Neural network cloud top pressure and height for MODIS

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

Atmospheric measurement techniques · 01 June 2018

In one sentence. Neural networks retrieve MODIS cloud-top height using CALIOP training labels, cutting mean absolute error by at least 33% against operational algorithms.

Abstract

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.

Labels

Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.