Articles | Volume 17, issue 8
https://doi.org/10.5194/acp-17-5185-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-17-5185-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
21 Apr 2017
Research article |
| 21 Apr 2017
Investigation of the aerosol–cloud–rainfall association over the Indian summer monsoon region
Chandan Sarangi
Department of Civil Engineering and Centre for Environmental Science and Engineering, India Institute of Technology, Kanpur, India
Department of Civil Engineering and Centre for Environmental Science and Engineering, India Institute of Technology, Kanpur, India
Vijay P. Kanawade
Department of Civil Engineering and Centre for Environmental Science and Engineering, India Institute of Technology, Kanpur, India
currently at: University Centre for Earth and Space Sciences, University of Hyderabad, Hyderabad, India
Ilan Koren
Department of Earth and Planetary Science, Weizmann Institute, Rehovot, Israel
D. Sivanand Pai
India Meteorological Department, Pune, India
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Atmos. Meas. Tech., 19, 617–627, https://doi.org/10.5194/amt-19-617-2026, https://doi.org/10.5194/amt-19-617-2026, 2026
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We advanced the Lightning Differential Space (LDS) method, which maps time and distance between lightning strokes, and demonstrated its use across three distinct meteorological regions. By comparing stroke strength and timing, our new Current Ratio approach reveals flash initiation patterns in different storm types. The method is straightforward and data-driven, enabling physical insight without model-based analysis or prior assumptions, and supports regional lightning analysis and forecasting.
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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Anchal Garg, Maximilien Desservettaz, Aliki Christodoulou, Theodoros Christoudias, Vijay Punjaji Kanawade, Chrysanthos Savvides, Mihalis Vrekoussis, Shahid Naqui, Tuija Jokinen, Joseph Byron, Jonathan Williams, Nikos Mihalopoulos, Eleni Liakakou, Jean Sciare, and Efstratios Bourtsoukidis
EGUsphere, https://doi.org/10.5194/egusphere-2025-5124, https://doi.org/10.5194/egusphere-2025-5124, 2025
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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
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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
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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.
Neha Deot, Vijay P. Kanawade, Alkistis Papetta, Rima Baalbaki, Michael Pikridas, Franco Marenco, Markku Kulmala, Jean Sciare, Katrianne Lehtipalo, and Tuija Jokinen
Aerosol Research, 3, 139–154, https://doi.org/10.5194/ar-3-139-2025, https://doi.org/10.5194/ar-3-139-2025, 2025
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Manuel Santos Gutiérrez, Mickaël David Chekroun, and Ilan Koren
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Preprint withdrawn
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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
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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
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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.
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
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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°.
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
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Huan Liu, Ilan Koren, Orit Altaratz, and Mickaël D. Chekroun
Atmos. Chem. Phys., 23, 6559–6569, https://doi.org/10.5194/acp-23-6559-2023, https://doi.org/10.5194/acp-23-6559-2023, 2023
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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
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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.
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
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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.
Elisa T. Sena, Ilan Koren, Orit Altaratz, and Alexander B. Kostinski
Atmos. Chem. Phys., 22, 16111–16122, https://doi.org/10.5194/acp-22-16111-2022, https://doi.org/10.5194/acp-22-16111-2022, 2022
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We used record-breaking statistics together with spatial information to create record-breaking SST maps. The maps reveal warming patterns in the overwhelming majority of the ocean and coherent islands of cooling, where low records occur more frequently than high ones. Some of these cooling spots are well known; however, a surprising elliptical area in the Southern Ocean is observed as well. Similar analyses can be performed on other key climatological variables to explore their trend patterns.
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
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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.
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
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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.
Mathew Sebastian, Sobhan Kumar Kompalli, Vasudevan Anil Kumar, Sandhya Jose, S. Suresh Babu, Govindan Pandithurai, Sachchidanand Singh, Rakesh K. Hooda, Vijay K. Soni, Jeffrey R. Pierce, Ville Vakkari, Eija Asmi, Daniel M. Westervelt, Antti-Pekka Hyvärinen, and Vijay P. Kanawade
Atmos. Chem. Phys., 22, 4491–4508, https://doi.org/10.5194/acp-22-4491-2022, https://doi.org/10.5194/acp-22-4491-2022, 2022
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Characteristics of particle number size distributions and new particle formation in six locations in India were analyzed. New particle formation occurred frequently during the pre-monsoon (spring) season and it significantly modulates the shape of the particle number size distributions. The contribution of newly formed particles to cloud condensation nuclei concentrations was ~3 times higher in urban locations than in mountain background locations.
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
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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.
Eshkol Eytan, Ilan Koren, Orit Altaratz, Mark Pinsky, and Alexander Khain
Atmos. Chem. Phys., 21, 16203–16217, https://doi.org/10.5194/acp-21-16203-2021, https://doi.org/10.5194/acp-21-16203-2021, 2021
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Describing cloud mixing processes is among the most challenging fronts in cloud physics. Therefore, the adiabatic fraction (AF) that serves as a mixing measure is a valuable metric. We use high-resolution (10 m) simulations of single clouds with a passive tracer to test the skill of different methods used to derive AF. We highlight a method that is insensitive to the available cloud samples and allows considering microphysical effects on AF estimations in different environmental conditions.
Tom Dror, Mickaël D. Chekroun, Orit Altaratz, and Ilan Koren
Atmos. Chem. Phys., 21, 12261–12272, https://doi.org/10.5194/acp-21-12261-2021, https://doi.org/10.5194/acp-21-12261-2021, 2021
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A part of continental shallow convective cumulus (Cu) was shown to share properties such as organization and formation over vegetated areas, thus named green Cu. Mechanisms behind the formed patterns are not understood. We use different metrics and an empirical orthogonal function (EOF) to decompose the dataset and quantify organization factors (cloud streets and gravity waves). We show that clouds form a highly organized grid structure over hundreds of kilometers at the field lifetime.
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
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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.
Cited articles
Abish, B. and Mohanakumar, K.: Role of fine mode aerosols in modulating cloud properties over industrial locations in north India, Ann. Geophys., 29, 1605–1612, https://doi.org/10.5194/angeo-29-1605-2011, 2011.
Ackerman, A. S., Toon, O. B., Stevens, D. E., Heymsfield, A. J., Ramanathan, V., and Welton, E. J.: Reduction of Tropical Cloudiness by Soot, Science, 288, 1042–1047, 2000.
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, 1989.
Altaratz, O., Bar-Or, R. Z., Wollner, U., and Koren, I.: Relative humidity and its effect on aerosol optical depth in the vicinity of convective clouds, Environ. Res. Lett., 8, 034025, https://doi.org/10.1088/1748-9326/8/3/034025, 2013.
Altaratz, O., Koren, I., Remer, L. A., and Hirsch, E.: Review: Cloud invigoration by aerosols – Coupling between microphysics and dynamics, Atmos. Res., 140–141, 38–60, 2014.
Andreae, M. O.: Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions, Atmos. Chem. Phys., 9, 543–556, https://doi.org/10.5194/acp-9-543-2009, 2009.
Andreae, M. O., Rosenfeld, D., Artaxo, P., Costa, A. A., Frank, G. P., Longo, K. M., and Silva-Dias, M. A. F.: Smoking Rain Clouds over the Amazon, Science, 303, 1337–1342, 2004.
Austin, R. T., Heymsfield, A. J., and Stephens, G. L.: Retrieval of ice cloud microphysical parameters using the CloudSat millimeter-wave radar and temperature, J. Geophys. Res.-Atmos., 114, D00A23, https://doi.org/10.1029/2008JD010049, 2009.
Bar-Or, R. Z., Koren, I., Altaratz, O., and Fredj, E.: Radiative properties of humidified aerosols in cloudy environment, Atmos. Res., 118, 280–294, 2012.
Bhattu, D. and Tripathi, S. N.: Inter-seasonal variability in size-resolved CCN properties at Kanpur, India, Atmos. Environ., 85, 161–168, 2014.
Bollasina, M. A., Ming, Y., and Ramaswamy, V.: Anthropogenic Aerosols and the Weakening of the South Asian Summer Monsoon, Science, 334, 502–505, 2011.
Bond, T. C., Streets, D. G., Yarber, K. F., Nelson, S. M., Woo, J.-H., and Klimont, Z.: A technology-based global inventory of black and organic carbon emissions from combustion, J. Geophys. Res.-Atmos., 109, D14203, https://doi.org/10.1029/2003JD003697, 2004.
Boucher, O. and Quaas, J.: Water vapour affects both rain and aerosol optical depth, Nat. Geosci., 6, 4–5, 2013.
Chakraborty, S., Fu, R., Wright, J. S., and Massie, S. T.: Relationships between convective structure and transport of aerosols to the upper troposphere deduced from satellite observations, J. Geophys. Res.-Atmos., 120, 6515–6536, 2015.
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, 2016.
Corrigan, C. E., Ramanathan, V., and Schauer, J. J.: Impact of monsoon transitions on the physical and optical properties of aerosols, J. Geophys. Res.-Atmos., 111, D18208, https://doi.org/10.1029/2005JD006370, 2006.
Dagan, G., Koren, I., and Altaratz, O.: Competition between core and periphery-based processes in warm convective clouds – from invigoration to suppression, Atmos. Chem. Phys., 15, 2749–2760, https://doi.org/10.5194/acp-15-2749-2015, 2015.
Dey, S. and Di Girolamo, L.: A decade of change in aerosol properties over the Indian subcontinent, Geophys. Res. Lett., 38, L14811, https://doi.org/10.1029/2011GL048153, 2011.
Dubovik, O. and King, M. D.: A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements, J. Geophys. Res.-Atmos., 105, 20673–20696, 2000.
Durre, I., Vose, R. S., and Wuertz, D. B.: Overview of the Integrated Global Radiosonde Archive, J. Climate, 19, 53–68, 2006.
Fan, J., Yuan, T., Comstock, J. M., Ghan, S., Khain, A., Leung, L. R., Li, Z., Martins, V. J., and Ovchinnikov, M.: Dominant role by vertical wind shear in regulating aerosol effects on deep convective clouds, J. Geophys. Res.-Atmos., 114, D22206, https://doi.org/10.1029/2009JD012352, 2009.
Fan, J., Leung, L. R., Rosenfeld, D., Chen, Q., Li, Z., Zhang, J., and Yan, H.: Microphysical effects determine macrophysical response for aerosol impacts on deep convective clouds, P. Natl. Acad. Sci. USA, 110, E4581–E4590, 2013.
Feingold, G., Remer, L. A., Ramaprasad, J., and Kaufman, Y. J.: Analysis of smoke impact on clouds in Brazilian biomass burning regions: An extension of Twomey's approach, J. Geophys. Res.-Atmos., 106, 22907–22922, 2001.
Feingold, G., McComiskey, A., Yamaguchi, T., Johnson, J. S., Carslaw, K. S., and Schmidt, K. S.: New approaches to quantifying aerosol influence on the cloud radiative effect, P. Natl. Acad. Sci. USA, 113, 5812–5819, 2016.
Fitzgerald, J. W. and Spyers-Duran, P. A.: Changes in Cloud Nucleus Concentration and Cloud Droplet Size Distribution Associated with Pollution from St. Louis, J. Appl. Meteorol., 12, 511–516, 1973.
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D. W., Haywood, J., Lean, J., Lowe, D. C., Myhre, G., Nganga, J., Prinn, R., Raga, G., Schulz, M., and Van Dorland, R.: Changes in Atmospheric Constituents and in Radiative Forcing, in: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change Rep., Cambridge, UK and New York, NY, USA, 2007.
Freud, E., Rosenfeld, D., and Kulkarni, J. R.: Resolving both entrainment-mixing and number of activated CCN in deep convective clouds, Atmos. Chem. Phys., 11, 12887–12900, https://doi.org/10.5194/acp-11-12887-2011, 2011.
Gadgil, S.: The Indian Monsoon and its Variability, Annu. Rev. Earth Pl. Sc., 31, 429–467, 2003.
Ganguly, D., Rasch, P. J., Wang, H., and Yoon, J.-H.: Climate response of the South Asian monsoon system to anthropogenic aerosols, J. Geophys. Res.-Atmos., 117, D13209, https://doi.org/10.1029/2012JD017508, 2012.
Gonçalves, W. A., Machado, L. A. T., and Kirstetter, P.-E.: Influence of biomass aerosol on precipitation over the Central Amazon: an observational study, Atmos. Chem. Phys., 15, 6789–6800, https://doi.org/10.5194/acp-15-6789-2015, 2015.
Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S., and Xavier, P. K.: Increasing Trend of Extreme Rain Events Over India in a Warming Environment, Science, 314, 1442–1445, 2006.
Grandey, B. S., Stier, P., and Wagner, T. M.: Investigating relationships between aerosol optical depth and cloud fraction using satellite, aerosol reanalysis and general circulation model data, Atmos. Chem. Phys., 13, 3177–3184, https://doi.org/10.5194/acp-13-3177-2013, 2013.
Grandey, B. S., Gururaj, A., Stier, P., and Wagner, T. M.: Rainfall-aerosol relationships explained by wet scavenging and humidity, Geophys. Res. Lett., 41, 5678–5684, 2014.
Gryspeerdt, E., Stier, P., and Grandey, B. S.: Cloud fraction mediates the aerosol optical depth-cloud top height relationship, Geophys. Res. Lett., 41, 3622–3627, 2014.
Gryspeerdt, E., Stier, P., White, B. A., and Kipling, Z.: Wet scavenging limits the detection of aerosol effects on precipitation, Atmos. Chem. Phys., 15, 7557–7570, https://doi.org/10.5194/acp-15-7557-2015, 2015.
Harikishan, G., Padmakumari, B., Maheskumar, R. S., Pandithurai, G., and Min, Q. L.: Aerosol indirect effects from ground-based retrievals over the rain shadow region in Indian subcontinent, J. Geophys. Res.-Atmos., 121, 2369–2382, 2016.
Hazra, A., Goswami, B. N., and Chen, J.-P.: Role of Interactions between Aerosol Radiative Effect, Dynamics, and Cloud Microphysics on Transitions of Monsoon Intraseasonal Oscillations, J. Atmos. Sci., 70, 2073–2087, 2013.
Heiblum, R. H., Koren, I., and Altaratz, O.: New evidence of cloud invigoration from TRMM measurements of rain center of gravity, Geophys. Res. Lett., 39, L08803, https://doi.org/10.1029/2012GL051158, 2012.
Heiblum, R. H., Altaratz, O., Koren, I., Feingold, G., Kostinski, A. B., Khain, A. P., Ovchinnikov, M., Fredj, E., Dagan, G., Pinto, L., Yaish, R., and Chen, Q.: Characterization of cumulus cloud fields using trajectories in the center of gravity versus water mass phase space: 2. Aerosol effects on warm convective clouds, J. Geophys. Res.-Atmos., 121, 6356–6373, 2016.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – A Federated Instrument Network and Data Archive for Aerosol Characterization, Remote Sens. Environ., 66, 1–16, 1998.
Huffman, G., Adler, R., Bolvin, D., and Nelkin, E.: The TRMM Multi-Satellite Precipitation Analysis (TMPA), in: Satellite Rainfall Applications for Surface Hydrology, edited by: Gebremichael, M. and Hossain, F., 3–22, Springer Netherlands, https://doi.org/10.1007/978-90-481-2915-7, 2010.
Iguchi, T., Matsui, T., Shi, J. J., Tao, W.-K., Khain, A. P., Hou, A., Cifelli, R., Heymsfield, A., and Tokay, A.: Numerical analysis using WRF-SBM for the cloud microphysical structures in the C3VP field campaign: Impacts of supercooled droplets and resultant riming on snow microphysics, J. Geophys. Res.-Atmos., 117, D23206, https://doi.org/10.1029/2012JD018101, 2012.
Ilan, K., Orit, A., and Guy, D.: Aerosol effect on the mobility of cloud droplets, Environ. Res. Lett., 10, 104011, https://doi.org/10.1088/1748-9326/10/10/104011, 2015.
Ilotoviz, E., Khain, A. P., Benmoshe, N., Phillips, V. T. J., and Ryzhkov, A. V.: Effect of Aerosols on Freezing Drops, Hail, and Precipitation in a Midlatitude Storm, J. Atmos. Sci., 73, 109–144, 2016.
Jai Devi, J., Tripathi, S. N., Gupta, T., Singh, B. N., Gopalakrishnan, V., and Dey, S.: Observation-based 3-D view of aerosol radiative properties over Indian Continental Tropical Convergence Zone: implications to regional climate, Tellus B, 63, 971–989, 2011.
Kaufman, Y. J., Tanre, D., and Boucher, O.: A satellite view of aerosols in the climate system, Nature, 419, 215–223, 2002.
Khain, A.: Notes on state-of-the-art investigations of aerosol effects on precipitation: a critical review, Environ. Res. Lett., 4, 015004, https://doi.org/10.1088/1748-9326/4/1/015004, 2009.
Khain, A. and Lynn, B.: Simulation of a supercell storm in clean and dirty atmosphere using weather research and forecast model with spectral bin microphysics, J. Geophys. Res.-Atmos., 114, D19209, https://doi.org/10.1029/2009JD011827, 2009.
Khain, A., Ovtchinnikov, M., Pinsky, M., Pokrovsky, A., and Krugliak, H.: Notes on the state-of-the-art numerical modeling of cloud microphysics, Atmos. Res., 55, 159–224, 2000.
Khain, A., Pokrovsky, A., Pinsky, M., Seifert, A., and Phillips, V.: Simulation of Effects of Atmospheric Aerosols on Deep Turbulent Convective Clouds Using a Spectral Microphysics Mixed-Phase Cumulus Cloud Model. Part I: Model Description and Possible Applications, J. Atmos. Sci., 61, 2963–2982, 2004.
Khain, A., Rosenfeld, D., and Pokrovsky, A.: Aerosol impact on the dynamics and microphysics of deep convective clouds, Q. J. Roy. Meteor. Soc., 131, 2639–2663, 2005.
Khain, A., BenMoshe, N., and Pokrovsky, A.: Factors Determining the Impact of Aerosols on Surface Precipitation from Clouds: An Attempt at Classification, J. Atmos. Sci., 65, 1721–1748, 2008.
Konwar, M., Maheskumar, R. S., Kulkarni, J. R., Freud, E., Goswami, B. N., and Rosenfeld, D.: Aerosol control on depth of warm rain in convective clouds, J. Geophys. Res.-Atmos., 117, D13204, https://doi.org/10.1029/2012JD017585, 2012.
Koren, I., Kaufman, Y. J., Remer, L. A., and Martins, J. V.: Measurement of the Effect of Amazon Smoke on Inhibition of Cloud Formation, Science, 303, 1342–1345, 2004.
Koren, I., Kaufman, Y. J., Rosenfeld, D., Remer, L. A., and Rudich, Y.: Aerosol invigoration and restructuring of Atlantic convective clouds, Geophys. Res. Lett., 32, L14828, https://doi.org/10.1029/2005GL023187, 2005.
Koren, I., Martins, J. V., Remer, L. A., and Afargan, H.: Smoke Invigoration Versus Inhibition of Clouds over the Amazon, Science, 321, 946–949, 2008.
Koren, I., Feingold, G., and Remer, L. A.: The invigoration of deep convective clouds over the Atlantic: aerosol effect, meteorology or retrieval artifact?, Atmos. Chem. Phys., 10, 8855–8872, https://doi.org/10.5194/acp-10-8855-2010, 2010a.
Koren, I., Remer, L. A., Altaratz, O., Martins, J. V., and Davidi, A.: Aerosol-induced changes of convective cloud anvils produce strong climate warming, Atmos. Chem. Phys., 10, 5001-5010, https://doi.org/10.5194/acp-10-5001-2010, 2010b.
Koren, I., Altaratz, O., Remer, L. A., Feingold, G., Martins, J. V., and Heiblum, R. H.: Aerosol-induced intensification of rain from the tropics to the mid-latitudes, Nat. Geosci., 5, 118–122, 2012.
Koren, I., Dagan, G., and Altaratz, O.: From aerosol-limited to invigoration of warm convective clouds, Science, 344, 1143–1146, 2014.
Kourtidis, K., Stathopoulos, S., Georgoulias, A. K., Alexandri, G., and Rapsomanikis, S.: A study of the impact of synoptic weather conditions and water vapor on aerosol–cloud relationships over major urban clusters of China, Atmos. Chem. Phys., 15, 10955–10964, https://doi.org/10.5194/acp-15-10955-2015, 2015.
Krishnamurthy, V. and Shukla, J.: Intraseasonal and Seasonally Persisting Patterns of Indian Monsoon Rainfall, J. Climate, 20, 3–20, 2007.
Krishnamurthy, V. and Shukla, J.: Seasonal persistence and propagation of intraseasonal patterns over the Indian monsoon region, Clim. Dynam., 30, 353–369, 2008.
Lau, K. M. and Kim, K. M.: Observational relationships between aerosol and Asian monsoon rainfall, and circulation, Geophys. Res. Lett., 33, L21810, https://doi.org/10.1029/2006GL027546, 2006.
Lee, S.-S.: Atmospheric science: Aerosols, clouds and climate, Nat. Geosci., 4, 826–827, 2011.
Lensky, I. M. and Rosenfeld, D.: The time-space exchangeability of satellite retrieved relations between cloud top temperature and particle effective radius, Atmos. Chem. Phys., 6, 2887–2894, https://doi.org/10.5194/acp-6-2887-2006, 2006.
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, 888–894, 2011.
Li, Z., Lau, W. K.-M., Ramanathan, V., Wu, G., Ding, Y., Manoj, M. G., Liu, J., Qian, Y., Li, J., Zhou, T., Fan, J., Rosenfeld, D., Ming, Y., Wang, Y., Huang, J., Wang, B., Xu, X., Lee, S.-S., Cribb, M., Zhang, F., Yang, X., Zhao, C., Takemura, T., Wang, K., Xia, X., Yin, Y., Zhang, H., Guo, J., Zhai, P. M., Sugimoto, N., Babu, S. S., and Brasseur, G. P.: Aerosol and monsoon climate interactions over Asia, Rev. Geophys., 54, 866–929, 2016.
Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review, Atmos. Chem. Phys., 5, 715–737, https://doi.org/10.5194/acp-5-715-2005, 2005.
Lynn, B. and Khain, A.: Utilization of spectral bin microphysics and bulk parameterization schemes to simulate the cloud structure and precipitation in a mesoscale rain event, J. Geophys. Res.-Atmos., 112, D22205, https://doi.org/10.1029/2007JD008475, 2007.
Manoj, M. G., Devara, P. C. S., Safai, P. D., and Goswami, B. N.: Absorbing aerosols facilitate transition of Indian monsoon breaks to active spells, Clim. Dynam., 37, 2181–2198, 2011.
Manoj, M. G., Devara, P. C. S., Joseph, S., and Sahai, A. K.: Aerosol indirect effect during the aberrant Indian Summer Monsoon breaks of 2009, Atmos. Environ., 60, 153–163, 2012.
Martins, J. V., Marshak, A., Remer, L. A., Rosenfeld, D., Kaufman, Y. J., Fernandez-Borda, R., Koren, I., Correia, A. L., Zubko, V., and Artaxo, P.: Remote sensing the vertical profile of cloud droplet effective radius, thermodynamic phase, and temperature, Atmos. Chem. Phys., 11, 9485–9501, https://doi.org/10.5194/acp-11-9485-2011, 2011.
Mishra, A. K. and Shibata, T.: Climatological aspects of seasonal variation of aerosol vertical distribution over central Indo-Gangetic belt (IGB) inferred by the space-borne lidar CALIOP, Atmos. Environ., 46, 365–375, 2012.
Misra, A., Tripathi, S. N., Kaul, D. S., and Welton, E. J.: Study of MPLNET-Derived Aerosol Climatology over Kanpur, India, and Validation of CALIPSO Level 2 Version 3 Backscatter and Extinction Products, J. Atmos. Ocean. Tech., 29, 1285–1294, 2012.
Myhre, G., Stordal, F., Johnsrud, M., Kaufman, Y. J., Rosenfeld, D., Storelvmo, T., Kristjansson, J. E., Berntsen, T. K., Myhre, A., and Isaksen, I. S. A.: Aerosol-cloud interaction inferred from MODIS satellite data and global aerosol models, Atmos. Chem. Phys., 7, 3081–3101, https://doi.org/10.5194/acp-7-3081-2007, 2007.
Padma Kumari, B. and Goswami, B. N.: Seminal role of clouds on solar dimming over the Indian monsoon region, Geophys. Res. Lett., 37, L06703, https://doi.org/10.1029/2009GL042133, 2010.
Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S., and Mukhopadhyay, B.: Development and analysis of a new high spatial resolution(0.25° × 0.25°) long period (1901–2010) daily gridded rainfall data set over India Rep., National Climate Centre, India Meteorological Department, Pune, India, 2013.
Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S., and Mukhopadhyay, B.: Analysis of daily rainfall events over India using a new long period (1901–2010) high resolution (0.25° × 0.25°) gridded rainfall data set, Clim. Dynam., 45, 755–776, https://doi.org/10.1007/s00382-014-2307-1, 2014.
Pandithurai, G., Dipu, S., Prabha, T. V., Maheskumar, R. S., Kulkarni, J. R., and Goswami, B. N.: Aerosol effect on droplet spectral dispersion in warm continental cumuli, J. Geophys. Res.-Atmos., 117, D16202, https://doi.org/10.1029/2011JD016532, 2012.
Panicker, A. S., Pandithurai, G., and Dipu, S.: Aerosol indirect effect during successive contrasting monsoon seasons over Indian subcontinent using MODIS data, Atmos. Environ., 44, 1937–1943, 2010.
Parrish, D. F. and Derber, J. C.: The National Meteorological Center's Spectral Statistical-Interpolation Analysis System, Mon. Weather Rev., 120, 1747–1763, 1992.
Pinsky, M., Mazin, I. P., Korolev, A., and Khain, A.: Supersaturation and Diffusional Droplet Growth in Liquid Clouds, J. Atmos. Sci., 70, 2778–2793, 2013.
Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A., Riedi, J. C., and Frey, R. A.: The MODIS cloud products: algorithms and examples from Terra, IEEE T. Geosci. Remote, 41, 459–473, 2003.
Prabha, T. V., Khain, A., Maheshkumar, R. S., Pandithurai, G., Kulkarni, J. R., Konwar, M., and Goswami, B. N.: Microphysics of Premonsoon and Monsoon Clouds as Seen from In Situ Measurements during the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX), J. Atmos. Sci., 68, 1882–1901, 2011.
Prabha, T. V., Patade, S., Pandithurai, G., Khain, A., Axisa, D., Pradeep-Kumar, P., Maheshkumar, R. S., Kulkarni, J. R., and Goswami, B. N.: Spectral width of premonsoon and monsoon clouds over Indo-Gangetic valley, J. Geophys. Res.-Atmos., 117, D20205, https://doi.org/10.1029/2011JD016837, 2012.
Preethi, B. and Revadekar, J. V.: Kharif foodgrain yield and daily summer monsoon precipitation over India, Int. J. Climatol., 33, 1978–1986, 2013.
Rajeevan, M., Bhate, J., and Jaswal, A. K.: Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data, Geophys. Res. Lett., 35, L18707, https://doi.org/10.1029/2008GL035143, 2008.
Ramachandran, S. and Kedia, S.: Aerosol, clouds and rainfall: inter-annual and regional variations over India, Clim. Dynam., 40, 1591–1610, 2013.
Ramanathan, V. and Carmichael, G.: Global and regional climate changes due to black carbon, Nat. Geosci., 1, 221–227, 2008.
Ramanathan, V., Crutzen, P. J., Kiehl, J. T., and Rosenfeld, D.: Aerosols, Climate, and the Hydrological Cycle, Science, 294, 2119–2124, 2001.
Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A., Martins, J. V., Li, R.-R., Ichoku, C., Levy, R. C., Kleidman, R. G., Eck, T. F., Vermote, E., and Holben, B. N.: The MODIS Aerosol Algorithm, Products, and Validation, J. Atmos. Sci., 62, 947–973, 2005.
Rosenfeld, D.: TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall, Geophys. Res. Lett., 26, 3105–3108, 1999.
Rosenfeld, D.: Suppression of Rain and Snow by Urban and Industrial Air Pollution, Science, 287, 1793–1796, 2000.
Rosenfeld, D. and Lensky, I. M.: Satellite–Based Insights into Precipitation Formation Processes in Continental and Maritime Convective Clouds, B. Am. Meteorol. Soc., 79, 2457–2476, 1998.
Rosenfeld, D. and Woodley, W. L.: Deep convective clouds with sustained supercooled liquid water down to −37.5 °C, Nature, 405, 440–442, 2000.
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, 2008.
Rosenfeld, D., Wang, H., and Rasch, P. J.: The roles of cloud drop effective radius and LWP in determining rain properties in marine stratocumulus, Geophys. Res. Lett., 39, L13801, https://doi.org/10.1029/2012GL052028, 2012.
Rosenfeld, D., Andreae, M. O., Asmi, A., Chin, M., de Leeuw, G., Donovan, D. P., Kahn, R., Kinne, S., Kivekäs, N., Kulmala, M., Lau, W., Schmidt, K. S., Suni, T., Wagner, T., Wild, M., and Quaas, J.: Global observations of aerosol-cloud-precipitation-climate interactions, Rev. Geophys., 52, 750–808, 2014.
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, 2015.
Seiki, T. and Nakajima, T.: Aerosol Effects of the Condensation Process on a Convective Cloud Simulation, J. Atmos. Sci., 71, 833–853, 2014.
Sengupta, K., Dey, S., and Sarkar, M.: Structural evolution of monsoon clouds in the Indian CTCZ, Geophys. Res. Lett., 40, 5295–5299, https://doi.org/10.1002/grl.50970, 2013.
Shepard, D.: A two-dimensional interpolation function for irregularly-spaced data, in: Proceedings of the 23rd ACM national conference, 27–29 August 1968, New York, NY, USA, ACM, 517–524, https://doi.org/10.1145/800186.810616, 1968.
Shrestha, P. and Barros, A. P.: Joint spatial variability of aerosol, clouds and rainfall in the Himalayas from satellite data, Atmos. Chem. Phys., 10, 8305–8317, https://doi.org/10.5194/acp-10-8305-2010, 2010.
Small, J. D., Jiang, J. H., Su, H., and Zhai, C.: Relationship between aerosol and cloud fraction over Australia, Geophys. Res. Lett., 38, L23802, https://doi.org/10.1029/2011GL049404, 2011.
Squires, P.: The Microstructure and Colloidal Stability of Warm Clouds, Tellus, 10, 256–261, 1958.
Squires, P. and Twomey, S.: The Relation Between Cloud Droplet Spectra and the Spectrum of Cloud Nuclei, in: Physics of Precipitation: Proceedings of the Cloud Physics Conference, Woods Hole, Massachusetts, USA, 3–5 June 1959, American Geophysical Union, 211–219, 2013.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat Science Team: The Cloudsat Mission and the A-Train, B. Am. Meteorol. Soc., 83, 1771–1790, 2002.
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.
Ten Hoeve, J. E., Remer, L. A., and Jacobson, M. Z.: Microphysical and radiative effects of aerosols on warm clouds during the Amazon biomass burning season as observed by MODIS: impacts of water vapor and land cover, Atmos. Chem. Phys., 11, 3021–3036, https://doi.org/10.5194/acp-11-3021-2011, 2011.
Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's global energy budget, B. Am. Meteorol. Soc., 90, 311–323, 2009.
Tripathi, S. N., Dey, S., Chandel, A., Srivastava, S., Singh, R. P., and Holben, B. N.: Comparison of MODIS and AERONET derived aerosol optical depth over the Ganga Basin, India, Ann. Geophys., 23, 1093–1101, https://doi.org/10.5194/angeo-23-1093-2005, 2005.
Tripathi, S. N.: In-situ measured rainfall over IIT Kanpur, Indian Institute of Technology, Kanpur, available at: http://www.mosdac.gov.in/ (last access: April 2017), 2006.
Tripathi, S. N., Pattnaik, A., and Dey, S.: Aerosol indirect effect over Indo-Gangetic plain, Atmos. Environ., 41, 7037–7047, 2007.
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, 1974.
Twomey, S.: The Influence of Pollution on the Shortwave Albedo of Clouds, J. Atmos. Sci., 34, 1149–1152, 1977.
van den Heever, S. C., Stephens, G. L., and Wood, N. B.: Aerosol indirect effects on tropical convection characteristics under conditions of radiative-convective equilibrium, J. Atmos. Sci., 68, 699–718, 2011.
Vernier, J. P., Fairlie, T. D., Natarajan, M., Wienhold, F. G., Bian, J., Martinsson, B. G., Crumeyrolle, S., Thomason, L. W., and Bedka, K. M.: Increase in upper tropospheric and lower stratospheric aerosol levels and its potential connection with Asian pollution, J. Geophys. Res.-Atmos., 120, 1608–1619, 2015.
Wang, C.: A modeling study of the response of tropical deep convection to the increase of cloud condensation nuclei concentration: 1. Dynamics and microphysics, J. Geophys. Res.-Atmos., 110, D21211, https://doi.org/10.1029/2004JD005720, 2005.
Warner, J. and Twomey, S.: The Production of Cloud Nuclei by Cane Fires and the Effect on Cloud Droplet Concentration, J. Atmos. Sci., 24, 704–706, 1967.
Webster, P. J., Magaña, V. O., Palmer, T. N., Shukla, J., Tomas, R. A., Yanai, M., and Yasunari, T.: Monsoons: Processes, predictability, and the prospects for prediction, J. Geophys. Res.-Oceans, 103, 14451–14510, 1998.
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee, R. B., Louis Smith, G., and Cooper, J. E.: Clouds and the Earth's Radiant Energy System (CERES): An Earth Observing System Experiment, B. Am. Meteorol. Soc., 77, 853–868, 1996.
Yan, H., Li, Z., Huang, J., Cribb, M., and Liu, J.: Long-term aerosol-mediated changes in cloud radiative forcing of deep clouds at the top and bottom of the atmosphere over the Southern Great Plains, Atmos. Chem. Phys., 14, 7113–7124, https://doi.org/10.5194/acp-14-7113-2014, 2014.
Yang, Y., Russell, L. M., Lou, S., Liu, Y., Singh, B., and Ghan, S. J.: Rain-aerosol relationships influenced by wind speed, Geophys. Res. Lett., 43, 2267–2274, 2016.
Yu, P., Toon, O. B., Neely, R. R., Martinsson, B. G., and Brenninkmeijer, C. A. M.: Composition and physical properties of the Asian Tropopause Aerosol Layer and the North American Tropospheric Aerosol Layer, Geophys. Res. Lett., 42, 2540–2546, 2015.
Short summary
Aerosol-induced perturbations in cloud systems and rainfall are very uncertain. This study provides observational evidence of a robust positive association between aerosol–cloud–rainfall properties over the Indian summer monsoon region. Observed and modeled aerosol–cloud microphysical changes illustrate that cloud invigoration under a high AOD scenario can explain most of the aerosol-associated changes in cloud fraction, cloud top pressure, and surface rainfall over this region.
Aerosol-induced perturbations in cloud systems and rainfall are very uncertain. This study...
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