Suchergebnisse

In the framework of theWorld Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 h using observations of aerosol optical depth (AOD) from the AErosol RObotic NETwork (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distribution was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile), synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the longrange transport towards northern Europe. Our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport. ; The authors acknowledge AERONET (http://aeronet.gsfc.nasa.gov) and thank the PIs of the AERONET stations used in this paper for maintaining the observation program and the AERONET-Europe TNA (EU-ACTRIS grant no. 262254) for contributing to calibration efforts. We also acknowledge the MERRA, CALIPSO and MODIS mission scientists and associated NASA personnel for the production of the data used in this research effort. MODIS data used in this paper were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC. S. Basart acknowledges the Catalan Government (BE-DGR-2012) as well as the CICYT project (CGL2010-19652 and CGL2013-46736) and Severo Ochoa (SEV-2011-00067) programme of the Spanish Government. The NMMB/BSC-Dust and BSC-DREAM8b simulations were performed on the MareNostrum supercomputer hosted by BSC. Stephanie Fiedler acknowledges the funding of the European Research Council through the starting grant of Peter Knippertz (no. 257543). Nicolas Huneeus acknowledges FONDAP 15110009 and FONDECYT 1150873. The database on dust concentrations at ground level was produced in the framework of the Grant Agreement LIFE10 ENV/IT/327 from the LIFE Programme of the European Commission. J. Pey has been partially funded by a Ramon y Cajal Grant (RYC-2013-14159) from the Spanish Ministry of Economy and Competitiveness. Carlos Pérez García-Pando acknowledges the Department of Energy (DE-SC0006713) and the NASA Modeling, Analysis and Prediction Program. The work was partly funded within MACC-II by the European Commission under the EU Seventh Research Framework Programme, contract number 283576 and MACC-III by the European Community's Horizon 2020 Programme under grant agreement no. 633080. ; Postprint (published version)

In the framework of theWorld Meteorological Organisation's Sand and Dust Storm Warning Advisory and Assessment System, we evaluated the predictions of five state-of-the-art dust forecast models during an intense Saharan dust outbreak affecting western and northern Europe in April 2011. We assessed the capacity of the models to predict the evolution of the dust cloud with lead times of up to 72 h using observations of aerosol optical depth (AOD) from the AErosol RObotic NETwork (AERONET) and the Moderate Resolution Imaging Spectroradiometer (MODIS) and dust surface concentrations from a ground-based measurement network. In addition, the predicted vertical dust distribution was evaluated with vertical extinction profiles from the Cloud and Aerosol Lidar with Orthogonal Polarization (CALIOP). To assess the diversity in forecast capability among the models, the analysis was extended to wind field (both surface and profile), synoptic conditions, emissions and deposition fluxes. Models predict the onset and evolution of the AOD for all analysed lead times. On average, differences among the models are larger than differences among lead times for each individual model. In spite of large differences in emission and deposition, the models present comparable skill for AOD. In general, models are better in predicting AOD than near-surface dust concentration over the Iberian Peninsula. Models tend to underestimate the longrange transport towards northern Europe. Our analysis suggests that this is partly due to difficulties in simulating the vertical distribution dust and horizontal wind. Differences in the size distribution and wet scavenging efficiency may also account for model diversity in long-range transport. ; The authors acknowledge AERONET (http://aeronet.gsfc.nasa.gov) and thank the PIs of the AERONET stations used in this paper for maintaining the observation program and the AERONET-Europe TNA (EU-ACTRIS grant no. 262254) for contributing to calibration efforts. We also acknowledge the MERRA, CALIPSO and MODIS mission scientists and associated NASA personnel for the production of the data used in this research effort. MODIS data used in this paper were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC. S. Basart acknowledges the Catalan Government (BE-DGR-2012) as well as the CICYT project (CGL2010-19652 and CGL2013-46736) and Severo Ochoa (SEV-2011-00067) programme of the Spanish Government. The NMMB/BSC-Dust and BSC-DREAM8b simulations were performed on the MareNostrum supercomputer hosted by BSC. Stephanie Fiedler acknowledges the funding of the European Research Council through the starting grant of Peter Knippertz (no. 257543). Nicolas Huneeus acknowledges FONDAP 15110009 and FONDECYT 1150873. The database on dust concentrations at ground level was produced in the framework of the Grant Agreement LIFE10 ENV/IT/327 from the LIFE Programme of the European Commission. J. Pey has been partially funded by a Ramon y Cajal Grant (RYC-2013-14159) from the Spanish Ministry of Economy and Competitiveness. Carlos Pérez García-Pando acknowledges the Department of Energy (DE-SC0006713) and the NASA Modeling, Analysis and Prediction Program. The work was partly funded within MACC-II by the European Commission under the EU Seventh Research Framework Programme, contract number 283576 and MACC-III by the European Community's Horizon 2020 Programme under grant agreement no. 633080. ; Postprint (published version)

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 ...

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 5 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 10 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 15 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 white paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centres. Some of the requirements are equally applicable to aerosol-climate research. However, the focus here is on the global operational prediction of aerosol properties such as mass concentrations ...