Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data

Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep learning models have become more common, RNNs have been used to forecast increasingly complicated systems. Dynamical spatio-temporal processes represent a class of complex systems that can potentially benefit from these types of models. Although the RNN literature is expansive and highly developed, uncertainty quantification is often ignored. Even when considered, the uncertainty is generally quantified without the use of a rigorous framework, such as a fully Bayesian setting. Here we attempt to quantify uncertainty in a more formal framework while maintaining the forecast accuracy that makes these models appealing, by presenting a Bayesian RNN model for nonlinear spatio-temporal forecasting. Additionally, we make simple modifications to the basic RNN to help accommodate the unique nature of nonlinear spatio-temporal data. The proposed model is applied to a Lorenz simulation and two real-world nonlinear spatio-temporal forecasting applications

Spatio-temporal precipitation patterns: from teleconnections to improved long-term forecasts

The standardized precipitation index (SPI) is an important yet easy-to-calculate means to describe wet or dry conditions in very different climates. In this work, a new scheme for obtaining improved forecasts of this index is developed. The methodology is tested over Russia and West Africa, proving that it can be successfully applied to different forecasting models and world regions. For testing, we use two forecasting models: the semi-implicit semi-Lagrangian vorticity-divergence (SL–AV) model of the Hydrometeorological Centre of Russia and the Institute of Numerical Mathematics of the Russian Academy of Sciences for Russia and the Climate Forecast System Version 2 (CFSv2) of the National Center for Environmental Prediction (NCEP) for West Africa. Based on hindcast simulations of both models, we demonstrate relatively poor skills in obtaining direct zero to three month lead-time SPI forecasts in the regions of interest during summer season. In order to improve the accuracy of these forecasts, we utilize surface temperature, mean sea level pressure and 500 hPa geopotential height fields, obtained from the outputs of both models. The spatial patterns of crosscorrelations between previously obtained climatological fields and our target variable (SPI-1) are studied to identify informative co-variates, potentially affecting monthly scale precipitation variability. The cross-correlation structures between the different fields reveal relevant interdependencies between SPI-1, sea surface temperature, mean sea level pressure and 500 hPa geopotential height in different regions. Subsequently, we employ two different regression models based on statistical post-processing of regional climate model output. In the first model, we consider all combinations of pairs of the previously identified predictors in a set of linear regression equations, which generates an ensemble of individual SPI-1 forecasts. The second model is based on a multiple linear regression approach comprising the dependency between all predictor variables and the predictand (SPI-1) in a single equation. The resulting SPI-1 forecasts obtained from both regression models are subsequently analysed in both deterministic and probabilistic ways and checked by various verification metrics. We identify that the first proposed model provides a significant improvement in the SPI forecasting, pointing to the potential for its implementation in operational monthly precipitation forecasts

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. Approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: Forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and, finally, discuss mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting both the foundations, practical applications, and opportunities of graph neural networks for time series analysis.Comment: 27 pages, 6 figures, 5 table

A Network of Portable, Low-Cost, X-Band Radars

Radar is a unique tool to get an overview on the weather situation, given its high spatio- temporal resolution. Over 60 years, researchers have been investigating ways for obtaining the best use of radar. As a result we often find assurances on how much radar is a useful tool, and it is! After this initial statement, however, regularly comes a long list on how to increase the accuracy of radar or in what direction to move for improving it. Perhaps we should rather ask: is the resulting data good enough for our application? The answers are often more complicated than desired. At first, some people expect miracles. Then, when their wishes are disappointed, they discard radar as a tool: both attitudes are wrong; radar is a unique tool to obtain an excellent overview on what is happening: when and where it is happening. At short ranges, we may even get good quantitative data. But at longer ranges it may be impossible to obtain the desired precision, e.g. the precision needed to alert people living in small catchments in mountainous terrain. We would have to set the critical limit for an alert so low that this limit would lead to an unacceptable rate of false alarm

A contour tree based spatio-temporal data model for oceanographic applications

To present the spatio/temporal data from oceanographic modeling in GIS has been a challenging task due to the highly dynamic characteristic and complex pattern of variables, in relation to time and space. This dissertation focuses the research on spatio-temporal GIS data model applied to oceanographic model data, especially to homogeneous iso-surface data. The available spatio-temporal data models are carefully reviewed and characteristics in spatial and temporal issues from oceanographic model data are discussed in detail. As an important tool for data modeling, ontology is introduced to categorize oceanographic model data and further set up fundamental software components in the new data model. The proposed data model is based on the concept of contour tree. By adding temporal information to each node and arc of the contour tree, and using multiple contour trees to represent different time steps in the temporal domain, the changes can be stored and tracked by the data model. In order to reduce the data volume and increase the data quality, the new data model integrates spatial and temporal interpolation methods within it. The spatial interpolation calculates the data that fall between neighboring contours at a single time step. The Inverse Distance Weighting (IDW) is applied as the main algorithm and the Minimum Bounding Rectangle (MBR) is used to enhance the spatial interpolation performance. The temporal interpolation calculates the data that are not recorded, which fall between neighboring contour trees for adjacent time steps. The “linear interpolation” algorithm is preferred to the “nearest neighbor’s value” and “spline” interpolation methods, for its modest accuracy and the simple implementation scheme. In order to evaluate the support functions of the new data model, a case study is presented with the motivation to show how this data model supports complicated spatio-temporal queries in forecasting applications. This dissertation also showcases some work in contour tree simplification. A new simplification algorithm is introduced to reduce the data complexity. This algorithm is based on the branch decomposition method and supports temporal information integrated into contour trees. Three types of criteria parameters are introduced to run different simplification methods for various applications

A virtual centre at the interface of basic and applied weather and climate research

The Hans-Ertel Centre for Weather Research is a network of German universities, research institutes and the German Weather Service (Deutscher Wetterdienst, DWD). It has been established to trigger and intensify basic research and education on weather forecasting and climate monitoring. The performed research ranges from nowcasting and short-term weather forecasting to convective-scale data assimilation, the development of parameterizations for numerical weather prediction models, climate monitoring and the communication and use of forecast information. Scientific findings from the network contribute to better understanding of the life-cycle of shallow and deep convection, representation of uncertainty in ensemble systems, effects of unresolved variability, regional climate variability, perception of forecasts and vulnerability of society. Concrete developments within the research network include dual observation-microphysics composites, satellite forward operators, tools to estimate observation impact, cloud and precipitation system tracking algorithms, large-eddy-simulations, a regional reanalysis and a probabilistic forecast test product. Within three years, the network has triggered a number of activities that include the training and education of young scientists besides the centre's core objective of complementing DWD's internal research with relevant basic research at universities and research institutes. The long term goal is to develop a self-sustaining research network that continues the close collaboration with DWD and the national and international research community