Articles | Volume 11, issue 5
Atmos. Meas. Tech., 11, 3177–3196, 2018
https://doi.org/10.5194/amt-11-3177-2018
Atmos. Meas. Tech., 11, 3177–3196, 2018
https://doi.org/10.5194/amt-11-3177-2018

Research article 01 Jun 2018

Research article | 01 Jun 2018

Neural network cloud top pressure and height for MODIS

Nina Håkansson et al.

Data sets

MODIS/Aqua dataset LAADS DAAC https://ladsweb.nascom.nasa.gov

CALIPSO-CALIOP datasets Atmospheric Science Data Center https://eosweb.larc.nasa.gov/

CPR (CloudSat) data CloudSat Data Processing Center http://www.cloudsat.cira.colostate.edu/order-data

NWP forecast data ECMWF https://www.ecmwf.int/en/forecasts/accessing-forecasts

OSISAF icemap data ocean and Sea Ice SAF http://osisaf.met.no/p/ice/

Model code and software

PPS software package NWC SAF http://nwc-saf.eumetsat.int

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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.