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 Flood Forecasting with Uncertainty Using a Fully Automated Flood Model Chain: a Case Study for the City of Kulmbach

10.29007/jb27 ◽  
2018 ◽  
Author(s):  
Md Nazmul Azim Beg ◽  
Jorge Leandro ◽  
Punit Bhola ◽  
Iris Konnerth ◽  
Kanwal Amin ◽  
...  
Keyword(s):  
Real Time ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Flood Forecast ◽  
The Public ◽  
Uncertainty Sources ◽  
Flood Model ◽  
The City ◽  
Model Chain

Real time flood forecasting can help authorities in providing reliable warnings to the public. This process is, however, non-deterministic such that uncertainty sources need to be accounted before issuing forecasts. In the FloodEvac project, we have developed a tool which takes as inputs rainfall forecasts and links a hydrological with a hydraulic model for producing flood forecasts. The tool is able to handle calibration/validation of the hydrological model (LARSIM) and produces real-time flood forecast with associated uncertainty of flood discharges and flood extents. In this case study, we focus on the linkage with the hydrological model and on the real-time discharge forecasts generated.

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 Assessing uncertainties in flood forecasts for decision making: prototype of an operational flood management system integrating ensemble predictions

2009 ◽  
Vol 9 (4) ◽  
pp. 1529-1540 ◽  
Author(s):  
J. Dietrich ◽  
A. H. Schumann ◽  
M. Redetzky ◽  
J. Walther ◽  
M. Denhard ◽  
...  
Keyword(s):  
Management System ◽  
A Priori ◽  
Decision Rules ◽  
Flood Management ◽  
Probabilistic Assessment ◽  
Computationally Efficient ◽  
Ensemble Forecasts ◽  
Meteorological Model ◽  
Prediction Systems ◽  
Model Chain

Abstract. Ensemble forecasts aim at framing the uncertainties of the potential future development of the hydro-meteorological situation. A probabilistic evaluation can be used to communicate forecast uncertainty to decision makers. Here an operational system for ensemble based flood forecasting is presented, which combines forecasts from the European COSMO-LEPS, SRNWP-PEPS and COSMO-DE prediction systems. A multi-model lagged average super-ensemble is generated by recombining members from different runs of these meteorological forecast systems. A subset of the super-ensemble is selected based on a priori model weights, which are obtained from ensemble calibration. Flood forecasts are simulated by the conceptual rainfall-runoff-model ArcEGMO. Parameter uncertainty of the model is represented by a parameter ensemble, which is a priori generated from a comprehensive uncertainty analysis during model calibration. The use of a computationally efficient hydrological model within a flood management system allows us to compute the hydro-meteorological model chain for all members of the sub-ensemble. The model chain is not re-computed before new ensemble forecasts are available, but the probabilistic assessment of the output is updated when new information from deterministic short range forecasts or from assimilation of measured data becomes available. For hydraulic modelling, with the desired result of a probabilistic inundation map with high spatial resolution, a replacement model can help to overcome computational limitations. A prototype of the developed framework has been applied for a case study in the Mulde river basin. However these techniques, in particular the probabilistic assessment and the derivation of decision rules are still in their infancy. Further research is necessary and promising.

scholarly journals Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

2018 ◽  
Vol 18 (8) ◽  
pp. 2183-2202 ◽  
Author(s):  
Ekrem Canli ◽  
Martin Mergili ◽  
Benni Thiebes ◽  
Thomas Glade
Keyword(s):  
High Performance ◽  
Weather Forecasting ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Operational Mode ◽  
Physically Based ◽  
Landslide Forecasting ◽  
Forecasting System ◽  
Prediction Systems ◽  
Performance Computing

Abstract. Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslide-triggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.

scholarly journals Flood forecasting using medium-range probabilistic weather prediction

2005 ◽  
Vol 9 (4) ◽  
pp. 365-380 ◽  
Author(s):  
B. T. Gouweleeuw ◽  
J. Thielen ◽  
G. Franchello ◽  
A. P. J. De Roo ◽  
R. Buizza
Keyword(s):  
Large Scale ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Probabilistic Approach ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Ensemble Techniques ◽  
Medium Range ◽  
Prediction Systems ◽  
Nwp Model

Abstract. Following the developments in short- and medium-range weather forecasting over the last decade, operational flood forecasting also appears to show a shift from a so-called single solution or 'best guess' deterministic approach towards a probabilistic approach based on ensemble techniques. While this probabilistic approach is now more or less common practice and well established in the meteorological community, operational flood forecasters have only started to look for ways to interpret and mitigate for end-users the prediction products obtained by combining so-called Ensemble Prediction Systems (EPS) of Numerical Weather Prediction (NWP) models with rainfall-runoff models. This paper presents initial results obtained by combining deterministic and EPS hindcasts of the global NWP model of the European Centre for Medium-Range Weather Forecasts (ECMWF) with the large-scale hydrological model LISFLOOD for two historic flood events: the river Meuse flood in January 1995 and the river Odra flood in July 1997. In addition, a possible way to interpret the obtained ensemble based stream flow prediction is proposed.

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

Secure Broadcast with One-Time Signatures in Controller Area Networks

2013 ◽  
Vol 5 (3) ◽  
pp. 1-18
Author(s):  
Bogdan Groza ◽  
Pal-Stefan Murvay
Keyword(s):  
Real Time ◽  
Time Synchronization ◽  
Public Key ◽  
Time Constraints ◽  
Signature Scheme ◽  
Computationally Efficient ◽  
The Public ◽  
Controller Area Networks ◽  
Trade Offs ◽  
Public Keys

Broadcast authentication in Controller Area Networks (CAN) is subject to real time constraints that are hard to satisfy by expensive public key primitives. For this purpose the authors study here the use of one-time signatures which can be built on the most computationally efficient one-way functions. The authors use an enhancement of the classical Merkle signature as well as the more recently proposed HORS signature scheme. Notably, these two proposals offer different trade-offs, and they can be efficiently paired with time synchronization to reduce the overhead caused by the re-initialization of the public keys, which would otherwise require expensive authentication trees. The authors do outline clear bounds on the performance of such a solution and provide experimental results on development boards equipped with Freescale S12X, a commonly used automotive grade micro-controller. The authors also benefit from the acceleration offered by the XGATE co-processor available on S12X derivatives which significantly increases the computational performances.

Design of Portable Electrocardiograph Monitoring System

2020 ◽  
Author(s):  
Jia Hua-Ping ◽  
Zhao Jun-Long ◽  
Liu Jun
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Data Storage ◽  
P Wave ◽  
Ecg Monitoring ◽  
The Public ◽  
Ecg Signals ◽  
Early Warning Mechanism ◽  
Timely Treatment ◽  
Ecg Data

Cardiovascular disease is one of the major diseases that threaten the human health. But the existing electrocardiograph (ECG) monitoring system has many limitations in practical application. In order to monitor ECG in real time, a portable ECG monitoring system based on the Android platform is developed to meet the needs of the public. The system uses BMD101 ECG chip to collect and process ECG signals in the Android system, where data storage and waveform display of ECG data can be realized. The Bluetooth HC-07 module is used for ECG data transmission. The abnormal ECG can be judged by P wave, QRS bandwidth, and RR interval. If abnormal ECG is found, an early warning mechanism will be activated to locate the user’s location in real time and send preset short messages, so that the user can get timely treatment, avoiding dangerous occurrence. The monitoring system is convenient and portable, which brings great convenie to the life of ordinary cardiovascular users.

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