A Non-singular Horizontal Position Representation

Gade, Kenneth

doi:10.1017/s0373463309990415

Cited by 128 publications

(55 citation statements)

References 12 publications

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“…After determining the ACNFS ice edge, the next step is to identify the ACNFS model grid point that contains the NIC Ice Edge product location. The closest ACNFS ice edge point to the NIC Ice Edge product location is found, and the distance between the two points is then computed using via the Haversine formula [ Gade , , equation (19)]

D_{e} = 2 r normalarcsin (|, \sqrt{\sin^{2} (\frac{Φ_{1} - Φ_{0}}{2}) + \cos (Φ_{0}) \cos (Φ_{1}) \sin^{2} (\frac{Θ_{1} - Θ_{0}}{2})})

…”

Section: Forecast Assessmentsmentioning

confidence: 97%

Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

et al. 2015

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In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1–7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short‐term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

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D_{e} = 2 r normalarcsin (|, \sqrt{\sin^{2} (\frac{Φ_{1} - Φ_{0}}{2}) + \cos (Φ_{0}) \cos (Φ_{1}) \sin^{2} (\frac{Θ_{1} - Θ_{0}}{2})})

…”

Section: Forecast Assessmentsmentioning

confidence: 97%

Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

et al. 2015

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“…Due to the small extension of a typical vineyard with respect to the Earth radius, the great circle distance calculation can be neglected and thus the distance d i between the two ending points A i and B i of each row can be calculated as the Euclidean distance on an equirectangular projection (Gade, 2010;Snyder, 1997).…”

Section: Identification Of Individual Plantsmentioning

confidence: 99%

Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

Primicerio

Caruso

Comba

et al. 2017

European Journal of Remote Sensing

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In the last few years, high-resolution imaging of vineyards, obtained by unmanned aerial vehicle recognitions, has provided new opportunities to obtain valuable information for precision farming applications. While available semi-automatic image processing algorithms are now able to detect parcels and extract vine rows from aerial images, the identification of single plant inside the rows is a problem still unaddressed. This study presents a new methodology for the segmentation of vine rows in virtual shapes, each representing a real plant. From the virtual shapes, an extensive set of features is discussed, extracted and coupled to a statistical classifier, to evaluate its performance in missing plant detection within a vineyard parcel. Passing from continuous images to a discrete set of individual plants results in a crucial simplification of the statistical investigation of the problem. ARTICLE HISTORY

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“…The first error component ̄d GPS is the familiar geospatial position bias from GPS trilateration that the literature has long established to have zero mean and normal distribution (Gade 2010). However, the literature seldom identifies or reports on the second error component ̄d lag .…”

Section: Gps-related Errorsmentioning

confidence: 99%

Accuracy enhancement of roadway anomaly localization using connected vehicles

Bridgelall

Tolliver

2016

International Journal of Pavement Engineering

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The timely identification and localisation of roadway anomalies that pose hazards to the traveling public is currently a critical but very expensive task. Hence, transportation agencies are evaluating emerging alternatives that use connected vehicles to lower the cost dramatically and to increase simultaneously both the monitoring frequency and the network coverage. Connected vehicle methods use conventional GPS receivers to tag the inertial data stream with geospatial position estimates. In addition to the anticipated GPS trilateration errors, numerous other factors reduce the accuracy of anomaly localisation. However, practitioners currently lack information about their characteristics and significance. This study developed error models to characterise the factors in position biases so that practitioners can estimate and remove them. The field studies revealed the typical and relative contributions of each factor, and validated the models by demonstrating agreement of their statistics with the anticipated norms. The results revealed a surprising potential for tagging errors from embedded systems latencies to exceed the typical GPS errors and become dominant at highway speeds.

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A Non-singular Horizontal Position Representation

Cited by 128 publications

References 12 publications

Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

Accuracy enhancement of roadway anomaly localization using connected vehicles

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