Search Results
You are looking at 1 - 2 of 2 items for :
- Author or Editor: S. Medina x
- Article x
- Refine by Access: All Content x
Abstract
The visual description of wave climate is usually limited to two-dimensional conditional histograms. In this work, self-organizing maps (SOMs), because of their visualization properties, are used to characterize multivariate wave climate. The SOMs are applied to time series of sea-state parameters at a particular location provided by ocean reanalysis databases. Trivariate (significant wave height, mean period, and mean direction), pentavariate (the previous wave parameters and wind velocity and direction), and hexavariate (three wave parameters of the sea and swell components; or the wave, wind, and storm surge) classifications are explored. This clustering technique is also applied to wave and wind data at several locations to analyze their spatial relationship. Several processes are established in order to improve the results, the most relevant being a preselection of data by means a maximum dissimilarity algorithm (MDA). Results show that the SOM identifies the relevant multivariate sea-state types at a particular location spanning the historical variability, and provides an outstanding analysis of the dependency between the different parameters by visual inspection. In the case of wave climate characterizations for several locations the SOM is able to extract the qualitative spatial sea-state patterns, allowing the analysis of the spatial variability and the relationship between different locations. Moreover, the distribution of sea states over the reanalysis period defines a probability density function on the lattice, providing a visual interpretation of the seasonality and interannuality of the multivariate wave climate.
Abstract
The visual description of wave climate is usually limited to two-dimensional conditional histograms. In this work, self-organizing maps (SOMs), because of their visualization properties, are used to characterize multivariate wave climate. The SOMs are applied to time series of sea-state parameters at a particular location provided by ocean reanalysis databases. Trivariate (significant wave height, mean period, and mean direction), pentavariate (the previous wave parameters and wind velocity and direction), and hexavariate (three wave parameters of the sea and swell components; or the wave, wind, and storm surge) classifications are explored. This clustering technique is also applied to wave and wind data at several locations to analyze their spatial relationship. Several processes are established in order to improve the results, the most relevant being a preselection of data by means a maximum dissimilarity algorithm (MDA). Results show that the SOM identifies the relevant multivariate sea-state types at a particular location spanning the historical variability, and provides an outstanding analysis of the dependency between the different parameters by visual inspection. In the case of wave climate characterizations for several locations the SOM is able to extract the qualitative spatial sea-state patterns, allowing the analysis of the spatial variability and the relationship between different locations. Moreover, the distribution of sea states over the reanalysis period defines a probability density function on the lattice, providing a visual interpretation of the seasonality and interannuality of the multivariate wave climate.
Abstract
This article provides an overview of the experimental design, execution, education and public outreach, data collection, and initial scientific results from the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. RELAMPAGO was a major field campaign conducted in the Córdoba and Mendoza provinces in Argentina and western Rio Grande do Sul State in Brazil in 2018–19 that involved more than 200 scientists and students from the United States, Argentina, and Brazil. This campaign was motivated by the physical processes and societal impacts of deep convection that frequently initiates in this region, often along the complex terrain of the Sierras de Córdoba and Andes, and often grows rapidly upscale into dangerous storms that impact society. Observed storms during the experiment produced copious hail, intense flash flooding, extreme lightning flash rates, and other unusual lightning phenomena, but few tornadoes. The five distinct scientific foci of RELAMPAGO—convection initiation, severe weather, upscale growth, hydrometeorology, and lightning and electrification—are described, as are the deployment strategies to observe physical processes relevant to these foci. The campaign’s international cooperation, forecasting efforts, and mission planning strategies enabled a successful data collection effort. In addition, the legacy of RELAMPAGO in South America, including extensive multinational education, public outreach, and social media data gathering associated with the campaign, is summarized.
Abstract
This article provides an overview of the experimental design, execution, education and public outreach, data collection, and initial scientific results from the Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign. RELAMPAGO was a major field campaign conducted in the Córdoba and Mendoza provinces in Argentina and western Rio Grande do Sul State in Brazil in 2018–19 that involved more than 200 scientists and students from the United States, Argentina, and Brazil. This campaign was motivated by the physical processes and societal impacts of deep convection that frequently initiates in this region, often along the complex terrain of the Sierras de Córdoba and Andes, and often grows rapidly upscale into dangerous storms that impact society. Observed storms during the experiment produced copious hail, intense flash flooding, extreme lightning flash rates, and other unusual lightning phenomena, but few tornadoes. The five distinct scientific foci of RELAMPAGO—convection initiation, severe weather, upscale growth, hydrometeorology, and lightning and electrification—are described, as are the deployment strategies to observe physical processes relevant to these foci. The campaign’s international cooperation, forecasting efforts, and mission planning strategies enabled a successful data collection effort. In addition, the legacy of RELAMPAGO in South America, including extensive multinational education, public outreach, and social media data gathering associated with the campaign, is summarized.
