The Influence of Assimilated Targeted Observations Upon Ensemble Forecasts of Convection Initiation

The influence of assimilating targeted meso-α- to synoptic-scale observations collected in the upstream, pre-convective environment upon subsequent short-range ensemble forecasts of convection initiation (CI) across the central United States for the fifteen aircraft missions conducted by the Mesoscale Predictability Experiment (MPEX) in May and June 2013 is evaluated in this study. Utilizing the ensemble Kalman filter implementation within the Data Assimilation Research Testbed software package as coupled to version 3.4.1 of the Advanced Research version of the Weather Research and Forecasting model, two nearly-identical thirty- member ensembles of short-range forecasts are conducted for each mission. Initial conditions for one ensemble are generated through a cycled data assimilation process that incorporates the targeted MPEX dropsonde observations from that day\u27s mission, and initial conditions for the other ensemble are generated through a cycled data assimilation process that excludes the targeted MPEX dropsonde observations. All forecasts for a given mission begin at 1500 UTC, extend forward 15 h, and are conducted on a domain encompassing the conterminous United States with 3 km horizontal grid spacing and 40 vertical levels. Verification is conducted over spatiotemporal thresholds of 50 km/0.5 h, 100 km/1 h, and 200 km/2 h of an observed CI event to assess the skill of probabilistic forecasts and quantify the influence that assimilating targeted observations has upon forecast skill for the events considered. Forecasts without the targeted observations have high probabilities of detection but also greatly overproduce CI, and the inclusion of targeted observations minimally improves some forecasts and minimally degrades other forecasts. Within the 100 km/1 h threshold, the targeted observations on average reduce distance errors between matched modeled and observed objects by 0.22 km while adding a time bias of 0.24 minutes. The forecast performance of specific cases as well as implications for CI predictability are discussed

Radar-Detected Mesocyclone Tilt in Tornadic and Nontornadic Supercells

While supercell thunderstorms are the storms with the greatest potential of producing tornadoes, the majority of supercells do not produce tornadoes. Recent work has demonstrated that low-level (LL) vertical wind shear and lifting condensation level (LCL) height in the storm inflow region are the most promising discriminators between tornadic and nontornadic supercells. It is anticipated that as the horizontal distance between the LL and mid-level (ML) mesocyclones (mesocyclone tilt) decreases, the likelihood and intensity of a tornado increase. It is expected that there is an orientation of both LL vertical shear and lower LCL height that results in a smaller mesocyclone tilt. This study builds a climatology of radar data to distinguish between tornadic and nontornadic supercells. Level-II and -III Weather Surveillance Radar-1988 Doppler data were collected and processed for a subset of isolated supercells in the contiguous United States from 2009 to 2015. From this initial climatology, LL and ML azimuthal wind shear maxima are located, representing the LL and ML mesocyclones, and the horizontal distance between each maximum is calculated during the evolution of each supercell. Results connecting the mesocyclone tilt to aspects of the near-storm environment, including LL shear magnitude and orientation and LCL height, will be discussed. Characteristics of the storm environment are obtained from proximity soundings derived from the Rapid Update Cycle and Rapid Refresh model analyses. Statistical and observational analyses of the climatology and of individual case studies will be presented Significantly tornadic supercells are associated with low LCL heights, strong southwesterly LL vertical wind shear, and critical angles below 100°. While smaller mesocyclone tilts are often associated with significant tornadoes, there is considerable overlap between distributions, suggesting that nontornadic and weakly tornadic storms may also have small tilts. There may be also a balance of shear orientation that moderates the position of outflow to result in a small positive or negative mesocyclone tilt. Further consideration should be given to the LL kinematic storm environment when discriminating between tornadic and nontornadic supercells

The Influence of PBL Parameterization on the Practical Predictability of Convection Initiation During the Mesoscale Predictability Experiment (MPEX)

This study evaluates the influence of planetary boundary layer (PBL) parameterizations on short-range (0-15 h) forecasts of convection initiation (CI) within convection-allowing ensembles that utilize sub-synoptic-scale observations collected during the Mesoscale Predictability Experiment (MPEX). Running five thirty-member ensembles with the Advanced Research Weather Research and Forecasting Model (WRF-ARW) with each differing only in the chosen PBL parameterization, forecast skill, PBL sensitivity on the environment in which CI occurred, and the variability within are examined. Three MPEX cases, 19-20 May 2013, 31 May-1 June 2013, and 8-9 June 2013 are considered, each characterized by a different large-scale flow pattern to analyze a wider spectrum of events. Using an object-based method to verify and analyze the forecasts of CI, it was found that none of the Five PBL schemes analyzed significantly improved the forecast skill. The non-local mixing PBL schemes, MYJ and QNSE, had in all cases higher probability of detection (POD) but consequently had a higher false alarm ratio (FAR) resulting from the models overproducing the number of CI objects, with all PBLs, and thus resulting in relative high bias scores as well. The CSI showed only subtle changes between PBL schemes suggesting no one PBL scheme drastically outperforms the other. The temporal distribution of errors associated with the “hits” in the CI object matching showed an approximate normal distribution around a mean of 0-s suggesting little systematic timing bias. While the spatial distribution of errors yielded skewed distributions with on average a mean (median) distance error of just over 44-km (28-km). Analysis of cumulative distribution functions (CDFs) of the “hits” highlighted limits to increased forecast skill beyond temporal and spatial thresholds of 60-min and 100-km. Mean error (ME) plots computed for surface features as well as vertical profiles in pre-convective environments highlighted biases in both the initial conditions as well as between ensembles. In agreement with previous studies, it was found that non-local mixing PBL schemes tend to produce PBLs that are too shallow, cool, and moist while local mixing schemes tend to be deeper, warmer, and drier as a function of the stronger (weaker) vertical mixing within the local (non-local) PBL schemes. Relative to the analysis of the vertical profiles, it was seen that the model has an inherent inability to accurately represent strong capping inversions in models across all PBL schemes suggesting an issue with the handling of vertical diffusion within the PBL and the implicit damping associated with the discretization schemes used within WRF

Radar-Detected Mesocyclone Tilt in Tornadic and Nontornadic Supercells

While supercell thunderstorms are the storms with the greatest potential of producing tornadoes, the majority of supercells do not produce tornadoes. Recent work has demonstrated that low-level (LL) vertical wind shear and lifting condensation level (LCL) height in the storm inflow region are the most promising discriminators between tornadic and nontornadic supercells. It is anticipated that as the horizontal distance between the LL and mid-level (ML) mesocyclones (mesocyclone tilt) decreases, the likelihood and intensity of a tornado increase. It is expected that there is an orientation of both LL vertical shear and lower LCL height that results in a smaller mesocyclone tilt. This study builds a climatology of radar data to distinguish between tornadic and nontornadic supercells. Level-II and -III Weather Surveillance Radar-1988 Doppler data were collected and processed for a subset of isolated supercells in the contiguous United States from 2009 to 2015. From this initial climatology, LL and ML azimuthal wind shear maxima are located, representing the LL and ML mesocyclones, and the horizontal distance between each maximum is calculated during the evolution of each supercell. Results connecting the mesocyclone tilt to aspects of the near-storm environment, including LL shear magnitude and orientation and LCL height, will be discussed. Characteristics of the storm environment are obtained from proximity soundings derived from the Rapid Update Cycle and Rapid Refresh model analyses. Statistical and observational analyses of the climatology and of individual case studies will be presented Significantly tornadic supercells are associated with low LCL heights, strong southwesterly LL vertical wind shear, and critical angles below 100°. While smaller mesocyclone tilts are often associated with significant tornadoes, there is considerable overlap between distributions, suggesting that nontornadic and weakly tornadic storms may also have small tilts. There may be also a balance of shear orientation that moderates the position of outflow to result in a small positive or negative mesocyclone tilt. Further consideration should be given to the LL kinematic storm environment when discriminating between tornadic and nontornadic supercells

Remote Sensing Applications in Coastal Environment

Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments

Geospatial Computing: Architectures and Algorithms for Mapping Applications

Beginning with the MapTube website (1), which was launched in 2007 for crowd-sourcing maps, this project investigates approaches to exploratory Geographic Information Systems (GIS) using web-based mapping, or ‘web GIS’. Users can log in to upload their own maps and overlay different layers of GIS data sets. This work looks into the theory behind how web-based mapping systems function and whether their performance can be modelled and predicted. One of the important questions when dealing with different geospatial data sets is how they relate to one another. Internet data stores provide another source of information, which can be exploited if more generic geospatial data mining techniques are developed. The identification of similarities between thousands of maps is a GIS technique that can give structure to the overall fabric of the data, once the problems of scalability and comparisons between different geographies are solved. After running MapTube for nine years to crowd-source data, this would mark a natural progression from visualisation of individual maps to wider questions about what additional knowledge can be discovered from the data collected. In the new ‘data science’ age, the introduction of real-time data sets introduces a new challenge for web-based mapping applications. The mapping of real-time geospatial systems is technically challenging, but has the potential to show inter-dependencies as they emerge in the time series. Combined geospatial and temporal data mining of realtime sources can provide archives of transport and environmental data from which to accurately model the systems under investigation. By using techniques from machine learning, the models can be built directly from the real-time data stream. These models can then be used for analysis and experimentation, being derived directly from city data. This then leads to an analysis of the behaviours of the interacting systems. (1) The MapTube website: http://www.maptube.org

INTELLIGENT CYBERINFRASTRUCTURE FOR BIG DATA ENABLED HYDROLOGICAL MODELING, PREDICTION, AND EVALUATION

Most hydrologic data are associated with spatiotemporal information, which is capable of presenting patterns and changes in both spatial and temporal aspects. The demands of retrieving, managing, analyzing, visualizing, and sharing these data have been continuously increasing. However, spatiotemporal hydrologic data are generally complex, which can be difficult to work with knowledge from hydrology alone. With the assistance of geographic information systems (GIS) and web-based technologies, a solution of establishing a cyberinfrastructure as the backbone to support such demands has emerged. This interdisciplinary dissertation described the advancement of traditional approaches for organizing and managing spatiotemporal hydrologic data, integrating and executing hydrologic models, analyzing and evaluating the results, and sharing the entire process. A pilot study was conducted in Chapter 2, in which a globally shared flood cyberinfrastructure was created to collect, organize, and manage flood databases that visually provide useful information to authorities and the public in real-time. The cyberinfrastructure used public cloud services provided by Google Fusion Table and crowdsourcing data collection methods to provide location-based visualization as well as statistical analysis and graphing capabilities. This study intended to engage citizen-scientists and presented an opportunity to modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters eventually. An observationally based monthly evapotranspiration (ET) product was produced in Chapter 3, using the simple water balance equation across the conterminous United States (CONUS). The best quality ground- and satellite-based observations of the water budget components, i.e., precipitation, runoff, and water storage change were adopted, while ET is computed as the residual. A land surface model-based downscaling approach to disaggregate the monthly GRACE equivalent water thickness (EWT) data to daily, 0.125º values was developed. The derived ET was evaluated against three sets of existing ET products and showed reliable results. The new ET product and the disaggregated GRACE data could be used as a benchmark dataset for researches in hydrological and climatological changes and terrestrial water and energy cycle dynamics over the CONUS. The study in Chapter 4 developed an automated hydrological modeling framework for any non-hydrologists with internet access, who can organize hydrologic data, execute hydrologic models, and visualize results graphically and statistically for further analysis in real-time. By adopting Hadoop distributed file system (HDFS) and Apache Hive, the efficiency of data processing and query were significantly increased. Two lumped hydrologic models, lumped Coupled Routing and Excess STorage (CREST) model and HyMOD model, were integrated as a proof of concept in this web framework. Evaluation of selected basins over the CONUS were performed as a demonstration. Our vision is to simplify the processes of using hydrologic models for researchers and modelers, as well as to unlock the potential and educate the less experienced public on hydrologic models

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications

Coastal management and adaptation: an integrated data-driven approach

Coastal regions are some of the most exposed to environmental hazards, yet the coast is the preferred settlement site for a high percentage of the global population, and most major global cities are located on or near the coast. This research adopts a predominantly anthropocentric approach to the analysis of coastal risk and resilience. This centres on the pervasive hazards of coastal flooding and erosion. Coastal management decision-making practices are shown to be reliant on access to current and accurate information. However, constraints have been imposed on information flows between scientists, policy makers and practitioners, due to a lack of awareness and utilisation of available data sources. This research seeks to tackle this issue in evaluating how innovations in the use of data and analytics can be applied to further the application of science within decision-making processes related to coastal risk adaptation. In achieving this aim a range of research methodologies have been employed and the progression of topics covered mark a shift from themes of risk to resilience. The work focuses on a case study region of East Anglia, UK, benefiting from the input of a partner organisation, responsible for the region’s coasts: Coastal Partnership East. An initial review revealed how data can be utilised effectively within coastal decision-making practices, highlighting scope for application of advanced Big Data techniques to the analysis of coastal datasets. The process of risk evaluation has been examined in detail, and the range of possibilities afforded by open source coastal datasets were revealed. Subsequently, open source coastal terrain and bathymetric, point cloud datasets were identified for 14 sites within the case study area. These were then utilised within a practical application of a geomorphological change detection (GCD) method. This revealed how analysis of high spatial and temporal resolution point cloud data can accurately reveal and quantify physical coastal impacts. Additionally, the research reveals how data innovations can facilitate adaptation through insurance; more specifically how the use of empirical evidence in pricing of coastal flood insurance can result in both communication and distribution of risk. The various strands of knowledge generated throughout this study reveal how an extensive range of data types, sources, and advanced forms of analysis, can together allow coastal resilience assessments to be founded on empirical evidence. This research serves to demonstrate how the application of advanced data-driven analytical processes can reduce levels of uncertainty and subjectivity inherent within current coastal environmental management practices. Adoption of methods presented within this research could further the possibilities for sustainable and resilient management of the incredibly valuable environmental resource which is the coast