Pub Date : 2024-12-23DOI: 10.1016/j.atmosres.2024.107889
Avirup Sen, Atiba A. Shaikh, Harilal B. Menon
The first comprehensive long-term observation of the variability in columnar aerosol optical depth (AOD) and ambient Black Carbon mass concentrations (MB) was conducted at a tropical coastal location neighboring the Arabian Sea (Goa; 15.45°N, 73.83°E) between December and May in two phases: 2008–2011 (Phase I) and 2017–2021 (Phase II). Inter-seasonal (winter monsoon season (WMS): December–February; spring inter-monsoon season (SIMS): March–April; and MAY) and interphase variability in aerosol types, potential source regions, aerosol direct radiative effects (ADRE), and heating rate (HR) were investigated. The slope of spectral AOD was steeper during WMS and SIMS than MAY in both phases. Relatively flat AOD spectra with low Ångström exponent (α < 1) prevailed during all seasons in Phase I and MAY in Phase II, implying the predominance of coarse-mode aerosols. However, increasing fine-mode aerosol dominance was observed during WMS and SIMS in Phase II (mean α ∼1.5). The highest and lowest mean MB were recorded during WMS of Phase I (2904.68 ± 787.20 ng m−3), and MAY of Phase II (531.12 ± 163.95 ng m−3), respectively. Further, urban/industrial aerosols increased over 3-fold during WMS and SIMS from Phase I to Phase II. Strong potential sources of fine-mode aerosols were interspersed across the Deccan Plateau, central India, and the east coast of India during WMS of Phase II. An investigation into the sources showed that the enhancement in power generation capacities of thermal power plants was a major contributor to fine-mode anthropogenic aerosols, along with increased vehicular density and agricultural activity at upwind locations in Phase II. The sharp rise in single scattering albedo (SSA) in Phase II implied a substantial increase in scattering aerosols. ADRE in the atmosphere (ADREATM) and HR were the highest during SIMS (63.76 ± 12.99 W m−2; 1.79 ± 0.36 K day−1) in Phase I. Low ADREATM and HR were recorded during SIMS (28.20 ± 13.84 W m−2; 0.79 ± 0.39 K day−1) and MAY (36.15 ± 9.15 W m−2; 1.06 ± 0.31 K day−1) in Phase II, which can be attributed to the rapid decline in absorbing aerosols during SIMS and MAY of 2020 and 2021, coinciding with the countrywide COVID-19 lockdown.
{"title":"Augmentation of fine-mode anthropogenic aerosols over a tropical coastal site in India adjoining Eastern Arabian Sea: Potential sources and direct radiative effects","authors":"Avirup Sen, Atiba A. Shaikh, Harilal B. Menon","doi":"10.1016/j.atmosres.2024.107889","DOIUrl":"https://doi.org/10.1016/j.atmosres.2024.107889","url":null,"abstract":"The first comprehensive long-term observation of the variability in columnar aerosol optical depth (AOD) and ambient Black Carbon mass concentrations (M<ce:inf loc=\"post\">B</ce:inf>) was conducted at a tropical coastal location neighboring the Arabian Sea (Goa; 15.45°N, 73.83°E) between December and May in two phases: 2008–2011 (Phase I) and 2017–2021 (Phase II). Inter-seasonal (winter monsoon season (WMS): December–February; spring inter-monsoon season (SIMS): March–April; and MAY) and interphase variability in aerosol types, potential source regions, aerosol direct radiative effects (ADRE), and heating rate (HR) were investigated. The slope of spectral AOD was steeper during WMS and SIMS than MAY in both phases. Relatively flat AOD spectra with low Ångström exponent (α < 1) prevailed during all seasons in Phase I and MAY in Phase II, implying the predominance of coarse-mode aerosols. However, increasing fine-mode aerosol dominance was observed during WMS and SIMS in Phase II (mean α <mml:math altimg=\"si11.svg\"><mml:mo>∼</mml:mo></mml:math>1.5). The highest and lowest mean M<ce:inf loc=\"post\">B</ce:inf> were recorded during WMS of Phase I (2904.68 ± 787.20 ng m<ce:sup loc=\"post\">−3</ce:sup>), and MAY of Phase II (531.12 ± 163.95 ng m<ce:sup loc=\"post\">−3</ce:sup>), respectively. Further, urban/industrial aerosols increased over 3-fold during WMS and SIMS from Phase I to Phase II. Strong potential sources of fine-mode aerosols were interspersed across the Deccan Plateau, central India, and the east coast of India during WMS of Phase II. An investigation into the sources showed that the enhancement in power generation capacities of thermal power plants was a major contributor to fine-mode anthropogenic aerosols, along with increased vehicular density and agricultural activity at upwind locations in Phase II. The sharp rise in single scattering albedo (SSA) in Phase II implied a substantial increase in scattering aerosols. ADRE in the atmosphere (ADRE<ce:inf loc=\"post\">ATM</ce:inf>) and HR were the highest during SIMS (63.76 ± 12.99 W m<ce:sup loc=\"post\">−2</ce:sup>; 1.79 ± 0.36 K day<ce:sup loc=\"post\">−1</ce:sup>) in Phase I. Low ADRE<ce:inf loc=\"post\">ATM</ce:inf> and HR were recorded during SIMS (28.20 ± 13.84 W m<ce:sup loc=\"post\">−2</ce:sup>; 0.79 ± 0.39 K day<ce:sup loc=\"post\">−1</ce:sup>) and MAY (36.15 ± 9.15 W m<ce:sup loc=\"post\">−2</ce:sup>; 1.06 ± 0.31 K day<ce:sup loc=\"post\">−1</ce:sup>) in Phase II, which can be attributed to the rapid decline in absorbing aerosols during SIMS and MAY of 2020 and 2021, coinciding with the countrywide COVID-19 lockdown.","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"53 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888668","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}
This study proposed a new method to retrieve aerosol single scattering albedo (SSA) over land for the Medium Resolution Spectral Imager-II (MERSI-II) onboard the Fengyun-3D (FY-3D). Considering both accuracy and retrieval efficiency, the method combines machine learning with an aerosol optical model constructed from mixed aerosol components. A sample dataset, containing 4 bands of apparent reflectance simulated by the radiative transfer model and corresponding geometric conditions, aerosol and land surface information, is constructed for training and validating machine learning models. Three Back Propagation Neural Network (BPNN) SSA retrieval models are built based on the theoretical basis of SSA retrieval, and the sensitivity of SSA retrieval accuracy to input parameter errors is analyzed. The results show that BPNN-based SSA retrieval models can replace the iterative optimal solution process to a certain extent, achieving quick retrieval of satellite SSA. The BPNN SSA retrieval models are applied to FY-3D MERSI-II observations and validated using AERONET SSA products. The results indicate that the BPNN SSA retrieval model, which uses solar zenith angle, satellite zenith angle, relative azimuth angle, aerosol optical depth (AOD), surface altitude, bi-directional reflectance distribution function (BRDF) parameters (bands 1–2), and apparent reflectance (bands 1–4) as inputs, performs better than others. The retrievals show good consistency with AERONET SSA products with a correlation coefficient of approximately 0.5 and a root mean square error (RMSE) of 0.045 (0.034) at 470 nm (550 nm). In addition, more than 66 % of the SSA retrievals are within the expected error of ±0.05.
{"title":"Retrieving aerosol single scattering albedo from FY-3D observations combining machine learning with radiative transfer model","authors":"Qingxin Wang, Siwei Li, Zhaoyang Zhang, Xingwen Lin, Yanmin Shuai, Xinyan Liu, Hao Lin","doi":"10.1016/j.atmosres.2024.107884","DOIUrl":"https://doi.org/10.1016/j.atmosres.2024.107884","url":null,"abstract":"This study proposed a new method to retrieve aerosol single scattering albedo (SSA) over land for the Medium Resolution Spectral Imager-II (MERSI-II) onboard the Fengyun-3D (FY-3D). Considering both accuracy and retrieval efficiency, the method combines machine learning with an aerosol optical model constructed from mixed aerosol components. A sample dataset, containing 4 bands of apparent reflectance simulated by the radiative transfer model and corresponding geometric conditions, aerosol and land surface information, is constructed for training and validating machine learning models. Three Back Propagation Neural Network (BPNN) SSA retrieval models are built based on the theoretical basis of SSA retrieval, and the sensitivity of SSA retrieval accuracy to input parameter errors is analyzed. The results show that BPNN-based SSA retrieval models can replace the iterative optimal solution process to a certain extent, achieving quick retrieval of satellite SSA. The BPNN SSA retrieval models are applied to FY-3D MERSI-II observations and validated using AERONET SSA products. The results indicate that the BPNN SSA retrieval model, which uses solar zenith angle, satellite zenith angle, relative azimuth angle, aerosol optical depth (AOD), surface altitude, bi-directional reflectance distribution function (BRDF) parameters (bands 1–2), and apparent reflectance (bands 1–4) as inputs, performs better than others. The retrievals show good consistency with AERONET SSA products with a correlation coefficient of approximately 0.5 and a root mean square error (RMSE) of 0.045 (0.034) at 470 nm (550 nm). In addition, more than 66 % of the SSA retrievals are within the expected error of ±0.05.","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"202 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888670","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}
To understand the formation and evolution of tropical rainfall, this study examines macro- and micro-physical features and vertical structures of tropical precipitation systems (TPSs) using 9-years observations from the Global Precipitation Measurement (GPM) mission's dual-frequency precipitation radar (DPR). TPSs are primarily convective-dominated, and their precipitation rate (PR) concentrated in 20–40 mm/h, which can be largely attributed to liquid hydrometeors, especially in convective regions. However, TPSs with low PR (below 10 mm/h) are stratiform-dominated. The mean levels of 0 °C and − 40 °C within the TPSs are 4.9 km and 11 km, respectively. Warm core is observed in the TPS, which is related to the development of precipitation system. TPSs have distinct characteristics during different stages of their lifecycle. Condensation and autoconversion processes in convective cores contribute to the formation of initial small droplet below 3 km. With the development of TPSs, strong updrafts in convective cores transport droplets from cloud base to higher levels, facilitating the collision-coalescence process in liquid phase layers. During the developing and mature stages, aggregation and riming processes become active above the melting layers. The large hydrometeors within the convective cores contribute to high PR of mature-stage TPSs. In stratiform region, droplets sizes are larger during mature stage than dissipating stage, and these larger droplets may detach from the convective cores. It makes the dominate microphysical process in stratiform regions of mature (dissipating) stage is breakup (evaporation) of raindrops. These results advance the understanding of tropical rainfall and establish a foundation for future research into validating and improving cloud microphysical parameterization schemes in numerical models.
{"title":"Observational structure and physical features of tropical precipitation systems","authors":"Yihao Chen, Donghai Wang, Zhilin Zeng, Lingdong Huang, Enguang Li, Yuting Xue","doi":"10.1016/j.atmosres.2024.107885","DOIUrl":"https://doi.org/10.1016/j.atmosres.2024.107885","url":null,"abstract":"To understand the formation and evolution of tropical rainfall, this study examines macro- and micro-physical features and vertical structures of tropical precipitation systems (TPSs) using 9-years observations from the Global Precipitation Measurement (GPM) mission's dual-frequency precipitation radar (DPR). TPSs are primarily convective-dominated, and their precipitation rate (PR) concentrated in 20–40 mm/h, which can be largely attributed to liquid hydrometeors, especially in convective regions. However, TPSs with low PR (below 10 mm/h) are stratiform-dominated. The mean levels of 0 °C and − 40 °C within the TPSs are 4.9 km and 11 km, respectively. Warm core is observed in the TPS, which is related to the development of precipitation system. TPSs have distinct characteristics during different stages of their lifecycle. Condensation and autoconversion processes in convective cores contribute to the formation of initial small droplet below 3 km. With the development of TPSs, strong updrafts in convective cores transport droplets from cloud base to higher levels, facilitating the collision-coalescence process in liquid phase layers. During the developing and mature stages, aggregation and riming processes become active above the melting layers. The large hydrometeors within the convective cores contribute to high PR of mature-stage TPSs. In stratiform region, droplets sizes are larger during mature stage than dissipating stage, and these larger droplets may detach from the convective cores. It makes the dominate microphysical process in stratiform regions of mature (dissipating) stage is breakup (evaporation) of raindrops. These results advance the understanding of tropical rainfall and establish a foundation for future research into validating and improving cloud microphysical parameterization schemes in numerical models.","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"1 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888672","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}
Cloud condensation nuclei (CCN) activation plays a crucial role in regional cloud-precipitation and global climate. However, inaccuracies in CCN activation parameterization, stemming from the presence of unactivated particles in CCN measurements that are mistakenly included in developing CCN activation parameterizations, can introduce biases in model predictions of cloud droplet number concentration, subsequently affecting cloud microphysics, precipitation initiation, and radiation. To address this issue, this study proposes correction coefficients for CCN activation parameterization using the Twomey power-law function and applies them in the large-eddy model to simulate continental shallow cumulus over the Southern Great Plains, USA. Results reveal that compared to simulations using uncorrected CCN parameterization, those using corrected parameterization decrease cloud droplet number concentration by 32.8 %, leading to an increase of cloud water autoconversion rate by 8.9 % and a decrease of cloud optical thickness by 17.3 %. This indicates a suppression of cloud-precipitation processes and an overestimation of cloud radiative cooling in the default scheme. Moreover, as aerosol loading increases, the differences between the corrected and uncorrected parameterization slightly diminish. Compared to uncorrected CCN parameterization, those using corrected parameterization exhibit stronger cloud sensitivity to aerosols, which partially mitigates the overestimation of cloud radiative cooling in the default scheme. The corrected CCN activation parameterization could help alleviate the overestimation of aerosol indirect effects, particularly in clouds with low supersaturation conditions, thereby contributing to reduced uncertainties in aerosol-cloud interaction simulations.
{"title":"Assessment of the corrected CCN activation parameterizations in simulating shallow cumulus using large-eddy simulations","authors":"Yuan Wang, Xiaoqi Xu, Chunsong Lu, Lei Zhu, Xinyi Wang, Ping Zhang","doi":"10.1016/j.atmosres.2024.107881","DOIUrl":"https://doi.org/10.1016/j.atmosres.2024.107881","url":null,"abstract":"Cloud condensation nuclei (CCN) activation plays a crucial role in regional cloud-precipitation and global climate. However, inaccuracies in CCN activation parameterization, stemming from the presence of unactivated particles in CCN measurements that are mistakenly included in developing CCN activation parameterizations, can introduce biases in model predictions of cloud droplet number concentration, subsequently affecting cloud microphysics, precipitation initiation, and radiation. To address this issue, this study proposes correction coefficients for CCN activation parameterization using the Twomey power-law function and applies them in the large-eddy model to simulate continental shallow cumulus over the Southern Great Plains, USA. Results reveal that compared to simulations using uncorrected CCN parameterization, those using corrected parameterization decrease cloud droplet number concentration by 32.8 %, leading to an increase of cloud water autoconversion rate by 8.9 % and a decrease of cloud optical thickness by 17.3 %. This indicates a suppression of cloud-precipitation processes and an overestimation of cloud radiative cooling in the default scheme. Moreover, as aerosol loading increases, the differences between the corrected and uncorrected parameterization slightly diminish. Compared to uncorrected CCN parameterization, those using corrected parameterization exhibit stronger cloud sensitivity to aerosols, which partially mitigates the overestimation of cloud radiative cooling in the default scheme. The corrected CCN activation parameterization could help alleviate the overestimation of aerosol indirect effects, particularly in clouds with low supersaturation conditions, thereby contributing to reduced uncertainties in aerosol-cloud interaction simulations.","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"41 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888836","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 : 2024-12-21DOI: 10.1016/j.atmosres.2024.107879
Livia J. Leganés, Andrés Navarro, Gyuwon Lee, Raúl Martín, Chris Kidd, Francisco J. Tapiador
Quantitative Precipitation Estimates (QPE) obtained from satellite data are essential for accurately assessing the hydrological cycle in both land and ocean. Early artificial Neural Networks (NN) methods were used previously either to merge infrared and microwave data or to derive better precipitation products from radar and radiometer measurements. Over the last 25 years, machine learning technology has advanced significantly, accompanied by the initiation of new satellites, such as the Global Precipitation Measurement Mission Core Observatory (GPM-CO). In addition, computing power has increased exponentially since the beginning of the 21st century. This paper compares the performance of a pure NN FORTRAN, originally designed to expedite the 2A12 TRMM (Tropical Rainfall Measuring Mission) algorithm, with a contemporary state-of-the-art NN in Python using the TensorFlow library (NN PYTHON). The performance of FORTRAN and Python approaches to QPE using GPM-CO data are compared with the goal of achieving a minimum NN architecture that at least matches the outcome of the Goddard Profiling Algorithm (GPROF) algorithm. Another conclusion is that the new NN PYTHON does not present significant advantages over the old FORTRAN code. The latter does not require dependencies, which has many practical advantages in operational use and therefore have an edge over more complex approaches in hydrometeorology.
{"title":"TRMM-era neural networks for GPM-era satellite quantitative precipitation estimation (QPE)","authors":"Livia J. Leganés, Andrés Navarro, Gyuwon Lee, Raúl Martín, Chris Kidd, Francisco J. Tapiador","doi":"10.1016/j.atmosres.2024.107879","DOIUrl":"https://doi.org/10.1016/j.atmosres.2024.107879","url":null,"abstract":"Quantitative Precipitation Estimates (QPE) obtained from satellite data are essential for accurately assessing the hydrological cycle in both land and ocean. Early artificial Neural Networks (NN) methods were used previously either to merge infrared and microwave data or to derive better precipitation products from radar and radiometer measurements. Over the last 25 years, machine learning technology has advanced significantly, accompanied by the initiation of new satellites, such as the Global Precipitation Measurement Mission Core Observatory (GPM-CO). In addition, computing power has increased exponentially since the beginning of the 21st century. This paper compares the performance of a pure NN FORTRAN, originally designed to expedite the 2A12 TRMM (Tropical Rainfall Measuring Mission) algorithm, with a contemporary state-of-the-art NN in Python using the TensorFlow library (NN PYTHON). The performance of FORTRAN and Python approaches to QPE using GPM-CO data are compared with the goal of achieving a <ce:italic>minimum</ce:italic> NN architecture that at least matches the outcome of the Goddard Profiling Algorithm (GPROF) algorithm. Another conclusion is that the new NN PYTHON does not present significant advantages over the old FORTRAN code. The latter does not require dependencies, which has many practical advantages in operational use and therefore have an edge over more complex approaches in hydrometeorology.","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"48 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888837","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 : 2024-12-20DOI: 10.1016/j.atmosres.2024.107875
Umberto Rizza, Fabio Massimo Grasso, Mauro Morichetti, Alessandro Tiesi, Elenio Avolio, Ferdinando de Tomasi, Mario Marcello Miglietta
The radiative effects caused by a massive desert dust outbreak that took place in the Western Sahara Desert, in the proximity of the Atlantic Ocean, in June 2020 are studied. This outbreak featured two significant dust plumes, the second of which is the focus of the present study.
{"title":"Evaluating the direct radiative forcing of a giant Saharan dust storm","authors":"Umberto Rizza, Fabio Massimo Grasso, Mauro Morichetti, Alessandro Tiesi, Elenio Avolio, Ferdinando de Tomasi, Mario Marcello Miglietta","doi":"10.1016/j.atmosres.2024.107875","DOIUrl":"https://doi.org/10.1016/j.atmosres.2024.107875","url":null,"abstract":"The radiative effects caused by a massive desert dust outbreak that took place in the Western Sahara Desert, in the proximity of the Atlantic Ocean, in June 2020 are studied. This outbreak featured two significant dust plumes, the second of which is the focus of the present study.","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"3 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888669","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 : 2024-12-13DOI: 10.1016/j.atmosres.2024.107870
Hamza Varikoden, V.H. Jamshadali, Catherine George, T. Reshma, R. Vishnu
The South Asian regions frequently encounter extreme rainfall events (EREs) during the summer monsoon season, causing property damage, environmental harm, and fatalities. Therefore, it is essential to comprehend how these events are evolving and may change in the future, particularly for developing countries like India. This study utilised selected models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to evaluate the intensity and contribution of EREs to seasonal rainfall during the historical period (1950–2014), as well as to anticipate the variability of extremes in the future period (2015–2100) during the summer monsoon period. Compared to observational rainfall data from the Climatic Research Unit, the selected models showed similar spatial patterns for mean and extreme rainfall in the historical period with regional biases across CMIP6 selected models, mainly in the monsoonal regions such as the west coast, central India, the Himalayas and its foothills, and the northeastern regions. Four EC-Earth models accurately projected mean and extreme rainfall for central India, the southern west coast, and the western Himalayan regions, while NorESM slightly overestimated central India and underestimated) west coast extremes. Moreover, the selected models are not adequate to realistically capture the extremes in northeastern and Myanmar coastal areas. Future scenarios predict significant changes in extreme rainfall over central India and the rain shadow regions of southeast India. The increased ERE in the northern regions of the west coast and southwestern regions of central India could lead to increased vulnerabilities in the area, especially in higher forcing scenarios. Moreover, the zone of extreme rainfall is projected the expansion of the rain shadow regions to the leeward side of the Western Ghats, particularly in the SSP5–8.5 scenario.
{"title":"Historical and future projections of southwest monsoon rainfall extremes: a comprehensive study using CMIP6 simulations","authors":"Hamza Varikoden, V.H. Jamshadali, Catherine George, T. Reshma, R. Vishnu","doi":"10.1016/j.atmosres.2024.107870","DOIUrl":"https://doi.org/10.1016/j.atmosres.2024.107870","url":null,"abstract":"The South Asian regions frequently encounter extreme rainfall events (EREs) during the summer monsoon season, causing property damage, environmental harm, and fatalities. Therefore, it is essential to comprehend how these events are evolving and may change in the future, particularly for developing countries like India. This study utilised selected models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to evaluate the intensity and contribution of EREs to seasonal rainfall during the historical period (1950–2014), as well as to anticipate the variability of extremes in the future period (2015–2100) during the summer monsoon period. Compared to observational rainfall data from the Climatic Research Unit, the selected models showed similar spatial patterns for mean and extreme rainfall in the historical period with regional biases across CMIP6 selected models, mainly in the monsoonal regions such as the west coast, central India, the Himalayas and its foothills, and the northeastern regions. Four EC-Earth models accurately projected mean and extreme rainfall for central India, the southern west coast, and the western Himalayan regions, while NorESM slightly overestimated central India and underestimated) west coast extremes. Moreover, the selected models are not adequate to realistically capture the extremes in northeastern and Myanmar coastal areas. Future scenarios predict significant changes in extreme rainfall over central India and the rain shadow regions of southeast India. The increased ERE in the northern regions of the west coast and southwestern regions of central India could lead to increased vulnerabilities in the area, especially in higher forcing scenarios. Moreover, the zone of extreme rainfall is projected the expansion of the rain shadow regions to the leeward side of the Western Ghats, particularly in the SSP5–8.5 scenario.","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"11 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867581","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 : 2024-12-11DOI: 10.1016/j.atmosres.2024.107859
Moumita Bhowmik, Anupam Hazra
We investigated the performance of mass transfer from cloud water to rainwater, commonly referred to as ‘autoconversion’, in cloud microphysical schemes within high-resolution numerical models, such as the Weather Research and Forecasting Model (WRF). The proper choice of autoconversion rate in the numerical model is crucial for droplet growth and conversion rates to form precipitation. The liquid water content and cloud properties, varying from shallow to convective clouds, are highly sensitive to autoconversion rates, and their sensitivity is closely linked with model biases. A parcel-bin model guided by observations can provide significant insights for selecting a more suitable suite of autoconversion rates for the forecasting model. The parcel-bin model is important for the explicit representation of hydrometeors population and calculating microphysical process rates. We calculated drop size distribution, relative dispersion, diffusion growth rate coefficient, and autoconversion rates for two environmental conditions using a small-scale model based on in situ airborne measurement data from the Cloud-Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) over the Indian subcontinent. We evaluated four different (Kessler, KES; Liu-Daum, LD; Khairoutdinov-Kogan, KK; Lee-Baik, LB) autoconversion rates in the WRF model for simulating two thunderstorm events over India. The LD, LB and KK autoconversion rates exhibited closer performance and demonstrated a better probability distribution of raindrop size and precipitation compared to KES. The present study highlights the importance of proper choice of autoconversion rates in numerical weather prediction models.
{"title":"Simulating rainfall during thunderstorm events: Insights into cloud-to-rain microphysical processes over the Indian subcontinent","authors":"Moumita Bhowmik, Anupam Hazra","doi":"10.1016/j.atmosres.2024.107859","DOIUrl":"https://doi.org/10.1016/j.atmosres.2024.107859","url":null,"abstract":"We investigated the performance of mass transfer from cloud water to rainwater, commonly referred to as ‘autoconversion’, in cloud microphysical schemes within high-resolution numerical models, such as the Weather Research and Forecasting Model (WRF). The proper choice of autoconversion rate in the numerical model is crucial for droplet growth and conversion rates to form precipitation. The liquid water content and cloud properties, varying from shallow to convective clouds, are highly sensitive to autoconversion rates, and their sensitivity is closely linked with model biases. A parcel-bin model guided by observations can provide significant insights for selecting a more suitable suite of autoconversion rates for the forecasting model. The parcel-bin model is important for the explicit representation of hydrometeors population and calculating microphysical process rates. We calculated drop size distribution, relative dispersion, diffusion growth rate coefficient, and autoconversion rates for two environmental conditions using a small-scale model based on in situ airborne measurement data from the Cloud-Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX) over the Indian subcontinent. We evaluated four different (Kessler, KES; Liu-Daum, LD; Khairoutdinov-Kogan, KK; Lee-Baik, LB) autoconversion rates in the WRF model for simulating two thunderstorm events over India. The LD, LB and KK autoconversion rates exhibited closer performance and demonstrated a better probability distribution of raindrop size and precipitation compared to KES. The present study highlights the importance of proper choice of autoconversion rates in numerical weather prediction models.","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"26 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867584","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 : 2024-12-10DOI: 10.1016/j.atmosres.2024.107868
Prerna Thapliyal, Ashish Soni, Tarun Gupta
Brown Carbon (BrC) is an organic component of aerosols with light-absorbing characteristics that have crucial consequences in atmospheric warming and the climate system, yet it carries significant uncertainty. This uncertainty is due to its non-static optical properties which provide a significant challenge in the measurement of the perturbation caused by them in the Earth radiation budget. The unpredictability in optical properties is because of the continuous formation of Secondary BrC and decay of existing BrC influenced by various physicochemical and meteorological factors in the ambient atmosphere. The dynamic behaviour of these chromophores can be impacted by the aerosol liquid water content (ALWC) and atmospheric acidity via influencing its atmospheric chemistry of formation and decay. The objective of this research is to investigate how the ALWC and acidity in terms of pH affect the BrC optical properties in the rarely examined Eastern part of India during extreme winters. Utilizing a thermal-optical carbon analyzer, the optical characteristics of BrC were estimated. The ISORROPIA II, thermodynamic model was employed to simulate ALWC and aerosol pH, yielding a mean pH value of 3.30 ± 0.16 for the study duration. The study provides the first in-field evidence of a linear increase of absorption coefficient with increasing pH or decreasing aerosol acidity in the ambient atmosphere. A 39.6 Mm−1 increase in absorption coefficient per unit increase in pH, shows that aerosol pH is one of the decisive elements influencing BrC chemistry. The results also showed the inverse relation of the absorption coefficient with ALWC. The findings indicate the sensitivity of BrC chemistry towards aerosol acidity and ALWC in the ambient atmosphere and its importance while evaluating BrC absorption.
{"title":"Field validation of brown carbon absorption dependence on acidity and aerosol liquid water content","authors":"Prerna Thapliyal, Ashish Soni, Tarun Gupta","doi":"10.1016/j.atmosres.2024.107868","DOIUrl":"https://doi.org/10.1016/j.atmosres.2024.107868","url":null,"abstract":"Brown Carbon (BrC) is an organic component of aerosols with light-absorbing characteristics that have crucial consequences in atmospheric warming and the climate system, yet it carries significant uncertainty. This uncertainty is due to its non-static optical properties which provide a significant challenge in the measurement of the perturbation caused by them in the Earth radiation budget. The unpredictability in optical properties is because of the continuous formation of Secondary BrC and decay of existing BrC influenced by various physicochemical and meteorological factors in the ambient atmosphere. The dynamic behaviour of these chromophores can be impacted by the aerosol liquid water content (ALWC) and atmospheric acidity via influencing its atmospheric chemistry of formation and decay. The objective of this research is to investigate how the ALWC and acidity in terms of pH affect the BrC optical properties in the rarely examined Eastern part of India during extreme winters. Utilizing a thermal-optical carbon analyzer, the optical characteristics of BrC were estimated. The ISORROPIA II, thermodynamic model was employed to simulate ALWC and aerosol pH, yielding a mean pH value of 3.30 ± 0.16 for the study duration. The study provides the first in-field evidence of a linear increase of absorption coefficient with increasing pH or decreasing aerosol acidity in the ambient atmosphere. A 39.6 Mm<ce:sup loc=\"post\">−1</ce:sup> increase in absorption coefficient per unit increase in pH, shows that aerosol pH is one of the decisive elements influencing BrC chemistry. The results also showed the inverse relation of the absorption coefficient with ALWC. The findings indicate the sensitivity of BrC chemistry towards aerosol acidity and ALWC in the ambient atmosphere and its importance while evaluating BrC absorption.","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"16 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816535","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 : 2024-12-09DOI: 10.1016/j.atmosres.2024.107864
Gabriel Bonow Münchow, Aline Macedo de Oliveira, Ediclê De Souza Fernandes Duarte, Daniel Camilo Fortunato dos Santos Oliveira, Bárbara Marinho Araujo, Nilton Manuel Évora do Rosário, Judith Johanna Hoelzemann
The atmosphere over the Northeastern region of Brazil (NEB) contains a variety of different aerosol types: marine, soil dust, biomass burning, urban pollution, as well as mixed species. However, very few reliable information on aerosols is available for the region. Satellite data provides spatial and temporal coverage in places with low density ground-based observational networks, with Aerosol Optical Depth (AOD) products as most important information on the atmospheric aerosol load. To find out how different AOD products from satellites behave over the NEB region we compared AOD at 550 nm from MODIS retrievals aboard both NASA's Terra and Aqua satellites, and ground-based derived AOD 550 nm from the two Aerosol Robotic Network (AERONET) sites in the region, located in cities of Petrolina and Natal, between 2015 and 2018. We based our analysis on the following MODIS AOD retrievals products: Dark Target (DT), Deep Blue (DB), the combined product (DTDB), DT 3 km resolution product (MxD3k) and MAIAC at 1 km resolution. Additionally, we used AOD from MERRA2 reanalysis for another comparison that may provide additional insight on which products perform best over the region. We also analyze and compare the seasonality of the AOD products to the four biomes of NEB, which are Amazon rainforest, Atlantic Forest, Cerrado and Caatinga (wooded savannah-like) biomes. Petrolina is located in the NEB largest biome, the Caatinga, while Natal is a coastal city in the Atlantic Forest. The results show that DB yields the best results for the Petrolina site. The analyses also show that in all biomes the AOD monthly averages decrease during austral autumn and winter and increase during spring and summer. The compared products show some differences in AOD, even though with similar patterns. MAIAC and DB show very similar values in all four biomes throughout the year, recommending the use of DB and MAIAC for studies in the Caatinga region.
{"title":"Aerosol optical depth over Northeastern Brazil: A multi-platform intercomparison study","authors":"Gabriel Bonow Münchow, Aline Macedo de Oliveira, Ediclê De Souza Fernandes Duarte, Daniel Camilo Fortunato dos Santos Oliveira, Bárbara Marinho Araujo, Nilton Manuel Évora do Rosário, Judith Johanna Hoelzemann","doi":"10.1016/j.atmosres.2024.107864","DOIUrl":"https://doi.org/10.1016/j.atmosres.2024.107864","url":null,"abstract":"The atmosphere over the Northeastern region of Brazil (NEB) contains a variety of different aerosol types: marine, soil dust, biomass burning, urban pollution, as well as mixed species. However, very few reliable information on aerosols is available for the region. Satellite data provides spatial and temporal coverage in places with low density ground-based observational networks, with Aerosol Optical Depth (AOD) products as most important information on the atmospheric aerosol load. To find out how different AOD products from satellites behave over the NEB region we compared AOD at 550 nm from MODIS retrievals aboard both NASA's Terra and Aqua satellites, and ground-based derived AOD 550 nm from the two Aerosol Robotic Network (AERONET) sites in the region, located in cities of Petrolina and Natal, between 2015 and 2018. We based our analysis on the following MODIS AOD retrievals products: Dark Target (DT), Deep Blue (DB), the combined product (DTDB), DT 3 km resolution product (MxD3k) and MAIAC at 1 km resolution. Additionally, we used AOD from MERRA2 reanalysis for another comparison that may provide additional insight on which products perform best over the region. We also analyze and compare the seasonality of the AOD products to the four biomes of NEB, which are Amazon rainforest, Atlantic Forest, Cerrado and Caatinga (wooded savannah-like) biomes. Petrolina is located in the NEB largest biome, the Caatinga, while Natal is a coastal city in the Atlantic Forest. The results show that DB yields the best results for the Petrolina site. The analyses also show that in all biomes the AOD monthly averages decrease during austral autumn and winter and increase during spring and summer. The compared products show some differences in AOD, even though with similar patterns. MAIAC and DB show very similar values in all four biomes throughout the year, recommending the use of DB and MAIAC for studies in the Caatinga region.","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816538","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：