Visualization in Meteorology-A Survey of Techniques and Tools for Data Analysis Tasks

Spatial distribution patterns and hotspots of extreme agro-climatic resources in the Horro Guduru Wollega Zone, Northwestern Ethiopia

Extreme temperatures and rainfall influence crop yields, soil health, and natural ecosystems. This study examined the extent of extreme agro-climatic factors in Northwestern Ethiopia, with a focus on identifying vulnerability hotspots. Rainfall and temperature data from 1982 to 2022 were collected from eight meteorological stations of the Ethiopian Meteorological Institute, and missing values and outliers were corrected using imputation and Z-scores. ClimPact2 software extracted agro-climatic indicators, and trend analyses were performed using the Mann-Kendall test and Sen's slope. Consecutive dry days (CDD) ranged from 27 in Fincha'a to 57 in Obora, with Obora showing an annual increase of 2.033 days. Consecutive wet days (CWD) varied from 12 in Obora to 138 in Fincha'a. A positive trend in the warmest maximum temperatures (TXx) and a negative trend in the cold night index (TN10P) were observed. The Amuru District recorded the highest vulnerability index at 61, with most districts ranging from 42 to 60. These variations may significantly affect agriculture and water management in the region, necessitating the adoption of heat-tolerant crops and improved irrigation practices to enhance climate resilience.

Online temperature-monitoring technology for grain storage: a three-dimensional visualization method based on an adaptive neighborhood clustering algorithm

BACKGROUND

Post-harvest quality assurance is a crucial link between grain production and end users. It is essential to ensure that grain does not deteriorate due to heating during storage. To visualize the temperature distribution of a grain pile, the present study proposed a three-dimensional (3D) temperature field visualization method based on an adaptive neighborhood clustering algorithm (ANCA). The ANCA-based visualization method contains four calculation modules. First, discrete grain temperature data, obtained by sensors, are collected and interpolated using back propagation (BP) neural networks to model the temperature field. Then a new adaptive neighborhood clustering algorithm is applied to divide interpolation data into different categories by combining spatial characteristics and spatiotemporal information. Next, the Quickhull algorithm is used to compute the boundary points of each cluster. Finally, the polyhedrons determined by boundary points are rendered into different colors and are constructed in a 3D temperature model of the grain pile.

RESULTS

The experimental results show that ANCA is much better than the DBSCAN and MeanShift algorithms on compactness (around 95.7% of tested cases) and separation (approximately 91.3% of tested cases). Moreover, the ANCA-based visualization method for grain pile temperatures has a shorter rendering time and better visual effects.

CONCLUSION

This research provides an efficient 3D visualization method that allows managers of grain depots to obtain temperature field information for bulk grain visually in real time to help them protect grain quality during storage. © 2023 Society of Chemical Industry.

EnConVis: A Unified Framework for Ensemble Contour Visualization

Ensemble simulation is a crucial method to handle potential uncertainty in modern simulation and has been widely applied in many disciplines. Many ensemble contour visualization methods have been introduced to facilitate ensemble data analysis. On the basis of deep exploration and summarization of existing techniques and domain requirements, we propose a unified framework of ensemble contour visualization, EnConVis (Ensemble Contour Visualization), which systematically combines state-of-the-art methods. We model ensemble contour visualization as a four-step pipeline consisting of four essential procedures: member filtering, point-wise modeling, uncertainty band extraction, and visual mapping. For each of the four essential procedures, we compare different methods they use, analyze their pros and cons, highlight research gaps, and attempt to fill them. Specifically, we add Kernel Density Estimation in the point-wise modeling procedure and multi-layer extraction in the uncertainty band extraction procedure. This step shows the ensemble data's details accurately and provides abstract levels. We also analyze existing methods from a global perspective. We investigate their mechanisms and compare their effects, on the basis of which, we offer selection guidelines for them. From the overall perspective of this framework, we find choices and combinations that have not been tried before, which can be well compensated by our method. Synthetic data and real-world data are leveraged to verify the efficacy of our method. Domain experts' feedback suggests that our approach helps them better understand ensemble data analysis.

A Brief Review on 4D Weather Visualization

The accelerated changes on our planet have led to a growing interest in climate change and its consequences: natural hazards and adverse socio-economic impacts. However, the development of climate research and the proliferation of datasets require an integrated and efficient approach to the analysis, investigation, and visualization of atmospheric meteorological data. Thus, we propose a literature review of existing systems viewing meteorological phenomena in four and three dimensions. Moreover, we evaluate meteorological occurrences to better understand the dynamics associated with a meteorological phenomenon and visualize different weather data. Based on the investigation of tools and methods, we consider the existence of different ways of representing meteorological data and methodologies. However, it was imperative to obtain knowledge and create our way of visualizing weather data. This article found eleven existing solutions for 4D meteorological visualization and meteorological phenomena.

A Probability Density-Based Visual Analytics Approach to Forecast Bias Calibration

Biases inevitably occur in numerical weather prediction (NWP) due to an idealized numerical assumption for modeling chaotic atmospheric systems. Therefore, the rapid and accurate identification and calibration of biases is crucial for NWP in weather forecasting. Conventional approaches, such as various analog post-processing forecast methods, have been designed to aid in bias calibration. However, these approaches fail to consider the spatiotemporal correlations of forecast bias, which can considerably affect calibration efficacy. In this article, we propose a novel bias pattern extraction approach based on forecasting-observation probability density by merging historical forecasting and observation datasets. Given a spatiotemporal scope, our approach extracts and fuses bias patterns and automatically divides regions with similar bias patterns. Termed BicaVis, our spatiotemporal bias pattern visual analytics system is proposed to assist experts in drafting calibration curves on the basis of these bias patterns. To verify the effectiveness of our approach, we conduct two case studies with real-world reanalysis datasets. The feedback collected from domain experts confirms the efficacy of our approach.

“My personal forecast”: the digital transformation of the weather forecast communication using a fuzzy logic recommendation system

Abstract. Communicating the scientific data of the weather forecasts to the general public has always been a challenge. Using computer graphics' visual representations to convey the message to average people has certainly helped a lot to popularize the weather forecast consumption by the general public. However, these representations are not information rich since they are abstractions; moreover they are not very actionable on the receiver side to help one decide how s/he will “live” the forecast weather conditions and prepare appropriately. Therefore, there is a need to personalize the forecast based on past experience of the individuals and their personal needs. The forecast has to become more human- and needs-oriented and more focused to the particular requirements of each individual person. We, thus, propose a new co-creation process in which the audience is called to provide a daily feedback on how they lived the weather conditions personally on a daily basis, so that, “my personal forecast” can be produced making the forecast more actionable on the user side. Preliminary such attempts focused solely on the “feels like” temperature forecasts. To arrive at the “my personal forecast”, artificial intelligence based recommender systems need to be applied, using fuzzy logic as the appropriate method for the user to express the individually perceived weather conditions.

Technological opportunities for sensing of the health effects of weather and climate change: a state-of-the-art-review

Sensing and measuring meteorological and physiological parameters of humans, animals, and plants are necessary to understand the complex interactions that occur between atmospheric processes and the health of the living organisms. Advanced sensing technologies have provided both meteorological and biological data across increasingly vast spatial, spectral, temporal, and thematic scales. Information and communication technologies have reduced barriers to data dissemination, enabling the circulation of information across different jurisdictions and disciplines. Due to the advancement and rapid dissemination of these technologies, a review of the opportunities for sensing the health effects of weather and climate change is necessary. This paper provides such an overview by focusing on existing and emerging technologies and their opportunities and challenges for studying the health effects of weather and climate change on humans, animals, and plants.

Augmented situated visualization methods towards electromagnetic compatibility testing

In electrical engineering, hardware experts often need to analyze electromagnetic radiation data to detect any external interference or anomaly. The field that studies this sort of assessment is called electromagnetic compatibility (EMC). As a way to support EMC analysis, we propose the use of Augmented Situated Visualization (ASV) to supply professionals with visual and interactive information that helps them to comprehend that data, however situating it where it is most relevant in its spatial context. Users are able to interact with the visualization by changing the attributes being displayed, comparing the overlaps of multiple fields, and extracting data, as a way to refine their search. The solutions being proposed in this work were tested against each other in comparable 2D and 3D interactive visualizations of the same data in a series of data-extraction assessments with users, as a means to validate the approaches. Results exposed a correctness-time trade-off between the interaction methods. The hand-based techniques (Hand Slider and Touch Lens) were the least error-prone, being near to half as error-inducing as the gaze-based method. Touch Lens also performed as the least time-consuming method, taking in average less than half of the average time required by the others. For the visualization methods tested, the 2D ray casts presented a higher usability score and lesser workload index than the 3D topology view, however exposing over two times the error ratio. Ultimately, this work exposes how AR can help users to have better performances in a decision-making context, particularly in EMC related tasks, while also furthering the research in the ASV field.

IsoTrotter: Visually Guided Empirical Modelling of Atmospheric Convection

Empirical models, fitted to data from observations, are often used in natural sciences to describe physical behaviour and support discoveries. However, with more complex models, the regression of parameters quickly becomes insufficient, requiring a visual parameter space analysis to understand and optimize the models. In this work, we present a design study for building a model describing atmospheric convection. We present a mixed-initiative approach to visually guided modelling, integrating an interactive visual parameter space analysis with partial automatic parameter optimization. Our approach includes a new, semi-automatic technique called IsoTrotting, where we optimize the procedure by navigating along isocontours of the model. We evaluate the model with unique observational data of atmospheric convection based on flight trajectories of paragliders.

Uncertainty-Oriented Ensemble Data Visualization and Exploration using Variable Spatial Spreading

As an important method of handling potential uncertainties in numerical simulations, ensemble simulation has been widely applied in many disciplines. Visualization is a promising and powerful ensemble simulation analysis method. However, conventional visualization methods mainly aim at data simplification and highlighting important information based on domain expertise instead of providing a flexible data exploration and intervention mechanism. Trial-and-error procedures have to be repeatedly conducted by such approaches. To resolve this issue, we propose a new perspective of ensemble data analysis using the attribute variable dimension as the primary analysis dimension. Particularly, we propose a variable uncertainty calculation method based on variable spatial spreading. Based on this method, we design an interactive ensemble analysis framework that provides a flexible interactive exploration of the ensemble data. Particularly, the proposed spreading curve view, the region stability heat map view, and the temporal analysis view, together with the commonly used 2D map view, jointly support uncertainty distribution perception, region selection, and temporal analysis, as well as other analysis requirements. We verify our approach by analyzing a real-world ensemble simulation dataset. Feedback collected from domain experts confirms the efficacy of our framework.

A workflow for spatio-seasonal hydro-chemical analysis using multivariate statistical techniques

Multivariate statistical techniques are powerful in data interpretation and pattern recognition, which play a vital role in pollutant source identification for water environment management. Despite of their wide application in hydro-chemical analysis, absence of a comprehensive workflow hinders the practices and further studies. The present study constructed a workflow on the application of multivariate statistical techniques in spatio-seasonal hydro-chemical analysis, which provided a basic guidance for practices and a systematic support to future exploration. Selection of the methods and work paths for spatio-seasonal analysis largely depends on the structure of data set and the requirements of specific tasks. Trial and adjustment could be repeatedly performed to optimize the analysis strategy and identify more underlying patterns. Given a multiscale dataset concerning complex spatio-seasonal variations, temporal or spatial grouping using appropriate methods to reasonably divide the complicated data set contributes to data interpretation and pattern recognition. The upper Yangtze River basin (UYRB, China) was employed for case analysis to demonstrate how the workflow guides an efficient and effective data exploration. Efforts could be made in future works to continually improve the workflow to involve more complicated analysis and techniques and the integrated application in various fields.

Visualization of Climate Science Simulation Data

Climate simulations belong to the most data-intensive scientific disciplines and are-in relation to one of humankind's largest challenges, i.e., facing anthropogenic climate change-ever more important. Not only are the outputs generated by current models increasing in size, due to an increase in resolution and the use of ensembles, but the complexity is also rising as a result of maturing models that are able to better describe the intricacies of our climate system. This article focuses on developments and trends in the scientific workflow for the analysis and visualization of climate simulation data, as well as on changes in the visualization techniques and tools that are available.

GenerativeMap: Visualization and Exploration of Dynamic Density Maps via Generative Learning Model

The density map is widely used for data sampling, time-varying detection, ensemble representation, etc. The visualization of dynamic evolution is a challenging task when exploring spatiotemporal data. Many approaches have been provided to explore the variation of data patterns over time, which commonly need multiple parameters and preprocessing works. Image generation is a well-known topic in deep learning, and a variety of generating models have been promoted in recent years. In this paper, we introduce a general pipeline called GenerativeMap to extract dynamics of density maps by generating interpolation information. First, a trained generative model comprises an important part of our approach, which can generate nonlinear and natural results by implementing a few parameters. Second, a visual presentation is proposed to show the density change, which is combined with the level of detail and blue noise sampling for a better visual effect. Third, for dynamic visualization of large-scale density maps, we extend this approach to show the evolution in regions of interest, which costs less to overcome the drawback of the learning-based generative model. We demonstrate our method on different types of cases, and we evaluate and compare the approach from multiple aspects. The results help identify the effectiveness of our approach and confirm its applicability in different scenarios.

Extraction and Visual Analysis of Potential Vorticity Banners around the Alps

Potential vorticity is among the most important scalar quantities in atmospheric dynamics. For instance, potential vorticity plays a key role in particularly strong wind peaks in extratropical cyclones and it is able to explain the occurrence of frontal rain bands. Potential vorticity combines the key quantities of atmospheric dynamics, namely rotation and stratification. Under suitable wind conditions elongated banners of potential vorticity appear in the lee of mountains. Their role in atmospheric dynamics has recently raised considerable interest in the meteorological community for instance due to their influence in aviation wind hazards and maritime transport. In order to support meteorologists and climatologists in the analysis of these structures, we developed an extraction algorithm and a visual exploration framework consisting of multiple linked views. For the extraction we apply a predictor-corrector algorithm that follows streamlines and realigns them with extremal lines of potential vorticity. Using the agglomerative hierarchical clustering algorithm, we group banners from different sources based on their proximity. To visually analyze the time-dependent banner geometry, we provide interactive overviews and enable the query for detail on demand, including the analysis of different time steps, potentially correlated scalar quantities, and the wind vector field. In particular, we study the relationship between relative humidity and the banners for their potential in indicating the development of precipitation. Working with our method, the collaborating meteorologists gained a deeper understanding of the three-dimensional processes, which may spur follow-up research in the future.

VAPOR: A Visualization Package Tailored to Analyze Simulation Data in Earth System Science

Visualization is an essential tool for analysis of data and communication of findings in the sciences, and the Earth System Sciences (ESS) are no exception. However, within ESS, specialized visualization requirements and data models, particularly for those data arising from numerical models, often make general purpose visualization packages difficult, if not impossible, to use effectively. This paper presents VAPOR: a domain-specific visualization package that targets the specialized needs of ESS modelers, particularly those working in research settings where highly-interactive exploratory visualization is beneficial. We specifically describe VAPOR’s ability to handle ESS simulation data from a wide variety of numerical models, as well as a multi-resolution representation that enables interactive visualization on very large data while using only commodity computing resources. We also describe VAPOR’s visualization capabilities, paying particular attention to features for geo-referenced data and advanced rendering algorithms suitable for time-varying, 3D data. Finally, we illustrate VAPOR’s utility in the study of a numerically- simulated tornado. Our results demonstrate both ease-of-use and the rich capabilities of VAPOR in such a use case.

Interactive 3D Visual Analysis of Atmospheric Fronts

Atmospheric fronts play a central role in meteorology, as the boundaries between different air masses and as fundamental features of extra-tropical cyclones. They appear in numerous conceptual model depictions of extra-tropical weather systems. Conceptually, fronts are three-dimensional surfaces in space possessing an innate structural complexity, yet in meteorology, both manual and objective identification and depiction have historically focused on the structure in two dimensions. In this work, we -a team of visualization scientists and meteorologists- propose a novel visualization approach to analyze the three-dimensional structure of atmospheric fronts and related physical and dynamical processes. We build upon existing approaches to objectively identify fronts as lines in two dimensions and extend these to obtain frontal surfaces in three dimensions, using the magnitude of temperature change along the gradient of a moist potential temperature field as the primary identifying factor. We introduce the use of normal curves in the temperature gradient field to visualize a frontal zone (i.e., the transitional zone between the air masses) and the distribution of atmospheric variables in such zones. To enable for the first time a statistical analysis of frontal zones, we present a new approach to obtain the volume enclosed by a zone, by classifying grid boxes that intersect with normal curves emanating from a selected front. We introduce our method by means of an idealized numerical simulation and demonstrate its use with two real-world cases using numerical weather prediction data.