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

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.

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 A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

scholarly journals An approach for tuning ensemble prediction systems

2013 ◽  
Vol 65 (1) ◽  
pp. 20594 ◽  
Author(s):  
Antti Solonen ◽  
Heikki Järvinen
Keyword(s):  
Ensemble Prediction ◽  
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 Evaluation of two hydrometeorological ensemble strategies for flash-flood forecasting over a catchment of the eastern Pyrenees

2020 ◽  
Vol 20 (2) ◽  
pp. 425-450 ◽  
Author(s):  
Hélène Roux ◽  
Arnau Amengual ◽  
Romu Romero ◽  
Ernest Bladé ◽  
Marcos Sanz-Ramos
Keyword(s):  
Mesoscale Model ◽  
Flash Flood ◽  
Ensemble Prediction ◽  
Flood Warning ◽  
Eastern Pyrenees ◽  
Streamflow Forecasts ◽  
Prediction Systems ◽  
Rainfall Runoff Model ◽  
Alert Threshold ◽  
Threshold Exceedance

Abstract. This study aims at evaluating the performances of flash-flood forecasts issued from deterministic and ensemble meteorological prognostic systems. The hydrometeorological modeling chain includes the Weather Research and Forecasting Model (WRF) forcing the rainfall-runoff model MARINE dedicated to flash floods. Two distinct ensemble prediction systems accounting for (i) perturbed initial and lateral boundary conditions of the meteorological state and (ii) mesoscale model physical parameterizations have been implemented on the Agly catchment of the eastern Pyrenees with three subcatchments exhibiting different rainfall regimes. Different evaluations of the performance of the hydrometeorological strategies have been performed: (i) verification of short-range ensemble prediction systems and corresponding streamflow forecasts, for a better understanding of how forecasts behave; (ii) usual measures derived from a contingency table approach, to test an alert threshold exceedance; and (iii) overall evaluation of the hydrometeorological chain using the continuous rank probability score, for a general quantification of the ensemble performances. Results show that the overall discharge forecast is improved by both ensemble strategies with respect to the deterministic forecast. Threshold exceedance detections for flood warning also benefit from large hydrometeorological ensemble spread. There are no substantial differences between both ensemble strategies on these test cases in terms of both the issuance of flood warnings and the overall performances, suggesting that both sources of external-scale uncertainty are important to take into account.

Intelligent Analysis of Software Maintenance Data

2009 ◽  
pp. 189-221
Author(s):  
Marek Reformat ◽  
Petr Musilek ◽  
Efe Igbide
Keyword(s):  
Software Engineering ◽  
Software Maintenance ◽  
Evidence Theory ◽  
Data Models ◽  
Ensemble Prediction ◽  
Software Companies ◽  
Single Defect ◽  
Engineering Data ◽  
Single Data ◽  
Prediction Systems

Amount of software engineering data gathered by software companies amplifies importance of tools and techniques dedicated to processing and analysis of data. More and more methods are being developed to extract knowledge from data and build data models. In such cases, selection of the most suitable data processing methods and quality of extracted knowledge is of great importance. Software maintenance is one of the most time and effort-consuming tasks among all phases of a software life cycle. Maintenance managers and personnel look for methods and tools supporting analysis of software maintenance data in order to gain knowledge needed to prepare better plans and schedules of software maintenance activities. Software engineering data models should provide quantitative as well as qualitative outputs. It is desirable to build these models based on a welldelineated logic structure. Such models would enhance maintainers’ understanding of factors which influence maintenance efforts. This chapter focuses on defect-related activities that are the core of corrective maintenance. Two aspects of these activities are considered: a number of software components that have to be examined during a defect removing process, and time needed to remove a single defect. Analysis of the available datasets leads to development of data models, extraction of IF-THEN rules from these models, and construction of ensemble-based prediction systems that are built based on these data models. The data models are developed using well-known tools such as See5/C5.0 and 4cRuleBuilder, and a new multi-level evolutionary-based algorithm. Single data models are put together into ensemble prediction systems that use elements of evidence theory for the purpose of inference about a degree of belief in the final prediction.

scholarly journals Evaluating U.S. East Coast Winter Storms in a Multimodel Ensemble Using EOF and Clustering Approaches

2019 ◽  
Vol 147 (6) ◽  
pp. 1967-1987 ◽  
Author(s):  
Minghua Zheng ◽  
Edmund K. M. Chang ◽  
Brian A. Colle
Keyword(s):  
Lead Time ◽  
East Coast ◽  
Time Frame ◽  
Ensemble Prediction ◽  
Multimodel Ensemble ◽  
Winter Storms ◽  
East Coast Winter Storms ◽  
Prediction Systems ◽  
Environmental Prediction ◽  
The Individual

Abstract Empirical orthogonal function (EOF) and fuzzy clustering tools were applied to generate and validate scenarios in operational ensemble prediction systems (EPSs) for U.S. East Coast winter storms. The National Centers for Environmental Prediction (NCEP), European Centre for Medium-Range Weather Forecasts (ECMWF), and Canadian Meteorological Centre (CMC) EPSs were validated in their ability to capture the analysis scenarios for historical East Coast cyclone cases at lead times of 1–9 days. The ECMWF ensemble has the best performance for the medium- to extended-range forecasts. During this time frame, NCEP and CMC did not perform as well, but a combination of the two models helps reduce the missing rate and alleviates the underdispersion. All ensembles are underdispersed at all ranges, with combined ensembles being less underdispersed than the individual EPSs. The number of outside-of-envelope cases increases with lead time. For a majority of the cases beyond the short range, the verifying analysis does not lie within the ensemble mean group of the multimodel ensemble or within the same direction indicated by any of the individual model means, suggesting that all possible scenarios need to be taken into account. Using the EOF patterns to validate the cyclone properties, the NCEP model tends to show less intensity and displacement biases during 1–3-day lead time, while the ECMWF model has the smallest biases during 4–6 days. Nevertheless, the ECMWF forecast position tends to be biased toward the southwest of the other two models and the analysis.

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