Information system for operation analysis of time series

This work is devoted to modern information technology of operational analysis of time series. The main purpose of data operational analysis is replenishment, data blurring, smoothing and forecasting of time series, obtaining conclusions about periodicity, cyclicity, character of the behavior of the observed process. Also attention is paid to the task of separating noise or observational errors from real data

Operational solar forecasting for the real-time market

Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world's fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones

A comparative study of forecasting container throughput through time series analysis

This paper shows different techniques used in the operational research to encounter with forecasting the total container throughput handling. Each techniques approached has its objective and constraints regarding to the research problem. The container throughput is responsible for large investments in port infrastructure development as the aims is to established a sufficiently accurate forecasting decision support system since they try to follow the global trends in the optimization of port operations and facilities

Continental and global scale flood forecasting systems

Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centres are increasingly using the meteorological output from these to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state-of-the-art operational large scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Currently, operational systems have the capability to produce coarse-scale discharge forecasts in the medium-range and disseminate forecasts and, in some cases, early warning products, in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale, alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction

Improving Operational Geomagnetic Index Forecasting

Space weather prediction is moving from an era of pure curiosity-driven research into an era of 24/7 operations. The interest in space weather forecasts has never been greater, with society becoming ever more reliant upon technology and infrastructure which are potentially at risk. Amongst space weather hazards, geomagnetic storms are potential threats to power-grid infrastructure, communication systems and oil and gas operations. Geomagnetic indices capture the severity of magnetic storms by summarising magnetic activity at spatially disparate locations. They have become almost ubiquitous as parameterisations of storm-time magnetic conditions and are required inputs for radiation belt,ionospheric and neutral atmosphere models. We present the first results from a study aiming to provide operational geomagnetic index prediction that is: robust and reliable, has high cadence, runs fast enough for real-time operations , and is accurate forecasting up to three days ahead. The predictive power of autoregressive and machine-learning techniques applied to combinations of solar, solar wind and geomagnetic data is investigated. The predictions presented will ultimately form part of the British Geological Survey’s space weather forecast operations

Summary of the Operational Applications of Satellite Snowcover Observations Working Session, 20 August 1975

Various techniques for reducing the satellite data to a form usable by the operational agencies were covered in mini-presentations by the operational satellite snow interpretive personnel. Similar discussions were made by operational agency stream flow forecasters on how satellite-derived snow data could be incorporated into runoff forecasting methods

Challenges of operational river forecasting

Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors

First in new environmental spacecraft series to be launched

A series of operational meteorological monitoring satellites (TIROS-N) is described. Emphasis is placed on environmental monitoring instruments onboard the satellites that provide technological advances over previous sensors. Benefits in the areas of weather forecasting, oceanography, water resource management, and flood forecasting are discussed along with the operational capability to collect and transmit environmental data from platforms on land, at sea, and airborne, and to track stations motion. The participation of Canada, Great Britain, and France is mentioned and a description of the launch vehicle is included