Suchergebnisse

Monitoring and describing the spatiotemporal variability in dust aerosols is crucial for understanding their multiple effects, related feedbacks, and impacts within the Earth system. This study describes the development of the ModIs Dust AeroSol (MIDAS) data set. MIDAS provides columnar daily dust optical depth (DOD) at 550 nm at a global scale and fine spatial resolution (0.1∘ × 0.1∘) over a 15-year period (2003–2017). This new data set combines quality filtered satellite aerosol optical depth (AOD) retrievals from MODIS-Aqua at swath level (Collection 6.1; Level 2), along with DOD-to-AOD ratios provided by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) reanalysis to derive DOD on the MODIS native grid. The uncertainties of the MODIS AOD and MERRA-2 dust fraction, with respect to the AEronet RObotic NETwork (AERONET) and LIdar climatology of vertical Aerosol Structure for space-based lidar simulation (LIVAS), respectively, are taken into account for the estimation of the total DOD uncertainty. MERRA-2 dust fractions are in very good agreement with those of LIVAS across the dust belt in the tropical Atlantic Ocean and the Arabian Sea; the agreement degrades in North America and the Southern Hemisphere, where dust sources are smaller. MIDAS, MERRA-2, and LIVAS DODs strongly agree when it comes to annual and seasonal spatial patterns, with colocated global DOD averages of 0.033, 0.031, and 0.029, respectively; however, deviations in dust loading are evident and regionally dependent. Overall, MIDAS is well correlated with AERONET-derived DODs (R=0.89) and only shows a small positive bias (0.004 or 2.7 %). Among the major dust areas of the planet, the highest R values (>0.9) are found at sites of North Africa, the Middle East, and Asia. MIDAS expands, complements, and upgrades the existing observational capabilities of dust aerosols, and it is suitable for dust climatological studies, model evaluation, and data assimilation. ; Antonis Gkikas acknowledges support from the European Union's Horizon 2020 Research and Innovation programme under the Marie Skłodowska-Curie Actions (grant no. 749461; DUST-GLASS). We would like to thank the principal investigators maintaining the AERONET sites used in the present work. We thank the NASA CALIPSO team and NASA/LaRC/ASDC for making the CALIPSO products available, which have been used to build the LIVAS products, and ESA, who funded the LIVAS project (contract no. 4000104106/11/NL/FF/fk). We are grateful to the AERIS/ICARE Data and Services Center for providing access to the CALIPSO data and their computational center (http://www.icare.univ-lille1.fr/, last access: 8 August 2019). Vassilis Amiridis acknowledges support from the European Research Council (grant no. 725698; D-TECT). Eleni Marinou was funded by a DLR VO-Ryoung investigator group and the Deutscher Akademischer Austauschdienst (grant no. 57370121). Carlos Pérez García-Pando acknowledges support from the European Research Council (grant no. 773051; FRAGMENT), the AXA Research Fund, and the Spanish Ministry of Science, Innovation and Universities (grant nos. RYC-2015-18690 and CGL2017-88911-R). The authors acknowledge support from the DustClim project as part of ERA4CS, an ERA-NET project initiated by JPI Climate and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), and ANR (FR), with cofunding by the European Union (grant no. 690462). PRACE (Partnership for Advanced Computing in Europe) is acknowledged for awarding access to the MareNostrum Supercomputer in the Barcelona Supercomputing Center. We acknowledge support of this work by the PANhellenic infrastructure for Atmospheric Composition and climatE chAnge (PANACEA) project (grant no. MIS 5021516), which is implemented under the Horizon 2020 Action of "Reinforcement of the Research and Innovation Infrastructure", funded by the Operational Programme Competitiveness, Entrepreneurship, and Innovation (NSRF 2014–2020) and cofinanced by Greece and the European Union (under the European Regional Development Fund). NOA members acknowledge support from the Stavros Niarchos Foundation (SNF). The authors would like to thank Andrew Mark Sayer for his valuable and constructive comments. The authors would like also to thank Thanasis Georgiou for developing the ftp server on which the MIDAS data set is stored. We would like to thank the two anonymous reviewers, who were very helpful and who provided constructive comments that improved the paper. ; Peer Reviewed ; Postprint (published version)

Quantifying the dust optical depth (DOD) and its uncertainty across spatiotemporal scales is key to understanding and constraining the dust cycle and its interactions with the Earth System. This study quantifies the DOD along with its monthly and year-to-year variability between 2003 and 2017 at global and regional levels based on the MIDAS (ModIs Dust AeroSol) dataset, which combines Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua retrievals and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis products. We also describe the annual and seasonal geographical distributions of DOD across the main dust source regions and transport pathways. MIDAS provides columnar mid-visible (550 nm) DOD at fine spatial resolution (), expanding the current observational capabilities for monitoring the highly variable spatiotemporal features of the dust burden. We obtain a global DOD of 0.032±0.003 – approximately a quarter (23.4 %±2.4 %) of the global aerosol optical depth (AOD) – with about 1 order of magnitude more DOD in the Northern Hemisphere (0.056±0.004; 31.8 %±2.7 %) than in the Southern Hemisphere (0.008±0.001; 8.2 %±1.1 %) and about 3.5 times more DOD over land (0.070±0.005) than over ocean (0.019±0.002). The Northern Hemisphere monthly DOD is highly correlated with the corresponding monthly AOD (R2=0.94) and contributes 20 % to 48 % of it, both indicating a dominant dust contribution. In contrast, the contribution of dust to the monthly AOD does not exceed 17 % in the Southern Hemisphere, although the uncertainty in this region is larger. Among the major dust sources of the planet, the maximum DODs (∼1.2) are recorded in the Bodélé Depression of the northern Lake Chad Basin, whereas moderate-to-high intensities are encountered in the Western Sahara (boreal summer), along the eastern parts of the Middle East (boreal summer) and in the Taklamakan Desert (spring). Over oceans, major long-range dust transport is observed primarily along the tropical Atlantic (intensified during boreal summer) and secondarily in the North Pacific (intensified during boreal spring). Our calculated global and regional averages and associated uncertainties are consistent with some but not all recent observation-based studies. Our work provides a simple yet flexible method to estimate consistent uncertainties across spatiotemporal scales, which will enhance the use of the MIDAS dataset in a variety of future studies. ; Antonis Gkikas acknowledges support by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the 2nd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers (ATLANTAS, project number 544), as well as support from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Actions (grant no. 749461; DUST-GLASS). Vassilis Amiridis acknowledges support from the European Research Council (grant no. 725698; D-TECT). Eleni Marinou was funded by a DLR VO-R young investigator group and the Deutscher Akademischer Austauschdienst (grant no. 57370121). Jasper F. Kok acknowledges support from National Science Foundation (NSF) grant 1552519. Carlos Pérez García-Pando acknowledges support from the European Research Council (grant no. 773051; FRAGMENT); the AXA Research Fund; and the Spanish Ministry of Science, Innovation and Universities (grant nos. RYC-2015-18690 and CGL2017-88911-R). The authors acknowledge support from the DustClim project as part of ERA4CS, an ERA-NET project initiated by JPI Climate and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), and ANR (FR), with cofunding by the European Union (grant no. 690462). PRACE (Partnership for Advanced Computing in Europe) and RES (Red Española de Supercomputación) are acknowledged for awarding access to the MareNostrum Supercomputer in the Barcelona Supercomputing Center. We acknowledge support of this work by the PANhellenic infrastructure for Atmospheric Composition and climatE chAnge (PANACEA) project (grant no. MIS 5021516), which is implemented under the Horizon 2020 Action of Reinforcement of the Research and Innovation Infrastructure, funded by the Operational Programme Competitiveness, Entrepreneurship, and Innovation (NSRF 2014–2020) and cofinanced by Greece and the European Union (under the European Regional Development Fund). NOA members acknowledge support from the Stavros Niarchos Foundation (SNF). The authors acknowledge support by the COST Action InDust (grant no. CA16202), supported by COST (European Cooperation in Science and Technology). The authors would like to thank Andrew Mark Sayer for his valuable and constructive comments. The authors would like also to thank Thanasis Georgiou for developing the ftp server on which the MIDAS dataset is stored. ; Peer Reviewed ; Postprint (published version)

The main aim of the present study is to describe the vertical structure of the intense Mediterranean dust outbreaks, based on the use of satellite and surface-based retrievals/measurements. Strong and extreme desert dust (DD) episodes are identified at 1° × 1° spatial resolution, over the period March 2000–February 2013, through the implementation of an updated objective and dynamic algorithm. According to the algorithm, strong DD episodes occurring at a specific place correspond to cases in which the daily aerosol optical depth at 550 nm (AOD550 nm) exceeds or equals the long-term mean AOD550 nm (Mean) plus two standard deviations (SD), which is also smaller than Mean+4 × SD. Extreme DD episodes correspond to cases in which the daily AOD550 nm value equals or exceeds Mean+4 × SD. For the identification of DD episodes, additional optical properties (Ångström exponent, fine fraction, effective radius and aerosol index) derived by the MODIS-Terra & Aqua (also AOD retrievals), OMI-Aura and EP-TOMS databases are used as inputs. According to the algorithm using MODIS-Terra data, over the period March 2000–February 2013, strong DD episodes occur more frequently (up to 9.9 episodes year−1) over the western Mediterranean, while the corresponding frequencies for the extreme ones are smaller (up to 3.3 episodes year−1, central Mediterranean Sea). In contrast to their frequency, dust episodes are more intense (AODs up to 4.1), over the central and eastern Mediterranean Sea, off the northern African coasts. Slightly lower frequencies and higher intensities are found when the satellite algorithm operates based on MODIS-Aqua retrievals, for the period 2003–2012. The consistency of the algorithm is successfully tested through the application of an alternative methodology for the determination of DD episodes, which produced similar features of the episodes' frequency and intensity, with just slightly higher frequencies and lower intensities. The performance of the satellite algorithm is assessed against surface-based daily data from 109 sun-photometric (AERONET) and 22 PM10 stations. The agreement between AERONET and MODIS AOD is satisfactory (R = 0.505 − 0.750) and improves considerably when MODIS level 3 retrievals with higher sub-grid spatial representativeness and homogeneity are considered. Through the comparison against PM10 concentrations, it is found that the presence of dust is justified in all ground stations with success scores ranging from 68 to 97 %. However, poor agreement is evident between satellite and ground PM10 observations in the western parts of the Mediterranean, which is attributed to the desert dust outbreaks' vertical extension and the high altitude of dust presence. The CALIOP vertical profiles of pure and polluted dust observations and the associated total backscatter coefficient at 532 nm (β532 nm), indicate that dust particles are mainly detected between 0.5 and 6 km, though they can reach 8 km between the parallels 32 and 38° N in warm seasons. An increased number of CALIOP dust records at higher altitudes is observed with increased latitude, northwards to 40° N, revealing an ascending mode of the dust transport. However, the overall intensity of DD episodes is maximum (up to 0.006 km−1 sr−1) below 2 km and at the southern parts of the study region (30–34° N). Additionally, the average thickness of dust layers gradually decreases from 4 to 2 km, moving from south to north. In spring, dust layers of moderate-to-high β532 nm values ( ∼ 0.004 km−1 sr−1) are detected over the Mediterranean (35–42° N), extending from 2 to 4 km. Over the western Mediterranean, dust layers are observed between 2 and 6 km, while their base height is decreased down to 0.5 km for increasing longitudes underlying the role of topography and thermal convection. The vertical profiles of CALIOP β532 nm confirm the multilayered structure of the Mediterranean desert dust outbreaks on both annual and seasonal bases, with several dust layers of variable geometrical characteristics and intensities. A detailed analysis of the vertical structure of specific DD episodes using CALIOP profiles reveals that the consideration of the dust vertical structure is necessary when attempting comparisons between columnar MODIS AOD retrievals and ground PM10 concentrations. ; The MDRAF project has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 622662. The Collection 051 MODIS-Terra and MODIS-Aqua data were obtained from NASA's level 1 and Atmosphere Archive and Distribution System (LAADS) website (ftp://ladsweb.nascom.nasa.gov/). The Earth Probe (TOMS) and OMI aerosol climatology is available from the Mirador ftp server (http://mirador.gsfc.nasa.gov/). The CALIPSO retrievals have been derived from NASA's Earth Observing System Data and Information System (http://reverb.echo.nasa.gov/). We would like to thank the principal investigators maintaining the AERONET sites used in the present work. We would like to acknowledge the EMEP Programme and the public European databases Airbase and ACTRIS, which supplied PM10 data used in this study. J. Pey benefits from a Ramón y Cajal Research Grant (RYC-2013-14159) from the Spanish Ministry of Economy and Competitiveness. S. Basart, O. Jorba, S. Gassó and J. M. Baldasano acknowledge the CICYT project CGL2013-46736 and Severo Ochoa (SEV-996 2011-00067) programme of the Spanish Government. The publication was supported by the European Union Seventh Framework Programme (FP-7-REGPOT-2012-2013-1), in the framework of the project BEYOND, under Grant Agreement No. 316210 (BEYOND – Building Capacity for a Centre of Excellence for EO-based monitoring of Natural Disasters. The Figs. 10, 11 and 12 have been produced with ccplot (http://ccplot.org/). This work contributes to the Chemistry-Aerosol Mediterranean Experiment (ChArMEx) coordinated effort for the long-term Mediterranean aerosol characterization using available remote sensing data sets. ; This paper was written under the framework of the International HYMEX project and the Spanish HOPE (CGL2014-52571-R) project. We would like to thank the "Agència Catalana de l'Aigua" and the "Real Academia de Ciencias y Artes de Barcelona" for the SAIH and Jardí data provided, respectively. This work was partially funded by the Project of Interest 'NextData' of the Italian Ministry for Education, University and Research. We thank the anonymous reviewers whose comments greatly helped us improve the presentation of the results. ; Peer Reviewed ; Postprint (published version)

The main aim of the present study is to describe the vertical structure of the intense Mediterranean dust outbreaks, based on the use of satellite and surface-based retrievals/measurements. Strong and extreme desert dust (DD) episodes are identified at 1° × 1° spatial resolution, over the period March 2000–February 2013, through the implementation of an updated objective and dynamic algorithm. According to the algorithm, strong DD episodes occurring at a specific place correspond to cases in which the daily aerosol optical depth at 550 nm (AOD550 nm) exceeds or equals the long-term mean AOD550 nm (Mean) plus two standard deviations (SD), which is also smaller than Mean+4 × SD. Extreme DD episodes correspond to cases in which the daily AOD550 nm value equals or exceeds Mean+4 × SD. For the identification of DD episodes, additional optical properties (Ångström exponent, fine fraction, effective radius and aerosol index) derived by the MODIS-Terra & Aqua (also AOD retrievals), OMI-Aura and EP-TOMS databases are used as inputs. According to the algorithm using MODIS-Terra data, over the period March 2000–February 2013, strong DD episodes occur more frequently (up to 9.9 episodes year−1) over the western Mediterranean, while the corresponding frequencies for the extreme ones are smaller (up to 3.3 episodes year−1, central Mediterranean Sea). In contrast to their frequency, dust episodes are more intense (AODs up to 4.1), over the central and eastern Mediterranean Sea, off the northern African coasts. Slightly lower frequencies and higher intensities are found when the satellite algorithm operates based on MODIS-Aqua retrievals, for the period 2003–2012. The consistency of the algorithm is successfully tested through the application of an alternative methodology for the determination of DD episodes, which produced similar features of the episodes' frequency and intensity, with just slightly higher frequencies and lower intensities. The performance of the satellite algorithm is assessed against surface-based daily data from 109 sun-photometric (AERONET) and 22 PM10 stations. The agreement between AERONET and MODIS AOD is satisfactory (R = 0.505 − 0.750) and improves considerably when MODIS level 3 retrievals with higher sub-grid spatial representativeness and homogeneity are considered. Through the comparison against PM10 concentrations, it is found that the presence of dust is justified in all ground stations with success scores ranging from 68 to 97 %. However, poor agreement is evident between satellite and ground PM10 observations in the western parts of the Mediterranean, which is attributed to the desert dust outbreaks' vertical extension and the high altitude of dust presence. The CALIOP vertical profiles of pure and polluted dust observations and the associated total backscatter coefficient at 532 nm (β532 nm), indicate that dust particles are mainly detected between 0.5 and 6 km, though they can reach 8 km between the parallels 32 and 38° N in warm seasons. An increased number of CALIOP dust records at higher altitudes is observed with increased latitude, northwards to 40° N, revealing an ascending mode of the dust transport. However, the overall intensity of DD episodes is maximum (up to 0.006 km−1 sr−1) below 2 km and at the southern parts of the study region (30–34° N). Additionally, the average thickness of dust layers gradually decreases from 4 to 2 km, moving from south to north. In spring, dust layers of moderate-to-high β532 nm values ( ∼ 0.004 km−1 sr−1) are detected over the Mediterranean (35–42° N), extending from 2 to 4 km. Over the western Mediterranean, dust layers are observed between 2 and 6 km, while their base height is decreased down to 0.5 km for increasing longitudes underlying the role of topography and thermal convection. The vertical profiles of CALIOP β532 nm confirm the multilayered structure of the Mediterranean desert dust outbreaks on both annual and seasonal bases, with several dust layers of variable geometrical characteristics and intensities. A detailed analysis of the vertical structure of specific DD episodes using CALIOP profiles reveals that the consideration of the dust vertical structure is necessary when attempting comparisons between columnar MODIS AOD retrievals and ground PM10 concentrations. ; The MDRAF project has received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 622662. The Collection 051 MODIS-Terra and MODIS-Aqua data were obtained from NASA's level 1 and Atmosphere Archive and Distribution System (LAADS) website (ftp://ladsweb.nascom.nasa.gov/). The Earth Probe (TOMS) and OMI aerosol climatology is available from the Mirador ftp server (http://mirador.gsfc.nasa.gov/). The CALIPSO retrievals have been derived from NASA's Earth Observing System Data and Information System (http://reverb.echo.nasa.gov/). We would like to thank the principal investigators maintaining the AERONET sites used in the present work. We would like to acknowledge the EMEP Programme and the public European databases Airbase and ACTRIS, which supplied PM10 data used in this study. J. Pey benefits from a Ramón y Cajal Research Grant (RYC-2013-14159) from the Spanish Ministry of Economy and Competitiveness. S. Basart, O. Jorba, S. Gassó and J. M. Baldasano acknowledge the CICYT project CGL2013-46736 and Severo Ochoa (SEV-996 2011-00067) programme of the Spanish Government. The publication was supported by the European Union Seventh Framework Programme (FP-7-REGPOT-2012-2013-1), in the framework of the project BEYOND, under Grant Agreement No. 316210 (BEYOND – Building Capacity for a Centre of Excellence for EO-based monitoring of Natural Disasters. The Figs. 10, 11 and 12 have been produced with ccplot (http://ccplot.org/). This work contributes to the Chemistry-Aerosol Mediterranean Experiment (ChArMEx) coordinated effort for the long-term Mediterranean aerosol characterization using available remote sensing data sets. ; This paper was written under the framework of the International HYMEX project and the Spanish HOPE (CGL2014-52571-R) project. We would like to thank the "Agència Catalana de l'Aigua" and the "Real Academia de Ciencias y Artes de Barcelona" for the SAIH and Jardí data provided, respectively. This work was partially funded by the Project of Interest 'NextData' of the Italian Ministry for Education, University and Research. We thank the anonymous reviewers whose comments greatly helped us improve the presentation of the results. ; Peer Reviewed ; Postprint (published version)

The aim of this paper is to analyze the suitability of the high-mountain stations Mauna Loa and Izaña for Langley plot calibration of Sun photometers. Thus the aerosol optical depth (AOD) characteristics and seasonality, as well as the cloudiness, have been investigated in order to provide a robust estimation of the calibration uncertainty as well as the number of days that are suitable for Langley calibrations. The data used for the investigations belong to the AERONET and GAW-PFR networks, which maintain reference Sun photometers at these stations with long measurement records: 22 years at Mauna Loa and 15 years at Izaña. In terms of clear-sky and stable aerosol conditions, Mauna Loa (3397ma.s.l.) exhibits on average 377 Langley plots (243 morning and 134 afternoon) per year suitable for Langley plot calibration, whereas Izaña (2373ma.s.l.) shows 343 Langley plots (187 morning and 155 afternoon) per year. The background AOD (500nm) values, on days that are favorable for Langley calibrations, are in the range 0.01–0.02 throughout the year, with well-defined seasonality that exhibits a spring maximum at both stations plus a slight summer increase at Izaña. The statistical analysis of the long-term determination of extraterrestrial signals yields to a calibration uncertainty of ∼ 0.25–0.5%, this uncertainty being smaller in the visible and near-infrared wavelengths and larger in the ultraviolet wavelengths. This is due to atmospheric variability produced by changes in several factors, mainly the AOD. The uncertainty cannot be reduced based only on quality criteria of individual Langley plots and averaging over several days is shown to reduce the uncertainty to the needed levels for reference Sun photometers. ; This research has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 654109 (ACTRIS-2). The funding by MINECO (CTM2015-66742-R) and Junta de Castilla y León (VA100P17) is also acknowledged.

This study introduces an Earth observation (EO)-based system which is capable of operationally estimating and continuously monitoring the ultraviolet index (UVI) in Europe. UVIOS (i.e., UV-Index Operating System) exploits a synergy of radiative transfer models with high-performance computing and EO data from satellites (Meteosat Second Generation and Meteorological Operational Satellite-B) and retrieval processes (Tropospheric Emission Monitoring Internet Service, Copernicus Atmosphere Monitoring Service and the Global Land Service). It provides a near-real-time nowcasting and short-term forecasting service for UV radiation over Europe. The main atmospheric inputs for the UVI simulations include ozone, clouds and aerosols, while the impacts of ground elevation and surface albedo are also taken into account. The UVIOS output is the UVI at high spatial and temporal resolution (5 km and 15 min, respectively) for Europe (i.e., 1.5 million pixels) in real time. The UVI is empirically related to biologically important UV dose rates, and the reliability of this EO-based solution was verified against ground-based measurements from 17 stations across Europe. Stations are equipped with spectral, broadband or multi-filter instruments and cover a range of topographic and atmospheric conditions. A period of over 1 year of forecasted 15 min retrievals under all-sky conditions was compared with the ground-based measurements. UVIOS forecasts were within ±0.5 of the measured UVI for at least 70 % of the data compared at all stations. For clear-sky conditions the agreement was better than 0.5 UVI for 80 % of the data. A sensitivity analysis of EO inputs and UVIOS outputs was performed in order to quantify the level of uncertainty in the derived products and to identify the covariance between the accuracy of the output and the spatial and temporal resolution and the quality of the inputs. Overall, UVIOS slightly overestimated the UVI due to observational uncertainties in inputs of cloud and aerosol. This service will hopefully contribute to EO capabilities and will assist the provision of operational early warning systems that will help raise awareness among European Union citizens of the health implications of high UVI doses. ; publishedVersion

Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas–aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite- and ground-based platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol–climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term "requirements" is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of aerosol prediction activities. The criteria on which the requirements for aerosol observations are based are also outlined. Assimilation and evaluation aspects are discussed from the perspective of the user requirements.

Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centres due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, providers of climate services, and health professionals. The prediction of aerosol particle properties in Numerical Weather Prediction (NWP) models faces a number of challenges owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions. Errors in aerosol prediction concern all processes involved in the aerosol life cycle. These include errors on the source terms (for both anthropogenic and natural emissions), errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), as well as errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transformation and growth, hygroscopicity). The main goal of current research on aerosol forecast consists in prioritizing these errors and trying to reduce the most important ones through model development and data assimilation. Aerosol particle observations from satellite and ground-based platforms have been crucial to guide model development of the recent years, and have been made more readily available for model evaluation and assimilation (.) ; Angela Benedetti has received funding from the H2020 Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS2, Grant Number 654109).

This study introduces an Earth observation (EO)-based system which is capable of operationally estimating and continuously monitoring the ultraviolet index (UVI) in Europe. UVIOS (i.e., UV-Index Operating System) exploits a synergy of radiative transfer models with high-performance computing and EO data from satellites (Meteosat Second Generation and Meteorological Operational Satellite-B) and retrieval processes (Tropospheric Emission Monitoring Internet Service, Copernicus Atmosphere Monitoring Service and the Global Land Service). It provides a near-real-time nowcasting and short-term forecasting service for UV radiation over Europe. The main atmospheric inputs for the UVI simulations include ozone, clouds and aerosols, while the impacts of ground elevation and surface albedo are also taken into account. The UVIOS output is the UVI at high spatial and temporal resolution (5 km and 15 min, respectively) for Europe (i.e., 1.5 million pixels) in real time. The UVI is empirically related to biologically important UV dose rates, and the reliability of this EO-based solution was verified against ground-based measurements from 17 stations across Europe. Stations are equipped with spectral, broadband or multi-filter instruments and cover a range of topographic and atmospheric conditions. A period of over 1 year of forecasted 15 min retrievals under all-sky conditions was compared with the ground-based measurements. UVIOS forecasts were within ±0.5 of the measured UVI for at least 70 % of the data compared at all stations. For clear-sky conditions the agreement was better than 0.5 UVI for 80 % of the data. A sensitivity analysis of EO inputs and UVIOS outputs was performed in order to quantify the level of uncertainty in the derived products and to identify the covariance between the accuracy of the output and the spatial and temporal resolution and the quality of the inputs. Overall, UVIOS slightly overestimated the UVI due to observational uncertainties in inputs of cloud and aerosol. This service will hopefully contribute to EO capabilities and will assist the provision of operational early warning systems that will help raise awareness among European Union citizens of the health implications of high UVI doses.

This study introduces an Earth observation (EO)-based system which is capable of operationally estimating and continuously monitoring the ultraviolet index (UVI) in Europe. The UVIOS (i.e. UV-Index Operating System) exploits a synergy of radiative transfer models with high performance computing and EO data from satellites (Meteosat Second Generation and Meteorological Operational Satellite-B), and retrieval processes (Tropospheric Emission Monitoring Internet Service, Copernicus Atmosphere Monitoring Service and the Global Land Service). It provides a near-real-time now-casting and short-term forecasting service for UV radiation over Europe. The main atmospheric inputs for the UVI simulations include ozone, clouds and aerosols while the impacts of ground elevation and surface albedo are also taken into account. The UVIOS output is the UVI at high spatial and temporal resolution (5 km and 15 minutes, respectively) for Europe (i.e. 1.5 million pixels) in real-time. The UVI is empirically related to biologically important UV dose rates and the reliability of this EO-based solution was verified against ground-based measurements from 17 stations across Europe. Stations are equipped with spectral, broadband or multi-filter instruments and cover a range of topographic and atmospheric conditions. A period of over one year of forecasted 15-min retrievals under all sky conditions were compared with the ground–based measurements. UVIOS forecasts were within ±0.5 of measured UVI for at least 70 % of the data compared at all stations. For clear sky conditions the agreement was better than 0.5 UVI for 80 % of the data. A sensitivity analysis of EO inputs and UVIOS outputs was performed in order to quantify the level of uncertainty in the derived products, and to identify the covariance between the accuracy of the output and the spatial and temporal resolution, and the quality of the inputs. Overall, UVIOS slightly overestimated UVI due to observational uncertainties in inputs of cloud and aerosol. This service will hopefully contribute to EO capabilities and will assist the provision of operational early warning systems that will help raise awareness among European Union citizens of the health implications of high UVI doses.

This study introduces an Earth observation (EO)-based system which is capable of operationally estimating and continuously monitoring the ultraviolet index (UVI) in Europe. UVIOS (i.e., UV-Index Operating System) exploits a synergy of radiative transfer models with high-performance computing and EO data from satellites (Meteosat Second Generation and Meteorological Operational Satellite-B) and retrieval processes (Tropospheric Emission Monitoring Internet Service, Copernicus Atmosphere Monitoring Service and the Global Land Service). It provides a near-real-time nowcasting and short-term forecasting service for UV radiation over Europe. The main atmospheric inputs for the UVI simulations include ozone, clouds and aerosols, while the impacts of ground elevation and surface albedo are also taken into account. The UVIOS output is the UVI at high spatial and temporal resolution (5 km and 15 min, respectively) for Europe (i.e., 1.5 million pixels) in real time. The UVI is empirically related to biologically important UV dose rates, and the reliability of this EO-based solution was verified against ground-based measurements from 17 stations across Europe. Stations are equipped with spectral, broadband or multi-filter instruments and cover a range of topographic and atmospheric conditions. A period of over 1 year of forecasted 15 min retrievals under all-sky conditions was compared with the ground-based measurements. UVIOS forecasts were within ± 0.5 of the measured UVI for at least 70 % of the data compared at all stations. For clear-sky conditions the agreement was better than 0.5 UVI for 80 % of the data. A sensitivity analysis of EO inputs and UVIOS outputs was performed in order to quantify the level of uncertainty in the derived products and to identify the covariance between the accuracy of the output and the spatial and temporal resolution and the quality of the inputs. Overall, UVIOS slightly overestimated the UVI due to observational uncertainties in inputs of cloud and aerosol. This service will hopefully contribute to EO capabilities and will assist the provision of operational early warning systems that will help raise awareness among European Union citizens of the health implications of high UVI doses.

Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas–aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite- and ground-based platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol–climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term "requirements" is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of aerosol prediction activities. The criteria on which the requirements for aerosol observations are based are also outlined. Assimilation and evaluation aspects are discussed from the perspective of the user requirements.

Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite- and ground-based platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol-climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term "requirements" is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of aerosol prediction activities. The criteria on which the requirements for aerosol observations are based are also outlined. Assimilation and evaluation aspects are discussed from the perspective of the user requirements. ; Peer reviewed

Abstract. Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas–aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite- and ground-based platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current ...