Neural network cloud top pressure and height for MODIS

Håkansson, Nina; Adok, Claudia; Thoss, Anke; Scheirer, Ronald; Hörnquist, Sara

doi:https://doi.org/10.5194/amt-11-3177-2018

Articles | Volume 11, issue 5

https://doi.org/10.5194/amt-11-3177-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-11-3177-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 11, issue 5

Research article

|

01 Jun 2018

Research article |

| 01 Jun 2018

Neural network cloud top pressure and height for MODIS

Nina Håkansson, Claudia Adok, Anke Thoss, Ronald Scheirer, and Sara Hörnquist

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

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.