Abstract. Water vapour is a critical component of the Earth system. Techniques to acquire and improve measurements of atmospheric water vapour and its isotopes are under active development. This work presents a detailed intercomparison of water vapour total column measurements taken between 2006 and 2014 at a Canadian High Arctic research site (Eureka, Nunavut). Instruments include radiosondes, sun photometers, a microwave radiometer, and emission and solar absorption Fourier transform infrared (FTIR) spectrometers. Close agreement is observed between all combination of datasets, with mean differences ≤ 1.0 kg m −2 and correlation coefficients ≥ 0.98. The one exception in the observed high correlation is the comparison between the microwave radiometer and a radiosonde product, which had a correlation coefficient of 0.92.A variety of biases affecting Eureka instruments are revealed and discussed. A subset of Eureka radiosonde measurements was processed by the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) for this study. Comparisons reveal a small dry bias in the standard radiosonde measurement water vapour total columns of approximately 4 %. A recently produced solar absorption FTIR spectrometer dataset resulting from the MU-SICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) retrieval technique is shown to offer accurate measurements of water vapour total columns (e.g. average agreement within −5.2 % of GRUAN and −6.5 % of a co-located emission FTIR spectrometer). However, comparisons show a small wet bias of approximately 6 % at the high-latitude Eureka site. In addition, a new dataset derived from Atmospheric Emitted Radiance Interferometer (AERI) measurements is shown to provide accurate water vapour measurements (e.g. average agreement was within 4 % of GRUAN), which usefully enables measurements to be taken during day and night (especially valuable during polar night).

No abstract

First and foremost, I would like to thank my parents, Angelina and Alessandro Spassiani, for all their love and support over the years, especially during my academic years abroad.Without them none of this would have been possible. I would also like to thank my siblings and their partners for always being there, Gian-Paolo and Elise Spassiani, Dr. Natasha Spassiani and Richard Pasquire, Lucas Spassiani and Alicia Marchini. Even though you were far away your support has meant a lot to me. I would have not gotten to the submission line without the help and support from the people I have met during my time at the University of Queensland (UQ). Especially without the help of my advisors, Dr. Matthew Mason and Dr. Richard Krupar III. Thank you both for your guidance and feedback throughout these four years. Thank you to the Wind Engineering Research group, Thomas Klöetzke, Ting Yang, Junwei Lyu, Razib Hussan and NipunMudiyanselage, for sitting and listening to me speak for countless hours about everything I did not understand. Thanks to all the friends I made during my time at UQ:

Abstract. Water vapour is a critical component of the Earth system. Techniques to acquire and improve measurements of atmospheric water vapour and its isotopes are under active development. This work presents a detailed intercomparison of water vapour total column measurements taken between 2006 and 2014 at a Canadian high Arctic research site. Instruments include radiosondes, sun photometers, a microwave radiometer, and emission and solar absorption Fourier transform spectrometers (FTSs). Good agreement is observed between all combination of datasets, with correlation coefficients ≥ 0.90 showing high correlations. A variety of biases and calibration issues are revealed and discussed for all instruments. A new FTS dataset, resulting from the MUSICA (Multi-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) retrieval technique, is shown to offer accurate measurements of water vapour total columns; however, measurements show a small wet bias of approximately 6 %. A new dataset derived from Atmospheric Emitted Radiance Interferometer (AERI) measurements is also shown to provide accurate water vapour measurements, which usefully enables measurements to be taken during day and night (especially valuable during Polar Night). In addition, limited profile comparisons are conducted using radiosonde and ground-based FTS measurements. Results show MUSICA FTS profiles were within 15 % of radiosonde measurements throughout the troposphere.

To quantify the hazard or risks associated with severe convective wind gusts, it is necessary to have a reliable and spatially complete climatology of these events. The coupling of observational and global reanalysis (ERA-Interim) data over the period 2005–2015 is used here to facilitate the development of a spatially complete convective wind gust climatology for Australia. This is done through the development of Bayesian Hierarchical models that use both weather station-based wind gust observations and seasonally averaged severe weather indices (SWI), calculated using reanalysis data, to estimate seasonal gust frequencies across the country while correcting for observational biases specifically, the sparse observational network to record events. Different SWI combinations were found to explain event counts for different seasons. For example, combinations of Lifted Index and low level wind shear were found to generate the best results for autumn and winter. While for spring and summer, the composite Microburst Index and the combination of most unstable CAPE and 0–1 km wind shear were found to be most successful. Results from these models showed a minimum in event counts during the winter months, with events that do occur mainly doing so along the southwest coast of Western Australia or along the coasts of Tasmania and Victoria. Summer is shown to have the largest event counts across the country, with the largest number of gusts occurring in northern Western Australia extending east into the Northern Territory with another maximum over northeast New South Wales. Similar trends were found with an extended application of the models to the period 1979–2015 when utilizing only reanalysis data as input. This implementation of the models highlights the versatility of the Bayesian hierarchical modelling approach and its ability, when trained, to be used in the absence of observations.