Atmospheric observations of the water vapour continuum in the near-infrared windows between 2500 and 6600 cm-1

Water vapour continuum absorption is potentially important for both closure of the Earth's energy budget and remote sensing applications. Currently, there are significant uncertainties in its characteristics in the near-infrared atmospheric windows at 2.1 and 1.6 µm. There have been several attempts to measure the continuum in the laboratory; not only are there significant differences amongst these measurements, but there are also difficulties in extrapolating the laboratory data taken at room temperature and above to temperatures more widely relevant to the atmosphere. Validation is therefore required using field observations of the real atmosphere. There are currently no published observations in atmospheric conditions with enough water vapour to detect a continuum signal within these windows or where the self-continuum component is significant. We present observations of the near-infrared water vapour continuum from Camborne, UK, at sea level using a Sun-pointing, radiometrically calibrated Fourier transform spectrometer in the window regions between 2000 and 10 000 cm−1. Analysis of these data is challenging, particularly because of the need to remove aerosol extinction and the large uncertainties associated with such field measurements. Nevertheless, we present data that are consistent with recent laboratory datasets in the 4 and 2.1 µm windows (when extrapolated to atmospheric temperatures). These results indicate that the most recent revision (3.2) of the MT_CKD foreign continuum, versions of which are widely used in atmospheric radiation models, requires strengthening by a factor of ∼5 in the centre of the 2.1 µm window. In the higher-wavenumber window at 1.6 µm, our estimated self- and foreign-continua are significantly stronger than MT_CKD. The possible contribution of the self- and foreign-continua to our derived total continuum optical depth is estimated by using laboratory or MT_CKD values of one, to estimate the other. The obtained self-continuum shows some consistency with temperature-extrapolated laboratory data in the centres of the 4 and 2.1 µm windows. The 1.6 µm region is more sensitive to atmospheric aerosol and continuum retrievals and therefore more uncertain than the more robust results at 2.1 and 4 µm. We highlight the difficulties in observing the atmospheric continuum and make the case for additional measurements in both the laboratory and field and discuss the requirements for any future field campaign

Post-hoc derivation of SOHO Michelson doppler imager flat fields

<p><b>Context:</b> The SOHO satellite now offers a unique perspective on the Sun as it is the only space-based instrument that can provide large, high-resolution data sets over an entire 11-year solar cycle. This unique property enables detailed studies of long-term variations in the Sun. One significant problem when looking for such changes is determining what component of any variation is due to deterioration of the instrument and what is due to the Sun itself. One of the key parameters that changes over time is the apparent sensitivity of individual pixels in the CCD array. This can change considerably as a result of optics damage, radiation damage, and aging of the sensor itself. In addition to reducing the sensitivity of the telescope over time, this damage significantly changes the uniformity of the flat field of the instrument, a property that is very hard to recalibrate in space. For procedures such as feature tracking and intensity analysis, this can cause significant errors.</p> <p><b>Aims:</b> We present a method for deriving high-precision flat fields for high-resolution MDI continuum data, using analysis of existing continuum and magnetogram data sets.</p> <p><b>Methods:</b> A flat field is constructed using a large set (1000-4000 frames) of cospatial magnetogram and continuum data. The magnetogram data is used to identify and mask out magnetically active regions on the continuum data, allowing systematic biases to be avoided. This flat field can then be used to correct individual continuum images from a similar time.</p> <p><b>Results:</b> This method allows us to reduce the residual flat field error by around a factor 6-30, depending on the area considered, enough to significantly change the results from correlation-tracking analysis. One significant advantage of this method is that it can be done retrospectively using archived data, without requiring any special satellite operations.</p&gt

Balltracking: an highly efficient method for tracking flow fields

We present a method for tracking solar photospheric flows that is highly efficient, and demonstrate it using high resolution MDI continuum images. The method involves making a surface from the photospheric granulation data, and allowing many small floating tracers or balls to be moved around by the evolving granulation pattern. The results are tested against synthesised granulation with known flow fields and compared to the results produced by Local Correlation tracking (LCT). The results from this new method have similar accuracy to those produced by LCT. We also investigate the maximum spatial and temporal resolution of the velocity field that it is possible to extract, based on the statistical properties of the granulation data. We conclude that both methods produce results that are close to the maximum resolution possible from granulation data. The code runs very significantly faster than our similarly optimised LCT code, making real time applications on large data sets possible. The tracking method is not limited to photospheric flows, and will also work on any velocity field where there are visible moving features of known scale length

Evidence of photospheric vortex flows at supergranular junctions observed by FG/SOT (Hinode)

Twisting motions of different nature are observed in several layers of the solar atmosphere. Chromospheric sunspot whorls and rotation of sunspots or even higher up in the lower corona sigmoids are examples of the large scale twisted topology of many solar features. Nevertheless, their occurrence at large scale in the quiet photosphere has not been investigated. The present study reveals the existence of vortex flows located at the supergranular junctions of the quiet Sun. We use a 1-hour and a 5-hour time series of the granulation in Blue continuum and G-band images from FG/SOT to derive the photospheric flows. A feature tracking technique called Balltracking is performed to track the granules and reveal the underlying flow fields. In both time series we identify long-lasting vortex flow located at supergranular junctions. The first vortex flow lasts at least 1 hour and is ~20-arcsec-wide (~15.5 Mm). The second vortex flow lasts more than 2 hours and is ~27-arcsec-wide (~21 Mm).Comment: 4 pages, 10 figure

Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses

peer-reviewedThe global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg−1 Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg−1 Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practice

Multiple linear regression modelling of on-farm direct water and electricity consumption on pasture based dairy farms

peer-reviewedAn analysis into the impact of milk production, stock numbers, infrastructural equipment, managerial procedures and environmental conditions on dairy farm electricity and water consumption using multiple linear regression (MLR) modelling was carried out. Electricity and water consumption data were attained through the utilisation of a remote monitoring system installed on a study sample of 58 pasture-based, Irish commercial dairy farms between 2014 and 2016. In total, 15 and 20 dairy farm variables were analysed on their ability to predict monthly electricity and water consumption, respectively. The subsets of variables that had the greatest prediction accuracy on unseen electricity and water consumption data were selected by applying a univariate variable selection technique, all subsets regression and 10-fold cross validation. Overall, electricity consumption was more accurately predicted than water consumption with relative prediction error values of 26% and 49% for electricity and water, respectively. Milk production and the total number of dairy cows had the largest impact on electricity consumption while milk production, automatic parlour washing and whether winter building troughs were reported to be leaking had the largest impact on water consumption. A standardised regression analysis found that utilising ground water for pre-cooling milk increased electricity consumption by 0.11 standard deviations, while increasing water consumption by 0.06 standard deviations when recycled in an open loop system. Milk production had a large influence on model overprediction with large negative correlations of −0.90 and −0.82 between milk production and mean percentage error for electricity and water prediction, respectively. This suggested that overprediction was inflated when milk production was low and vice versa. Governing bodies, farmers and/or policy makers may use the developed MLR models to calculate the impact of Irish dairy farming on natural resources or as decision support tools to calculate potential impacts of on-farm mitigation practises

Comparative evaluation of machine learning models for groundwater quality assessment

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration ranges of nitrate and pesticide. This study evaluates the classification performance of the models for two, three, and four class scenarios and compares them with the corresponding regression models. The study also examines the issue of class imbalance and tests the efficacy of three class imbalance mitigation techniques: oversampling, weighting, and oversampling and weighting, for all the scenarios. The models’ performance is reported using multiple metrics, both insensitive to class imbalance (accuracy) and sensitive to class imbalance (F1 score and MCC). Finally, the study assesses the importance of features using game-theoretic Shapley values to rank features consistently and offer model interpretability

Phylogeography and dispersal in the velvet gecko (Oedura lesueurii), and potential implications for conservation of an endangered snake (Hoplocephalus bungaroides).

ABSTRACT: BACKGROUND: To conserve critically endangered predators, we also need to conserve the prey species upon which they depend. Velvet geckos (Oedura lesueurii) are a primary prey for the endangered broad-headed snake (Hoplocephalus bungaroides), which is restricted to sandstone habitats in southeastern Australia. We sequenced the ND2 gene from 179 velvet geckos, to clarify the lizards' phylogeographic history and landscape genetics. We also analysed 260 records from a longterm (3-year) capture-mark-recapture program at three sites, to evaluate dispersal rates of geckos as a function of locality, sex and body size. RESULTS: The genetic analyses revealed three ancient lineages in the north, south and centre of the species' current range. Estimates of gene flow suggest low dispersal rates, constrained by the availability of contiguous rocky habitat. Mark-recapture records confirm that these lizards are highly sedentary, with most animals moving < 30 m from their original capture site even over multi-year periods. CONCLUSION: The low vagility of these lizards suggests that they will be slow to colonise vacant habitat patches; and hence, efforts to restore degraded habitats for broad-headed snakes may need to include translocation of lizards