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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 https://doi.org/10.5281/zenodo.4811488 (Lamy and Portafaix, 2021a).

Fitting a line to two measured variables is considered one of the simplest statistical procedures researchers can carry out. However, this simplicity is deceptive as the line-fitting procedure is actually quite a complex problem. Atmospheric measurement data never come without some measurement error. Too often, these errors are neglected when researchers make inferences from their data. To demonstrate the problem, we simulated datasets with different numbers of data points and different amounts of error, mimicking the dependence of the atmospheric new particle formation rate (J1.7) on the sulfuric acid concentration (H2SO4). Both variables have substantial measurement error and, thus, are good test variables for our study. We show that ordinary least squares (OLS) regression results in strongly biased slope values compared with six error-in-variables (EIV) regression methods (Deming regression, principal component analysis, orthogonal regression, Bayesian EIV and two different bivariate regression methods) that are known to take errors in the variables into account.

We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15 %) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

A new method has been developed to estimate the global and direct solar irradiance in the UV-A and UV-B at ground level in cloud-free conditions. It is based on a resampling technique applied to the results of the k-distribution method and the correlated-k approximation of Kato et al. (1999) over the UV band. Its inputs are the aerosol properties and total column ozone that are produced by the Copernicus Atmosphere Monitoring Service (CAMS). The estimates from this new method have been compared to instantaneous measurements of global UV irradiances made in cloud-free conditions at five stations at high latitudes in various climates. For the UV-A irradiance, the bias ranges between −0.8 W m−2 (−3 % of the mean of all data) and −0.2 W m−2 (−1 %). The root mean square error (RMSE) ranges from 1.1 W m−2 (6 %) to 1.9 W m−2 (9 %). The coefficient of determination R2 is greater than 0.98. The bias for UV-B is between −0.04 W m−2 (−4 %) and 0.08 W m−2 (+13 %) and the RMSE is 0.1 W m−2 (between 12 and 18 %). R2 ranges between 0.97 and 0.99. This work demonstrates the quality of the proposed method combined with the CAMS products. Improvements, especially in the modeling of the reflectivity of the Earth's surface in the UV region, are necessary prior to its inclusion into an operational tool.

AERONET (AErosol RObotic NETwork), which is a network of ground-based sun photometers, produces a data product called the aerosol spectral deconvolution algorithm (SDA) that utilizes spectral total aerosol optical depth (AOD) data to infer the component fine- and coarse-mode optical depths at 500 nm. Based on its assumptions, SDA identifies cloud optical depth as the coarse-mode AOD component and therefore effectively computes the fine-mode AOD also in mixed cloud–aerosol observations. Therefore, it can be argued that the more representative AOD for fine-mode fraction should be based on all direct sun measurements and not only on those cloud screened for clear-sky conditions, i.e., on those from level 1 (L1) instead of level 2 (L2) in AERONET. The objective of our study was to assess, including all the available AERONET sites, how the fine-mode AOD is enhanced in cloudy conditions, contrasting SDA L1 and L2 in our analysis. Assuming that the cloud screening correctly separates the cloudy and clear-sky conditions, then the increases in fine-mode AOD can be due to various cloud-related processes, mainly by the strong hygroscopic growth of particles in the vicinity of clouds and in-cloud processing leading to growth of accumulation mode particles. We estimated these cloud-related enhancements in fine-mode AOD seasonally and found, for instance, that in June–August season the average over all the AERONET sites was 0.011, when total fine-mode AOD from L2 data was 0.154; therefore, the relative enhancement was 7 %. The enhancements were largest, both absolutely and relatively, in East Asia; for example, in June–August season the absolute and relative differences in fine-mode AOD, between L1 and L2 measurements, were 0.022 and 10 %, respectively. Corresponding values in North America and Europe were about 0.01 and 6–7 %. In some highly polluted areas, the enhancement is greater than these regional averages, e.g., in Beijing region and in June–July–August (JJA) season the corresponding absolute values were about 0.1. It is difficult to separate the fine-mode AOD enhancements due to in-cloud processing and hygroscopic growth, but we attempted to get some understanding by conducting a similar analysis for SDA-based fine-mode Ångström exponent (AE) patterns. Moreover, we exploited a cloud parcel model, in order to understand in detail the relative role of different processes. We found that in marine conditions, were aerosol concentration are low and cloud scavenging is efficient, the AE changes in opposite direction than in the more polluted conditions, were hygroscopic growth of particles leads to a negative AE change.

Spectral solar UV radiation measurements are performed in France using three spectroradiometers located at very different sites. One is installed in Villeneuve d'Ascq, in the north of France (VDA). It is an urban site in a topographically flat region. Another instrument is installed in Observatoire de Haute-Provence, located in the southern French Alps (OHP). It is a rural mountainous site. The third instrument is installed in Saint-Denis, Réunion Island (SDR). It is a coastal urban site on a small mountainous island in the southern tropics. The three instruments are affiliated with the Network for the Detection of Atmospheric Composition Change (NDACC) and carry out routine measurements to monitor the spectral solar UV radiation and enable derivation of UV index (UVI). The ground-based UVI values observed at solar noon are compared to similar quantities derived from the Ozone Monitoring Instrument (OMI, onboard the Aura satellite) and the second Global Ozone Monitoring Experiment (GOME-2, onboard the Metop-A satellite) measurements for validation of these satellite-based products. The present study concerns the period 2009–September 2012, date of the implementation of a new OMI processing tool. The new version (v1.3) introduces a correction for absorbing aerosols that were not considered in the old version (v1.2). Both versions of the OMI UVI products were available before September 2012 and are used to assess the improvement of the new processing tool. On average, estimates from satellite instruments always overestimate surface UVI at solar noon. Under cloudless conditions, the satellite-derived estimates of UVI compare satisfactorily with ground-based data: the median relative bias is less than 8 % at VDA and 4 % at SDR for both OMI v1.3 and GOME-2, and about 6 % for OMI v1.3 and 2 % for GOME-2 at OHP. The correlation between satellite-based and ground-based data is better at VDA and OHP (about 0.99) than at SDR (0.96) for both space-borne instruments. For all sky conditions, the median relative biases are much larger, with large dispersion for both instruments at all sites (VDA: about 12 %; OHP: 9 %; SDR: 11 %). Correlation between satellite-based and ground-based data is still better at VDA and OHP (about 0.95) than at SDR (about 0.73) for both satellite instruments. These results are explained considering the time of overpass of the two satellites, which is far from solar noon, preventing a good estimation of the cloud cover necessary for a good modelling of the UVI. Site topography and environment are shown to have a non-significant influence. At VDA and OHP, OMI v1.3 shows a significant improvement with respect to v1.2, which did not account for absorbing aerosols.

The importance of light-absorbing organic aerosols, often called brown carbon (BrC), has become evident in recent years. However, there have been relatively few measurement-based estimates for the direct radiative effect of BrC so far. In earlier studies, the AErosol RObotic NETwork (AERONET)-measured aerosol absorption optical depth (AAOD) and absorption Angstrom exponent (AAE) were exploited. However, these two pieces of information are clearly not sufficient to separate properly carbonaceous aerosols from dust, while imaginary indices of refraction would contain more and better justified information for this purpose. This is first time that the direct radiative effect (DRE) of BrC is estimated by exploiting the AERONET-retrieved imaginary indices. We estimated it for four sites in the Indo-Gangetic Plain (IGP), Karachi, Lahore, Kanpur and Gandhi College. We found a distinct seasonality, which was generally similar among all the sites, but with slightly different strengths. The monthly warming effect up to 0.5 W m−2 takes place during the spring season. On the other hand, BrC results in an overall cooling effect in the winter season, which can reach levels close to −1 W m−2. We then estimated similarly also the DRE of black carbon and total aerosol, in order to assess the relative significance of the BrC radiative effect in the radiative effects of other components. Even though BrC impact seems minor in this context, we demonstrated that it is not insignificant. Moreover, we demonstrated that it is crucial to perform spectrally resolved radiative transfer calculations to obtain good estimates for the DRE of BrC.

The k-distribution method and the correlated-k approximation of Kato et al. (1999) is a computationally efficient approach originally designed for calculations of the broadband solar radiation at ground level by dividing the solar spectrum in 32 specific spectral bands from 240 to 4606 nm. Compared to a spectrally resolved computation, its performance in the UV band appears to be inaccurate, especially in the spectral intervals #3 [283, 307] nm and #4 [307, 328] nm because of inaccuracy in modeling the transmissivity due to ozone absorption. Numerical simulations presented in this paper indicate that a single effective ozone cross section is insufficient to accurately represent the transmissivity over each spectral interval. A novel parameterization of the transmissivity using more quadrature points yields maximum errors of respectively 0.0006 and 0.0143 for intervals #3 and #4. How to practically implement this new parameterization in a radiative transfer model is discussed for the case of libRadtran (library for radiative transfer). The new parameterization considerably improves the accuracy of the retrieval of irradiances in UV bands.

This paper presents validation results of the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System MACC (Monitoring Atmospheric Composition and Climate) re-analysis aerosol optical depth (AOD) for the period 2003–2006. We evaluate the MACC AOD at a UV wavelength (340 nm) and at mid-visible (500 and 550 nm) by comparing against ground-based AERONET measurements at 12 sites. The AERONET sites cover various parts of the globe and are categorized in three groups: urban/anthropogenic, biomass burning and dust, depending on the typically dominating aerosol type. This is the first time a global model such as the ECMWF has been evaluated for the performance of AOD at a UV wavelength. The results show that the MACC system generally provides a good representation of the AOD on a monthly basis, showing a realistic seasonal cycle. The model is mostly able to capture major dust load events and also the peak months of biomass burning correctly. For Kanpur and Solar Village, however, the model overestimates the AOD during the monsoon period when the aerosol load is generally low. When comparing hourly AOD values, the model–measurement agreement is better for biomass burning and dust sites than for urban sites, with an average correlation coefficient around 0.90 for biomass burning sites, around 0.77 for dust sites, and below 0.70 for urban sites. The AOD at 500 nm averaged over all sites shows only a small systematic difference between modeled and measured values, with a relative mean bias of 0.02. However, for the AOD at 340 nm the relative mean bias is −0.2. All sites included in the study show a relative mean bias at 340 nm smaller (or more negative) than that at 500 nm, indicating a strong wavelength dependence in the performance of the AOD in the MACC system. A comparison against fine and coarse mode AOD of the AERONET indicates that this has to do with the size distribution of the model: generally, the ECMWF model overestimates the contribution by coarse mode particles.