31 research outputs found
EARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product
We present the evaluation activity of the European Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of the Level 2 aerosol backscatter coefficient product derived by the Cloud-Aerosol Transport System (CATS) aboard the International Space Station (ISS; Rodier et al., 2015). The study employs correlative CATS and EARLINET backscatter measurements within a 50¿km distance between the ground station and the ISS overpass and as close in time as possible, typically with the starting time or stopping time of the EARLINET performed measurement time window within 90¿min of the ISS overpass, for the period from February 2015 to September 2016. The results demonstrate the good agreement of the CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig, Germany; Évora, Portugal; and Dushanbe, Tajikistan, are analyzed here to demonstrate the performance of the CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions, CATS is in good agreement with EARLINET, independent of daytime and nighttime conditions. CATS low negative biases are observed, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds; these are biases which may lead to systematic deviations and slight underestimations of the total aerosol optical depth (AOD) in climate studies. In addition, CATS misclassification of aerosol layers as clouds, and vice versa, in cases of coexistent and/or adjacent aerosol and cloud features, occasionally leads to non-representative, unrealistic, and cloud-contaminated aerosol profiles. Regarding solar illumination conditions, low negative biases in CATS backscatter coefficient profiles, of the order of 6.1¿%, indicate the good nighttime performance of CATS. During daytime, a reduced signal-to-noise ratio by solar background illumination prevents retrievals of weakly scattering atmospheric layers that would otherwise be detectable during nighttime, leading to higher negative biases, of the order of 22.3¿%.Peer ReviewedPostprint (published version
Earlinet validation of CATS L2 product
The Cloud-Aerosol Transport System (CATS) onboard the International Space Station (ISS), is a lidar system providing vertically resolved aerosol and cloud profiles since February 2015. In this study, the CATS aerosol product is validated against the aerosol profiles provided by the European Aerosol Research Lidar Network (EARLINET). This validation activity is based on collocated CATS-EARLINET measurements and the comparison of the particle backscatter coefficient at 1064nm.Peer ReviewedPostprint (published version
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Dust impact on surface solar irradiance assessed with model simulations, satellite observations and ground-based measurements
This study assesses the impact of dust on surface solar radiation focussing on an extreme dust event. For this purpose, we exploited the synergy of AERONET measurements and passive and active satellite remote sensing (MODIS and CALIPSO) observations, in conjunction with radiative transfer model (RTM) and chemical transport model (CTM) simulations and the 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The area of interest is the eastern Mediterranean where anomalously high aerosol loads were recorded between 30 January and 3 February 2015. The intensity of the event was extremely high, with aerosol optical depth (AOD) reaching 3.5, and optical/microphysical properties suggesting aged dust. RTM and CTM simulations were able to quantify the extent of dust impact on surface irradiances and reveal substantial reduction in solar energy exploitation capacity of PV and CSP installations under this high aerosol load. We found that such an extreme dust event can result in Global Horizontal Irradiance (GHI) attenuation by as much as 40–50 % and a much stronger Direct Normal Irradiance (DNI) decrease (80–90 %), while spectrally this attenuation is distributed to 37 % in the UV region, 33 % in the visible and around 30 % in the infrared. CAMS forecasts provided a reliable available energy assessment (accuracy within 10 % of that obtained from MODIS). Spatially, the dust plume resulted in a zonally averaged reduction of GHI and DNI of the order of 150 W/m^2 in southern Greece, and a mean increase of 20 W/m^2 in the northern Greece as a result of lower AOD values combined with local atmospheric processes. This analysis of a real-world scenario contributes to the understanding and quantification of the impact range of high aerosol loads on solar energy and the potential for forecasting power generation failures at sunshine-privileged locations where solar power plants exist, are under construction or are being planned
Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations – Part 1: ATSR (1995–2011) and MODIS C6.1 (2000–2017)
Aerosol optical
depth (AOD) patterns and interannual and seasonal variations over China are
discussed based on the AOD retrieved from the Along-Track Scanning Radiometer
(ATSR-2, 1995–2002), the Advanced ATSR (AATSR, 2002–2012) (together ATSR) and
the MODerate resolution Imaging Spectroradiometer (MODIS) aboard the Terra
satellite (2000–2017). The AOD products used were the ATSR Dual View (ADV)
v2.31 AOD and the MODIS/Terra Collection 6.1 (C6.1) merged dark target (DT) and
deep blue (DB) AOD product. Together these datasets provide an AOD time
series for 23Â years, from 1995 to 2017. The difference between the AOD values
retrieved from ATSR-2 and AATSR is small, as shown by pixel-by-pixel and
monthly aggregate comparisons as well as validation results. This allows for
the combination of the ATSR-2 and AATSR AOD time series into one dataset without offset
correction.ADV and MODIS AOD validation results show similar high correlations with the
Aerosol Robotic Network (AERONET) AOD (0.88 and 0.92, respectively), while
the corresponding bias is positive for MODIS (0.06) and negative for ADV
(−0.07). Validation of the AOD products in similar conditions, when ATSR and
MODIS/Terra overpasses are within 90 min of each other and when both
ADV and MODIS retrieve AOD around AERONET locations, show that ADV performs
better than MODIS in autumn, while MODIS performs slightly better in spring
and summer. In winter, both ADV and MODIS underestimate the AERONET AOD.Similar AOD patterns are observed by ADV and MODIS in annual and seasonal
aggregates as well as in time series. ADV–MODIS difference maps show that
MODIS AOD is generally higher than that from ADV. Both ADV and MODIS show
similar seasonal AOD behavior. The AOD maxima shift from spring in the south to summer along the eastern coast further north.The agreement between sensors regarding year-to-year AOD changes is quite good.
During the period from 1995 to 2006 AOD increased in the southeast (SE) of China. Between
2006 and 2011 AOD did not change much, showing minor minima in 2008–2009.
From 2011 onward AOD decreased in the SE of China. Similar patterns
exist in year-to-year ADV and MODIS annual AOD tendencies in the overlapping
period. However, regional differences between the ATSR and MODIS AODs are
quite large. The consistency between ATSR and MODIS with regards to the AOD
tendencies in the overlapping period is rather strong in summer, autumn and
overall for the yearly average; however, in winter and spring, when there is a
difference in coverage between the two instruments, the agreement between
ATSR and MODIS is lower.AOD tendencies in China during the 1995–2017 period will be discussed in more
detail in Part 2 (a following paper: Sogacheva et al., 2018), where a method to combine AOD time series from ADV and
MODIS is introduced, and combined AOD time series are analyzed.</p
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LIVAS: A 3-D multi-wavelength aerosol/cloud database based on CALIPSO and EARLINET
We present LIVAS (LIdar climatology of Vertical Aerosol Structure for space-based lidar simulation studies), a 3-D multi-wavelength global aerosol and cloud optical database, optimized to be used for future space-based lidar end-to-end simulations of realistic atmospheric scenarios as well as retrieval algorithm testing activities. The LIVAS database provides averaged profiles of aerosol optical properties for the potential spaceborne laser operating wavelengths of 355, 532, 1064, 1570 and 2050 nm and of cloud optical properties at the wavelength of 532 nm. The global database is based on CALIPSO observations at 532 and 1064 nm and on aerosol-type-dependent backscatter- and extinction-related Ångström exponents, derived from EARLINET (European Aerosol Research Lidar Network) ground-based measurements for the UV and scattering calculations for the IR wavelengths, using a combination of input data from AERONET, suitable aerosol models and recent literature. The required spectral conversions are calculated for each of the CALIPSO aerosol types and are applied to CALIPSO backscatter and extinction data corresponding to the aerosol type retrieved by the CALIPSO aerosol classification scheme. A cloud optical database based on CALIPSO measurements at 532 nm is also provided, neglecting wavelength conversion due to approximately neutral scattering behavior of clouds along the spectral range of LIVAS. Averages of particle linear depolarization ratio profiles at 532 nm are provided as well. Finally, vertical distributions for a set of selected scenes of specific atmospheric phenomena (e.g., dust outbreaks, volcanic eruptions, wild fires, polar stratospheric clouds) are analyzed and spectrally converted so as to be used as case studies for spaceborne lidar performance assessments. The final global data set includes 4-year (1 January 2008–31 December 2011) time-averaged CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) data on a uniform grid of 1° × 1° with the original high vertical resolution of CALIPSO in order to ensure realistic simulations of the atmospheric variability in lidar end-to-end simulations
The impact of using assimilated Aeolus wind data on regional WRF-Chem dust simulations
Land–atmosphere interactions govern the process of dust emission and transport. An accurate depiction of these physical processes within numerical weather prediction models allows for better estimating the spatial and temporal distribution of the dust burden and the characterisation of source and recipient areas. In the presented study, the ECMWF-IFS (European Centre for Medium-Range Weather Forecast – Integrated Forecasting System) outputs, produced with and without the assimilation of Aeolus quality-assured Rayleigh–clear and Mie–cloudy horizontal line-of-sight wind profiles, are used as initial or boundary conditions in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to simulate 2-month periods in the spring and autumn of 2020, focusing on a case study in October. The experiments have been performed over the broader eastern Mediterranean and Middle East (EMME) region, which is frequently subjected to dust transport, as it encompasses some of the most active erodible dust sources. Aerosol- and dust-related model outputs (extinction coefficient, optical depth and concentrations) are qualitatively and quantitatively evaluated against ground- and satellite-based observations. Ground-based columnar and vertically resolved aerosol optical properties are acquired through AERONET sun photometers and PollyXT lidar, while near-surface concentrations are taken from EMEP. Satellite-derived vertical dust and columnar aerosol optical properties are acquired through LIVAS (LIdar climatology of Vertical Aerosol Structure) and MIDAS (ModIs Dust AeroSol), respectively.
Overall, in cases of either high or low aerosol loadings, the model predictive skill is improved when WRF-Chem simulations are initialised with the meteorological fields of Aeolus wind profiles assimilated by the IFS. The improvement varies in space and time, with the most significant impact observed during the autumn months in the study region. Comparison with observation datasets saw a remarkable improvement in columnar aerosol optical depths, vertically resolved dust mass concentrations and near-surface particulate concentrations in the assimilated run against the control run. Reductions in model biases, either positive or negative, and an increase in the correlation between simulated and observed values was achieved for October 2020.</p
Fully Dynamic High–Resolution Model for Dispersion of Icelandic Airborne Mineral Dust
Icelandic topsoil sediments, as confirmed by numerous scientific studies, represent the largest and the most important European source of mineral dust. Strong winds, connected with the intensive cyclonic circulation in the North Atlantic, induce intense emissions of mineral dust from local sources all year and carry away these fine aerosol particles for thousands of kilometers. Various impacts of airborne mineral dust particles on local air quality, human health, transportation, climate and marine ecosystems motivated us to design a fully dynamic coupled atmosphere–dust numerical modelling system in order to simulate, predict and quantify the Icelandic mineral dust process including: local measurements and source specification over Iceland. In this study, we used the Dust Regional Atmospheric Model (DREAM) with improved Icelandic high resolution dust source specification and implemented spatially variable particle size distribution, variable snow cover and soil wetness. Three case studies of intense short- and long-range transport were selected to evaluate the model performance. Results demonstrated the model’s capability to forecast major transport features, such as timing, and horizontal and vertical distribution of the processes. This modelling system can be used as an operational forecasting system, but also as a reliable tool for assessing climate and environmental Icelandic dust impacts. © 2022 by the authors
Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns
The retrieval of aerosol properties from satellite observations
provides their spatial distribution over a wide area in cloud-free
conditions. As such, they complement ground-based measurements by providing
information over sparsely instrumented areas, albeit that significant
differences may exist in both the type of information obtained and the
temporal information from satellite and ground-based observations. In this
paper, information from different types of satellite-based instruments is
used to provide a 3-D climatology of aerosol properties over mainland China,
i.e., vertical profiles of extinction coefficients from the Cloud-Aerosol
Lidar with Orthogonal Polarization (CALIOP), a lidar flying
aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
(CALIPSO) satellite and the column-integrated extinction (aerosol optical depth – AOD)
available from three radiometers: the European Space Agency (ESA)'s Along-Track
Scanning Radiometer version 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR) (together referred to
as ATSR) and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, together spanning the period 1995–2015. AOD
data are retrieved from ATSR using the ATSR dual view (ADV) v2.31 algorithm, while for MODIS
Collection 6 (C6) the AOD data set is used that was obtained from merging the
AODs obtained from the dark target (DT) and deep blue (DB) algorithms, further
referred to as the DTDB merged AOD product. These data sets are
validated and differences are compared using Aerosol Robotic Network (AERONET) version 2 L2.0 AOD data
as reference. The results show that, over China, ATSR slightly underestimates
the AOD and MODIS slightly overestimates the AOD. Consequently, ATSR AOD is
overall lower than that from MODIS, and the difference increases with
increasing AOD. The comparison also shows that neither of the ATSR and MODIS AOD
data sets is better than the other one everywhere. However, ATSR ADV has
limitations over bright surfaces which the MODIS DB was designed for. To
allow for comparison of MODIS C6 results with previous analyses where MODIS
Collection 5.1 (C5.1) data were used, also the difference between the C6 and
C5.1 merged DTDB data sets from MODIS/Terra over China is briefly discussed.
The AOD data sets show strong seasonal differences and the seasonal features
vary with latitude and longitude across China. Two-decadal AOD time series,
averaged over all of mainland China, are presented and briefly discussed.
Using the 17Â years of ATSR data as the basis and MODIS/Terra to follow
the temporal evolution in recent years when the environmental satellite Envisat was lost requires a
comparison of the data sets for the overlapping period to show their
complementarity. ATSR precedes the MODIS time series between 1995 and 2000
and shows a distinct increase in the AOD over this period. The two data
series show similar variations during the overlapping period between 2000 and
2011, with minima and maxima in the same years. MODIS extends this time
series beyond the end of the Envisat period in 2012, showing decreasing AOD
EARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product
We present the evaluation activity of the European Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of the Level 2 aerosol backscatter coefficient product derived by the Cloud-Aerosol Transport System (CATS) aboard the International Space Station (ISS; Rodier et al., 2015). The study employs correlative CATS and EARLINET backscatter measurements within a 50¿km distance between the ground station and the ISS overpass and as close in time as possible, typically with the starting time or stopping time of the EARLINET performed measurement time window within 90¿min of the ISS overpass, for the period from February 2015 to September 2016. The results demonstrate the good agreement of the CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig, Germany; Évora, Portugal; and Dushanbe, Tajikistan, are analyzed here to demonstrate the performance of the CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions, CATS is in good agreement with EARLINET, independent of daytime and nighttime conditions. CATS low negative biases are observed, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds; these are biases which may lead to systematic deviations and slight underestimations of the total aerosol optical depth (AOD) in climate studies. In addition, CATS misclassification of aerosol layers as clouds, and vice versa, in cases of coexistent and/or adjacent aerosol and cloud features, occasionally leads to non-representative, unrealistic, and cloud-contaminated aerosol profiles. Regarding solar illumination conditions, low negative biases in CATS backscatter coefficient profiles, of the order of 6.1¿%, indicate the good nighttime performance of CATS. During daytime, a reduced signal-to-noise ratio by solar background illumination prevents retrievals of weakly scattering atmospheric layers that would otherwise be detectable during nighttime, leading to higher negative biases, of the order of 22.3¿%.Peer Reviewe