Chapter 22 Regional Real-Time Smoke Prediction Systems

2008 ◽  
pp. 499-534 ◽  
Author(s):  
Susan M. O’Neill ◽  
Narasimhan (Sim) K. Larkin ◽  
Jeanne Hoadley ◽  
Graham Mills ◽  
Joseph K. Vaughan ◽  
...  
Keyword(s):  
Real Time ◽  
Prediction Systems

Data Reduction Techniques for Near Real-Time Decision Making in Fall Prediction Systems

2018 ◽  
pp. 52-64
Author(s):  
Masoud Hemmatpour ◽  
Renato Ferrero ◽  
Filippo Gandino ◽  
Bartolomeo Montrucchio ◽  
Maurizio Rebaudengo
Keyword(s):  
Real Time ◽  
High Speed ◽  
Time Data ◽  
Reduction Techniques ◽  
Fall Prediction ◽  
Real Time Data ◽  
Data Volume ◽  
Prediction Systems ◽  
Unintentional Falls ◽  
Health Service Costs

Unintentional falls are a frequent cause of hospitalization that mostly increases health service costs due to injuries. Fall prediction systems strive to reduce injuries and provide fast help to the users. Typically, such systems collect data continuously at a high speed through a device directly attached to the user. Whereas such systems are implemented in devices with limited resources, data volume is significantly important. In this chapter, a real-time data analyzer and reducer is proposed in order to manage the data volume of fall prediction systems.

scholarly journals PostProcessing and Visualization Techniques for Convection-Allowing Ensembles

2019 ◽  
Vol 100 (7) ◽  
pp. 1245-1258 ◽  
Author(s):  
Brett Roberts ◽  
Israel L. Jirak ◽  
Adam J. Clark ◽  
Steven J. Weiss ◽  
John S. Kain
Keyword(s):  
Real Time ◽  
Data Extraction ◽  
Deep Convection ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Data Volume ◽  
Explicit Simulation ◽  
Prediction Systems ◽  
Visualization Techniques

AbstractSince the early 2000s, growing computing resources for numerical weather prediction (NWP) and scientific advances enabled development and testing of experimental, real-time deterministic convection-allowing models (CAMs). By the late 2000s, continued advancements spurred development of CAM ensemble forecast systems, through which a broad range of successful forecasting applications have been demonstrated. This work has prepared the National Weather Service (NWS) for practical usage of the High Resolution Ensemble Forecast (HREF) system, which was implemented operationally in November 2017. Historically, methods for postprocessing and visualizing products from regional and global ensemble prediction systems (e.g., ensemble means and spaghetti plots) have been applied to fields that provide information on mesoscale to synoptic-scale processes. However, much of the value from CAMs is derived from the explicit simulation of deep convection and associated storm-attribute fields like updraft helicity and simulated reflectivity. Thus, fully exploiting CAM ensembles for forecasting applications has required the development of fundamentally new data extraction, postprocessing, and visualization strategies. In the process, challenges imposed by the immense data volume inherent to these systems required new approaches when considering diverse factors like forecaster interpretation and computational expense. In this article, we review the current state of postprocessing and visualization for CAM ensembles, with a particular focus on forecast applications for severe convective hazards that have been evaluated within NOAA’s Hazardous Weather Testbed. The HREF web viewer implemented at the NWS Storm Prediction Center (SPC) is presented as a prototype for deploying these techniques in real time on a flexible and widely accessible platform.

An Overview of Drought Monitoring and Prediction Systems at Regional and Global Scales

2017 ◽  
Vol 98 (9) ◽  
pp. 1879-1896 ◽  
Author(s):  
Zengchao Hao ◽  
Xing Yuan ◽  
Youlong Xia ◽  
Fanghua Hao ◽  
Vijay P. Singh
Keyword(s):  
Real Time ◽  
Land Surface ◽  
Information Dissemination ◽  
Data Sources ◽  
Drought Monitoring ◽  
Severe Drought ◽  
Drought Prediction ◽  
Multiple Data ◽  
Challenges And Opportunities ◽  
Prediction Systems

Abstract In past decades, severe drought events have struck different regions around the world, leading to huge losses to a wide array of environmental and societal sectors. Because of wide impacts of drought, it is of critical importance to monitor drought in near–real time and provide early warning. This article provides an overview of the development of drought monitoring and prediction systems (DMAPS) at regional and global scales. After introducing drought indicators, drought monitoring (based on different data sources and tools) is summarized, along with an introduction of statistical and dynamical drought prediction approaches. The current progress of the development and implementation of DMAPS with various indicators at different temporal and/or spatial resolutions, based on the land surface modeling, remote sensing, and seasonal climate forecast, at the regional and global scales is then reviewed. Advances in drought monitoring with multiple data sources and tools and prediction from multimodel ensembles are highlighted. Also highlighted are challenges and opportunities, including near-real-time and long-term data products, indicator linkage to impacts, prediction skill improvement, and information dissemination/communication. The review of different components of these systems will provide useful guidelines and insights for the future development of effective DMAPS to aid drought modeling and management.

A Metamodel for Estimating Error Bounds in Real-Time Traffic Prediction Systems

2014 ◽  
Vol 15 (3) ◽  
pp. 1310-1322 ◽  
Author(s):  
Francisco C. Pereira ◽  
Constantinos Antoniou ◽  
Joan Aguilar Fargas ◽  
Moshe Ben-Akiva
Keyword(s):  
Real Time ◽  
Error Bounds ◽  
Traffic Prediction ◽  
Real Time Traffic ◽  
Prediction Systems

Lessons learned from the implementation of the near real-time S2S4E Decision Support Tool

2021 ◽  
Author(s):  
Lluís Palma ◽  
Andrea Manrique ◽  
Llorenç Lledó ◽  
Andria Nicodemou ◽  
Pierre-Antoine Bretonnière ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Climate Models ◽  
Decision Support Tool ◽  
Lessons Learned ◽  
Cloud Infrastructure ◽  
Time Service ◽  
Support Tool ◽  
Wide Range ◽  
Prediction Systems

<p>Under the context of the H2020 S2S4E project, industrial and research partners co-developed a fully-operational Decision Support Tool (DST) providing during 18 months near real-time subseasonal and seasonal  forecasts tailored to the specific needs of the renewable energy sector. The tool aimed to breach the last mile gap between climate information and the end-user by paying attention to the interaction with agents from the sector, already used to work with weather information, and willing to extend their forecasting horizon by incorporating climate predictions into their daily operations.</p><p>With this purpose, the tool gathered a heterogeneous dataset of seven different essential climate variables and nine energy indicators, providing for each of them bias-adjusted probabilistic information paired with a reference skill metric. To achieve this, data from state-of-the-art prediction systems and reanalysis needed to be downloaded and post-processed, fulfilling a set of quality requirements that ensure the proper functioning of the operational service. During the design, implementation, and testing phases, a wide range of scientific and technical choices had to be made, making clear the difficulties of transferring scientific research to a user-oriented real-time service. A brief showcase will be presented, exemplifying the different tools, methodologies, and best practices applied to the data workflow, together with a case study performed in Oracle’s cloud infrastructure. We expect that by making a clear description of the process and the problems encountered, we will provide a valuable experience for both, upcoming attempts of similar implementations, and the organizations providing data from climate models and reanalysis.</p>

scholarly journals Discharge Interval method for uncertain flood forecasts using a flood model chain: city of Kulmbach

2019 ◽  
Vol 21 (5) ◽  
pp. 925-944
Author(s):  
Md Nazmul Azim Beg ◽  
Jorge Leandro ◽  
Punit Bhola ◽  
Iris Konnerth ◽  
Winfried Willems ◽  
...  
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Historical Event ◽  
Computationally Efficient ◽  
Interval Method ◽  
The Public ◽  
Uncertainty Sources ◽  
Prediction Systems ◽  
Model Chain

Abstract Real-time flood forecasting can help authorities in providing reliable warnings to the public. Ensemble prediction systems (EPS) have been progressively used for operational flood forecasting by European hydrometeorological agencies in recent years. This process, however, is non-deterministic such that uncertainty sources need to be considered before issuing forecasts. In this study, a new methodology for flood forecasting named Discharge Interval method is proposed. This method uses at least one historical event hindcast data, run in several ensembles and selects a pair of best ensemble discharge results for every certain discharge level. Later, the method uses the same parameter settings of the chosen ensemble discharge pair to forecast any certain flood discharge level. The methodology was implemented within the FloodEvac tool. The tool can handle calibration/validation of the hydrological model (LARSIM) and produces real-time flood forecasts with the associated uncertainty of the flood discharges. The proposed methodology is computationally efficient and suitable for real-time forecasts with uncertainty. The results using the Discharge Interval method were found comparable to the 90th percentile forecasted discharge range obtained with the Ensemble method.

scholarly journals Lessons Learned from Coupled Fire-Atmosphere Research and Implications for Operational Fire Prediction and Meteorological Products Provided by the Bureau of Meteorology to Australian Fire Agencies

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1380
Author(s):  
Mika Peace ◽  
Joseph Charney ◽  
John Bally
Keyword(s):  
Research And Development ◽  
Real Time ◽  
Combustion Process ◽  
Lessons Learned ◽  
Future Directions ◽  
Hazard Management ◽  
Atmospheric Component ◽  
Atmospheric Processes ◽  
Prediction Systems ◽  
Protection Of Life

Coupled fire-atmosphere models are simulators that integrate a fire component and an atmospheric component, with the objective of capturing interactions between the fire and atmosphere. As a fire releases energy in the combustion process, the surrounding atmosphere adjusts in response to the energy fluxes; coupled fire-atmosphere (CFA) models aim to resolve the processes through which these adjustments occur. Several CFA models have been developed internationally, mostly by meteorological institutions and primarily for use as a research tool. Research studies have provided valuable insights into some of the atmospheric processes surrounding a fire. The potential to run CFA models in real time is currently limited due to the intensive computational requirements. In addition, there is a need for systematic verification to establish their accuracy and the appropriate circumstances for their use. The Bureau of Meteorology (the Bureau) is responsible for providing relevant and accurate meteorological information to Australian fire agencies to inform decisions for the protection of life and property and to support hazard management activities. The inclusion of temporally and spatially detailed meteorological fields that adjust in response to the energy released by a fire is seen as a component in developing fire prediction systems that capture some of the most impactful fire and weather behavior. The Bureau’s ten-year research and development plan includes a commitment to developing CFA models, with the objective of providing enhanced services to Australian fire agencies. This paper discusses the operational use of fire predictions and simulators, learnings from CFA models and potential future directions for the Bureau in using CFA models to support fire prediction activities.

scholarly journals Skill of Real-Time Seasonal ENSO Model Predictions during 2002–11: Is Our Capability Increasing?

2012 ◽  
Vol 93 (5) ◽  
pp. 631-651 ◽  
Author(s):  
Anthony G. Barnston ◽  
Michael K. Tippett ◽  
Michelle L. L'Heureux ◽  
Shuhua Li ◽  
David G. DeWitt
Keyword(s):  
Real Time ◽  
Prediction Models ◽  
Difficulty Level ◽  
Dynamical Models ◽  
Time Model ◽  
Skill Levels ◽  
Model Predictions ◽  
Large Ensembles ◽  
Decadal Variations ◽  
Prediction Systems

Real-time model predictions of ENSO conditions during the 2002–11 period are evaluated and compared to skill levels documented in studies of the 1990s. ENSO conditions are represented by the Niño- 3.4 SST index in the east-central tropical Pacific. The skills of 20 prediction models (12 dynamical, 8 statistical) are examined. Results indicate skills somewhat lower than those found for the less advanced models of the 1980s and 1990s. Using hindcasts spanning 1981–2011, this finding is explained by the relatively greater predictive challenge posed by the 2002–11 period and suggests that decadal variations in the character of ENSO variability are a greater skill-determining factor than the steady but gradual trend toward improved ENSO prediction science and models. After adjusting for the varying difficulty level, the skills of 2002–11 are slightly higher than those of earlier decades. Unlike earlier results, the average skill of dynamical models slightly, but statistically significantly, exceeds that of statistical models for start times just before the middle of the year when prediction has proven most difficult. The greater skill of dynamical models is largely attributable to the subset of dynamical models with the most advanced, highresolution, fully coupled ocean–atmosphere prediction systems using sophisticated data assimilation systems and large ensembles. This finding suggests that additional advances in skill remain likely, with the expected implementation of better physics, numeric and assimilation schemes, finer resolution, and larger ensemble sizes.

scholarly journals MAP D-PHASE: real-time demonstration of hydrological ensemble prediction systems

2008 ◽  
Vol 9 (2) ◽  
pp. 80-87 ◽  
Author(s):  
Massimiliano Zappa ◽  
Mathias W. Rotach ◽  
Marco Arpagaus ◽  
Manfred Dorninger ◽  
Christoph Hegg ◽  
...  
Keyword(s):  
Real Time ◽  
Ensemble Prediction ◽  
Prediction Systems
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