Predictability of variable solar-terrestrial coupling

In October 2017, the Scientific Committee on Solar-Terrestrial Physics (SCOSTEP) Bureau established a committee for the design of SCOSTEP's Next Scientific Programme (NSP). The NSP committee members and authors of this paper decided from the very beginning of their deliberations that the predictability of the Sun-Earth System from a few hours to centuries is a timely scientific topic, combining the interests of different topical communities in a relevant way. Accordingly, the NSP was christened PRESTO - PREdictability of the variable Solar-Terrestrial cOupling. This paper presents a detailed account of PRESTO; we show the key milestones of the PRESTO roadmap for the next 5 years, review the current state of the art and discuss future studies required for the most effective development of solar-terrestrial physics.Peer reviewe

Machine Learning for Earth Systems Modeling, Analysis and Predictability

Artificial intelligence (AI) and machine learning (ML) methods and applications have been continuously explored in many areas of scientific research. While these methods have lead to many advances in climate science, there remains room for growth especially in Earth System Modeling, analysis and predictability. Due to their high computational expense and large volumes of complex data they produce, earth system models (ESMs) provide an abundance of potential for enhancing both our understanding of the climate system as well as improving performance of ESMs themselves using ML techniques. Here I demonstrate 3 specific areas of development using ML: statistical downscaling, predictability using non-linear latent spaces and emulation of complex parametrization. These three areas of research illustrate the ability of innovative ML methods to advance our understanding of climate systems through ESMs. In Aim 1, I present a first application of a fast super resolution convolutional neural network (FSRCNN) based approach for downscaling earth system model (ESM) simulations. We adapt the FSRCNN to improve reconstruction on ESM data, we term the FSRCNN-ESM. We find that FSRCNN-ESM outperforms FSRCNN and other super-resolution methods in reconstructing high resolution images producing finer spatial scale features with better accuracy for surface temperature, surface radiative fluxes and precipitation. In Aim 2, I construct a novel Multi-Input Multi-Output Autoencoder-decoder (MIMO-AE) in an application of multi-task learning to capture the non-linear relationship of Southern California precipitation (SC-PRECIP) and tropical Pacific Ocean sea surface temperature (TP-SST) on monthly time-scales. I find that the MIMO-AE index provides enhanced predictability of SC-PRECIP for a lead-time of up-to four months as compared to Ni{\~n}o 3.4 index and the El Ni{\~n}o Southern Oscillation Longitudinal Index. I also use a MTL method to expand on a convolutional long short term memory (conv-LSTM) to predict Nino 3.4 index by including multiple input variables known to be associated with ENSO, namely sea level pressure (SLP), outgoing longwave radiation (ORL) and surface level zonal winds (U). In Aim 3, I demonstrate the capability of DNNs for learning computationally expensive parameterizations in ESMs. This study develops a DNN to replace the full radiation model in the E3SM

Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges

With the rapid development of marine activities, there has been an increasing number of maritime mobile terminals, as well as a growing demand for high-speed and ultra-reliable maritime communications to keep them connected. Traditionally, the maritime Internet of Things (IoT) is enabled by maritime satellites. However, satellites are seriously restricted by their high latency and relatively low data rate. As an alternative, shore & island-based base stations (BSs) can be built to extend the coverage of terrestrial networks using fourth-generation (4G), fifth-generation (5G), and beyond 5G services. Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs. Despite of all these approaches, there are still open issues for an efficient maritime communication network (MCN). For example, due to the complicated electromagnetic propagation environment, the limited geometrically available BS sites, and rigorous service demands from mission-critical applications, conventional communication and networking theories and methods should be tailored for maritime scenarios. Towards this end, we provide a survey on the demand for maritime communications, the state-of-the-art MCNs, and key technologies for enhancing transmission efficiency, extending network coverage, and provisioning maritime-specific services. Future challenges in developing an environment-aware, service-driven, and integrated satellite-air-ground MCN to be smart enough to utilize external auxiliary information, e.g., sea state and atmosphere conditions, are also discussed

25 Years of Self-Organized Criticality: Solar and Astrophysics

Shortly after the seminal paper {\sl "Self-Organized Criticality: An explanation of 1/f noise"} by Bak, Tang, and Wiesenfeld (1987), the idea has been applied to solar physics, in {\sl "Avalanches and the Distribution of Solar Flares"} by Lu and Hamilton (1991). In the following years, an inspiring cross-fertilization from complexity theory to solar and astrophysics took place, where the SOC concept was initially applied to solar flares, stellar flares, and magnetospheric substorms, and later extended to the radiation belt, the heliosphere, lunar craters, the asteroid belt, the Saturn ring, pulsar glitches, soft X-ray repeaters, blazars, black-hole objects, cosmic rays, and boson clouds. The application of SOC concepts has been performed by numerical cellular automaton simulations, by analytical calculations of statistical (powerlaw-like) distributions based on physical scaling laws, and by observational tests of theoretically predicted size distributions and waiting time distributions. Attempts have been undertaken to import physical models into the numerical SOC toy models, such as the discretization of magneto-hydrodynamics (MHD) processes. The novel applications stimulated also vigorous debates about the discrimination between SOC models, SOC-like, and non-SOC processes, such as phase transitions, turbulence, random-walk diffusion, percolation, branching processes, network theory, chaos theory, fractality, multi-scale, and other complexity phenomena. We review SOC studies from the last 25 years and highlight new trends, open questions, and future challenges, as discussed during two recent ISSI workshops on this theme.Comment: 139 pages, 28 figures, Review based on ISSI workshops "Self-Organized Criticality and Turbulence" (2012, 2013, Bern, Switzerland

On the predictability of exceptional error events in wind power forecasting —an ultra large ensemble approach—

Exceptional error events in wind power forecasting impose a major obstacle to today’s reliable power supply. The predictability of such error events is fundamentally restricted by the underlying weather forecast, resting on limitations of state-of-the-art numerical prediction systems. This work aims to identify such imminent forecast errors applying a probabilistic approach. To this end, the standard sizes of meteorological ensembles are increased from O(10) to an ultra large ensemble size of O(1000) members to accomplish an improved approximation of the probability density function. For this purpose, a novel approach of an ensemble control system named ESIAS-met has been developed on a Petaflop architecture. Further, an increased ensemble size favors the application of nonlinear data assimilation techniques based on the particle filter, while imposing the challenge of growing computational expenses of a resampling step within the particle filter algorithm. ESIAS-met presents a computationally efficient solution to the problem by realizing a parallel execution of the ensemble. Performance measurements demonstrate strong scalability of the system with up to 4096 members. Moreover, the computational expenses of a particle filter resampling step are shown to become independent of the ensemble size. The ESIAS-met system is further applied to investigate the benefit of an increased ensemble size on the predictability of recent exceptional error events. The analysis reveals, that despite the large ensemble size, the forecast error is only represented by single outliers. Higher order moments prove to provide a robust measure of the proper direction of forecast error and assess their likelihood of appearance. It is shown, that at least O(100) ensemble members are needed to resolve the higher order moments sufficiently well. Hence, the results achieved in this work yield important potential for future warning capabilities of exceptional error events

Correlated power time series of individual wind turbines: A data driven model approach

Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with cross-over behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model ofpower time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function.Comment: 20 pages, 8 figure

North Atlantic Climate Variability: Phenomena, Impacts and Mechanisms

Variability of the North Atlantic Oscillation and the Tropical Atlantic dominate the climate of the North Atlantic sector, the underlying ocean and surrounding continents on interannual to decadal time scales. Here we review these phenomena, their climatic impacts and our present state of understanding of their underlying caus