Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning

Fuchs, Julia; Čermák, Jan; Andersen, Hendrik

doi:10.5194/acp-18-16537-2018

Cited by 35 publications

(41 citation statements)

References 70 publications

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“…On seasonal scales, the spatiotemporal FLC occurrence indicates a connection to the southeastern Atlantic stratocumulus cloud deck. As such, process knowledge from studies on the heavily investigated stratocumulus clouds in this region (e.g., Adebiyi and Zuidema, 2018;Andersen and Cermak, 2015;Diamond et al, 2018;Fuchs et al, 2017Fuchs et al, , 2018Gordon et al, 2018;Painemal et al, 2014;Yuter et al, 2018) may be applicable to Namib-region FLCs, and vice versa.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal dynamics of fog and low clouds in the Namib unveiled with ground- and space-based observations

et al. 2019

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Abstract. Fog is an essential component of Namib-region ecosystems. Current knowledge on Namib-region fog patterns and processes is limited by a lack of coherent observations in space and time. In this study, data from multiple satellite platforms and station measurements paint a coherent picture of the spatiotemporal dynamics of fog and low cloud (FLC) distribution. It is found that observed seasonal FLC patterns derived from satellite observations differ from fog measurements at coastal station locations, whereas they agree further inland. This is linked to an observed seasonal cycle in the vertical structure of FLCs that determines the probability of low-level clouds touching the ground. For the first time, these observations are complemented by spatially coherent statistics concerning the diurnal cycle of FLCs using geostationary satellite data. The average timing of the start of the diurnal FLC cycle is found to strongly depend on the distance to the coastline (correlation ≈0.85 north of 25∘ S), a clear indication of dominant advective processes. In the central Namib, FLCs typically occur 2–4 h later than in other coastal regions, possibly due to local advection patterns. The findings lead to a new conceptual model of the spatiotemporal dynamics of fog and low clouds in the Namib.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal dynamics of fog and low clouds in the Namib unveiled with ground- and space-based observations

et al. 2019

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“…The above-cloud aerosol shortwave absorption can warm the free troposphere, all else being equal, strengthening the capping inversion and reducing entrainment (Johnson et al, 2004;Gordon et al, 2018;Herbert et al, 2019). This may explain why stratocumulus thickens in September-October (Wilcox, 2010), and cloud cover and top-of-atmosphere allsky albedo increase when more smoke is present (Adebiyi et al, 2015;Wilcox, 2012), aided perhaps by changes in other cloud-controlling factors and aerosol indirect effects (Fuchs et al, 2018;Adebiyi and Zuidema, 2018).…”

Section: Introductionmentioning

confidence: 99%

The diurnal cycle of the smoky marine boundary layer observed during August in the remote southeast Atlantic

Zhang

Zuidema²

2019

Atmos. Chem. Phys.

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Abstract. Ascension Island (8∘ S, 14.5∘ W) is located at the northwestern edge of the south Atlantic stratocumulus deck, with most clouds in August characterized by surface observers as “stratocumulus and cumulus with bases at different levels”, and secondarily as “cumulus of limited vertical extent” and occurring within a typically decoupled boundary layer. Field measurements have previously shown that the highest amounts of sunlight-absorbing smoke occur annually within the marine boundary layer during August. On more smoke-free days, the diurnal cycle in cloudiness includes a nighttime maximum in cloud liquid water path and rain, an afternoon cloud minimum, and a secondary late-afternoon increase in cumulus and rain. The afternoon low-cloud minimum is more pronounced on days with a smokier boundary layer. The cloud liquid water paths are also reduced throughout most of the diurnal cycle when more smoke is present, with the difference from cleaner conditions most pronounced at night. Precipitation is infrequent. An exception is the mid-morning, when the boundary layer deepens and liquid water paths increase. The data support a view that a radiatively enhanced decoupling persisting throughout the night is key to understanding the changes in the cloud diurnal cycle when the boundary layer is smokier. Under these conditions, the nighttime stratiform cloud layer does not always recouple to the sub-cloud layer, and the decoupling maintains more moisture within the sub-cloud layer. After the sun rises, enhanced shortwave absorption in a smokier boundary layer can drive a vertical ascent that momentarily couples the sub-cloud layer to the cloud layer, deepening the boundary layer and ventilating moisture throughout, a process that may also be aided by a shift to smaller droplets. After noon, shortwave absorption within smokier boundary layers again reduces the upper-level stratiform cloud and the sub-cloud relative humidity, discouraging further cumulus development and again strengthening a decoupling that lasts longer into the night. The novel diurnal mechanism provides a new challenge for cloud models to emulate. The lower free troposphere above cloud is more likely to be cooler, when boundary layer smoke is present, and lower free-tropospheric winds are stronger and more northeasterly, with both (meteorological) influences supporting further smoke entrainment into the boundary layer from above.

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“…An advantage of tree-based methods such as RF or GBRT is that, compared to deep learning methods, (Elith et al, 2008). GBRT have shown to have good predictive power in previous studies (Elith et al, 2008;Fuchs et al, 2018;Just et al, 2018). The general framework of setting up the model is shown in Figure 2 and includes input feature selection, hyperparameter tuning, model training, and model validation.…”

Section: Gradient Boosted Regression Trees 251 Model Specificationsmentioning

confidence: 99%

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

et al. 2020

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The quantification of factors leading to harmfully high levels of particulate matter (PM) remains challenging. This study presents a novel approach using a statistical model that is trained to predict hourly concentrations of particles smaller than 10 μm (PM10) by combining satellite‐borne aerosol optical depth (AOD) with meteorological and land‐use parameters. The model is shown to accurately predict PM10 (overall R 2 = 0.77, RMSE = 7.44 μg/m 3) for measurement sites in Germany. The capability of satellite observations to map and monitor surface air pollution is assessed by investigating the relationship between AOD and PM10 in the same modeling setup. Sensitivity analyses show that important drivers of modeled PM10 include multiday mean wind flow, boundary layer height (BLH), day of year (DOY), and temperature. Different mechanisms associated with elevated PM10 concentrations are identified in winter and summer. In winter, mean predictions of PM10 concentrations >35 μg/m 3 occur when BLH is below ∼500 m. Paired with multiday easterly wind flow, mean model predictions surpass 40 μg/m 3 of PM10. In summer, PM10 concentrations seemingly are less driven by meteorology, but by emission or chemical particle formation processes, which are not included in the model. The relationship between AOD and predicted PM10 concentrations depends to a large extent on ambient meteorological conditions. Results suggest that AOD can be used to assess air quality at ground level in a machine learning approach linking it with meteorological conditions.

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Building a cloud in the southeast Atlantic: understanding low-cloud controls based on satellite observations with machine learning

Cited by 35 publications

References 70 publications

Spatiotemporal dynamics of fog and low clouds in the Namib unveiled with ground- and space-based observations

Spatiotemporal dynamics of fog and low clouds in the Namib unveiled with ground- and space-based observations

The diurnal cycle of the smoky marine boundary layer observed during August in the remote southeast Atlantic

Mapping and Understanding Patterns of Air Quality Using Satellite Data and Machine Learning

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