Ensemble Numerical Weather Prediction Research Articles

Seasonal Hydropower Planning for Data‐Scarce Regions Using Multimodel Ensemble Forecasts, Remote Sensing Data, and Stochastic Programming

AbstractIn data‐scarce regions, seasonal hydropower planning is hindered by the unavailability of reliable long‐term streamflow observations, which are required for the construction of inflow scenario trees. In this study, we develop a methodological framework to overcome the problem of streamflow data scarcity by combining precipitation forecasts from ensemble numerical weather prediction models, spatially distributed hydrologic models, and stochastic programming. We use evapotranspiration as a proxy for streamflow in generating reliable reservoir inflow forecasts. Using the framework, we compare three different formulations of inflow scenario structures and their applicability to data‐scarce regions: (1) a single deterministic forecast, (2) a scenario fan with the first stage deterministic, and (3) a scenario fan with all stages stochastic. We apply the framework to a cascade of two reservoirs in the Omo‐Gibe River basin in Ethiopia. Future reservoir inflows are generated using a 3‐model 30‐member ensemble seasonal precipitation forecast from the North American Multimodel Ensemble and the Noah‐MP hydrologic model. We then perform deterministic and stochastic optimization for hydropower operation and planning. Comparing the results from the three different inflow scenario structures, we observe that the uncertainty in reservoir inflows is significant only for the dry stages of the planning horizon. In addition, we find that the impact of model parameter uncertainty on hydropower production is significant (0.14–0.18×106 MWh).

Spatial interpolation of two‐metre temperature over Norway based on the combination of numerical weather prediction ensembles and in situ observations

AbstractAccurate hourly two‐metre temperature gridded fields available in near real‐time are valuable products for numerous applications, such as civil protection and energy production planning. An analysis ensemble of temperature is obtained from the combination of a numerical weather prediction ensemble (background) and in situ observations. At the core of the flow‐dependent spatial interpolation method lies the analysis step of the local ensemble transform Kalman filter (LETKF). A scaling factor and a localization procedure have been added to correct for deficiencies of the background. Each observation is characterized by its own representativeness, which is allowed to vary in time. We call the method described here an Ensemble‐based Statistical Interpolation (EnSI) scheme for spatial analysis and it has been integrated into the operational post‐processing systems in use at the Norwegian Meteorological Institute (MET Norway). The benefits of the analysis are assessed over a 1‐year time period (July 2017–July 2018) and a case‐study is presented for a challenging situation over complex terrain. EnSI gives more accurate results than an interpolation method based exclusively on observations. The analysis ensemble provides a more informative representation of the uncertainty than a spatial analysis based on a single‐field background. EnSI reduces the number of large prediction errors in the analysis compared to the background by almost 50%, reduces the ensemble spread and increases its accuracy.

Deep Learning for Spatially Explicit Prediction of Synoptic-Scale Fronts

AbstractThis paper describes the use of convolutional neural nets (CNN), a type of deep learning, to identify fronts in gridded data, followed by a novel postprocessing method that converts probability grids to objects. Synoptic-scale fronts are often associated with extreme weather in the midlatitudes. Predictors are 1000-mb (1 mb = 1 hPa) grids of wind velocity, temperature, specific humidity, wet-bulb potential temperature, and/or geopotential height from the North American Regional Reanalysis. Labels are human-drawn fronts from Weather Prediction Center bulletins. We present two experiments to optimize parameters of the CNN and object conversion. To evaluate our system, we compare the objects (predicted warm and cold fronts) with human-analyzed warm and cold fronts, matching fronts of the same type within a 100- or 250-km neighborhood distance. At 250 km our system obtains a probability of detection of 0.73, success ratio of 0.65 (or false-alarm rate of 0.35), and critical success index of 0.52. These values drastically outperform the baseline, which is a traditional method from numerical frontal analysis. Our system is not intended to replace human meteorologists, but to provide an objective method that can be applied consistently and easily to a large number of cases. Our system could be used, for example, to create climatologies and quantify the spread in forecast frontal properties across members of a numerical weather prediction ensemble.

Probabilistic Solar Irradiance Forecasting by Conditioning Joint Probability Method and Its Application to Electric Power Trading

This study presents a method for probabilistic forecasting of solar irradiance based on the joint probability distribution function (PDF) of irradiance predicted by numerical weather prediction (NWP) and irradiance observed. Multidimensional kernel density estimation was used to construct this joint PDF. The probabilistic forecast is obtained by deriving a conditional PDF given a current NWP by using the Bayes rule. The proposed method can naturally handle the nonlinear nature of the relation between observed and predicted irradiance. The method showed better statistical performance in terms of the continuous ranked probability score, reliability, and sharpness than an existing probabilistic forecasting method based on ensemble NWP. Simulation of solar power trading based on pricing of an actual electric power market also confirmed that the proposed method results in greater profit by suppressing penalties imposed due to overbidding.

NWP-based lightning prediction using flexible count data regression

Abstract. A method to predict lightning by postprocessing numerical weather prediction (NWP) output is developed for the region of the European Eastern Alps. Cloud-to-ground (CG) flashes – detected by the ground-based Austrian Lightning Detection & Information System (ALDIS) network – are counted on the 18×18 km2 grid of the 51-member NWP ensemble of the European Centre for Medium-Range Weather Forecasts (ECMWF). These counts serve as the target quantity in count data regression models for the occurrence of lightning events and flash counts of CG. The probability of lightning occurrence is modelled by a Bernoulli distribution. The flash counts are modelled with a hurdle approach where the Bernoulli distribution is combined with a zero-truncated negative binomial. In the statistical models the parameters of the distributions are described by additive predictors, which are assembled using potentially nonlinear functions of NWP covariates. Measures of location and spread of 100 direct and derived NWP covariates provide a pool of candidates for the nonlinear terms. A combination of stability selection and gradient boosting identifies the nine (three) most influential terms for the parameters of the Bernoulli (zero-truncated negative binomial) distribution, most of which turn out to be associated with either convective available potential energy (CAPE) or convective precipitation. Markov chain Monte Carlo (MCMC) sampling estimates the final model to provide credible inference of effects, scores, and predictions. The selection of terms and MCMC sampling are applied for data of the year 2016, and out-of-sample performance is evaluated for 2017. The occurrence model outperforms a reference climatology – based on 7 years of data – up to a forecast horizon of 5 days. The flash count model is calibrated and also outperforms climatology for exceedance probabilities, quantiles, and full predictive distributions.

Развитие систем детерминированного и ансамблевого численного прогнозирования погоды на основе глобальной спектральной модели атмосферы Гидрометцентра России в 2009–2019 гг.

Развитие систем детерминированного и ансамблевого численного прогнозирования погоды на основе глобальной спектральной модели атмосферы Гидрометцентра России в 2009–2019 гг.

Improvements in Nonconvective Aviation Turbulence Prediction for the World Area Forecast System

AbstractFor the next generation of the World Area Forecast System (WAFS), the global Graphical Turbulence Guidance (G-GTG) has been developed using global numerical weather prediction (NWP) model outputs as an input to compute a set of turbulence diagnostics, identifying strong spatial gradients of meteorological variables associated with clear-air turbulence (CAT) and mountain-wave turbulence (MWT). The G-GTG provides an atmospheric turbulence intensity metric of energy dissipation rate (EDR) to the 1/3 power (m2/3 s–1), which is the International Civil Aviation Organization (ICAO) standard for aircraft reporting. Deterministic CAT and MWT EDR forecasts are derived from ensembles of calibrated multiple CAT and MWT diagnostics, respectively, with the final forecast provided by the gridpoint-by-gridpoint maximum of the CAT and MWT ensemble means. In addition, a probabilistic EDR forecast is produced by the percentage agreement of the individual CAT and MWT diagnostics that exceed a certain EDR threshold for turbulence (i.e., multidiagnostic ensemble). Objective evaluations of the G-GTG against global in situ EDR measurement data show that both deterministic and probabilistic G-GTG significantly improve the current WAFS CAT product, mainly because the G-GTG takes into account turbulence from various sources related to CAT and MWT. The probabilistic G-GTG forecast is more reliable at predicting light-or-greater (EDR > 0.15)- than moderate-or-greater (EDR > 0.22)-level turbulence, although it suffers from overforecasting. This will be improved in the future when we use this methodology with NWP ensembles and more observation data will be available for calibration. We expect that the new G-GTG forecasts will be beneficial to aviation users globally.

Typhoon Rainfall Forecasting by Means of Ensemble Numerical Weather Predictions with a GA-Based Integration Strategy

Rainfall during typhoons is one of the most important water resources in Taiwan, but heavy typhoon rainfall often leads to serious disasters and consequently results in loss of lives and property. Hence, accurate forecasts of typhoon rainfall are always required as important information for water resources management and rainfall-induced disaster warning system. In this study, a methodology is proposed for providing quantitative forecasts of 24 h cumulative rainfall during typhoons. Firstly, ensemble forecasts of typhoon rainfall are obtained from an ensemble numerical weather prediction (NWP) system. Then, an evolutionary algorithm, i.e., genetic algorithm (GA), is adopted to real-time decide the weights for optimally combining these ensemble forecasts. That is, the novelty of this proposed methodology is the effective integration of the NWP-based ensemble forecasts through an evolutionary algorithm-based strategy. An actual application is conducted to verify the forecasts resulting from the proposed methodology, namely NWP-based ensemble forecasts with a GA-based integration strategy. The results confirm that the forecasts from the proposed methodology are in good agreement with observations. Besides, the results from the GA-based strategy are more accurate as compared to those by simply averaging all ensemble forecasts. On average, the root mean square error decreases about 7%. In conclusion, more accurate typhoon rainfall forecasts are obtained by the proposed methodology, and they are expected to be useful for disaster warning system and water resources management during typhoons.

Ensemble size: How suboptimal is less than infinity?

Ensemble forecasts are the method of choice in numerical weather prediction (NWP) to generate probabilistic forecasts. The number of members in an ensemble is an important factor in determining how well a probability distribution of a weather‐related variable can be estimated. Having only a finite number of members reduces the average skill such a probabilistic forecast can have. Increasing ensemble size is therefore desirable; however, ensemble size is also proportional to the computational cost. Having a small ensemble size limits the cost and makes other improvements, such as increases in spatial resolution, feasible.This article examines how average skill measures with metrics such as the continuous ranked probability score, the quantile score, and the Dawid–Sebastiani score converge with ensemble size. A numerical experiment with a 200 member ensemble using the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) model at a resolution of 29 km and a forecast range of 15 days provides data to compare the convergence of probabilistic skill in a current NWP system with theoretical expectations derived for perfectly reliable ensembles with exchangeable members.Results in the first part of the article can help users of operational NWP ensemble forecasts formulate their minimum requirement in terms of ensemble size. In the second part, requirements for scientists who test changes to NWP systems are examined. Using proper scores and fair scores, it is explored whether testing changes in the ensemble forecasts can be meaningful with fewer members than in the operational configuration. Results are based on medium‐range numerical experiments with 50 members. Two experiments test the activation of a representation of model uncertainty and three other experiments test changes in horizontal resolution from 29 to 18 km and from 29 to 45 km.

FACETs: A Proposed Next-Generation Paradigm for High-Impact Weather Forecasting

AbstractRecommendations by the National Research Council (NRC), the National Institute of Standards and Technology (NIST), and Weather-Ready Nation workshop participants have encouraged the National Oceanic and Atmospheric Administration (NOAA) and the broader weather enterprise to explore and expand the use of probabilistic information to convey weather forecast uncertainty. Forecasting a Continuum of Environmental Threats (FACETs) is a concept being explored by NOAA to address those recommendations and also potentially shift the National Weather Service (NWS) from (primarily) teletype-era, deterministic watch–warning products to high-resolution, probabilistic hazard information (PHI) spanning periods from days (and longer) to within minutes of high-impact weather and water events. FACETs simultaneously i) considers a reinvention of the NWS hazard forecasting and communication paradigm so as to deliver multiscale, user-specific probabilistic guidance from numerical weather prediction ensembles and ii) provides a comprehensive framework to organize the physical, social, and behavioral sciences, the technology, and the practices needed to achieve that reinvention. The first applications of FACETs have focused on thunderstorm phenomena, but the FACETs concept is envisioned to extend to the attributes of any environmental hazards that can be described probabilistically (e.g., winter, tropical, and aviation weather). This paper introduces the FACETs vision, the motivation for its creation, the research and development under way to explore that vision, its relevance to operational forecasting and society, and possible strategies for implementation.

Assessment of Ensemble Flood Forecasting with Numerical Weather Prediction by considering Spatial Shift of Rainfall Fields

This study evaluated the ensemble flood forecasting by using ensemble forecast outputs of the Numerical Weather Prediction (NWP) model with a distributed hydrologic model. Ensemble NWP rainfall with a 2 km horizontal resolution and 30 hour forecast time was assessed whether it can produce suitable rainfall compared with the deterministic rainfall forecast during the typhoon TALAS, 2011. The ensemble flood forecast based on ensemble NWP rainfall was also assessed for hydrological applications. This study presents a methodology using the spatial movement of ensemble rainfall field in order to improve the accuracy of ensemble flood prediction considering the location correction of Quantitative Precipitation Forecast (QPF). This study was conducted using the largest flood events occurred by typhoon TALAS in 2011 with regard to the Futatsuno (356.1 km2) and Nanairo (182.1 km2) dam catchments in the Shingu river basin (2,360 km2) located in Kii Peninsula of Japan. As a result, the flood prediction using the spatial movement of ensemble rainfall field could improve the under-predicted interval of the flood prediction using the original ensemble rainfall. In addition, it was able to reproduce the peak discharge in the first and second prediction intervals for both catchments and to cover the observed value. The method we proposed in this study can be used in hydrological applications such as real-time flood prediction including flood prediction and warning systems, optimized discharge flow for dam operation.

Ensemble flood forecasting based on ensemble NWP and the GMKHM distributed hydrological model

The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a new opportunity for flood forecast. The GMKHM distributed hydrological model, which is based on a mixed runoff generation model and overland flow routing model based on kinematic wave theory, and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the GMKHM model and the TIGGE database gives a promising tool for the anticipation of flood events several days ahead,, comparable with that driven by raingauge observation.

Bayesian Model Averaging for Wind Speed Ensemble Forecasts Using Wind Speed and Direction

Abstract In this paper, probabilistic wind speed forecasts are constructed based on ensemble numerical weather prediction (NWP) forecasts for both wind speed and wind direction. Including other NWP variables in addition to the one subject to forecasting is common for statistical calibration of deterministic forecasts. However, this practice is rarely seen for ensemble forecasts, probably because of a lack of methods. A Bayesian modeling approach (BMA) is adopted, and a flexible model class based on splines is introduced for the mean model. The spline model allows both wind speed and wind direction to be included nonlinearly. The proposed methodology is tested for forecasting hourly maximum 10-min wind speeds based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts at 204 locations in Norway for lead times from +12 to +108 h. An improvement in the continuous ranked probability score is seen for approximately 85% of the locations using the proposed method compared to standard BMA based on only wind speed forecasts. For moderate-to-strong wind the improvement is substantial, while for low wind speeds there is generally less or no improvement. On average, the improvement is 5%. The proposed methodology can be extended to include more NWP variables in the calibration and can also be applied to other variables.

Nowcasting algorithm for wind fields using ensemble forecasting and aircraft flight data

This study proposes an algorithm that combines ensemble numerical weather-prediction model data and aircraft flight data in a wind nowcasting system for safe and efficient aircraft operation. It uses an ensemble-weighted average method based on sequential importance sampling (SIS), which is a particle filter method for forecasting the wind field in real time. SIS is applied to the ensemble forecast data and control run data of the European Centre for Medium-Range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), Korea Meteorological Administration (KMA), National Centers for Environmental Prediction (NCEP) and United Kingdom Met Office (UKMO) for the two case studies that use flight data from 72 commercial aircraft flights. The results show that SIS can forecast better than the other four methods: direct ensemble average (DEA), elite strategy (ES), and selective ensemble average (SEAV) and weighted average (SEWE), with average improvements in forecast performance of about 10–15%, even at 300 min ahead. In addition, the overall forecast performance between the forecast wind and observation of the radiosonde of SIS was slightly better than DEA. In both cases, the forecast performance was significantly improved on points along the flight path of the aircraft used for this study. Case analyses and the impact of differences in the hyper-parameters of SIS on forecast performance are also presented in this study.

Effective Use of Ensemble Numerical Weather Predictions in Taiwan by Means of a SOM-Based Cluster Analysis Technique

Typhoon rainfall is one of the most important water resources in Taiwan. However, heavy rainfall during typhoons often leads to serious disasters. Therefore, accurate typhoon rainfall forecasts are always desired for water resources managers and disaster warning systems. In this study, the quantitative rainfall forecasts from an ensemble numerical weather prediction system in Taiwan are used. Furthermore, a novel strategy, which is based on the use of a self-organizing map (SOM) based cluster analysis technique, is proposed to integrate these ensemble forecasts. By means of the SOM-based cluster analysis technique, ensemble forecasts that have similar features are clustered. That is helpful for users to effectively combine these ensemble forecasts for providing better typhoon rainfall forecasts. To clearly demonstrate the advantage of the proposed strategy, actual application is conducted during five typhoon events. The results indicate that the ensemble rainfall forecasts from numerical weather prediction models are well categorized by the SOM-based cluster analysis technique. Moreover, the integrated typhoon rainfall forecasts resulting from the proposed strategy are more accurate when compared to those from the conventional method (i.e., the ensemble mean of all forecasts). In conclusion, the proposed strategy provides improved forecasts of typhoon rainfall. The improved quantitative rainfall forecasts are expected to be useful to support disaster warning systems as well as water resources management systems during typhoons.

Comparison of extended medium-range forecast skill between KMA ensemble, ocean coupled ensemble, and GloSea5

This article describes a three way inter-comparison of forecast skill on an extended medium-range time scale using the Korea Meteorological Administration (KMA) operational ensemble numerical weather prediction (NWP) systems (i.e., atmosphere-only global ensemble prediction system (EPSG) and ocean-atmosphere coupledEPSG) and KMA operational seasonal prediction system, the Global Seasonal forecast system version 5 (GloSea5). The main motivation is to investigate whether the ensemble NWP system can provide advantage over the existing seasonal prediction system for the extended medium-range forecast (30 days) even with putting extra resources in extended integration or coupling with ocean with NWP system. Two types of evaluation statistics are examined: the basic verification statistics - the anomaly correlation and RMSE of 500-hPa geopotential height and 1.5-meter surface temperature for the global and East Asia area, and the other is the Real-time Multivariate Madden and Julian Oscillation (MJO) indices (RMM1 and RMM2) - which is used to examine the MJO prediction skill. The MJO is regarded as a main source of forecast skill in the tropics linked to the mid-latitude weather on monthly time scale. Under limited number of experiment cases, the coupled NWP extends the forecast skill of the NWP by a few more days, and thereafter such forecast skill is overtaken by that of the seasonal prediction system. At present stage, it seems there is little gain from the coupled NWP even though more resources are put into it. Considering this, the best combination of numerical product guidance for operational forecasters for an extended medium-range is extension of the forecast lead time of the current ensemble NWP (EPSG) up to 20 days and use of the seasonal prediction system (GloSea5) forecast thereafter, though there exists a matter of consistency between the two systems.

Distribution of Single-Banded Snowfall in Central U.S. Cyclones

Abstract A climatology of single-banded snowfall in the central United States and the variability of processes at work in its formation are presented. Ninety-eight snowbands are identified in association with 66 cyclones over 5 yr spanning the winters from 2006/07 through 2010/11. An additional 38 cyclones featured nonbanded snowfall exceeding 4 in. (10.2 cm). Nearly twice as many bands were observed to the northeast of the surface low than to the northwest. Over each snowband’s life cycle, the median (mean) snowband lasted 4.0 (5.2) h, was 42 (45) km wide, 388 (428) km long, and had an aspect ratio of 10.2 (10.8). A common appearance exists for snowbands in different large-scale flow regimes and locations relative to the surface cyclone. The median snowband elongates during the first half of its life span, with its width remaining constant. During the second half of the median snowband’s life span, the length and width contract. Composite analysis of the synoptic and broad mesoscale environments that snowbands form in illustrates that the juxtaposition of the ingredients necessary for snowbands are similar no matter which quadrant of the surface low the band is located in, indicating that the synoptic-scale flow determines where these ingredients are organized with respect to the cyclone. The frequency of banded snowfall within each northern quadrant of the surface low, the typical snowband characteristics and their evolution, and the patterns that give rise to snowbands documented by this work can all prove useful to forecasters tasked with maintaining situational awareness in the presence of many solutions provided by ensemble numerical weather prediction.

The very short-term rainfall forecasting for a mountainous watershed by means of an ensemble numerical weather prediction system in Taiwan

During typhoons, accurate forecasts of rainfall are always desired for various kinds of disaster warning systems to reduce the impact of rainfall-induced disasters. However, rainfall forecasting, especially the very short-term (hourly) rainfall, is one of the most difficult tasks in hydrology due to the high variability in space and time and the complex physical process. In this study, the purpose is to provide effective forecasts of very short-term rainfall by means of the ensemble numerical weather prediction system in Taiwan. To this end, the ensemble forecasts of hourly rainfall from this ensemble numerical weather prediction system are analyzed to evaluate the performance. Furthermore, a methodology, which is based on the principle of analogue prediction, is proposed to effectively process these ensemble forecasts for improving the performance on very short-term rainfall forecasting. To clearly demonstrate the advantage of the proposed methodology, actual application is conducted on a mountainous watershed to yield 1- to 6-h ahead forecasts during typhoon events. The results indicate that the proposed methodology is better performed and more flexible than the conventional one. Generally, the proposed methodology provides improved performance for very short-term rainfall forecasting, especially for 1- to 2-h ahead forecasting. The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems, such as flash-flood, landslide, and debris flow warning systems, during typhoons.

Distinguishing high and low flow domains in urban drainage systems 2 days ahead using numerical weather prediction ensembles

Precipitation constitutes a major contribution to the flow in urban storm- and wastewater systems. Forecasts of the anticipated runoff flows, created from radar extrapolation and/or numerical weather predictions, can potentially be used to optimize operation in both wet and dry weather periods. However, flow forecasts are inevitably uncertain and their use will ultimately require a trade-off between the value of knowing what will happen in the future and the probability and consequence of being wrong.In this study we examine how ensemble forecasts from the HIRLAM-DMI-S05 numerical weather prediction (NWP) model subject to three different ensemble post-processing approaches can be used to forecast flow exceedance in a combined sewer for a wide range of ratios between the probability of detection (POD) and the probability of false detection (POFD). We use a hydrological rainfall-runoff model to transform the forecasted rainfall into forecasted flow series and evaluate three different approaches to establishing the relative operating characteristics (ROC) diagram of the forecast, which is a plot of POD against POFD for each fraction of concordant ensemble members and can be used to select the weight of evidence that matches the desired trade-off between POD and POFD. In the first approach, the rainfall input to the model is calculated for each of 25 ensemble members as a weighted average of rainfall from the NWP cells over the catchment where the weights are proportional to the areal intersection between the catchment and the NWP cells. In the second approach, a total of 2825 flow ensembles are generated using rainfall input from the neighbouring NWP cells up to approximately 6 cells in all directions from the catchment. In the third approach, the first approach is extended spatially by successively increasing the area covered and for each spatial increase and each time step selecting only the cell with the highest intensity resulting in a total of 175 ensemble members. While the first and second approaches have the disadvantage of not covering the full range of the ROC diagram and being computationally heavy, respectively, the third approach leads to both a broad coverage of the ROC diagram range at a relatively low computational cost. A broad coverage of the ROC diagram offers a larger selection of prediction skill to choose from to best match to the prediction purpose.The study distinguishes itself from earlier research in being the first application to urban hydrology, with fast runoff and small catchments that are highly sensitive to local extremes. Furthermore, no earlier reference has been found on the highly efficient third approach using only neighbouring cells with the highest threat to expand the range of the ROC diagram. This study provides an efficient and robust approach to using ensemble rainfall forecasts affected by bias and misplacement errors for predicting flow threshold exceedance in urban drainage systems.

Improving the accuracy of flood forecasting with transpositions of ensemble NWP rainfall fields considering orographic effects

Summary The use of meteorological ensembles to produce sets of hydrological predictions increased the capability to issue flood warnings. However, space scale of the hydrological domain is still much finer than meteorological model, and NWP models have challenges with displacement. The main objective of this study to enhance the transposition method proposed in Yu et al. (2014) and to suggest the post-processing ensemble flood forecasting method for the real-time updating and the accuracy improvement of flood forecasts that considers the separation of the orographic rainfall and the correction of misplaced rain distributions using additional ensemble information through the transposition of rain distributions. In the first step of the proposed method, ensemble forecast rainfalls from a numerical weather prediction (NWP) model are separated into orographic and non-orographic rainfall fields using atmospheric variables and the extraction of topographic effect. Then the non-orographic rainfall fields are examined by the transposition scheme to produce additional ensemble information and new ensemble NWP rainfall fields are calculated by recombining the transposition results of non-orographic rain fields with separated orographic rainfall fields for a generation of place-corrected ensemble information. Then, the additional ensemble information is applied into a hydrologic model for post-flood forecasting with a 6-h interval. The newly proposed method has a clear advantage to improve the accuracy of mean value of ensemble flood forecasting. Our study is carried out and verified using the largest flood event by typhoon ‘Talas’ of 2011 over the two catchments, which are Futatsuno (356.1 km 2 ) and Nanairo (182.1 km 2 ) dam catchments of Shingu river basin (2360 km 2 ), which is located in the Kii peninsula, Japan.