Articles | Volume 20, issue 23
https://doi.org/10.5194/acp-20-15389-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-15389-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Dec 2020
Research article |
| 11 Dec 2020
| 11 Dec 2020
Aerosol-enhanced high precipitation events near the Himalayan foothills
Department of Earth and Atmospheric Sciences, National Institute of Technology Rourkela, Rourkela 769008, Odisha, India
Leipzig Institute for Meteorology (LIM), University of Leipzig, Stephanstraße 3, 04103 Leipzig, Germany
Department of Earth and Atmospheric Sciences, National Institute of Technology Rourkela, Rourkela 769008, Odisha, India
Naresh Krishna Vissa
Department of Earth and Atmospheric Sciences, National Institute of Technology Rourkela, Rourkela 769008, Odisha, India
Jyotsna Singh
Shanti Raj Bhawan, Paramhans Nagar, Kandwa, Varanasi 221106, India
Chandan Sarangi
Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600024, India
Sachchida Nand Tripathi
Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India
Department of Earth Sciences, Indian Institute of Technology Kanpur, Kanpur 208016, India
Matthias Tesche
Leipzig Institute for Meteorology (LIM), University of Leipzig, Stephanstraße 3, 04103 Leipzig, Germany
Related authors
Yun He, Goutam Choudhury, Matthias Tesche, Albert Ansmann, Fan Yi, Detlef Müller, and Zhenping Yin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2666, https://doi.org/10.5194/egusphere-2025-2666, 2025
Short summary
Short summary
We present a global data set of POLIPHON dust conversion factors at 532 nm obtained using Aerosol RObotic NETwork (AERONET) observations at 137 sites for INP and 123 sites for CCN calculations. We also conduct a comparison of dust CCN concentration profiles derived using both POLIPHON and the independent OMCAM (Optical Modelling of the CALIPSO Aerosol Microphysics) retrieval.
Goutam Choudhury, Karoline Block, Mahnoosh Haghighatnasab, Johannes Quaas, Tom Goren, and Matthias Tesche
Atmos. Chem. Phys., 25, 3841–3856, https://doi.org/10.5194/acp-25-3841-2025, https://doi.org/10.5194/acp-25-3841-2025, 2025
Short summary
Short summary
Aerosol particles in the atmosphere increase cloud reflectivity, thereby cooling the Earth. Accurate global measurements of these particles are crucial for estimating this cooling effect. This study compares and harmonizes two newly developed global aerosol datasets, offering insights for their future use and refinement. We identify pristine oceans as a significant source of uncertainty in the datasets and, therefore, in quantifying the role of aerosols in Earth's climate.
Tom Goren, Goutam Choudhury, Jan Kretzschmar, and Isabel McCoy
Atmos. Chem. Phys., 25, 3413–3423, https://doi.org/10.5194/acp-25-3413-2025, https://doi.org/10.5194/acp-25-3413-2025, 2025
Short summary
Short summary
Many studies have identified an inverted-V relationship between the liquid water path (LWP) and droplet concentration (Nd), where LWP increases and then decreases with Nd. Using satellite observations and meteorological data, we demonstrate that the inverted V primarily reflects co-variability between LWP and Nd. We suggest taking a holistic approach that considers this co-variability when assessing the climatological sensitivity of LWP to anthropogenic aerosols.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
Short summary
Short summary
We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Goutam Choudhury and Matthias Tesche
Earth Syst. Sci. Data, 15, 3747–3760, https://doi.org/10.5194/essd-15-3747-2023, https://doi.org/10.5194/essd-15-3747-2023, 2023
Short summary
Short summary
Aerosols in the atmosphere that can form liquid cloud droplets are called cloud condensation nuclei (CCN). Accurate measurements of CCN, especially CCN of anthropogenic origin, are necessary to quantify the effect of anthropogenic aerosols on the present-day as well as future climate. In this paper, we describe a novel global 3D CCN data set calculated from satellite measurements. We also discuss the potential applications of the data in the context of aerosol–cloud interactions.
Goutam Choudhury, Albert Ansmann, and Matthias Tesche
Atmos. Chem. Phys., 22, 7143–7161, https://doi.org/10.5194/acp-22-7143-2022, https://doi.org/10.5194/acp-22-7143-2022, 2022
Short summary
Short summary
Lidars provide height-resolved type-specific aerosol properties and are key in studying vertically collocated aerosols and clouds. In this study, we compare the aerosol number concentrations derived from spaceborne lidar with the in situ flight measurements. Our results show a reasonable agreement between both datasets. Such an agreement has not been achieved yet. It shows the potential of spaceborne lidar in studying aerosol–cloud interactions, which is needed to improve our climate forecasts.
Goutam Choudhury and Matthias Tesche
Atmos. Meas. Tech., 15, 639–654, https://doi.org/10.5194/amt-15-639-2022, https://doi.org/10.5194/amt-15-639-2022, 2022
Short summary
Short summary
Aerosols are tiny particles suspended in the atmosphere. A fraction of these particles can form clouds and are called cloud condensation nuclei (CCN). Measurements of such aerosol particles are necessary to study the aerosol–cloud interactions and reduce the uncertainty in our future climate predictions. We present a novel methodology to estimate global 3D CCN concentrations from the CALIPSO satellite measurements. The final data set will be used to study the aerosol–cloud interactions.
Saleem Ali, Chandan Sarangi, and Sanjay Kumar Mehta
Atmos. Chem. Phys., 25, 8769–8783, https://doi.org/10.5194/acp-25-8769-2025, https://doi.org/10.5194/acp-25-8769-2025, 2025
Short summary
Short summary
The pollutants over northern India are transported towards southern India under the influence of the prevalent wind system, especially during the winter season. This long-range transport induces widespread haziness over southern India, lasting for days. We evaluated the occurrence of such transport episodes over southern India using observational methods and found that it suppresses the boundary layer height by approximately 40 % compared to clear days, while exacerbating the surface pollution by approximately 50 %–60 %.
Peggy Achtert, Torsten Seelig, Gabriella Wallentin, Luisa Ickes, Matthew D. Shupe, Corinna Hoose, and Matthias Tesche
EGUsphere, https://doi.org/10.5194/egusphere-2025-3529, https://doi.org/10.5194/egusphere-2025-3529, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
We quantify the occurrence of single- and multi-layer clouds in the Arctic based on combining soundings with cloud-radar observations. We also assess the rate of ice-crystal seeding in multi-layer cloud systems as this is an important initiator of glaciation in super-cooled liquid cloud layers. We find an abundance of multi-layer clouds in the Arctic with seeding in about half to two thirds of cases in which the gap between upper and lower layers ranges between 100 and 1000 m.
Gabriella Wallentin, Annika Oertel, Luisa Ickes, Peggy Achtert, Matthias Tesche, and Corinna Hoose
Atmos. Chem. Phys., 25, 6607–6631, https://doi.org/10.5194/acp-25-6607-2025, https://doi.org/10.5194/acp-25-6607-2025, 2025
Short summary
Short summary
Multilayer clouds are common in the Arctic but remain underrepresented. We use an atmospheric model to simulate multilayer cloud cases from the Arctic expedition MOSAiC 2019/2020. We find that it is complex to accurately model these cloud layers due to the lack of correct temperature profiles. The model also struggles to capture the observed cloud phase and the relative concentration of cloud droplets and cloud ice. We constrain our model to measured aerosols to mitigate this issue.
Yun He, Goutam Choudhury, Matthias Tesche, Albert Ansmann, Fan Yi, Detlef Müller, and Zhenping Yin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2666, https://doi.org/10.5194/egusphere-2025-2666, 2025
Short summary
Short summary
We present a global data set of POLIPHON dust conversion factors at 532 nm obtained using Aerosol RObotic NETwork (AERONET) observations at 137 sites for INP and 123 sites for CCN calculations. We also conduct a comparison of dust CCN concentration profiles derived using both POLIPHON and the independent OMCAM (Optical Modelling of the CALIPSO Aerosol Microphysics) retrieval.
Sougat Kumar Sarangi, Chandan Sarangi, Niravkumar Patel, Bomidi Lakshmi Madhavan, Shantikumar Singh Ningombam, Belur Ravindra, and Madineni Venkat Ratnam
EGUsphere, https://doi.org/10.5194/egusphere-2024-3364, https://doi.org/10.5194/egusphere-2024-3364, 2025
Short summary
Short summary
This study introduces a new approach to measure cloud cover from image data taken by ground based sky observations. Our method used diverse sky images taken from various locations across the globe to train our machine learning model. We achieved a very high accuracy in detecting cloud cover, even in polluted areas. Our model surpasses traditional methods by running efficiently with minimal computational needs.
Zhenyu Zhang, Jing Li, Huizheng Che, Yueming Dong, Oleg Dubovik, Thomas Eck, Pawan Gupta, Brent Holben, Jhoon Kim, Elena Lind, Trailokya Saud, Sachchida Nand Tripathi, and Tong Ying
Atmos. Chem. Phys., 25, 4617–4637, https://doi.org/10.5194/acp-25-4617-2025, https://doi.org/10.5194/acp-25-4617-2025, 2025
Short summary
Short summary
We used ground-based remote sensing data from the Aerosol Robotic Network to examine long-term trends in aerosol characteristics. We found aerosol loadings generally decreased globally, and aerosols became more scattering. These changes are closely related to variations in aerosol compositions, such as decreased anthropogenic emissions over East Asia, Europe, and North America; increased anthropogenic sources over northern India; and increased dust activity over the Arabian Peninsula.
Goutam Choudhury, Karoline Block, Mahnoosh Haghighatnasab, Johannes Quaas, Tom Goren, and Matthias Tesche
Atmos. Chem. Phys., 25, 3841–3856, https://doi.org/10.5194/acp-25-3841-2025, https://doi.org/10.5194/acp-25-3841-2025, 2025
Short summary
Short summary
Aerosol particles in the atmosphere increase cloud reflectivity, thereby cooling the Earth. Accurate global measurements of these particles are crucial for estimating this cooling effect. This study compares and harmonizes two newly developed global aerosol datasets, offering insights for their future use and refinement. We identify pristine oceans as a significant source of uncertainty in the datasets and, therefore, in quantifying the role of aerosols in Earth's climate.
Ashutosh K. Shukla, Sachchida N. Tripathi, Shamitaksha Talukdar, Vishnu Murari, Sreenivas Gaddamidi, Manousos-Ioannis Manousakas, Vipul Lalchandani, Kuldeep Dixit, Vinayak M. Ruge, Peeyush Khare, Mayank Kumar, Vikram Singh, Neeraj Rastogi, Suresh Tiwari, Atul K. Srivastava, Dilip Ganguly, Kaspar Rudolf Daellenbach, and André S. H. Prévôt
Atmos. Chem. Phys., 25, 3765–3784, https://doi.org/10.5194/acp-25-3765-2025, https://doi.org/10.5194/acp-25-3765-2025, 2025
Short summary
Short summary
Our study delves into the elemental composition of aerosols at three sites across the Indo-Gangetic Plain (IGP), revealing distinct patterns during pollution episodes. We found significant increases in chlorine (Cl)-rich and solid fuel combustion (SFC) sources, indicating dynamic emission sources, agricultural burning impacts, and meteorological influences. Surges in Cl-rich particles during cold periods highlight their role in particle growth under high-relative-humidity conditions.
Tom Goren, Goutam Choudhury, Jan Kretzschmar, and Isabel McCoy
Atmos. Chem. Phys., 25, 3413–3423, https://doi.org/10.5194/acp-25-3413-2025, https://doi.org/10.5194/acp-25-3413-2025, 2025
Short summary
Short summary
Many studies have identified an inverted-V relationship between the liquid water path (LWP) and droplet concentration (Nd), where LWP increases and then decreases with Nd. Using satellite observations and meteorological data, we demonstrate that the inverted V primarily reflects co-variability between LWP and Nd. We suggest taking a holistic approach that considers this co-variability when assessing the climatological sensitivity of LWP to anthropogenic aerosols.
Sohee Joo, Juseon Shin, Matthias Tesche, Naghmeh Dehkhoda, Taegyeong Kim, and Youngmin Noh
Atmos. Chem. Phys., 25, 1023–1036, https://doi.org/10.5194/acp-25-1023-2025, https://doi.org/10.5194/acp-25-1023-2025, 2025
Short summary
Short summary
In our study, we investigated why, in northeast Asia, visibility has not improved even though air pollution levels have decreased. By examining trends in Seoul and Ulsan, we found that the particles in the air are getting smaller, which scatters light more effectively and reduces how far we can see. Our findings suggest that changes in particle properties adversely affected public perception of air quality improvement even though the PM2.5 mass concentration is continuously decreasing.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
Short summary
Short summary
We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Nishant Ajnoti, Hemant Gehlot, and Sachchida Nand Tripathi
Atmos. Meas. Tech., 17, 1651–1664, https://doi.org/10.5194/amt-17-1651-2024, https://doi.org/10.5194/amt-17-1651-2024, 2024
Short summary
Short summary
This research focuses on the optimal placement of hybrid instruments (sensors and monitors) to maximize satisfaction function considering population, PM2.5 concentration, budget, and other factors. Two algorithms are developed in this study: a genetic algorithm and a greedy algorithm. We tested these algorithms on various regions. The insights of this work aid in quantitative placement of air quality monitoring instruments in large cities, moving away from ad hoc approaches.
Wei Huang, Cheng Wu, Linyu Gao, Yvette Gramlich, Sophie L. Haslett, Joel Thornton, Felipe D. Lopez-Hilfiker, Ben H. Lee, Junwei Song, Harald Saathoff, Xiaoli Shen, Ramakrishna Ramisetty, Sachchida N. Tripathi, Dilip Ganguly, Feng Jiang, Magdalena Vallon, Siegfried Schobesberger, Taina Yli-Juuti, and Claudia Mohr
Atmos. Chem. Phys., 24, 2607–2624, https://doi.org/10.5194/acp-24-2607-2024, https://doi.org/10.5194/acp-24-2607-2024, 2024
Short summary
Short summary
We present distinct molecular composition and volatility of oxygenated organic aerosol particles in different rural, urban, and mountain environments. We do a comprehensive investigation of the relationship between the chemical composition and volatility of oxygenated organic aerosol particles across different systems and environments. This study provides implications for volatility descriptions of oxygenated organic aerosol particles in different model frameworks.
Juseon Shin, Gahyeong Kim, Dukhyeon Kim, Matthias Tesche, Gahyeon Park, and Youngmin Noh
Atmos. Meas. Tech., 17, 397–406, https://doi.org/10.5194/amt-17-397-2024, https://doi.org/10.5194/amt-17-397-2024, 2024
Short summary
Short summary
We introduce the multi-section method, a novel approach for stable extinction coefficient retrievals in horizontally scanning aerosol lidar measurements, in this study. Our method effectively removes signal–noise-induced irregular peaks and derives a reference extinction coefficient, αref, from multiple scans, resulting in a strong correlation (>0.74) with PM2.5 mass concentrations. Case studies demonstrate its utility in retrieving spatio-temporal aerosol distributions and PM2.5 concentrations.
Matthias Kohl, Jos Lelieveld, Sourangsu Chowdhury, Sebastian Ehrhart, Disha Sharma, Yafang Cheng, Sachchida Nand Tripathi, Mathew Sebastian, Govindan Pandithurai, Hongli Wang, and Andrea Pozzer
Atmos. Chem. Phys., 23, 13191–13215, https://doi.org/10.5194/acp-23-13191-2023, https://doi.org/10.5194/acp-23-13191-2023, 2023
Short summary
Short summary
Knowledge on atmospheric ultrafine particles (UFPs) with a diameter smaller than 100 nm is crucial for public health and the hydrological cycle. We present a new global dataset of UFP concentrations at the Earth's surface derived with a comprehensive chemistry–climate model and evaluated with ground-based observations. The evaluation results are combined with high-resolution primary emissions to downscale UFP concentrations to an unprecedented horizontal resolution of 0.1° × 0.1°.
Goutam Choudhury and Matthias Tesche
Earth Syst. Sci. Data, 15, 3747–3760, https://doi.org/10.5194/essd-15-3747-2023, https://doi.org/10.5194/essd-15-3747-2023, 2023
Short summary
Short summary
Aerosols in the atmosphere that can form liquid cloud droplets are called cloud condensation nuclei (CCN). Accurate measurements of CCN, especially CCN of anthropogenic origin, are necessary to quantify the effect of anthropogenic aerosols on the present-day as well as future climate. In this paper, we describe a novel global 3D CCN data set calculated from satellite measurements. We also discuss the potential applications of the data in the context of aerosol–cloud interactions.
Sophie L. Haslett, David M. Bell, Varun Kumar, Jay G. Slowik, Dongyu S. Wang, Suneeti Mishra, Neeraj Rastogi, Atinderpal Singh, Dilip Ganguly, Joel Thornton, Feixue Zheng, Yuanyuan Li, Wei Nie, Yongchun Liu, Wei Ma, Chao Yan, Markku Kulmala, Kaspar R. Daellenbach, David Hadden, Urs Baltensperger, Andre S. H. Prevot, Sachchida N. Tripathi, and Claudia Mohr
Atmos. Chem. Phys., 23, 9023–9036, https://doi.org/10.5194/acp-23-9023-2023, https://doi.org/10.5194/acp-23-9023-2023, 2023
Short summary
Short summary
In Delhi, some aspects of daytime and nighttime atmospheric chemistry are inverted, and parodoxically, vehicle emissions may be limiting other forms of particle production. This is because the nighttime emissions of nitrogen oxide (NO) by traffic and biomass burning prevent some chemical processes that would otherwise create even more particles and worsen the urban haze.
Vaishali Jain, Nidhi Tripathi, Sachchida N. Tripathi, Mansi Gupta, Lokesh K. Sahu, Vishnu Murari, Sreenivas Gaddamidi, Ashutosh K. Shukla, and Andre S. H. Prevot
Atmos. Chem. Phys., 23, 3383–3408, https://doi.org/10.5194/acp-23-3383-2023, https://doi.org/10.5194/acp-23-3383-2023, 2023
Short summary
Short summary
This research chemically characterises 173 different NMVOCs (non-methane volatile organic compounds) measured in real time for three seasons in the city of the central Indo-Gangetic basin of India, Lucknow. Receptor modelling is used to analyse probable sources of NMVOCs and their crucial role in forming ozone and secondary organic aerosols. It is observed that vehicular emissions and solid fuel combustion are the highest contributors to the emission of primary and secondary NMVOCs.
Chandan Sarangi, Yun Qian, L. Ruby Leung, Yang Zhang, Yufei Zou, and Yuhang Wang
Atmos. Chem. Phys., 23, 1769–1783, https://doi.org/10.5194/acp-23-1769-2023, https://doi.org/10.5194/acp-23-1769-2023, 2023
Short summary
Short summary
We show that for air quality, the densely populated eastern US may see even larger impacts of wildfires due to long-distance smoke transport and associated positive climatic impacts, partially compensating the improvements from regulations on anthropogenic emissions. This study highlights the tension between natural and anthropogenic contributions and the non-local nature of air pollution that complicate regulatory strategies for improving future regional air quality for human health.
Sudipta Ghosh, Sagnik Dey, Sushant Das, Nicole Riemer, Graziano Giuliani, Dilip Ganguly, Chandra Venkataraman, Filippo Giorgi, Sachchida Nand Tripathi, Srikanthan Ramachandran, Thazhathakal Ayyappen Rajesh, Harish Gadhavi, and Atul Kumar Srivastava
Geosci. Model Dev., 16, 1–15, https://doi.org/10.5194/gmd-16-1-2023, https://doi.org/10.5194/gmd-16-1-2023, 2023
Short summary
Short summary
Accurate representation of aerosols in climate models is critical for minimizing the uncertainty in climate projections. Here, we implement region-specific emission fluxes and a more accurate scheme for carbonaceous aerosol ageing processes in a regional climate model (RegCM4) and show that it improves model performance significantly against in situ, reanalysis, and satellite data over the Indian subcontinent. We recommend improving the model performance before using them for climate studies.
Peter Bräuer and Matthias Tesche
Geosci. Model Dev., 15, 7557–7572, https://doi.org/10.5194/gmd-15-7557-2022, https://doi.org/10.5194/gmd-15-7557-2022, 2022
Short summary
Short summary
This paper presents a tool for (i) finding temporally and spatially resolved intersections between two- or three-dimensional geographical tracks (trajectories) and (ii) extracting of data in the vicinity of intersections to achieve the optimal combination of various data sets.
Matthias Tesche and Vincent Noel
Atmos. Meas. Tech., 15, 4225–4240, https://doi.org/10.5194/amt-15-4225-2022, https://doi.org/10.5194/amt-15-4225-2022, 2022
Short summary
Short summary
Mid-level and high clouds can be considered natural laboratories for studying cloud glaciation in the atmosphere. While they can be conveniently observed from ground with lidar, such measurements require a clear line of sight between the instrument and the target cloud. Here, observations of clouds with two spaceborne lidars are used to assess where ground-based lidar measurements of mid- and upper-level clouds are least affected by the light-attenuating effect of low-level clouds.
Varun Kumar, Stamatios Giannoukos, Sophie L. Haslett, Yandong Tong, Atinderpal Singh, Amelie Bertrand, Chuan Ping Lee, Dongyu S. Wang, Deepika Bhattu, Giulia Stefenelli, Jay S. Dave, Joseph V. Puthussery, Lu Qi, Pawan Vats, Pragati Rai, Roberto Casotto, Rangu Satish, Suneeti Mishra, Veronika Pospisilova, Claudia Mohr, David M. Bell, Dilip Ganguly, Vishal Verma, Neeraj Rastogi, Urs Baltensperger, Sachchida N. Tripathi, André S. H. Prévôt, and Jay G. Slowik
Atmos. Chem. Phys., 22, 7739–7761, https://doi.org/10.5194/acp-22-7739-2022, https://doi.org/10.5194/acp-22-7739-2022, 2022
Short summary
Short summary
Here we present source apportionment results from the first field deployment in Delhi of an extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF). The EESI-TOF is a recently developed instrument capable of providing uniquely detailed online chemical characterization of organic aerosol (OA), in particular the secondary OA (SOA) fraction. Here, we are able to apportion not only primary OA but also SOA to specific sources, which is performed for the first time in Delhi.
Goutam Choudhury, Albert Ansmann, and Matthias Tesche
Atmos. Chem. Phys., 22, 7143–7161, https://doi.org/10.5194/acp-22-7143-2022, https://doi.org/10.5194/acp-22-7143-2022, 2022
Short summary
Short summary
Lidars provide height-resolved type-specific aerosol properties and are key in studying vertically collocated aerosols and clouds. In this study, we compare the aerosol number concentrations derived from spaceborne lidar with the in situ flight measurements. Our results show a reasonable agreement between both datasets. Such an agreement has not been achieved yet. It shows the potential of spaceborne lidar in studying aerosol–cloud interactions, which is needed to improve our climate forecasts.
Himadri Sekhar Bhowmik, Ashutosh Shukla, Vipul Lalchandani, Jay Dave, Neeraj Rastogi, Mayank Kumar, Vikram Singh, and Sachchida Nand Tripathi
Atmos. Meas. Tech., 15, 2667–2684, https://doi.org/10.5194/amt-15-2667-2022, https://doi.org/10.5194/amt-15-2667-2022, 2022
Short summary
Short summary
This study presents comparisons between online and offline measurements of both refractory and non-refractory aerosol. This study shows differences between the measurements, related to either the limitations of the instrument (e.g., aerosol mass spectrometer only observing non-refractory aerosol) or known interferences with the technique (e.g., volatilization or reactions). The findings highlight the measurement methods' accuracy and imply the particular type of measurements needed.
Juseon Shin, Juhyeon Sim, Naghmeh Dehkhoda, Sohee Joo, Taekyung Kim, Gahyung Kim, Detlef Müller, Matthias Tesche, Sungkyun Shin, Dongho Shin, and Youngmin Noh
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-219, https://doi.org/10.5194/acp-2022-219, 2022
Preprint withdrawn
Short summary
Short summary
We analyzed long-term AERONET sun/sky radiometer for 6 continentals to verify the trend of aerosol physical properties depending on sources (dust or pollution) and size (fine or coarse mode). We identified the trend of classified aerosol optical depth (AOD) and size change over 9 years. Especially, we find out aerosol properties causing AOD variations are different from regions and fine aerosol particle in most regions has become smaller using MK-test for trend analysis.
Chandan Sarangi, TC Chakraborty, Sachchidanand Tripathi, Mithun Krishnan, Ross Morrison, Jonathan Evans, and Lina M. Mercado
Atmos. Chem. Phys., 22, 3615–3629, https://doi.org/10.5194/acp-22-3615-2022, https://doi.org/10.5194/acp-22-3615-2022, 2022
Short summary
Short summary
Transpiration fluxes by vegetation are reduced under heat stress to conserve water. However, in situ observations over northern India show that the strength of the inverse association between transpiration and atmospheric vapor pressure deficit is weakening in the presence of heavy aerosol loading. This finding not only implicates the significant role of aerosols in modifying the evaporative fraction (EF) but also warrants an in-depth analysis of the aerosol–plant–temperature–EF continuum.
Goutam Choudhury and Matthias Tesche
Atmos. Meas. Tech., 15, 639–654, https://doi.org/10.5194/amt-15-639-2022, https://doi.org/10.5194/amt-15-639-2022, 2022
Short summary
Short summary
Aerosols are tiny particles suspended in the atmosphere. A fraction of these particles can form clouds and are called cloud condensation nuclei (CCN). Measurements of such aerosol particles are necessary to study the aerosol–cloud interactions and reduce the uncertainty in our future climate predictions. We present a novel methodology to estimate global 3D CCN concentrations from the CALIPSO satellite measurements. The final data set will be used to study the aerosol–cloud interactions.
Maria Kezoudi, Matthias Tesche, Helen Smith, Alexandra Tsekeri, Holger Baars, Maximilian Dollner, Víctor Estellés, Johannes Bühl, Bernadett Weinzierl, Zbigniew Ulanowski, Detlef Müller, and Vassilis Amiridis
Atmos. Chem. Phys., 21, 6781–6797, https://doi.org/10.5194/acp-21-6781-2021, https://doi.org/10.5194/acp-21-6781-2021, 2021
Short summary
Short summary
Mineral dust concentrations in the diameter range from 0.4 to 14.0 μm were measured with the balloon-borne UCASS optical particle counter. Launches were coordinated with ground-based remote-sensing and airborne in situ measurements during a Saharan dust outbreak over Cyprus. Particle number concentrations reached 50 cm−3 for the diameter range 0.8–13.9 μm. Comparisons with aircraft data show reasonable agreement in magnitude and shape of the particle size distribution.
Karn Vohra, Eloise A. Marais, Shannen Suckra, Louisa Kramer, William J. Bloss, Ravi Sahu, Abhishek Gaur, Sachchida N. Tripathi, Martin Van Damme, Lieven Clarisse, and Pierre-F. Coheur
Atmos. Chem. Phys., 21, 6275–6296, https://doi.org/10.5194/acp-21-6275-2021, https://doi.org/10.5194/acp-21-6275-2021, 2021
Short summary
Short summary
We find satellite observations of atmospheric composition generally reproduce variability in surface air pollution, so we use their long record to estimate air quality trends in major UK and Indian cities. Our trend analysis shows that pollutants targeted with air quality policies have not declined in Delhi and Kanpur but have in London and Birmingham, with the exception of a recent and dramatic increase in reactive volatile organics in London. Unregulated ammonia has increased only in Delhi.
Pragati Rai, Jay G. Slowik, Markus Furger, Imad El Haddad, Suzanne Visser, Yandong Tong, Atinderpal Singh, Günther Wehrle, Varun Kumar, Anna K. Tobler, Deepika Bhattu, Liwei Wang, Dilip Ganguly, Neeraj Rastogi, Ru-Jin Huang, Jaroslaw Necki, Junji Cao, Sachchida N. Tripathi, Urs Baltensperger, and André S. H. Prévôt
Atmos. Chem. Phys., 21, 717–730, https://doi.org/10.5194/acp-21-717-2021, https://doi.org/10.5194/acp-21-717-2021, 2021
Short summary
Short summary
We present a simple conceptual framework based on elemental size distributions and enrichment factors that allows for a characterization of major sources, site-to-site similarities, and local differences and the identification of key information required for efficient policy development. Absolute concentrations are by far the highest in Delhi, followed by Beijing, and then the European cities.
Matthias Tesche, Peggy Achtert, and Michael C. Pitts
Atmos. Chem. Phys., 21, 505–516, https://doi.org/10.5194/acp-21-505-2021, https://doi.org/10.5194/acp-21-505-2021, 2021
Short summary
Short summary
We combine spaceborne lidar observations of clouds in the troposphere and stratosphere to assess the outcome of ground-based polar stratospheric cloud (PSC) observations that are often performed at the mercy of tropospheric clouds. We find that the outcome of ground-based lidar measurements of PSCs depends on the location of the measurement. We also provide recommendations regarding the most suitable sites in the Arctic and Antarctic.
Ravi Sahu, Ayush Nagal, Kuldeep Kumar Dixit, Harshavardhan Unnibhavi, Srikanth Mantravadi, Srijith Nair, Yogesh Simmhan, Brijesh Mishra, Rajesh Zele, Ronak Sutaria, Vidyanand Motiram Motghare, Purushottam Kar, and Sachchida Nand Tripathi
Atmos. Meas. Tech., 14, 37–52, https://doi.org/10.5194/amt-14-37-2021, https://doi.org/10.5194/amt-14-37-2021, 2021
Short summary
Short summary
A unique feature of our low-cost sensor deployment is a swap-out experiment wherein four of the six sensors were relocated to different sites in the two phases. The swap-out experiment is crucial in investigating the efficacy of calibration models when applied to weather and air quality conditions vastly different from those present during calibration. We developed a novel local calibration algorithm based on metric learning that offers stable and accurate calibration performance.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce E. Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, https://doi.org/10.5194/acp-20-15079-2020, 2020
Short summary
Short summary
Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds' reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models and requires satellite data for better quantification. The review summarizes the challenges in properly doing so and outlines avenues for progress towards a better use of aerosol retrievals and better retrievals of droplet concentrations.
Liwei Wang, Jay G. Slowik, Nidhi Tripathi, Deepika Bhattu, Pragati Rai, Varun Kumar, Pawan Vats, Rangu Satish, Urs Baltensperger, Dilip Ganguly, Neeraj Rastogi, Lokesh K. Sahu, Sachchida N. Tripathi, and André S. H. Prévôt
Atmos. Chem. Phys., 20, 9753–9770, https://doi.org/10.5194/acp-20-9753-2020, https://doi.org/10.5194/acp-20-9753-2020, 2020
Cited articles
Altaratz, O., Koren, I., Remer, L. A., and Hirsch, E.:
Cloud invigoration by aerosols – Coupling between microphysics and dynamics,
Atmos. Res.,
140, 38–60, https://doi.org/10.1016/j.atmosres.2014.01.009, 2014. a
Benn, D. I. and Owen, L. A.:
The role of the Indian summer monsoon and the mid-latitude westerlies in Himalayan glaciation: Review and speculative discussion,
J. Geol. Soc. London,
155, 353–363, https://doi.org/10.1144/gsjgs.155.2.0353, 1998. a
Bilal, M., Qiu, Z., Campbell, J., Spak, S., Shen, X., and Nazeer, M.:
A new MODIS C6 Dark Target and Deep Blue merged aerosol product on a 3 km spatial grid,
Remote Sens.-Basel,
10, 463, https://doi.org/10.3390/rs10030463, 2018. a
Bohlinger, P. and Sorteberg, A.:
A comprehensive view on trends in extreme precipitation in Nepal and their spatial distribution,
Int. J. Climatol.,
38, 1833–1845, https://doi.org/10.1002/joc.5299, 2018. a, b
Bohlinger, P., Sorteberg, A., and Sodemann, H.:
Synoptic conditions and moisture sources actuating extreme precipitation in Nepal,
J. Geophys. Res.-Atmos.,
122, 12653–12671, https://doi.org/10.1002/2017JD027543, 2017. a, b, c, d
Bookhagen, B.:
Appearance of extreme monsoonal rainfall events and their impact on erosion in the Himalaya,
Geomat. Nat. Haz. Risk,
1, 37–50, https://doi.org/10.1080/19475701003625737, 2010. a
Bosilovich, M. G., Lucchesi, R., and Suarez, M.: MERRA-2: File specification, Global Modeling and Assimilation Office GMAO, Office Note No. 9, Version 1.1, 73 pp., available at: https://gmao.gsfc.nasa.gov/pubs/office_notes (last access: 5 May 2020), 2015. a
Brooks, J., Allan, J. D., Williams, P. I., Liu, D., Fox, C., Haywood, J., Langridge, J. M., Highwood, E. J., Kompalli, S. K., O'Sullivan, D., Babu, S. S., Satheesh, S. K., Turner, A. G., and Coe, H.: Vertical and horizontal distribution of submicron aerosol chemical composition and physical characteristics across northern India during pre-monsoon and monsoon seasons, Atmos. Chem. Phys., 19, 5615–5634, https://doi.org/10.5194/acp-19-5615-2019, 2019. a
Buchard, V., Randles, C. A., da Silva, A. M., Darmenov, A., Colarco, P. R., Govindaraju, R., Ferrare, R., Hair, J., Beyersdorf, A. J., Ziemba, L. D., and Yu, H.:
The MERRA-2 aerosol reanalysis, 1980 onward. Part II: Evaluation and case studies,
J. Climate,
30, 6851–6872, https://doi.org/10.1175/JCLI-D-16-0613.1, 2017. a
Carrió, G. G., Cotton, W. R., and Cheng, W. Y. Y.:
Urban growth and aerosol effects on convection over Houston: Part 1. The August 2000 case,
Atmos. Res.,
96, 560–574, https://doi.org/10.1016/j.atmosres.2010.01.005, 2010. a
Chakraborty, S., Fu, R., Massie, S. T., and Stephens, G.:
Relative influence of meteorological conditions and aerosols on the lifetime of mesoscale convective systems,
P. Natl. Acad. Sci. USA,
113, 7426–7431, https://doi.org/10.1073/pnas.1601935113, 2016. a
Choudhury, G., Tyagi, B., Singh, J., Sarangi, C., and Tripathi, S. N.:
Aerosol-orography-precipitation – A critical assessment,
Atmos. Environ.,
214, https://doi.org/10.1016/j.atmosenv.2019.116831, 2019. a
Dahutia, P., Pathak, B., and Bhuyan, P. K.:
Aerosols characteristics, trends and their climatic implications over Northeast India and adjoining South Asia,
Int. J. Climatol.,
38, 1234–1256, https://doi.org/10.1002/joc.5240, 2018. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, D. P., and Bechtold, P.: The ERA Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Dhar, O. N. and Nandargi, S. S.:
Spatial distribution of severe rainstorms over India and their associated areal raindepths,
Mausam,
44, 373–380, 1993. a
Dimri, A. P., Chevuturi, A., Niyogi, D., Thayyen, R. J., Ray, K., Tripathi, S. N., Pandey, A. K., and Mohanty, U. C.:
Cloudbursts in Indian Himalayas: A review,
Earth-Sci. Rev.,
168, 1–23, https://doi.org/10.1016/j.earscirev.2017.03.006, 2017. a, b
Ding, Q., Sun, J., Huang, X., Ding, A., Zou, J., Yang, X., and Fu, C.: Impacts of black carbon on the formation of advection–radiation fog during a haze pollution episode in eastern China, Atmos. Chem. Phys., 19, 7759–7774, https://doi.org/10.5194/acp-19-7759-2019, 2019. a
ECMWF (European Centre for Medium-Range Weather Forecasts): ERA-Interim Project, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, updated monthly, https://doi.org/10.5065/D6CR5RD9, 2009. a
Fan, J., Leung, L. R., DeMott, P. J., Comstock, J. M., Singh, B., Rosenfeld, D., Tomlinson, J. M., White, A., Prather, K. A., Minnis, P., Ayers, J. K., and Min, Q.: Aerosol impacts on California winter clouds and precipitation during CalWater 2011: local pollution versus long-range transported dust, Atmos. Chem. Phys., 14, 81–101, https://doi.org/10.5194/acp-14-81-2014, 2014. a, b, c
Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.:
Review of aerosol–cloud interactions: Mechanisms, significance, and challenges,
J. Atmos. Sci.,
73, 4221–4252, https://doi.org/10.1175/JAS-D-16-0037.1, 2016. a, b
Fan, J., Leung, L. R., Rosenfeld, D., and DeMott, P. J.: Effects of cloud condensation nuclei and ice nucleating particles on precipitation processes and supercooled liquid in mixed-phase orographic clouds, Atmos. Chem. Phys., 17, 1017–1035, https://doi.org/10.5194/acp-17-1017-2017, 2017. a, b
Fan, J., Rosenfeld, D., Zhang, Y., Giangrande, S. E., Li, Z., Machado, L. A., Martin, S. T., Yang, Y., Wang, J., Artaxo, P., and Barbosa, H. M.:
Substantial convection and precipitation enhancements by ultrafine aerosol particles,
Science,
359, 411–418, https://doi.org/10.1126/science.aan8461, 2018. a, b
Gadgil, S.:
The Indian monsoon and its variability,
Annu. Rev. Earth Pl. Sc.,
31, 429–467, https://doi.org/10.1146/annurev.earth.31.100901.141251, 2003. a
Gadgil, S., Rajeevan, M., and Nanjundiah, R.: Monsoon prediction – Why yet another failure, Curr. Sci. India, 88, 1389–1400,
available at: http://www.jstor.org/stable/24110705 (last access: 5 May 2020), 2005. a
Gautam, R., Hsu, N. C., Lau, W. K. M., and Yasunari, T. J.:
Satellite observations of desert dust-induced Himalayan snow darkening,
Geophys. Res. Lett.,
40, 988–993, https://doi.org/10.1002/grl.50226, 2013. a
GMAO (Global Modeling and Assimilation Office): MERRA-2 inst3_2d_gas_Nx: 2d,3-Hourly,Instantaneous,Single-Level,Assimilation,Aerosol Optical Depth Analysis V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/HNGA0EWW0R09, 2015a. a
GMAO (Global Modeling and Assimilation Office): MERRA-2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/QBZ6MG944HW0, 2015b. a
Han, J.-Y., Baik, J.-J., and Khain, A. P.: A numerical study of urban aerosol impacts on clouds and precipitation, J. Atmos. Sci., 69, 504–520, https://doi.org/10.1175/JAS-D-11-071.1, 2012. a
Hsu, N. C., Jeong, M. J., Bettenhausen, C., Sayer, A. M., Hansell, R., Seftor, C. S., Huang, J., and Tsay, S. C.: Enhanced Deep Blue aerosol retrieval algorithm: The second generation, J. Geophys. Res.-Atmos.,
118, 9296–9315, https://doi.org/10.1002/jgrd.50712, 2013. a
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales, J. Hydrometeorol., 8, 38–55, https://doi.org/10.1175/JHM560.1, 2007. a
Indrani, P. and Al-Tabbaa, A.: Monsoon rainfall extreme indices and tendencies from 1954–2003 in Kerala, India, Climatic Change,
106, 407–419, https://doi.org/10.1007/s10584-011-0044-6, 2011. a
Inoue, T., Satoh, M., Miura, H., and Mapes, B.: Characteristics of cloud size of deep convection simulated by a global cloud resolving model over the western tropical Pacific, Journal of the Meteorological Society of Japan Ser. I, 86, 1–15, https://doi.org/10.2151/jmsj.86A.1, 2008. a
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., https://doi.org/10.1017/CBO9781107415324, 2013. a
Iqbal, M. F. and Athar, H.:
Validation of satellite based precipitation over diverse topography of Pakistan,
Atmos. Res.,
201, 247–260, https://doi.org/10.1016/j.atmosres.2017.10.026, 2018. a, b
Kant, S., Panda, J., Gautam, R., Wang, P. K., and Singh, S. P.:
Significance of aerosols influencing weather and climate over Indian region,
International Journal of Earth and Atmospheric Science, 4, 1–20, 2017. a
Kant, S., Panda, J., Pani, S. K., and Wang, P. K.:
Long-term study of aerosol–cloud–precipitation interaction over the eastern part of India using satellite observations during pre-monsoon season,
Theor. Appl. Climatol.,
136, 605–626, https://doi.org/10.1007/s00704-018-2509-2, 2019. a
Kiranmayi, L. and Maloney, E. D.:
Intraseasonal moist static energy budget in reanalysis data,
J. Geophys. Res., 116, D21117, https://doi.org/10.1029/2011JD016031, 2011. a
Koo, M. S., Hong, S. Y., and Kim, J.:
An evaluation of the tropical rainfall measuring mission (TRMM) multi-satellite precipitation analysis (TMPA) data over South Korea, Asia-Pacific
J. Atmos. Sci.,
45, 265–282, 2009. a
Krishnamurti, T. N.:
Summer monsoon experiment – A review,
Mon. Weather Rev.,
113, 1590–1626, https://doi.org/10.1175/1520-0493(1985)113<1590:SMER>2.0.CO;2, 1985. a
Krishnan, R., Zhang, C., and Sugi, M.:
Dynamics of breaks in the Indian summer monsoon,
J. Atmos. Sci.,
57, 1354–1372, https://doi.org/10.1175/1520-0469(2000)057<1354:DOBITI>2.0.CO;2, 2000. a
Kumar, K. K., Soman, M. K., and Kumar, K. R.:
Seasonal forecasting of Indian summer monsoon rainfall: A review,
Weather,
50, 449–467, https://doi.org/10.1002/j.1477-8696.1995.tb06071.x, 1995. a
Lau, W. K., Kim, M. K., Kim, K. M., and Lee, W. S.:
Enhanced surface warming and accelerated snow melt in the Himalayas and Tibetan Plateau induced by absorbing aerosols,
Environ. Res. Lett.,
5, 025204, https://doi.org/10.1088/1748-9326/5/2/025204, 2010. a
Lau, W. K., Kim, K. M., Shi, J. J., Matsui, T., Chin, M., Tan, Q., Peters-Lidard, C., and Tao, W. K.:
Impacts of aerosol–monsoon interaction on rainfall and circulation over Northern India and the Himalaya Foothills,
Clim. Dynam.,
49, 1945–1960, https://doi.org/10.1007/s00382-016-3430-y, 2017. a, b, c
Letcher, T. and Cotton, W. R.:
The effect of pollution aerosol on wintertime orographic precipitation in the Colorado Rockies using a simplified emissions scheme to predict CCN concentrations,
J. Appl. Meteorol. Clim.,
53, 859–872, https://doi.org/10.1175/JAMC-D-13-0166.1, 2014. a
Levy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., and Kaufman, Y. J.:
Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance,
J. Geophys. Res., 112, D13211, https://doi.org/10.1029/2006JD007811, 2007. a
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013. a
Li, Z., Niu, F., Fan, J., Liu, Y., Rosenfeld, D., and Ding, Y.:
Long-term impacts of aerosols on the vertical development of clouds and precipitation, Nat. Geosci., 4, 888, https://doi.org/10.1038/ngeo1313, 2011. a
Li, Z., Rosenfeld, D., and Fan, J.: Aerosols and their impact on radiation, clouds, precipitation, and severe weather events, Oxford Research Encyclopedia of Environmental Science, https://doi.org/10.1093/acrefore/9780199389414.013.126, 2017. a
Liebmann, B. and Smith, C. A.:
Description of a complete (interpolated) outgoing longwave radiation dataset,
B. Am. Meteorol. Soc., 77, 1275–1277, available at: https://www.jstor.org/stable/26233278 (last access: 5 May 2020), 1996. a
Mayer, M. and Haimberger, L.:
Poleward atmospheric energy transports and their variability as evaluated from ECMWF reanalysis data,
J. Climate,
25, 734–752, https://doi.org/10.1175/JCLI-D-11-00202.1, 2012. a
Nandargi, S. and Dhar, O. N.:
Extreme rainfall events over the Himalayas between 1871 and 2007,
Hydrolog. Sci. J., 56, 930–945, https://doi.org/10.1080/02626667.2011.595373, 2011. a, b
NCAR/NOAA: NOAA Physical Sciences Laboratory (PSL), available at: https://www.esrl.noaa.gov/psd (last access: 5 May 2020), 1996. a
Ochoa, A., Pineda, L., Crespo, P., and Willems, P.: Evaluation of TRMM 3B42 precipitation estimates and WRF retrospective precipitation simulation over the Pacific–Andean region of Ecuador and Peru, Hydrol. Earth Syst. Sci., 18, 3179–3193, https://doi.org/10.5194/hess-18-3179-2014, 2014. a
Pillai, P. A. and Sahai, A. K.: Moist dynamics of active/break cycle of Indian summer monsoon rainfall from NCEPR2 and MERRA reanalysis, Int. J. Climatol., 34, 1429–1444, https://doi.org/10.1002/joc.3774, 2014. a
Platnick, S., Hubanks, P., Meyer, K., and King, M. D.: MODIS Atmosphere L3 Daily Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA, https://doi.org/10.5067/MODIS/MOD08_D3.061, 2015. a
Rajeevan, M., Gadgil, S., and Bhate, J.: Active and break spells of the Indian summer monsoon, J. Earth Syst. Sci., 119, 229–247, https://doi.org/10.1007/s12040-010-0019-4, 2010. a, b
Ramachandran, S., Kedia, S., and Srivastava, R.: Aerosol optical depth trends over different regions of India, Atmos. Environ., 49, 338–347, https://doi.org/10.1016/j.atmosenv.2011.11.017, 2012. a
Rana, A., Jia, S., and Sarkar, S.:
Black carbon aerosol in India: A comprehensive review of current status and future prospects,
Atmos. Res.,
218, 207–230, https://doi.org/10.1016/j.atmosres.2018.12.002, 2019. a
Reddy, M. D., Srivastava, A. K., Bisht, D. S., Singh, D., Soni, V. K., Singh, S., and Tiwari, S.:
Columnar Aerosol Characteristics over a Highly Polluted Urban Station in North India Using Sun/Sky Radiometer Measurements, International Journal of Earth and Atmospheric Science, 5, 47–61, 2018. a
Rosenfeld, D., Lohmann, U., Raga, G. B., O'Dowd, C. D., Kulmala, M., Fuzzi, S., Reissell, A., and Andreae, M. O.:
Flood or drought: how do aerosols affect precipitation?,
Science,
321, 1309–1313, https://doi.org/10.1126/science.1160606, 2008. a, b
Saleeby, S. M., Cotton, W. R., Lowenthal, D., and Messina, J.:
Aerosol impacts on the microphysical growth processes of orographic snowfall,
J. Appl. Meteorol. Clim.,
52, 834–852, https://doi.org/10.1175/JAMC-D-12-0193.1, 2013. a
Sapiano, M. R. P. and Arkin, P. A.:
An intercomparison and validation of high-resolution satellite precipitation estimates with 3-hourly gauge data,
J. Hydrometeorol.,
10, 149–166, https://doi.org/10.1175/2008JHM1052.1, 2009. a
Sarangi, C., Tripathi, S. N., Tripathi, S., and Barth, M. C.:
Aerosol-cloud associations over Gangetic Basin during a typical monsoon depression event using WRF-Chem simulation,
J. Geophys. Res.-Atmos.,
120, 10974–10995, https://doi.org/10.1002/2015JD023634, 2015. a
Sarangi, C., Tripathi, S. N., Kanawade, V. P., Koren, I., and Pai, D. S.: Investigation of the aerosol–cloud–rainfall association over the Indian summer monsoon region, Atmos. Chem. Phys., 17, 5185–5204, https://doi.org/10.5194/acp-17-5185-2017, 2017. a
Sarangi, C., Kanawade, V. P., Tripathi, S. N., Thomas, A., and Ganguly, D.:
Aerosol-induced intensification of cooling effect of clouds during Indian summer monsoon,
Nat. Commun.,
9, 3754, https://doi.org/10.1038/s41467-018-06015-5, 2018. a, b
Scheel, M. L. M., Rohrer, M., Huggel, Ch., Santos Villar, D., Silvestre, E., and Huffman, G. J.: Evaluation of TRMM Multi-satellite Precipitation Analysis (TMPA) performance in the Central Andes region and its dependency on spatial and temporal resolution, Hydrol. Earth Syst. Sci., 15, 2649–2663, https://doi.org/10.5194/hess-15-2649-2011, 2011. a
Shi, H., Xiao, Z., Zhan, X., Ma, H., and Tian, X.:
Evaluation of MODIS and two reanalysis aerosol optical depth products over AERONET sites,
Atmos. Res.,
220, 75–80, https://doi.org/10.1016/j.atmosres.2019.01.009, 2019. a
Shrestha, R. K., Connolly, P. J., and Gallagher, M. W.:
Sensitivity of precipitation to aerosol and temperature perturbation over the foothills of the Nepal Himalayas,
Proceedings,
1, 144, https://doi.org/10.3390/ecas2017-04146, 2017. a
Shrestha, S., Yao, T., and Adhikari, T. R.:
Analysis of rainfall trends of two complex mountain river basins on the southern slopes of the Central Himalayas,
Atmos. Res.,
215, 99–115, https://doi.org/10.1007/s00704-019-02897-7, 2019. a
Takemura, T., Nozawa, T., Emori, S., Nakajima, T. Y., and Nakajima, T.:
Simulation of climate response to aerosol direct and indirect effects with aerosol transport-radiation model,
J. Geophys. Res., 110, D02202, https://doi.org/10.1029/2004JD005029, 2005. a
Tao, W. K., Chen, J. P., Li, Z., Wang, C., and Zhang, C.:
Impact of aerosols on convective clouds and precipitation,
Rev. Geophys., 50, RG2001, https://doi.org/10.1029/2011RG000369, 2012. a
TRMM (Tropical Rainfall Measuring Mission): TRMM (TMPA) Rainfall Estimate L3 3 hour 0.25 degree x 0.25 degree V7, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), https://doi.org/10.5067/TRMM/TMPA/3H/7, 2011. a
Van den Heever, S. C. and Cotton, W. R.:
Urban aerosol impacts on downwind convective storms,
J. Appl. Meteorol. Clim.,
46, 828–850, https://doi.org/10.1175/JAM2492.1, 2007. a
Varikoden, H. and Revadekar, J. V.:
On the extreme rainfall events during the southwest monsoon season in northeast regions of the Indian subcontinent,
Meteorol. Appl.,
27, 1822, https://doi.org/10.1002/met.1822, 2019. a
Vinoj, V., Rasch, P. J., Wang, H., Yoon, J. H., Ma, P. L., Landu, K., and Singh, B.:
Short-term modulation of Indian summer monsoon rainfall by West Asian dust,
Nat. Geosci.,
7, 308, https://doi.org/10.1038/ngeo2107, 2014. a
Wang, Y., Khalizov, A., Levy, M., and Zhang, R.:
New Directions: Light absorbing aerosols and their atmospheric impacts,
Atmos. Environ.,
81, 713–715, https://doi.org/10.1016/j.atmosenv.2013.09.034, 2013. a
Wei, J., Li, Z., Peng, Y., and Sun, L.: MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison, Atmos. Environ., 201, 428–440, https://doi.org/10.1016/j.atmosenv.2018.12.004, 2019a.
Wei, J., Li, Z., Sun, L., Peng, Y., and Wang, L.: Improved merge schemes for MODIS Collection 6.1 Dark Target and Deep Blue combined aerosol products, Atmos. Environ., 202, 315–327, https://doi.org/10.1016/j.atmosenv.2019.01.016, 2019b.
Wei, J., Peng, Y., Guo, J., and Sun, L.: Performance of MODIS Collection 6.1 Level 3 aerosol products in spatial-temporal variations over land,
Atmos. Environ., 206, 30–44, https://doi.org/10.1016/j.atmosenv.2019.03.001, 2019c.
a
Wendisch, M., Hellmuth, O., Ansmann, A., Heintzenberg, J., Engelmann, R., Althausen, D., Eichler, H., Müller, D., Hu, M., Zhang, Y., and Mao, J.:
Radiative and dynamic effects of absorbing aerosol particles over the Pearl River Delta, China,
Atmos. Environ.,
42, 6405–6416, https://doi.org/10.1016/j.atmosenv.2008.02.033, 2008. a
Xiao, H., Yin, Y., Jin, L., Chen, Q., and Chen, J.:
Simulation of the effects of aerosol on mixed phase orographic clouds using the WRF model with a detailed bin microphysics scheme,
J. Geophys. Res.-Atmos.,
120, 8345–8358, https://doi.org/10.1002/2014JD022988, 2015. a, b
Yang, K., Koike, T., Fujii, H., Tamura, T., Xu, X., Bian, L., and Zhou, M.:
The daytime evolution of the atmospheric boundary layer and convection over the Tibetan Plateau: observations and simulations,
J. Meteorol. Soc. Jpn., Ser. II, 82, 1777–1792, https://doi.org/10.2151/jmsj.82.1777, 2004. a
Yang, X. and Li, Z.: Increases in thunderstorm activity and relationships with air pollution in southeast China, J. Geophys. Res.-Atmos.,
119, 1835–1844, https://doi.org/10.1002/2013JD021224, 2014. a
Zubler, E. M., Lohmann, U., Lüthi, D., Schär, C., and Muhlbauer, A.:
Statistical analysis of aerosol effects on simulated mixed-phase clouds and precipitation in the Alps, J. Atmos. Sci., 68, 1474–1492, https://doi.org/10.1175/2011JAS3632.1, 2011. a
Short summary
This study uses 17 years (2001–2017) of observed rain rate, aerosol optical depth (AOD), meteorological reanalysis fields and outgoing long-wave radiation to investigate high precipitation events at the foothills of the Himalayas. Composite analysis of all data sets for high precipitation events (daily rainfall > 95th percentile) indicates clear and robust associations between high precipitation events, high aerosol loading and high moist static energy values.
This study uses 17 years (2001–2017) of observed rain rate, aerosol optical depth (AOD),...
Altmetrics
Final-revised paper
Preprint