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• W4200218717 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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 1710 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 <span class=inline-formula>d</span> over 2005–2020. A comparison to the current MLS cloudiness flag used in “Level 2” processing reveals a huge improvement in classification performance. For previously unseen data, the algorithm successfully detects <span class=inline-formula>></span> 93 % of profiles affected by clouds, up from <span class=inline-formula>∼</span> 16 % for the Level 2 flagging. At the same time, false positives reported for actually clear profiles are comparable to the Level 2 results. The classification performance is not dependent on geolocation but slightly decreases over low-cloud-cover regions. The new cloudiness flag is applied to determine average global cloud cover maps over 2015–2019, successfully reproducing the spatial patterns of mid-level to high clouds seen in MODIS data. It is also applied to four example cloud fields to illustrate its reliable performance for different cloud structures with varying degrees of complexity. Training a similar model on MODIS-retrieved cloud top pressure (<span class=inline-formula><i>p</i>_CT</span>) yields reliable predictions with correlation coefficients <span class=inline-formula>></span> 0.82. It is shown that the model can correctly identify <span class=inline-formula>></span> 85 % of profiles with <span class=inline-formula><i>p</i>_CT</span> <span class=inline-formula><</span> 400 <span class=inline-formula>hPa</span>. Similar to the cloud classification model, global maps and example cloud fields are provided, which reveal good agreement with MODIS results. The combination of the cloudiness flag and predicted cloud top pressure provides the means to identify MLS profiles in the presence of high-reaching convection." @default.
• W4200218717 created "2021-12-31" @default.
• W4200218717 creator A5000420548 @default.
• W4200218717 date "2021-09-30" @default.
• W4200218717 modified "2023-10-03" @default.
• W4200218717 title "Reply on RC1" @default.
• W4200218717 doi "https://doi.org/10.5194/amt-2021-146-ac1" @default.
• W4200218717 hasPublicationYear "2021" @default.
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