Identification of storm eye from Satellite image data using fuzzy logic with machine learning

This research presents a study of a unique technique for identifying storm eye that is based on fuzzy logic and image processing with the help of cloud images. Fuzzy logic is a term that refers to complicated systems with unclear behaviour caused by a number of different circumstances. It provides the ability to model the dynamic behavior of the storm and determines the location of the best eye in an area of interest. After that, image processing is applied to enable accurate eye positioning based on the search results. The experimental results are analyzing the storm eye position with approxiamtely $98\%$ accurate compared to the India meteorological department provided best track data and Cooperative Institute for Meteorological Satellite Studies provided Advances Dvorak Technique data. As a result, the identification of storm's eye location using this technique can be found to improve significantly. Using the present technique, it is possible to determine the eye entirely automatically, which replacing the manual method that has been employed in the past

Effective Visualizations of the Uncertainty in Hurricane Forecasts

The track forecast cone developed by the U.S. National Hurricane Center is the one most universally adopted by the general public, the news media, and governmental officials to enhance viewers\u27 understanding of the forecasts and their underlying uncertainties. However, current research has experimentally shown that it has limitations that result in misconceptions of the uncertainty included. Most importantly, the area covered by the cone tends to be misinterpreted as the region affected by the hurricane. In addition, the cone summarizes forecasts for the next three days into a single representation and, thus, makes it difficult for viewers to accurately determine crucial time-specific information. To address these limitations, this research develops novel alternative visualizations. It begins by developing a technique that generates and smoothly interpolates robust statistics from ensembles of hurricane predictions, thus creating visualizations that inherently include the spatial uncertainty by displaying three levels of positional storm strike risk at a specific point in time. To address the misconception of the area covered by the cone, this research develops time-specific visualizations depicting spatial information based on a sampling technique that selects a small, representative subset from an ensemble of points. It also allows depictions of such important storm characteristics as size and intensity. Further, this research generalizes the representative sampling framework to process ensembles of forecast tracks, selecting a subset of tracks accurately preserving the original distributions of available storm characteristics and keeping appropriately defined spatial separations. This framework supports an additional hurricane visualization portraying prediction uncertainties implicitly by directly showing the members of the subset without the visual clutter. We collaborated on cognitive studies that suggest that these visualizations enhance viewers\u27 ability to understand the forecasts because they are potentially interpreted more like uncertainty distributions. In addition to benefiting the field of hurricane forecasting, this research potentially enhances the visualization community more generally. For instance, the representative sampling framework for processing 2D points developed here can be applied to enhancing the standard scatter plots and density plots by reducing sizes of data sets. Further, as the idea of direct ensemble displays can possibly be extended to more general numerical simulations, it, thus, has potential impacts on a wide range of ensemble visualizations

1988 CIRA satellite research workshop

Includes bibliographical references.This document reports on a Satellite Research Workshop sponsored by the Cooperative Institute for Research in the Atmosphere (CIRA) that was held at the Colorado State University's Pingree Park campus from September 21-23, 1988. The workshop was designed to investigate research and applications opportunities using data from the next generation GOES and TIROS satellites

Rainfall threshold for initiating effective stress decrease and failure in weathered tephra slopes

Rainfall is one of the most important triggers of slope failure. Weathered pyroclastic (tephra) deposits are especially vulnerable to slope failure because they commonly form slopes of high porosity and high clay content. Empirically derived thresholds for the triggering of landslides are commonly based on rainfall conditions and have been widely applied in volcanic soils. However, so far only few researchers utilized pore water pressure in the slope as additional variable for the threshold calibration. Here, we derived a new rainfall threshold for initiating the decrease in effective stress in the slope by analyzing a long-term record of rainfall and piezometer data from a slide-prone coastal area in northern New Zealand that consists of clayey, halloysitic tephra deposits. The level of effective stress decrease increased with rainfall intensity and duration. We observed highest effective stress decrease of up to 36% during rainfall events that triggered landslides in our study area. The effective stress threshold exhibits a satisfactory predictive capability. The probability of correctly predicting a decrease in effective stress is 53%. The effective stress threshold contributes towards the implementation of the decrease in effective stress into rainfall thresholds for the occurrence of landslides

Fatigue Analysis on Wind Turbine Towers in Cyclone-Prone Areas

Wind energy is developing at a rapid pace both onshore and offshore to realize the carbon neutral ambitions set by different countries. However, the promotion of wind turbine in tropical/ subtropical regions encounters challenges from local extreme weathers, e.g., tropical cyclone. The intensity of a common tropical cyclone is far beyond the cut-out wind speed, which will result in drastic vibration of the wind turbine tower. Most of previous research are focused on the strength or stability failure of a wind turbine under cyclones, which is explicitly reflected from several forensic onsite investigations. Nonetheless, this thesis reveals that the implicit fatigue damage accumulation that incurred by cyclones can also be important. To quantify the additional fatigue damage induced by cyclones, the dynamics of an onshore or offshore wind turbine under both normal wind and cyclones should be accurately described. An improved decoupled theory is proposed based on an improved aerodynamic damping theory to successfully realize the soil-structure interaction and the possible nonlinear behavior of a tower, which still cannot be achieved in many wind turbine design software. A basic analysis framework is constructed to include the various external and internal conditions of a wind turbine under both the normal wind and cyclones. Based on this, a novel wind turbine evaluation framework under cyclones is created to classify the potential failure modes of wind turbine towers into three distinct categories with high computational efficiency. In addition, an upgraded fatigue analysis framework for wind turbine under cyclones is proposed with consideration of meteorological data supported time-varying cyclone features, e.g., cyclone direction, intensity, translation speed and track straightness, together with other important cyclone characteristics, e.g., cyclone average recurrence interval (ARI) and cyclone-normal-wind direction misalignment. Last but not least, parametric analysis in terms of wind turbine scale, parking status, wind intensity, section slenderness and material model is presented through this thesis to reflect the significance of the factors concerned. The proposed fatigue analysis frameworks can also be extended to other structures, e.g., the hybrid wind-tidal energy conversion system in cyclone-prone regions

Tropical Cyclone Storm Surge Detection in Slash Pine Radial Growth along the Northern Gulf of Mexico Coastline

My thesis examines the ecological impact of tropical cyclone (TCs) storm surge on coastal slash pine (Pinus elliottii var. elliottii Engelm) communities along the Gulf of Mexico in the southern United States (U.S.). Previous research has shown slash pine radial growth trends can be examined to identify long and short-term growth changes associated with TC passage, providing insight into overall stand health and resiliency through time. However, this previous research encompasses just one site in Mississippi. My thesis expands the spatial footprint of TC-surge impact on slash pine radial growth with the addition of three new sites. I examined seasonally resolved tree-ring data from two sites in Alabama and one in Florida and discovered differences in geography and seasonality to suppressions and recovery. The Weeks Bay, Fairhope, Alabama site was the most responsive to storm-surge suppressions, and this was perhaps due to lack of dune protection and proximity to a concave coastline. Latewood growth recorded the highest percentage of suppressions associated with storm surge and was generally the quickest growth metric to recover to normal growing conditions. TCs are predicted to become larger and more powerful in the 21st century, and it will be necessary to consider the negative impacts that these storms can have on coastal pine savannas while constructing plans to protect and preserve these unique environments