A cloud particle sensor (CPS) sonde is an observing system to obtain the signals of the phase, size, and the number of cloud particles. Based on the field experiments in the Arctic regions and numerical experiments, we proposed a method to correct the CPS sonde data and found that the CPS sonde system can appropriately observe the liquid cloud if our correction method is applied.

In this study, we developed a new algorithm for cloud classification using solar radiation and cloud base height measurements. Our objective was to develop a simple and inexpensive but effective algorithm for the needs of studies related to ecosystem and atmosphere interactions. In the present study, we used the algorithm for obtaining cloud statistics at a measurement station in southern Finland, and we discuss the advantages and shortcomings of the algorithm.

Images of cloud particles give important information for improving our understanding of microphysical cloud processes. For phase-resolved measurements, a large number of water droplets and ice crystals need to be classified by an automated approach. In this study, a convolutional neural network was designed, which exceeds the classification ability of traditional methods and therefore shortens the analysis procedure of cloud particle images.

In the changing Arctic, quantifying the resulting variability of incoming solar radiation is important to better elucidate the net radiative effect of clouds. As part of a multidisciplinary expedition in the central Arctic held in early summer 2017, a novel network of pyranometers was deployed over an ice floe to investigate the spatiotemporal variability of solar radiation under different sky conditions. This study presents the collected data and an analysis of the spatiotemporal variability.

How cirroform clouds affect the radiation balance of the atmosphere depends on their properties, including ice particle types such as crystals, pellets, and fragments. Ice halos form if ice particles in these clouds are in a smooth hexagonal crystalline form. This paper introduces a method to search long-term records of sky images for ice halos, as gathered by total sky imagers (TSIs). Such an analysis will allow one to explore geographical and seasonal variations in cirrus cloud particle types.

A statistical tool frequently utilized to measure scale-dependent departures from perfect randomness is the radial distribution function. This tool has many strengths, but it is not easy to calculate for particle detections within a three-dimensional sample volume. In this manuscript, we introduce and test a new method to estimate the three-dimensional radial distribution function in realistic measurement volumes.

Existing chamber technologies for direct measurements of number concentration of cloud condensation nuclei (NCCN) are sophisticated and expensive. In this paper, a new method is proposed to calculate NCCN based only on measurements of a humidified nephelometer system which have accounted for influences of both aerosol size and aerosol hygroscopicity on NCCN calculation. This new method makes NCCN measurements more convenient and is capable of obtaining NCCN at lower supersaturations.

The current study analyses the cloud radiative effect during the daytime depending on cloud fraction and cloud type at two stations in Switzerland over a time period of 3–5 years. Information about fractional cloud coverage and cloud type is retrieved from images taken by visible all-sky cameras. Cloud cover, cloud type and other atmospheric parameters have an influence on the magnitude of the longwave cloud effect as well as on the shortwave.

In this study we evaluate a method to estimate cloud water content (CWC) knowing cloud reflectivity. Ice hydrometeors are replace by ice oblate spheroids to simulate their reflectivity. There is no assumption on the relation between mass and their size. Then, a broad range of CWCs are compared with direct measurements of CWC. The accuracy of the method is ~ ±32 %. This study is performed in areas of convective clouds where reflectivity and CWC are especially high, what makes it unique.

A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection results is helpful for cloud classification, tracking and solar irradiance prediction. The features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We have shown that taking advantages of multiple classifiers and various patch sizes is able to increase the detection accuracy.

This paper proposes a new cloud classification method, named bag of micro-structures (BoMS), for whole-sky imagers. BoMS treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. BoMS identifies five different sky conditions: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness (often appearing in all-sky images but seldom addressed in the literature). The performance of BoMS overperforms those of traditional methods.

Cloud base height observations from Ceilometer CL31 were extensively studied during May 2013 to January 2015 over Ahmedabad (23.03°N, 72.54°E), India. Results indicate that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds, and that the MODIS satellite provides accurate retrieval of high-level clouds over this region.

Measurements of clouds with an aircraft are essential to understand how clouds form and how they affect the Earth's climate. These measurements are used in climate models to help predict how our climate might develop in the next century. Aircraft measurements are, however, difficult for modellers to interpret because the way they were acquired and analyzed varies from one team of scientists to the next. We present a software platform for scientists to share and compare their analysis tools.

This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.