Contrasting characteristics of open- and closed-cellular stratocumulus cloud in the eastern North Atlantic

Extensive regions of marine boundary layer cloud impact the radiative balance through their significant shortwave albedo while having little impact on outgoing longwave radiation. Despite this importance, these cloud systems remain poorly represented in large-scale models due to difficulty in representing the processes that drive their life cycle and coverage. In particular, the mesoscale organization and cellular structure of marine boundary clouds have important implications for the subsequent cloud feedbacks. In this study, we use long-term (2013–2018) observations from the Atmospheric Radiation Measurement (ARM) Facility's Eastern North Atlantic (ENA) site on Graciosa Island, Azores, Portugal, to identify cloud cases with open- or closed-cellular organization. More than 500 h of each organization type are identified. The ARM observations are combined with reanalysis and satellite products to quantify the cloud, precipitation, aerosol, thermodynamic, and large-scale synoptic characteristics associated with these cloud types. Our analysis shows that both cloud organization populations occur during similar sea surface temperature conditions, but the open-cell cases are distinguished by stronger cold-air advection and large-scale subsidence compared to the closed-cell cases, consistent with their formation during cold-air outbreaks. We also find that the open-cell cases were associated with deeper boundary layers, stronger low-level winds, and higher rain rates compared to their closed-cell counterparts. Finally, raindrops with diameters larger than 1 mm were routinely recorded at the surface during both populations, with a higher number of large drops during the open-cellular cases. The similarities and differences noted herein provide important insights into the environmental and cloud characteristics during varying marine boundary layer cloud mesoscale organization and will be useful for the evaluation of model simulations for ENA marine clouds.

Investigation of Applicability of Impact Factors to Estimate Solar Irradiance: Comparative Analysis Using Machine Learning Algorithms

This study explores investigation of applicability of impact factors to estimate solar irradiance by four machine learning algorithms using climatic elements as comparative analysis: linear regression, support vector machines (SVM), a multi-layer neural network (MLNN), and a long short-term memory (LSTM) neural network. The methods show how actual climate factors impact on solar irradiation, and the possibility of estimating one year local solar irradiance using machine learning methodologies with four different algorithms. This study conducted readily accessible local weather data including temperature, wind velocity and direction, air pressure, the amount of total cloud cover, the amount of middle and low-layer cloud cover, and humidity. The results show that the artificial neural network (ANN) models provided more close information on solar irradiance than the conventional techniques (linear regression and SVM). Between the two ANN models, the LSTM model achieved better performance, improving accuracy by 31.7% compared to the MLNN model. Impact factor analysis also revealed that temperature and the amount of total cloud cover are the dominant factors affecting solar irradiance, and the amount of middle and low-layer cloud cover is also an important factor. The results from this work demonstrate that ANN models, especially ones based on LSTM, can provide accurate information of local solar irradiance using weather data without installing and maintaining on-site solar irradiance sensors.

Evaluating cloud liquid detection using cloud radar Doppler spectra in a pre-trained artificial neural network against Cloudnet liquid detection

Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-basedremote sensing instruments still pose observational challenges yet improvements are crucial since the existence of multi-layerliquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud life time, and precipitation formationprocesses. Hydrometeor target classifications such as Cloudnet that require a lidar signal for the classification of liquid arelimited to the maximum height of lidar signal penetration and thus often lead to underestimations of liquid-containing cloudlayers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphologicalfeatures in cloud-penetrating cloud radar Doppler spectra measurements in a artificial neural network (ANN) approach toclassify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscattercoefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained in Arctic conditionscan successfully be applied to observations in the mid-latitudes obtained during the seven-week long ACCEPT field experimentin Cabauw, the Netherlands, 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detectionthresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification.Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based techniqueis realized by comparisons to observations of microwave radiometer liquid water path, ceilometer liquid-layer base altitude,and radiosonde relative humidity. Four conclusions were drawn from the investigation: First, it was found that the thresholdselection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second,nevertheless, all threshold values used in the ANN-framework were found to outperform the Cloudnet target classification fordeep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud reflectivityin higher cloud layers was sufficiently high to be detectable by the cloud radar. Third, in convective situations for whichlidar data is available and for which the imprint of cloud microphysics on the radar Doppler spectrum is decreased, Cloudnetoutperforms the ANN retrieval. Fourth, in high-level clouds both approaches (Cloudnet and the ANN technique), are limited.

How frequent is natural cloud seeding from ice cloud layers ( < −35 °C) over Switzerland?

Clouds and cloud feedbacks represent one of the largest uncertainties in climate projections. As the ice phase influences many key cloud properties and their lifetime, its formation needs to be better understood in order to improve climate and weather prediction models. Ice crystals sedimenting out of a cloud do not sublimate immediately but can survive certain distances and eventually fall into a cloud below. This natural cloud seeding can trigger glaciation and has been shown to enhance precipitation formation. However, to date, an estimate of its occurrence frequency is lacking. In this study, we estimate the occurrence frequency of natural cloud seeding over Switzerland from satellite data and sublimation calculations. We use the DARDAR (radar lidar) satellite product between April 2006 and October 2017 to estimate the occurrence frequency of multi-layer cloud situations, where a cirrus cloud at T < −35 ∘C can provide seeds to a lower-lying feeder cloud. These situations are found to occur in 31 % of the observations. Of these, 42 % have a cirrus cloud above another cloud, separated, while in 58 % the cirrus is part of a thicker cloud, with a potential for in-cloud seeding. Vertical distances between the cirrus and the lower-lying cloud are distributed uniformly between 100 m and 10 km. They are found to not vary with topography. Seasonally, winter nights have the most multi-layer cloud occurrences, in 38 % of the measurements. Additionally, in situ and liquid origin cirrus cloud size modes can be identified according to the ice crystal mean effective radius in the DARDAR data. Using sublimation calculations, we show that in a significant number of cases the seeding ice crystals do not sublimate before reaching the lower-lying feeder cloud. Depending on whether bullet rosette, plate-like or spherical crystals were assumed, 10 %, 11 % or 20 % of the crystals, respectively, could provide seeds after sedimenting 2 km. The high occurrence frequency of seeding situations and the survival of the ice crystals indicate that the seeder–feeder process and natural cloud seeding are widespread phenomena over Switzerland. This hints at a large potential for natural cloud seeding to influence cloud properties and thereby the Earth's radiative budget and water cycle, which should be studied globally. Further investigations of the magnitude of the seeding ice crystals' effect on lower-lying clouds are necessary to estimate the contribution of natural cloud seeding to precipitation.

Long-Term Change in Characteristics of Cloud Vertical Structures Over Sumatra from Radiosonde Observations

Study on the vertical structure of cloud in Indonesia in terms of climate change is still very limited. We investigated the long-term change in characteristics of cloud vertical structures over Sumatra from three radiosonde observation stations in this work. The cloud base height (CBH), cloud top height (CT), and the number of cloud layers were retrieved using relative humidity (RH) profiles from radiosonde observation. The height of the cloud base is determined by taking the height of the layer with relative humidity (RH) value > 84% with at least a 3% jump in the RH from the ground level. Sumatra’s most frequently observed cloud layer is a one-layer cloud with an average occurrence rate of > 60%, which is slightly larger than the one-layer cloud globally. The percentage of appearance values at the Padang station, Pangkal Pinang, and Medan are 63.58%, 69.50% and 66.05%. The appearance of low-level clouds also dominates in Sumatra compared to other cloud types. CT and CBH increase with the number of years including all seasons. This is in line with the increase in temperature in Indonesia reported by previous researchers. On the other hand, the clouds’ thickness, especially for the cloud with one layer, varies from one location to another. The thickness of clouds decreases at Padang station and does not change at Pangkal Pinang and Medan stations.

Contrasting characteristics of open- and closed- cellular stratocumulus cloud in the Eastern North Atlantic

Extensive regions of marine boundary layer cloud impact the radiative balance through their significant shortwave albedo while having little impact on outgoing longwave radiation. Despite this importance, these cloud systems remain poorly represented in large-scale models due to difficulty in representing the processes that drive their lifecycle and coverage. In particular, the mesoscale organization, and cellular structure of marine boundary clouds has important implications for the subsequent cloud feedbacks. In this study, we use long-term (2013–2018) observations from the Atmospheric Radiation Measurement (ARM) Facility's Eastern North Atlantic (ENA) site on Graciosa Island, Azores, Portugal to identify cloud cases with open- or closed-cellular organization. More than 500 hours of each organization type are identified. The ARM observations are combined with reanalysis and satellite products to quantify the cloud, precipitation, aerosol, thermodynamic and large-scale synoptic characteristics associated with these cloud types. Our analysis shows that both cloud organization populations occur during similar sea surface temperature conditions, but the open-cell cases are distinguished by stronger cold-air advection and large-scale subsidence compared to the closed-cell cases, consistent with their formation during cold-air outbreaks. We also find that the open-cell cases were associated with deeper boundary layers, stronger low-level winds, and higher-rain rates compared to their closed-cell counterparts. Finally, raindrops with diameters larger than one millimeter were routinely recorded at the surface during both populations, with a higher number of large drops during the open-cellular cases. The similarities and differences noted herein provide important insights into the environmental and cloud characteristics during varying marine boundary layer cloud mesoscale organization and will be useful for the evaluation of model simulations for ENA marine clouds.

Ontology-Based Human-Computer Cloud for Decision Support

A variety of information processing and decision support tasks (especially in the context of smart city or smart tourist destination) rely both on the automated and human-based procedures. The article proposes a multi-layer cloud environment that, first, unifies various kinds of resources used by these information processing and decision-support scenarios (hardware, software, and human), and second, implements an ontology-based automatic service composition procedures that can be used to build ad hoc decision-support services for problems unknown in advance. The service composition is based on uniform description of all parts of the environment with a help of ontologies. The article describes the architecture and models of the novel human-computer cloud environment. It also describes several scenarios of decision support in tourism leveraging the proposed human-computer cloud concept.