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| Titel |
Reconstruction of high-resolution time series from slow-response atmospheric measurements by deconvolution |
| VerfasserIn |
André Ehrlich, Manfred Wendisch |
| 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 |
250148115
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| Publikation (Nr.) |
 EGU/EGU2017-12344.pdf |
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| Zusammenfassung |
| Measurements of high temporal resolution are often needed to study the spatial or
temporal variation of atmospheric parameters. An efficient method to enhance the
temporal resolution of slow-response measurements is introduced. It is based on
the deconvolution theorem of Fourier transform to restore amplitude and phase
shift of high frequent fluctuations. It is shown that the quality of reconstruction
depends on the instrument noise, the sensor response time and the frequency of
the oscillations. The method is demonstrated by application to measurements of
broadband terrestrial irradiance using pyrgeometer and temperature and humidity
measurements by drop sondes. Using a CGR-4 pyrgeometer with response time
of 3 s, the method is tested in laboratory measurements for synthetic time series
including a boxcar function and periodic oscillations. The originally slow-response
pyrgeometer data were reconstructed to higher resolution and compared to the predefined
synthetic time series. The reconstruction of the time series worked up to oscillations of
0.5 Hz frequency and 2 W m−2 amplitude if the sampling frequency of the data
acquisition is 16 kHz or higher. For oscillations faster than 2 Hz, the instrument
noise exceeded the reduced amplitude of the oscillations in the measurements and
the reconstruction failed. The method was applied to airborne measurements of
upward terrestrial irradiance and drop sonde profiles from the VERDI (Vertical
Distribution of Ice in Arctic Clouds) field campaign. Pyrgeometer data above open
leads in sea ice and a broken cloud field were reconstructed and compared to KT19
infrared thermometer data. The reconstruction of amplitude and phase shift of the
deconvoluted data improved the agreement with the KT19 data and removed biases for the
maximum and minimum values. By application to temperature and humidity profiles
measured by drop sonde profiles, the resolution of the cloud top inversion cloud be
improved. |
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