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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 5 | Copyright
Atmos. Meas. Tech., 11, 3177-3196, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 01 Jun 2018

Research article | 01 Jun 2018

Neural network cloud top pressure and height for MODIS

Nina Håkansson1, Claudia Adok2, Anke Thoss1, Ronald Scheirer1, and Sara Hörnquist1 Nina Håkansson et al.
  • 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 (Moderate Resolution Imaging Spectroradiometer) is presented. The neural networks are trained using cloud top layer pressure data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) 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 in the 2014 version of the NWC SAF (EUMETSAT (European Organization for the Exploitation of Meteorological Satellites) Satellite Application Facility on Support to Nowcasting and Very Short Range Forecasting) PPS (Polar Platform System). All three techniques are evaluated using both CALIOP and CPR (Cloud Profiling Radar for CloudSat (CLOUD SATellite)) height.

Instruments like AVHRR (Advanced Very High Resolution Radiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) 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 neighboring pixels are very important.

The error distributions of the involved cloud top height algorithms are found to be non-Gaussian. Different descriptive statistic measures are presented and it is exemplified that bias and SD (standard deviation) can be misleading for non-Gaussian distributions. The median and mode are found to better describe the tendency of the error distributions and IQR (interquartile range) and MAE (mean absolute error) are found to give the most useful information of the spread of the errors.

For all descriptive statistics presented MAE, IQR, RMSE (root mean square error), SD, mode, median, bias and percentage of absolute errors above 0.25, 0.5, 1 and 2km the neural network perform better than the reference algorithms both validated with CALIOP and CPR (CloudSat). The neural networks using the brightness temperatures at 11 and 12µm show at least 32% (or 623m) lower MAE compared to the two operational reference algorithms when validating with CALIOP height. Validation with CPR (CloudSat) height gives at least 25% (or 430m) reduction of MAE.

Publications Copernicus
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.
In this paper a new algorithm for cloud top height retrieval from imager instruments like MODIS...