Validation of the NANA (Novel Assessment of Nutrition and Ageing) touch screen system for use at home by older adults

Prospective measurement of nutrition, cognition, and physical activity in later life would facilitate early detection of detrimental change and early intervention but is hard to achieve in community settings. Technology can simplify the task and facilitate daily data collection. The Novel Assessment of Nutrition and Ageing (NANA) toolkit was developed to provide a holistic picture of an individual's function including diet, cognition and activity levels. This study aimed to validate the NANA toolkit for data collection in the community. Forty participants aged 65 years and over trialled the NANA toolkit in their homes for three 7-day periods at four-week intervals. Data collected using the NANA toolkit were compared with standard measures of diet (four-day food diary), cognitive ability (processing speed) and physical activity (self-report). Bland–Altman analysis of dietary intake (energy, carbohydrates, protein fat) found a good relationship with the food diary and cognitive processing speed and physical activity (hours) were significantly correlated with their standard counterparts. The NANA toolkit enables daily reporting of data that would otherwise be collected sporadically while reducing demands on participants; older adults can complete the daily reporting at home without a researcher being present; and it enables prospective investigation of several domains at once

Validation of Canopy Height Profile methodology for small-footprint full-waveform airborne LiDAR data in a discontinuous canopy environment

A Canopy Height Profile (CHP) procedure presented in Harding et al. (2001) for large footprint LiDAR data was tested in a closed canopy environment as a way of extracting vertical foliage profiles from LiDAR raw-waveform. In this study, an adaptation of this method to small-footprint data has been shown, tested and validated in an Australian sparse canopy forest at plot- and site-level. Further, the methodology itself has been enhanced by implementing a dataset-adjusted reflectance ratio calculation according to Armston et al. (2013) in the processing chain, and tested against a fixed ratio of 0.5 estimated for the laser wavelength of 1550nm. As a by-product of the methodology, effective leaf area index (LAIe) estimates were derived and compared to hemispherical photography-derived values. To assess the influence of LiDAR aggregation area size on the estimates in a sparse canopy environment, LiDAR CHPs and LAIes were generated by aggregating waveforms to plot- and site-level footprints (plot/site-aggregated) as well as in 5m grids (grid-processed). LiDAR profiles were then compared to leaf biomass field profiles generated based on field tree measurements. The correlation between field and LiDAR profiles was very high, with a mean R2 of 0.75 at plot-level and 0.86 at site-level for 55 plots and the corresponding 11 sites. Gridding had almost no impact on the correlation between LiDAR and field profiles (only marginally improvement), nor did the dataset-adjusted reflectance ratio. However, gridding and the dataset-adjusted reflectance ratio were found to improve the correlation between raw-waveform LiDAR and hemispherical photography LAIe estimates, yielding the highest correlations of 0.61 at plot-level and of 0.83 at site-level. This proved the validity of the approach and superiority of dataset-adjusted reflectance ratio of Armston et al. (2013) over a fixed ratio of 0.5 for LAIe estimation, as well as showed the adequacy of small-footprint LiDAR data for LAIe estimation in discontinuous canopy forests.

Recent lake-level and outflow variations at Lake Viljandi, Estonia: validation of a coupled lake-catchment modelling scheme for climate change studies

Changes in the depth of Lake Viljandi between 1940 and 1990 were simulated using a lake water and energy-balance model driven by standard monthly weather data. Catchment runoff was simulated using a one-dimensional hydrological model, with a two-layer soil, a single-layer snowpack, a simple representation of vegetation cover and similarly modest input requirements. Outflow was modelled as a function of lake level. The simulated record of lake level and outflow matched observations of lake-level variations (r = 0.78) and streamflow (r = 0.87) well. The ability of the model to capture both intra- and inter-annual variations in the behaviour of a specific lake, despite the relatively simple input requirements, makes it extremely suitable for investigations of the impacts of climate change on lake water balance.

Effects of agricultural management practices on earthworm populations and crop yield: validation and application of a mechanistic modelling approach

There is little consensus on how agriculture will meet future food demands sustainably. Soils and their biota play a crucial role by mediating ecosystem services that support agricultural productivity. However, a multitude of site-specific environmental factors and management practices interact to affect the ability of soil biota to perform vital functions, confounding the interpretation of results from experimental approaches. Insights can be gained through models, which integrate the physiological, biological and ecological mechanisms underpinning soil functions. We present a powerful modelling approach for predicting how agricultural management practices (pesticide applications and tillage) affect soil functioning through earthworm populations. By combining energy budgets and individual-based simulation models, and integrating key behavioural and ecological drivers, we accurately predict population responses to pesticide applications in different climatic conditions. We use the model to analyse the ecological consequences of different weed management practices. Our results demonstrate that an important link between agricultural management (herbicide applications and zero, reduced and conventional tillage) and earthworms is the maintenance of soil organic matter (SOM). We show how zero and reduced tillage practices can increase crop yields while preserving natural ecosystem functions. This demonstrates how management practices which aim to sustain agricultural productivity should account for their effects on earthworm populations, as their proliferation stimulates agricultural productivity. Synthesis and applications. Our results indicate that conventional tillage practices have longer term effects on soil biota than pesticide control, if the pesticide has a short dissipation time. The risk of earthworm populations becoming exposed to toxic pesticides will be reduced under dry soil conditions. Similarly, an increase in soil organic matter could increase the recovery rate of earthworm populations. However, effects are not necessarily additive and the impact of different management practices on earthworms depends on their timing and the prevailing environmental conditions. Our model can be used to determine which combinations of crop management practices and climatic conditions pose least overall risk to earthworm populations. Linking our model mechanistically to crop yield models would aid the optimization of crop management systems by exploring the trade-off between different ecosystem services.

Simple measures of climate, soil properties and plant traits predict national scale grassland soil carbon stocks

1. Soil carbon (C) storage is a key ecosystem service. Soil C stocks play a vital role in soil fertility and climate regulation, but the factors that control these stocks at regional and national scales are unknown, particularly when their composition and stability are considered. As a result, their mapping relies on either unreliable proxy measures or laborious direct measurements. 2. Using data from an extensive national survey of English grasslands we show that surface soil (0-7cm) C stocks in size fractions of varying stability can be predicted at both regional and national scales from plant traits and simple measures of soil and climatic conditions. 3. Soil C stocks in the largest pool, of intermediate particle size (50-250 µm), were best explained by mean annual temperature (MAT), soil pH and soil moisture content. The second largest C pool, highly stable physically and biochemically protected particles (0.45-50 µm), was explained by soil pH and the community abundance weighted mean (CWM) leaf nitrogen (N) content, with the highest soil C stocks under N rich vegetation. The C stock in the small active fraction (250-4000 µm) was explained by a wide range of variables: MAT, mean annual precipitation, mean growing season length, soil pH and CWM specific leaf area; stocks were higher under vegetation with thick and/or dense leaves. 4. Testing the models describing these fractions against data from an independent English region indicated moderately strong correlation between predicted and actual values and no systematic bias, with the exception of the active fraction, for which predictions were inaccurate. 5. Synthesis and Applications: Validation indicates that readily available climate, soils and plant survey data can be effective in making local- to landscape-scale (1-100,000 km2) soil C stock predictions. Such predictions are a crucial component of effective management strategies to protect C stocks and enhance soil C sequestration.

Validation of a priori CME arrival predictions made using real-time heliospheric imager observations

Between December 2010 and March 2013, volunteers for the Solar Stormwatch (SSW) Citizen Science project have identified and analyzed coronal mass ejections (CMEs) in the near real-time Solar Terrestrial Relations Observatory Heliospheric Imager observations, in order to make “Fearless Forecasts” of CME arrival times and speeds at Earth. Of the 60 predictions of Earth-directed CMEs, 20 resulted in an identifiable Interplanetary CME (ICME) at Earth within 1.5–6 days, with an average error in predicted transit time of 22 h, and average transit time of 82.3 h. The average error in predicting arrival speed is 151 km s−1, with an average arrival speed of 425km s−1. In the same time period, there were 44 CMEs for which there are no corresponding SSW predictions, and there were 600 days on which there was neither a CME predicted nor observed. A number of metrics show that the SSW predictions do have useful forecast skill; however, there is still much room for improvement. We investigate potential improvements by using SSW inputs in three models of ICME propagation: two of constant acceleration and one of aerodynamic drag. We find that taking account of interplanetary acceleration can improve the average errors of transit time to 19 h and arrival speed to 77 km s−1.

Development and validation of the Morphing Fear Questionnaire (MFQ)

Morphing fears (also called transformation obsessions) involve concerns that a person may become contaminated by and acquire undesirable characteristics of others. These symptoms are found in patients with OCD and are thought to be related to mental contamination. Given the high levels of distress and interference morphing fears can cause, a reliable and valid assessment measure is needed. This article describes the development and evaluation of the Morphing Fear Questionnaire (MFQ), a 13-item measure designed to assess for the presence and severity of morphing fears. A sample of 900 participants took part in the research. Of these, 140 reported having a current diagnosis of OCD (SR-OCD) and 760 reported never having had OCD (N-OCD; of whom 24 reported a diagnosis of an anxiety disorder and 23 reported a diagnosis of depression). Factor structure, reliability, and construct and criterion related validity were investigated. Exploratory and confirmatory factor analyses supported a one-factor structure replicable across the N-OCD and SR-OCD group. The MFQ was found to have high internal consistency and good temporal stability, and showed significantly greater associations with convergent measures (assessing obsessive-compulsive symptoms, mental contamination, thought-action fusion and magical thinking) than with divergent measures (assessing depression and anxiety). Moreover, the MFQ successfully discriminated between the SR-OCD sample and the N-OCD group, anxiety disorder sample, and depression sample. These findings suggest that the MFQ has sound psychometric properties and that it can be used to assess morphing fear. Clinical implications are discussed.

Statistical characterisation of the growth and spatial scales of the substorm onset arc

We present the first multi-event study of the spatial and temporal structuring of the aurora to provide statistical evidence of the near-Earth plasma instability which causes the substorm onset arc. Using data from ground-based auroral imagers, we study repeatable signatures of along-arc auroral beads, which are thought to represent the ionospheric projection of magnetospheric instability in the near-Earth plasma sheet. We show that the growth and spatial scales of these wave-like fluctuations are similar across multiple events, indicating that each sudden auroral brightening has a common explanation. We find statistically that growth rates for auroral beads peak at low wavenumber with the most unstable spatial scales mapping to an azimuthal wavelength λ≈1700 − 2500 km in the equatorial magnetosphere at around 9-12 RE. We compare growth rates and spatial scales with a range of theoretical predictions of magnetotail instabilities, including the cross-field current instability and the shear-flow ballooning instability. We conclude that, although the cross-field current instability can generate similar magnitude of growth rates, the range of unstable wavenumbers indicates that the shear-flow ballooning instability is the most likely explanation for our observations.

Designing a sampling scheme to reveal correlations between weeds and soil properties at multiple spatial scales

Weeds tend to aggregate in patches within fields and there is evidence that this is partly owing to variation in soil properties. Because the processes driving soil heterogeneity operate at different scales, the strength of the relationships between soil properties and weed density would also be expected to be scale-dependent. Quantifying these effects of scale on weed patch dynamics is essential to guide the design of discrete sampling protocols for mapping weed distribution. We have developed a general method that uses novel within-field nested sampling and residual maximum likelihood (REML) estimation to explore scale-dependent relationships between weeds and soil properties. We have validated the method using a case study of Alopecurus myosuroides in winter wheat. Using REML, we partitioned the variance and covariance into scale-specific components and estimated the correlations between the weed counts and soil properties at each scale. We used variograms to quantify the spatial structure in the data and to map variables by kriging. Our methodology successfully captured the effect of scale on a number of edaphic drivers of weed patchiness. The overall Pearson correlations between A. myosuroides and soil organic matter and clay content were weak and masked the stronger correlations at >50 m. Knowing how the variance was partitioned across the spatial scales we optimized the sampling design to focus sampling effort at those scales that contributed most to the total variance. The methods have the potential to guide patch spraying of weeds by identifying areas of the field that are vulnerable to weed establishment.

Regionally coupled atmosphere-ocean-sea ice-marine biogeochemistry model ROM: 1. Description and validation

The general circulation models used to simulate global climate typically feature resolution too coarse to reproduce many smaller-scale processes, which are crucial to determining the regional responses to climate change. A novel approach to downscale climate change scenarios is presented which includes the interactions between the North Atlantic Ocean and the European shelves as well as their impact on the North Atlantic and European climate. The goal of this paper is to introduce the global ocean-regional atmosphere coupling concept and to show the potential benefits of this model system to simulate present-day climate. A global ocean-sea ice-marine biogeochemistry model (MPIOM/HAMOCC) with regionally high horizontal resolution is coupled to an atmospheric regional model (REMO) and global terrestrial hydrology model (HD) via the OASIS coupler. Moreover, results obtained with ROM using NCEP/NCAR reanalysis and ECHAM5/MPIOM CMIP3 historical simulations as boundary conditions are presented and discussed for the North Atlantic and North European region. The validation of all the model components, i.e., ocean, atmosphere, terrestrial hydrology, and ocean biogeochemistry is performed and discussed. The careful and detailed validation of ROM provides evidence that the proposed model system improves the simulation of many aspects of the regional climate, remarkably the ocean, even though some biases persist in other model components, thus leaving potential for future improvement. We conclude that ROM is a powerful tool to estimate possible impacts of climate change on the regional scale.

Methane cross-validation between three Fourier Transform Spectrometers: SCISAT ACE-FTS, GOSAT TANSO-FTS, and ground-based FTS measurements in the Canadian high Arctic

We present cross-validation of remote sensing measurements of methane profiles in the Canadian high Arctic. Accurate and precise measurements of methane are essential to understand quantitatively its role in the climate system and in global change. Here, we show a cross-validation between three datasets: two from spaceborne instruments and one from a ground-based instrument. All are Fourier Transform Spectrometers (FTSs). We consider the Canadian SCISAT Atmospheric Chemistry Experiment (ACE)-FTS, a solar occultation infrared spectrometer operating since 2004, and the thermal infrared band of the Japanese Greenhouse Gases Observing Satellite (GOSAT) Thermal And Near infrared Sensor for carbon Observation (TANSO)-FTS, a nadir/off-nadir scanning FTS instrument operating at solar and terrestrial infrared wavelengths, since 2009. The ground-based instrument is a Bruker 125HR Fourier Transform Infrared (FTIR) spectrometer, measuring mid-infrared solar absorption spectra at the Polar Environment Atmospheric Research Laboratory (PEARL) Ridge Lab at Eureka, Nunavut (80° N, 86° W) since 2006. For each pair of instruments, measurements are collocated within 500 km and 24 h. An additional criterion based on potential vorticity values was found not to significantly affect differences between measurements. Profiles are regridded to a common vertical grid for each comparison set. To account for differing vertical resolutions, ACE-FTS measurements are smoothed to the resolution of either PEARL-FTS or TANSO-FTS, and PEARL-FTS measurements are smoothed to the TANSO-FTS resolution. Differences for each pair are examined in terms of profile and partial columns. During the period considered, the number of collocations for each pair is large enough to obtain a good sample size (from several hundred to tens of thousands depending on pair and configuration). Considering full profiles, the degrees of freedom for signal (DOFS) are between 0.2 and 0.7 for TANSO-FTS and between 1.5 and 3 for PEARL-FTS, while ACE-FTS has considerably more information (roughly 1° of freedom per altitude level). We take partial columns between roughly 5 and 30 km for the ACE-FTS–PEARL-FTS comparison, and between 5 and 10 km for the other pairs. The DOFS for the partial columns are between 1.2 and 2 for PEARL-FTS collocated with ACE-FTS, between 0.1 and 0.5 for PEARL-FTS collocated with TANSO-FTS or for TANSO-FTS collocated with either other instrument, while ACE-FTS has much higher information content. For all pairs, the partial column differences are within ± 3 × 1022 molecules cm−2. Expressed as median ± median absolute deviation (expressed in absolute or relative terms), these differences are 0.11 ± 9.60 × 10^20 molecules cm−2 (0.012 ± 1.018 %) for TANSO-FTS–PEARL-FTS, −2.6 ± 2.6 × 10^21 molecules cm−2 (−1.6 ± 1.6 %) for ACE-FTS–PEARL-FTS, and 7.4 ± 6.0 × 10^20 molecules cm−2 (0.78 ± 0.64 %) for TANSO-FTS–ACE-FTS. The differences for ACE-FTS–PEARL-FTS and TANSO-FTS–PEARL-FTS partial columns decrease significantly as a function of PEARL partial columns, whereas the range of partial column values for TANSO-FTS–ACE-FTS collocations is too small to draw any conclusion on its dependence on ACE-FTS partial columns.

Intercomparison and validation of the mixed layer depth fields of global ocean syntheses

Intercomparison and evaluation of the global ocean surface mixed layer depth (MLD) fields estimated from a suite of major ocean syntheses are conducted. Compared with the reference MLDs calculated from individual profiles, MLDs calculated from monthly mean and gridded profiles show negative biases of 10–20 m in early spring related to the re-stratification process of relatively deep mixed layers. Vertical resolution of profiles also influences the MLD estimation. MLDs are underestimated by approximately 5–7 (14–16) m with the vertical resolution of 25 (50) m when the criterion of potential density exceeding the 10-m value by 0.03 kg m−3 is used for the MLD estimation. Using the larger criterion (0.125 kg m−3) generally reduces the underestimations. In addition, positive biases greater than 100 m are found in wintertime subpolar regions when MLD criteria based on temperature are used. Biases of the reanalyses are due to both model errors and errors related to differences between the assimilation methods. The result shows that these errors are partially cancelled out through the ensemble averaging. Moreover, the bias in the ensemble mean field of the reanalyses is smaller than in the observation-only analyses. This is largely attributed to comparably higher resolutions of the reanalyses. The robust reproduction of both the seasonal cycle and interannual variability by the ensemble mean of the reanalyses indicates a great potential of the ensemble mean MLD field for investigating and monitoring upper ocean processes.

The BALTIMOS (BALTEX Integrated Model System) fi eld experiments: A comprehensive atmospheric boundary layer data set for model validation over the open and ice- covered Baltic Sea

A comprehensive atmospheric boundary layer (ABL) data set was collected in eight fi eld experiments (two during each season) over open water and sea ice in the Baltic Sea during 1998–2001 with the primary objective to validate the coupled atmospheric- ice-ocean-land surface model BALTIMOS (BALTEX Integrated Model System). Measurements were taken by aircraft, ships and surface stations and cover the mean and turbulent structure of the ABL including turbulent fl uxes, radiation fl uxes, and cloud conditions. Measurement examples of the spatial variability of the ABL over the ice edge zone and of the stable ABL over open water demonstrate the wide range of ABL conditions collected and the strength of the data set which can also be used to validate other regional models.

Independent uncertainty estimates for coefficient based sea surface temperature retrieval from the Along-Trck Scanning Radiometer instruments

We establish a methodology for calculating uncertainties in sea surface temperature estimates from coefficient based satellite retrievals. The uncertainty estimates are derived independently of in-situ data. This enables validation of both the retrieved SSTs and their uncertainty estimate using in-situ data records. The total uncertainty budget is comprised of a number of components, arising from uncorrelated (eg. noise), locally systematic (eg. atmospheric), large scale systematic and sampling effects (for gridded products). The importance of distinguishing these components arises in propagating uncertainty across spatio-temporal scales. We apply the method to SST data retrieved from the Advanced Along Track Scanning Radiometer (AATSR) and validate the results for two different SST retrieval algorithms, both at a per pixel level and for gridded data. We find good agreement between our estimated uncertainties and validation data. This approach to calculating uncertainties in SST retrievals has a wider application to data from other instruments and retrieval of other geophysical variables.