Cluster analysisCluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical data analysis, used in many fields, including pattern recognition, , information retrieval, bioinformatics, data compression, computer graphics and machine learning.
Weather forecastingWeather forecasting is the application of science and technology to predict the conditions of the atmosphere for a given location and time. People have attempted to predict the weather informally for millennia and formally since the 19th century. Weather forecasts are made by collecting quantitative data about the current state of the atmosphere, land, and ocean and using meteorology to project how the atmosphere will change at a given place.
Convective storm detectionConvective storm detection is the meteorological observation, and short-term prediction, of deep moist convection (DMC). DMC describes atmospheric conditions producing single or clusters of large vertical extension clouds ranging from cumulus congestus to cumulonimbus, the latter producing thunderstorms associated with lightning and thunder. Those two types of clouds can produce severe weather at the surface and aloft.
Hierarchical clusteringIn data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis that seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two categories: Agglomerative: This is a "bottom-up" approach: Each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy. Divisive: This is a "top-down" approach: All observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
DBSCANDensity-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996. It is a density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away).
Clustering high-dimensional dataClustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions equals the size of the vocabulary.
K-means clusteringk-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances.
LightningLightning is a natural phenomenon formed by electrostatic discharges through the atmosphere between two electrically charged regions, either both in the atmosphere or with one in the atmosphere and on the ground, temporarily neutralizing these in a near-instantaneous release of an average of one gigajoule of energy. This discharge may produce a wide range of electromagnetic radiation, from heat created by the rapid movement of electrons, to brilliant flashes of visible light in the form of black-body radiation.
Correlation clusteringClustering is the problem of partitioning data points into groups based on their similarity. Correlation clustering provides a method for clustering a set of objects into the optimum number of clusters without specifying that number in advance. Cluster analysis In machine learning, correlation clustering or cluster editing operates in a scenario where the relationships between the objects are known instead of the actual representations of the objects.
Lightning detectionA lightning detector is a device that detects lightning produced by thunderstorms. There are three primary types of detectors: ground-based systems using multiple antennas, mobile systems using a direction and a sense antenna in the same location (often aboard an aircraft), and space-based systems. The first such device was invented in 1894 by Alexander Stepanovich Popov. It also was the first radio receiver in the world.
Spectral clusteringIn multivariate statistics, spectral clustering techniques make use of the spectrum (eigenvalues) of the similarity matrix of the data to perform dimensionality reduction before clustering in fewer dimensions. The similarity matrix is provided as an input and consists of a quantitative assessment of the relative similarity of each pair of points in the dataset. In application to image segmentation, spectral clustering is known as segmentation-based object categorization.
MeteorologyMeteorology is a branch of the atmospheric sciences (which include atmospheric chemistry and physics) with a major focus on weather forecasting. The study of meteorology dates back millennia, though significant progress in meteorology did not begin until the 18th century. The 19th century saw modest progress in the field after weather observation networks were formed across broad regions. Prior attempts at prediction of weather depended on historical data.
Numerical weather predictionNumerical weather prediction (NWP) uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs.
Density estimationIn statistics, probability density estimation or simply density estimation is the construction of an estimate, based on observed data, of an unobservable underlying probability density function. The unobservable density function is thought of as the density according to which a large population is distributed; the data are usually thought of as a random sample from that population. A variety of approaches to density estimation are used, including Parzen windows and a range of data clustering techniques, including vector quantization.
ThunderstormA thunderstorm, also known as an electrical storm or a lightning storm, is a storm characterized by the presence of lightning and its acoustic effect on the Earth's atmosphere, known as thunder. Relatively weak thunderstorms are sometimes called thundershowers. Thunderstorms occur in a type of cloud known as a cumulonimbus. They are usually accompanied by strong winds and often produce heavy rain and sometimes snow, sleet, or hail, but some thunderstorms produce little precipitation or no precipitation at all.
National Weather ServiceThe National Weather Service (NWS) is an agency of the United States federal government that is tasked with providing weather forecasts, warnings of hazardous weather, and other weather-related products to organizations and the public for the purposes of protection, safety, and general information. It is a part of the National Oceanic and Atmospheric Administration (NOAA) branch of the Department of Commerce, and is headquartered in Silver Spring, Maryland, within the Washington metropolitan area.
BiclusteringBiclustering, block clustering, Co-clustering or two-mode clustering is a data mining technique which allows simultaneous clustering of the rows and columns of a matrix. The term was first introduced by Boris Mirkin to name a technique introduced many years earlier, in 1972, by John A. Hartigan. Given a set of samples represented by an -dimensional feature vector, the entire dataset can be represented as rows in columns (i.e., an matrix). The Biclustering algorithm generates Biclusters.
Lightning strikeA lightning strike is a lightning event in which the electric discharge takes place between the atmosphere and the ground. Most originate in a cumulonimbus cloud and terminate on the ground, called cloud-to-ground (CG) lightning. A less common type of strike, ground-to-cloud (GC) lightning, is upward-propagating lightning initiated from a tall grounded object and reaching into the clouds. About 25% of all lightning events worldwide are strikes between the atmosphere and earth-bound objects.
Storm chasingStorm chasing is broadly defined as the deliberate pursuit of any severe weather phenomenon, regardless of motive, but most commonly for curiosity, adventure, scientific investigation, or for news or media coverage. A person who chases storms is known as a storm chaser or simply a chaser. While witnessing a tornado is the single biggest objective for most chasers, many chase thunderstorms and delight in viewing cumulonimbus and related cloud structures, watching a barrage of hail and lightning, and seeing what skyscapes unfold.
DownburstIn meteorology, a downburst is a strong downward and outward gushing wind system that emanates from a point source above and blows radially, that is, in straight lines in all directions from the area of impact at surface level. Capable of producing damaging winds, it may sometimes be confused with a tornado, where high-velocity winds circle a central area, and air moves inward and upward. These usually last for seconds to minutes.
