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Comparison of the United States Precision Lightning Network (USPLN) and the Cloud-to-Ground Lightning Surveillance System (CGLSS)
Get PDFWSI Corporation requested a performance evaluation of their United States Precision Lightning Network™ (USPLN™), which is co-owned by TOA Systems, Inc. The USPLN is a national lightning detection network with over 160 sensors placed across the North American continent. Previous performance evaluations of the network had been limited to simulated lightning events and individual fixed tower case studies. Thus, a longer evaluation of the network had yet to be completed, which this study attempts to achieve. As a validation tool, the second generation of the Cloud-to-Ground Lightning Surveillance System (CGLSS-II) was selected. CGLSS-II is a local detection network used for critical lightning surveillance at Kennedy Space Center and Cape Canaveral Air Force Station (KSC/CCAFS). The network of six sensors has been certified by the U.S. Air Force since 1989, and is constantly monitored and evaluated. CGLSS-II and the USPLN share numerous similarities including: the processing of all lightning strokes, GPS timing, and the time-of-arrival technique for triangulating stroke locations. Stroke data for CGLSS-II and USPLN were acquired and quality controlled for the selected study period of 20 May 2008 to 31 August 2010. The study period was further divided into sub-periods based on changes to CGLSS-II performance, and data were restricted to a region surrounding KSC/CCAFS. A correlation procedure was selected which matched strokes between the two networks using time and distance thresholds, creating a comparative dataset. Data from the Four Dimensional Lightning Surveillance System (4DLSS) was also collected as a means to classify cloud-to-ground (CG) and intra-cloud (IC) strokes. Melbourne (KMLB) composite reflectivity radar imagery was also acquired to further evaluate USPLN performance. Several analyses of USPLN stroke detection efficiency (DE) and location accuracy were conducted to first determine average performance and then to examine specific case studies. Analyses of USPLN stroke DE revealed several strengths and weaknesses to the network. Simple weighted average analyses of each sub-period revealed that the USPLN failed to detect a significant portion of the CGLSS-II strokes. Logistic regression and plots of USPLN stroke DE versus CGLSS-II peak current (Ip) indicated that most of the missed detections were due to low current strokes, while the USPLN excelled at detecting high current strokes. A pseudo-flash DE analysis concluded that perhaps many of the undetected low current strokes were subsequent strokes in a lightning flash. Additionally, performance was found to be degraded when the USPLN sensor baseline was altered significantly by sensor outages. Temporally, the USPLN stroke DE improved with time until around 1 July 2010, after which performance decreased. Analyses of USPLN location accuracy showed a similar temporal performance improvement up to around 1 July 2010, with a significant decrease thereafter. The 95% confidence USPLN location accuracy metrics were on the order of 600 m during peak performance from February to June 2010. Similar decreases in performance were also discovered when USPLN sensor outages occurred, coinciding with the loss of DE. An analysis of directional variation between matching CGLSS-II and USPLN stroke locations revealed a prominent northeast and less prominent southwest bias for the USPLN over the study region. Four case study days were examined to determine potential causes for USPLN strokes that were not matched with CGLSS-II. Comparisons to 4DLSS data indicated that the majority of these unmatched strokes were truly IC strokes detected by the USPLN and falsely classified as CG strokes. Any phantom" strokes those which seemed to occur with little or no 4DLSS activity were examined with radar imagery. The radar analyses produced primarily inconclusive results. Additional case study analyses revealed that the USPLN rarely falsely categorizes a true CG stroke as IC or reports the incorrect polarity of a stroke. This study revealed several strengths and weaknesses for the USPLN. It is unclear exactly why low current strokes were frequently missed but hypotheses were introduced regarding sensor sensitivity and other factors. An investigation into the 1 July 2010 performance decrease uncovered a software change to the USPLN which likely was the cause for the performance decrease. Additionally an investigation into a poorly analyzed CGLSS-II event was also initiated during this study. Future work should include more performance evaluations of the USPLN with other local and national networks a review of the quality control methods the addition of new methodologies and perhaps additional CGLSS-II comparisons with other stroke-based networks.Electronic Thesis or Dissertatio
Seasonal movement and habitat use of eastern brook trout, Salvelinus fontinalis, in a mountain stream in northern NH
No full textWe conducted a study of brook trout, Salvelinus fontinalis, movement and habitat use in an unnamed tributary (hereafter referred to as Emerson Brook)in the Nash Stream watershed, Coos County, NH in 2010.We measured movement and dispersal in this small, fragmented mountain stream population of brook trout using Passive Integrated Transponder (PIT) tags to track movement from May to November. Emerson Brook is a small, mountain stream that flows into Nash stream on its western side that is divided into three discrete reaches by two waterfalls. We hypothesized that the geomorphology of these two waterfalls would prevent any upstream movement between reaches, therefore decreasing genetic mixing. Through instream monitoring of fish, we discovered that the upper waterfall was impassable to PIT-tagged brook trout and the lower waterfall was navigated by five PIT-tagged fish that moved upstream through the water fall in 2010. These findings were corroborated by genetic analysis based on microsatellite-based markers showing that little introgression occurred across these geomorphological features.
We collected data over the course of the 2010 season to address the following objectives: 1) to describe habitat and instream wood use by brook trout, 2) to quantify brook trout movement, and 3) to determine the relationship between fish movement and habitat use. Brook trout in Emerson Brook (on average 107 mm in total length) moved continually throughout the season with an average total movement of 98 m over the course of the season in 2010. Habitat use by brook trout in Emerson Brook was similar to that reported for other salmonids in that they were keying into pools and habitat with instream wood nearby. Juvenile brook trout preferred pool habitat (Pearson Chi-Square = 21.836, df = 2, p < 0.001) and habitat with wood jams close by (Pearson Chi-Square = 10.880 df = 3, p < 0.001). Adults also preferred pools with both logs and wood jams (compared to pools without wood) and riffles without wood (Pearson Chi-Square = 121.406, df = 4, p < 0.001). We found positive significant relationships between movement and fish total length (df = 134, R2= 0.148, p < 0.001) and movement of fish and age (df = 13, R2= 0.316, p = 0.036). Fish in the upper reaches of Emerson Brook moved significantly less than those by its confluence with Nash Stream (df = 112, R2= 0.037, p = 0.042). Also, brook trout in Emerson Brook moved more to access pool habitat than riffle habitat (Paired T-test, T = 4.22, p < 0.001). The findings of this study support that brook trout in small, mountain streams need habitat diversity and specifically prefer pool habitat and instream wood.
Mountain streams that have fragmented subpopulations, as seems likely for Emerson Brook, should be carefully considered in watershed-wide management. Tributaries like Emerson Brook provide cold water input into Nash Stream, constitute a refuge for mainstem brook trout when temperatures increase, and offer an opportunity for genetic mixing when mainstem brook trout enter the lower, easily accessible reaches of the tributary to spawn.Electronic Thesis or Dissertatio
A statistical analysis of icing prediction in complex terrains
Get PDFThe issue of icing has been around for decades in aviation industry, and while notable improvements have been made in the study of the formation and process of icing, the prediction of icing events is a challenge that has yet to be completely overcome. Low level icing prediction, particularly in complex terrain, has been bumped to the back burner in an attempt to perfect the models created for in-flight icing. However, over the years there have been a number of different, non-model methods used to better refine the variable involved in low-level icing prediction. One of those methods comes through statistical analysis and modeling, particularly through the use of the Classification and Regression Tree (CART) techniques. These techniques examine the statistical significance of each predictor within a data set to determine various decision rules. Those rules in which the overall misclassification error is the smallest are then used to construct a decision tree and can be used to create a forecast for icing events. Using adiabatically adjusted Rapid Update Cycle (RUC) interpolated sounding data these CART techniques are used in this study to examine icing events in the White Mountains of New Hampshire, specifically on the summit of Mount Washington. The Mount Washington Observatory (MWO), which sits on the summit and is manned year around by weather observers, is no stranger to icing occurrences. In fact, the summit sees icing events from October all the way until April, and occasionally even into May. In this study, these events are examined in detail for the October 2010 to April 2011 season, and five CART models generated for icing in general, rime icing, and glaze icing in attempt to create a decision tree or trees with a high predictive accuracy. Also examined in this study for the October 2010 to April 2011 icing season is the Air Weather Service Pamphlet(AWSP) algorithm, a decision tree model currently in use by the Air Force to predict icing events. Producing an icing forecast with this model requires the user to manually work through each branch. Previous work to this end was completed by Stanley et al. 2002, and the goal of this study is to further that work by automating the AWSP using the adiabatically adjusted RUC interpolated sounding data as a test set in an attempt to produce an effective automated forecast tool for icing events in complex terrain.Electronic Thesis or Dissertatio
Beginning to understand why New Hampshire's rural educators chose not to join New Hampshire's newly developed on-line professional learning communities: a quantitative and qualitative study
Get PDFRural educators face many barriers when trying to participate in high quality professional development, including isolation, funding issues, distance, and lack of temporary replacements. Technological solutions can assist rural educators in overcoming these barriers. Participating in on-line professional learning communities can provide New Hampshire's rural educators opportunities for professional development that might be otherwise unavailable to them. The purpose of this study is to begin to develop a framework for understanding why rural New Hampshire educators chose not to join newly developed on-line professional learning communities. This research can lead to further investigation regarding the choices New Hampshire's rural educators make with regard to participation in technologically mediated professional development opportunities.Electronic Thesis or Dissertatio
Estimating Fine-Scale Movement Patterns of Black Bear using GPS Telemetry
Get PDFAnimal behavior is guided by needs and constraints in response to surroundings
and is measured by the individual’s location. Our goal was to explore the use of fine-
scale movement data collected with GPS telemetry. The objectives were to evaluate low-
cost Global Positioning System (GPS) telemetry collars and programming required to
obtain fine-scale movement data of American black bear (Ursus americanus). The data
provided insight to landscape factors influencing access by black bears to human-related
food sources. Technology costs have been reduced substantially in the past few years,
making short-term deployments of GPS telemetry collars a cost effective method of
collecting fine-scale spatial and temporal data. The study was conducted during spring
and summer 2007-2009 with planned collar deployments at four weeks and GPS
collection at 10-minute intervals to distinguish bear activity within the community. A
Brownian Bridge Movement Model (Horne et al. 2007) was used to determine
probability of occurrence distributions based on GPS locations with measured error.
Fixed kernel density functions show spatial clustering and define areas of animal activity.
Brownian Bridge probability densities show spatial pattern of animal movement. The
Brownian Bridge probability surfaces were used to select values of co-occurring
landscape features and analyzed using a classification tree method to determine which
landscape features bears selected when moving through human communities. Fine-scale
movement data could also be used to relate continuous probability of occurrence to
landscape features, using the Marzluff et al. (2004) Resource Utilization Function
approach to remove spatial autocorrelation. Identifying fine-scale habitat selection or use
of travel corridors will require short GPS sampling intervals to determine which
landscape factors influence movement.Mary Ann McGarry, Major Advisor, Associate Professor of Science Education, Center
for the Environment and Department of Environmental Science and Policy
_____________________________________________________
Thomas R. Boucher, Associate Professor and Director, Statistical Consulting Center
Department of Mathematics, Plymouth State University
_____________________________________________________
Andrew Timmins, Bear Project Leader, New Hampshire Fish and Game Department
_____________________________________________________
Brian Eisenhauer, Interim Director of the Center for the Environment; , Graduate
Coordinator of the M.S. in Environmental Science and Policy, and Associate Professor of
Sociology, Plymouth State Universit
Validation and development of existing and new RAOB-based warm-season convective wind forecasting tools for Cape Canaveral Air Force Station and Kennedy Space Center
Get PDFUsing a 15-year (1995 to 2009) climatology of 1500 UTC warm-season (May through September) rawinsonde observation (RAOB) data from the Cape Canaveral Air Force Station (CCAFS) Skid Strip (KXMR) and 5 minute wind data from 36 wind towers on CCAFS and Kennedy Space Center (KSC), several convective wind forecasting techniques currently employed by the 45th Weather Squadron (45 WS) were evaluated. Present forecasting methods under evaluation include examining the vertical equivalent potential temperature (θe) profile, vertical profiles of wind spend and direction, and several wet downburst forecasting indices. Although previous research found that currently used wet downburst forecasting methods showed little promise for forecasting convective winds, it was carried out with a very small sample, limiting the reliability of the results. Evaluation versus a larger 15-year dataset was performed to truly assess the forecasting utility of these methods in the central Florida warm-season convective environment. In addition, several new predictive analytic based forecast methods for predicting the occurrence of warm-season convection and its associated wind gusts were developed and validated. This research was performed in order to help the 45 WS better forecast not only which days are more likely to produce convective wind gusts, but also to better predict which days are more likely to yield warning criteria wind events of 35 knots or greater, should convection be forecasted. Convective wind forecasting is a very challenging problem that requires new statistically based modeling techniques since conventional meteorologically based methods do not perform well. New predictive analytic based forecasting methods were constructed using R statistical software and incorporate several techniques including multiple linear regression, logistic regression, multinomial logistic regression, classification and regression trees (CART), and ensemble CART using bootstrapping. All of these techniques except the ensemble CART methods were built with data from the 1995 to 2007 warm-seasons and validated with a separate independent dataset from the 2008 and 2009 warm-seasons. Ensemble CART models were built using randomly selected data from the 1995 to 2009 RAOB dataset and validated with data not used in constructing the models. Three different ensemble CART algorithms including the random forests, bagging, and boosting algorithms were tested to find the best performing model. Quantitative verification results suggest that the presently used convection and wet downburst forecasting techniques do not show much operational promise. As such, it is not recommended that the 45 WS use vertical profiles of θe, wind speed, or wind direction to make specific predictions for which days are likely to produce convection or warning threshold wind gusts. None of the wet downburst indices used displayed much potential either. Although, the linear regression based predictive analytic models do not perform too well, CART based models perform better, especially those that utilize a binary response variable. Of the new techniques, the ensemble CART models displayed the most promise with the boosting algorithm showing nearly perfect results for predicting which days would produce convection and which days would produce warning threshold winds should convection be predicted.Electronic Thesis or Dissertatio
Prioritizing conservation efforts in the Squam Lakes watershed using knowledge-based models
Get PDFAs an increasing number of Americans are leaving urban and suburban areas to live in rural and exurban regions, agricultural and forested land continues to be converted for residential development. This conversion pattern is most prominent in rural regions with attractive natural resource amenities such as aesthetic value and recreational opportunities. One method of preserving rural landscapes has been through the acquisition of conservation easements held by local, regional, or national land trusts. However, no precise formula exists for committing private land to a conservation easement. This project created logic-based models using conservation criteria established by a land trust, the Squam Lakes Conservation Society (SLCS) in central New Hampshire, to address the need for a systematic, data-driven approach to prioritizing conservation easements. The model prioritized areas within the Squam Lakes watershed and evaluated existing conservation easements based on the SLCS criteria. The model was created so that it can adapt as the needs and values of the community that operates through SLCS can change through time.Electronic Thesis or Dissertatio
Encouraging environmentally responsible lawn care behavior in New England: utilizing social science to develop successful outreach and education
Get PDFNutrient losses from common lawn care practices have been identified as significant contributors to nonpoint source (NPS) pollution in New England's watersheds. Lawn care practices that potentially contribute to NPS pollution have been a target for university Extension programs for some time; however little research exists that explores either the social dynamics involved or the means of achieving behavioral changes with lawn care practices. Following the principles of Community Based Social Marketing (CBSM), this thesis first conducted an initial study to examine New England lawn care behavior and the results from this study were used to guide the development of region specific outreach and education. This thesis then used the empirical results of the initial study to develop and implement a successful educational outreach campaign that was then implemented in the Bangor area of Maine collaborating with the University of Maine Cooperative Extension. After the implementation of the campaign this thesis conducted an evaluation study to test the effectiveness of the campaign at encouraging desired behavioral change and also to test the effectiveness of using normatively framed campaign messages. To ensure that the results from the initial study were theoretically sound and useful for Extension staff, the theory of planned behavior (TPB), a proven social/psychological theoretical framework, was employed. The TPB helped to structure the initial study, aid with analysis, and produce more meaningful results. The thesis produced meaningful empirical data that were analyzed and the results used to develop outreach and education that changed behavior thereby helping to protect regional water quality.Electronic Thesis or Dissertatio
Classification of mesoscale snow banding events in the northeast United States
Get PDFWintertime precipitation has a large impact on the Northeastern United States each year. Within any given storm, there is potential for banded snow events, some of which can produce heavy amounts of snowfall. Previous studies have shown that heavy banded snow events require strong values of frontogenesis, weak moist symmetric stability, and moisture to form within any given storm. This study seeks to find answers to two questions. The first is whether it is possible to predict mesoscale snow banding events based on independent variables. The second question is whether these variables can be combined into a forecasting tool to predict the occurrence of snow banding through statistical correlation. For this study, storms were first identified using GOES satellite archive data, obtained from the NCDC GIBBS archive (http://www.ncdc.noaa.gov/gibbs). Storms seen on satellite imagery were gathered into a database. Using WSR-88D radar data from the NCDC, banding events were identified in the storm database with the storms containing changes in 24 hour snow depth of 8 inches or greater. Using classification systems developed by Novak et al.(2004), the type of banding was identified within storms. A gridded area was created over the Northeastern United States with 20 km spacing in between grid points. The grid was rotated 45 degrees to better align with the axis of the Appalachian Mountains. Topography for the region of study was classified based on elevation using the Integrated Data Viewer (IDV) data source of the National Geophysical Data Center (NGDC) ETOPO1 dataset. Heights were interpolated to the grid points developed in the 20 km resolution grid. Basic classification schemes involving elevation were employed to correlate the existence of a banding event with the topographical classification. Model data from the North American Regional Reanalysis (NARR-A) was obtained and used in General Meteorological Package (GEMPAK), with data interpolated to the 20 km resolution grid. Results from synoptic composites based on banding type observed in storm events will be presented, along with predictability of banding by variables such as topographic slope, frontogenesis, cross-shore potential temperature gradient, and the existence of a coastal front in the gridded domain. To statistically analyze the predictor set determined from the synoptic composites, a best subsets analysis was generated to determine the best variables to use in a regression equation. All three variables (topographic slope, frontogenesis, and cross-shore potential temperature gradient) were used in the regression equations for each banding type. Results of the best subsets analyses were used to determine the best variables to use in the linear regression analyses. These results are shown in Chapter 5. Linear regression analysis was also completed for all banding types following the best subsets analysis. The results of this test showed that the generated equation could not predict the occurrence of a banding event. In order to improve on this, other techniques should be used as means for analysis of banding. This indicates the need for inclusion of additional predictors, due to the influence of uncontrolled variables in the analysis. These results and future suggestions are discussed in Chapter 5.Electronic Thesis or Dissertatio
Pharmaceuticals and personal care products in the environment
Get PDFPharmaceuticals and personal care products (PPCPs) are a class of emerging contaminants that include, but are not limited to, prescription and non-prescription drugs, perfumes, detergents and soaps, body lotions and sun block. PPCPs reach the environment primarily through two routes, the release of treated waste via wastewater treatment plants' effluent stream and through agricultural run-off. Since the 1980s, PPCPs have been recognized as having the potential to cause adverse effects in the environment and are identified by the US EPA as potentially hazardous compounds, even at low parts-per-billion or parts-per trillion concentrations. Among other effects, studies have linked PPCPs to antibiotic resistance in bacteria and viruses and to the feminization of certain fish species. Unfortunately, there is a significant limit in the peer reviewed literature on both the occurrence of these compounds and their effects in the environment.
One reason for this information gap is the lack of analytical equipment/protocols with sensitivities low enough to detect these compounds at their environmental concentrations. The purpose of this study was to establish a reliable method to detect four PPCPs in aquatic samples within the state of New Hampshire, and to pilot test the method on environmental samples from rivers, lakes and private septic systems in Central New Hampshire.
The method this study adapted was originally established by the US Geological Survey's (USGS) National Water Quality Lab in Denver, CO. It uses solid phase extraction (SPE) and high performance liquid chromatography coupled with mass spectroscopy (HPLC/MS) to identify and quantitatively measure 14 different PPCPs. Generally, SPE separates compound targets from the sample of water, while HPLC separates the target compounds from each other and MS produces a signal from each compound that is proportional to its concentration. This study adapted the USGS method to use methanol instead of acetonitrile as the HPLC mobile phase and limited the detection to four PPCPs, each from different therapeutic drug classes: acetaminophen (a common analgesic), caffeine (a stimulant), carbamazepine (an anti-epileptic, mood stabilizer) and trimethoprim (an antibiotic).
As a result of these adaptations, many instrumental parameters were optimized for instrument sensitivity. The adapted method has interim reporting levels ranging between 8 and 100 ng/L for the targeted compounds while the USGS reports detection limits of 25 ng/L to 40 ng/L for these compounds. The adapted method demonstrates fair accuracy; mean percent recovery of compounds in reagent-free water was within 20% of the true value, while fortified environmental samples had mean percent recoveries within 35% of the true value. A standard operating procedure for this method was written and is on-file with the NH Department of Environmental Services.
A pilot study used this adapted method to document the occurrence of these four compounds in water resources in Central New Hampshire, US. A total of 16 samples were collected: 6 lake samples were collected along with 7 river samples, 2 samples from wastewater treatment plants' wastewater effluent stream and 1 from the distribution box of a private septic system. None of the compounds were detected in any of the lake samples. One river sample had 79 ng/L caffeine. One wastewater treatment plant was found to have acetaminophen (720 ng/L), caffeine (1200 ng/L) and carbamazepine (280 ng/L) while the other was found to have only carbamazepine (330 ng/L). The private septic system's distribution box was found to have >2,000ng/L of both acetaminophen and caffeine. The most commonly occurring PPCP was caffeine (three occurrences: 19%), followed by carbamazepine and acetaminophen (two occurrences each: 12.5%), trimethoprim was not detected in any of the 16 samples collected