1,720,962 research outputs found
Localising Global Climate Projections: Advanced Deep Learning Techniques for Downscaling Extreme Events
No full textGlobal Climate Models (GCMs) simulate low-resolution climate projections on a global
scale. The native resolution of GCMs is generally too low for societal-level decisionmaking. Downscaling is often applied to GCM output to enhance the spatial resolution.
Statistical downscaling techniques, in particular, are well-established as a cost-effective
approach. They require significantly less computational time than physics-based dynamical downscaling. In recent years, deep learning has gained prominence in statistical
downscaling, demonstrating significantly lower error rates compared to traditional statistical methods. However, regression-based deep learning techniques, in particular, tend
to overfit the mean sample magnitude. As a result, extreme values are often underestimated. Furthermore, regression-based methods characteristically over-smooth the areas
surrounding sample extremes. The exact location of the sample extremes subsequently
becomes ambiguous. Problematically, extreme events have the largest societal impact,
e.g. substantial damage to infrastructure.
We propose Quantile-Regression-Ensemble (QRE), an innovative deep learning algorithm inspired by boosting methods. Its primary objective is to avoid trade-offs between
fitting to sample means and extreme values by training independent models on a partitioned dataset. Our QRE is robust to redundant models and not susceptible to explosive
ensemble weights, ensuring a reliable training process. Diffusion models can capture
high-frequency details of complex spatial patterns, providing a potential solution to over
smoothing. However, there is limited research on utilising their generative capacity in a
regression task, where sample magnitude may vary drastically. We introduce the SemDiff framework, which uses transfer learning on a surrogate dataset to train ScaleNet, a
network that scales diffusion samples to match the magnitude of the ground truth.
QRE achieves a lower Mean Squared Error (MSE) than various baseline models. In
particular, our algorithm has a lower error for high-intensity precipitation events over
New Zealand, highlighting the ability to represent extreme events reliably. SemDiff exhibits comparable capture of sample magnitude over baseline techniques while achieving
significantly higher SSIM and PSD. Therefore, Our proposed methods allow for reduced
uncertainty over extreme events, particularly when downscaling GCM projections
Mesoscale Cellular Convection: Determining the radiative effect and the large-scale meteorological controls
No full textFull Text is available to authenticated members of The University of Auckland only.This study develops an Artifcial Neural Network (ANN) to classify satellite imagery from Multi-angle Imaging SpectroRadiometer (MISR) in domains of (200 km)2 into four categories of marine low-clouds based on their type of Mesoscale Cellular Convection (MCC). These categories are (i) closedcelled MCC (ii) open-celled MCC (iii) disorganised MCC and (iv) No MCC. These different types of MCC are usefully defined as low-clouds of different morphologies. These classifcations are used to investigate the large-scale meteorological controls on MCC. The large-scale meteorological variables that were used in this study are sea-surface temperature (SST) and Lower- Tropospheric Stability (LTS). Changes in large-scale meteorology are found to impact the occurrence of each MCC type disproportionately. We also investigated relationships between the El Ni~no Southern Oscillation (ENSO) and MCC. MCC is found to be strongly inuenced by the SST anomaly patterns that arise during El Ni~no and LaNi~na. Changes in the coverage of MCC during ENSO phases are found to have significant impacts on the Top-Of-Atmosphere albedo. Classifcations from the ANN are also combined with satellite observations from MISR to develop relationships between cloud morphology, domain albedo, cloud fraction and a cloud heterogeneity. Cloud morphology is found to play an essential role in modulating these relationships. The cloud fraction-albedo relationships are found to be directly a function of cloud morphology. Relationships between domain albedo and cloud heterogeneity are also found to be a function of MCC type. Our results strongly indicate that the albedo has a strong dependence on cloud morphology and cloud heterogeneity. Understanding both the physical properties and the meteorological controls on MCC has important implications for understanding low-cloud behaviour and improving their representation in General Circulation Models
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation
No full text<h1>Dataset Description for "A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</h1>
<p>This dataset accompanies the research paper titled <strong>"A Robust Generative Adversarial Network Approach for Climate Downscaling and Weather Generation"</strong>, currently under review for the AGU Journal JAMES. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/A-Robust-Generative-Adversarial-Network-Approach-for-Climate-Downscaling" target="_new">GitHub repository</a>.</p>
<h2>Aims</h2>
<p>Our study's overarching goal was to assess the effectiveness of Generative Adversarial Networks (GANs) in a climate downscaling context and is structured around two aims. The first aim of our study is to examine whether GANs can overcome several important limitations of regression-based climate downscaling algorithms (i.e. underestimating the magnitude of extreme events). The second and most important aim of our study is to assess the robustness GAN performance to different training hyperparameters. Our robustness assessment thoroughly scrutinizes GANs for their application in climate downscaling contexts, ensuring that they can learn and capture regional climate processes</p>
<h2>Geographic Focus</h2>
<p>Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).</p>
<p> </p>
<h2>Data Overview</h2>
<h3>Training and Evaluation Data</h3>
<p>The training data used in this study (for our RCM emulator) only spans the historical period of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables. </p>
<ul>
<li>
<p><strong>Resolution:</strong> The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation. </p>
</li>
<li>
<p><strong>Period Coverage:</strong></p>
<ul>
<li>Training Data: 1960-2014</li>
<li>Validation Data: 1986-2005</li>
</ul>
</li>
<li>
<p><strong>Models:</strong></p>
<ul>
<li>Training on: ACCESS-CM2</li>
<li>Validated on: EC-Earth3, NorESM2-MM</li>
</ul>
</li>
</ul>
<h3>File Structure</h3>
<ul>
<li>
<p><strong>Training Data:</strong></p>
<ul>
<li>Target/Ground Truth (Y): <code>predictor_ACCESS-CM2_hist.nc</code></li>
<li>Predictor (X): <code>pr_ACCESS-CM2_hist.nc</code></li>
</ul>
</li>
<li>
<p><strong>Evaluation Data:</strong></p>
<ul>
<li><strong>NorESM2-MM:</strong>
<ul>
<li>Target (Y): <code>NorESM2-MM_historical_precip_compressed.nc</code></li>
<li>Predictor (X): <code>NorESM2-MM_histupdated_compressed.nc</code></li>
</ul>
</li>
<li><strong>EC-Earth3:</strong>
<ul>
<li>Target: <code>EC-Earth3_historical_precip_compressed.nc</code></li>
<li>Predictor: <code>EC-Earth3_histupdated_compressed.nc</code></li>
</ul>
</li>
</ul>
</li>
</ul>
<h2>Methodological Insights</h2>
<ul>
<li>
<p><strong>Regional Climate Model</strong>, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>.</p>
</li>
<li>
<p><strong>Predictor and Target Variables:</strong> Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository.</p>
</li>
<li>
<p><strong>Training Framework:</strong> Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a></p>
</li>
</ul>
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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