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dc.contributor.authorKral, Stephan
dc.date.accessioned2020-11-10T13:47:36Z
dc.date.available2020-11-10T13:47:36Z
dc.date.issued2020-11-20
dc.date.submitted2020-11-02T13:14:19.702Z
dc.identifiercontainer/bd/7d/1e/7d/bd7d1e7d-199c-4337-a4d1-fc81b67594c6
dc.identifier.isbn9788230866238
dc.identifier.isbn9788230863077
dc.identifier.urihttps://hdl.handle.net/1956/24469
dc.description.abstractIn this thesis, consisting of five scientific papers, I investigate the potential of unmanned aircraft systems (UAS) in stable boundary layer (SBL) research, by developing and applying a new innovative observation strategy. In this strategy we supplement ground-based micrometeorological observations from masts and remote-sensing systems with a number of different UAS. To achieve good agreement between the different systems employed in this approach, I further investigate the quality and intercomparability of UAS-based observations of atmospheric temperature, humidity, pressure and wind, and develop and apply common, best-practice data processing methods. In Paper I we give a brief introduction to the ISOBAR project and provide an overview over the first SBL campaign at Hailuoto and the prevailing synoptic, sea-ice and micrometeorological conditions. We demonstrate the quality of our measurement approach by combining UAS profile data with observations from the wind and temperature sensing systems. Repeated UAS temperature profiles give detailed insight into the temporal evolution of the SBL, which we find was often subject to rapid temperature changes affecting the entire depth of the SBL. We further highlight the potential of the sampled data by detailed investigations of a case study, featuring rapid shifts in turbulent regimes and strong elevated thermal instabilities, which were likely to result from the instability of an elevated internal gravity wave. In Paper II we assess the quality and intercomparability of UAS-based atmospheric observations from the most extensive intercomparison experiment to date. We evaluate the precision and bias of temperature, humidity, pressure, wind speed and direction observations from 38 individual UAS with 23 unique sensor configurations based on observations next to a 18-m mast equipped with reference instruments. In addition, we investigate the influence of sensor response on the quality of temperature and humidity profiles. By grouping the different sensor–platform combinations with respect to the type of aircraft, sensor type and sensor integration (i.e., measures for aspiration and radiation shielding), we attempt to draw general conclusions from the intercomparison results. Overall, we find most observation systems in good agreement with the reference observations, however, some systems showed fairly large biases. In general, hovering multicopters showed less variability than fixed-wing systems and we attribute this finding to the difference in sampling strategies. The most consistent observations of the mean wind were achieved by multicopter-mounted sonic anemometers. Sensor response errors were smaller for fine-bed thermistors compared to temperature sensors of integrated-circuit type, and sensor aspiration proofed to be substantially relevant. We conclude, that sensor integration considerations, like radiation shielding and aspiration, are likely to be as important as the choice of the sensor type, and give a couple of recommendations for future perspectives on UAS-based atmospheric measurements. Paper III presents the ISOBAR project to a broader scientific audience, including a description of the two measurement campaigns, ISOBAR17 and ISOBAR18 and the contrasting meteorological and sea ice conditions. We further provide an overview on the micrometeorological conditions during the 13 intensive observational periods (IOPs), which resulted in detailed data sets on the SBL in unprecedented spatiotemporal resolution. Numerous cases with very-stable stratification under clear-sky and weak-wind conditions were observed, featuring a variety of different SBL processes. These processes resulted in rapid changes in the SBL’s vertical structure. Based on selected in-depth case studies, we investigate the interactions of turbulence in the very stable boundary layer (VSBL) with different processes, i.e., a shear instability, associated with a low-level jet; a rapid and strong cooling event, observed a couple of meters above the ground; and a wave-breaking event, caused by the enhancement of wind shear. In a first qualitative model validation experiment we use data from one IOP to assess the performance of three different types of numerical models. Only the turbulence resolving large-eddy simulation model is found capable of reproducing a VSBL structure similar to the one observed during the IOP. The other models, i.e., an operational weather prediction and a single-column model, substantially overestimated the depth of the SBL. Paper IV introduces a new fixed-wing UAS for turbulence observations and first results from validation experiments carried out during ISOBAR18. Airborne observations of mechanical turbulence from straight horizontal flight paths are compared to corresponding eddy-covariance measurements mounted on a 10-m mast during weakly stable conditions with moderate wind speeds. Different average and spectral turbulence quantities, as well as mean wind speed and direction were computed for both systems and compared to each other. The UAS observations of mean wind and turbulence are in good agreement with the reference observations and the turbulence spectra agree qualitatively in the onset of the inertial subrange and the turbulence production range. Minor differences are likely to be caused by a slightly elevated UAS flight level and additional small altitude variations in the presence of relatively strong vertical gradients. In a second comparison, vertical profiles of mean wind and turbulence variables, determined from straight horizontal UAS flights at several different levels are compared qualitatively to profile observations from the 10-m mast and a phased-array sodar system providing 10-min averaged wind and vertical velocity variance profiles above 35 m. Qualitatively, the results agree well for the first two out of three profiles. During the third profile, the UAS data indicate the existence of a low-level jet but not an upside-down boundary layer structure, which would be expected due to the elevated source of turbulence. This observation is, however, not supported by the other measurement systems. Instead, the sodar data indicate a strong decrease in wind speed during the time of this profile. The fact that the lower part of the UAS profile was sampled before the start of the strongest transition, resulted in a seemingly wrong shape of the vertical profiles. This finding highlights the relevance of non-stationarity and the importance of additional reference systems for the correct interpretation of UAS sampled turbulence profiles. Paper V explores the potential of a new method to estimate profiles of turbulence variables in the SBL. In this method we apply a gradient-based scaling scheme for SBL turbulence to multicopter profiles of temperature and wind, sampled during ISOBAR18. We first validate this method by scaling turbulence observations from three levels on a 10-m mast with the corresponding scaling parameters, and comparing the resulting non- dimensional parameters to the semi-empirical stability functions proposed for this scheme. The scaled data from the three levels largely collapse to the predicted curves, however, minor differences between the three levels are evident. We attribute this discrepancy to the non-ideal observation heights for the determination of vertical gradients at the upper turbulence observation level. After the successful validation we apply this method to UAS profiles, by computing profiles of the gradient Richardson number to which we then apply the stability functions to derive turbulence variables. We demonstrate this approach based on three case studies covering a broad range of SBL conditions and boundary layer heights. Since the application of this scaling scheme is only valid within the SBL, we estimate the boundary layer height from the sodar and two different methods based on UAS data. Comparisons at the lowest levels against turbulence variables from the 10-m mast and at higher levels against a Doppler wind lidar, which also provides estimates of some turbulence variables, indicate broad agreement and physical meaningful results of this method. Supplementing the findings from the five scientific papers, this thesis also provides the detailed description on the methodology and data processing procedures, I applied for the synthesis of observations from UAS, micrometeorological masts and boundary layer remote-sensing systems. Furthermore, I present results on the validation of the different wind observation methods, using lidar wind observations as the common reference. Finally, I provide an outlook on future perspectives of SBL and UAS-based boundary-layer research, and how further developments in SBL observation strategies may benefit from recent and future developments.eng
dc.language.isoengeng
dc.publisherThe University of Bergeneng
dc.relation.haspartPaper I: Kral, S. T., J. Reuder, T. Vihma, I. Suomi, E. O’Connor, R. Kouznetsov, B. Wrenger, A. Rautenberg, G. Urbancic, M. O. Jonassen, L. Båserud, B. Maronga, S. Mayer, T. Lorenz, A. A. M. Holtslag, G.-J. Steeneveld, A. Seidl, M. Müller, C. Lindenberg, C. Langohr, H. Voss, J. Bange, M. Hundhausen, P. Hilsheimer and M. Schygulla, 2018: Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR) —The Hailuoto 2017 Campaign. Atmosphere, 9 (7), 268. The article is available at: <a href="http://hdl.handle.net/1956/20755" target="blank">http://hdl.handle.net/1956/20755</a>eng
dc.relation.haspartPaper II: Barbieri, L., S. T. Kral, S. C. C. Bailey, A. E. Frazier, J. D. Jacob, J. Reuder, D. Brus, P. B. Chilson, C. Crick, C. Detweiler, A. Doddi, J. Elston, H. Foroutan, J. González-Rocha, B. R. Greene, M. I. Guzman, A. L. Houston, A. Islam, O. Kemppinen, D. Lawrence, E. A. Pillar-Little, S. D. Ross, M. P. Sama, D. G. Schmale, T. J. Schuyler, A. Shankar, S.W. Smith, S.Waugh, C. Dixon, S. Borenstein and G. de Boer, 2019: Intercomparison of Small Unmanned Aircraft System (sUAS) Measurements for Atmospheric Science during the LAPSE-RATE Campaign. Sensors, 19 (9), 2179. The article is available at: <a href="http://hdl.handle.net/1956/23725" target="blank">http://hdl.handle.net/1956/23725</a>eng
dc.relation.haspartPaper III: Kral, S. T., J. Reuder, T. Vihma, I. Suomi, K. Flacké Haualand, G. H. Urbancic, B. R. Greene, G.-J. Steeneveld, T. Lorenz, B. Maronga, M. O. Jonassen, H. Ajosenpää, L. Båserud, P. B. Chilson, A. A. M. Holtslag, A. D. Jenkins, R. Kouznetsov, S. Mayer, E. A. Pillar-Little, A. Rautenberg, J. Schwenkel, A. Seidl and B. Wrenger, 2020: The Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer Project (ISOBAR)—Unique fine-scale observations under stable and very stable conditions. Bull. Amer. Meteor. Soc. The article is available in the thesis file. The article is also available at: <a href="https://doi.org/10.1175/BAMS-D-19-0212.1" target="blank">https://doi.org/10.1175/BAMS-D-19-0212.1</a>eng
dc.relation.haspartPaper IV: Rautenberg, A., M. Schön, K. zum Berge, M. Mauz, P. Manz, A. Platis, B. van Kesteren, I. Suomi, S. T. Kral, and J. Bange, 2019: The Multi-Purpose Airborne Sensor Carrier MASC-3 for Wind and Turbulence Measurements in the Atmospheric Boundary Layer. Sensors, 19 (10), 2292. The article is available at: <a href="http://hdl.handle.net/1956/22163" target="blank">http://hdl.handle.net/1956/22163</a>eng
dc.relation.haspartPaper V: Greene, B. R., S. T. Kral, P. B. Chilson and J. Reuder, 2020: Gradient-based turbulence estimates from multicopter profiles of the stable boundary layer during ISOBAR18. In preparation for Bound.-Layer Meteor. The article is not available in BORA.eng
dc.rightsAttribution (CC BY)eng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/eng
dc.titleInnovative Strategies for Observations in the Arctic Atmospheric Boundary Layereng
dc.typeDoctoral thesiseng
dc.date.updated2020-11-02T13:14:19.702Z
dc.rights.holderCopyright the Author.eng
dc.contributor.orcid0000-0002-7966-8585
dc.description.degreeDoktorgradsavhandling
fs.unitcode12-44-0


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