Improved cloud detection for the Aura Microwave Limb Sounder: Training an artificial neural network on colocated MLS and Aqua-MODIS data

Abstract

Abstract. An improved cloud detection algorithm for the Aura Microwave Limb Sounder (MLS) is presented. This new algorithm is based on a feedforward artificial neural network and uses as input, for each MLS limb scan, a vector consisting of 1,710 brightness temperatures provided by MLS observations from 15 different tangent altitudes and up to 13 spectral channels in each of 10 different MLS bands. The model has been trained on global cloud properties reported by Aqua’s Moderate Resolution Imaging Spectroradiometer (MODIS). In total, the colocated MLS-MODIS data set consists of 162,117 combined scenes sampled on 208 days over 2005–2020. We show that the algorithm can correctly classify > 96 % of cloudy and clear instances for previously unseen MLS scans. A comparison to the current MLS cloudiness flag used in “Level 2” processing reveals a huge improvement in classification performance. For all profiles in the colocated MLS-MODIS data set, the algorithm successfully detects 97.8 % of profiles affected by clouds, up from 15.8 % for the Level 2 flagging. Meanwhile, false positives reported for actually clear profiles are reduced to 1.7 %, down from 6.2 % in Level 2. The classification performance is not dependent on geolocation. The new cloudiness flag is applied to determine average global cloud cover between 2015 and 2019, successfully reproducing the spatial patterns of mid-level to high clouds reported in previous studies. It is also applied to four example cloud fields to illustrate the reliable performance for different cloud structures with varying degrees of complexity. Training a similar model on MODIS-retrieved cloud top pressure yields reliable predictions with correlation coefficients greater than 0.99. The combination of cloudiness flag and predicted cloud top pressure provides the means to identify MLS profiles in the presence of high-reaching convection.