Academic literature on the topic 'Hydrologic Ensemble Forecast Service'
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Journal articles on the topic "Hydrologic Ensemble Forecast Service"
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Hapuarachchi, Hapu Arachchige Prasantha, Mohammed Abdul Bari, Aynul Kabir, Mohammad Mahadi Hasan, Fitsum Markos Woldemeskel, Nilantha Gamage, Patrick Daniel Sunter, et al. "Development of a national 7-day ensemble streamflow forecasting service for Australia." Hydrology and Earth System Sciences 26, no. 18 (September 29, 2022): 4801–21. http://dx.doi.org/10.5194/hess-26-4801-2022.
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Abstract. Reliable streamflow forecasts with associated uncertainty estimates are essential to manage and make better use of Australia's scarce surface water resources. Here we present the development of an operational 7 d ensemble streamflow forecasting service for Australia to meet the growing needs of users, primarily water and river managers, for probabilistic forecasts to support their decision making. We test the modelling methodology for 100 catchments to learn the characteristics of different rainfall forecasts from Numerical Weather Prediction (NWP) models, the effect of statistical processing on streamflow forecasts, the optimal ensemble size, and parameters of a bootstrapping technique for calculating forecast skill. A conceptual rainfall–runoff model, GR4H (hourly), and lag and route channel routing model that are in-built in the Short-term Water Information Forecasting Tools (SWIFT) hydrologic modelling package are used to simulate streamflow from input rainfall and potential evaporation. The statistical catchment hydrologic pre-processor (CHyPP) is used for calibrating rainfall forecasts, and the error reduction and representation in stages (ERRIS) model is used to reduce hydrological errors and quantify hydrological uncertainty. Calibrating raw forecast rainfall with CHyPP is an efficient method to significantly reduce bias and improve reliability for up to 7 lead days. We demonstrate that ERRIS significantly improves forecast skill up to 7 lead days. Forecast skills are highest in temperate perennially flowing rivers, while it is lowest in intermittently flowing rivers. A sensitivity analysis for optimising the number of streamflow ensemble members for the operational service shows that more than 200 members are needed to represent the forecast uncertainty. We show that the bootstrapping block size is sensitive to the forecast skill calculation. A bootstrapping block size of 1 month is recommended to capture maximum possible uncertainty. We present benchmark criteria for accepting forecast locations for the public service. Based on the criteria, 209 forecast locations out of a possible 283 are selected in different hydro-climatic regions across Australia for the public service. The service, which has been operational since 2019, provides daily updates of graphical and tabular products of ensemble streamflow forecasts along with performance information, for up to 7 lead days.
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Brown, James D., and Dong-Jun Seo. "A Nonparametric Postprocessor for Bias Correction of Hydrometeorological and Hydrologic Ensemble Forecasts." Journal of Hydrometeorology 11, no. 3 (June 1, 2010): 642–65. http://dx.doi.org/10.1175/2009jhm1188.1.
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Abstract This paper describes a technique for quantifying and removing biases from ensemble forecasts of hydrometeorological and hydrologic variables. The technique makes no a priori assumptions about the distributional form of the variables, which is often unknown or difficult to model parametrically. The aim is to estimate the conditional cumulative distribution function (ccdf) of the observed variable given a (possibly biased) real-time ensemble forecast. This ccdf represents the “true” probability distribution of the forecast variable, subject to sampling uncertainties. In the absence of a known distributional form, the ccdf should be estimated nonparametrically. It is noted that the probability of exceeding a threshold of the observed variable, such as flood stage, is equivalent to the expectation of an indicator variable defined for that threshold. The ccdf is then modeled through a linear combination of the indicator variables of the forecast ensemble members. The technique is based on Bayesian optimal linear estimation of indicator variables and is analogous to indicator cokriging (ICK) in geostatistics. By developing linear estimators for the conditional expectation of the observed variable at many thresholds, ICK provides a discrete approximation of the full ccdf. Since ICK minimizes the conditional error variance of the indicator variable at each threshold, it effectively minimizes the continuous ranked probability score (CRPS) when infinitely many thresholds are employed. The technique is used to bias-correct precipitation ensemble forecasts from the NCEP Global Ensemble Forecast System (GEFS) and streamflow ensemble forecasts from the National Weather Service (NWS) River Forecast Centers (RFCs). Split-sample validation results are presented for several attributes of ensemble forecast quality, including reliability and discrimination. In general, the forecast biases were substantially reduced following ICK. Overall, the technique shows significant potential for bias-correcting ensemble forecasts whose distributional form is unknown or nonparametric.
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Yuan, Xing, Joshua K. Roundy, Eric F. Wood, and Justin Sheffield. "Seasonal Forecasting of Global Hydrologic Extremes: System Development and Evaluation over GEWEX Basins." Bulletin of the American Meteorological Society 96, no. 11 (November 1, 2015): 1895–912. http://dx.doi.org/10.1175/bams-d-14-00003.1.
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Abstract Seasonal hydrologic extremes in the form of droughts and wet spells have devastating impacts on human and natural systems. Improving understanding and predictive capability of hydrologic extremes, and facilitating adaptations through establishing climate service systems at regional to global scales are among the grand challenges proposed by the World Climate Research Programme (WCRP) and are the core themes of the Regional Hydroclimate Projects (RHP) under the Global Energy and Water Cycle Experiment (GEWEX). An experimental global seasonal hydrologic forecasting system has been developed that is based on coupled climate forecast models participating in the North American Multimodel Ensemble (NMME) project and an advanced land surface hydrologic model. The system is evaluated over major GEWEX RHP river basins by comparing with ensemble streamflow prediction (ESP). The multimodel seasonal forecast system provides higher detectability for soil moisture droughts, more reliable low and high f low ensemble forecasts, and better “real time” prediction for the 2012 North American extreme drought. The association of the onset of extreme hydrologic events with oceanic and land precursors is also investigated based on the joint distribution of forecasts and observations. Climate models have a higher probability of missing the onset of hydrologic extremes when there is no oceanic precursor. But oceanic precursor alone is insufficient to guarantee a correct forecast—a land precursor is also critical in avoiding a false alarm for forecasting extremes. This study is targeted at providing the scientific underpinning for the predictability of hydrologic extremes over GEWEX RHP basins and serves as a prototype for seasonal hydrologic forecasts within the Global Framework for Climate Services (GFCS).
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Schaake, J., J. Demargne, R. Hartman, M. Mullusky, E. Welles, L. Wu, H. Herr, X. Fan, and D. J. Seo. "Precipitation and temperature ensemble forecasts from single-value forecasts." Hydrology and Earth System Sciences Discussions 4, no. 2 (April 2, 2007): 655–717. http://dx.doi.org/10.5194/hessd-4-655-2007.
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Abstract. A procedure is presented to construct ensemble forecasts from single-value forecasts of precipitation and temperature. This involves dividing the spatial forecast domain and total forecast period into a number of parts that are treated as separate forecast events. The spatial domain is divided into hydrologic sub-basins. The total forecast period is divided into time periods, one for each model time step. For each event archived values of forecasts and corresponding observations are used to model the joint distribution of forecasts and observations. The conditional distribution of observations for a given single-value forecast is used to represent the corresponding probability distribution of events that may occur for that forecast. This conditional forecast distribution subsequently is used to create ensemble members that vary in space and time using the "Schaake Shuffle" (Clark et al, 2004). The resulting ensemble members have the same space-time patterns as historical observations so that space-time joint relationships between events that have a significant effect on hydrological response tend to be preserved. Forecast uncertainty is space and time-scale dependent. For a given lead time to the beginning of the valid period of an event, forecast uncertainty depends on the length of the forecast valid time period and the spatial area to which the forecast applies. Although the "Schaake Shuffle" procedure, when applied to construct ensemble members from a time-series of single value forecasts, may preserve some of this scale dependency, it may not be sufficient without additional constraint. To account more fully for the time-dependent structure of forecast uncertainty, events for additional "aggregate" forecast periods are defined as accumulations of different "base" forecast periods. The generated ensemble members can be ingested by an Ensemble Streamflow Prediction system to produce ensemble forecasts of streamflow and other hydrological variables that reflect the meteorological uncertainty. The methodology is illustrated by an application to generate temperature and precipitation ensemble forecasts for the American River in California. Parameter estimation and dependent validation results are presented based on operational single-value forecasts archives of short-range River Forecast Center (RFC) forecasts and medium-range ensemble mean forecasts from the National Weather Service (NWS) Global Forecast System (GFS).
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Demargne, Julie, Limin Wu, Satish K. Regonda, James D. Brown, Haksu Lee, Minxue He, Dong-Jun Seo, et al. "The Science of NOAA's Operational Hydrologic Ensemble Forecast Service." Bulletin of the American Meteorological Society 95, no. 1 (January 2014): 79–98. http://dx.doi.org/10.1175/bams-d-12-00081.1.
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Kim, Sunghee, Hossein Sadeghi, Reza Ahmad Limon, Manabendra Saharia, Dong-Jun Seo, Andrew Philpott, Frank Bell, James Brown, and Minxue He. "Assessing the Skill of Medium-Range Ensemble Precipitation and Streamflow Forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for the Upper Trinity River Basin in North Texas." Journal of Hydrometeorology 19, no. 9 (September 1, 2018): 1467–83. http://dx.doi.org/10.1175/jhm-d-18-0027.1.
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Abstract To issue early warnings for the public to act, for emergency managers to take preventive actions, and for water managers to operate their systems cost-effectively, it is necessary to maximize the time horizon over which streamflow forecasts are skillful. In this work, we assess the value of medium-range ensemble precipitation forecasts generated with the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service (NWS) in increasing the lead time and skill of streamflow forecasts for five headwater basins in the upper Trinity River basin in north-central Texas. The HEFS uses ensemble mean precipitation forecasts from the Global Ensemble Forecast System (GEFS) of the National Centers for Environment Prediction (NCEP). For comparative evaluation, we verify ensemble streamflow forecasts generated with the HEFS forced by the GEFS forecast with those forced by the short-range quantitative precipitation forecasts (QPFs) from the NWS West Gulf River Forecast Center (WGRFC) based on guidance from the NCEP’s Weather Prediction Center. We also assess the benefits of postprocessing the raw ensemble streamflow forecasts and evaluate the impact of selected parameters within the HEFS on forecast quality. The results show that the use of medium-range precipitation forecasts from the GEFS with the HEFS extends the time horizon for skillful forecasting of mean daily streamflow by 1–3 days for significant events when compared with using only the 72-h River Forecast Center (RFC) QPF with the HEFS. The HEFS forced by the GEFS also improves the skill of two-week-ahead biweekly streamflow forecast by about 20% over climatological forecast for the largest 1% of the observed biweekly flow.
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Porter, James H., Adão H. Matonse, and Allan Frei. "The New York City Operations Support Tool (OST): Managing Water for Millions of People in an Era of Changing Climate and Extreme Hydrological Events." Journal of Extreme Events 02, no. 02 (December 2015): 1550008. http://dx.doi.org/10.1142/s2345737615500086.
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With an average daily delivery of 1.1 billion gallons ([Formula: see text]) of drinking water to approximately nine million people in New York City (NYC) and four upstate counties, the NYC Water Supply is among the world’s largest unfiltered systems. In addition to reliably supplying water in terms of quantity and quality, the city has to fulfill other flow objectives to serve downstream communities. At times, such as during extreme hydrological events, water quality issues may restrict water usage from parts of the system; the city is proactively implementing a number of programs to monitor and minimize the impact. To help guide operations and planning, NYC has developed the Operations Support Tool (OST), a decision support system that utilizes ensemble forecasts provided by the National Weather Service (NWS) Hydrologic Ensemble Forecast Service (HEFS). This paper provides an overview of OST and shows two operations case studies to illustrate how OST is used to support risk-based water supply management. As the modeling uncertainty is strongly impacted by the forecast skill, we also discuss how changes in patterns of hydrological extreme events elevate the challenge faced by water supply managers and the role of the scientific community to integrate non-stationarity approaches in hydrologic forecasting.
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Franz, K. J., and T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community." Hydrology and Earth System Sciences 15, no. 11 (November 15, 2011): 3367–82. http://dx.doi.org/10.5194/hess-15-3367-2011.
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Abstract. The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP) systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences, yet few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from two different parameter uncertainty estimation methods: the Generalized Uncertainty Likelihood Estimator (GLUE), and the Shuffle Complex Evolution Metropolis (SCEM). Model ensembles are generated for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA) model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the presented probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of model performance associated with parameter uncertainty. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
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Carlberg, Bradley, Kristie Franz, and William Gallus. "A Method to Account for QPF Spatial Displacement Errors in Short-Term Ensemble Streamflow Forecasting." Water 12, no. 12 (December 13, 2020): 3505. http://dx.doi.org/10.3390/w12123505.
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To account for spatial displacement errors common in quantitative precipitation forecasts (QPFs), a method using systematic shifting of QPF fields was tested to create ensemble streamflow forecasts. While previous studies addressed spatial displacement using neighborhood approaches, shifting of QPF accounts for those errors while maintaining the structure of predicted systems, a feature important in hydrologic forecasts. QPFs from the nine-member High-Resolution Rapid Refresh Ensemble were analyzed for 46 forecasts from 6 cases covering 17 basins within the National Weather Service North Central River Forecast Center forecasting region. Shifts of 55.5 and 111 km were made in the four cardinal and intermediate directions, increasing the ensemble size to 81 members. These members were input into a distributed hydrologic model to create an ensemble streamflow prediction. Overall, the ensemble using the shifted QPFs had an improved frequency of non-exceedance and probability of detection, and thus better predicted flood occurrence. However, false alarm ratio did not improve, likely because shifting multiple QPF ensembles increases the potential to place heavy precipitation in a basin where none actually occurred. A weighting scheme based on a climatology of displacements was tested, improving overall performance slightly compared to the approach using non-weighted members.
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Franz, K. J., and T. S. Hogue. "Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community." Hydrology and Earth System Sciences Discussions 8, no. 2 (March 30, 2011): 3085–131. http://dx.doi.org/10.5194/hessd-8-3085-2011.
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Abstract. The hydrologic community is generally moving towards the use of probabilistic estimates of streamflow, primarily through the implementation of Ensemble Streamflow Prediction (ESP) systems, ensemble data assimilation methods, or multi-modeling platforms. However, evaluation of probabilistic outputs has not necessarily kept pace with ensemble generation. Much of the modeling community is still performing model evaluation using standard deterministic measures, such as error, correlation, or bias, typically applied to the ensemble mean or median. Probabilistic forecast verification methods have been well developed, particularly in the atmospheric sciences yet, few have been adopted for evaluating uncertainty estimates in hydrologic model simulations. In the current paper, we overview existing probabilistic forecast verification methods and apply the methods to evaluate and compare model ensembles produced from different parameter uncertainty estimation methods. The Generalized Uncertainty Likelihood Estimator (GLUE), a modified version of GLUE, and the Shuffle Complex Evolution Metropolis (SCEM) are used to generate model ensembles for the National Weather Service SACramento Soil Moisture Accounting (SAC-SMA) model for 12 forecast basins located in the Southeastern United States. We evaluate the model ensembles using relevant metrics in the following categories: distribution, correlation, accuracy, conditional statistics, and categorical statistics. We show that the probabilistic metrics are easily adapted to model simulation ensembles and provide a robust analysis of parameter uncertainty, one that is commensurate with the dimension of the ensembles themselves. Application of these methods requires no information in addition to what is already available as part of traditional model validation methodology and considers the entire ensemble or uncertainty range in the approach.
Dissertations / Theses on the topic "Hydrologic Ensemble Forecast Service"
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Adams, Thomas Edwin III. "The Use of Central Tendency Measures from an Operational Short Lead-time Hydrologic Ensemble Forecast System for Real-time Forecasts." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83461.
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A principal factor contributing to hydrologic prediction uncertainty is modeling error intro- duced by the measurement and prediction of precipitation. The research presented demon- strates the necessity for using probabilistic methods to quantify hydrologic forecast uncer- tainty due to the magnitude of precipitation errors. Significant improvements have been made in precipitation estimation that have lead to greatly improved hydrologic simulations. However, advancements in the prediction of future precipitation have been marginal. This research shows that gains in forecasted precipitation accuracy have not significantly improved hydrologic forecasting accuracy. The use of forecasted precipitation, referred to as quantita- tive precipitation forecast (QPF), in hydrologic forecasting remains commonplace. Non-zero QPF is shown to improve hydrologic forecasts, but QPF duration should be limited to 6 to 12 hours for flood forecasting, particularly for fast responding watersheds. Probabilistic hydrologic forecasting captures hydrologic forecast error introduced by QPF for all forecast durations. However, public acceptance of probabilistic hydrologic forecasts is problematic. Central tendency measures from a probabilistic hydrologic forecast, such as the ensemble median or mean, have the appearance of a single-valued deterministic forecast. The research presented shows that hydrologic ensemble median and mean forecasts of river stage have smaller forecast errors than current operational methods with forecast lead-time beginning at 36-hours for fast response basins. Overall, hydrologic ensemble median and mean forecasts display smaller forecast error than current operational forecasts.Ph. D.
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Sarath, M. "At-site and Multisite Probabilistic Forecasting of Streamflow." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/5126.
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Streamflow forecasts are very useful for a variety of applications such as flood warning, reservoir operation and water resources planning and management, especially in countries like India where streamflow can be highly variable. Methods available for streamflow forecasting can be broadly classified as process-driven and data-driven methods. Forecasts always have uncertainty associated with them due to limitations in modelling complex processes in the hydrologic system, and factors such as scarcity of data and measurement errors. It is important to quantify the forecast uncertainty for making informed decisions.
Hydrologic Ensemble Prediction Systems (HEPS), which use ensembles in process-driven approach for generating probabilistic forecasts to quantify uncertainty are gaining popularity in the world. However, there is dearth of studies on application of HEPS for forecasting streamflows in Indian rivers. Recently, United States (US) National Weather Service developed a HEPS called Hydrologic Ensemble Forecast Service (HEFS) to generate seamless probabilistic hydrologic forecasts from short to long lead times. The first objective of this thesis is to investigate the potential of HEFS in generating skilful streamflow forecasts for an Indian river, as there is no prior application of HEFS outside US. Tel river, which is one of the tributaries of Mahanadi river (which is frequently prone to floods) was chosen for case study. Forecasts of meteorological variables (precipitation and temperature) required as input to HEFS were obtained from Global Ensemble Forecast System (GEFS). The HEFS consists of three main components - (i) Meteorological Ensemble Forecast Processor (MEFP), (ii) Hydrologic Processor and (iii) Hydrologic Ensemble Postprocessor (EnsPost). MEFP accounts for meteorological uncertainty by generating bias corrected ensemble meteorological forecast which is subsequently propagated through the Hydrologic Processor initialised with basin conditions. The resulting hydrologic ensemble forecast is input to EnsPost to generate
postprocessed hydrologic ensemble forecast which reflects the total forecast uncertainty accounting for both meteorological and hydrologic uncertainties. A lumped rainfall-runoff model called GR4J was used as the Hydrologic Processor. Verification of retrospective daily streamflow forecasts generated using HEFS for Tel river against corresponding observations indicated that the forecasts have fairly good skill at short lead times (1 to 3 days). The forecasts were found to have higher skill compared to climatological forecasts and forecasts generated by an ARIMA model.
Statistical methods are widely used operationally for forecasting streamflow at coarser time scales such as seasonal. For some applications (e.g., coordinated operation of a system of reservoirs), contemporaneous streamflow forecasts may be required at many sites in a basin. Forecasts generated using separate statistical models for each site may not preserve spatial correlation structure between flows at different sites. The second objective of this thesis is to explore the potential of regularised Multivariate Multiple Linear Regression (MMLR) models in generating skilful multisite streamflow forecasts. Three regularisation methods namely ridge regression, lasso and MRCE (Multivariate Regression with Covariance Estimation) were considered. The potential of the regularised MMLR models was examined through a case study on seasonal streamflow forecasting in upper Colorado river basin of US. Performance of the models was compared with that of four other multisite forecasting methods based on (i) Schaake Shuffle, (ii) Principal Component Analysis, (iii) disaggregation and (iv) k-nearest neighbour resampling, which were available in literature. Considering both forecast skill and ability to preserve inter-site correlations, the method based on MMLR and ridge regression was found to perform better than the other methods considered.
Book chapters on the topic "Hydrologic Ensemble Forecast Service"
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Larson, Lee W., and John C. Monro. "Precipitation Modeling in Mountainous Areas for the National Weather Service River Forecast System." In Precipitation Analysis for Hydrologic Modeling, 189–99. Washington, D. C.: American Geophysical Union, 2013. http://dx.doi.org/10.1029/sp004p0189.
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Saito, Kazuo, Yoshinori Shoji, Seiji Origuchi, and Le Duc. "GPS PWV Assimilation with the JMA Nonhydrostatic 4DVAR and Cloud Resolving Ensemble Forecast for the 2008 August Tokyo Metropolitan Area Local Heavy Rainfalls." In Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. III), 383–404. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43415-5_17.
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Vallette, Anne, Quentin Gunti, Fatimatou Coulibaly, and Anne-Laure Beck. "Implementation of a Hydrologic Model as an Element of the Litter-TEP Service—Marine Litter Tracking and Stranding Forecast—Or for the Understanding of the Coastal Patterns Change." In Advances in Hydroinformatics, 921–36. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1600-7_57.
Full textConference papers on the topic "Hydrologic Ensemble Forecast Service"
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Chen, Yuwen, Jian Cao, Shanshan Feng, and Yudong Tan. "An ensemble learning based approach for building airfare forecast service." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363846.
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Steele, Edward, Hannah Brown, Christopher Bunney, Philip Gill, Kenneth Mylne, Andrew Saulter, Jessica Standen, Liam Blair, Stewart Cruickshank, and Morten Gulbrandsen. "Using Metocean Forecast Verification Information to Effectively Enhance Operational Decision-Making." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31253-ms.
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Abstract Metocean forecast verification statistics (or ‘skill scores’), for variables such as significant wave height, are typically computed as a means of assessing the (past) weather model performance over the particular area of interest. For developers, this information is important for the measurement of model improvement, while for consumers this is commonly applied for the comparison/evaluation of potential service providers. However, an opportunity missed by many is also its considerable benefit to users in enhancing operational decision-making on a real-time (future) basis, when combined with an awareness of the context of the specific decision being made. Here, we present two categorical verification techniques and demonstrate their application in simplifying the interpretation of ensemble (probabilistic) wave forecasts out to 15 days ahead, as pioneered – in operation – in Summer 2020 to support the recent weather sensitive installation of the first phase of a 36 km subsea pipeline in the Fenja field in the North Sea. Categorical verification information (based on whether forecast and observations exceed the user-defined operational weather limits) was constructed from 1460 archive wave forecasts, issued for the two-year period 2017 to 2018, and used to characterise the past performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system (EPS) in the form of Receiver Operating Characteristic (ROC) and Relative Economic Value (REV) analysis. These data were then combined with a bespoke parameterization of the impact of adverse weather on the planned operation, allowing the relevant go/no-go ensemble probability threshold (i.e. the number of individual/constituent forecast members that must predict favourable/unfavourable conditions) for the interpretation of future forecasts to be determined. Following the computation of the probability thresholds for the Fenja location, trials on an unseen nine-month period of data from the site (Spring to Autumn 2019) confirm these approaches facilitate a simple technique for processing/interpreting the ensemble forecast, able to be readily tailored to the particular decision being made. The use of these methods achieves a considerably greater value (benefit) than equivalent deterministic (single) forecasts or traditional climate-based options at all lead times up to 15 days ahead, promising a more robust basis for effective planning than typically considered by the offshore industry. This is particularly important for tasks requiring early identification of long weather windows (e.g. for the Fenja tie-ins), but similarly relevant for maximising the exploitation of any ensemble forecast, providing a practical approach for how such data are handled and used to promote safe, efficient and successful operations.