Articles | Volume 11, issue 5
Atmos. Meas. Tech., 11, 3177–3196, 2018
Atmos. Meas. Tech., 11, 3177–3196, 2018

Research article 01 Jun 2018

Research article | 01 Jun 2018

Neural network cloud top pressure and height for MODIS

Nina Håkansson et al.

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Cited articles

Ackerman, S., Menzel, P., and Frey, R.: MODIS Atmosphere L2 Cloud Product (06_L2),, 2015. a, b
Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E., Ackerman, S. A., Heidinger, A. K., and Yang, P.: MODIS Cloud-Top Property Refinements for Collection 6, J. Appl. Meteorol. Clim., 51, 1145–1163,, 2012. a, b, c
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Cotter, A., Shamir, O., Srebro, N., and Sridharan, K.: Better Mini-Batch Algorithms via Accelerated Gradient Methods, in: Advances in Neural Information Processing Systems 24, edited by: Shawe-Taylor, J., Zemel, R. S., Bartlett, P. L., Pereira, F., and Weinberger, K. Q., 1647–1655, Curran Associates, Inc., available at:, 2011. a
Derrien, M., Lavanant, L., and Le Gleau, H.: Retrieval of the cloud top temperature of semi-transparent clouds with AVHRR, in: Proceedings of the IRS'88, 199–202, Deepak Publ., Hampton, Lille, France, 1988. a
Short summary
In this paper a new algorithm for cloud top height retrieval from imager instruments like MODIS is presented. It uses artificial neural networks and reduces the mean absolute error by 32 % compared to two other operational cloud height algorithms. This means that improved cloud height retrieval for nowcasting, as input to models and in cloud climatologies is possible.