This paper describes an enhanced 0–2-h convective initiation (CI) nowcasting algorithm known as Satellite Convection Analysis and Tracking, version 2 (SATCASTv2). Tracking of developing cumulus cloud “objects” in advance of CI was developed as a means of reducing errors caused by tracking single satellite pixels of cumulus clouds, as identified in Geostationary Operational Environmental Satellite (GOES) output. The method rests on the idea that cloud objects at one time, when extrapolated forward in space and time using mesoscale atmospheric motion vectors, will overlap with the same actual cloud objects at a later time. Significant overlapping confirms that a coherent cumulus cloud is present and trackable in GOES data and that it is persistent enough that various infrared threshold–based tests may be performed to assess cloud growth. Validation of the new object-tracking approach to nowcasting CI was performed over four regions in the United States: 1) Melbourne, Florida; 2) Memphis, Tennessee; 3) the central United States/Great Plains; and 4) the northeastern United States as a means of evaluating algorithm performance in various convective environments. In this study, 9943 CI nowcasts and 804 CI events were analyzed. Optimal results occurred in the central U.S./Great Plains domain, where the probability of detection (POD) and false-alarm ratio (FAR) reached 85% and 55%, respectively, for tracked cloud objects. The FARs were partially attributed to difficulties inherent to the CI nowcasting problem. PODs were seen to decrease for CI events in Florida. Discussion is provided on how SATCASTv2 performed, as well as on how certain problems may be mitigated, especially in light of enhanced geostationary-satellite systems.

The authenticity of a specific brand of Scotch whisky may be confirmed by comparing analytical data for suspect samples with reference to analytical ranges for the genuine brand. Wider generic authenticity issues exist when a product purports to be Scotch whisky when it has not been produced in Scotland in accordance with the legal definition of Scotch whisky. When such cases reach litigation, courts may ask chemists to analyse suspect products and draw conclusions on authenticity. This paper presents analytical profiles generated from a survey of Malt, Grain and Blended Scotch whiskies and compares the results with whiskies of other origins and examples of a diverse range of suspect products purporting to be Scotch whisky. The concentrations and ratios of concentrations of the major volatile compounds (or congeners), particularly methanol, n‐propanol, isobutanol and 2‐ and 3‐methyl butanol, were found to be important factors in the authenticity decision‐making process. In addition, the absence of known Scotch whisky congeners, the presence of compounds known to be absent from genuine whisky and abnormal maturation congener profiles all contributed to the decision process. From this review of genuine analytical profiles, an experimental protocol for determining the authenticity of Scotch whisky is proposed.

Infrared (IR) data from the Meteosat Second Generation (MSG) satellite are used to understand cloud-top signatures for growing cumulus clouds prior to known convective initiation (CI) events, or the first occurrence of a ≥35-dBZ echo from a new convective cloud. In the process, this study proposes how MSG IR fields may be used to infer three physical attributes of growing cumuli, cloud depth, cloud-top glaciation, and updraft strength, with limited information redundancy. These three aspects are observed as unique signatures within MSG IR data, for which this study seeks to relate to previous research, as well as develop a new understanding on which subset of IR information best identifies these attributes. Data from 123 subjectively identified CI events observed during the 2007 Convection and Orograpically Induced Precipitation Study (COPS) field experiment conducted over southern Germany and northeastern France are processed, per convective cell, to meet this study’s objectives. A total of 67 IR “interest fields” are initially assessed for growing cumulus clouds, with correlation and principal component analyses used to highlight the top 21 fields that are considered the best candidates for describing the three attributes. Using between 6 and 8 fields per category, a method is then proposed on how growing convective clouds may be quantified per 3-km2 pixel (or per cumulus cloud object) toward inferring each attribute. No independent CI-nowcasting analysis is performed, which instead is the subject of ongoing research.

This paper describes a statistical clustering approach toward the classification of cloud types within meteorological satellite imagery, specifically, visible and infrared data. The method is based on the Standard Deviation Limited Adaptive Clustering (SDLAC) procedure, which has been used to classify a variety of features within both polar orbiting and geostationary imagery, including land cover, volcanic ash, dust, and clouds of various types. In this study, the focus is on classifying cumulus clouds of various types (e.g., “fair weather, ”towering, and newly glaciated cumulus, in addition to cumulonimbus). The SDLAC algorithm is demonstrated by showing examples using Geostationary Operational Environmental Satellite (GOES) 12, Meteosat Second Generation's (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI), and the Moderate Resolution Infrared Spectrometer (MODIS). Results indicate that the method performs well, classifying cumulus similarly between MODIS, SEVIRI, and GOES, despite the obvious channel and resolution differences between these three sensors. The SDLAC methodology has been used in several research activities related to convective weather forecasting, which offers some proof of concept for its value.