MULTIPLE HYPOTHESIS TRACKING AND STRONG POINT ANALYSIS FOR STORM TRACKING WITH WEATHER RADAR

Storm tracking using radar observations of weather is a valuable tool both for the study of weather conditions and for the issuance of advance warning when such conditions are severe. Centroid-based storm tracking algorithms are a common tool used to provide short-term forecasting of storms. In these algorithms, cells representing intense areas of storms are identified. The cells are compared to cells identified at regular intervals, and similar cells are assigned to tracks which indicate the history of the underlying storm. These tracks may then be used to generate forecasts. The Strong Point Analysis, Multiple Hypothesis Tracking (SPA-MHT) technique is a centroid-based algorithm which draws on methods effective in storm tracking and in the more general field of object tracking. Strong Point Analysis identifies storm cells using the erosion and dilation operations from the field of mathematical morphology. Multiple Hypothesis Tracking assigns these cells to tracks, resolving ambiguities by incorporating data from multiple sequential sets of observations. In this thesis, the motivation for the design of SPA-MHT is presented in the context of previous storm tracking algorithms. The suitability of Strong Point Analysis for storm identification and Multiple Hypothesis Tracking for track association is considered. An implementation of each technique is also presented. The combined implementation is evaluated on radar data from the NEXRAD network of Doppler weather radars. This constitutes the first evaluation of Multiple Hypothesis Tracking on actual weather radar data. Both qualitative visual comparisons and quantitative comparisons are considered. SPA-MHT is found to outperform the current NEXRAD tracking algorithm when tracking storms that are isolated or forming clusters, but performance between the two methods is more comparable in challenging situations such as squall lines

Making hurricane track data accessible

Our interactive tool allows the exploration, validation and presentation of hundreds of years of dynamically simulated storm tracks. The tracks were generated as part of a research project to improve the risk assessment of tropical storm damage by the insurance industry. The main impact of the tool is that exploratory interactive visualisation is now being used by the storm track modellers to (a) validate and improve model outputs, (b) discuss outputs with their peers (c) obtain a better understanding of the formation and development of tropical storms and (d) present examples of the behaviour of storms under different conditions to the insurance industry and others. Insights into tropical storm behaviour have been obtained and these insights are being articulated

Evaluation of Continuous Monitoring as a Tool for Municipal Stormwater Management Programs

The purpose of this study is to evaluate the uncertainty attributable to inadequate temporal sampling of stormwater discharge and water quality, and understand its implications for meeting monitoring objectives relevant to municipal separate storm sewer systems (MS4s). A methodology is presented to evaluate uncertainty attributable to inadequate temporal sampling of continuous stormflow and water quality, and a case study demonstrates the application of the methodology to six small urban watersheds (0.8-6.8 km2) and six large rural watersheds (30-16,192 km2) in Virginia. Results indicate the necessity of high-frequency continuous monitoring for accurately capturing multiple monitoring objectives, including illicit discharges, acute toxicity events, and stormflow pollutant concentrations and loads, as compared to traditional methods of sampling. For example, 1-h sampling in small urban watersheds and daily sampling in large rural watersheds would introduce uncertainty in capturing pollutant loads of 3–46% and 10–28%, respectively. Overall, the outcomes from this study highlight how MS4s can leverage continuous monitoring to meet multiple objectives under current and future regulatory environments

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone

A framework for exploration and cleaning of environmental data : Tehran air quality data experience

Management and cleaning of large environmental monitored data sets is a specific challenge. In this article, the authors present a novel framework for exploring and cleaning large datasets. As a case study, we applied the method on air quality data of Tehran, Iran from 1996 to 2013. ; The framework consists of data acquisition [here, data of particulate matter with aerodynamic diameter ≤10 µm (PM10)], development of databases, initial descriptive analyses, removing inconsistent data with plausibility range, and detection of missing pattern. Additionally, we developed a novel tool entitled spatiotemporal screening tool (SST), which considers both spatial and temporal nature of data in process of outlier detection. We also evaluated the effect of dust storm in outlier detection phase.; The raw mean concentration of PM10 before implementation of algorithms was 88.96 µg/m3 for 1996-2013 in Tehran. After implementing the algorithms, in total, 5.7% of data points were recognized as unacceptable outliers, from which 69% data points were detected by SST and 1% data points were detected via dust storm algorithm. In addition, 29% of unacceptable outlier values were not in the PR.  The mean concentration of PM10 after implementation of algorithms was 88.41 µg/m3. However, the standard deviation was significantly decreased from 90.86 µg/m3 to 61.64 µg/m3 after implementation of the algorithms. There was no distinguishable significant pattern according to hour, day, month, and year in missing data.; We developed a novel framework for cleaning of large environmental monitored data, which can identify hidden patterns. We also presented a complete picture of PM10 from 1996 to 2013 in Tehran. Finally, we propose implementation of our framework on large spatiotemporal databases, especially in developing countries

FIJICLIM description and users guide

The FIJICLIM prototype is based on PACCLIM which was developed by the International Global Change Institute (IGCI) as part of the Pacific Islands Climate Change Assistance Programme (PICCAP) executed by the South Pacific Regional Environment Programme (SPREP). Both FIJICLIM and PACCLIM build directly on a comparable model development for New Zealand, known as the CLIMPACTS system (Kenny et al., 1995, 1999; Warrick et al., 1996, 1999). The development of CLIMPACTS has been funded by the Foundation for Research Science and Technology since 1993. Its core components, which include a graphic user interface (GUI), a customised geographic information system (GIS), and data compression routines, have provided the basis for the development of FIJICLIM. The development of FIJICLIM is complementary to similar developments that have evolved from CLIMPACTS, for Bangladesh (BDCLIM), Australia (OZCLIM), and for training in climate change V&A assessment (VANDACLIM)

Management of an Urban Stormwater System Using Projected Future Scenarios of Climate Models: A Watershed-Based Modeling Approach

Anticipating a proper management needs for urban stormwater due to climate change is becoming a critical concern to water resources managers. In an effort to identify best management practices and understand the probable future climate scenarios, this study used high-resolution climate model data in conjunction with advanced statistical methods and computer simulation. Climate model data from the North American Regional Climate Change Assessment Program (NARCCAP) were used to calculate the design storm depths for the Gowan Watershed of Las Vegas Valley, Nevada. The Storm Water Management Model (SWMM), developed by the Environmental Protection Agency (EPA), was used for hydrological modeling. Two low-impact development techniques – Permeable Pavement and Green Roof – were implemented in the EPA SWMM hydrological modeling to attenuate excess surface runoff that was induced by climate change. The method adopted in this study was effective in mitigating the challenges in managing changes in urban stormwater amounts due to climate change

Cross-Talk-Free Multi-Color STORM Imaging Using a Single Fluorophore

Multi-color stochastic optical reconstruction microscopy (STORM) is routinely performed; however, the various approaches for achieving multiple colors have important caveats. Color cross-talk, limited availability of spectrally distinct fluorophores with optimal brightness and duty cycle, incompatibility of imaging buffers for different fluorophores, and chromatic aberrations impact the spatial resolution and ultimately the number of colors that can be achieved. We overcome these complexities and develop a simple approach for multi-color STORM imaging using a single fluorophore and sequential labelling. In addition, we present a simple and versatile method to locate the same region of interest on different days and even on different microscopes. In combination, these approaches enable cross-talk-free multi-color imaging of sub-cellular structures.Peer ReviewedPostprint (published version