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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
30 Jan 2018
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.
Neural network cloud top pressure and height for MODIS
Nina Håkansson1, Claudia Adok2, Anke Thoss1, Ronald Scheirer1, and Sara Hörnquist1 1Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
2Regional Cancer Center Western Sweden, Gothenburg, Sweden
Abstract. Cloud top height retrieval from imager instruments is important for Nowcasting and for satellite climate data records. A neural network approach for cloud top height retrieval from the imager instrument MODIS is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP dataset.

Results are compared with two operational reference algorithms for cloud top height: the MODIS Collection 6 level 2 height product and the cloud top temperature and height algorithm (CTTH) in the 2014 version of the NWCSAF Polar Platform System (PPS-v2014). All three techniques are evaluated using both CALIOP and CPR (CloudSat) height.

Instruments like AVHRR and VIIRS contain fewer channels useful for cloud top height retrievals than MODIS, therefore several different neural networks are investigated to test how infrared channel selection influences retrieval performance.

Also a network with only channels available for the AVHRR1 instrument is trained and evaluated. To examine the contribution of different variables, networks with fewer variables are trained. It is shown that variables containing imager information for neighbouring pixels are very important.

Overall results for the neural network height retrievals are very promising. The neural networks using the brightness temperatures at 11 μm and 12 μm show at least 33 % (or 627 m) lower mean absolute error (MAE) compared to the two operational reference algorithms when validating with CALIOP height. Validation with CPR (CloudSat) height gives at least 25 % (or 433 m) reduction of MAE. For the network trained with a channel combination available for AVHRR1, the MAE is at least 542 m better when validated with CALIOP and 414 m when validated with CPR (CloudSat) compared to the two operational reference algorithms. The NWCSAF PPS-2018 release will contain a neural network based cloud height algorithm.

Citation: Håkansson, N., Adok, C., Thoss, A., Scheirer, R., and Hörnquist, S.: Neural network cloud top pressure and height for MODIS, Atmos. Meas. Tech. Discuss.,, in review, 2018.
Nina Håkansson et al.
Nina Håkansson et al.


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Short summary
In the 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 with 33 % compared to two operational cloud height algorithms. This means that improved cloud height retrieval for nowcasting, as input to models and in cloud climatologies, is possible.
In the paper a new algorithm for cloud top height retrieval from imager instruments like MODIS...