Development and applications of a cloud regime dataset over Europe using satellite observations

Within the framework of the present dissertation, a cloud regime dataset was developed over Europe using satellite observations. The climatology of the “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS) is thoroughly investigated and the derived cloud regimes are used in a number of applications. The cloud regimes are generated over Europe on a 1°x1° spatial resolution and extend over a 14-year period (from 2004 until 2017) with a high temporal resolution of 15-minutes. In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 are used in order to compute 2D histograms. Then, the k-means clustering algorithm is applied to the generated 2D histograms for the derivation of the cloud regimes. Eight cloud regimes are identified, which assist in providing a detailed description of the climate of the cloud properties over Europe. Three of these cloud regimes are associated with high-level clouds and, in particular, with cirrus, cirrostratus and dense cirrus from deep convection and frontal systems. An additional cloud regime is connected to alto- and nimbo-type clouds, while a fifth one is related to mid-level clouds. Two cloud regimes are found to represent low-level clouds, specifically stratocumulus, shallow cumulus and fog. The final cloud regime is associated mainly with fair weather clouds and joint cloud histograms with no distinguishable structure. The spatiotemporal variability and changes of the CRAAS cloud regimes are investigated over the European region from 2004 until 2017. The last cloud regime is found to be the most dominant, in terms of frequency of occurrence, reaching values up to 85.5% over regions of lower latitude. The annual and diurnal cycles are also dominated by the last cloud regime, while features of the main representative cloud type of each cloud regime can be observed as well. The biggest decrease in the frequency of occurrence, -0.65% during the time period of the study, is observed for the cloud regime representing the alto- and nimbo-type clouds. In contrast, the largest increase, +0.70% during the time period of the study, is noted for the cloud regime associated with shallow cumulus and fog. The majority of the statistically significant trends are observed over lower latitudes, focusing mostly over the Mediterranean. An intercomparison against a similar classification, based on cloud optical properties from the International Satellite Cloud Climatology Project (ISCCP), showed that it is important to distinguish between regionally and globally derived cloud regimes. Furthermore, in order to identify relations between large-scale weather patterns and the cloud regimes, co-occurrences between the cloud regimes and the Objective Weather Type Classification are studied. Finally, the combination of the cloud regimes with vertical profiles from ground based measurements is used for a detailed description of cloud structures and their temporal evolution in frontal systems

CRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record

Given the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively)

Development and applications of a cloud regime dataset over Europe using satellite observations

Within the framework of the present dissertation, a cloud regime dataset was developed over Europe using satellite observations. The climatology of the “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS) is thoroughly investigated and the derived cloud regimes are used in a number of applications. The cloud regimes are generated over Europe on a 1°x1° spatial resolution and extend over a 14-year period (from 2004 until 2017) with a high temporal resolution of 15-minutes. In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 are used in order to compute 2D histograms. Then, the k-means clustering algorithm is applied to the generated 2D histograms for the derivation of the cloud regimes. Eight cloud regimes are identified, which assist in providing a detailed description of the climate of the cloud properties over Europe. Three of these cloud regimes are associated with high-level clouds and, in particular, with cirrus, cirrostratus and dense cirrus from deep convection and frontal systems. An additional cloud regime is connected to alto- and nimbo-type clouds, while a fifth one is related to mid-level clouds. Two cloud regimes are found to represent low-level clouds, specifically stratocumulus, shallow cumulus and fog. The final cloud regime is associated mainly with fair weather clouds and joint cloud histograms with no distinguishable structure. The spatiotemporal variability and changes of the CRAAS cloud regimes are investigated over the European region from 2004 until 2017. The last cloud regime is found to be the most dominant, in terms of frequency of occurrence, reaching values up to 85.5% over regions of lower latitude. The annual and diurnal cycles are also dominated by the last cloud regime, while features of the main representative cloud type of each cloud regime can be observed as well. The biggest decrease in the frequency of occurrence, -0.65% during the time period of the study, is observed for the cloud regime representing the alto- and nimbo-type clouds. In contrast, the largest increase, +0.70% during the time period of the study, is noted for the cloud regime associated with shallow cumulus and fog. The majority of the statistically significant trends are observed over lower latitudes, focusing mostly over the Mediterranean. An intercomparison against a similar classification, based on cloud optical properties from the International Satellite Cloud Climatology Project (ISCCP), showed that it is important to distinguish between regionally and globally derived cloud regimes. Furthermore, in order to identify relations between large-scale weather patterns and the cloud regimes, co-occurrences between the cloud regimes and the Objective Weather Type Classification are studied. Finally, the combination of the cloud regimes with vertical profiles from ground based measurements is used for a detailed description of cloud structures and their temporal evolution in frontal systems

Development and applications of a cloud regime dataset over Europe using satellite observations

Within the framework of the present dissertation, a cloud regime dataset was developed over Europe using satellite observations. The climatology of the “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS) is thoroughly investigated and the derived cloud regimes are used in a number of applications. The cloud regimes are generated over Europe on a 1°x1° spatial resolution and extend over a 14-year period (from 2004 until 2017) with a high temporal resolution of 15-minutes. In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 are used in order to compute 2D histograms. Then, the k-means clustering algorithm is applied to the generated 2D histograms for the derivation of the cloud regimes. Eight cloud regimes are identified, which assist in providing a detailed description of the climate of the cloud properties over Europe. Three of these cloud regimes are associated with high-level clouds and, in particular, with cirrus, cirrostratus and dense cirrus from deep convection and frontal systems. An additional cloud regime is connected to alto- and nimbo-type clouds, while a fifth one is related to mid-level clouds. Two cloud regimes are found to represent low-level clouds, specifically stratocumulus, shallow cumulus and fog. The final cloud regime is associated mainly with fair weather clouds and joint cloud histograms with no distinguishable structure. The spatiotemporal variability and changes of the CRAAS cloud regimes are investigated over the European region from 2004 until 2017. The last cloud regime is found to be the most dominant, in terms of frequency of occurrence, reaching values up to 85.5% over regions of lower latitude. The annual and diurnal cycles are also dominated by the last cloud regime, while features of the main representative cloud type of each cloud regime can be observed as well. The biggest decrease in the frequency of occurrence, -0.65% during the time period of the study, is observed for the cloud regime representing the alto- and nimbo-type clouds. In contrast, the largest increase, +0.70% during the time period of the study, is noted for the cloud regime associated with shallow cumulus and fog. The majority of the statistically significant trends are observed over lower latitudes, focusing mostly over the Mediterranean. An intercomparison against a similar classification, based on cloud optical properties from the International Satellite Cloud Climatology Project (ISCCP), showed that it is important to distinguish between regionally and globally derived cloud regimes. Furthermore, in order to identify relations between large-scale weather patterns and the cloud regimes, co-occurrences between the cloud regimes and the Objective Weather Type Classification are studied. Finally, the combination of the cloud regimes with vertical profiles from ground based measurements is used for a detailed description of cloud structures and their temporal evolution in frontal systems

An Improved Controlled Random Search Method

A modified version of a common global optimization method named controlled random search is presented here. This method is designed to estimate the global minimum of multidimensional symmetric and asymmetric functional problems. The new method modifies the original algorithm by incorporating a new sampling method, a new termination rule and the periodical application of a local search optimization algorithm to the points sampled. The new version is compared against the original using some benchmark functions from the relevant literature

An Improved Medical Image Compression Method Based on Wavelet Difference Reduction

Advanced microscopic techniques such as high-throughput, high-content, multispectral, and 3D imaging could include many images per experiment requiring hundreds of gigabytes (GBs) of memory. Efficient lossy image-compression methods such as joint photographic experts group (JPEG) and JPEG 2000 are crucial to managing these large amounts of data. However, these methods can get visual quality with high compression ratios but do not necessarily maintain the medical data and information integrity. This paper proposes a novel and improved medical image compression method based on color wavelet difference reduction. Specifically, the proposed method is an extension of the standard wavelet difference reduction (WDR) method using mean co-located pixel difference to select the optimum quantity of color images that present the highest similarity in the spatial and temporal domain. The images with large spatiotemporal coherence are encoded as one volume and evaluated regarding the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed method is evaluated in the challenging histopathological microscopy image analysis field using 31 slides of colorectal cancer. It is found that the perceptual quality of the medical image is remarkably high. The results indicate that the PSNR improvement over existing schemes may reach up to 22.65 dB compared to JPEG 2000. Also, it can reach up to 10.33dB compared to a method utilizing discrete wavelet transform (DWT), leading us to implement a mobile and web platform that can be used for compressing and transmitting microscopic medical images in real time

CRAAS: A European Cloud Regime dAtAset Based on the CLAAS-2.1 Climate Data Record

Given the important role of clouds in our planet’s climate system, it is crucial to further improve our understanding of their governing processes as well as the resulting spatio-temporal variability of their properties. This co-variability of different cloud optical properties is adequately represented through the well-established concept of cloud regimes. The focus of the present study lies on the creation of a cloud regime dataset over Europe, named “Cloud Regime dAtAset based on the CLAAS-2.1 climate data record” (CRAAS), in order to analyze their variability and their changes at different spatio-temporal scales. In addition, co-occurrences between the cloud regimes and large-scale weather patterns are investigated. The CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the Satellite Application Facility on Climate Monitoring (CM SAF), was used as the basis for the derivation of the cloud regimes over Europe for a 14-year period (2004–2017). In particular, the cloud optical thickness (COT) and cloud top pressure (CTP) products of CLAAS-2.1 were used in order to compute 2D histograms. Then, the k-means clustering algorithm was applied to the generated 2D histograms in order to derive the cloud regimes. Eight cloud regimes were identified, which, along with the geographical distribution of their frequency of occurrence, assisted in providing a detailed description of the climate of the cloud properties over Europe. The annual and diurnal variabilities of the eight cloud regimes were studied, and trends in their frequency of occurrence were also examined. Larger changes in the frequency of occurrence of the produced cloud regimes were found for a regime associated to alto- and nimbo-type clouds and for a regime connected to shallow cumulus clouds and fog (−0.65% and +0.70% for the time period of the study, respectively)

Evaluation of CLARA-A2 and ISCCP-H Cloud Cover Climate Data Records over Europe with ECA&D Ground-Based Measurements

Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data

EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests

Discrimination of eye movements and visual states is a flourishing field of research and there is an urgent need for non-manual EEG-based wheelchair control and navigation systems. This paper presents a novel system that utilizes a brain–computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while eye movement and subsequently classify them into six categories by applying a random forests (RF) classification algorithm. RF is an ensemble learning method that constructs a series of decision trees where each tree gives a class prediction, and the class with the highest number of class predictions becomes the model’s prediction. The categories of the proposed random forests brain–computer interface (RF-BCI) are defined according to the position of the subject’s eyes: open, closed, left, right, up, and down. The purpose of RF-BCI is to be utilized as an EEG-based control system for driving an electromechanical wheelchair (rehabilitation device). The proposed approach has been tested using a dataset containing 219 records taken from 10 different patients. The BCI implemented the EPOC Flex head cap system, which includes 32 saline felt sensors for capturing the subjects’ EEG signals. Each sensor caught four different brain waves (delta, theta, alpha, and beta) per second. Then, these signals were split in 4-second windows resulting in 512 samples per record and the band energy was extracted for each EEG rhythm. The proposed system was compared with naïve Bayes, Bayes Network, k-nearest neighbors (K-NN), multilayer perceptron (MLP), support vector machine (SVM), J48-C4.5 decision tree, and Bagging classification algorithms. The experimental results showed that the RF algorithm outperformed compared to the other approaches and high levels of accuracy (85.39%) for a 6-class classification are obtained. This method exploits high spatial information acquired from the Emotiv EPOC Flex wearable EEG recording device and examines successfully the potential of this device to be used for BCI wheelchair technology