global climate change in the Antarctic region. The quality assurance includes regular absolute calibrations and solar comparisons performed at Marambio and at Sodankylä, Finland. The measurements continue observations at Marambio that were performed with (Norwegian Institute for Air Re-search UV Radiometer (NILU-UV) radiometers between 2000 and 2010 as part of the Antarctic NILU-UV network. These measurements are ideally suited for assessing the effects of the ongoing stratospheric ozone recovery on the ecosystem, as the data products include information on radiation at various wavelengths ranging from UV to VIS so that changes on biologically effective radiation due to ozone can be separated from those due to other factors. Data products include total ozone, photosynthetically active radiation (PAR), visible (VIS) radiation at 555 nm, UV index, UV irradiance at 5 channels, UVB and UVA dose rate and daily dose, and biologically weighted UV dose rate and daily dose, calculated with 10 different action spectra. The data from the last 5 d and the daily maximum UV index time series are plotted and updated daily on the following web page: http://fmiarc.fmi.fi/sub%5fsites/GUVant/ (last access: 17 April 2020). The first 2 years of UV measurements were very different in terms of the results: for October, November and December the monthly average of daily maximum UVB dose rates were clearly higher in 2018 than in 2017. The largest difference was observed in October, when the average of daily maximum UVB dose rates was 76 and 102 µ W cm -2 in 2017 and 2018, respectively. Monthly averages of the 3 months were similar in 2018, while in 2017 the monthly average of October was lower than those of November and December. The VIS and PAR time series show that daily maxima in 2018–2019 exceed those in 2017–2018 during late spring and summer (mid-November–January). The studied dataset, including daily maximum irradiances at five UV channels and one VIS channel; daily maximum UVB, UVA, and PAR dose rates; noon UVB, UVA, and PAR dose rates; noon total column ozone; and UVB and UVA daily doses, is freely accessible at 10.5281/zenodo.3688700. [ABSTRACT FROM AUTHOR]

Climate impacts agriculture in various complex ways at different levels and scales depending on the local natural crop growth limitations. Our objective in this study, therefore, is to understand how different is the atmosphere–biosphere exchange of CO2 under contrasting subtropical and boreal agricultural (an oilseed crop and a bioenergy crop, respectively) climates. The oilseed crop in subtropical climate continued to uptake CO2 from the atmosphere throughout the year, with maximum uptake occurring in the monsoon season, and drastically reduced uptake during drought. The boreal ecosystem, on the other hand, was a sustained, small source of CO2 to the atmosphere during the snow-covered winter season. Higher rates of CO2 uptake were observed owing to greater day-length in the growing season in the boreal ecosystem. The optimal temperature for photosynthesis by the subtropical ecosystem was close to the regional normal mean temperature. An enhanced photosynthetic response to the incident radiation was found for the boreal ecosystem implying the bioenergy crop to be more efficient than the oilseed crop in utilizing the available light. This comparison of the CO2 exchange patterns will help strategising the carbon management under different climatic conditions. [ABSTRACT FROM AUTHOR]

A GUV multifilter radiometer was set up at Marambio, 64° S 56° W, Antarctica, in 2017. The instrument measures continuously ultraviolet (UV) radiation, visible (VIS) radiation and photosynthetically active radiation (PAR). The measurements are designed for providing high quality long-term time series which can be used to assess the impact of global climate change in the Antarctic region. The quality assurance includes regular absolute calibrations and solar comparisons performed at the site and at Sodankylä, Finland. The actual measurements continue the time series measured at Marambio with NILU-UV radiometers during 2000–2010 as part of the Antarctic NILU-UV network. They are optimal for assessing the effects of the ongoing stratospheric ozone recovery on the ecosystem as the data products include information on radiation at various wavelengths ranging from UV to VIS so that changes on biologically effective radiation due to ozone can be separated from those due to other factors. The final data products are total ozone, PAR, VIS radiation at 555 nm, UV index, UV irradiance at 5 channels, UVB and UVA dose rate/daily dose, and biologically weighted UV dose rate/daily dose, including 10 different action spectra. The data from the last five days and the daily maximum UV index time series are plotted and updated daily on the web page fmiarc.fmi.fi/sub_sites/GUVant/. The first two years of UV measurements were very different in terms of the results: The monthly average of daily maximum UVB dose rates were clearly higher in 2018 than in 2017 during the period from October to December. The largest difference was observed in October, when the average of daily maximum UVB dose rates was 7.6 kWm−2 and 10.2 kWm−2 in 2017 and 2018, respectively. The monthly averages were close to each other for all the three months in 2018, while in 2017 the monthly average of October was lower than those of November and December. VIS and PAR time series show that daily maxima in 2018–2019 exceed those in 2017–2018 during the late spring and the summer (mid-November–January). The studied dataset is freely accessible at https://doi.org/10.5281/zenodo.3553634 (Lakkala et al.,2019). [ABSTRACT FROM AUTHOR]

The aim of this study is to validate the Ozone Monitoring Instrument (OMI) erythemal dose rates using ground-based measurements in Thessaloniki, Greece. In the Laboratory of Atmospheric Physics of the Aristotle University of Thessaloniki, a Yankee Environmental System UVB-1 radiometer measures the erythemal dose rates every minute, and a Norsk Institutt for Luftforskning (NILU) multi-filter radiometer provides multi-filter based irradiances that were used to derive erythemal dose rates for the period 2005–2014. Both these datasets were independently validated against collocated UV irradiance spectra from a Brewer MkIII spectrophotometer. Cloud detection was performed based on measurements of the global horizontal radiation from a Kipp & Zonen pyranometer and from NILU measurements in the visible range. The satellite versus ground observation validation was performed taking into account the effect of temporal averaging, limitations related to OMI quality control criteria, cloud conditions, the solar zenith angle and atmospheric aerosol loading. Aerosol optical depth was also retrieved using a collocated CIMEL sunphotometer in order to assess its impact on the comparisons. The effect of total ozone columns satellite versus ground-based differences on the erythemal dose comparisons was also investigated. Since most of the public awareness alerts are based on UV Index (UVI) classifications, an analysis and assessment of OMI capability for retrieving UVIs was also performed. An overestimation of the OMI erythemal product by 3–6% and 4–8% with respect to ground measurements is observed when examining overpass and noontime estimates respectively. The comparisons revealed a relatively small solar zenith angle dependence, with the OMI data showing a slight dependence on aerosol load, especially at high aerosol optical depth values. A mean underestimation of 2% in OMI total ozone columns under cloud-free conditions was found to lead to an overestimation in OMI erythemal doses of 1–5%.While OMI overestimated the erythemal dose rates over the range of cloudiness conditions examined, its UVIs were found to be reliable for the purpose of characterizing the ambient UV radiation impact. [ABSTRACT FROM AUTHOR]

Wildfire activity in the western United States during August to October 2020 was exceptional in terms of the fire severity and area burned. Extremely dry biomass fuels from near historic multi-year drought conditions were further exacerbated with very hot and dry conditions in 2020. These conditions when coupled with strong offshore flow allowed many ignitions to grow into extremely large and severe wildfires. Long-term monitoring at a few AERONET sites in California showed that the number of days with high Aerosol Optical Depth at 440 nm (AOD 440 >1) in 2020 was greater than any other year going back to the beginning of the data records in 2002. A wide range of fine mode particle volume median radii were retrieved from AERONET data over the course of these fires suggesting significant variability in combustion conditions and aging processes. Additionally, the fine mode radii in some of these smoke plumes in 2020 were very large especially at high AOD (∼0.22–0.32 μm volume median radius), likely due to both coagulation and condensation occurring during aging at very high particulate concentrations. The largest fine mode particle radii combined with narrow distributions resulted in some very rare AOD spectra showing peak AOD at 500 nm and decreasing to lower AOD at both shorter and longer wavelengths. The most extreme retrieved size distributions and associated measured AOD spectra were principally observed in long-distance transported smoke plumes from these western United States fires at sites in Colorado, Maryland and Virginia, possibly due to further aging during transport. Additionally, strong absorption was sometimes observed at short wavelengths with much lower single scattering albedo at 440 nm compared to 675 nm in some plumes consistent with significant brown carbon (BrC) and/or coated black carbon (BC) absorption in biomass burning particles. This strong spectral absorption signature observed at some California sites and dates remained similarly strong in some smoke plumes observed at some east coast sites in Maryland and Virginia, thereby suggesting that the lifetime of these particular BrC and/or coated BC absorbing species was greater than 5 days. • AOD from forest fire smoke in the western US was exceptionally high in 2020. • Some smoke had very large volume radius (>0.30 micron) and narrow fine width. • These extreme size distributions resulted in maximum AOD at 500 nm in some cases. • Large SSA differences (675-440 nm) indicated BrC and/or coated BC absorption. • These smoke SSA differences remained large even after 4–5 days transport and aging. [ABSTRACT FROM AUTHOR]

We have used total and diffuse UV irradiance measurements from a multi-filter rotating shadow-band radiometer (UVMFR) in order to investigate aerosol absorption in the UV range for a 5-year period in Athens, Greece. This dataset was used as input to a radiative transfer model and the single scattering albedo (SSA) at 368 and 332 nm was calculated. Retrievals from a collocated CIMEL sun photometer were used to evaluate the products and study the absorption spectral behavior of retrieved SSA values. The UVMFR SSA, together with synchronous, CIMEL-derived retrievals of SSA at 440 nm, had a mean of 0.90, 0.87 and 0.83, with lowest values (higher absorption) encountered at the shorter wavelengths. In addition, noticeable diurnal variation of the SSA in all wavelengths is shown, with amplitudes up to 0.05. Strong SSA wavelength dependence is revealed for cases of low Ångström exponents, accompanied by a SSA decrease with decreasing extinction optical depth, suggesting varying influence under different aerosol composition. However, part of this dependence for low aerosol optical depths is masked by the enhanced SSA retrieval uncertainty. Dust and brown carbon UV absorbing properties were also investigated to explain seasonal patterns. [ABSTRACT FROM AUTHOR]

The main aim of this work is to evaluate the NASA EOS AURA Ozone Monitoring Instrument (OMI) UV irradiance estimates through ground-based measurements performed by a NILU-UV multichannel radiometer (NILU-UV) operating in Thessaloniki, Greece, for the time period between January 2005 and December 2014. NILU-UV multi-filter radiometers can provide measurements at 5 UV wavelength bands with full width at half maximum (FWHM) of 10 nm approximately and a time analysis of 1 min. An additional channel measuring the Photosynthetically Active Radiation (PAR) is also incorporated to the instrument and is used for the stringent characterization of the cloud free instances. The OMI instrument estimates solar UV irradiances at four wavelengths close to those of the NILU-UV in Thessaloniki. Clear and all-sky overpass-time, as well as solar local-noon time, UV estimates are provided by the NASA Aura Data Validation Center. Spectra measured from a collocated MKIII Brewer spectrophotometer with serial number 086 (Brewer #086) were utilized for the whole period (2005–2014) in order to estimate the NILU-UV irradiances at the OMI wavelength irradiances and therefore provide a direct comparison and validation to the NILU UV measurements provided by OMI. For the nominal comparisons, using un-flagged OMI data within a 50 km radius from Thessaloniki, the linear determination coefficient, R 2 , ranges between 0.91 and 0.97 for the 305 nm and between 0.75 and 0.92 for 380 nm depending on the choice of overpass or local-noon time data and the cloudiness flags. The best agreement is found for the clear-sky overpass-time comparisons as well as the both PAR- and satellite algorithm-deduced clear-sky overpass and local-noon comparisons for all wavelengths. The OMI irradiances were found to overestimate the NILU-UV observations in Thessaloniki between ∼4.5% and 13.5% for the 305 nm and between ∼1.5% and ∼10.0% for the 310 nm wavelength depending on the choice of time [overpass vs local noon] and cloudiness restrictions [only satellite-deduced or both PAR- and satellite-deduced]. For the 324 nm and 380 nm wavelengths, the satellite-deduced local-noon time clear skies comparisons show a satellite under-estimation of −3.75% and −4.15% respectively whereas the overpass-time comparisons range between −1.55% and −1.90% for the same wavelengths. When imposing the PAR-deduced clear-sky assessment as well, the comparisons show a satellite over-estimation of 2.50% and 2.75% for the local-noon and 3.70% and 4.10% for the overpass-time cases for the 324 nm and 380 nm wavelengths. The effect of a stricter radius of collocation selection (10 km and 25 km vs 50 km) appears to have a negligible impact on the comparisons however results in a smaller statistical sample. When flagging restrictions are applied to the quality indicators of the OMI data, the amount of co-locations decreases appreciatively but the correlations improve for most cases (R 2 > 0.9 in most cases and wavelengths). The effect of the temporal averaging window of the ground-based measurements, of around 10 and 60 min to the overpass time, was also investigated. The differences of the time averaging span result in a more obvious improvement of the agreement for the all-sky cases; the R 2 factor improves to 0.94, 0.90, 0.87 and 0.85 from the original 0.91, 0.87, 0.82 and 0.78 for the local-noon comparisons and to 0.96, 0.94, 0.92 and 0.91 from the original 0.93, 0.90, 0.80 and 0.83 for the overpass-time comparisons for the 305 nm, 310 nm, 324 nm and 380 nm wavelengths respectively. Hence, it is shown that this type of temporal averaging can implicitly compensate for the changes of cloud position and optical properties when comparing UV measurements from ground and space. For the detection of the seasonal characteristics of the satellite irradiances it was shown that the 380 nm wavelength can be safely studied. The standard deviation of the strict cloud free overpass-time comparisons of ±12% may form a dependable measure of the seasonality imposed by the satellite retrieval algorithm assumptions. Additionally, the aerosol load at 340 nm and the possible solar zenith angle (SZA) effect on the comparisons was considered. No marked aerosol dependence was found with the ratio of satellite-to-ground irradiances remaining 1.1 ± 1.3 for the all-sky comparisons and 1 ± 0.1 for the clear-sky comparisons, while the SZA effect becomes appreciable only for angles higher than 70° with ratios 1.1 ± 1.0 for all-skies and 1.0 ± 0.1 for the clear-sky cases below that threshold. [ABSTRACT FROM AUTHOR]

Abstract: Lidar measurements of the vertical distribution of the aerosol extinction and backscatter coefficient and the corresponding extinction to backscatter ratio (so-called lidar ratio) at 355nm have been performed at Thessaloniki, Greece using a Raman lidar system in the frame of the EARLINET for the period 2001–2005. Coincident spectral UV irradiance measurements, total ozone observations and aerosol optical depth estimates were available from a double Brewer spectroradiometer. The retrieval of single scattering albedo employed the Brewer global irradiance measurements and radiative transfer modeling. Vertically averaged values of the lidar ratio ranged from a minimum of 16sr to a maximum value of 90sr, while the effective single scattering albedo ranged from 0.78 to 1.00. The mean value of the lidar ratio for the dataset under study was 45.5±21.0sr while the average value of the single scattering albedo was 0.94±0.05. For the majority of our measurements (80%) the single scattering albedo found to be greater than 0.90. Using additional information from backward trajectory calculations and lidar-derived free tropospheric contribution of aerosols in the columnar aerosol optical depth, it is shown that the combined use of the directly measured lidar ratio, and the indirectly estimated single scattering albedo, leads to a better characterization of the aerosol type probed. [Copyright &y& Elsevier]

The Ozone Monitoring Instrument (OMI) onboard the NASA Earth Observing System (EOS) Aura spacecraft is a nadir-viewing spectrometer that measures solar reflected and backscattered light in a selected range of the ultraviolet and visible spectrum. The instrument has a 2600-km-wide viewing swath, and it is capable of daily, global contiguous mapping. We developed and implemented a surface ultraviolet (UV) irradiance algorithm for OMI that produces noontime surface spectral UV irradiance estimates at four wavelengths (305, 310, 324, and 380 nm). Additionally, noontime erythemal dose rate and the erythemal daily dose are estimated. The OMI surface UV algorithm inherits from the surface UV algorithm developed by NASA Goddard Space Flight Center for the Total Ozone Mapping Spectrometer (TOMS). The OMI surface UV irradiance products are produced and archived in HDF5-EOS format by Finnish Meteorological Institute. The accuracy of the surface UV estimates depend on UV wavelength and atmospheric and other geolocation specific conditions ranging from 7%to 30%. A postprocessing aerosol correction can be applied at sites with additional ground-based measurements of the aerosol absorption optical thickness. The current OMI surface UV product validation plan is presented. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the coming years, Earth Observation missions like the FLuorescence EXplorer (FLEX) will acquire the radiance signal from the visible to the near-infrared at a very high spectral resolution, enabling exciting prospects for new insights in satellite-based photosynthetic studies. In this context, the process of de-coupling atmospheric and vegetation-related spectral signatures will become essential to guarantee a reliable estimation of the vegetation photosynthetic activity from space. Dynamic changes related to the vegetation photosynthetic status result in subtle contributions to the top of atmosphere radiance signal, e.g. due to the emission of the solar-induced chlorophyll fluorescence (~ 650–800 nm) or due to changes in surface reflectance spectra (500–600 nm) indicating variations in the vegetation photoprotection and light use efficiency. Conversely, atmospheric effects (molecular and aerosol absorption and scattering) dominate the spectral interval of interest for vegetation studies. This article presents a comprehensive overview of the atmospheric radiative effects caused by aerosols, ozone (O 3), water vapor (H 2 O), oxygen (O 2), and atmospheric pressure and temperature changes within the visible and near-infrared spectral interval, and paying special attention to the co-occurring vegetation-related spectral changes associated with the fluorescence emission and the activation of the photoprotection mechanisms. Since the largest uncertainties in the atmospheric correction process are associated with the characterization of the aerosol radiative effects, this work largely concentrates on the satellite retrieval-related implications under different aerosol absorbing and scattering scenarios on a global scale. Through a simulation exercise, it is evaluated to what extent aerosol climatology could influence the accuracy of satellite-derived surface apparent reflectance spectra impacting; therefore, any vegetation-related satellite product on a seasonal and global scale. • Impact of aerosol characterization inaccuracies on the PRI region • Assessment of atmospheric effects on the fluorescence emission range • Aerosol error propagation assessment on vegetation dynamics using climatology data • O2-A and -B features: evaluation of aerosol, temperature and pressure conditions [ABSTRACT FROM AUTHOR]

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'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. [ABSTRACT FROM AUTHOR]

AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) is a three-year project supported by the European Union in the frame of its H2020 Call (EO-2-2015) for "Stimulating wider research use of Copernicus Sentinel Data". The project addresses key scientific issues relevant for synergistic exploitation of data acquired in different spectral ranges by different instruments on board the atmospheric Sentinels. A novel approach, based on the assimilation of geosynchronous equatorial orbit (GEO) and low Earth orbit (LEO) fused products by application of an innovative algorithm to Sentinel-4 (S-4) and Sentinel-5 (S-5) synthetic data, is adopted to assess the quality of the unique ozone vertical profile obtained in a context simulating the operational environment. The first priority is then attributed to the lower atmosphere with calculation of tropospheric columns and ultraviolet (UV) surface radiation from the resulting ozone vertical distribution. Here we provide details on the surface UV algorithm of AURORA. Both UV index (UVI) and UV-A irradiance are provided from synthetic satellite measurements, which in turn are based on atmospheric scenarios from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) re-analysis. The UV algorithm is implemented in a software tool integrated in the technological infrastructure developed in the context of AURORA for the management of the synthetic data and for supporting the data processing. This was closely linked to the application-oriented activities of the project, aimed to improve the performance and functionality of a downstream application for personal UV dosimetry based on satellite data. The use of synthetic measurements from MERRA-2 gives us also a "ground truth", against which to evaluate the performance of our UV model with varying inputs. In this study we both describe the UV algorithm itself and assess the influence that changes in ozone profiles, due to the fusion and assimilation, can cause in surface UV levels. [ABSTRACT FROM AUTHOR]

A clear–sky method to estimate the photosynthetically active radiation (PAR) at the surface level in cloudless atmospheres is presented and validated. It uses a fast and accurate approximation adopted in several radiative transfer models, known as the k-distribution method and the correlated-k approximation, which gives a set of fluxes accumulated over 32 established wavelength intervals. A resampling technique, followed by a summation, are applied over the wavelength range [0.4, 0.7] µm in order to retrieve the PAR fluxes. The method uses as inputs the total column contents of ozone and water vapor, and optical properties of aerosols provided by the Copernicus Atmosphere Monitoring Service. To validate the method, its outcomes were compared to instantaneous global photosynthetic photon flux density (PPFD) measurements acquired at seven experimental sites of the Surface Radiation Budget Network (SURFRAD) located in various climates in the USA. The bias lies in the interval [−12, 61] µmol m−2 s−1 ([−1, 5] % in values relative to the means of the measurements at each station). The root mean square error ranges between 37 µmol m−2 s−1 (3%) and 82 µmol m−2 s−1 (6%). The squared correlation coefficient fluctuates from 0.97 to 0.99. This comparison demonstrates the high level of accuracy of the presented method, which offers an accurate estimate of PAR fluxes in cloudless atmospheres at high spatial and temporal resolutions useful for several bio geophysical models. [ABSTRACT FROM AUTHOR]

One major source of uncertainty in the cloud-mediated aerosol forcing arises from the magnitude of the cloud liquid water path (LWP) adjustment to aerosol-cloud interactions, which is poorly constrained by observations. Many of the recent satellite-based studies have observed a decreasing LWP as a function of cloud droplet number concentration (CDNC) as the dominating behavior. Estimating the LWP response to the CDNC changes is a complex task since various confounding factors need to be isolated. However, an important aspect has not been sufficiently considered: the propagation of natural spatial variability and errors in satellite retrievals of cloud optical depth and cloud effective radius to estimates of CDNC and LWP. Here we use satellite and simulated measurements to demonstrate that, because of this propagation, even a positive LWP adjustment is likely to be misinterpreted as negative. This biasing effect therefore leads to an underestimate of the aerosol-cloud-climate cooling and must be properly considered in future studies., (© 2022. The Author(s).)

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.

Aerosol particles cool the climate by scattering solar radiation and by acting as cloud condensation nuclei. Higher temperatures resulting from increased greenhouse gas levels have been suggested to lead to increased biogenic secondary organic aerosol and cloud condensation nuclei concentrations creating a negative climate feedback mechanism. Here, we present direct observations on this feedback mechanism utilizing collocated long term aerosol chemical composition measurements and remote sensing observations on aerosol and cloud properties. Summer time organic aerosol loadings showed a clear increase with temperature, with simultaneous increase in cloud condensation nuclei concentration in a boreal forest environment. Remote sensing observations revealed a change in cloud properties with an increase in cloud reflectivity in concert with increasing organic aerosol loadings in the area. The results provide direct observational evidence on the significance of this negative climate feedback mechanism., (© 2021. The Author(s).)