Flood Forecasting Based on TIGGE Precipitation Ensemble Forecast

TIGGE (THORPEX International Grand Global Ensemble) was a major part of the THORPEX (Observing System Research and Predictability Experiment). It integrates ensemble precipitation products from all the major forecast centers in the world and provides systematic evaluation on the multimodel ensemble prediction system. Development of meteorologic-hydrologic coupled flood forecasting model and early warning model based on the TIGGE precipitation ensemble forecast can provide flood probability forecast, extend the lead time of the flood forecast, and gain more time for decision-makers to make the right decision. In this study, precipitation ensemble forecast products from ECMWF, NCEP, and CMA are used to drive distributed hydrologic model TOPX. We focus on Yi River catchment and aim to build a flood forecast and early warning system. The results show that the meteorologic-hydrologic coupled model can satisfactorily predict the flow-process of four flood events. The predicted occurrence time of peak discharges is close to the observations. However, the magnitude of the peak discharges is significantly different due to various performances of the ensemble prediction systems. The coupled forecasting model can accurately predict occurrence of the peak time and the corresponding risk probability of peak discharge based on the probability distribution of peak time and flood warning, which can provide users a strong theoretical foundation and valuable information as a promising new approach.

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Pluvial flood forecasting in urban data-scarce regions: Influence of rainfall spatio-temporal data (in)accuracy on decision-making

10.5194/egusphere-egu2020-9492 ◽

2020 ◽

Author(s):

Adele Young ◽

Biswa Bhattacharya ◽

Chris Zevenbergen

Keyword(s):

Decision Making ◽

Input Data ◽

Early Warning Systems ◽

Flood Forecasting ◽

Ensemble Forecast ◽

Water Levels ◽

Precipitation Forecast ◽

Ensemble Prediction ◽

Flood Forecast ◽

Urban Data

Pluvial flooding is on the rise as more cities are challenged by a changing climate and local drivers: increased urbanisation and inadequate sewer system capacity. Flood forecasting and early warning systems have been proposed as a &#8220;low regret&#8221; measure to reduce flood risk and increase preparedness through forecast-based actions.&#160; However there are multiple sources of uncertainty from meteorological forecast, model parameters and structure and inadequate calibration. &#160;In data-scarce cities, there are additional challenges to produce high-quality rainfall forecast and well-calibrated flood forecast (timing, water levels, extent and impact). As a result, there is a cascading effect on the ability to make and provide good reliable decisions given the uncertainty in the forecast or inaccuracy in the input data.Ensemble prediction systems (EPS) have been proposed as a means to quantify uncertainty in forecast and compared to deterministic forecast, facilitate a probabilistic framework in decision making. Probabilistic information has been applied to cost loss ratio approaches and Bayesian decision under uncertainty. However, to what extent inherent spatiotemporal inaccuracies of meteorological inputs influence this posterior probability and the resultant decision has not been considered in data scare regions. In this regard, this research focuses on providing understanding on how the influence of the varying degrees of input data, particularly forecast rainfall spatial and temporal distributions will ultimately affect the ability to make an optimal decision; i.e. the recommended decision given the information available at the time of the forecast.Using a study area in the Alexandria city, Egypt, this research proposes a framework for decision making under uncertainty in an urban data-scarce city using a Weather Research Forecast (WRF) model to simulate downscaled rainfall ensemble forecast and remotely sensed rainfall products to supplement data gaps. Adopting a probabilistic approach, uncertainty in the flood forecast predictions will be represented from an urban rainfall-runoff model driven by ensemble precipitation forecast. The objective of this research is not to make forecast more accurate but rather to highlight the interdependences of the flood forecast and decision-making chain in order to address what decision can be made given the quality of forecast.Keywords: Pluvial flood forecasting, Ensemble forecast, Decision making, Data-scarce Alexandria, Egypt

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The Study of Flood Forecast Based on Precipitation Ensemble Forecast

MATEC Web of Conferences ◽

10.1051/matecconf/201824601051 ◽

2018 ◽

Vol 246 ◽

pp. 01051

Author(s):

Xiao Yang ◽

Wu Xin ◽

Ye Wang ◽

Deqin Cao

Keyword(s):

Flood Forecasting ◽

Ensemble Forecast ◽

Probabilistic Forecast ◽

Forecasting Model ◽

Precipitation Data ◽

Flood Forecast ◽

Runoff Simulation ◽

Deterministic Process ◽

Process Line

Ensemble forecast which makes up for the lack of the single forecast, is a shift from deterministic forecast to probabilistic forecast. Based on the above ideas, this paper takes the Jianghua as study basin,and uses the ECWMF ensemble forecast precipitation data to drive the flood forecasting model for flood forecasting. The result shows that the ensemble forecast flood forecasting can get the range of runoff simulation. And 75% of the process line Q75 is used as a deterministic process line which can simulate the flood well. The method not only ensures the accuracy of flood forecast, but also prevents the period of flood. Reliability of the application of ensemble forecast in flood forecast is proved.

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Model Uncertainty in a Mesoscale Ensemble Prediction System: Stochastic versus Multiphysics Representations

Monthly Weather Review ◽

10.1175/2010mwr3595.1 ◽

2011 ◽

Vol 139 (6) ◽

pp. 1972-1995 ◽

Cited By ~ 128

Author(s):

J. Berner ◽

S.-Y. Ha ◽

J. P. Hacker ◽

A. Fournier ◽

C. Snyder

Keyword(s):

Kinetic Energy ◽

Model Uncertainty ◽

Model Error ◽

Ensemble Forecast ◽

Ensemble Member ◽

Ensemble Prediction ◽

Weather Research And Forecasting ◽

Forecasting Model ◽

Prediction System ◽

Ensemble Prediction System

Abstract A multiphysics and a stochastic kinetic-energy backscatter scheme are employed to represent model uncertainty in a mesoscale ensemble prediction system using the Weather Research and Forecasting model. Both model-error schemes lead to significant improvements over the control ensemble system that is simply a downscaled global ensemble forecast with the same physics for each ensemble member. The improvements are evident in verification against both observations and analyses, but different in some details. Overall the stochastic kinetic-energy backscatter scheme outperforms the multiphysics scheme, except near the surface. Best results are obtained when both schemes are used simultaneously, indicating that the model error can best be captured by a combination of multiple schemes.

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Initial perturbations based on Ensemble Transfrom Kalman Filter with rescaling method for ensemble forecast

Weather and Forecasting ◽

10.1175/waf-d-20-0176.1 ◽

2021 ◽

Author(s):

Jingzhuo Wang ◽

Jing Chen ◽

Hanbin Zhang ◽

Hua Tian ◽

Yining Shi

Keyword(s):

Growth Rate ◽

Kalman Filter ◽

Weather Forecasting ◽

Initial Perturbation ◽

Ensemble Forecast ◽

Ensemble Prediction ◽

Forecast Errors ◽

Model Uncertainties ◽

Ensemble Prediction System ◽

Probabilistic Forecasts

AbstractEnsemble forecast is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by Ensemble Transform Kalman Filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread-skill relationship, a faster ensemble spread growth rate and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

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Multi-Step Sequence Flood Forecasting Based on MSBP Model

Water ◽

10.3390/w13152095 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2095

Author(s):

Yue Zhang ◽

Juanhui Ren ◽

Rui Wang ◽

Feiteng Fang ◽

Wen Zheng

Keyword(s):

River Basin ◽

River Flow ◽

Back Propagation ◽

Engineering Application ◽

Flood Forecasting ◽

Propagation Model ◽

Forecasting Model ◽

Peak Time ◽

Model Based ◽

Simple Neural Network

Establishing a model predicting river flow can effectively reduce huge losses caused by floods. This paper proposes a multi-step time series forecasting model based on multiple input and multiple output strategies, and this model is applied to the flood forecasting process of a river basin in Shanxi, which effectively improves the engineering application value of the flood forecasting model based on deep learning. The experimental results show that after considering the seasonal characteristics of the river channel and screening the influencing factors, a simple neural network model can accurately predict the peak value, the peak time and flood trends. On this basis, we proposed the MSBP (Multi-step Back Propagation) model, which can accurately predict the flow trend of the river basin 20 h in advance, and the NSE (Nash Efficiency) is 0.89. The MSBP model can improve the reliability of flood forecasting and increase the internal interpretability of the model, which is of great significance for effectively improving the effect of flood forecasting.

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Summary Verification Measures and Their Interpretation for Ensemble Forecasts

Monthly Weather Review ◽

10.1175/2010mwr3305.1 ◽

2011 ◽

Vol 139 (9) ◽

pp. 3075-3089 ◽

Cited By ~ 36

Author(s):

A. Allen Bradley ◽

Stuart S. Schwartz

Keyword(s):

Weighted Average ◽

Threshold Value ◽

Skill Score ◽

Ensemble Forecast ◽

Ensemble Prediction ◽

Threshold Values ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Prediction Systems ◽

Forecast Quality

Ensemble prediction systems produce forecasts that represent the probability distribution of a continuous forecast variable. Most often, the verification problem is simplified by transforming the ensemble forecast into probability forecasts for discrete events, where the events are defined by one or more threshold values. Then, skill is evaluated using the mean-square error (MSE; i.e., Brier) skill score for binary events, or the ranked probability skill score (RPSS) for multicategory events. A framework is introduced that generalizes this approach, by describing the forecast quality of ensemble forecasts as a continuous function of the threshold value. Viewing ensemble forecast quality this way leads to the interpretation of the RPSS and the continuous ranked probability skill score (CRPSS) as measures of the weighted-average skill over the threshold values. It also motivates additional measures, derived to summarize other features of a continuous forecast quality function, which can be interpreted as descriptions of the function’s geometric shape. The measures can be computed not only for skill, but also for skill score decompositions, which characterize the resolution, reliability, discrimination, and other aspects of forecast quality. Collectively, they provide convenient metrics for comparing the performance of an ensemble prediction system at different locations, lead times, or issuance times, or for comparing alternative forecasting systems.

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Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system

10.5194/hess-2017-514 ◽

2017 ◽

Cited By ~ 1

Author(s):

Sanjib Sharma ◽

Ridwan Siddique ◽

Seann Reed ◽

Peter Ahnert ◽

Pablo Mendoza ◽

...

Keyword(s):

Surface Temperature ◽

Warm Season ◽

Hydrologic Model ◽

Laboratory Research ◽

Ensemble Prediction ◽

Lead Times ◽

Prediction System ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Near Surface

Abstract. The relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7) are investigated. For this purpose, a regional hydrologic ensemble prediction system (RHEPS) is developed and implemented. The RHEPS is comprised by the following components: i) hydrometeorological observations (multisensor precipitation estimates, gridded surface temperature, and gauged streamflow); ii) weather ensemble forecasts (precipitation and near-surface temperature) from the National Centers for Environmental Prediction 11-member Global Ensemble Forecast System Reforecast version 2 (GEFSRv2); iii) NOAA’s Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM); iv) heteroscedastic censored logistic regression (HCLR) as the statistical preprocessor; v) two statistical postprocessors, an autoregressive model with a single exogenous variable (ARX(1,1)) and quantile regression (QR); and vi) a comprehensive verification strategy. To implement the RHEPS, 1 to 7 days weather forecasts from the GEFSRv2 are used to force HL-RDHM and generate raw ensemble streamflow forecasts. Forecasting experiments are conducted in four nested basins in the U.S. middle Atlantic region, ranging in size from 381 to 12,362 km2. Results show that the HCLR preprocessed ensemble precipitation forecasts have greater skill than the raw forecasts. These improvements are more noticeable in the warm season at the longer lead times (> 3 days). Both postprocessors, ARX(1,1) and QR, show gains in skill relative to the raw ensemble flood forecasts but QR outperforms ARX(1,1). Preprocessing alone has little effect on improving the skill of the ensemble flood forecasts. Indeed, postprocessing alone performs similar, in terms of the relative mean error, skill, and reliability, to the more involved scenario that includes both preprocessing and postprocessing. We conclude that statistical preprocessing may not always be a necessary component of the ensemble flood forecasting chain.

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Detection of Severe Weather Events in a High-Resolution Ensemble Prediction System Using the Extreme Forecast Index (EFI) and Shift of Tails (SOT)

Weather and Forecasting ◽

10.1175/waf-d-17-0183.1 ◽

2018 ◽

Vol 33 (4) ◽

pp. 901-908

Author(s):

Laure Raynaud ◽

Benoît Touzé ◽

Philippe Arbogast

Keyword(s):

Reference Model ◽

Weather Forecast ◽

Ensemble Forecast ◽

Severe Weather ◽

Ensemble Prediction ◽

False Alarms ◽

Prediction System ◽

Ensemble Prediction System ◽

Wind Gusts ◽

Weather Events

Abstract The extreme forecast index (EFI) and shift of tails (SOT) are commonly used to compare an ensemble forecast to a reference model climatology, in order to measure the severity of the current weather forecast. In this study, the feasibility and the relevance of EFI and SOT computations are examined within the convection-permitting Application of Research to Operations at Mesoscale (AROME-France) ensemble prediction system (EPS). First, different climate configurations are proposed and discussed, in order to overcome the small size of the ensemble and the short climate sampling length. Subjective and objective evaluations of EFI and SOT for wind gusts and precipitation forecasts are then presented. It is shown that these indices can provide relevant early warnings and, based on a trade-off between hits and false alarms, optimal EFI thresholds can be determined for decision-making.

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Probabilistic Flood Forecasting with a Limited-Area Ensemble Prediction System: Selected Case Studies

Journal of Hydrometeorology ◽

10.1175/jhm594.1 ◽

2007 ◽

Vol 8 (4) ◽

pp. 897-909 ◽

Cited By ~ 63

Author(s):

M. Verbunt ◽

A. Walser ◽

J. Gurtz ◽

A. Montani ◽

C. Schär

Keyword(s):

Case Studies ◽

Heavy Precipitation ◽

Hydrologic Model ◽

Ensemble Prediction ◽

Limited Area ◽

Prediction System ◽

Rhine River ◽

Time Step ◽

Ensemble Prediction System ◽

Runoff Forecasting

Abstract A high-resolution atmospheric ensemble forecasting system is coupled to a hydrologic model to investigate probabilistic runoff forecasts for the alpine tributaries of the Rhine River basin (34 550 km2). Five-day ensemble forecasts consisting of 51 members, generated with the global ensemble prediction system (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF), are downscaled with the limited-area model Lokal Modell (LM). The resulting limited-area ensemble prediction system (LEPS) uses a horizontal grid spacing of 10 km and provides one-hourly output for driving the distributed hydrologic model Precipitation–Runoff–Evapotranspiration–Hydrotope (PREVAH) hydrologic response unit (HRU) with a resolution of 500 × 500 m2 and a time step of 1 h. The hydrologic model component is calibrated for the river catchments considered, which are characterized by highly complex topography, for the period 1997–98 using surface observations, and validated for 1999–2002. This study explores the feasibility of atmospheric ensemble predictions for runoff forecasting, in comparison with deterministic atmospheric forcing. Detailed analysis is presented for two case studies: the spring 1999 flood event affecting central Europe due to a combination of snowmelt and heavy precipitation, and the November 2002 flood in the Alpine Rhine catchment. For both cases, the deterministic simulations yield forecast failures, while the coupled atmospheric–hydrologic EPS provides appropriate probabilistic forecast guidance with early indications for extreme floods. It is further shown that probabilistic runoff forecasts using a subsample of EPS members, selected by a cluster analysis, properly represent the forecasts using all 51 EPS members, while forecasts from randomly chosen subsamples reveal a reduced spread compared to the representative members. Additional analyses show that the representation of horizontal advection of precipitation in the atmospheric model may be crucial for flood forecasts in alpine catchments.

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Ensemble Flood Forecasting of Typhoons Talas and Roke at Hiyoshi Dam Basin

Journal of Disaster Research ◽

10.20965/jdr.2016.p1032 ◽

2016 ◽

Vol 11 (6) ◽

pp. 1032-1039 ◽

Cited By ~ 5

Author(s):

Tomoki Ushiyama ◽

◽

Takahiro Sayama ◽

Yoichi Iwami ◽

◽

...

Keyword(s):

Weather Prediction ◽

Flood Forecasting ◽

Quantitative Prediction ◽

Ensemble Prediction ◽

Distributed Hydrological Model ◽

Ensemble Prediction System ◽

Forecasting System ◽

Flood Warnings ◽

Better Than ◽

Small Basin

In order to be able to issue flood warnings not hours but days in advance, numerical weather prediction (NWP) is essential to the forecasting of flood-producing rainfall. The regional ensemble prediction system (EPS), advanced NWP on a local scale, has a high potential to improve flood forecasting through the quantitative prediction of precipitation. In this study, the predictability of floods using the ensemble flood forecasting system, which is composed of regional EPS and a distributed hydrological model, was investigated. Two flood events which took place in a small basin in Japan in 2010 and which were caused by typhoons Talas and Roke were examined. As the forecasting system predicted the probability of flood occurrence at least 24 h beforehand in the case of both typhoons, these forecasts were better than deterministic forecasts. However, the system underestimated the peak of the flooding in the typhoon Roke event, and it was too early in its prediction of the appearance of the peak of the flooding in the Talas event. Although the system has its limitations, it has proved to have the potential to produce early flood warnings.

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