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S. R. Proud
University of Oxford

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Article
Published: 23 June 2021
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Growth in aviation contributes more to global warming than is generally appreciated because of the mix of climate pollutants it generates: aviation contributed approximately 4% to observed human-induced global warming to date, despite being responsible for only 2.4% of global annual emissions of CO 2. Aviation is projected to have caused a total of about 0.1˚C of warming by 2050, half of it to date and the other half over the next three decades. Should aviation's pre-COVID growth resume, the industry will contribute a 6-17% share to the remaining 0.3-0.8˚C to not exceed 1.5-2˚C of global warming. Under this scenario, the reduction due to COVID-19 to date is small and is projected to only delay aviation's warming contribution by about 5 years. But the leveraging impact of growth also represents an opportunity: Aviation's contribution to further warming would be immediately halted by either a sustained annual 2.5% decrease in flights under the existing fuel mix, or a transition to a 90% carbon-neutral fuel mix by 2050.

ACS Style

Milan Klöwerid; Myles Allen; David Lee; Simon ProudiD; Leo Gallagher; Agnieszka Skowron. Quantifying aviation's contribution to global warming. 2021, 1 .

AMA Style

Milan Klöwerid, Myles Allen, David Lee, Simon ProudiD, Leo Gallagher, Agnieszka Skowron. Quantifying aviation's contribution to global warming. . 2021; ():1.

Chicago/Turabian Style

Milan Klöwerid; Myles Allen; David Lee; Simon ProudiD; Leo Gallagher; Agnieszka Skowron. 2021. "Quantifying aviation's contribution to global warming." , no. : 1.

Research letter
Published: 22 March 2021 in Geophysical Research Letters
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Earth‐orbiting satellites have long been used to examine meteorological processes. In the context of severe weather, brightness temperatures (BTs) at infrared wavelengths allow the determination of convective cloud properties. The anvils of cumulonimbus clouds, for example, typically produce BTs close to the tropopause temperature. Particularly severe storms generate overshoots that penetrate the stratosphere and are cooler than the anvil. In this study, we describe clustered storm overshoots in the tropical West Pacific on December 29, 2018 that resulted in the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard NOAA‐20 measuring a temperature of 161.96K (−111.2°C), which is, to our knowledge, the coldest on record. We describe the local meteorological conditions, examine the VIIRS overpass that produced the cold temperature, compare VIIRS with other sensors that observed the region and, finally, analyze the historical context provided by two other satellite instruments to show that such cold temperatures may be becoming more common.

ACS Style

Simon Richard Proud; Scott Bachmeier. Record‐Low Cloud Temperatures Associated With a Tropical Deep Convective Event. Geophysical Research Letters 2021, 48, 1 .

AMA Style

Simon Richard Proud, Scott Bachmeier. Record‐Low Cloud Temperatures Associated With a Tropical Deep Convective Event. Geophysical Research Letters. 2021; 48 (6):1.

Chicago/Turabian Style

Simon Richard Proud; Scott Bachmeier. 2021. "Record‐Low Cloud Temperatures Associated With a Tropical Deep Convective Event." Geophysical Research Letters 48, no. 6: 1.

Data description paper
Published: 09 September 2020 in Earth System Science Data
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We present version 3 (V3) of the Cloud_cci Along-Track Scanning Radiometer (ATSR) and Advanced ATSR (AATSR) data set. The data set was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) programme. The cloud properties were retrieved from the second ATSR (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the AATSR on board Envisat, which spanned 2002–2012. The data are comprised of a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius, and optical thickness, alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top- and bottom-of-atmosphere shortwave and longwave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with co-located CALIPSO data. The cloud masking has improved significantly, particularly in its ability to detect clear pixels. The Kuiper Skill score has increased from 0.49 to 0.66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulus cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments. The correlation with MAC-LWP increased from 0.4 to over 0.8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth's Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new data set is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol–cloud interactions, and providing contextual information for co-located ATSR-2/AATSR surface temperature and aerosol products. The following new digital identifier has been issued for the Cloud_cci ATSR-2/AATSRv3 data set: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 (Poulsen et al., 2019).

ACS Style

Caroline A. Poulsen; Gregory R. McGarragh; Gareth E. Thomas; Martin Stengel; Matthew W. Christensen; Adam C. Povey; Simon R. Proud; Elisa Carboni; Rainer Hollmann; Roy G. Grainger. Cloud_cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties. Earth System Science Data 2020, 12, 2121 -2135.

AMA Style

Caroline A. Poulsen, Gregory R. McGarragh, Gareth E. Thomas, Martin Stengel, Matthew W. Christensen, Adam C. Povey, Simon R. Proud, Elisa Carboni, Rainer Hollmann, Roy G. Grainger. Cloud_cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties. Earth System Science Data. 2020; 12 (3):2121-2135.

Chicago/Turabian Style

Caroline A. Poulsen; Gregory R. McGarragh; Gareth E. Thomas; Martin Stengel; Matthew W. Christensen; Adam C. Povey; Simon R. Proud; Elisa Carboni; Rainer Hollmann; Roy G. Grainger. 2020. "Cloud_cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties." Earth System Science Data 12, no. 3: 2121-2135.

Report
Published: 23 July 2020 in Science
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Human activity causes vibrations that propagate into the ground as high-frequency seismic waves. Measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic caused widespread changes in human activity, leading to a months-long reduction in seismic noise of up to 50%. The 2020 seismic noise quiet period is the longest and most prominent global anthropogenic seismic noise reduction on record. Although the reduction is strongest at surface seismometers in populated areas, this seismic quiescence extends for many kilometers radially and hundreds of meters in depth. This quiet period provides an opportunity to detect subtle signals from subsurface seismic sources that would have been concealed in noisier times and to benchmark sources of anthropogenic noise. A strong correlation between seismic noise and independent measurements of human mobility suggests that seismology provides an absolute, real-time estimate of human activities.

ACS Style

Thomas Lecocq; Stephen P. Hicks; Koen Van Noten; Kasper Van Wijk; Paula Koelemeijer; Raphael S. M. De Plaen; Frédérick Massin; Gregor Hillers; Robert E. Anthony; Maria-Theresia Apoloner; Mario Arroyo-Solórzano; Jelle D. Assink; Pinar Büyükakpınar; Andrea Cannata; Flavio Cannavo; Sebastian Carrasco; Corentin Caudron; Esteban J. Chaves; David G. Cornwell; David Craig; Olivier F. C. Den Ouden; Jordi Diaz; Stefanie Donner; Christos P. Evangelidis; Läslo Evers; Benoit Fauville; Gonzalo A. Fernandez; Dimitrios Giannopoulos; Steven J. Gibbons; Társilo Girona; Bogdan Grecu; Marc Grunberg; György Hetényi; Anna Horleston; Adolfo Inza; Jessica C. E. Irving; Mohammadreza Jamalreyhani; Alan Kafka; Mathijs R. Koymans; Celeste R. Labedz; Eric Larose; Nathaniel J. Lindsey; Mika McKinnon; Tobias Megies; Meghan S. Miller; William Minarik; Louis Moresi; Víctor H. Márquez-Ramírez; Martin Möllhoff; Ian M. Nesbitt; Shankho Niyogi; Javier Ojeda; Adrien Oth; Simon Proud; Jay Pulli; Lise Retailleau; Annukka E. Rintamäki; Claudio Satriano; Martha K. Savage; Shahar Shani-Kadmiel; Reinoud Sleeman; Efthimios Sokos; Klaus Stammler; Alexander E. Stott; Shiba Subedi; Mathilde B. Sørensen; Taka'aki Taira; Mar Tapia; Fatih Turhan; Ben Van Der Pluijm; Mark Vanstone; Jerome Vergne; Tommi A. T. Vuorinen; Tristram Warren; Joachim Wassermann; Han Xiao. Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures. Science 2020, 369, 1338 -1343.

AMA Style

Thomas Lecocq, Stephen P. Hicks, Koen Van Noten, Kasper Van Wijk, Paula Koelemeijer, Raphael S. M. De Plaen, Frédérick Massin, Gregor Hillers, Robert E. Anthony, Maria-Theresia Apoloner, Mario Arroyo-Solórzano, Jelle D. Assink, Pinar Büyükakpınar, Andrea Cannata, Flavio Cannavo, Sebastian Carrasco, Corentin Caudron, Esteban J. Chaves, David G. Cornwell, David Craig, Olivier F. C. Den Ouden, Jordi Diaz, Stefanie Donner, Christos P. Evangelidis, Läslo Evers, Benoit Fauville, Gonzalo A. Fernandez, Dimitrios Giannopoulos, Steven J. Gibbons, Társilo Girona, Bogdan Grecu, Marc Grunberg, György Hetényi, Anna Horleston, Adolfo Inza, Jessica C. E. Irving, Mohammadreza Jamalreyhani, Alan Kafka, Mathijs R. Koymans, Celeste R. Labedz, Eric Larose, Nathaniel J. Lindsey, Mika McKinnon, Tobias Megies, Meghan S. Miller, William Minarik, Louis Moresi, Víctor H. Márquez-Ramírez, Martin Möllhoff, Ian M. Nesbitt, Shankho Niyogi, Javier Ojeda, Adrien Oth, Simon Proud, Jay Pulli, Lise Retailleau, Annukka E. Rintamäki, Claudio Satriano, Martha K. Savage, Shahar Shani-Kadmiel, Reinoud Sleeman, Efthimios Sokos, Klaus Stammler, Alexander E. Stott, Shiba Subedi, Mathilde B. Sørensen, Taka'aki Taira, Mar Tapia, Fatih Turhan, Ben Van Der Pluijm, Mark Vanstone, Jerome Vergne, Tommi A. T. Vuorinen, Tristram Warren, Joachim Wassermann, Han Xiao. Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures. Science. 2020; 369 (6509):1338-1343.

Chicago/Turabian Style

Thomas Lecocq; Stephen P. Hicks; Koen Van Noten; Kasper Van Wijk; Paula Koelemeijer; Raphael S. M. De Plaen; Frédérick Massin; Gregor Hillers; Robert E. Anthony; Maria-Theresia Apoloner; Mario Arroyo-Solórzano; Jelle D. Assink; Pinar Büyükakpınar; Andrea Cannata; Flavio Cannavo; Sebastian Carrasco; Corentin Caudron; Esteban J. Chaves; David G. Cornwell; David Craig; Olivier F. C. Den Ouden; Jordi Diaz; Stefanie Donner; Christos P. Evangelidis; Läslo Evers; Benoit Fauville; Gonzalo A. Fernandez; Dimitrios Giannopoulos; Steven J. Gibbons; Társilo Girona; Bogdan Grecu; Marc Grunberg; György Hetényi; Anna Horleston; Adolfo Inza; Jessica C. E. Irving; Mohammadreza Jamalreyhani; Alan Kafka; Mathijs R. Koymans; Celeste R. Labedz; Eric Larose; Nathaniel J. Lindsey; Mika McKinnon; Tobias Megies; Meghan S. Miller; William Minarik; Louis Moresi; Víctor H. Márquez-Ramírez; Martin Möllhoff; Ian M. Nesbitt; Shankho Niyogi; Javier Ojeda; Adrien Oth; Simon Proud; Jay Pulli; Lise Retailleau; Annukka E. Rintamäki; Claudio Satriano; Martha K. Savage; Shahar Shani-Kadmiel; Reinoud Sleeman; Efthimios Sokos; Klaus Stammler; Alexander E. Stott; Shiba Subedi; Mathilde B. Sørensen; Taka'aki Taira; Mar Tapia; Fatih Turhan; Ben Van Der Pluijm; Mark Vanstone; Jerome Vergne; Tommi A. T. Vuorinen; Tristram Warren; Joachim Wassermann; Han Xiao. 2020. "Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures." Science 369, no. 6509: 1338-1343.

Journal article
Published: 21 February 2020 in Aerospace
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The decision of a flight crew to undertake a go-around, aborting a landing attempt, is primarily to ensure the safe conduct of a flight. Although go-arounds are rare, they do cause air traffic disruption, especially in busy airspace, due to the need to accommodate an aircraft in an unusual position, and a go-around can also result in knock-on delays due to the time taken for the aircraft to re-position, fit into the landing sequence and execute a successful landing. Therefore, it is important to understand and alleviate the factors that can result in a go-around. In this paper, I present a new method for automatically detecting go-around events in aircraft position data, such as that sent via the ADS-B system, and apply the method to one year of approach data for Chhatrapati Shivaji Maharaj International Airport (VABB) in Mumbai, India. I show that the method is significantly more accurate than other methods, detecting go-arounds with very few false positives or negatives. Finally, I use the new method to reveal that while there is no one cause for go-arounds at this airport, the majority can be attributed to weather and/or an unstable approach. I also show that one runway (14/32) has a significantly higher proportion of go-arounds than the other (09/27).

ACS Style

Simon Richard Proud. Go-Around Detection Using Crowd-Sourced ADS-B Position Data. Aerospace 2020, 7, 16 .

AMA Style

Simon Richard Proud. Go-Around Detection Using Crowd-Sourced ADS-B Position Data. Aerospace. 2020; 7 (2):16.

Chicago/Turabian Style

Simon Richard Proud. 2020. "Go-Around Detection Using Crowd-Sourced ADS-B Position Data." Aerospace 7, no. 2: 16.

Preprint content
Published: 11 December 2019
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We present version 3 (V3) of the Cloud_cci ATSR-2/AATSR dataset. The dataset was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) program. The cloud properties were retrieved from the second Along- Track Scanning Radiometer (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the Advanced ATSR (AATSR) on board Envisat, which spanned 2002–2012. The data comprises a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius and optical thickness alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top and bottom-of-atmosphere short- and long-wave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with collocated CALIPSO data. The cloud masking has improved significantly, particularly the ability to detect clear pixels The Kuiper Skill score has increased from .49 to .66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulous cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments, the Correlation with MAC-LWP increase from .4 to over .8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth’s Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new dataset is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol-cloud interactions, and providing contextual information for collocated ATSR-2/AATSR surface temperature and aerosol products. For the Cloud_cci ATSR-2/AATSRv3 dataset a new digital identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 Poulsen et al. (2019).

ACS Style

Caroline A. Poulsen; Gregory R. McGarragh; Gareth E. Thomas; Martin Stengel; Matthew W. Christiensen; Adam C. Povey; Simon R. Proud; Elisa Carboni; Rainer Hollmann; Roy G. Grainger. Cloud_cci ATSR-2 and AATSR dataset version 3: a 17-yearclimatology of global cloud and radiation properties. 2019, 2019, 1 -21.

AMA Style

Caroline A. Poulsen, Gregory R. McGarragh, Gareth E. Thomas, Martin Stengel, Matthew W. Christiensen, Adam C. Povey, Simon R. Proud, Elisa Carboni, Rainer Hollmann, Roy G. Grainger. Cloud_cci ATSR-2 and AATSR dataset version 3: a 17-yearclimatology of global cloud and radiation properties. . 2019; 2019 ():1-21.

Chicago/Turabian Style

Caroline A. Poulsen; Gregory R. McGarragh; Gareth E. Thomas; Martin Stengel; Matthew W. Christiensen; Adam C. Povey; Simon R. Proud; Elisa Carboni; Rainer Hollmann; Roy G. Grainger. 2019. "Cloud_cci ATSR-2 and AATSR dataset version 3: a 17-yearclimatology of global cloud and radiation properties." 2019, no. : 1-21.

Journal article
Published: 13 June 2018 in Atmospheric Measurement Techniques
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The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20 %.

ACS Style

Gregory R. McGarragh; Caroline Poulsen; Gareth E. Thomas; Adam Povey; Oliver Sus; Stefan Stapelberg; Cornelia Schlundt; Simon Proud; Matthew W. Christensen; Martin Stengel; Rainer Hollmann; Roy G. Grainger. The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach. Atmospheric Measurement Techniques 2018, 11, 3397 -3431.

AMA Style

Gregory R. McGarragh, Caroline Poulsen, Gareth E. Thomas, Adam Povey, Oliver Sus, Stefan Stapelberg, Cornelia Schlundt, Simon Proud, Matthew W. Christensen, Martin Stengel, Rainer Hollmann, Roy G. Grainger. The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach. Atmospheric Measurement Techniques. 2018; 11 (6):3397-3431.

Chicago/Turabian Style

Gregory R. McGarragh; Caroline Poulsen; Gareth E. Thomas; Adam Povey; Oliver Sus; Stefan Stapelberg; Cornelia Schlundt; Simon Proud; Matthew W. Christensen; Martin Stengel; Rainer Hollmann; Roy G. Grainger. 2018. "The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach." Atmospheric Measurement Techniques 11, no. 6: 3397-3431.

Journal article
Published: 13 June 2018 in Atmospheric Measurement Techniques
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We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02∘. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.

ACS Style

Oliver Sus; Martin Stengel; Stefan Stapelberg; Gregory McGarragh; Caroline Poulsen; Adam C. Povey; Cornelia Schlundt; Gareth Thomas; Matthew Christensen; Simon Proud; Matthias Jerg; Roy Grainger; Rainer Hollmann. The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors. Atmospheric Measurement Techniques 2018, 11, 3373 -3396.

AMA Style

Oliver Sus, Martin Stengel, Stefan Stapelberg, Gregory McGarragh, Caroline Poulsen, Adam C. Povey, Cornelia Schlundt, Gareth Thomas, Matthew Christensen, Simon Proud, Matthias Jerg, Roy Grainger, Rainer Hollmann. The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors. Atmospheric Measurement Techniques. 2018; 11 (6):3373-3396.

Chicago/Turabian Style

Oliver Sus; Martin Stengel; Stefan Stapelberg; Gregory McGarragh; Caroline Poulsen; Adam C. Povey; Cornelia Schlundt; Gareth Thomas; Matthew Christensen; Simon Proud; Matthias Jerg; Roy Grainger; Rainer Hollmann. 2018. "The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors." Atmospheric Measurement Techniques 11, no. 6: 3373-3396.

Journal article
Published: 23 November 2017 in Earth System Science Data
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New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude–longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.For each dataset a digital object identifier has been issued:Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

ACS Style

Martin Stengel; Stefan Stapelberg; Oliver Sus; Cornelia Schlundt; Caroline Poulsen; Gareth Thomas; Matthew Christensen; Cintia Carbajal Henken; Rene Preusker; Jürgen Fischer; Abhay Devasthale; Ulrika Willén; Karl-Göran Karlsson; Gregory R. McGarragh; Simon Proud; Adam Povey; Roy G. Grainger; Jan Fokke Meirink; Artem Feofilov; Ralf Bennartz; Jedrzej S. Bojanowski; Rainer Hollmann. Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project. Earth System Science Data 2017, 9, 881 -904.

AMA Style

Martin Stengel, Stefan Stapelberg, Oliver Sus, Cornelia Schlundt, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Cintia Carbajal Henken, Rene Preusker, Jürgen Fischer, Abhay Devasthale, Ulrika Willén, Karl-Göran Karlsson, Gregory R. McGarragh, Simon Proud, Adam Povey, Roy G. Grainger, Jan Fokke Meirink, Artem Feofilov, Ralf Bennartz, Jedrzej S. Bojanowski, Rainer Hollmann. Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project. Earth System Science Data. 2017; 9 (2):881-904.

Chicago/Turabian Style

Martin Stengel; Stefan Stapelberg; Oliver Sus; Cornelia Schlundt; Caroline Poulsen; Gareth Thomas; Matthew Christensen; Cintia Carbajal Henken; Rene Preusker; Jürgen Fischer; Abhay Devasthale; Ulrika Willén; Karl-Göran Karlsson; Gregory R. McGarragh; Simon Proud; Adam Povey; Roy G. Grainger; Jan Fokke Meirink; Artem Feofilov; Ralf Bennartz; Jedrzej S. Bojanowski; Rainer Hollmann. 2017. "Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project." Earth System Science Data 9, no. 2: 881-904.

Journal article
Published: 07 November 2017 in Atmospheric Chemistry and Physics
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Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloud–aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (−0.28±0.26 W m−2) compared to those including pairs that are within 15 km of the nearest cloud (−0.49±0.18 W m−2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds.

ACS Style

Matthew W. Christensen; David Neubauer; Caroline Poulsen; Gareth E. Thomas; Gregory R. McGarragh; Adam Povey; Simon R. Proud; Roy G. Grainger. Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate. Atmospheric Chemistry and Physics 2017, 17, 13151 -13164.

AMA Style

Matthew W. Christensen, David Neubauer, Caroline Poulsen, Gareth E. Thomas, Gregory R. McGarragh, Adam Povey, Simon R. Proud, Roy G. Grainger. Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate. Atmospheric Chemistry and Physics. 2017; 17 (21):13151-13164.

Chicago/Turabian Style

Matthew W. Christensen; David Neubauer; Caroline Poulsen; Gareth E. Thomas; Gregory R. McGarragh; Adam Povey; Simon R. Proud; Roy G. Grainger. 2017. "Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate." Atmospheric Chemistry and Physics 17, no. 21: 13151-13164.

Preprint content
Published: 18 October 2017
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The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model which, includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), the "fast" radiative transfer solution (which includes a multiple scattering treatment) All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modelling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the non-linear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors ranging up to 20 %.

ACS Style

Gregory R. McGarragh; Caroline A. Poulsen; Gareth E. Thomas; Adam C. Povey; Oliver Sus; Stefan Stapelberg; Cornelia Schlundt; Simon Proud; Matthew W. Christensen; Martin Stengel; Rainer Hollmann; Roy G. Grainger. The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach. 2017, 2017, 1 -55.

AMA Style

Gregory R. McGarragh, Caroline A. Poulsen, Gareth E. Thomas, Adam C. Povey, Oliver Sus, Stefan Stapelberg, Cornelia Schlundt, Simon Proud, Matthew W. Christensen, Martin Stengel, Rainer Hollmann, Roy G. Grainger. The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach. . 2017; 2017 ():1-55.

Chicago/Turabian Style

Gregory R. McGarragh; Caroline A. Poulsen; Gareth E. Thomas; Adam C. Povey; Oliver Sus; Stefan Stapelberg; Cornelia Schlundt; Simon Proud; Matthew W. Christensen; Martin Stengel; Rainer Hollmann; Roy G. Grainger. 2017. "The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach." 2017, no. : 1-55.

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Published: 17 October 2017
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We present the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time-series provided at various resolutions, from 0.5° to 0.02°. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework, and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high-latitudes and over the Gulf of Guinea/West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one data set, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.

ACS Style

Oliver Sus; Martin Stengel; Stefan Stapelberg; Gregory McGarragh; Caroline Poulsen; Adam C. Povey; Cornelia Schlundt; Gareth Thomas; Matthew Christensen; Simon Proud; Matthias Jerg; Roy Grainger; Rainer Hollmann. The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors. 2017, 2017, 1 -42.

AMA Style

Oliver Sus, Martin Stengel, Stefan Stapelberg, Gregory McGarragh, Caroline Poulsen, Adam C. Povey, Cornelia Schlundt, Gareth Thomas, Matthew Christensen, Simon Proud, Matthias Jerg, Roy Grainger, Rainer Hollmann. The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors. . 2017; 2017 ():1-42.

Chicago/Turabian Style

Oliver Sus; Martin Stengel; Stefan Stapelberg; Gregory McGarragh; Caroline Poulsen; Adam C. Povey; Cornelia Schlundt; Gareth Thomas; Matthew Christensen; Simon Proud; Matthias Jerg; Roy Grainger; Rainer Hollmann. 2017. "The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors." 2017, no. : 1-42.

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Published: 20 June 2017
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New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude-longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named: AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. Together with the ensured spectral consistency and rigorous uncertainty propagation though all processing levels, the Cloud_cci datasets approach new benchmarks for climate data records of cloud properties based on passive imaging sensors. For each dataset a Digital Object Identifier has been issued: Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002 Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002 Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002 Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002 Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002 Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

ACS Style

Martin Stengel; Stefan Stapelberg; Oliver Sus; Cornelia Schlundt; Caroline Poulsen; Gareth Thomas; Matthew Christensen; Cintia Carbajal Henken; Rene Preusker; Jürgen Fischer; Abhay Devasthale; Ulrika Willén; Karl-Göran Karlsson; Gregory R. McGarragh; Simon Proud; Adam C. Povey; Don G. Grainger; Jan Fokke Meirink; Artem Feofilov; Ralf Bennartz; Jedrzej Bojanowski; Rainer Hollmann. Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project. 2017, 2017, 1 -34.

AMA Style

Martin Stengel, Stefan Stapelberg, Oliver Sus, Cornelia Schlundt, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Cintia Carbajal Henken, Rene Preusker, Jürgen Fischer, Abhay Devasthale, Ulrika Willén, Karl-Göran Karlsson, Gregory R. McGarragh, Simon Proud, Adam C. Povey, Don G. Grainger, Jan Fokke Meirink, Artem Feofilov, Ralf Bennartz, Jedrzej Bojanowski, Rainer Hollmann. Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project. . 2017; 2017 ():1-34.

Chicago/Turabian Style

Martin Stengel; Stefan Stapelberg; Oliver Sus; Cornelia Schlundt; Caroline Poulsen; Gareth Thomas; Matthew Christensen; Cintia Carbajal Henken; Rene Preusker; Jürgen Fischer; Abhay Devasthale; Ulrika Willén; Karl-Göran Karlsson; Gregory R. McGarragh; Simon Proud; Adam C. Povey; Don G. Grainger; Jan Fokke Meirink; Artem Feofilov; Ralf Bennartz; Jedrzej Bojanowski; Rainer Hollmann. 2017. "Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project." 2017, no. : 1-34.

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Published: 19 May 2017
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Increased concentrations of aerosol can enhance the albedo of warm lowlevel cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3D radiative transfer) clouds. To screen for this contamination, we have developed a new 5 Cloud-Aerosol Pairing Algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. Results from two independent satellite imagers, the Advanced Along Track Scanning Radiometer (AATSR) and MODerate Resolution Imaging Spectroradiometer (MODIS) show a marked reduction in the strength of the intrinsic aerosol indirect forcing when selecting aerosol pairs that are located farther away from the clouds (−0.28 ± 0.26 W/m2) compared to those 10 including pairs that are within 15 km of the nearest cloud (−0.49 ± 0.18 W/m2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to including retrieval artefacts in the aerosol located near clouds.

ACS Style

Matthew W. Christensen; David Neubauer; Caroline Poulsen; Gareth Thomas; Greg McGarragh; Adam C. Povey; Simon Proud; Roy G. Grainger. Unveiling aerosol-cloud interactions Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate. 2017, 2017, 1 -21.

AMA Style

Matthew W. Christensen, David Neubauer, Caroline Poulsen, Gareth Thomas, Greg McGarragh, Adam C. Povey, Simon Proud, Roy G. Grainger. Unveiling aerosol-cloud interactions Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate. . 2017; 2017 ():1-21.

Chicago/Turabian Style

Matthew W. Christensen; David Neubauer; Caroline Poulsen; Gareth Thomas; Greg McGarragh; Adam C. Povey; Simon Proud; Roy G. Grainger. 2017. "Unveiling aerosol-cloud interactions Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate." 2017, no. : 1-21.

Journal article
Published: 01 October 2015 in Weather
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Simon Richard Proud. Analysis of aircraft flights near convective weather over Europe. Weather 2015, 70, 292 -296.

AMA Style

Simon Richard Proud. Analysis of aircraft flights near convective weather over Europe. Weather. 2015; 70 (10):292-296.

Chicago/Turabian Style

Simon Richard Proud. 2015. "Analysis of aircraft flights near convective weather over Europe." Weather 70, no. 10: 292-296.

Journal article
Published: 13 August 2015 in Atmospheric Chemistry and Physics
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Atmospheric aerosol particles are often partially or completely composed of inorganic salts, such as ammonium sulfate and sodium chloride, and therefore exhibit hygroscopic properties. Many inorganic salts have well-defined deliquescence and efflorescence points at which they take up and lose water, respectively. Field measurements have shown that atmospheric aerosols are not typically pure inorganic salt, instead, they often also contain organic species. There is ample evidence from laboratory studies that suggests that mixed particles exist in a phase-separated state, with an aqueous inorganic core and organic shell. Although phase separation has not been measured in situ, there is no reason it would not also take place in the atmosphere. Here, we investigate the deliquescence and efflorescence points, phase separation and ability to exchange gas-phase components of mixed organic and inorganic aerosol using a flow tube coupled with FTIR (Fourier transform infrared) spectroscopy. Ammonium sulfate aerosol mixed with organic polyols with different O : C ratios, including 1,4-butanediol, glycerol, 1,2,6-hexanetriol, 1,2-hexanediol, and 1,5-pentanediol have been investigated. Those constituents correspond to materials found in the atmosphere in great abundance and, therefore, particles prepared in this study should mimic atmospheric mixed-phase aerosol particles. Some results of this study tend to be in agreement with previous microscopy experiments, but others, such as phase separation properties of 1,2,6-hexanetriol, do not agree with previous work. Because the particles studied in this experiment are of a smaller size than those used in microscopy studies, the discrepancies found could be a size-related effect.

ACS Style

M. A. Zawadowicz; Simon Proud; S. S. Seppalainen; D. J. Cziczo. Hygroscopic and phase separation properties of ammonium sulfate/organics/water ternary solutions. Atmospheric Chemistry and Physics 2015, 15, 8975 -8986.

AMA Style

M. A. Zawadowicz, Simon Proud, S. S. Seppalainen, D. J. Cziczo. Hygroscopic and phase separation properties of ammonium sulfate/organics/water ternary solutions. Atmospheric Chemistry and Physics. 2015; 15 (15):8975-8986.

Chicago/Turabian Style

M. A. Zawadowicz; Simon Proud; S. S. Seppalainen; D. J. Cziczo. 2015. "Hygroscopic and phase separation properties of ammonium sulfate/organics/water ternary solutions." Atmospheric Chemistry and Physics 15, no. 15: 8975-8986.

Journal article
Published: 01 March 2015 in Remote Sensing of Environment
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Jørgen L. Olsen; Simon Stisen; Simon R. Proud; Rasmus Fensholt. Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin. Remote Sensing of Environment 2015, 159, 57 -69.

AMA Style

Jørgen L. Olsen, Simon Stisen, Simon R. Proud, Rasmus Fensholt. Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin. Remote Sensing of Environment. 2015; 159 ():57-69.

Chicago/Turabian Style

Jørgen L. Olsen; Simon Stisen; Simon R. Proud; Rasmus Fensholt. 2015. "Evaluating EO-based canopy water stress from seasonally detrended NDVI and SIWSI with modeled evapotranspiration in the Senegal River Basin." Remote Sensing of Environment 159, no. : 57-69.

Journal article
Published: 24 February 2015 in IEEE Geoscience and Remote Sensing Letters
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Polar mesospheric clouds (PMCs) form at very high altitude (around 80 km) over high latitudes in the Northern and Southern Hemispheres. Because of their altitude, seasonality, and tenuous nature, PMCs are not well understood. This letter presents a method for detecting PMCs using geostationary satellite images of the Earth's limb. The method is tested against data from the Aeronomy of Ice in the Mesosphere mission that was specifically designed for PMC detection. It is found that the new method is successful in detecting seasonal trends in PMC formation and, due to the larger data set of geostationary images, may allow examination of the temporal properties of PMCs in greater detail than possible by satellites in polar orbit.

ACS Style

Simon Proud. Observation of Polar Mesospheric Clouds by Geostationary Satellite Sensors. IEEE Geoscience and Remote Sensing Letters 2015, 12, 1332 -1336.

AMA Style

Simon Proud. Observation of Polar Mesospheric Clouds by Geostationary Satellite Sensors. IEEE Geoscience and Remote Sensing Letters. 2015; 12 (6):1332-1336.

Chicago/Turabian Style

Simon Proud. 2015. "Observation of Polar Mesospheric Clouds by Geostationary Satellite Sensors." IEEE Geoscience and Remote Sensing Letters 12, no. 6: 1332-1336.

Journal article
Published: 02 February 2015 in Remote Sensing
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A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.

ACS Style

Alireza Taravat; Simon Proud; Simone Peronaci; Fabio Del Frate; Natascha Oppelt. Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking. Remote Sensing 2015, 7, 1529 -1539.

AMA Style

Alireza Taravat, Simon Proud, Simone Peronaci, Fabio Del Frate, Natascha Oppelt. Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking. Remote Sensing. 2015; 7 (2):1529-1539.

Chicago/Turabian Style

Alireza Taravat; Simon Proud; Simone Peronaci; Fabio Del Frate; Natascha Oppelt. 2015. "Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking." Remote Sensing 7, no. 2: 1529-1539.

Journal article
Published: 16 July 2014 in Quarterly Journal of the Royal Meteorological Society
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Simon R. Proud. Analysis of overshooting top detections by Meteosat Second Generation: a 5-year dataset. Quarterly Journal of the Royal Meteorological Society 2014, 141, 909 -915.

AMA Style

Simon R. Proud. Analysis of overshooting top detections by Meteosat Second Generation: a 5-year dataset. Quarterly Journal of the Royal Meteorological Society. 2014; 141 (688):909-915.

Chicago/Turabian Style

Simon R. Proud. 2014. "Analysis of overshooting top detections by Meteosat Second Generation: a 5-year dataset." Quarterly Journal of the Royal Meteorological Society 141, no. 688: 909-915.