Articles | Volume 18, issue 4
Atmos. Chem. Phys., 18, 3065–3082, 2018
https://doi.org/10.5194/acp-18-3065-2018
Atmos. Chem. Phys., 18, 3065–3082, 2018
https://doi.org/10.5194/acp-18-3065-2018

Research article 02 Mar 2018

Research article | 02 Mar 2018

Contrasting the co-variability of daytime cloud and precipitation over tropical land and ocean

Daeho Jin et al.

Related authors

Identifying meteorological influences on marine low-cloud mesoscale morphology using satellite classifications
Johannes Mohrmann, Robert Wood, Tianle Yuan, Hua Song, Ryan Eastman, and Lazaros Oreopoulos
Atmos. Chem. Phys., 21, 9629–9642, https://doi.org/10.5194/acp-21-9629-2021,https://doi.org/10.5194/acp-21-9629-2021, 2021
Short summary
Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology
Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick
Atmos. Meas. Tech., 13, 6989–6997, https://doi.org/10.5194/amt-13-6989-2020,https://doi.org/10.5194/amt-13-6989-2020, 2020
Short summary
An evaluation of clouds and radiation in a large-scale atmospheric model using a cloud vertical structure classification
Dongmin Lee, Lazaros Oreopoulos, and Nayeong Cho
Geosci. Model Dev., 13, 673–684, https://doi.org/10.5194/gmd-13-673-2020,https://doi.org/10.5194/gmd-13-673-2020, 2020
Short summary
Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria
Sungmin O, Ulrich Foelsche, Gottfried Kirchengast, Juergen Fuchsberger, Jackson Tan, and Walter A. Petersen
Hydrol. Earth Syst. Sci., 21, 6559–6572, https://doi.org/10.5194/hess-21-6559-2017,https://doi.org/10.5194/hess-21-6559-2017, 2017
Short summary
Modeling the influences of aerosols on pre-monsoon circulation and rainfall over Southeast Asia
D. Lee, Y. C. Sud, L. Oreopoulos, K.-M. Kim, W. K. Lau, and I.-S. Kang
Atmos. Chem. Phys., 14, 6853–6866, https://doi.org/10.5194/acp-14-6853-2014,https://doi.org/10.5194/acp-14-6853-2014, 2014

Related subject area

Subject: Clouds and Precipitation | Research Activity: Remote Sensing | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
Identifying meteorological influences on marine low-cloud mesoscale morphology using satellite classifications
Johannes Mohrmann, Robert Wood, Tianle Yuan, Hua Song, Ryan Eastman, and Lazaros Oreopoulos
Atmos. Chem. Phys., 21, 9629–9642, https://doi.org/10.5194/acp-21-9629-2021,https://doi.org/10.5194/acp-21-9629-2021, 2021
Short summary
Lidar observations of cirrus clouds in Palau (7°33′ N, 134°48′ E)
Francesco Cairo, Mauro De Muro, Marcel Snels, Luca Di Liberto, Silvia Bucci, Bernard Legras, Ajil Kottayil, Andrea Scoccione, and Stefano Ghisu
Atmos. Chem. Phys., 21, 7947–7961, https://doi.org/10.5194/acp-21-7947-2021,https://doi.org/10.5194/acp-21-7947-2021, 2021
Short summary
Observing the timescales of aerosol–cloud interactions in snapshot satellite images
Edward Gryspeerdt, Tom Goren, and Tristan W. P. Smith
Atmos. Chem. Phys., 21, 6093–6109, https://doi.org/10.5194/acp-21-6093-2021,https://doi.org/10.5194/acp-21-6093-2021, 2021
Short summary
Potential impact of aerosols on convective clouds revealed by Himawari-8 observations over different terrain types in eastern China
Tianmeng Chen, Zhanqing Li, Ralph A. Kahn, Chuanfeng Zhao, Daniel Rosenfeld, Jianping Guo, Wenchao Han, and Dandan Chen
Atmos. Chem. Phys., 21, 6199–6220, https://doi.org/10.5194/acp-21-6199-2021,https://doi.org/10.5194/acp-21-6199-2021, 2021
Short summary
How frequent is natural cloud seeding from ice cloud layers ( < −35 °C) over Switzerland?
Ulrike Proske, Verena Bessenbacher, Zane Dedekind, Ulrike Lohmann, and David Neubauer
Atmos. Chem. Phys., 21, 5195–5216, https://doi.org/10.5194/acp-21-5195-2021,https://doi.org/10.5194/acp-21-5195-2021, 2021
Short summary

Cited articles

Carroll, M. L., Townshend, J. R., DiMiceli, C. M., Noojipady, P., and Sohlberg, R. A.: A new global raster water mask at 250 m resolution, Int. J. Digit. Earth, 2, 291–308, https://doi.org/10.1080/17538940902951401, 2009. 
Cetrone, J. and Houze, R. A.: Anvil clouds of tropical mesoscale convective systems in monsoon regions, Q. J. Roy. Meteor. Soc., 135, 305–317, https://doi.org/10.1002/qj.389, 2009. 
Chen, T., Rossow, W. B., and Zhang, Y.: Radiative effects of cloud-type variations, J. Climate, 13, 264–286, https://doi.org/10.1175/1520-0442(2000)013<0264:REOCTV>2.0.CO;2, 2000. 
Cho, H.-M., Zhang, Z., Meyer, K., Lebsock, M., Platnick, S., Ackerman, A. S., Di Girolamo, L., C.-Labonnote, L., Cornet, C., Riedi, J., and Holz, R. E.: Frequency and causes of failed MODIS cloud property retrievals for liquid phase clouds over global oceans, J. Geophys. Res.-Atmos., 120, 4132–4154, https://doi.org/10.1002/2015JD023161, 2015. 
Field, P. R. and Heymsfield, A. J.: Importance of snow to global precipitation, Geophys. Res. Lett., 42, 9512–9520, https://doi.org/10.1002/2015GL065497, 2015. 
Download
Short summary
To what degree can precipitation be predicted given information about clouds? Or, conversely, with precipitation information at hand, can we provide good guesses about the clouds responsible? To answer these questions, we performed joint analysis of rainfall and cloud data, which are significantly decoupled. We find that only for the deepest and thickest clouds does cloud amount relate strongly with the intensity of rainfall, and that the details are different over oceans and land.
Altmetrics
Final-revised paper
Preprint