A data based mechanistic real-time flood forecasting module for NFFS FEWS

Abstract. The data based mechanistic (DBM) approach for identifying and estimating rainfall to level, and level to level models has been shown to perform well for flood forecasting in several studies. The DELFT-FEWS open shell operational flood forecasting system provides a framework linking hydrological/meteorological real-time data, real-time forecast models, and a human/computer interaction interface. This infrastructure is used by the UK National Flood Forecasting System (NFFS) and the European Flood Alert System (EFAS) among others. The open shell nature of the FEWS framework has been specifically designed to make it easy to add new forecasting models written as FEWS modules. This paper shows the development of the DBM forecast model as a FEWS module and presents results for the Eden catchment (Cumbria UK) as a case study.

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Application of data-based mechanistic modelling for flood forecasting at multiple locations in the Eden catchment in the National Flood Forecasting System (England and Wales)

Hydrology and Earth System Sciences ◽

10.5194/hess-17-177-2013 ◽

2013 ◽

Vol 17 (1) ◽

pp. 177-185 ◽

Cited By ~ 10

Author(s):

D. Leedal ◽

A. H. Weerts ◽

P. J. Smith ◽

K. J. Beven

Keyword(s):

Warning System ◽

Open Shell ◽

Flood Forecasting ◽

Mechanistic Modelling ◽

Forecasting Models ◽

Forecasting System ◽

Environment Agency ◽

The Uk ◽

Modelling Approach

Abstract. The Delft Flood Early Warning System provides a versatile framework for real-time flood forecasting. The UK Environment Agency has adopted the Delft framework to deliver its National Flood Forecasting System. The Delft system incorporates new flood forecasting models very easily using an "open shell" framework. This paper describes how we added the data-based mechanistic modelling approach to the model inventory and presents a case study for the Eden catchment (Cumbria, UK).

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Development of a Real-Time Flood Forecasting System by Hydraulic Flood Routing

Bridging the Gap ◽

10.1061/40569(2001)432 ◽

2001 ◽

Author(s):

Joo Heon Lee ◽

Do Hun Lee ◽

Sang Man Jeong ◽

Eun Tae Lee

Keyword(s):

Real Time ◽

Flood Forecasting ◽

Flood Routing ◽

Forecasting System

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How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-17-0231.1 ◽

2019 ◽

Vol 100 (4) ◽

pp. 605-619 ◽

Cited By ~ 14

Author(s):

A. J. Illingworth ◽

D. Cimini ◽

A. Haefele ◽

M. Haeffelin ◽

M. Hervo ◽

...

Keyword(s):

Real Time ◽

Traffic Control ◽

Positive Impact ◽

Weather Forecast ◽

Weather Conditions ◽

Time Data ◽

Weather Forecasts ◽

Forecast Models ◽

Wind Lidar ◽

Microwave Radiometers

Abstract To realize the promise of improved predictions of hazardous weather such as flash floods, wind storms, fog, and poor air quality from high-resolution mesoscale models, the forecast models must be initialized with an accurate representation of the current state of the atmosphere, but the lowest few kilometers are hardly accessible by satellite, especially in dynamically active conditions. We report on recent European developments in the exploitation of existing ground-based profiling instruments so that they are networked and able to send data in real time to forecast centers. The three classes of instruments are i) automatic lidars and ceilometers providing backscatter profiles of clouds, aerosols, dust, fog, and volcanic ash, the last two being especially important for air traffic control; ii) Doppler wind lidars deriving profiles of wind, turbulence, wind shear, wind gusts, and low-level jets; and iii) microwave radiometers estimating profiles of temperature and humidity in nearly all weather conditions. The project includes collaboration from 22 European countries and 15 European national weather services, which involves the implementation of common operating procedures, instrument calibrations, data formats, and retrieval algorithms. Currently, data from 265 ceilometers in 19 countries are being distributed in near–real time to national weather forecast centers; this should soon rise to many hundreds. One wind lidar is currently delivering real time data rising to 5 by the end of 2019, and the plan is to incorporate radiometers in 2020. Initial data assimilation tests indicate a positive impact of the new data.

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A Robust Skewed Boxplot for Detecting Outliers in Rainfall Observations in Real-Time Flood Forecasting

Advances in Meteorology ◽

10.1155/2019/1795673 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7

Author(s):

Chao Zhao ◽

Jinyan Yang

Keyword(s):

Real Time ◽

Exponential Function ◽

Flood Forecasting ◽

Interquartile Range ◽

Skewed Data ◽

Symmetric Distributions ◽

Contaminated Data ◽

Forecasting System

The standard boxplot is one of the most popular nonparametric tools for detecting outliers in univariate datasets. For Gaussian or symmetric distributions, the chance of data occurring outside of the standard boxplot fence is only 0.7%. However, for skewed data, such as telemetric rain observations in a real-time flood forecasting system, the probability is significantly higher. To overcome this problem, a medcouple (MC) that is robust to resisting outliers and sensitive to detecting skewness was introduced to construct a new robust skewed boxplot fence. Three types of boxplot fences related to MC were analyzed and compared, and the exponential function boxplot fence was selected. Operating on uncontaminated as well as simulated contaminated data, the results showed that the proposed method could produce a lower swamping rate and higher accuracy than the standard boxplot and semi-interquartile range boxplot. The outcomes of this study demonstrated that it is reasonable to use the new robust skewed boxplot method to detect outliers in skewed rain distributions.

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Improving probabilistic flood forecasting through a data assimilation scheme based on genetic programming

Natural Hazards and Earth System Science ◽

10.5194/nhess-12-3719-2012 ◽

2012 ◽

Vol 12 (12) ◽

pp. 3719-3732 ◽

Cited By ~ 4

Author(s):

L. Mediero ◽

L. Garrote ◽

A. Chavez-Jimenez

Keyword(s):

Data Assimilation ◽

Genetic Programming ◽

Real Time ◽

High Performance ◽

Forecast Model ◽

Flood Forecasting ◽

Distribution Functions ◽

Model Parameters ◽

Hydrological Process ◽

Model Based

Abstract. Opportunities offered by high performance computing provide a significant degree of promise in the enhancement of the performance of real-time flood forecasting systems. In this paper, a real-time framework for probabilistic flood forecasting through data assimilation is presented. The distributed rainfall-runoff real-time interactive basin simulator (RIBS) model is selected to simulate the hydrological process in the basin. Although the RIBS model is deterministic, it is run in a probabilistic way through the results of calibration developed in a previous work performed by the authors that identifies the probability distribution functions that best characterise the most relevant model parameters. Adaptive techniques improve the result of flood forecasts because the model can be adapted to observations in real time as new information is available. The new adaptive forecast model based on genetic programming as a data assimilation technique is compared with the previously developed flood forecast model based on the calibration results. Both models are probabilistic as they generate an ensemble of hydrographs, taking the different uncertainties inherent in any forecast process into account. The Manzanares River basin was selected as a case study, with the process being computationally intensive as it requires simulation of many replicas of the ensemble in real time.

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Sensitivities of the Madden–Julian oscillation forecasts to configurations of physics in the ECMWF global model

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-4759-2021 ◽

2021 ◽

Vol 21 (6) ◽

pp. 4759-4778

Author(s):

Jun-Ichi Yano ◽

Nils P. Wedi

Keyword(s):

Forecast Model ◽

Global Model ◽

Madden Julian Oscillation ◽

Forecast Performance ◽

Theoretical Investigations ◽

Forecast Models ◽

Forecasting System ◽

The Rich ◽

The Tropics ◽

Original Motivation

Abstract. The sensitivities of the Madden–Julian oscillation (MJO) forecasts to various different configurations of the parameterized physics are examined with the global model of ECMWF's Integrated Forecasting System (IFS). The motivation for the study was to simulate the MJO as a nonlinear free wave under active interactions with higher-latitude Rossby waves. To emulate free dynamics in the IFS, various momentum-dissipation terms (“friction”) as well as diabatic heating were selectively turned off over the tropics for the range of the latitudes from 20∘ S to 20∘ N. The reduction of friction sometimes improves the MJO forecasts, although without any systematic tendency. Contrary to the original motivation, emulating free dynamics with an operational forecast model turned out to be rather difficult, because forecast performance sensitively depends on the specific type of friction turned off. The result suggests the need for theoretical investigations that much more closely follow the actual formulations of model physics: a naive approach with a dichotomy of with or without friction simply fails to elucidate the rich behaviour of complex operational models. The paper further exposes the importance of physical processes other than convection for simulating the MJO in global forecast models.

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A quality improvement (QI) project on improving trainee confidence in conducting remote psychiatric consultations at Pennine Care National Health Service (NHS) Foundation Trust in the United Kingdom (UK)

BJPsych Open ◽

10.1192/bjo.2021.560 ◽

2021 ◽

Vol 7 (S1) ◽

pp. S209-S210

Author(s):

Rachel Moir ◽

Roshelle Ramkisson ◽

Seri Abraham ◽

Shevonne Matheiken

Keyword(s):

Real Time ◽

Response Rates ◽

Psychiatric Consultation ◽

Time Data ◽

Remote Consultation ◽

Safe Patient Care ◽

Pdsa Cycle ◽

Real Time Data ◽

The Uk ◽

Remote Consultations

AimsWhen the coronavirus disease 2019 pandemic hit the UK, clinicians within Pennine Care NHS Foundation Trust (a five-borough mental health trust) were faced with the challenge of rapidly switching to a novel way of assessing patients remotely.The idea for a QI project on trainees’ experience with remote consultations was conceived in April 2020. We present our February 2021 results here.We aimed to improve trainee confidence in conducting remote psychiatric assessments by at least 40%, to ensure effective and safe patient care during their 6 months placement.MethodOur discovery process included surveying trainees in April 2020 to explore experiences with remote psychiatric consultations, a literature search of current UK guidance and a local audit. The audit reviewed documentation of consent to remote consultations, with reference to standards as per NHS England remote consultation guidance. Key change ideas included publication of an article, ‘Remote consultations – top tips for clinical practitioners’, video-simulated remote consultations and a session on remote consultations in the trainee induction.In the first ‘plan-do-study-act’ (PDSA) cycle, we presented key findings from the article in a video presentation, which was sent trust-wide. We measured confidence in conducting remote assessments pre- and post-presentation via a feedback survey. Unfortunately, response rates were low and in the second PDSA cycle we targeted a smaller cohort of trainees at the August 2020 induction, although encountered similar difficulties. In the third PDSA cycle, we collected real-time data using an interactive app at the February 2021 trainee induction, and measured pre- and post- confidence following a presentation and a video-simulated remote consultation.Result2/34 respondents had accessed previous remote psychiatric consultation training and12/35 had some telepsychiatry experience. Pre-induction trainee confidence results revealed: extremely uncomfortable (16%), not confident (31%), neutral (47%), confident (6%) and very confident (0%) and post-induction confidence was 0%, 22%, 52%, 26% and 0%, respectively.ConclusionOur project started during the first peak of the pandemic, which may be a reason for initial limited response rates. Our results suggest that the remote psychiatric consultation trainee induction session has shown some improvement in trainee confidence; the ‘confident’ cohort improved from 6% to 26%.Our next steps include collecting similar real-time data, mid-rotation and uploading video-simulated remote consultations to the Trust Intranet. We plan to complete the local audit cycle. We also plan to incorporate patient experience (from an ongoing systematic review) to inform a potential triage process post-pandemic, choosing between face-to-face versus remote consultations.

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Proposed Intelligent Pre-Processing Model of Real-Time Flood Forecasting and Warning for Data Classification and Aggregation

International Journal of Online Engineering (iJOE) ◽

10.3991/ijoe.v13i11.7382 ◽

2017 ◽

Vol 13 (11) ◽

pp. 4 ◽

Cited By ~ 1

Author(s):

Marouane El Mabrouk ◽

Salma Gaou

Keyword(s):

Wireless Sensor Networks ◽

Sensor Networks ◽

Real Time ◽

Wireless Sensors ◽

Data Classification ◽

Flood Forecasting ◽

Base Stations ◽

Wireless Sensor ◽

Time Data ◽

Forecasting And Warning

A wireless sensor network is a network that can design a self-organizing structure and provides effective support for several protocols such as routing, locating, discovering services, etc. It is composed of several nodes called sensors grouped together into a network to communicate with each other and with the base stations. Nowadays, the use of Wireless sensor networks increased considerably. It can collect physical data and transform it into a digital values in real-time to monitor in a continuous manner different disaster like flood. However, due to various factors that can affect the wireless sensor networks namely, environmental, manufacturing errors hardware and software problems etc... It is necessary to carefully select and filter the data from the wireless sensors since we are providing a decision support system for flood forecasting and warning. In this paper, we presents an intelligent Pre-Processing model of real-time flood forecasting and warning for data classification and aggregation. The proposed model consists on several stages to monitor the wireless sensors and its proper functioning, to provide the most appropriate data received from the wireless sensor networks in order to guarantee the best accuracy in terms of real-time data and to generate a historical data to be used in the further flood forecasting.

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