Ensemble hydrological forecasting for flood warning in small catchments in Saxony, Germany

2020 ◽  
Author(s):  
Jens Grundmann ◽  
Achim Six ◽  
Andy Philipp
Keyword(s):  
Extreme Precipitation ◽  
Disaster Response ◽  
Lead Times ◽  
Rainfall Runoff ◽  
Ensemble Forecasts ◽  
Processing Scheme ◽  
Hydrological Forecasting ◽  
Flood Warning ◽  
Training Concept ◽  
Small Catchments

<p>Reliable warnings and forecasts of extreme precipitation and the resulting floods are an important prerequisite for disaster response. Especially for small catchments, warning and forecasting systems are challenging due to the short response time of the catchments and the uncertainties of the meteorological forecast products. Thus, ensemble forecasts of precipitation are an option to portray these inherent uncertainties. By this contribution, we present our operational processing scheme for ensemble hydrological forecasting. We use the COSMO-D2-EPS product of the German Weather Service, which provides an ensemble of 20 members each three hours, for lead times up to 27 hours. Each member is evaluated regarding specific extreme precipitation thresholds for predefined hydrological warning regions. If these thresholds are exceeded in a specific region, rainfall-runoff models for the associated catchments are started to propagate the meteorological uncertainty into the resulting runoff, followed by statistical post processing and visualization. In addition, a communication and training concept based on a series of workshops with the locally responsible civil protection forces to deal with the uncertainties in the forecast is associated. Results are presented by a re-analysis of the flood in the upper Weiße Elster catchment in May 2018 in the Vogtland region of Saxony. Rainfall amounts larger than 140mm in 6 hours led to the highest flood warning levels in the region. Analysis show that such extreme amounts of precipitation are only predicted by one member of the COSMO-D2-EPS ensemble forecast. The deterministic COSMO-D2 model run does not show this, which underlines the benefit and the potential of the ensemble predictions, but also the need for a suitable communication of the uncertainties.</p>

A web based demonstration platform for flood warning in small catchments using ensemble hydrological forecasting

2021 ◽  
Author(s):  
Jens Grundmann ◽  
Achim Six ◽  
Andy Philipp
Keyword(s):  
Extreme Precipitation ◽  
Disaster Response ◽  
Ensemble Forecasts ◽  
Web Based ◽  
Forecast Performance ◽  
Hydrological Forecasting ◽  
Flood Warning ◽  
First Results ◽  
Small Catchments ◽  
The Web

<p>Reliable warnings and forecasts of extreme precipitation and resulting floods are an important prerequisite for disaster response. Especially for small catchments, warning and forecasting systems are challenging due to the short response time of the catchments and the uncertainties of the meteorological forecasts. Thus, ensemble forecasts of precipitation are an option to portray these inherent uncertainties. By this contribution, we present our operational web-based demonstration platform for ensemble hydrological forecasting in small catchments of Saxony, Germany. We use the ICON/COSMO-D2-EPS product of the German Weather Service, which provides an ensemble of 20 members each three hours, for lead times up to 27 hours. Each member is evaluated regarding specific extreme precipitation thresholds for predefined hydrological warning regions. If these thresholds are exceeded in a specific region, rainfall-runoff models for the associated catchments are started to propagate the meteorological uncertainty into the resulting runoff, followed by statistical post processing and visualization. Different options for the visualization of the uncertainty information were discussed and evaluated by a series of (virtual) workshops with locally responsible civil protection forces and water authorities. This leads to the current design of the web-based demonstration platform in an iterative process, which is still ongoing. The web-based demonstration platform is established for three pilot regions with different hydrological settings in Saxony, Germany. Besides layout and technical issues, first experiences with the demonstration platform are presented as well as first results regarding forecast performance in the small pilot regions.</p>

scholarly journals Predictive skill for atmospheric rivers in the western Iberian Peninsula

2020 ◽  
Vol 20 (3) ◽  
pp. 877-888 ◽  
Author(s):  
Alexandre M. Ramos ◽  
Pedro M. Sousa ◽  
Emanuel Dutra ◽  
Ricardo M. Trigo
Keyword(s):  
Iberian Peninsula ◽  
Extreme Precipitation ◽  
Western Europe ◽  
Large Fraction ◽  
Water Vapor Transport ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Atmospheric Rivers ◽  
Predictive Skill ◽  
Probabilistic Evaluation

Abstract. A large fraction of extreme precipitation and flood events across western Europe are triggered by atmospheric rivers (ARs). The association between ARs and extreme precipitation days over the Iberian Peninsula has been well documented for western river basins. Since ARs are often associated with high impact weather, it is important to study their medium-range predictability. Here we perform such an assessment using the ECMWF ensemble forecasts up to 15 d for events where ARs made landfall in the western Iberian Peninsula during the winters spanning between 2012–2013 and 2015–2016. Vertically integrated horizontal water vapor transport (IVT) and precipitation from the 51 ensemble members of the ECMWF Integrated Forecasting System (IFS) ensemble (ENS) were processed over a domain including western Europe and the contiguous North Atlantic Ocean. Metrics concerning AR location, intensity, and orientation were computed, in order to compare the predictive skill (for different prediction lead times) of IVT and precipitation. We considered several regional boxes over western Iberia, where the presence of ARs is detected in analysis/forecasts, enabling the construction of contingency tables and probabilistic evaluation for further objective verification of forecast accuracy. Our results indicate that the ensemble forecasts have skill in detecting upcoming AR events, which can be particularly useful to better predict potential hydrometeorological extremes. We also characterized how the ENS dispersion and confidence curves change with increasing forecast lead times for each sub-domain. The probabilistic evaluation, using receiver operating characteristic (ROC) analysis, shows that for short lead times precipitation forecasts are more accurate than IVT forecasts, while for longer lead times this reverses (∼10 d). Furthermore, we show that this reversal occurs for shorter lead times in areas where the AR contribution is more relevant for winter precipitation totals (e.g., northwestern Iberia).

scholarly journals A stochastic space-time rainfall forecasting system for real time flow forecasting II: Application of SHETRAN and ARNO rainfall runoff models to the Brue catchment

2000 ◽  
Vol 4 (4) ◽  
pp. 617-626 ◽  
Author(s):  
D. Mellor ◽  
J. Sheffield ◽  
P. E. O’Connell ◽  
A. V. Metcalfe
Keyword(s):  
Real Time ◽  
Rainfall Runoff ◽  
Rainfall Forecasting ◽  
Ensemble Forecasts ◽  
Flood Warning ◽  
Distributed Modelling ◽  
Time Flow ◽  
Physically Based ◽  
The Impact ◽  
Modelling System

Abstract. Key issues involved in converting MTB ensemble forecasts of rainfall into ensemble forecasts of runoff are addressed. The physically-based distributed modelling system, SHETRAN, is parameterised for the Brue catchment, and used to assess the impact of averaging spatially variable MTB rainfall inputs on the accuracy of simulated runoff response. Averaging is found to have little impact for wet antecedent conditions and to lead to some underestimation of peak discharge under dry catchment conditions. The simpler ARNO modelling system is also parameterised for the Brue and SHETRAN and ARNO calibration and validation results are found to be similar. Ensemble forecasts of runoff generated using both SHETRAN and the simpler ARNO modelling system are compared. The ensemble is more spread out with the SHETRAN model, and a likely explanation is that the ARNO model introduces too much smoothing. Nevertheless, the forecasting performance of the simpler model could be adequate for flood warning purposes. Keywords: SHETRAN, ARNO, HYREX, rainfall-runoff model, Brue, real-time flow forecasting

scholarly journals Predictive skill of Atmospheric Rivers in western Iberian Peninsula

2019 ◽  
Author(s):  
Alexandre M. Ramos ◽  
Pedro M. Sousa ◽  
Emanuel Dutra ◽  
Ricardo M. Trigo
Keyword(s):  
Iberian Peninsula ◽  
Extreme Precipitation ◽  
Western Europe ◽  
North Atlantic Ocean ◽  
Large Fraction ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Atmospheric Rivers ◽  
Predictive Skill ◽  
Probabilistic Evaluation

Abstract. It is now clear that a large fraction of extreme precipitation and flood events across Western Europe are triggered by Atmospheric Rivers (ARs). The association between ARs and extreme precipitation days over the Iberian Peninsula has been well documented for western river basins. Since ARs are often associated with high impact weather, it is important to study their medium-range predictability. Here we perform such an assessment using the ECMWF ensemble forecasts up to 15 days for events that made landfall in western Iberian Peninsula during the winters spanning between 2012/2013 and 2015/16. IVT and precipitation from the 51 ensemble members of the ECMWF Integrated Forecasting System (IFS) ensemble (ENS) were processed over a domain including western Europe and contiguous North Atlantic Ocean. Metrics concerning the ARs location, intensity and orientation were computed, in order to compare the predictive skill (for different prediction lead times) of IVT and precipitation analyses in the IFS. We considered several regional boxes over Western Iberia, where the presence of ARs is detected in analysis/forecasts, enabling the construction of contingency tables and probabilistic evaluation for further objective verification of forecast accuracy. Our results indicate that the ensemble forecasts have skill to detect upcoming ARs events, which can be particularly useful to better predict potential hydrometeorological extremes. We also characterized how the ENS dispersion and confidence curves change with increasing forecast lead times for each sub-domain. The probabilistic evaluation, using ROC analysis, shows that for short lead times precipitation forecasts are more accurate than IVT forecasts, while for longer lead times this reverses (~10 days). Furthermore, we show that this reversal occurs for shorter lead times in areas where the ARs contribution is more relevant for winter precipitation totals (e.g. northwestern Iberia).

scholarly journals Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 253
Author(s):  
Luying Ji ◽  
Qixiang Luo ◽  
Yan Ji ◽  
Xiefei Zhi
Keyword(s):  
Northern Hemisphere ◽  
Geopotential Height ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Lead Times ◽  
Height Distribution ◽  
Height Field ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Geopotential Height Field

Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.

scholarly journals Intrinsic Predictability of the 20 May 2013 Tornadic Thunderstorm Event in Oklahoma at Storm Scales

2016 ◽  
Vol 144 (4) ◽  
pp. 1273-1298 ◽  
Author(s):  
Yunji Zhang ◽  
Fuqing Zhang ◽  
David J. Stensrud ◽  
Zhiyong Meng
Keyword(s):  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Lead Times ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Positive Feedbacks ◽  
Convective Storms ◽  
Cold Pools ◽  
Initial Errors ◽  
Environmental Instability

Abstract Using a high-resolution convection-allowing numerical weather prediction model, this study seeks to explore the intrinsic predictability of the severe tornadic thunderstorm event on 20 May 2013 in Oklahoma from its preinitiation environment to initiation, upscale organization, and interaction with other convective storms. This is accomplished through ensemble forecasts perturbed with minute initial condition uncertainties that were beyond detection capabilities of any current observational platforms. It was found that these small perturbations, too small to modify the initial mesoscale environmental instability and moisture fields, will be propagated and evolved via turbulence within the PBL and rapidly amplified in moist convective processes through positive feedbacks associated with updrafts, phase transitions of water species, and cold pools, thus greatly affecting the appearance, organization, and development of thunderstorms. The forecast errors remain nearly unchanged even when the initial perturbations (errors) were reduced by as much as 90%, which strongly suggests an inherently limited predictability for this thunderstorm event for lead times as short as 3–6 h. Further scale decomposition reveals rapid error growth and saturation in meso-γ scales (regardless of the magnitude of initial errors) and subsequent upscale growth into meso-β scales.

Influence of the Madden–Julian Oscillation on Forecasts of Extreme Precipitation in the Contiguous United States

2011 ◽  
Vol 139 (2) ◽  
pp. 332-350 ◽  
Author(s):  
Charles Jones ◽  
Jon Gottschalck ◽  
Leila M. V. Carvalho ◽  
Wayne Higgins
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Western Hemisphere ◽  
Boreal Winter ◽  
Lead Times ◽  
Madden Julian Oscillation ◽  
Extreme Precipitation Events ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Precipitation Events

Abstract Extreme precipitation events are among the most devastating weather phenomena since they are frequently accompanied by loss of life and property. This study uses reforecasts of the NCEP Climate Forecast System (CFS.v1) to evaluate the skill of nonprobabilistic and probabilistic forecasts of extreme precipitation in the contiguous United States (CONUS) during boreal winter for lead times up to two weeks. The CFS model realistically simulates the spatial patterns of extreme precipitation events over the CONUS, although the magnitudes of the extremes in the model are much larger than in the observations. Heidke skill scores (HSS) for forecasts of extreme precipitation at the 75th and 90th percentiles showed that the CFS model has good skill at week 1 and modest skill at week 2. Forecast skill is usually higher when the Madden–Julian oscillation (MJO) is active and has enhanced convection occurring over the Western Hemisphere, Africa, and/or the western Indian Ocean than in quiescent periods. HSS greater than 0.1 extends to lead times of up to two weeks in these situations. Approximately 10%–30% of the CONUS has HSS greater than 0.1 at lead times of 1–14 days when the MJO is active. Probabilistic forecasts for extreme precipitation events at the 75th percentile show improvements over climatology of 0%–40% at 1-day lead and 0%–5% at 7-day leads. The CFS has better skill in forecasting severe extremes (i.e., events exceeding the 90th percentile) at longer leads than moderate extremes (75th percentile). Improvements over climatology between 10% and 30% at leads of 3 days are observed over several areas across the CONUS—especially in California and in the Midwest.

Coherent intra-annual ensemble forecasts of surface and groundwater availability

2021 ◽  
Author(s):  
David Robertson ◽  
Guobin Fu ◽  
Olga Barron ◽  
Geoff Hodgson ◽  
Andrew Schepen
Keyword(s):  
Surface Water ◽  
Water Resources ◽  
Groundwater Level ◽  
Forecast Skill ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Error Models ◽  
Groundwater Availability ◽  
Surface Water And Groundwater ◽  
Surface And Groundwater

<p>In many parts of the world, surface water and groundwater are used complementarily to supply agricultural production and to meet urban water demands. Conjunctive management of these water resources requires balancing of the different characteristics of surface water and groundwater with respect to availability, quality and cost of supply. Ensemble forecasts of surface water and groundwater availability can inform management decisions but require explicit representation of the complex processes controlling surface and groundwater interactions. While many methods and operational services exist that provide independent forecasts for surface and groundwater availability, to our knowledge no approaches for coupled forecasting have been developed yet.</p><p>In this presentation we introduce an approach that generates coupled forecasts of surface water and groundwater availability. It extends the Forecast Guided Stochastic Scenarios (FoGSS) (Bennett et al., 2016) approach to forecast groundwater level at specified locations, in addition to streamflow totals, to lead times of 12 months at monthly time steps. We adapt a conceptual hydrological model to improve predictions of streamflow and, as a by-product, groundwater level. We then apply independent error models to streamflow and groundwater level to reduce bias, update predictions using recent observations and quantify residual uncertainty. Ensemble streamflow and groundwater forecasts are generated by forcing the hydrological and error models with ensemble rainfall forecasts generated by post-processing ECMWF System 5 outputs. The skill, bias and reliability of the rainfall, streamflow and groundwater level forecasts were assessed for a case-study catchment in South-East Queensland, Australia. We find that skill of forecasts is dependent on the forecast issue month and lead time, with groundwater level forecasts displaying significant skill to lead times of 12 months, while streamflow forecast skill rarely persists beyond 3 months.  We conclude by describing opportunities to improve forecast skill and some of the challenges that may be faced in the operational delivery of water resource forecasts in real-time.</p><p>Reference</p><p>Bennett, J. C., Wang, Q. J., Li, M., Robertson, D. E., and Schepen, A.: Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model, Water Resources Research, 52, 8238-8259, 10.1002/2016WR019193, 2016.</p>

scholarly journals Simultaneous Assimilation of Radar and All-Sky Satellite Infrared Radiance Observations for Convection-Allowing Ensemble Analysis and Prediction of Severe Thunderstorms

2019 ◽  
Vol 147 (12) ◽  
pp. 4389-4409 ◽  
Author(s):  
Yunji Zhang ◽  
David J. Stensrud ◽  
Fuqing Zhang
Keyword(s):  
Doppler Radar ◽  
Three Dimensional ◽  
Lead Times ◽  
Probabilistic Prediction ◽  
Ensemble Forecasts ◽  
Radar Observations ◽  
Weather Radars ◽  
Infrared Radiance ◽  
Severe Thunderstorms ◽  
Ensemble Analysis

Abstract This study explores the benefits of assimilating infrared (IR) brightness temperature (BT) observations from geostationary satellites jointly with radial velocity (Vr) and reflectivity (Z) observations from Doppler weather radars within an ensemble Kalman filter (EnKF) data assimilation system to the convection-allowing ensemble analysis and prediction of a tornadic supercell thunderstorm event on 12 June 2017 across Wyoming and Nebraska. While radar observations sample the three-dimensional storm structures with high fidelity, BT observations provide information about clouds prior to the formation of precipitation particles when in-storm radar observations are not yet available and also provide information on the environment outside the thunderstorms. To better understand the strengths and limitations of each observation type, the satellite and Doppler radar observations are assimilated separately and jointly, and the ensemble analyses and forecasts are compared with available observations. Results show that assimilating BT observations has the potential to increase the forecast and warning lead times of severe weather events compared with radar observations and may also potentially complement the sparse surface observations in some regions as revealed by the probabilistic prediction of mesocyclone tracks initialized from EnKF analyses as various times. Additionally, the assimilation of both BT and Vr observations yields the best ensemble forecasts, providing higher confidence, improved accuracy, and longer lead times on the probabilistic prediction of midlevel mesocyclones.

Sign in / Sign up

Export Citation Format

Share Document