Hydrological ensemble prediction systems

This study presents findings from a real-time forecast experiment that compares legacy deterministic hydrologic stage forecasts to ensemble mean and median stage forecasts from the NOAA/NWS Meteorological Model-Based Ensemble Forecast System (MMEFS). The NOAA/NWS Ohio River Forecast Center (OHRFC) area of responsibility defines the experimental region. Real-time forecasts from subbasins at 54 forecast point locations, ranging in drainage area, geographic location within the Ohio River valley, and watershed response time serve as the basis for analyses. In the experiment, operational hydrologic forecasts, with a 24-h quantitative precipitation forecast (QPF) and forecast temperatures, are compared to MMEFS hydrologic ensemble mean and median forecasts, with model forcings from the NOAA/NWS National Centers for Environmental Prediction (NCEP) North American Ensemble Forecast System (NAEFS), over the period from 30 November 2010 through 24 May 2012. Experiments indicate that MMEFS ensemble mean and median forecasts exhibit lower errors beginning at about lead time 90 h when forecasts at all locations are aggregated. With fast response basins that peak at ≤24 h, ensemble mean and median forecasts exhibit lower errors much earlier, beginning at about lead time 36 h, which suggests the viability of using MMEFS ensemble forecasts as an alternative to OHRFC legacy forecasts. Analyses show that ensemble median forecasts generally exhibit smaller errors than ensemble mean forecasts for all stage ranges. Verification results suggest that OHRFC MMEFS NAEFS ensemble forecasts are reasonable, but needed improvements are identified.

Given the increasing vulnerability due to more frequent and severe hydrological hazards under a changing climate, it is imperative to develop accurate and reliable global and local hydrological prediction systems at subseasonal-to-seasonal (S2S) timescales. However, operational forecasts tailored to specific local regions remain limited due to lack of both local observations and regional hydrological models, while global hydrological models often lack calibration for local conditions, making it challenging for them to capture local-scale dynamics. Additionally, users and decision-makers face difficulties in effectively interpreting ensemble forecasts from multiple hydrological models in operational settings especially when compared to the simplicity and clarity of a single model approach. To bridge this gap, it is essential to integrate existing hydrological forecasting systems across global, regional, and local scales, with the goal of delivering skilful, standardized, and comprehensible predictions. As part of the World Meteorological Organization's (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we are exploring various approaches to: (i) validate and enhance the skill of current hydrological probabilistic forecasts, and (ii) blend multi-model ensemble simulations to develop integrated and reliable operational forecasts. Here, we aim to develop a framework for bias-correcting and blending global multi-model ensemble forecasts, based on the skill of each modelling system for each catchment, to deliver unique probabilistic forecasts. Our research, using global hindcasts from various modelling systems, has demonstrated that applying this framework to post-process raw model simulations can deliver reliable S2S hydrological forecasts across diverse global catchments operationally. This approach has the potential for improved water resource management and hydrological hazard mitigation, particularly in data-sparse regions.

We conducted 6-month deterministic and ensemble forecasts initialized from the ensemble mean analyses and 128-member ensemble analyses, respectively, on the first day of each month from 2016 to 2018 (i.e., 36 cases in total) to compare the forecast accuracies of the Kuroshio south of Japan. The results showed that the ensemble mean forecasts captured the Kuroshio paths better in general compared with the deterministic forecasts, especially during the Kuroshio large meander period after the summer of 2017. For 2016–2018, the predictability limit for the ensemble mean forecast is estimated to be 108 days on average, demonstrating for the first time that the ensemble mean forecasts achieve a significantly longer predictability limit than the 74-day limit of the deterministic forecasts for the Kuroshio. We also investigated the sensitivity of the forecast accuracy to the ensemble size using a resampling method. The results indicated that for the Kuroshio prediction, the ensemble mean forecasts are more accurate than the deterministic forecasts when the ensemble size exceeds 32 members. When the ensemble size is limited to 32 members or less, combining the deterministic and ensemble forecasts could improve the short-term Kuroshio forecasting. This study also identified substantial northward shift biases in the Kuroshio forecasts, suggesting that further system development is necessary to achieve more significant improvements in the forecast accuracy.

WIREs WaterVolume 2, Issue 5 p. 593-593 ErratumFree Access Understanding the roles of modernity, science, and risk in shaping flood management This article corrects the following: Understanding the roles of modernity, science, and risk in shaping flood management Sébastien Nobert, Kristian Krieger, Florian Pappenberger, Volume 2Issue 3Wiley Interdisciplinary Reviews: Water pages: 245-258 First Published online: March 3, 2015 First published: 08 August 2015 https://doi.org/10.1002/wat2.1095AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL In Nobert et al.1, the following error was published in Figure 1 legend. Deterministic and probabilistic forecasts from the European Flood Awareness System. The blue solid line is a flood forecast using the deterministic model of the German weather service (Deutscher Wetterdienst). The blue solid line is the high-resolution forecast of ECMWF and the box-plots show the ensemble forecast of ECMWF. The text was incorrect and should have read: Deterministic and probabilistic forecasts from the European Flood Awareness System. The blue solid line is a flood forecast using the deterministic model of the German weather service (Deutscher Wetterdienst). The red solid line is the high-resolution forecast of ECMWF and the box-plots show the ensemble forecast of ECMWF. We apologize for this error. REFERENCE 1Nobert S, Krieger K, Pappenberger F. Understanding the roles of modernity, science, and risk in shaping flood management. WIREs Water 2015, 2: 245– 58. Volume2, Issue5September/October 2015Pages 593-593 ReferencesRelatedInformation

This paper deals with the assessment of hydrological predictions driven by probabilistic hybrid rainfall forecasts obtained by merging numerical weather prediction and radar‐based rainfall forecasts. Deterministic and ensemble hybrid rainfall forecasts have been utilized to provide river flow predictions on a catchment study located in the UK. A novel assessment methodology has been developed and implemented for the purpose of comparing deterministic and ensemble river flow predictions. The assessment methodology provides a measure of the degree of underestimation/overestimation characterizing the forecasts in comparison with a series of normalized reference thresholds. The analysis of the results obtained by implementing this evaluation methodology shows that the overall performance of the deterministic forecasts is very similar to that of the ensemble forecasts, with a slightly larger degree of underestimation affecting the ensemble forecasts in comparison with the deterministic forecasts. Copyright © 2012 John Wiley & Sons, Ltd.

U.S. National Weather Service (NWS) River Forecast Centers (RFCs) have produced monthly to seasonal hydrologic forecast ensemble outlooks for many years using historical precipitation and temperature as the forcing data for the ensembles within the Advanced Hydrologic Prediction System (AHPS). This has worked well for many water resources applications relying on long lead-time ensembles of greater than thirty days. In the eastern United States, there is considerable interest in shorter lead-time ensembles, particularly in the period of one to seven days that convey hydrologic forecast uncertainty resulting directly from the uncertainties in precipitation and temperature forecasts, with a focus on flood forecasting. This paper demonstrates the use of numerical weather prediction (NWP) model ensembles as the forcing for hydrologic models within the NWS hydrologic forecasting system, the Deltares Flood Early Warning System (FEWS) based Community Hydrologic Prediction System (CHPS), utilizing Ensemble Streamflow Prediction (ESP) system techniques. This approach makes it possible to produce probabilistic hydrologic forecasts that incorporate the skill and uncertainty of atmospheric models. Significant issues exist with directly using the meteorological ensembles, including biases and spatial resolution of the available data, but the approach allows for the generation of valuable information for end-user decision makers, covering a time frame for which there is little current probabilistic hydrologic guidance available. This paper presents a description of a joint project among four NWS RFCs to incorporate ensemble forecasts from a variety of meteorological model ensembles. These include the National Oceanic and Atmospheric Administration (NOAA) NWS National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS), North American Ensemble Forecast System (NAEFS), and Short Range Ensemble Forecast System (SREF). Verification results are presented for operational NAEFS and SREF based MMEFS real-time probabilistic hydrologic forecasts covering multi-year period. Improvements to the MMEFS are also recommended.

The National Center for Environmental Predictions (NCEP) has produced an ensemble meteorological reforecast product by using a fixed version of Global Forecast System (GFS) ensemble prediction system since 1 January 1979. The 15‐member ensemble product, with a global coverage at a 2.5° × 2.5° spatial resolution and a 14‐day lead time, has been used successfully by the River Forecast Centers of the National Weather Service (NWS) to produce basin scale precipitation and temperature ensemble forecasts in the US for several years now. This study evaluates the predictive skill of post‐processed ensemble forecasts based on GFS precipitation reforecast in China's Huai river basin. The evaluation is carried out in 15 sub‐areas of the Huai river basin and covers the 1/1/1981–31/12/2003 period. The Ensemble Pre‐Processing system version 3 (EPP3), developed at NWS, is used to develop joint probability distributions between forecasted ensemble mean precipitation and corresponding observations and to generate individual ensemble members that preserve space–time correlation of the observed precipitation data. Several statistical verification measures are used to quantify the goodness of fit between post‐processed (i.e. EPP3 processed) ensemble mean and observation and to assess the ensemble spread. Results indicate that the post‐processed forecasts have meaningful predictive skill for the first few days for ensemble daily precipitation forecasts. Predictive skill of ensemble forecasts of cumulative precipitation for lead times up to 14 days are significant. The forecast skill is highly dependent on seasonality, with relatively lower skills seen for wet summer season, when convective storm patterns dominate, as compared with other seasons. The predictive skill of the post‐processed ensemble precipitation is much better than the raw forecasts and the climatological ensemble forecasts. The results from this study suggest that the NCEP's GFS reforecasts can be a valuable resource for places other than the US. Copyright © 2012 John Wiley & Sons, Ltd.

Flood forecasting is becoming increasingly important across the world. The exposure of people and property to flooding is increasing and society is demanding improved management of flood risk. At the same time, technological and data advances are enabling improvements in forecasting capabilities. One area where flood forecasting is seeing technical developments is in the use of probabilistic forecasts—these provide a range of possible forecast outcomes that indicate the probability or chance of a flood occurring. While probabilistic forecasts have some distinct benefits, they pose an additional decision-making challenge to those that use them: with a range of forecasts to pick from, which one is right? (or rather, which one(s) can enable me to make the correct decision?). This paper describes an innovative and transferable approach for aiding decision-making with probabilistic forecasts. The proposed risk-based decision-support framework has been tested in a range of flood risk environments: from coastal surge to fluvial catchments to urban storm water scales. The outputs have been designed to be practical and proportionate to the level of flood risk at any location and to be easy to apply in an operational flood forecasting and warning context. The benefits of employing a benefit-cost inspired decision-support framework are that flood forecasting decision-making can be undertaken objectively, with confidence and an understanding of uncertainty, and can save unnecessary effort on flood incident actions. The method described is flexible such that it can be used for a wide range of flood environments with multiple flood incident management actions. It uses a risk-based approach taking into account both the probability and the level of impact of a flood event. A key feature of the framework is that it is based on a full assessment of the flood-related risk, taking into account both the probability and the level of impact of a flood event. A recommendation for action may be triggered by either a higher probability of a lower impact flood or a low probability of a very severe flood. Hence, it is highly innovative as it is the first application of such a risk-based method for flood forecasting and warning purposes. A final benefit is that it is considered to be transferrable to other countries.

Ensemble prediction system (EPS) meteorological forecasts are increasingly being used as input to hydrological models in order to extend flood‐warning lead‐times and to improve forecasts and the knowledge of their uncertainty. Probabilistic skill scores classically used in meteorology are usually also utilized for assessing the quality of hydrological ensemble forecasts. However, the different river discharge magnitudes can make difficult the interpretation and comparisons of these scores, as it is for the continuous rank probability score (CRPS). In this letter, a novel ‘Reduction’ CRPS (RCRPS), which takes into account the different river discharge magnitudes, is proposed, and its usefulness is exhibited. Copyright © 2013 Royal Meteorological Society

Bias correction of ensemble precipitation forecasts in the improvement of summer streamflow prediction skill

Forecast verification is important across scientific disciplines, as it provides a framework for evaluating the performance of a forecasting system. In the atmospheric sciences, probabilistic skill scores are often used for verification, as they provide a way of ranking the performance of different probabilistic forecasts unambiguously. In order to be useful, a skill score must be proper: it must encourage honesty in the forecaster and reward forecasts that are reliable and have good resolution. A new score, the error‐spread score (ES), is proposed, which is particularly suitable for evaluation of ensemble forecasts. It is formulated with respect to the moments of the forecast. The ES is confirmed to be a proper score and is therefore sensitive to both resolution and reliability. The ES is tested on forecasts made using the Lorenz ′96 system and found to be useful for summarizing the skill of the forecasts. The European Centre for Medium‐Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) is evaluated using the ES. Its performance is compared with a perfect statistical probabilistic forecast: the ECMWF high‐resolution deterministic forecast dressed with the observed error distribution. This generates a forecast that is perfectly reliable if considered over all time, but does not vary from day to day with the predictability of the atmospheric flow. The ES distinguishes between the dynamically reliable EPS forecasts and the statically reliable dressed deterministic forecasts. Other skill scores are tested and found to be comparatively insensitive to this desirable forecast quality. The ES is used to evaluate seasonal range ensemble forecasts made with the ECMWF System 4. The ensemble forecasts are found to be skilful when compared with climatological or persistence forecasts, though this skill is dependent on the region and time of year.

Given increasing computing power, an important question is whether additional computational resources would be better spent reducing the horizontal grid spacing of a convection-allowing model (CAM) or adding members to form CAM ensembles. The present study investigates this question as it applies to CAM-derived next-day probabilistic severe weather forecasts created by using forecast updraft helicity as a severe weather proxy for 63 days of the 2010 and 2011 NOAA Hazardous Weather Testbed Spring Forecasting Experiments. Forecasts derived from three sets of Weather Research and Forecasting Model configurations are tested: a 1-km deterministic model, a 4-km deterministic model, and an 11-member, 4-km ensemble. Forecast quality is evaluated using relative operating characteristic (ROC) curves, attributes diagrams, and performance diagrams, and forecasts from five representative cases are analyzed to investigate their relative quality and value in a variety of situations. While no statistically significant differences exist between the 4- and 1-km deterministic forecasts in terms of area under ROC curves, the 4-km ensemble forecasts offer weakly significant improvements over the 4-km deterministic forecasts over the entire 63-day dataset. Further, the 4-km ensemble forecasts generally provide greater forecast quality relative to either of the deterministic forecasts on an individual day. Collectively, these results suggest that, for purposes of improving next-day CAM-derived probabilistic severe weather forecasts, additional computing resources may be better spent on adding members to form CAM ensembles than on reducing the horizontal grid spacing of a deterministic model below 4 km.

Ensemble predictions and perceptions of risk, uncertainty, and error in flood forecasting

Hydrologic forecasts are essential for mitigating water‐related risks. However, little work has explored whether operational ensemble forecasts have improved over time, particularly with respect to probabilistic performance and in cases with limited data. This study contributes a retrospective analysis of short‐ and medium‐range (1–14 days ahead) streamflow forecasts issued by the California Nevada River Forecast Center (RFC) at 97 sites between water years 2014–2025, using the National Weather Service (NWS)'s Hydrologic Ensemble Forecast Service (HEFS). We develop a novel and generalizable hierarchical Bayesian model to partially pool data across sites and quantify regional trends in deterministic and probabilistic forecast performance. Results suggest improved performance for moderate and high flow events, potentially linked to meteorological model upgrades and enhanced data assimilation. However, the degree of improvement depends on performance metric and lead time, with stronger trends for deterministic performance at shorter leads and only weak evidence for improvements in attributes of ensemble spread.

Problems and prospects in long and medium range weather forecasting