F. Bender, Tobias Lord, Anna Staffansdotter, Verena Jung, Sabine Undorf
Aerosol effects on cloud properties are notoriously difficult to disentangle from variations driven by meteorological factors. Here, a machine learning model is trained on reanalysis data and satellite retrievals to predict cloud microphysical properties, as a way to illustrate the relative importance of meteorology and aerosol, respectively, on cloud properties. It is found that cloud droplet effective radius can be predicted with some skill from only meteorological information, including estimated air mass origin and cloud top height. For ten geographical regions the mean coefficient of determination is 0.41 and normalised root-mean square error 24%. The machine learning model thereby performs better than a reference linear regression model, and a model predicting the climatological mean. A gradient boosting regression performs on par with a neural network regression model. Adding aerosol information as input to the model improves its skill somewhat, but the difference is small and the direction of the influence of changing aerosol burden on cloud droplet effective radius is not consistent across regions, and thereby also not always consistent with what is expected from cloud brightening.
{"title":"Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties","authors":"F. Bender, Tobias Lord, Anna Staffansdotter, Verena Jung, Sabine Undorf","doi":"10.16993/tellusb.1868","DOIUrl":"https://doi.org/10.16993/tellusb.1868","url":null,"abstract":"Aerosol effects on cloud properties are notoriously difficult to disentangle from variations driven by meteorological factors. Here, a machine learning model is trained on reanalysis data and satellite retrievals to predict cloud microphysical properties, as a way to illustrate the relative importance of meteorology and aerosol, respectively, on cloud properties. It is found that cloud droplet effective radius can be predicted with some skill from only meteorological information, including estimated air mass origin and cloud top height. For ten geographical regions the mean coefficient of determination is 0.41 and normalised root-mean square error 24%. The machine learning model thereby performs better than a reference linear regression model, and a model predicting the climatological mean. A gradient boosting regression performs on par with a neural network regression model. Adding aerosol information as input to the model improves its skill somewhat, but the difference is small and the direction of the influence of changing aerosol burden on cloud droplet effective radius is not consistent across regions, and thereby also not always consistent with what is expected from cloud brightening.","PeriodicalId":22320,"journal":{"name":"Tellus B: Chemical and Physical Meteorology","volume":"7 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate quantification of air-sea gas transfer velocity is critical for our understanding of air-sea CO2 gas fluxes, global carbon budget and climate responses. CO2 transfer velocity is predominantly subject to constraints of wave-related dynamic processes at the ocean surface layer but is typically parameterized with wind speed. This study proposes and compares two parameterizations which accommodate dimensionless wave terms. The validations are conducted using both laboratory and field measurements of CO2 transfer and wave statistics. A scaling of bubble-mediated gas transfer is implemented into the formula that is linked to wave breaking probability. The improved parameterizations are capable of collapsing combined laboratory and field data sets which comprise diversified conditions of wind, wave and wave breaking.
{"title":"Dimensionless Parameterizations of Air-Sea CO2 Gas Transfer Velocity on Surface Waves","authors":"Shuo Li, A. Babanin, Changlong Guan","doi":"10.16993/tellusb.1897","DOIUrl":"https://doi.org/10.16993/tellusb.1897","url":null,"abstract":"Accurate quantification of air-sea gas transfer velocity is critical for our understanding of air-sea CO2 gas fluxes, global carbon budget and climate responses. CO2 transfer velocity is predominantly subject to constraints of wave-related dynamic processes at the ocean surface layer but is typically parameterized with wind speed. This study proposes and compares two parameterizations which accommodate dimensionless wave terms. The validations are conducted using both laboratory and field measurements of CO2 transfer and wave statistics. A scaling of bubble-mediated gas transfer is implemented into the formula that is linked to wave breaking probability. The improved parameterizations are capable of collapsing combined laboratory and field data sets which comprise diversified conditions of wind, wave and wave breaking.","PeriodicalId":22320,"journal":{"name":"Tellus B: Chemical and Physical Meteorology","volume":"304 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88036897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sebastian Böö, A. Ekman, G. Svensson, A. Devasthale
{"title":"Transport of Mineral Dust Into the Arctic in Two Reanalysis Datasets of Atmospheric Composition","authors":"Sebastian Böö, A. Ekman, G. Svensson, A. Devasthale","doi":"10.16993/tellusb.1866","DOIUrl":"https://doi.org/10.16993/tellusb.1866","url":null,"abstract":"","PeriodicalId":22320,"journal":{"name":"Tellus B: Chemical and Physical Meteorology","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82516387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. Artaxo, H. Hansson, M. Andreae, J. Bäck, E. Alves, H. Barbosa, F. Bender, E. Bourtsoukidis, S. Carbone, J. Chi, S. Decesari, V. Després, F. Ditas, E. Ezhova, S. Fuzzi, N. Hasselquist, J. Heintzenberg, B. Holanda, A. Guenther, H. Hakola, L. Heikkinen, V. Kerminen, J. Kontkanen, R. Krejci, M. Kulmala, J. Lavrič, G. de Leeuw, K. Lehtipalo, L. Machado, G. Mcfiggans, M. A. Franco, B. Meller, F. Morais, C. Mohr, W. Morgan, M. Nilsson, M. Peichl, T. Petäjä, M. Prass, C. Pöhlker, M. Pöhlker, U. Pöschl, C. von Randow, I. Riipinen, J. Rinne, Luciana V. Rizzo, D. Rosenfeld, M. Dias, L. Sogacheva, P. Stier, E. Swietlicki, M. Sörgel, P. Tunved, A. Virkkula, Jian Wang, B. Weber, A. Yáñez-Serrano, P. Zieger, E. Mikhailov, J. Smith, J. Kesselmeier
{"title":"Tropical and Boreal Forest – Atmosphere Interactions: A Review","authors":"P. Artaxo, H. Hansson, M. Andreae, J. Bäck, E. Alves, H. Barbosa, F. Bender, E. Bourtsoukidis, S. Carbone, J. Chi, S. Decesari, V. Després, F. Ditas, E. Ezhova, S. Fuzzi, N. Hasselquist, J. Heintzenberg, B. Holanda, A. Guenther, H. Hakola, L. Heikkinen, V. Kerminen, J. Kontkanen, R. Krejci, M. Kulmala, J. Lavrič, G. de Leeuw, K. Lehtipalo, L. Machado, G. Mcfiggans, M. A. Franco, B. Meller, F. Morais, C. Mohr, W. Morgan, M. Nilsson, M. Peichl, T. Petäjä, M. Prass, C. Pöhlker, M. Pöhlker, U. Pöschl, C. von Randow, I. Riipinen, J. Rinne, Luciana V. Rizzo, D. Rosenfeld, M. Dias, L. Sogacheva, P. Stier, E. Swietlicki, M. Sörgel, P. Tunved, A. Virkkula, Jian Wang, B. Weber, A. Yáñez-Serrano, P. Zieger, E. Mikhailov, J. Smith, J. Kesselmeier","doi":"10.16993/tellusb.34","DOIUrl":"https://doi.org/10.16993/tellusb.34","url":null,"abstract":"","PeriodicalId":22320,"journal":{"name":"Tellus B: Chemical and Physical Meteorology","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82230861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Climatic Role of Interactive Leaf Phenology in the Vegetation-Atmosphere System of Radiative-Convective Equilibrium Storm-Resolving Simulations","authors":"Junhong Lee, C. Hohenegger, A. Chlond, R. Schnur","doi":"10.16993/tellusb.26","DOIUrl":"https://doi.org/10.16993/tellusb.26","url":null,"abstract":"","PeriodicalId":22320,"journal":{"name":"Tellus B: Chemical and Physical Meteorology","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77893986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Dipu, M. Schwarz, A. Ekman, E. Gryspeerdt, T. Goren, O. Sourdeval, J. Mülmenstädt, J. Quaas
Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (Nd) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between Nd and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses adiabatic assumption to derive the Nd (NAd), we have also considered NAd from the LES model for comparison. The NAd-LWP relationship in the satellite and the LES model show similar, generally positive, but non-monotonic relations. This case over continent thus behaves differently compared to previously-published analysis of oceanic clouds, and the analysis illustrates a regime dependency (marine and continental) in the NAd-LWP relation in the satellite retrievals. The study further explores the impact of the satellite retrieval assumptions on the Nd-LWP relationship. When considering the relationship of the actually simulated cloud-top Nd, rather than NAd, with LWP, the result shows a much more nonlinear relationship. The difference is much less pronounced, however, for shallow stratiform than for convective clouds. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear Nd-LWP relationship. Hence a regime based Nd-LWP analysis is even more relevant when it comes to continental clouds.
{"title":"Exploring satellite-derived relationships between cloud droplet number concentration and liquid water path using a large-domain large-eddy simulation","authors":"S. Dipu, M. Schwarz, A. Ekman, E. Gryspeerdt, T. Goren, O. Sourdeval, J. Mülmenstädt, J. Quaas","doi":"10.5194/dach2022-252","DOIUrl":"https://doi.org/10.5194/dach2022-252","url":null,"abstract":"<div class=\"page\" title=\"Page 1\">\u0000<div class=\"layoutArea\">\u0000<div class=\"column\">\u0000<p>Important aspects of the adjustments to aerosol-cloud interactions can be examined using the relationship between cloud droplet number concentration (Nd) and liquid water path (LWP). Specifically, this relation can constrain the role of aerosols in leading to thicker or thinner clouds in response to adjustment mechanisms. This study investigates the satellite retrieved relationship between Nd and LWP for a selected case of mid-latitude continental clouds using high-resolution Large-eddy simulations (LES) over a large domain in weather prediction mode. Since the satellite retrieval uses adiabatic assumption to derive the Nd (NAd), we have also considered NAd from the LES model for comparison. The NAd-LWP relationship in the satellite and the LES model show similar, generally positive, but non-monotonic relations. This case over continent thus behaves differently compared to previously-published analysis of oceanic clouds, and the analysis illustrates a regime dependency (marine and continental) in the NAd-LWP relation in the satellite retrievals. The study further explores the impact of the satellite retrieval assumptions on the Nd-LWP relationship. When considering the relationship of the actually simulated cloud-top Nd, rather than NAd, with LWP, the result shows a much more nonlinear relationship. The difference is much less pronounced, however, for shallow stratiform than for convective clouds. Comparing local vs large-scale statistics from satellite data shows that continental clouds exhibit only a weak nonlinear Nd-LWP relationship. Hence a regime based Nd-LWP analysis is even more relevant when it comes to continental clouds.</p>\u0000</div>\u0000</div>\u0000</div>","PeriodicalId":22320,"journal":{"name":"Tellus B: Chemical and Physical Meteorology","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84614772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/16000889.2021.1971925
H. Andersen, J. Cermak, Roland Stirnberg, J. Fuchs, Miae Kim, E. Pauli
Abstract Aerosols are a critical component of the climate system and a risk to human health. Here, the lockdown response to the coronavirus outbreak is used to analyse effects of dramatic reduction in anthropogenic aerosol sources on satellite-retrieved aerosol optical depth (AOD). A machine learning model is applied to estimate daily AOD during the initial lockdown in China in early 2020. The model uses information on aerosol climatology, geography and meteorological conditions, and explains 69% of the day-to-day AOD variability. A comparison of model-expected and observed AOD shows that no clear, systematic decrease in AOD is apparent during the lockdown in China. During March 2020, regional AOD is observed to be significantly lower than expected by the machine learning model in some coastal regions of the North China Plains and extending to the Korean peninsula. While this may possibly indicate a small lockdown effect on regional AOD, and potentially pointing trans-boundary effects of the lockdown measures, due to uncertainties associated with the method and the limited sample sizes, this AOD decrease cannot be unequivocally attributed to reduced anthropogenic emissions. Climatologically expected AOD is compared to a weather-adjusted expectation of AOD, indicating that meteorological influences have acted to significantly increase AOD during this time, in agreement with recent literature. The findings highlight the complexity of aerosol variability and the challenges of observation-based attribution of columnar aerosol changes.
{"title":"Assessment of COVID-19 effects on satellite-observed aerosol loading over China with machine learning","authors":"H. Andersen, J. Cermak, Roland Stirnberg, J. Fuchs, Miae Kim, E. Pauli","doi":"10.1080/16000889.2021.1971925","DOIUrl":"https://doi.org/10.1080/16000889.2021.1971925","url":null,"abstract":"Abstract Aerosols are a critical component of the climate system and a risk to human health. Here, the lockdown response to the coronavirus outbreak is used to analyse effects of dramatic reduction in anthropogenic aerosol sources on satellite-retrieved aerosol optical depth (AOD). A machine learning model is applied to estimate daily AOD during the initial lockdown in China in early 2020. The model uses information on aerosol climatology, geography and meteorological conditions, and explains 69% of the day-to-day AOD variability. A comparison of model-expected and observed AOD shows that no clear, systematic decrease in AOD is apparent during the lockdown in China. During March 2020, regional AOD is observed to be significantly lower than expected by the machine learning model in some coastal regions of the North China Plains and extending to the Korean peninsula. While this may possibly indicate a small lockdown effect on regional AOD, and potentially pointing trans-boundary effects of the lockdown measures, due to uncertainties associated with the method and the limited sample sizes, this AOD decrease cannot be unequivocally attributed to reduced anthropogenic emissions. Climatologically expected AOD is compared to a weather-adjusted expectation of AOD, indicating that meteorological influences have acted to significantly increase AOD during this time, in agreement with recent literature. The findings highlight the complexity of aerosol variability and the challenges of observation-based attribution of columnar aerosol changes.","PeriodicalId":22320,"journal":{"name":"Tellus B: Chemical and Physical Meteorology","volume":"99 1","pages":"1 - 14"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72901084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/16000889.2021.1894879
Tingting Qin, Ju Wang, Ran Li, Chunsheng Fang
Abstract With the rapid development of industry and economy, the problem of ozone (O3) pollution in China is of increasing concern in recent years. The problem of ozone pollution has become worse in Baicheng City which belongs to the province of Jilin. We analyze the spatial and temporal distribution of surface ozone and its correlation with precursors and meteorological factors during 2015 and 2019 using data from the automatic Atmospheric Environmental Quality Monitoring Station located in Baicheng. Results showed that the daily and monthly mean values of O3 concentration were both single-peak patterns, and interannual variation presented a inverted ‘V’ pattern in Baicheng. The high concentration of O3 in a year generally occurs from May to July, with the highest daily concentration occurring in the afternoon. The total number of days with O3 concentration exceeding the limits was 35 days from 2015 to 2019, among which the number of days exceeding the maximal daily average eight-hour (MDA8) O3 of 100 μg/m3 was the highest in 2018, and the 90th percentile MDA8 showed an overall growth pattern. Seasonal variations in O3 concentration in 2018 and 2019 were not consistent with the three previous years.
{"title":"Diurnal and inter-annual variability of surface ozone in Baicheng region, China","authors":"Tingting Qin, Ju Wang, Ran Li, Chunsheng Fang","doi":"10.1080/16000889.2021.1894879","DOIUrl":"https://doi.org/10.1080/16000889.2021.1894879","url":null,"abstract":"Abstract With the rapid development of industry and economy, the problem of ozone (O3) pollution in China is of increasing concern in recent years. The problem of ozone pollution has become worse in Baicheng City which belongs to the province of Jilin. We analyze the spatial and temporal distribution of surface ozone and its correlation with precursors and meteorological factors during 2015 and 2019 using data from the automatic Atmospheric Environmental Quality Monitoring Station located in Baicheng. Results showed that the daily and monthly mean values of O3 concentration were both single-peak patterns, and interannual variation presented a inverted ‘V’ pattern in Baicheng. The high concentration of O3 in a year generally occurs from May to July, with the highest daily concentration occurring in the afternoon. The total number of days with O3 concentration exceeding the limits was 35 days from 2015 to 2019, among which the number of days exceeding the maximal daily average eight-hour (MDA8) O3 of 100 μg/m3 was the highest in 2018, and the 90th percentile MDA8 showed an overall growth pattern. Seasonal variations in O3 concentration in 2018 and 2019 were not consistent with the three previous years.","PeriodicalId":22320,"journal":{"name":"Tellus B: Chemical and Physical Meteorology","volume":"78 1","pages":"1 - 10"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74027431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/16000889.2021.1940758
Lijuan Chen, Ren Wang, Jiamei Han, Y. Zha
Abstract Use of multi-angle sensors, which can perform ground observations in different view directions, provides an important technical means to acquire more comprehensive and detailed remote sensing information. This study explored the effect of angle changes on satellite-based aerosol optical depth (AOD) retrieval using 2016–2018 Multi-angle Imaging SpectroRadiometer (MISR) data in the Yangtze River Delta. The Moderate resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by the MODIS V5.2 algorithm and MODIS BRDF data are used to obtain MISR surface reflectance at different angles after a spectral conversion between MODIS and MISR, and then MISR AOD with different angles were retrieved based on the MISR surface reflectance at different angles. Moreover, the errors and their formation mechanism in retrieving AOD using MISR remote sensing data at different angles were analyzed based on the Global Aerosol Robotic Network (AERONET) ground observations. We used six indicators, including correlation coefficient (R), significant level (P value), mean absolute error (MAE), mean relative error (MRE) root mean square error (RMSE) and relative mean bias (RMB), and AERONET AOD is used to verify the MISR AOD. The results show that at Taihu and Xuzhou stations, the highest R values are 0.81 (Aa AOD) and 0.75 (An AOD), and the minimum RMSE values at Taihu and Xuzhou-CUMT stations are 0.11 (Aa AOD) and 0.20 (An AOD), respectively. Incorrect surface reflectance measurement is the root cause of the angle effect in MISR AOD retrieval. With the same surface reflectance deviation, the larger the MISR observation angle, the closer the retrieved MISR AOD is to the AOD observed by AERONET. The AOD retrieval of the forward observation angle is closer to AERONET AOD than that of backward observation angle, and the retrieved error is greatest at the vertical angle (nadir). Therefore, sensors for vertical ground observation are not suitable for AOD retrieval, and sensors in non-vertical directions, especially forward to the ground, are more favorable to MISR AOD retrieval.
{"title":"Influence of observation angle change on satellite retrieval of aerosol optical depth","authors":"Lijuan Chen, Ren Wang, Jiamei Han, Y. Zha","doi":"10.1080/16000889.2021.1940758","DOIUrl":"https://doi.org/10.1080/16000889.2021.1940758","url":null,"abstract":"Abstract Use of multi-angle sensors, which can perform ground observations in different view directions, provides an important technical means to acquire more comprehensive and detailed remote sensing information. This study explored the effect of angle changes on satellite-based aerosol optical depth (AOD) retrieval using 2016–2018 Multi-angle Imaging SpectroRadiometer (MISR) data in the Yangtze River Delta. The Moderate resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by the MODIS V5.2 algorithm and MODIS BRDF data are used to obtain MISR surface reflectance at different angles after a spectral conversion between MODIS and MISR, and then MISR AOD with different angles were retrieved based on the MISR surface reflectance at different angles. Moreover, the errors and their formation mechanism in retrieving AOD using MISR remote sensing data at different angles were analyzed based on the Global Aerosol Robotic Network (AERONET) ground observations. We used six indicators, including correlation coefficient (R), significant level (P value), mean absolute error (MAE), mean relative error (MRE) root mean square error (RMSE) and relative mean bias (RMB), and AERONET AOD is used to verify the MISR AOD. The results show that at Taihu and Xuzhou stations, the highest R values are 0.81 (Aa AOD) and 0.75 (An AOD), and the minimum RMSE values at Taihu and Xuzhou-CUMT stations are 0.11 (Aa AOD) and 0.20 (An AOD), respectively. Incorrect surface reflectance measurement is the root cause of the angle effect in MISR AOD retrieval. With the same surface reflectance deviation, the larger the MISR observation angle, the closer the retrieved MISR AOD is to the AOD observed by AERONET. The AOD retrieval of the forward observation angle is closer to AERONET AOD than that of backward observation angle, and the retrieved error is greatest at the vertical angle (nadir). Therefore, sensors for vertical ground observation are not suitable for AOD retrieval, and sensors in non-vertical directions, especially forward to the ground, are more favorable to MISR AOD retrieval.","PeriodicalId":22320,"journal":{"name":"Tellus B: Chemical and Physical Meteorology","volume":"47 1","pages":"1 - 14"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89960284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1080/16000889.2021.1912958
J. Krzyścin, B. Rajewska-Więch, J. Borkowski
Abstract Total column ozone (TCO) monitoring with the Dobson spectrophotometer no. 84 have been carried out at Belsk (51°50′, 20°47′), Poland, since 23 March 1963. TCO observations were made for various combinations of double wavelength pairs (AD, CD, CC′) and instrument settings (direct Sun, zenith blue, and zenith cloudy). In total, results of 115,736 manual observations were recorded in the period 1963–2019. The following metrics of the intraday TCO variability are examined: standard deviation divided by the mean value and the difference between the daily maximum and minimum divided by the mean value. The mean value, standard deviation, and 5th–95th percentile range for the intraday changes of the metrics are {1.6%, 0.8%, 2.5%} and {4.3%, 2.3%, 7.3%,}, respectively. To examine interday TCO variability, one-day changes of the metrics and the daily mean TCO are analysed. The corresponding statistics for one-day change of TCO are {−0.2%, 6.9%, 22.6%}. The short-term TCO variability changed only slightly (if ever) since the beginning of the ozone observations at Belsk.
{"title":"Short-term variability of total column ozone from the Dobson spectrophotometer measurements at Belsk, Poland, in the period 23 March 1963–31 December 2019","authors":"J. Krzyścin, B. Rajewska-Więch, J. Borkowski","doi":"10.1080/16000889.2021.1912958","DOIUrl":"https://doi.org/10.1080/16000889.2021.1912958","url":null,"abstract":"Abstract Total column ozone (TCO) monitoring with the Dobson spectrophotometer no. 84 have been carried out at Belsk (51°50′, 20°47′), Poland, since 23 March 1963. TCO observations were made for various combinations of double wavelength pairs (AD, CD, CC′) and instrument settings (direct Sun, zenith blue, and zenith cloudy). In total, results of 115,736 manual observations were recorded in the period 1963–2019. The following metrics of the intraday TCO variability are examined: standard deviation divided by the mean value and the difference between the daily maximum and minimum divided by the mean value. The mean value, standard deviation, and 5th–95th percentile range for the intraday changes of the metrics are {1.6%, 0.8%, 2.5%} and {4.3%, 2.3%, 7.3%,}, respectively. To examine interday TCO variability, one-day changes of the metrics and the daily mean TCO are analysed. The corresponding statistics for one-day change of TCO are {−0.2%, 6.9%, 22.6%}. The short-term TCO variability changed only slightly (if ever) since the beginning of the ozone observations at Belsk.","PeriodicalId":22320,"journal":{"name":"Tellus B: Chemical and Physical Meteorology","volume":"66 1","pages":"1 - 10"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87168868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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