Missing data imputation of high-resolution temporal climate time series data

© 2020 The Authors. Meteorological Applications published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. Analysis of high-resolution data offers greater opportunity to understand the nature of data variability, behaviours, trends and to detect small changes. Climate studies often require complete time series data which, in the presence of missing data, means imputation must be undertaken. Research on the imputation of high-resolution temporal climate time series data is still at an early phase. In this study, multiple approaches to the imputation of missing values were evaluated, including a structural time series model with Kalman smoothing, an autoregressive integrated moving average (ARIMA) model with Kalman smoothing and multiple linear regression. The methods were applied to complete subsets of data from 12 month time series of hourly temperature, humidity and wind speed data from four locations along the coast of Western Australia. Assuming that observations were missing at random, artificial gaps of missing observations were studied using a five-fold cross-validation methodology with the proportion of missing data set to 10%. The techniques were compared using the pooled mean absolute error, root mean square error and symmetric mean absolute percentage error. The multiple linear regression model was generally the best model based on the pooled performance indicators, followed by the ARIMA with Kalman smoothing. However, the low error values obtained from each of the approaches suggested that the models competed closely and imputed highly plausible values. To some extent, the performance of the models varied among locations. It can be concluded that the modelling approaches studied have demonstrated suitability in imputing missing data in hourly temperature, humidity and wind speed data and are therefore recommended for application in other fields where high-resolution data with missing values are common

Detector Description and Performance for the First Coincidence Observations between LIGO and GEO

For 17 days in August and September 2002, the LIGO and GEO interferometer gravitational wave detectors were operated in coincidence to produce their first data for scientific analysis. Although the detectors were still far from their design sensitivity levels, the data can be used to place better upper limits on the flux of gravitational waves incident on the earth than previous direct measurements. This paper describes the instruments and the data in some detail, as a companion to analysis papers based on the first data.Comment: 41 pages, 9 figures 17 Sept 03: author list amended, minor editorial change

Superconducting shielding

Superconducting shields offer the possibility of obtaining truly zero magnetic fields due to the phenomenon of flux quantization. They also offer excellent shielding from external time varying fields. Various techniques of superconducting shielding will be surveyed and recent results discussed

Some results from a continuous wave search using the ALLEGRO antenna

We have analysed the ALLEGRO data for the years 1999 and 2000 looking for continuous wave signals coming from some specific directions in the sky. No signal was found for these directions in long time integrations. However, we found a 'signal' at 919.535 Hz in a 50 min time integration search, which appeared on 13 August 1999 and disappeared on 12 December 1999. We present here the results of these searches and the reasons why we believe this 'signal' is probably noise

Allegro: noise performance and the ongoing search for gravitational waves

The noise performance of Allegro since 1993 is summarized. We show that the noise level of Allegro is, in general, stationary. Non-Gaussian impulse excitations persist despite efforts to isolate the detector from environmental disturbances. Some excitations are caused by seismic activity and flux jumps in the SQUID. Algorithms to identify and automatically veto these events are presented. Also, the contribution of Allegro to collaborations with other resonant-mass detectors via the International Gravitational Event Collaboration and with LIGO is reviewed

Calibration of the ALLEGRO resonant detector

We describe a method for calibrating the ALLEGRO resonant detector. The resulting response function can be used to transform the observed data backwards to gravitational strain data. These data are the input to a cross-correlation analysis to search for stochastic gravitational waves