Pub Date : 2024-09-27DOI: 10.1016/j.atmosres.2024.107701
Bingchun Liu , Mingzhao Lai , Peng Zeng , Jiali Chen
Heatwaves pose significant threats to urban environments, affecting both ecological systems and public health, primarily through the exacerbation of air pollution. Accurate prediction of air pollutant concentrations during heatwave periods is crucial for authorities to develop timely prevention and control strategies. Thus, we developed the 1D-CNN-BiLSTM-attention model, specifically designed to account for the unique data characteristics associated with heatwave conditions. Our model leverages an attention mechanism to enhance its ability to learn and predict air pollutant behavior during heatwaves. Across six scenario-based experiments, the model demonstrated high predictive accuracy, achieving a MAPE of 2.93 %. The model integrates meteorological indicators such as temperature, humidity, wind speed, cloud cover, and precipitation, extending its predictive capability across a spatial range of 150 km. In experiments testing the model's applicability to three typical city types in the Yangtze River Delta region, the results confirmed its effectiveness in predicting air pollutants. These findings highlight the model's usefulness for studying air pollution during urban heatwave periods on a regional scale, demonstrating its robustness and reliability under varying weather conditions.
{"title":"Air pollutant prediction based on a attention mechanism model of the Yangtze River Delta region in frequent heatwaves","authors":"Bingchun Liu , Mingzhao Lai , Peng Zeng , Jiali Chen","doi":"10.1016/j.atmosres.2024.107701","DOIUrl":"10.1016/j.atmosres.2024.107701","url":null,"abstract":"<div><div>Heatwaves pose significant threats to urban environments, affecting both ecological systems and public health, primarily through the exacerbation of air pollution. Accurate prediction of air pollutant concentrations during heatwave periods is crucial for authorities to develop timely prevention and control strategies. Thus, we developed the 1D-CNN-BiLSTM-attention model, specifically designed to account for the unique data characteristics associated with heatwave conditions. Our model leverages an attention mechanism to enhance its ability to learn and predict air pollutant behavior during heatwaves. Across six scenario-based experiments, the model demonstrated high predictive accuracy, achieving a MAPE of 2.93 %. The model integrates meteorological indicators such as temperature, humidity, wind speed, cloud cover, and precipitation, extending its predictive capability across a spatial range of 150 km. In experiments testing the model's applicability to three typical city types in the Yangtze River Delta region, the results confirmed its effectiveness in predicting air pollutants. These findings highlight the model's usefulness for studying air pollution during urban heatwave periods on a regional scale, demonstrating its robustness and reliability under varying weather conditions.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"311 ","pages":"Article 107701"},"PeriodicalIF":4.5,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.atmosres.2024.107702
Junkai Liu , Xinwei Qian , Lu Peng , Dan Lou , Yiwen Li
In recent years, deep learning has been widely applied to meteorological radar extrapolation due to the shortcomings of traditional optical flow methods in predicting the genesis and dissipation of radar echoes. However, it still faces challenges in addressing issues of clarity and overall intensity attenuation caused by uncertainty. This study implemented a dual-path spatiotemporal attention network that integrates optical flow techniques by employing intra-frame static attention and inter-frame dynamic attention, which could simulate motion fields and the overall intensity distribution of radar echoes separately. Our approach effectively resolve the issues of systematic intensity attenuation and clarity degradation introduced by deep learning methods. Through the comparisons of key metrics such as MSE, SSIM, CSI20, CSI30, and CSI40, the results demonstrated significant improvements over traditional approaches, particularly in CSI30 and CSI40, where the metrics improved by more than 35 %.
{"title":"TEDR: A spatiotemporal attention radar extrapolation network constrained by optical flow and distribution correction","authors":"Junkai Liu , Xinwei Qian , Lu Peng , Dan Lou , Yiwen Li","doi":"10.1016/j.atmosres.2024.107702","DOIUrl":"10.1016/j.atmosres.2024.107702","url":null,"abstract":"<div><div>In recent years, deep learning has been widely applied to meteorological radar extrapolation due to the shortcomings of traditional optical flow methods in predicting the genesis and dissipation of radar echoes. However, it still faces challenges in addressing issues of clarity and overall intensity attenuation caused by uncertainty. This study implemented a dual-path spatiotemporal attention network that integrates optical flow techniques by employing intra-frame static attention and inter-frame dynamic attention, which could simulate motion fields and the overall intensity distribution of radar echoes separately. Our approach effectively resolve the issues of systematic intensity attenuation and clarity degradation introduced by deep learning methods. Through the comparisons of key metrics such as MSE, SSIM, CSI20, CSI30, and CSI40, the results demonstrated significant improvements over traditional approaches, particularly in CSI30 and CSI40, where the metrics improved by more than 35 %.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"311 ","pages":"Article 107702"},"PeriodicalIF":4.5,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.atmosres.2024.107706
Xin Li , Xiaolei Zou , Mingjian Zeng , Xiaoyong Zhuge , Yang Wu , Ning Wang
This study explores a possibility of improving Advanced Himawari Imager (AHI) surface-sensitive brightness temperature (TB) simulations over land by assimilating land surface temperature (LST) observations from the National Basic Meteorological Observing Stations of China. The Gridpoint Statistical Interpolation 3D-Var regional data assimilation (DA) system is modified to add LST as a new control variable and its background error variances, horizontal correlations and cross-correlations. The background covariances of LST with other control variables are calculated separately for daytime and nighttime samples in summer and winter seasons. A control experiment (ExpCTL) and three LST DA experiments with (ExpLST) and without (ExpLST_NBC) bias correction or with an average of LST within 2° × 2° grid boxes (ExpLST_SO) are conducted. Considering the fact that surface station observations are point measurements while the satellite TBs measure the total radiation effect of earth's surface within fields-of-view, a bias correction is found necessary for LST DA during daytimes (ExpLST). The biases are quantified by the differences from the Moderate-resolution Imaging Spectroradiometer LST retrievals to compensate for the representative differences. The analyzed fields are then used as input to the Community Radiative Transfer Model to simulate TBs of AHI surface-sensitive channels over land. A long-period statistics shows that ExpLST significantly reduces the observations minus simulations (OB) biases and standard deviations of surface-sensitive TBs in terms of reducing the diurnal variations and season dependences of TB biases over different surface types, which also outperforms ExpLST_NBC and ExpLST_SO at daytime. This study suggests a potential benefit of combining the use of LST observations for assimilating surface-sensitive infrared TBs.
{"title":"Surface temperature assimilation improving geostationary meteorological satellite surface-sensitive brightness temperature simulations over land","authors":"Xin Li , Xiaolei Zou , Mingjian Zeng , Xiaoyong Zhuge , Yang Wu , Ning Wang","doi":"10.1016/j.atmosres.2024.107706","DOIUrl":"10.1016/j.atmosres.2024.107706","url":null,"abstract":"<div><div>This study explores a possibility of improving Advanced Himawari Imager (AHI) surface-sensitive brightness temperature (TB) simulations over land by assimilating land surface temperature (LST) observations from the National Basic Meteorological Observing Stations of China. The Gridpoint Statistical Interpolation 3D-Var regional data assimilation (DA) system is modified to add LST as a new control variable and its background error variances, horizontal correlations and cross-correlations. The background covariances of LST with other control variables are calculated separately for daytime and nighttime samples in summer and winter seasons. A control experiment (ExpCTL) and three LST DA experiments with (ExpLST) and without (ExpLST_NBC) bias correction or with an average of LST within 2° × 2° grid boxes (ExpLST_SO) are conducted. Considering the fact that surface station observations are point measurements while the satellite TBs measure the total radiation effect of earth's surface within fields-of-view, a bias correction is found necessary for LST DA during daytimes (ExpLST). The biases are quantified by the differences from the Moderate-resolution Imaging Spectroradiometer LST retrievals to compensate for the representative differences. The analyzed fields are then used as input to the Community Radiative Transfer Model to simulate TBs of AHI surface-sensitive channels over land. A long-period statistics shows that ExpLST significantly reduces the observations minus simulations (O<img>B) biases and standard deviations of surface-sensitive TBs in terms of reducing the diurnal variations and season dependences of TB biases over different surface types, which also outperforms ExpLST_NBC and ExpLST_SO at daytime. This study suggests a potential benefit of combining the use of LST observations for assimilating surface-sensitive infrared TBs.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"311 ","pages":"Article 107706"},"PeriodicalIF":4.5,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.atmosres.2024.107708
Lin Liang , Zhiwei Han , Weiwei Chen , Jiawei Li , Mingjie Liang , Shujing Shen
A Regional Air Quality Model System (RAQMS) driven by WRF was applied to explore the emission, transport, deposition, and direct radiative effect of mineral dust over western China in July 2022, with focus on the Tibetan Plateau (TP). Model validation against ground and satellite observations demonstrated the model reproduced meteorological variables, PM10 concentration, aerosol optical depth (AOD), and extinction coefficients in the vertical reasonably well. There was a dust event during 3–7 July 2022, which was originated from the Taklimakan Desert (TKD) and affected eastern and central TP under anticyclonic flows, resulting in the maximum hourly PM10 concentration exceeding 50 μg m−3 in Lhasa. Shortwave radiation was reduced considerably by dust aerosols over eastern TP, with the maximum decrease in daytime mean shortwave radiation reaching 30 W m−2 around Nyingchi on 5 July. Anthropogenic aerosols dominated PM10 mass in the capital cities of western China (54–67 %), while dust aerosols were dominant in the cities near the deserts. During this dust event, dust aerosols from TKD and Qaidam Desert (QDD) significantly influenced eastern TP, with dust contributions to PM10 mass concentration of 52 %, 76 % and 69 %, respectively, in Chamdo, Lhasa and Nyingzhi, respectively. The total dust emission in western China was about 10.6 Tg in July 2022, with the largest contribution from TKD (63.5 %), followed by Gobi Desert (GB) (26 %). The total deposition of dust was estimated to be 6.2 Tg, in which TKD and GB contributed 66 % and 22 %, respectively. During the study period, about 418 Gg dust aerosols were deposited on TP, 49 % of which was from TKD and 25 % from QDD. Foreign dust sources contributed approximately 7 % and 9 % to dust concentration and total deposition over TP, respectively. Over southern TP, the source contribution to dust deposition was estimated to be 42 %, 24 % and 21 % from TKD, foreign sources and QDD, respectively, suggesting potentially important impact of long-range transboundary dust transport on deposition, surface albedo and climate over TP.
{"title":"The source, transport, deposition and direct radiative effect of mineral dust over western China: A modeling study of July 2022 with focus on the Tibetan Plateau","authors":"Lin Liang , Zhiwei Han , Weiwei Chen , Jiawei Li , Mingjie Liang , Shujing Shen","doi":"10.1016/j.atmosres.2024.107708","DOIUrl":"10.1016/j.atmosres.2024.107708","url":null,"abstract":"<div><div>A Regional Air Quality Model System (RAQMS) driven by WRF was applied to explore the emission, transport, deposition, and direct radiative effect of mineral dust over western China in July 2022, with focus on the Tibetan Plateau (TP). Model validation against ground and satellite observations demonstrated the model reproduced meteorological variables, PM<sub>10</sub> concentration, aerosol optical depth (AOD), and extinction coefficients in the vertical reasonably well. There was a dust event during 3–7 July 2022, which was originated from the Taklimakan Desert (TKD) and affected eastern and central TP under anticyclonic flows, resulting in the maximum hourly PM<sub>10</sub> concentration exceeding 50 μg m<sup>−3</sup> in Lhasa. Shortwave radiation was reduced considerably by dust aerosols over eastern TP, with the maximum decrease in daytime mean shortwave radiation reaching 30 W m<sup>−2</sup> around Nyingchi on 5 July. Anthropogenic aerosols dominated PM<sub>10</sub> mass in the capital cities of western China (54–67 %), while dust aerosols were dominant in the cities near the deserts. During this dust event, dust aerosols from TKD and Qaidam Desert (QDD) significantly influenced eastern TP, with dust contributions to PM<sub>10</sub> mass concentration of 52 %, 76 % and 69 %, respectively, in Chamdo, Lhasa and Nyingzhi, respectively. The total dust emission in western China was about 10.6 Tg in July 2022, with the largest contribution from TKD (63.5 %), followed by Gobi Desert (GB) (26 %). The total deposition of dust was estimated to be 6.2 Tg, in which TKD and GB contributed 66 % and 22 %, respectively. During the study period, about 418 Gg dust aerosols were deposited on TP, 49 % of which was from TKD and 25 % from QDD. Foreign dust sources contributed approximately 7 % and 9 % to dust concentration and total deposition over TP, respectively. Over southern TP, the source contribution to dust deposition was estimated to be 42 %, 24 % and 21 % from TKD, foreign sources and QDD, respectively, suggesting potentially important impact of long-range transboundary dust transport on deposition, surface albedo and climate over TP.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"311 ","pages":"Article 107708"},"PeriodicalIF":4.5,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1016/j.atmosres.2024.107705
Hanmeng Xia , Kaicun Wang
Long-term series of high-resolution gridded precipitation datasets are essential for hydrological and meteorological research. Producing high-resolution precipitation data from regional models demands substantial computational resources and labor. Global Reanalyses offer long-term coverage and effectively capture annual and seasonal precipitation patterns. However, they have inadequate resolution and frequently have difficulties depicting extreme conditions. This study proposes an efficient and accurate approach for generating long-term series of high spatial and temporal resolution precipitation. It is achieved by leveraging deep learning techniques to integrate the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) global climate reanalysis (ERA5, 0.25°, hourly) data with high-resolution precipitation fusion datasets. Considering the heavy-tailed distribution of precipitation, we developed the PreciDBPN model structure, which combines a classification network with a super-resolution network and incorporates physically relevant indices into the model's input. We trained and evaluated the PreciDBPN and baseline models in eastern China using the China Meteorological Administration Land Data Assimilation System (CLDAS) precipitation dataset (0.0625°, hourly, 2017–2022). When compared to baseline methods and ERA5, our model excels in multiple metrics and provides a more precise representation of relative rainfall frequency. Independent verification was performed using station observations during the period of 2010–2015 when CLDAS data were unavailable. During this verification, the PreciDBPN demonstrated exceptional performance and greater robustness compared to the baseline models. Because our method can efficiently downscale precipitation and bias-correct reanalysis data using minimal computational resources, it can be used to generate high-resolution precipitation datasets (0.0625°, hourly) from 1979 to 2022 while correcting for heavy precipitation underestimations in reanalysis data.
{"title":"PreciDBPN: A customized deep learning approach for hourly precipitation downscaling in eastern China","authors":"Hanmeng Xia , Kaicun Wang","doi":"10.1016/j.atmosres.2024.107705","DOIUrl":"10.1016/j.atmosres.2024.107705","url":null,"abstract":"<div><div>Long-term series of high-resolution gridded precipitation datasets are essential for hydrological and meteorological research. Producing high-resolution precipitation data from regional models demands substantial computational resources and labor. Global Reanalyses offer long-term coverage and effectively capture annual and seasonal precipitation patterns. However, they have inadequate resolution and frequently have difficulties depicting extreme conditions. This study proposes an efficient and accurate approach for generating long-term series of high spatial and temporal resolution precipitation. It is achieved by leveraging deep learning techniques to integrate the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) global climate reanalysis (ERA5, 0.25°, hourly) data with high-resolution precipitation fusion datasets. Considering the heavy-tailed distribution of precipitation, we developed the PreciDBPN model structure, which combines a classification network with a super-resolution network and incorporates physically relevant indices into the model's input. We trained and evaluated the PreciDBPN and baseline models in eastern China using the China Meteorological Administration Land Data Assimilation System (CLDAS) precipitation dataset (0.0625°, hourly, 2017–2022). When compared to baseline methods and ERA5, our model excels in multiple metrics and provides a more precise representation of relative rainfall frequency. Independent verification was performed using station observations during the period of 2010–2015 when CLDAS data were unavailable. During this verification, the PreciDBPN demonstrated exceptional performance and greater robustness compared to the baseline models. Because our method can efficiently downscale precipitation and bias-correct reanalysis data using minimal computational resources, it can be used to generate high-resolution precipitation datasets (0.0625°, hourly) from 1979 to 2022 while correcting for heavy precipitation underestimations in reanalysis data.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"311 ","pages":"Article 107705"},"PeriodicalIF":4.5,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142329802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1016/j.atmosres.2024.107709
Yu Yang, Yali Yang, Cao Jie, Xizhou Cai, Jiantong Yu
The timing of the Bay of Bengal summer monsoon (BOBSM) onset has implications for the evolution of the Asian summer monsoon and associated precipitation. This study employs Japanese 55-year Reanalysis and NOAA's Gridded Precipitation Reconstruction over Land data to explore the spatiotemporal variation in the thermal influence of the Southeast Asian low-latitude highlands (SEALLH) and its effect on the BOBSM onset date. There is a significant correlation between pre-monsoon season (February–April) shortwave radiative heating (SWH) in the SEALLH and BOBSM onset in the interannual time scale. During the enhanced SWH, an anomalous vertical circulation, which converges and ascends in the lower troposphere over the SEALLH and converges and descends in the upper troposphere north of the Bay of Bengal (BOB), develops over the SEALLH–north of BOB during the pre-monsoon season with anticlockwise rotation. The warmer air mass resulting from the adiabatic heating is accumulated around the north of BOB by the insulation effect related to the anomalous vertical circulation. By facilitating the reversal of the land-sea thermal contrast, the warmer upper troposphere over the north of the BOB causes a 16-day earlier onset of the BOBSM than the case of weakened SWH. The findings shed light on subsequent research into the thermal effects of the SEALLH on Asian summer monsoon variability.
{"title":"Thermal effect of the Southeast Asian low-latitude highlands on interannual variability in the date of the Bay of Bengal summer monsoon onset","authors":"Yu Yang, Yali Yang, Cao Jie, Xizhou Cai, Jiantong Yu","doi":"10.1016/j.atmosres.2024.107709","DOIUrl":"10.1016/j.atmosres.2024.107709","url":null,"abstract":"<div><div>The timing of the Bay of Bengal summer monsoon (BOBSM) onset has implications for the evolution of the Asian summer monsoon and associated precipitation. This study employs Japanese 55-year Reanalysis and NOAA's Gridded Precipitation Reconstruction over Land data to explore the spatiotemporal variation in the thermal influence of the Southeast Asian low-latitude highlands (SEALLH) and its effect on the BOBSM onset date. There is a significant correlation between pre-monsoon season (February–April) shortwave radiative heating (SWH) in the SEALLH and BOBSM onset in the interannual time scale. During the enhanced SWH, an anomalous vertical circulation, which converges and ascends in the lower troposphere over the SEALLH and converges and descends in the upper troposphere north of the Bay of Bengal (BOB), develops over the SEALLH–north of BOB during the pre-monsoon season with anticlockwise rotation. The warmer air mass resulting from the adiabatic heating is accumulated around the north of BOB by the insulation effect related to the anomalous vertical circulation. By facilitating the reversal of the land-sea thermal contrast, the warmer upper troposphere over the north of the BOB causes a 16-day earlier onset of the BOBSM than the case of weakened SWH. The findings shed light on subsequent research into the thermal effects of the SEALLH on Asian summer monsoon variability.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"311 ","pages":"Article 107709"},"PeriodicalIF":4.5,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In Mediterranean countries, such as Greece, the frequency of forest fires has increased in recent years, primarily due to the widespread impacts of climate change. At the end of August 2023, Greece experienced a record-breaking heatwave that triggered severe wildfire incidents, significantly impacting the atmospheric conditions of several cities, among them Thessaloniki. A significant number of wildfires occurred in northern Greece (Evros), burning thousands of hectares of the protected Dadia forest (Natura 2000) and releasing a significant load of smoke into the atmosphere. According to the European Strategy Forum on Research Infrastructures (ESFRI) a total area of 90,000 ha was burnt collectively by the Evros extreme wildfires during August 2023. The emissions led to severely adverse air pollution conditions, causing reduced visibility across an extended eastern Mediterranean region for several days. In this work, we analyze the influence of the transported biomass burning particles on the aerosol properties in the free troposphere, as well as on the surface UV radiation levels over Thessaloniki during the last week of August 2023. The transported smoke plume was detected over the Laboratory of Atmospheric Physics (LAP) in Thessaloniki, approximately 240 km away from the burning area, from August 22nd to the 25th. During this period, the presence of smoke led to exceptionally increased levels of aerosol optical depth (AOD), reaching up to 3.4 at 340 nm (the highest ever recorded in Thessaloniki), as well as high Ångström exponent (AE) values, peaking at 2.4 for the 440–870 nm range, followed by high Fine Mode Fractions (FMFs), indicating the prevalence of fine-mode smoke aerosol particle. Moreover, during the event, the presence of the biomass burning aerosols led to a strong attenuation of the solar UV irradiance by up to 90 %, reaching unprecedented levels, similar of a solar eclipse. The primary goal of this study is to highlight the extensive impacts that wildfires have, which are anticipated to increase in frequency in the near future, due to the predicted rise in the rate of occurrence of summer heatwaves especially for Mediterranean areas and specifically for Greece. Furthermore, the great value of the synergistic monitoring of the event with ground-based remote sensing instrumentation, along with satellite aerosol observations and modeling tools, is made prominent.
{"title":"Extreme wildfires over northern Greece during summer 2023 – Part A: Effects on aerosol optical properties and solar UV radiation","authors":"Konstantinos Michailidis , Katerina Garane , Dimitris Karagkiozidis , Georgia Peletidou , Kalliopi-Artemis Voudouri , Dimitris Balis , Alkiviadis Bais","doi":"10.1016/j.atmosres.2024.107700","DOIUrl":"10.1016/j.atmosres.2024.107700","url":null,"abstract":"<div><div>In Mediterranean countries, such as Greece, the frequency of forest fires has increased in recent years, primarily due to the widespread impacts of climate change. At the end of August 2023, Greece experienced a record-breaking heatwave that triggered severe wildfire incidents, significantly impacting the atmospheric conditions of several cities, among them Thessaloniki. A significant number of wildfires occurred in northern Greece (Evros), burning thousands of hectares of the protected Dadia forest (Natura 2000) and releasing a significant load of smoke into the atmosphere. According to the European Strategy Forum on Research Infrastructures (ESFRI) a total area of 90,000<!--> <!-->ha was burnt collectively by the Evros extreme wildfires during August 2023. The emissions led to severely adverse air pollution conditions, causing reduced visibility across an extended eastern Mediterranean region for several days. In this work, we analyze the influence of the transported biomass burning particles on the aerosol properties in the free troposphere, as well as on the surface UV radiation levels over Thessaloniki during the last week of August 2023. The transported smoke plume was detected over the Laboratory of Atmospheric Physics (LAP) in Thessaloniki, approximately 240 km away from the burning area, from August 22nd to the 25th. During this period, the presence of smoke led to exceptionally increased levels of aerosol optical depth (AOD), reaching up to 3.4 at 340<!--> <!-->nm (the highest ever recorded in Thessaloniki), as well as high Ångström exponent (AE) values, peaking at 2.4 for the 440–870<!--> <!-->nm range, followed by high Fine Mode Fractions (FMFs), indicating the prevalence of fine-mode smoke aerosol particle. Moreover, during the event, the presence of the biomass burning aerosols led to a strong attenuation of the solar UV irradiance by up to 90 %, reaching unprecedented levels, similar of a solar eclipse. The primary goal of this study is to highlight the extensive impacts that wildfires have, which are anticipated to increase in frequency in the near future, due to the predicted rise in the rate of occurrence of summer heatwaves especially for Mediterranean areas and specifically for Greece. Furthermore, the great value of the synergistic monitoring of the event with ground-based remote sensing instrumentation, along with satellite aerosol observations and modeling tools, is made prominent.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"311 ","pages":"Article 107700"},"PeriodicalIF":4.5,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-21DOI: 10.1016/j.atmosres.2024.107699
Min Shao , Shun Lv , Yueming Song , Rui Liu , Qili Dai
China's Clean Air Actions have substantially improved ambient PM2.5 air quality since 2013. However, ozone (O3) pollution in urban areas has worsened in recent years, particularly in the eastern region. The formation of O3 in these high emission areas is highly complex and regulated by numerous factors. Utilizing the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), this study conducted a comprehensive analysis through nine sensitivity experiments for the years 2013 and 2017, aimed at disentangling the relative contributions of major factors (e.g., meteorological factors, anthropogenic emissions, biogenic emissions, and aerosol feedback mechanisms) to the observed O3 concentration trends in Jiangsu, a developed region of China with increasing trend of O3 level. Our findings indicate a pronounced increase in mean daily maximum 8-h average (MDA8) O3 levels in the economically vibrant southern Jiangsu, contrasted with a decrease in the less developed northern regions. The study identifies anthropogenic emissions change as the primary driver of O3 variations, with significant impacts also attributed to meteorological conditions and aerosol feedback effects, while biogenic emission shifts play a lesser role. In terms of meteorological factors, we discovered that the alteration in meteorological thermal factors (enhanced solar radiation and temperatures) in 2017, compared to 2013, exerted a more pronounced influence on the formation of O3 than the change in thermally driven dynamic factors (boundary layer height and wind speed). Moreover, the study observes a shift in O3 formation sensitivity from VOC-sensitive to transitional or NOX-sensitive regimes, signifying a notable transformation in the regional atmospheric chemistry conducive to O3 generation. Aerosol feedback effects, through complex pathways including photolysis rate alterations and modifications in the vertical O3 distribution, further compound the challenge of mitigating O3 levels. Our research underscores the necessity for adaptive, region-specific strategies to mitigate O3 pollution, providing potential insights for policymakers to formulate effective control measures.
{"title":"Disentangling the effects of meteorology and emissions from anthropogenic and biogenic sources on the increased surface ozone in Eastern China","authors":"Min Shao , Shun Lv , Yueming Song , Rui Liu , Qili Dai","doi":"10.1016/j.atmosres.2024.107699","DOIUrl":"10.1016/j.atmosres.2024.107699","url":null,"abstract":"<div><div>China's Clean Air Actions have substantially improved ambient PM<sub>2.5</sub> air quality since 2013. However, ozone (O<sub>3</sub>) pollution in urban areas has worsened in recent years, particularly in the eastern region. The formation of O<sub>3</sub> in these high emission areas is highly complex and regulated by numerous factors. Utilizing the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), this study conducted a comprehensive analysis through nine sensitivity experiments for the years 2013 and 2017, aimed at disentangling the relative contributions of major factors (e.g., meteorological factors, anthropogenic emissions, biogenic emissions, and aerosol feedback mechanisms) to the observed O<sub>3</sub> concentration trends in Jiangsu, a developed region of China with increasing trend of O<sub>3</sub> level. Our findings indicate a pronounced increase in mean daily maximum 8-h average (MDA8) O<sub>3</sub> levels in the economically vibrant southern Jiangsu, contrasted with a decrease in the less developed northern regions. The study identifies anthropogenic emissions change as the primary driver of O<sub>3</sub> variations, with significant impacts also attributed to meteorological conditions and aerosol feedback effects, while biogenic emission shifts play a lesser role. In terms of meteorological factors, we discovered that the alteration in meteorological thermal factors (enhanced solar radiation and temperatures) in 2017, compared to 2013, exerted a more pronounced influence on the formation of O<sub>3</sub> than the change in thermally driven dynamic factors (boundary layer height and wind speed). Moreover, the study observes a shift in O<sub>3</sub> formation sensitivity from VOC-sensitive to transitional or NO<sub>X</sub>-sensitive regimes, signifying a notable transformation in the regional atmospheric chemistry conducive to O<sub>3</sub> generation. Aerosol feedback effects, through complex pathways including photolysis rate alterations and modifications in the vertical O<sub>3</sub> distribution, further compound the challenge of mitigating O<sub>3</sub> levels. Our research underscores the necessity for adaptive, region-specific strategies to mitigate O<sub>3</sub> pollution, providing potential insights for policymakers to formulate effective control measures.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"311 ","pages":"Article 107699"},"PeriodicalIF":4.5,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142417058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1016/j.atmosres.2024.107698
Yushi Gan , Yuechen Li , Lihong Wang , Long Zhao , Lei Fan , Haichao Xu , Zhe Yin
High-quality precipitation data are essential for research in hydrology, meteorology and ecology. Nevertheless, in mountainous regions with intricate terrain, the reliability of gridded precipitation data derived from station data interpolation is low due to the limited number of stations caused by the difficulty of station setup. Current satellite precipitation products suffer from low spatial resolution, making them unsuitable for hydrological and meteorological research at small and medium scales. Their application in mountainous regions with significant spatiotemporal heterogeneity is even more challenging. To this end, downscaling satellite precipitation products has become an effective method for obtaining accurate spatial distribution information of precipitation in these regions. This study employs a method of first calibration followed by downscaling analysis of GPM daily precipitation product in the Chongqing area using random forest (RF), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) algorithms. Ultimately, the spatial resolution of GPM product is improved from 0.1° to 0.01° (∼1 km). The findings demonstrate that: (1) the station-calibrated GPM precipitation product performed better than the original GPM product, and it is closer to the station measurements; (2) in practical applications, the LSTM downscaling algorithm can effectively enhance spatial resolution without compromising accuracy, whereas RF and XGBoost incur considerable accuracy loss when enhancing spatial resolution; (3) the downscaled results from all three algorithms were consistent with the calibrated GPM precipitation maps and significantly improved the spatial details of precipitation. Among them, the results of the LSTM method exhibited greater continuity in the spatial distribution of precipitation, aligning more closely with the characteristics of precipitation distribution. In summary, the LSTM algorithm demonstrates greater potential for the downscaling of GPM precipitation product in the study area. This research provides a promising high-quality precipitation data generation scheme for mountainous regions with sparse station coverage and complex terrain and landforms.
{"title":"Machine-learning downscaling of GPM satellite precipitation products in mountainous regions: A case study in Chongqing","authors":"Yushi Gan , Yuechen Li , Lihong Wang , Long Zhao , Lei Fan , Haichao Xu , Zhe Yin","doi":"10.1016/j.atmosres.2024.107698","DOIUrl":"10.1016/j.atmosres.2024.107698","url":null,"abstract":"<div><div>High-quality precipitation data are essential for research in hydrology, meteorology and ecology. Nevertheless, in mountainous regions with intricate terrain, the reliability of gridded precipitation data derived from station data interpolation is low due to the limited number of stations caused by the difficulty of station setup. Current satellite precipitation products suffer from low spatial resolution, making them unsuitable for hydrological and meteorological research at small and medium scales. Their application in mountainous regions with significant spatiotemporal heterogeneity is even more challenging. To this end, downscaling satellite precipitation products has become an effective method for obtaining accurate spatial distribution information of precipitation in these regions. This study employs a method of first calibration followed by downscaling analysis of GPM daily precipitation product in the Chongqing area using random forest (RF), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) algorithms. Ultimately, the spatial resolution of GPM product is improved from 0.1° to 0.01° (∼1 km). The findings demonstrate that: (1) the station-calibrated GPM precipitation product performed better than the original GPM product, and it is closer to the station measurements; (2) in practical applications, the LSTM downscaling algorithm can effectively enhance spatial resolution without compromising accuracy, whereas RF and XGBoost incur considerable accuracy loss when enhancing spatial resolution; (3) the downscaled results from all three algorithms were consistent with the calibrated GPM precipitation maps and significantly improved the spatial details of precipitation. Among them, the results of the LSTM method exhibited greater continuity in the spatial distribution of precipitation, aligning more closely with the characteristics of precipitation distribution. In summary, the LSTM algorithm demonstrates greater potential for the downscaling of GPM precipitation product in the study area. This research provides a promising high-quality precipitation data generation scheme for mountainous regions with sparse station coverage and complex terrain and landforms.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"311 ","pages":"Article 107698"},"PeriodicalIF":4.5,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142311720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1016/j.atmosres.2024.107667
Mauro Mazzola , Robert S. Stone , Natalia Kouremeti , Vito Vitale , Julian Gröbner , Kerstin Stebel , Georg H. Hansen , Thomas C. Stone , Christoph Ritter , Simone Pulimeno
Moon-photometric measurements were made at two locations in the Arctic during winter nights using two different modified Sun photometers; a Carter Scott SP02 and a Precision Filter Radiometer (PFR) developed at PMOD/WRC. Values of aerosol optical depth (AOD) were derived from spectral irradiance measurements made at four wavelengths for each of the devices. The SP02 was located near Barrow, Alaska and recorded data from November 2012 to March 2013, spanning five lunar cycles, while the PFR was deployed to Ny-Ålesund, Svalbard each winter from February 2014 to February 2019 for a total of 56 measurement periods. A methodology was developed to process the raw data, involving calibration of the instruments and normalizing measured spectral irradiance values in accordance with site-specific determinations of the extraterrestrial atmospheric irradiance (ETI) as Moon phase cycled. Uncertainties of the derived AOD values were also evaluated and found to be in the range, 0.006–0.030, depending on wavelength and which device was evaluated.
The magnitudes of AOD determined for the two sites were in general agreement with those reported in the literature for sunlit periods just before and after the dark periods of Arctic night. Those for the PFR were also compared with data obtained using star photometers and a Cimel CE318-T, recently deployed to Ny-Ålesund, showing that Moon photometry is viable as a means to monitor AOD during the Arctic night. Such data are valuable for more complete assessments of the role aerosols play in modulating climate, the validation of AOD derived using various remote sensing techniques, and applications related to climate modeling.
{"title":"Monitoring aerosol optical depth during the Arctic night: Instrument development and first results","authors":"Mauro Mazzola , Robert S. Stone , Natalia Kouremeti , Vito Vitale , Julian Gröbner , Kerstin Stebel , Georg H. Hansen , Thomas C. Stone , Christoph Ritter , Simone Pulimeno","doi":"10.1016/j.atmosres.2024.107667","DOIUrl":"10.1016/j.atmosres.2024.107667","url":null,"abstract":"<div><div>Moon-photometric measurements were made at two locations in the Arctic during winter nights using two different modified Sun photometers; a Carter Scott SP02 and a Precision Filter Radiometer (PFR) developed at PMOD/WRC. Values of aerosol optical depth (AOD) were derived from spectral irradiance measurements made at four wavelengths for each of the devices. The SP02 was located near Barrow, Alaska and recorded data from November 2012 to March 2013, spanning five lunar cycles, while the PFR was deployed to Ny-Ålesund, Svalbard each winter from February 2014 to February 2019 for a total of 56 measurement periods. A methodology was developed to process the raw data, involving calibration of the instruments and normalizing measured spectral irradiance values in accordance with site-specific determinations of the extraterrestrial atmospheric irradiance (ETI) as Moon phase cycled. Uncertainties of the derived AOD values were also evaluated and found to be in the range, 0.006–0.030, depending on wavelength and which device was evaluated.</div><div>The magnitudes of AOD determined for the two sites were in general agreement with those reported in the literature for sunlit periods just before and after the dark periods of Arctic night. Those for the PFR were also compared with data obtained using star photometers and a Cimel CE318-T, recently deployed to Ny-Ålesund, showing that Moon photometry is viable as a means to monitor AOD during the Arctic night. Such data are valuable for more complete assessments of the role aerosols play in modulating climate, the validation of AOD derived using various remote sensing techniques, and applications related to climate modeling.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"311 ","pages":"Article 107667"},"PeriodicalIF":4.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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