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description Publicationkeyboard_double_arrow_right Article , Other literature type , Preprint 2017Publisher:Copernicus GmbH Funded by:EC | ACTRIS-2, EC | ACTRIS, EC | BACCHUS +2 projectsEC| ACTRIS-2 ,EC| ACTRIS ,EC| BACCHUS ,EC| GEO-CRADLE ,EC| EXCELSIORDai, Guangyao; Althausen, Dietrich; Hofer, Julian; Engelmann, Ronny; Seifert, Patric; Bühl, Johannes; Mamouri, Rodanthi-Elisavet; Wu, Songhua; Ansmann, Albert;doi: 10.5194/amt-2017-452
We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar PollyXT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor from the lidar observations is used for the calibration of the lidar measurements with the sun photometer measurements. The retrieved calibrated water vapor mixing ratio from the lidar measurements has a relative uncertainty of 11 % in which the error is mainly caused by the error of the sun photometer measurements. During CyCARE, nine measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the PollyXT Raman lidar is 6.56 g kg−1 ± 0.72 g kg−1 (with a statistical uncertainty of 0.08 g kg−1 and an instrumental uncertainty of 0.72 g kg−1). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R2 of 0.99 is found. One example is presented to demonstrate the full potential of a well-calibrated Raman lidar. The relative humidity profiles from lidar, GDAS (simulation) and radiosonde are compared, too. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with the relative uncertainty of 10–20 %.
Atmospheric Measurem... arrow_drop_down Atmospheric Measurement Techniques (AMT)Other literature type . 2019Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/amt-2017-452&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert Atmospheric Measurem... arrow_drop_down Atmospheric Measurement Techniques (AMT)Other literature type . 2019Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/amt-2017-452&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2017 Netherlands, Netherlands, United States, Netherlands, Belgium, United KingdomPublisher:Springer Science and Business Media LLC Funded by:EC | COMPARE, NSF | BIGDATA: Small: DA: Patie..., EC | PATHPHYLODYN +13 projectsEC| COMPARE ,NSF| BIGDATA: Small: DA: Patient-level predictive modeling from massive longitudinal databases ,EC| PATHPHYLODYN ,NIH| Scripps Translational Science Institute ,NIH| Tracking Evolution and Spread of Viral Genomes by Geospatial Observation Error ,EC| EVIDENT ,NSF| Advancing the Molecular Epidemiology of Infectious Diseases through Bayesian Phylogenetics ,NIH| Genomics, GPUs, and Next Generation Computational Statistics ,EC| VIROGENESIS ,WT| Seasonal drivers of human movement and aggregation in a changing climate: consequences for infectious disease dynamics and control ,EC| VIRALPHYLOGEOGRAPHY ,EC| PREDEMICS ,NIH| Elucidating Genetic Determinants of Resistance to Lassa Hemorrhagic Fever ,NIH| BAYESIAN MODELING AND DATA INTEGRATION IN INFECTIOUS DISEASE PHYLODYNAMICS ,NIH| Host and Microbial Genetic Determinants of Febrile Illness in West Africa ,NIH| Genomic Center for Infectious DiseaseGytis Dudas; Luiz Max Carvalho; Trevor Bedford; Andrew J. Tatem; Guy Baele; Nuno R. Faria; Daniel J. Park; Jason T. Ladner; Armando Arias; Danny Asogun; Filip Bielejec; Sarah L Caddy; Matthew Cotten; Jonathan D'ambrozio; Simon Dellicour; Antonino Di Caro; Joseph W. Diclaro; Sophie Duraffour; Michael J. Elmore; Lawrence Fakoli; Ousmane Faye; Merle L. Gilbert; Sahr M. Gevao; Stephen K. Gire; Adrianne Gladden-Young; Andreas Gnirke; Augustine Goba; Donald S. Grant; Bart L. Haagmans; Julian A. Hiscox; Umaru Jah; Jeffrey R. Kugelman; Di Liu; Jia Lu; Christine M. Malboeuf; Suzanne Mate; David A. Matthews; Christian B. Matranga; Luke W. Meredith; James Qu; Joshua Quick; Susan D. Pas; My V. T. Phan; Georgios Pollakis; Chantal B.E.M. Reusken; Mariano Sanchez-Lockhart; Stephen F. Schaffner; John S. Schieffelin; Rachel Sealfon; Etienne Simon-Loriere; Saskia L. Smits; Kilian Stoecker; Lucy Thorne; Ekaete Alice Tobin; Mohamed A. Vandi; Simon J. Watson; Kendra West; Shannon L.M. Whitmer; Michael R. Wiley; Sarah M. Winnicki; Shirlee Wohl; Roman Wölfel; Nathan L. Yozwiak; Kristian G. Andersen; Sylvia O. Blyden; Fatorma K. Bolay; Miles W. Carroll; Bernice Dahn; Boubacar Diallo; Pierre Formenty; Christophe Fraser; George F. Gao; Robert F. Garry; Ian Goodfellow; Stephan Günther; Christian T. Happi; Edward C. Holmes; Brima Kargbo; Sakoba Keita; Paul Kellam; Marion Koopmans; Jens H. Kuhn; Nicholas J. Loman; N’Faly Magassouba; Dhamari Naidoo; Stuart T. Nichol; Tolbert Nyenswah; Gustavo Palacios; Oliver G. Pybus; Pardis C. Sabeti; Amadou A. Sall; Ute Ströher; Isatta Wurie; Marc A. Suchard; Philippe Lemey; Andrew Rambaut;pmc: PMC5712493
pmid: 28405027
The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics. ispartof: Nature vol:544 issue:7650 pages:309- ispartof: location:England status: published
OpenAIRE; CORE (RIOX... arrow_drop_down OpenAIRE; CORE (RIOXX-UK Aggregator)Article . 2017eScholarship - University of CaliforniaArticle . 2017Data sources: eScholarship - University of CaliforniaSpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital RepositoryNARCIS; NatureArticle . 2017add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/nature22040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 318 citations 318 popularity Top 0.1% influence Top 1% impulse Top 0.1% Powered by BIP!visibility 21visibility views 21 download downloads 157 Powered bymore_vert OpenAIRE; CORE (RIOX... arrow_drop_down OpenAIRE; CORE (RIOXX-UK Aggregator)Article . 2017eScholarship - University of CaliforniaArticle . 2017Data sources: eScholarship - University of CaliforniaSpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital RepositoryNARCIS; NatureArticle . 2017add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/nature22040&type=result"></script>'); --> </script>
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description Publicationkeyboard_double_arrow_right Article , Other literature type , Preprint 2017Publisher:Copernicus GmbH Funded by:EC | ACTRIS-2, EC | ACTRIS, EC | BACCHUS +2 projectsEC| ACTRIS-2 ,EC| ACTRIS ,EC| BACCHUS ,EC| GEO-CRADLE ,EC| EXCELSIORDai, Guangyao; Althausen, Dietrich; Hofer, Julian; Engelmann, Ronny; Seifert, Patric; Bühl, Johannes; Mamouri, Rodanthi-Elisavet; Wu, Songhua; Ansmann, Albert;doi: 10.5194/amt-2017-452
We present a practical method to continuously calibrate Raman lidar observations of water vapor mixing ratio profiles. The water vapor profile measured with the multiwavelength polarization Raman lidar PollyXT is calibrated by means of co-located AErosol RObotic NETwork (AERONET) sun photometer observations and Global Data Assimilation System (GDAS) temperature and pressure profiles. This method is applied to lidar observations conducted during the Cyprus Cloud Aerosol and Rain Experiment (CyCARE) in Limassol, Cyprus. We use the GDAS temperature and pressure profiles to retrieve the water vapor density. In the next step, the precipitable water vapor from the lidar observations is used for the calibration of the lidar measurements with the sun photometer measurements. The retrieved calibrated water vapor mixing ratio from the lidar measurements has a relative uncertainty of 11 % in which the error is mainly caused by the error of the sun photometer measurements. During CyCARE, nine measurement cases with cloud-free and stable meteorological conditions are selected to calculate the precipitable water vapor from the lidar and the sun photometer observations. The ratio of these two precipitable water vapor values yields the water vapor calibration constant. The calibration constant for the PollyXT Raman lidar is 6.56 g kg−1 ± 0.72 g kg−1 (with a statistical uncertainty of 0.08 g kg−1 and an instrumental uncertainty of 0.72 g kg−1). To check the quality of the water vapor calibration, the water vapor mixing ratio profiles from the simultaneous nighttime observations with Raman lidar and Vaisala radiosonde sounding are compared. The correlation of the water vapor mixing ratios from these two instruments is determined by using all of the 19 simultaneous nighttime measurements during CyCARE. Excellent agreement with the slope of 1.01 and the R2 of 0.99 is found. One example is presented to demonstrate the full potential of a well-calibrated Raman lidar. The relative humidity profiles from lidar, GDAS (simulation) and radiosonde are compared, too. It is found that the combination of water vapor mixing ratio and GDAS temperature profiles allow us to derive relative humidity profiles with the relative uncertainty of 10–20 %.
Atmospheric Measurem... arrow_drop_down Atmospheric Measurement Techniques (AMT)Other literature type . 2019Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/amt-2017-452&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesgold 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!more_vert Atmospheric Measurem... arrow_drop_down Atmospheric Measurement Techniques (AMT)Other literature type . 2019Data sources: Copernicus Publicationsadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5194/amt-2017-452&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2017 Netherlands, Netherlands, United States, Netherlands, Belgium, United KingdomPublisher:Springer Science and Business Media LLC Funded by:EC | COMPARE, NSF | BIGDATA: Small: DA: Patie..., EC | PATHPHYLODYN +13 projectsEC| COMPARE ,NSF| BIGDATA: Small: DA: Patient-level predictive modeling from massive longitudinal databases ,EC| PATHPHYLODYN ,NIH| Scripps Translational Science Institute ,NIH| Tracking Evolution and Spread of Viral Genomes by Geospatial Observation Error ,EC| EVIDENT ,NSF| Advancing the Molecular Epidemiology of Infectious Diseases through Bayesian Phylogenetics ,NIH| Genomics, GPUs, and Next Generation Computational Statistics ,EC| VIROGENESIS ,WT| Seasonal drivers of human movement and aggregation in a changing climate: consequences for infectious disease dynamics and control ,EC| VIRALPHYLOGEOGRAPHY ,EC| PREDEMICS ,NIH| Elucidating Genetic Determinants of Resistance to Lassa Hemorrhagic Fever ,NIH| BAYESIAN MODELING AND DATA INTEGRATION IN INFECTIOUS DISEASE PHYLODYNAMICS ,NIH| Host and Microbial Genetic Determinants of Febrile Illness in West Africa ,NIH| Genomic Center for Infectious DiseaseGytis Dudas; Luiz Max Carvalho; Trevor Bedford; Andrew J. Tatem; Guy Baele; Nuno R. Faria; Daniel J. Park; Jason T. Ladner; Armando Arias; Danny Asogun; Filip Bielejec; Sarah L Caddy; Matthew Cotten; Jonathan D'ambrozio; Simon Dellicour; Antonino Di Caro; Joseph W. Diclaro; Sophie Duraffour; Michael J. Elmore; Lawrence Fakoli; Ousmane Faye; Merle L. Gilbert; Sahr M. Gevao; Stephen K. Gire; Adrianne Gladden-Young; Andreas Gnirke; Augustine Goba; Donald S. Grant; Bart L. Haagmans; Julian A. Hiscox; Umaru Jah; Jeffrey R. Kugelman; Di Liu; Jia Lu; Christine M. Malboeuf; Suzanne Mate; David A. Matthews; Christian B. Matranga; Luke W. Meredith; James Qu; Joshua Quick; Susan D. Pas; My V. T. Phan; Georgios Pollakis; Chantal B.E.M. Reusken; Mariano Sanchez-Lockhart; Stephen F. Schaffner; John S. Schieffelin; Rachel Sealfon; Etienne Simon-Loriere; Saskia L. Smits; Kilian Stoecker; Lucy Thorne; Ekaete Alice Tobin; Mohamed A. Vandi; Simon J. Watson; Kendra West; Shannon L.M. Whitmer; Michael R. Wiley; Sarah M. Winnicki; Shirlee Wohl; Roman Wölfel; Nathan L. Yozwiak; Kristian G. Andersen; Sylvia O. Blyden; Fatorma K. Bolay; Miles W. Carroll; Bernice Dahn; Boubacar Diallo; Pierre Formenty; Christophe Fraser; George F. Gao; Robert F. Garry; Ian Goodfellow; Stephan Günther; Christian T. Happi; Edward C. Holmes; Brima Kargbo; Sakoba Keita; Paul Kellam; Marion Koopmans; Jens H. Kuhn; Nicholas J. Loman; N’Faly Magassouba; Dhamari Naidoo; Stuart T. Nichol; Tolbert Nyenswah; Gustavo Palacios; Oliver G. Pybus; Pardis C. Sabeti; Amadou A. Sall; Ute Ströher; Isatta Wurie; Marc A. Suchard; Philippe Lemey; Andrew Rambaut;pmc: PMC5712493
pmid: 28405027
The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics. ispartof: Nature vol:544 issue:7650 pages:309- ispartof: location:England status: published
OpenAIRE; CORE (RIOX... arrow_drop_down OpenAIRE; CORE (RIOXX-UK Aggregator)Article . 2017eScholarship - University of CaliforniaArticle . 2017Data sources: eScholarship - University of CaliforniaSpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital RepositoryNARCIS; NatureArticle . 2017add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/nature22040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen bronze 318 citations 318 popularity Top 0.1% influence Top 1% impulse Top 0.1% Powered by BIP!visibility 21visibility views 21 download downloads 157 Powered bymore_vert OpenAIRE; CORE (RIOX... arrow_drop_down OpenAIRE; CORE (RIOXX-UK Aggregator)Article . 2017eScholarship - University of CaliforniaArticle . 2017Data sources: eScholarship - University of CaliforniaSpiral - Imperial College Digital RepositoryArticle . 2017Data sources: Spiral - Imperial College Digital RepositoryNARCIS; NatureArticle . 2017add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/nature22040&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu