Interpreting self-organizing maps through space--time data models

Self-organizing maps (SOMs) are a technique that has been used with high-dimensional data vectors to develop an archetypal set of states (nodes) that span, in some sense, the high-dimensional space. Noteworthy applications include weather states as described by weather variables over a region and speech patterns as characterized by frequencies in time. The SOM approach is essentially a neural network model that implements a nonlinear projection from a high-dimensional input space to a low-dimensional array of neurons. In the process, it also becomes a clustering technique, assigning to any vector in the high-dimensional data space the node (neuron) to which it is closest (using, say, Euclidean distance) in the data space. The number of nodes is thus equal to the number of clusters. However, the primary use for the SOM is as a representation technique, that is, finding a set of nodes which representatively span the high-dimensional space. These nodes are typically displayed using maps to enable visualization of the continuum of the data space. The technique does not appear to have been discussed in the statistics literature so it is our intent here to bring it to the attention of the community. The technique is implemented algorithmically through a training set of vectors. However, through the introduction of stochasticity in the form of a space--time process model, we seek to illuminate and interpret its performance in the context of application to daily data collection. That is, the observed daily state vectors are viewed as a time series of multivariate process realizations which we try to understand under the dimension reduction achieved by the SOM procedure.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS174 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Climate change in South Africa: Risks and opportunities for climate-resilient development in the IPCC Sixth Assessment WGII Report

South Africa is wrestling with increasing climate change impacts and how to respond. The 2022 IPCC Working Group II Report synthesises the latest evidence on climate change impacts, vulnerability and adaptation, and what this means for climate-resilient development. In this commentary, South African authors on the Report reflect on its key findings and the implications for the country. The commentary highlights challenges and opportunities for cities, the food-water-energy-nature nexus, knowledge and capacity strengthening (which includes climate services, climate change literacy, and indigenous and local knowledge), climate finance, equity, justice and social protection, and climate-resilient development pathways. The piece closes with a reflection on research gaps requiring attention and the importance of urgently ramping up climate action to secure a liveable future for all South Africans

Establishment guide for sub-tropical grasses : key steps to success

Sub-tropical perennial grasses are now widely sown in the Northern Agricultural region (NAR) and on the south coast of Western Australia (WA). Since 2000, more than 50 000 ha have been sown to perennial grasses in the NAR and about 150 000 ha on the south coast, mainly kikuyu.https://researchlibrary.agric.wa.gov.au/bulletins/1034/thumbnail.jp

A metrics-based analysis of seasonal daily precipitation and near-surface temperature within seven Coordinated Regional Climate Downscaling Experiment domains

We compare ensemble mean daily precipitation and near-surface temperatures from regional climate model simulations over seven Coordinated Regional Climate Downscaling Experiment domains for the winter and summer seasons. We use Taylor diagrams to show the domain-wide pattern similarity between the model ensemble and the observational data sets. We use the Climatic Research Unit (CRU) and the University of Delaware gridded observations and ERA-Interim reanalysis data as an additional observationally based estimate of historical climatology. Taylor diagrams determine the relative skill of the seven sets of simulations and quantify these results in terms of center pattern root-mean square error and correlation coefficient. Results suggest that there is good agreement between the models and the CRU, in terms of their respective seasonal cycles, as shown in Taylor diagrams and bias plots. There is also good agreement between both gridded observation sets. In addition, downscaled ERA-Interim precipitation is closer to observations than raw ERA-Interim precipitation. Domains located in the low latitudes and those having high topography appear to have larger biases, especially precipitation.Fil: Glisan, Justin M.. IOWA STATE UNIVERSITY (ISU);Fil: Jones, Richard. No especifíca;Fil: Lennard, Chris. University of Cape Town; SudáfricaFil: Castillo Pérez, Nadia Itzel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Lucas Picher, Philippe. Université du Québec a Montreal; CanadáFil: Rinke, Annette. Helmholtz Centre for Polar and Marine Research; AlemaniaFil: Solman, Silvina Alicia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Gutowski, William J.. IOWA STATE UNIVERSITY (ISU)

Rapid on-site identification of hazardous organic compounds at fire scenes using person-portable gas chromatography-mass spectrometry (GC-MS). Part 2: Water sampling and analysis

Building and factory fires pose a great risk to human and environmental health, due to the release of hazardous by-products of combustion. These hazardous compounds can dissipate into the environment through fire water run-off, and the impact can be immediate or chronic. Current laboratory-based methods do not report hazardous compounds released from a fire scene at the time and location of the event. Reporting of results is often delayed due to the complexities and logistics of laboratory-based sampling and analysis. These delays pose a risk to the health and wellbeing of the environment and exposed community. Recent developments in person-portable instrumentation have the potential to provide rapid analysis of samples in the field. A portable gas chromatograph-mass spectrometer (GC-MS) was evaluated for the on-site analysis of water samples for the identification of hazardous organic compounds at fire scenes. The portable GC-MS was capable of detecting and identifying a range of volatile and semi-volatile organic compounds in fire water run-off, and can be used in conjunction with conventional laboratory analysis methods for a comprehensive understanding of hazardous organics released at fire scenes. Deployment of this portable instrumentation provides first responders with a rapid, on-site screening tool to appropriately manage the run-off water from firefighting activities. This ensures that environmental and human health is proactively protected

Rapid on-site identification of hazardous organic compounds at fire scenes using person-portable gas chromatography-mass spectrometry (GC-MS). Part 1: Air sampling and analysis

Recent advancements in person-portable instrumentation have resulted in the potential to provide contemporaneous results through rapid in-field analyses. These technologies can be utilised in emergency response scenarios to aid first responders in appropriate site risk assessment and management. Large metropolitan fires can pose great risk to human and environmental health due to the rapid release of hazardous compounds into the atmosphere. Understanding the release of these hazardous organics is critical in understanding their associated risks. Person-portable gas chromatography-mass spectrometry (GC-MS) was evaluated for its potential to provide rapid on-site analysis for real-time monitoring of hazardous organic compounds at fire scenes. Air sampling and analysis methods were developed for scenes of this nature. Controlled field testing demonstrated that the portable GC-MS was able to provide preliminary analytical results on the volatile organic compounds present in air samples collected from both active and extinguished fires. In-field results were confirmed using conventional laboratory-based air sampling and analysis procedures. The deployment of portable instrumentation could provide first responders with a rapid on-site assessment tool for the appropriate management of scenes, thereby ensuring environmental and human health is proactively protected and scientifically informed decisions are made for the provision of timely advice to stakeholders