Stats4Grads: Patterns and Processes Revealed in High-Frequency Environmental Data
28 January 2015 13:00 in CM105
Advances in sensor technology enable monitoring programmes to record and store measurements at a high temporal resolution, enhancing the capacity to detect and understand short duration changes that would not have been apparent in the past with monthly, fortnightly or even daily sampling. Although these high-frequency data are advantageous, there are challenges in their processing and analysis such as the large volumes of data, their complex behaviour over the different timescales and the strong correlation structure that persists over a large number of lags. The aim of this talk is to present the complexities of modelling high-frequency data which arise from environmental applications. Surface waters are considered as key sources of atmospheric CO2, thus comprehensive understanding of the CO2 dynamics in surface waters is valuable. We consider a 15-minute resolution sensor-generated time series of the over-saturation of CO2, EpCO2, in a small order river system of the River Dee. Advanced statistical approaches used to analyse and model the data, which include visualization tools for exploratory analysis, wavelets, generalized additive models and functional data analysis, will be presented. These methods reveal the complex dynamics of EpCO2 over different timescales, the multivariate relationships of EpCO2 with hydrology and the temporal auto-correlation structures, which are time and scale dependent.
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