scholarly journals Comment on 'Temporal and spectral cloud screening of polar-winter aerosol optical depth (AOD) : impact of homogeneous and inhomogeneous clouds and crystal layers on climatological-scale AODs' by N. T. O'Neill et al.

2016 ◽  
Author(s):  
Anonymous
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Cloud Screening

scholarly journals Daytime and nighttime aerosol optical depth implementation in CÆLIS

2020 ◽  
Vol 9 (2) ◽  
pp. 417-433 ◽  
Author(s):  
Ramiro González ◽  
Carlos Toledano ◽  
Roberto Román ◽  
David Fuertes ◽  
Alberto Berjón ◽  
...  
Keyword(s):  
Quality Control ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Processing System ◽  
Software Tool ◽  
Mean Differences ◽  
The University ◽  
Phase Angles ◽  
Climate Studies ◽  
Cloud Screening

Abstract. The University of Valladolid (UVa, Spain) has managed a calibration center of the AErosol RObotic NETwork (AERONET) since 2006. The CÆLIS software tool, developed by UVa, was created to manage the data generated by AERONET photometers for calibration, quality control and data processing purposes. This paper exploits the potential of this tool in order to obtain products like the aerosol optical depth (AOD) and Ångström exponent (AE), which are of high interest for atmospheric and climate studies, as well as to enhance the quality control of the instruments and data managed by CÆLIS. The AOD and cloud screening algorithms implemented in CÆLIS, both based on AERONET version 3, are described in detail. The obtained products are compared with the AERONET database. In general, the differences in daytime AOD between CÆLIS and AERONET are far below the expected uncertainty of the instrument, ranging in mean differences between -1.3×10-4 at 870 nm and 6.2×10-4 at 380 nm. The standard deviations of the differences range from 2.8×10-4 at 675 nm to 8.1×10-4 at 340 nm. The AOD and AE at nighttime calculated by CÆLIS from Moon observations are also presented, showing good continuity between day and nighttime for different locations, aerosol loads and Moon phase angles. Regarding cloud screening, around 99.9 % of the observations classified as cloud-free by CÆLIS are also assumed cloud-free by AERONET; this percentage is similar for the cases considered cloud-contaminated by both databases. The obtained results point out the capability of CÆLIS as a processing system. The AOD algorithm provides the opportunity to use this tool with other instrument types and to retrieve other aerosol products in the future.

scholarly journals Day- and night-time aerosol optical depth implementation in CÆLIS

2020 ◽  
Author(s):  
Ramiro González ◽  
Carlos Toledano ◽  
Roberto Román ◽  
David Fuertes ◽  
Alberto Berjón ◽  
...  
Keyword(s):  
Quality Control ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Processing System ◽  
Software Tool ◽  
Night Time ◽  
Mean Differences ◽  
The University ◽  
Phase Angles ◽  
Cloud Screening

Abstract. The University of Valladolid (UVa, Spain) manages since 2006 a calibration center of the AErosol RObotic NETwork (AERONET). The CÆLIS software tool, developed by UVa, was created to manage the data generated by the AERONET photometers, for calibration, quality control and data processing purposes. This paper exploits the potential of this tool in order to obtain products like the aerosol optical depth (AOD) and Angstrom exponent (AE), which are of high interest for atmospheric and climate studies, as well as to enhance the quality control of the instruments and data managed by CÆLIS. The AOD and cloud screening algorithms implemented in CÆLIS, both based on AERONET version 3, are described in detail. The obtained products are compared with the AERONET database. In general, the differences in daytime AOD between CÆLIS and AERONET are far below the expected uncertainty of the instrument, ranging the mean differences between −1.3×10−4 at 870 nm and 6.2×10−4 at 380 nm. The standard deviations of the differences range from 2.8×10−4 at 675 nm to 8.1×10−4 at 340 nm. The AOD and AE at night-time calculated by CÆLIS from Moon observations are also presented, showing good continuity between day and night-time for different locations, aerosol loads and moon phase angles. Regarding cloud screening, around 99.9 % of the observations classified as cloud-free by CÆLIS are also assumed cloud-free by AERONET; this percentage is similar for the cases considered as cloud-contaminated by both databases. The obtained results point out the capability of CÆLIS as processing system. The AOD algorithm provides the opportunity to use this tool with other instrument types and to retrieve other aerosol products in the future.

scholarly journals Reducing cloud contamination in aerosol optical depth (AOD) measurements

2021 ◽  
Vol 14 (4) ◽  
pp. 2787-2798
Author(s):  
Verena Schenzinger ◽  
Axel Kreuter
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Screening Method ◽  
Nearest Neighbour ◽  
Cloud Detection ◽  
Simple Method ◽  
Scanning Time ◽  
Filter Radiometer ◽  
Cloud Screening

Abstract. We propose a new cloud screening method for sun photometry that is designed to effectively filter thin clouds. Our method is based on a k-nearest-neighbour algorithm instead of scanning time series of aerosol optical depth. Using 10 years of data from a precision filter radiometer in Innsbruck, we compare our new method and the currently employed screening technique. We exemplify the performance of the two routines in different cloud conditions. While both algorithms agree on the classification of a data point as clear or cloudy in a majority of the cases, the new routine is found to be more effective in flagging thin clouds. We conclude that this simple method can serve as a valid alternative for cloud detection, and we discuss the generalizability to other observation sites.

scholarly journals Advancements in the Aerosol Robotic Network (AERONET) Version 3 Database – Automated Near Real-Time Quality Control Algorithm with Improved Cloud Screening for Sun Photometer Aerosol Optical Depth (AOD) Measurements

2018 ◽  
Author(s):  
David M. Giles ◽  
Alexander Sinyuk ◽  
Mikhail S. Sorokin ◽  
Joel S. Schafer ◽  
Alexander Smirnov ◽  
...  
Keyword(s):  
Quality Control ◽  
Standard Deviation ◽  
Real Time ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Near Infrared ◽  
Data Set ◽  
Automatic Data ◽  
Field Calibration ◽  
Cloud Screening

Abstract. The Aerosol Robotic Network (AERONET) provides highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun/Sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near real-time AOD was semi-automatically quality controlled utilizing mainly cloud screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to manually quality control millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near real-time data as well as post-field deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near real-time uncertainty estimate where average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 standard deviation, yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 standard deviation. The high statistical agreement in multi-year monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.

scholarly journals Aerosol Optical Depth comparison between GAW-PFR and AERONET-Cimel radiometers from long term (2005–2015) 1-minute synchronous measurements

2018 ◽  
Author(s):  
Emilio Cuevas ◽  
Pedro Miguel Romero-Campos ◽  
Natalia Kouremeti ◽  
Stelios Kazadzis ◽  
Rosa Delia García ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Rayleigh Scattering ◽  
Field Of View ◽  
Filter Radiometer ◽  
The World ◽  
Comprehensive Comparison ◽  
Cloud Screening ◽  
Fixed Period

Abstract. A comprehensive comparison of more than 70000 synchronous 1-minute aerosol optical depth (AOD) data from three Global Atmosphere Watch-Precision Filter Radiometer (GAW-PFR) and 15 Aerosol Robotic Network-Cimel (AERONET-Cimel) radiometers was performed for the four nearby wavelengths (380, 440, 500 and 870 nm) in the period 2005–2015. The goal of this study is to assess whether, despite the marked differences between both networks and the number of instruments used, their long term AOD data are comparable and consistent. AOD traceability established by the World Meteorological Organization (WMO) consists in determining the percentage of synchronous data within specific limits. If, at least, 95 % of the AOD differences of an instrument compared to the WMO standards lie within these limits, both data populations are considered equivalent. The percentage of traceable data is 92.7 % (380 nm), 95.7 % (440 nm), 95.8 % (500 nm) and 98.0 % (870 nm). When small misalignments in GAW-PFR sun-pointing were fixed (period 2010–2015), the percentage of traceable data increased. The contribution of calibration related aspects to comparison outside the 95 % traceability limits is insignificant in all channels, except in 380 nm. The simultaneous failure of both cloud screening algorithms might occur only under the presence of cirrus, or altostratus clouds on the top of a dust-laden Saharan air layer. Differences in the calculation of the optical depth contribution due to Rayleigh scattering, and O3 and NO2 absorption have a negligible impact. For AOD > 0.1, a non-negligible percentage (≈ 1.9 %) of the AOD data outside the 95 % traceability limits at 380 nm can be partly assigned to the different field of view of the instruments. The comparison of the Angström exponent (AE) shows that under non-pristine conditions (AOD > 0.03 and AE

scholarly journals Reducing cloud contamination in AOD measurements

2020 ◽  
Author(s):  
Verena Schenzinger ◽  
Axel Kreuter
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Screening Method ◽  
New Method ◽  
Nearest Neighbour ◽  
Cloud Detection ◽  
Simple Method ◽  
Filter Radiometer ◽  
Cloud Screening

Abstract. We propose a new cloud screening method for sun photometry that is designed to effectively filter thin clouds. Our method is based on a k-nearest neighbour algorithm instead of scanning timeseries of aerosol optical depth. Using ten years of data from a precision filter radiometer in Innsbruck, we compare our new method and the currently employed screening technique. We exemplify the performance of the two routines in different cloud conditions. While both algorithms agree on the classification of a datapoint as clear or cloudy in a majority of the cases, the new routine is found to be more effective in flagging thin clouds. We conclude that this simple method can serve as a valid alternative for cloud detection, and discuss the generalizability to other observation sites.

scholarly journals Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements

2019 ◽  
Vol 12 (1) ◽  
pp. 169-209 ◽  
Author(s):  
David M. Giles ◽  
Alexander Sinyuk ◽  
Mikhail G. Sorokin ◽  
Joel S. Schafer ◽  
Alexander Smirnov ◽  
...  
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Near Infrared ◽  
Time Data ◽  
Data Set ◽  
Automatic Data ◽  
Field Calibration ◽  
Cloud Screening

Abstract. The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.

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