Search

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. [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]

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. [ABSTRACT FROM AUTHOR]

Long term statistics of atmospheric aerosol and especially cloud scavenging were studied at the Puijo measurement station in Kuopio, Finland, during October 2010-November 2014. Aerosol size distributions, scattering coefficients at three different wavelengths (450 nm, 550 nm, and 700 nm), and absorption coefficient at wavelength 637 nm were measured with a special inlet system to sample interstitial and total aerosol in clouds. On average, accumulation mode particle concentration was found to be temperature dependent with lowest average concentrations of 200 cm-3 around 0 °C increasing to more than 800 cm-3 for temperatures higher than 20 °C. From the in-cloud measurements, both scattering and absorbing material scavenging efficiencies were observed to have slightly increasing temperature dependence. At 0 °C the efficiencies of scattering and absorbing matter were 0.85 and 0.55 with slopes of 0.005 °C-1 and 0.003 °C-1, respectively. Additionally, scavenging efficiencies were studied as a function of the diameter at which half of the particles are activated into cloud droplets. This analysis indicated that the is a higher fraction of absorbing material, typically black carbon, in smaller sizes so that at least 20-30 % of interstitial particles within clouds consist of absorbing material. In addition, the PM1-inlet revealed that approximately 20 % of absorbing material was observed to reside in particles with ambient diameter larger than ~ 1 µm at relative humidity below 90 %. Similarly, 40 % of scattering material was seen to be in particles larger than 1 µm. Altogether, this dataset provides information on size dependent aerosol composition that can be applied in evaluating how well large-scale aerosol models reproduce aerosol composition, especially with respect to scavenging in stratus clouds. [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 x 3.5 km2 (5.6 x 3.5 km2 since 6 August 2019) at nadir. The Finnish Meteorological Institute (FMI) is responsible for the development and 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 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 % from 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 clear sky TROPOMI data were within ±20 % from ground-based measurements. Generally median relative differences between TROPOMI data and ground-based measurements were a little biased towards negative values, but at high latitudes where non-homogeneous topography and albedo/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 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. [ABSTRACT FROM AUTHOR]

The new platforms for Earth observation from space are characterized by measurements made with great spatial and temporal resolution. While this abundance of information makes it possible to detect and study localized phenomena, on the other hand it may be difficult to manage this large amount of data in 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. A simple procedure to overcome this problem is to extract a subset of the original measurements. However, this procedure involves a loss of information and is therefore justifiable only as a temporary solution. A more refined solution is to resort to the so-called fusion algorithms, capable of compressing the size of the dataset limiting the information loss. A novel data fusion method, the Complete Data Fusion, was recently developed to merge a-posteriori a set of retrieved products in a single one. In the present paper, the Complete Data Fusion method is applied to ozone profile measurements simulated in the thermal infrared and ultraviolet bands, in a realistic scenario, according to the specifications of the Sentinel 4 and 5 missions of the Copernicus programme. Then the fused products are compared with the input profiles; comparisons show that the output products of data fusion have in general smaller errors and higher information contents. The most significant improvement is an increased vertical resolution together with a reduction of the errors. The comparisons of the fused with the fusing products are presented both at single fusion grid-box scale and with a statistical analysis. The grid box size impact was also evaluated, showing that the Complete Data Fusion method can be used with a wide range of grid-box size, the quality of the products improving with larger grid boxes. [ABSTRACT FROM AUTHOR]

In March 2017, ultraviolet (UV) radiation measurements with a multichannel GUV-2511 radiometer were started in Marambio, Antarctica (64.23° S; 56.62° W), by the Finnish Meteorological Institute (FMI) in collaboration with the Argentinian National Meteorological Service (SMN). These measurements were analysed and the results were compared to previous measurements at the same site with NILU-UV radiometer during 2000-2008 and to data from five stations across Antarctica. Measurements in Marambio showed lower UV radiation levels in 2017/2018 compared to those measured during 2000-2008. Also at several other stations in Antarctica the radiation levels were below average in that period. The maximum UV index (UVI) in Marambio was only 6.2, while, during the time period 2000-2008, the maximum was 12. In 2018/2019, the radiation levels were higher than in the previous year and the maximum UVI recorded in Marambio was 9.5. In Marambio, the largest variation of the UV radiation are during the spring and early summer when the stratospheric ozone concentration is at a minimum (the so-called ozone hole). Beside cloud cover, the strength of the polar vortex and the stratospheric ozone depletion are the primary factors that influence the surface UV radiation levels in Antarctica. As the recovery of the ozone layer is slow, the continuation of the measurements is crucial in order to be able to detect long-term changes in UV levels in Antarctica. [ABSTRACT FROM AUTHOR]

The evaluation of modeling diagnostics with appropriate observations is an important task that establishes the capabilities and reliability of models. In this study we compare aerosol and cloud properties obtained from three different climate models ECHAM-HAM, ECHAM-HAM-SALSA, and NorESM with satellite observations using MOderate Resolution Imaging Spectrometer (MODIS) data. The simulator MODIS-COSP version 1.4 was implemented into the climate models to obtain MODIS-like cloud diagnostics, thus enabling model to model and model to satellite comparisons. Cloud droplet number concentrations (CDNC) are derived identically from MODIS-COSP simulated and MODIS-retrieved values of cloud optical depth and effective radius. For CDNC, the models capture the observed spatial distribution of higher values typically found near the coasts, downwind of the major continents, and lower values over the remote ocean and land areas. However, the COSP-simulated CDNC values are higher than those observed, whilst the direct model CDNC output is significantly lower than the MODIS-COSP diagnostics. NorESM produces large spatial biases for ice cloud properties and thick clouds over land. Despite having identical cloud modules, ECHAM-HAM and ECHAM-HAM-SALSA diverge in their representation of spatial and vertical distribution of clouds. From the spatial distributions of aerosol optical depth (AOD) and aerosol index (AI), we find that NorESM shows large biases for AOD over bright land surfaces, while discrepancies between ECHAM-HAM and ECHAM-HAM-SALSA can be observed mainly over oceans. Overall, the AIs from the different models are in good agreement globally, with higher negative biases on the Northern Hemisphere. We computed the aerosol-cloud interactions as the sensitivity of dln(CDNC)/dln(AI) on a global scale. However, one year of data may be considered not enough to assess the similarity or dissimilarities of the models due to large temporal variability in cloud properties. This study shows how simulators facilitate the evaluation of cloud properties and expose model deficiencies which are necessary steps to further improve the parametrization in climate models. [ABSTRACT FROM AUTHOR]