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| Titel |
Using a balloon-borne accelerometer to improve understanding of the turbulent structure of the atmosphere for aviation. |
| VerfasserIn |
Graeme Marlton, Giles Harrison, Keri Nicoll, Paul Williams |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250143168
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| Publikation (Nr.) |
 EGU/EGU2017-6869.pdf |
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| Zusammenfassung |
| This work describes the instrument development, characterisation and data analysis from
51 radiosondes specially equipped with accelerometers to measure atmospheric turbulence.
Turbulence is hazardous to aircraft as it cannot be observed in advance. It is estimated that
turbulence costs the airline industry millions of US dollars a year through damage to aircraft
and injuries to passengers and crew. To avoid turbulence pilots and passengers rely on Clear
Air Turbulence forecasts, which have limited skill. One limitation in this area is lack of
quantitative unbiased observations. The main source of turbulence observations is from
commercial airline pilot reports, which are subjective, biased by the size of aircraft and pilot
experience.
This work seeks to improve understanding of turbulence through a standardised method
of turbulence observations amenable throughout the troposphere. A sensing package has been
developed to measure the acceleration of the radiosonde as it swings in response to turbulent
agitation of its carrier balloon. The accelerometer radiosonde has been compared against
multiple turbulence remote sensing methods to characterise its measurements including
calibration with Doppler lidar eddy dissipation rate in the boundary layer. A further
relationship has been found by comparison with the spectral width of a Mesospheric,
Stratospheric and Tropospheric (MST) radar. From the full dataset of accelerometer sonde
ascents a standard deviation of 5 m s−2 is defined as a threshold for significant turbulence.
The dataset spans turbulence generated in meteorological phenomena such as jet
streams, clouds and in the presence of convection. The analysis revealed that 77% of
observed turbulence could be explained by the aforementioned phenomena. In jet
streams, turbulence generation was often caused by horizontal processes such as
deformation. In convection, turbulence is found to form when CAPE >150 J kg−1.
Deeper clouds were found to be more turbulent due to the increased intensity of
in-cloud processes. The accelerometer data were used to verify the skill of turbulence
diagnostics, in order to assess which diagnostics are best at forecasting turbulence. It was
found using a Receiver Operating Characteristics curve analysis that turbulence
diagnostics calculated using ECMWF high resolution data that featured wind speed,
deformation and relative vorticity advection predicted turbulence best with area under
curve values of 0.7,0.66 and 0.62 respectively. This work provides a new, safe and
inexpensive method to retrieve in-situ information about the turbulent structure of
the atmosphere. It can inform the aviation industry through identifying turbulence
generation regions and assess which predictive diagnostics are the most skilful. |
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