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- Author or Editor: Robin L. Vaughan x
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Abstract
Aircraft distance-measuring-equipment (DME) data are used to update position, velocity, and wind measurements from inertial navigation systems (INS) measurements. Data from conventional single-channel DME sets, suitably calibrated, are shown to be adequate to resolve the Schuler oscillation and correct INS positions to better than 1-km accuracy. The satellite-based NAVSTAR global position system (GPS) is rapidly superseding other systems for external position reference. However, DME is reliable and very accurate and has been recorded on many research datasets. The principal limitation of the DME is that it is restricted to land-based navigation. The regression technique used does not necessitate multiple DME receivers or station switching and involves few restrictions on the collection of the data. However, the results improve when more than one station is used. Comparisons with other navigation systems (interferometer and loran) demonstrate the method's skill in resolving INS errors. Intercomparisons among several research aircraft flying in close formation support the method's usefulness in correcting biases in INS data.
Abstract
Aircraft distance-measuring-equipment (DME) data are used to update position, velocity, and wind measurements from inertial navigation systems (INS) measurements. Data from conventional single-channel DME sets, suitably calibrated, are shown to be adequate to resolve the Schuler oscillation and correct INS positions to better than 1-km accuracy. The satellite-based NAVSTAR global position system (GPS) is rapidly superseding other systems for external position reference. However, DME is reliable and very accurate and has been recorded on many research datasets. The principal limitation of the DME is that it is restricted to land-based navigation. The regression technique used does not necessitate multiple DME receivers or station switching and involves few restrictions on the collection of the data. However, the results improve when more than one station is used. Comparisons with other navigation systems (interferometer and loran) demonstrate the method's skill in resolving INS errors. Intercomparisons among several research aircraft flying in close formation support the method's usefulness in correcting biases in INS data.
Abstract
During the 1981 summer season within a 70 000 km2 area surrounding Miles City, Montana, researchers from approximately twenty institutions participated in the Cooperative Convective Precipitation Experiment (CCOPE). The measurements collected during this project comprise one of the most comprehensive datasets ever acquired in and around individual convective storms on the high plains of North America. Principal data systems utilized during CCOPE included 8 ground-based radar (7 of which had Doppler capability), 12 instrumented research aircraft, and a network of 123 surface stations.
A major data processing goal has been to combine these independently acquired mesoscale measurements into a numerical description of observed atmospheric conditions at any point in time. Using the CCOPE data archive as an example, this paper describes the procedures used to reduce these high resolution observations to a common spatial and temporal framework. The final product is a digital description of the environment similar to that employed by most modelers—a three-dimensional Cartesian coordinate system containing fields that represent the instantaneous state of the atmosphere at discrete times across the period of interest. A software package designed to facilitate the construction and analysis of these composite data structures will also be discussed.
Abstract
During the 1981 summer season within a 70 000 km2 area surrounding Miles City, Montana, researchers from approximately twenty institutions participated in the Cooperative Convective Precipitation Experiment (CCOPE). The measurements collected during this project comprise one of the most comprehensive datasets ever acquired in and around individual convective storms on the high plains of North America. Principal data systems utilized during CCOPE included 8 ground-based radar (7 of which had Doppler capability), 12 instrumented research aircraft, and a network of 123 surface stations.
A major data processing goal has been to combine these independently acquired mesoscale measurements into a numerical description of observed atmospheric conditions at any point in time. Using the CCOPE data archive as an example, this paper describes the procedures used to reduce these high resolution observations to a common spatial and temporal framework. The final product is a digital description of the environment similar to that employed by most modelers—a three-dimensional Cartesian coordinate system containing fields that represent the instantaneous state of the atmosphere at discrete times across the period of interest. A software package designed to facilitate the construction and analysis of these composite data structures will also be discussed.
