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 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 d 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 > 93 % of profiles affected by clouds, up from ∼ 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 (pCT) yields reliable predictions with correlation coefficients > 0.82. It is shown that the model can correctly identify > 85 % of profiles with pCT < 400 hPa. 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.
Highlights
The impact of clouds on Earth’s hydrological, chemical, and radiative budget is well established (e.g., Warren et al, 1988; Ramanathan et al, 1989; Stephens, 2005)
The current Microwave Limb Sounder (MLS) “Level 2” cloud detection algorithm is based on the computation of cloud-induced radiances (Tcir), which represent the difference between individual observations and calculated clear-sky radiances (Wu et al, 2006)
MLS makes ∼ 3500 daily vertical limb scans, each consisting of 125 minor frames (MIFs) that can be associated with tangent pressures at different altitudes in the atmosphere
Summary
The impact of clouds on Earth’s hydrological, chemical, and radiative budget is well established (e.g., Warren et al, 1988; Ramanathan et al, 1989; Stephens, 2005). The current MLS “Level 2” cloud detection algorithm is based on the computation of cloud-induced radiances (Tcir), which represent the difference between individual observations and calculated clear-sky radiances (Wu et al, 2006) The latter are derived after the retrieval of the other MLS data products. This study describes the training and validation of an improved MLS cloud detection scheme employing a feedforward artificial neural network (“ANN” hereinafter) This algorithm is derived from colocated MLS samples and MODIS cloud products and is designed to classify clear and cloudy conditions for individual MLS profiles. MLS makes ∼ 3500 daily vertical limb scans (called major frames, MAFs), each consisting of 125 minor frames (MIFs) that can be associated with tangent pressures (ptan) at different altitudes in the atmosphere These observations provide the input for retrievals of profiles of a wide-ranging set of atmospheric trace gas concentrations.
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