Probabilistic downscaling of remote sensing data with applications for multi-scale biogeochemical flux modeling

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Rayleigh scattering by hexagonal ice crystals and the interpretation of dual-polarisation radar measurements

Dual-polarisation radar measurements provide valuable information about the shapes and orientations of atmospheric ice particles. For quantitative interpretation of these data in the Rayleigh regime, common practice is to approximate the true ice crystal shape with that of a spheroid. Calculations using the discrete dipole approximation for a wide range of crystal aspect ratios demonstrate that approximating hexagonal plates as spheroids leads to significant errors in the predicted differential reflectivity, by as much as 1.5 dB. An empirical modification of the shape factors in Gans's spheroid theory was made using the numerical data. The resulting simple expressions, like Gans's theory, can be applied to crystals in any desired orientation, illuminated by an arbitrarily polarised wave, but are much more accurate for hexagonal particles. Calculations of the scattering from more complex branched and dendritic crystals indicate that these may be accurately modelled using the new expression, but with a reduced permittivity dependent on the volume of ice relative to an enclosing hexagonal prism.

Interpretation of ambiguity: differences between children and adolescents with and without an anxiety disorder

Background: Theory and treatment of anxiety disorders in young people are commonly based on the premise that interpretation biases found in anxious adults are also found in children and adolescents. Although there is some evidence that this may be the case, studies have not typically taken age into account, which is surprising given the normative changes in cognition that occur throughout childhood. The aim of the current study was to identify whether associations between anxiety disorder status and interpretation biases differed in children and adolescents. Methods: The responses of children (7-10 years) and adolescents (13-16 years) with and without anxiety disorders (n = 120) were compared on an ambiguous scenarios task. Results: Children and adolescents with an anxiety disorder showed significantly higher levels of threat interpretation and avoidant strategies than non-anxious children and adolescents. However, age significantly moderated the effect of anxiety disorder status on interpretation of ambiguity, in that adolescents with anxiety disorders showed significantly higher levels of threat interpretation and associated negative emotion than non-anxious adolescents, but a similar relationship was not observed among children. Conclusions: The findings suggest that theoretical accounts of interpretation biases in anxiety disorders in children and adolescents should distinguish between different developmental periods. For both ages, treatment that targets behavioral avoidance appears warranted. However, while adolescents are likely to benefit from treatment that addresses interpretation biases, there may be limited benefit for children under the age of ten.

‘It Was All My Fault’: negative interpretation bias in depressed adolescents

The extent to which cognitive models of development and maintenance of depression apply to adolescents is largely untested, despite the widespread application of Cognitive Behavior Therapy (CBT) for depressed adolescents. Cognitive models suggest that negative cognitions, including interpretation bias, play a role in etiology and maintenance of depression. Given that cognitive development is incomplete by the teenage years and that CBT is not superior to non-cognitive treatments in the treatment of adolescent depression, it is important to test the underlying model. The primary aim of this study was to test the hypothesis that interpretation biases are exhibited by depressed adolescents. Four groups of adolescents were recruited: clinically-referred depressed (n = 27), clinically-referred non-depressed (n = 24), community with elevated depression symptoms (n = 42) and healthy community (n = 150). Participants completed a 20 item ambiguous scenarios questionnaire. Clinically-referred depressed adolescents made significantly more negative interpretations and rated scenarios as less pleasant than all other groups. The results suggest that this element of the cognitive model of depression is applicable to adolescents. Other aspects of the model should be tested so that cognitive treatment can be modified or adapted if necessary.

Threat interpretation bias in anxious children and their mothers

The role of parents in the development of anxiety disorders in children is of increasing research and clinical interest. This study investigated interpretation biases of anxious children and their mothers using the ambiguous stimuli task developed by Hadwin, Frost, French, and Richards (1997). Three groups of children (aged 7 to 12 years) and their mothers were recruited; 23 non-clinical controls, 18 children with an anxiety disorder and 15 children with an externalising disorder. Following diagnostic assessments of the children, children and their mothers independently completed the homophone task and self-report measures of anxiety. Mothers of anxious children had significantly higher self-reported anxiety than mothers of non-clinical children. As hypothesised, children in the anxious group had higher threat interpretation scores than the non-clinical group. The hypothesis that mothers of anxious children would make more threat interpretations was not supported. Paired correlations showed no significant association between threat interpretations made by children and their mothers. There was a significant positive correlation between maternal threat interpretation and child anxiety. The results suggest that there is a complex association between mother's anxiety and cognitions and those of their children, which requires further examination in controlled observational and experimental studies, including treatment trials.

Assessing the reliability of probabilistic flood inundation model predictions

An ability to quantify the reliability of probabilistic flood inundation predictions is a requirement not only for guiding model development but also for their successful application. Probabilistic flood inundation predictions are usually produced by choosing a method of weighting the model parameter space, but previous study suggests that this choice leads to clear differences in inundation probabilities. This study aims to address the evaluation of the reliability of these probabilistic predictions. However, a lack of an adequate number of observations of flood inundation for a catchment limits the application of conventional methods of evaluating predictive reliability. Consequently, attempts have been made to assess the reliability of probabilistic predictions using multiple observations from a single flood event. Here, a LISFLOOD-FP hydraulic model of an extreme (>1 in 1000 years) flood event in Cockermouth, UK, is constructed and calibrated using multiple performance measures from both peak flood wrack mark data and aerial photography captured post-peak. These measures are used in weighting the parameter space to produce multiple probabilistic predictions for the event. Two methods of assessing the reliability of these probabilistic predictions using limited observations are utilized; an existing method assessing the binary pattern of flooding, and a method developed in this paper to assess predictions of water surface elevation. This study finds that the water surface elevation method has both a better diagnostic and discriminatory ability, but this result is likely to be sensitive to the unknown uncertainties in the upstream boundary condition

Probabilistic wind power forecasts with an inverse power curve transformation and censored regression

Forecasting wind power is an important part of a successful integration of wind power into the power grid. Forecasts with lead times longer than 6 h are generally made by using statistical methods to post-process forecasts from numerical weather prediction systems. Two major problems that complicate this approach are the non-linear relationship between wind speed and power production and the limited range of power production between zero and nominal power of the turbine. In practice, these problems are often tackled by using non-linear non-parametric regression models. However, such an approach ignores valuable and readily available information: the power curve of the turbine's manufacturer. Much of the non-linearity can be directly accounted for by transforming the observed power production into wind speed via the inverse power curve so that simpler linear regression models can be used. Furthermore, the fact that the transformed power production has a limited range can be taken care of by employing censored regression models. In this study, we evaluate quantile forecasts from a range of methods: (i) using parametric and non-parametric models, (ii) with and without the proposed inverse power curve transformation and (iii) with and without censoring. The results show that with our inverse (power-to-wind) transformation, simpler linear regression models with censoring perform equally or better than non-linear models with or without the frequently used wind-to-power transformation.

A global empirical system for probabilistic seasonal climate prediction

Preparing for episodes with risks of anomalous weather a month to a year ahead is an important challenge for governments, non-governmental organisations, and private companies and is dependent on the availability of reliable forecasts. The majority of operational seasonal forecasts are made using process-based dynamical models, which are complex, computationally challenging and prone to biases. Empirical forecast approaches built on statistical models to represent physical processes offer an alternative to dynamical systems and can provide either a benchmark for comparison or independent supplementary forecasts. Here, we present a simple empirical system based on multiple linear regression for producing probabilistic forecasts of seasonal surface air temperature and precipitation across the globe. The global CO2-equivalent concentration is taken as the primary predictor; subsequent predictors, including large-scale modes of variability in the climate system and local-scale information, are selected on the basis of their physical relationship with the predictand. The focus given to the climate change signal as a source of skill and the probabilistic nature of the forecasts produced constitute a novel approach to global empirical prediction. Hindcasts for the period 1961–2013 are validated against observations using deterministic (correlation of seasonal means) and probabilistic (continuous rank probability skill scores) metrics. Good skill is found in many regions, particularly for surface air temperature and most notably in much of Europe during the spring and summer seasons. For precipitation, skill is generally limited to regions with known El Niño–Southern Oscillation (ENSO) teleconnections. The system is used in a quasi-operational framework to generate empirical seasonal forecasts on a monthly basis.

Data Assimilation and Inverse Methods in Terms of a Probabilistic Formulation

The weak-constraint inverse for nonlinear dynamical models is discussed and derived in terms of a probabilistic formulation. The well-known result that for Gaussian error statistics the minimum of the weak-constraint inverse is equal to the maximum-likelihood estimate is rederived. Then several methods based on ensemble statistics that can be used to find the smoother (as opposed to the filter) solution are introduced and compared to traditional methods. A strong point of the new methods is that they avoid the integration of adjoint equations, which is a complex task for real oceanographic or atmospheric applications. they also avoid iterative searches in a Hilbert space, and error estimates can be obtained without much additional computational effort. the feasibility of the new methods is illustrated in a two-layer quasigeostrophic model.

The Cold War and its aftermath in the Americas: the search for a synthetic interpretation of U.S. policy

More than two decades have passed since the fall of the Berlin Wall and the transfer of the Cold War file from a daily preoccupation of policy makers to a more detached assessment by historians. Scholars of U.S.-Latin American relations are beginning to take advantage both of the distance in time and of newly opened archives to reflect on the four decades that, from the 1940s to the 1980s, divided the Americas, as they did much of the world. Others are seeking to understand U.S. policy and inter-American relations in the post-Cold War era, a period that not only lacks a clear definition but also still has no name. Still others have turned their gaze forward to offer policies in regard to the region for the new Obama administration. Numerous books and review essays have addressed these three subjects—the Cold War, the post-Cold War era, and current and future issues on the inter-American agenda. Few of these studies attempt, however, to connect the three subjects or to offer new and comprehensive theories to explain the course of U.S. policies from the beginning of the twentieth century until the present. Indeed, some works and policy makers continue to use the mind-sets of the Cold War as though that conflict were still being fought. With the benefit of newly opened archives, some scholars have nevertheless drawn insights from the depths of the Cold War that improve our understanding of U.S. policies and inter-American relations, but they do not address the question as to whether the United States has escaped the longer cycle of intervention followed by neglect that has characterized its relations with Latin America. Another question is whether U.S. policies differ markedly before, during, and after the Cold War. In what follows, we ask whether the books reviewed here provide any insights in this regard and whether they offer a compass for the future of inter-American relations. We also offer our own thoughts as to how their various perspectives could be synthesized to address these questions more comprehensively.