scholarly journals Cloud and aerosol classification for 2 1/2 years of MAX-DOAS observations in Wuxi (China) and comparison to independent data sets

2015 ◽  
Vol 8 (5) ◽  
pp. 4653-4709 ◽  
Author(s):  
Y. Wang ◽  
M. Penning de Vries ◽  
P. H. Xie ◽  
S. Beirle ◽  
S. Dörner ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Original Algorithm ◽  
Clear Sky ◽  
Cloud Parameters ◽  
Cloud Classification ◽  
Aerosol Classification ◽  
Cloud Optical Thickness ◽  
Sky Conditions

Abstract. Multi-Axis-Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of trace gases can be strongly influenced by clouds and aerosols. Thus it is important to identify clouds and characterise their properties. In a recent study Wagner et al. (2014) developed a cloud classification scheme based on the MAX-DOAS measurements themselves with which different "sky conditions" (e.g. clear sky, continuous clouds, broken clouds) can be distinguished. Here we apply this scheme to long term MAX-DOAS measurements from 2011 to 2013 in Wuxi, China (31.57° N, 120.31° E). The original algorithm has been modified, in particular in order to account for smaller solar zenith angles (SZA). Instrumental degradation is accounted for to avoid artificial trends of the cloud classification. We compared the results of the MAX-DOAS cloud classification scheme to several independent measurements: aerosol optical depth from a nearby AERONET station and from MODIS, visibility derived from a visibility meter; and various cloud parameters from different satellite instruments (MODIS, OMI, and GOME-2). The most important findings from these comparisons are: (1) most cases characterized as clear sky with low or high aerosol load were associated with the respective AOD ranges obtained by AERONET and MODIS, (2) the observed dependences of MAX-DOAS results on cloud optical thickness and effective cloud fraction from satellite indicate that the cloud classification scheme is sensitive to cloud (optical) properties, (3) separation of cloudy scenes by cloud pressure shows that the MAX-DOAS cloud classification scheme is also capable of detecting high clouds, (4) some clear sky conditions, especially with high aerosol load, classified from MAX-DOAS observations corresponding to the optically thin and low clouds derived by satellite observations probably indicate that the satellite cloud products contain valuable information on aerosols.

scholarly journals Cloud and aerosol classification for 2.5 years of MAX-DOAS observations in Wuxi (China) and comparison to independent data sets

2015 ◽  
Vol 8 (12) ◽  
pp. 5133-5156 ◽  
Author(s):  
Y. Wang ◽  
M. Penning de Vries ◽  
P. H. Xie ◽  
S. Beirle ◽  
S. Dörner ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Classification Scheme ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Independent Data ◽  
Original Algorithm ◽  
Clear Sky ◽  
Cloud Classification ◽  
Ozone Monitoring

Abstract. Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of trace gases can be strongly influenced by clouds and aerosols. Thus it is important to identify clouds and characterize their properties. In a recent study Wagner et al. (2014) developed a cloud classification scheme based on the MAX-DOAS measurements themselves with which different "sky conditions" (e.g., clear sky, continuous clouds, broken clouds) can be distinguished. Here we apply this scheme to long-term MAX-DOAS measurements from 2011 to 2013 in Wuxi, China (31.57° N, 120.31° E). The original algorithm has been adapted to the characteristics of the Wuxi instrument, and extended towards smaller solar zenith angles (SZA). Moreover, a method for the determination and correction of instrumental degradation is developed to avoid artificial trends of the cloud classification results. We compared the results of the MAX-DOAS cloud classification scheme to several independent measurements: aerosol optical depth from a nearby Aerosol Robotic Network (AERONET) station and from two Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, visibility derived from a visibility meter and various cloud parameters from different satellite instruments (MODIS, the Ozone Monitoring Instrument (OMI) and the Global Ozone Monitoring Experiment (GOME-2)). Here it should be noted that no quantitative comparison between the MAX-DOAS results and the independent data sets is possible, because (a) not exactly the same quantities are measured, and (b) the spatial and temporal sampling is quite different. Thus our comparison is performed in a semi-quantitative way: the MAX-DOAS cloud classification results are studied as a function of the external quantities. The most important findings from these comparisons are as follows: (1) most cases characterized as clear sky with low or high aerosol load were associated with the respective aerosol optical depth (AOD) ranges obtained by AERONET and MODIS; (2) the observed dependences of MAX-DOAS results on cloud optical thickness and effective cloud fraction from satellite confirm that the MAX-DOAS cloud classification scheme is sensitive to cloud (optical) properties; (3) the separation of cloudy scenes by cloud pressure shows that the MAX-DOAS cloud classification scheme is also capable of detecting high clouds; (4) for some cloud-free conditions, especially with high aerosol load, the coincident satellite observations indicated optically thin and low clouds. This finding indicates that the satellite cloud products contain valuable information on aerosols.

scholarly journals Cloud detection and classification based on MAX-DOAS observations

2013 ◽  
Vol 6 (6) ◽  
pp. 10297-10360 ◽  
Author(s):  
T. Wagner ◽  
S. Beirle ◽  
S. Dörner ◽  
U. Friess ◽  
J. Remmers ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Scattered Light ◽  
Trace Gases ◽  
Colour Index ◽  
Optical Absorption Spectroscopy ◽  
Cloud Detection ◽  
Clear Sky ◽  
Cloud Classification ◽  
Good Agreement ◽  
Light Ring

Abstract. Multi-AXis-Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of aerosols and trace gases can be strongly influenced by clouds. Thus it is important to identify clouds and characterise their properties. In this study we investigate the effects of clouds on several quantities which can be derived from MAX-DOAS observations, like the radiance, the colour index (radiance ratio at two selected wavelengths), the absorption of the oxygen dimer O4 and the fraction of inelastically scattered light (Ring effect). To identify clouds, these quantities can be either compared to their corresponding clear sky reference values, or their dependencies on time or viewing direction can be analysed. From the investigation of the temporal variability the influence of clouds can be identified even for individual measurements. Based on our investigations we developed a cloud classification scheme, which can be applied in a flexible way to MAX-DOAS or zenith DOAS observations: in its simplest version, zenith observations of the colour index are used to identify the presence of clouds (or high aerosol load). In more sophisticated versions, also other quantities and viewing directions are considered, which allows sub-classifications like e.g. thin or thick clouds, or fog. We applied our cloud classification scheme to MAX-DOAS observations during the CINDI campaign in the Netherlands in Summer 2009 and found very good agreement with sky images taken from ground.

scholarly journals Absolute calibration of the colour index and O<sub>4</sub> absorption derived from Multi AXis (MAX-)DOAS measurements and their application to a standardised cloud classification algorithm

2016 ◽  
Vol 9 (9) ◽  
pp. 4803-4823 ◽  
Author(s):  
Thomas Wagner ◽  
Steffen Beirle ◽  
Julia Remmers ◽  
Reza Shaiganfar ◽  
Yang Wang
Keyword(s):  
Classification Scheme ◽  
Classification Algorithm ◽  
Colour Index ◽  
Optical Absorption Spectroscopy ◽  
Future Research ◽  
Absolute Calibration ◽  
Atmospheric Conditions ◽  
Threshold Values ◽  
Original Algorithm ◽  
Cloud Classification

Abstract. A method is developed for the calibration of the colour index (CI) and the O4 absorption derived from differential optical absorption spectroscopy (DOAS) measurements of scattered sunlight. The method is based on the comparison of measurements and radiative transfer simulations for well-defined atmospheric conditions and viewing geometries. Calibrated measurements of the CI and the O4 absorption are important for the detection and classification of clouds from MAX-DOAS observations. Such information is needed for the identification and correction of the cloud influence on Multi AXis (MAX-)DOAS profile inversion results, but might be also be of interest on their own, e.g. for meteorological applications. The calibration algorithm was successfully applied to measurements at two locations: Cabauw in the Netherlands and Wuxi in China. We used CI and O4 observations calibrated by the new method as input for our recently developed cloud classification scheme and also adapted the corresponding threshold values accordingly. For the observations at Cabauw, good agreement is found with the results of the original algorithm. Together with the calibration procedure of the CI and O4 absorption, the cloud classification scheme, which has been tuned to specific locations/conditions so far, can now be applied consistently to MAX-DOAS measurements at different locations. In addition to the new threshold values, further improvements were introduced to the cloud classification algorithm, namely a better description of the SZA (solar zenith angle) dependence of the threshold values and a new set of wavelengths for the determination of the CI. We also indicate specific areas for future research to further improve the cloud classification scheme.

scholarly journals Cloud detection and classification based on MAX-DOAS observations

2014 ◽  
Vol 7 (5) ◽  
pp. 1289-1320 ◽  
Author(s):  
T. Wagner ◽  
A. Apituley ◽  
S. Beirle ◽  
S. Dörner ◽  
U. Friess ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Scattered Light ◽  
Colour Index ◽  
Optical Absorption Spectroscopy ◽  
Measuring Instruments ◽  
Cloud Detection ◽  
Clear Sky ◽  
Cloud Classification ◽  
Good Agreement ◽  
Light Ring

Abstract. Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of aerosols and trace gases can be strongly influenced by clouds. Thus, it is important to identify clouds and characterise their properties. In this study we investigate the effects of clouds on several quantities which can be derived from MAX-DOAS observations, like radiance, the colour index (radiance ratio at two selected wavelengths), the absorption of the oxygen dimer O4 and the fraction of inelastically scattered light (Ring effect). To identify clouds, these quantities can be either compared to their corresponding clear-sky reference values, or their dependencies on time or viewing direction can be analysed. From the investigation of the temporal variability the influence of clouds can be identified even for individual measurements. Based on our investigations we developed a cloud classification scheme, which can be applied in a flexible way to MAX-DOAS or zenith DOAS observations: in its simplest version, zenith observations of the colour index are used to identify the presence of clouds (or high aerosol load). In more sophisticated versions, other quantities and viewing directions are also considered, which allows subclassifications like, e.g., thin or thick clouds, or fog. We applied our cloud classification scheme to MAX-DOAS observations during the Cabauw intercomparison campaign of Nitrogen Dioxide measuring instruments (CINDI) campaign in the Netherlands in summer 2009 and found very good agreement with sky images taken from the ground and backscatter profiles from a lidar.

scholarly journals Absolute calibration of the colour index and O<sub>4</sub> absorption derived from Multi-AXis (MAX-) DOAS measurements and their application to a standardised cloud classification algorithm

2016 ◽  
Author(s):  
Thomas Wagner ◽  
Steffen Beirle ◽  
Julia Remmers ◽  
Reza Shaiganfar ◽  
Yang Wang
Keyword(s):  
Classification Scheme ◽  
Classification Algorithm ◽  
Colour Index ◽  
Optical Absorption Spectroscopy ◽  
Future Research ◽  
Absolute Calibration ◽  
Atmospheric Conditions ◽  
Threshold Values ◽  
Original Algorithm ◽  
Cloud Classification

Abstract. A method is developed for the calibration of the colour index (CI) and the O4 absorption derived from Differential Optical Absorption Spectroscopy (DOAS) measurements of scattered sunlight. The method is based on the comparison of measurements and radiative transfer simulations for well-defined atmospheric conditions and viewing geometries. Calibrated measurements of the CI and the O4 absorption are important for the detection and classification of clouds from MAX-DOAS observations. Such information is needed for the identification and correction of the cloud influence on Multi-AXis (MAX-) DOAS profile inversion results, but might be also be of interest on their own, e.g. for meteorological applications. The calibration algorithm was successfully applied to measurements at two locations: Cabauw in the Netherlands and Wuxi in China. We used CI and O4 observations calibrated by the new method as input to our recently developed cloud classification scheme and adapted also the corresponding threshold values accordingly. For the observations at Cabauw good agreement is found with the results of the original algorithm. Together with the calibration procedure of the CI and O4 absorption the cloud classification scheme, which was tuned to specific locations/conditions so far, can now be applied consistently to MAX-DOAS measurements at different locations. In addition to the new threshold values, further improvements were introduced to the cloud classification algorithm, namely a better description of the SZA dependence of the threshold values and a new set of wavelengths for the determination of the CI. We also indicate specific areas for future research to further improve the cloud classification scheme.

scholarly journals Total Column Water Vapour Retrieval from S-5P/TROPOMI in the Visible Blue Spectral Range

2020 ◽  
Author(s):  
Christian Borger ◽  
Steffen Beirle ◽  
Steffen Dörner ◽  
Holger Sihler ◽  
Thomas Wagner
Keyword(s):  
Water Vapour ◽  
Spectral Range ◽  
Input Data ◽  
Reference Data ◽  
Boreal Summer ◽  
Data Sets ◽  
Clear Sky ◽  
Sky Conditions ◽  
Total Column ◽  
Good Agreement

Abstract. Total column water vapour has been retrieved from TROPOMI measurements in the visible blue spectral range and compared to a variety of different reference data sets for clear-sky conditions during boreal summer and winter. The retrieval consists of the common two-step DOAS approach: first the spectral analysis is performed within a linearized scheme and then the retrieved slant column densities are converted to vertical columns using an iterative scheme for the water vapour a priori profile shape which is based on an empirical parameterization of the water vapour scale height. Moreover, a modified albedo map was used combining the OMI LER albedo and scaled MODIS albedo map. The use of the alternative albedo is especially important over regions with very low albedo and high probability of clouds like the Amazon region. The errors of the TCWV retrieval have been theoretically estimated considering the contribution of a variety of different uncertainty sources. For observations during clear-sky conditions, over ocean surface, and at low solar zenith angles the error typically is around values of 10–20 % and during cloudy-sky conditions, over land surface, and at high solar zenith angles it reaches values around 20–50 %. In the framework of a validation study the retrieval demonstrates that it can well capture the global water vapour distribution: the retrieved H2O VCDs show very good agreement to the reference data sets over ocean for boreal summer and winter whereby the modified albedo map substantially improves the retrieval's consistency to the reference data sets in particular over tropical landmasses. However over land the retrieval underestimates the VCD by about 10 %, particularly during summertime. Our investigations show that this underestimation is likely caused by uncertainties within the surface albedo and the cloud input data: Low level clouds cause an underestimation but for mid to high level clouds good agreement is found. In addition, our investigations indicate that these biases can probably be further reduced by the use of updated cloud input data. The TCWV retrieval can be easily applied to further satellite sensors (e.g. GOME-2 or OMI) for creating uniform measurement data sets on longterm which is particularly interesting for climate and trend studies of water vapour.

Clarity Index Analysis and Modeling Using Probability Distribution Functions in Campo Grande-MS, Brazil

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Amaury de Souza ◽  
Razika Ihaddadene ◽  
Nabila Ihaddadene ◽  
Pelumi E. Oguntunde
Keyword(s):  
Low Frequency ◽  
Distribution Functions ◽  
Data Sets ◽  
Atmospheric Conditions ◽  
Mato Grosso ◽  
Clear Sky ◽  
Probability Distribution Functions ◽  
Index Analysis ◽  
Sky Conditions ◽  
Mato Grosso Do Sul

The importance of statistical analysis in the field of energy for environmental engineering is shown in this research paper, in which the adequacy of the data sets of clarity index with the model of “best” probability (based on the criteria used) was studied. In Campo Grande which is the capital of the Brazilian state of Mato Grosso do Sul, located in the Center-West region of the country, there is a predominance of the atmospheric conditions of low cloudiness, with a high frequency of days with a clear sky and in consequence a low-frequency of days with cloudy sky. The aerosols resulting from the burning of sugarcane influence the sky conditions in Campo Grande thus reducing the frequency of the clear sky.

scholarly journals From pixels to patches: a cloud classification method based on bag of micro-structures

2015 ◽  
Vol 8 (10) ◽  
pp. 10213-10247 ◽  
Author(s):  
Q. Li ◽  
Z. Zhang ◽  
W. Lu ◽  
J. Yang ◽  
Y. Ma ◽  
...  
Keyword(s):  
Image Representation ◽  
Feature Space ◽  
Classification Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Clear Sky ◽  
Cloud Classification ◽  
Image Patches ◽  
Sky Conditions ◽  
Micro Structures

Abstract. Automatic cloud classification has attracted more and more attention with the increasing development of whole sky imagers, but it is still in progress for ground-based cloud observation. This paper proposes a new cloud classification method, named bag of micro-structures (BoMS). This method treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. And then it constructs an image representation with a weighted histogram of micro-structures. Lastly, a support vector machine (SVM) classifier is applied on the image representation because SVM is appealing for sparse and high dimensional feature space. Five different sky conditions are identified: cirriform, cumuliform, stratiform, clear sky and mixed cloudiness that often appears in all-sky images but is seldom addressed in literature. BoMS is evaluated on a large dataset, which contains 5000 all-sky images that are captured by a total-sky cloud imager located in Tibet (29.25° N, 88.88° E). BoMS achieves an accuracy of 90.9 % for 10 fold cross-validation, and it outperforms the state-of-the-art method with an increase of about 19 %. Furthermore, influence of key parameters in BoMS are investigated to verify their robustness.

scholarly journals Estimates of free-tropospheric NO<sub>2</sub> and HCHO mixing ratios derived from high-altitude mountain MAX-DOAS observations at midlatitudes and in the tropics

2016 ◽  
Vol 16 (5) ◽  
pp. 2803-2817 ◽  
Author(s):  
Stefan F. Schreier ◽  
Andreas Richter ◽  
Folkard Wittrock ◽  
John P. Burrows
Keyword(s):  
Optical Path Length ◽  
Radiative Transfer Model ◽  
Absolute Difference ◽  
Optical Absorption Spectroscopy ◽  
Ultraviolet Region ◽  
Transfer Model ◽  
Geometrical Approach ◽  
Mixing Ratios ◽  
Clear Sky ◽  
Sky Conditions

Abstract. In this study, mixing ratios of NO2 (XNO2) and HCHO (XHCHO) in the free troposphere are derived from two multi-axis differential optical absorption spectroscopy (MAX-DOAS) data sets collected at Zugspitze (2650 m a.s.l., Germany) and Pico Espejo (4765 m a.s.l., Venezuela). The estimation of NO2 and HCHO mixing ratios is based on the modified geometrical approach, which assumes a single-scattering geometry and a scattering point altitude close to the instrument altitude. Firstly, the horizontal optical path length (hOPL) is obtained from O4 differential slant column densities (DSCDs) in the horizontal (0°) and vertical (90°) viewing directions. Secondly, XNO2 and XHCHO are estimated from the NO2 and HCHO DSCDs at the 0° and 90° viewing directions and averaged along the obtained hOPLs. As the MAX-DOAS instrument was performing measurements in the ultraviolet region, wavelength ranges of 346–372 and 338–357 nm are selected for the DOAS analysis to retrieve NO2 and HCHO DSCDs, respectively. In order to compare the measured O4 DSCDs and moreover to perform some sensitivity tests, the radiative transfer model SCIATRAN with adapted altitude settings for mountainous terrain is operated to simulate synthetic spectra, on which the DOAS analysis is also applied. The overall agreement between measured and synthetic O4 DSCDs is better for the higher Pico Espejo station than for Zugspitze. Further sensitivity analysis shows that a change in surface albedo (from 0.05 to 0.7) can influence the O4 DSCDs, with a larger absolute difference observed for the horizontal viewing direction. Consequently, the hOPL can vary by about 5 % throughout the season, for example when winter snow cover fully disappears in summer. Typical values of hOPLs during clear-sky conditions are 19 km (14 km) at Zugspitze and 34 km (26.5 km) at Pico Espejo when using the 346–372 (338–357 nm) fitting window. The estimated monthly values of XNO2 (XHCHO), averaged over these hOPLs during clear-sky conditions, are in the range of 60–100 ppt (500–950 ppt) at Zugspitze and 8.5–15.5 ppt (255–385 ppt) at Pico Espejo. Interestingly, multi-year-averaged monthly means of XNO2 and XHCHO increase towards the end of the dry season at the Pico Espejo site, suggesting that both trace gases are frequently lifted above the boundary layer as a result of South American biomass burning.

scholarly journals Automatic cloud classification of whole sky images

2010 ◽  
Vol 3 (3) ◽  
pp. 557-567 ◽  
Author(s):  
A. Heinle ◽  
A. Macke ◽  
A. Srivastav
Keyword(s):  
High Performance ◽  
Clear Sky ◽  
Cloud Classification ◽  
Stratiform Clouds ◽  
Stratocumulus Clouds ◽  
Temporal And Spatial ◽  
Nearest Neighbour Classifier ◽  
Leave One Out ◽  
Sky Conditions

Abstract. The recently increasing development of whole sky imagers enables temporal and spatial high-resolution sky observations. One application already performed in most cases is the estimation of fractional sky cover. A distinction between different cloud types, however, is still in progress. Here, an automatic cloud classification algorithm is presented, based on a set of mainly statistical features describing the color as well as the texture of an image. The k-nearest-neighbour classifier is used due to its high performance in solving complex issues, simplicity of implementation and low computational complexity. Seven different sky conditions are distinguished: high thin clouds (cirrus and cirrostratus), high patched cumuliform clouds (cirrocumulus and altocumulus), stratocumulus clouds, low cumuliform clouds, thick clouds (cumulonimbus and nimbostratus), stratiform clouds and clear sky. Based on the Leave-One-Out Cross-Validation the algorithm achieves an accuracy of about 97%. In addition, a test run of random images is presented, still outperforming previous algorithms by yielding a success rate of about 75%, or up to 88% if only "serious" errors with respect to radiation impact are considered. Reasons for the decrement in accuracy are discussed, and ideas to further improve the classification results, especially in problematic cases, are investigated.

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