Relevant bibliographies by topics / Hydrologic Ensemble Forecast Service

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Hydrologic Ensemble Forecast Service'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Hydrologic Ensemble Forecast Service.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Hydrologic Ensemble Forecast Service"

1

Hapuarachchi, Hapu Arachchige Prasantha, Mohammed Abdul Bari, Aynul Kabir, et al. "Development of a national 7-day ensemble streamflow forecasting service for Australia." Hydrology and Earth System Sciences 26, no. 18 (2022): 4801–21. http://dx.doi.org/10.5194/hess-26-4801-2022.

Full text
Abstract:
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 p
APA, Harvard, Vancouver, ISO, and other styles
2

Bari, Mohammed Abdul, Mohammad Mahadi Hasan, Gnanathikkam Emmanual Amirthanathan, et al. "Performance Evaluation of a National Seven-Day Ensemble Streamflow Forecast Service for Australia." Water 16, no. 10 (2024): 1438. http://dx.doi.org/10.3390/w16101438.

Full text
Abstract:
The Australian Bureau of Meteorology offers a national operational 7-day ensemble streamflow forecast service covering regions of high environmental, economic, and social significance. This semi-automated service generates streamflow forecasts every morning and is seamlessly integrated into the Bureau’s Hydrologic Forecasting System (HyFS). Ensemble rainfall forecasts, European Centre for Medium-Range Weather Forecasts (ECMWF), and Poor Man’s Ensemble (PME), available in the Numerical Weather Prediction (NWP) suite, are used to generate these streamflow forecasts. The NWP rainfall undergoes pr
APA, Harvard, Vancouver, ISO, and other styles
3

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 (2015): 1895–912. http://dx.doi.org/10.1175/bams-d-14-00003.1.

Full text
Abstract:
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 devel
APA, Harvard, Vancouver, ISO, and other styles
4

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 (2010): 642–65. http://dx.doi.org/10.1175/2009jhm1188.1.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles
5

Schaake, J., J. Demargne, R. Hartman, et al. "Precipitation and temperature ensemble forecasts from single-value forecasts." Hydrology and Earth System Sciences Discussions 4, no. 2 (2007): 655–717. http://dx.doi.org/10.5194/hessd-4-655-2007.

Full text
Abstract:
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 ob
APA, Harvard, Vancouver, ISO, and other styles
6

Kim, Sunghee, Hossein Sadeghi, Reza Ahmad Limon, et al. "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 (2018): 1467–83. http://dx.doi.org/10.1175/jhm-d-18-0027.1.

Full text
Abstract:
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. Th
APA, Harvard, Vancouver, ISO, and other styles
7

Demargne, Julie, Limin Wu, Satish K. Regonda, et al. "The Science of NOAA's Operational Hydrologic Ensemble Forecast Service." Bulletin of the American Meteorological Society 95, no. 1 (2014): 79–98. http://dx.doi.org/10.1175/bams-d-12-00081.1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

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 (2015): 1550008. http://dx.doi.org/10.1142/s2345737615500086.

Full text
Abstract:
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 a
APA, Harvard, Vancouver, ISO, and other styles
9

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 (2020): 3505. http://dx.doi.org/10.3390/w12123505.

Full text
Abstract:
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 Centr
APA, Harvard, Vancouver, ISO, and other styles
10

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 (2011): 3367–82. http://dx.doi.org/10.5194/hess-15-3367-2011.

Full text
Abstract:
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 metho
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Hydrologic Ensemble Forecast Service"

1

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.

Full text
Abstract:
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 ac
APA, Harvard, Vancouver, ISO, and other styles
2

Sarath, M. "At-site and Multisite Probabilistic Forecasting of Streamflow." Thesis, 2019. https://etd.iisc.ac.in/handle/2005/5126.

Full text
Abstract:
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
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Hydrologic Ensemble Forecast Service"

1

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. American Geophysical Union, 2013. http://dx.doi.org/10.1029/sp004p0189.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

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). Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43415-5_17.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

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. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1600-7_57.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Hydrologic Ensemble Forecast Service"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Steele, Edward, Hannah Brown, Christopher Bunney, et al. "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.

Full text
Abstract:
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 combin
APA, Harvard, Vancouver, ISO, and other styles
3

Kliegrová, Stanislava, Michal Belda, Ladislav Metelka, and Petr Štěpánek. "Long-range forecast of air temperature and precipitation for summer months in the Czech Republic." In První konference PERUN. Český hydrometeorologický ústav, 2023. http://dx.doi.org/10.59984/978-80-7653-063-8.05.

Full text
Abstract:
Long-range forecasts, providing information on expected future atmospheric and oceanic conditions for periods of one to three months, are attractive to many sectors. This study analyses selected global models for long-range forecasts available in the Copernicus Climate Change Service (C3S) archive, which provide air temperature and near-surface precipitation data at a spatial resolution of 1° x 1°, focusing on the forecast of the summer months (starting date 1 May) in the period 2000–2016 (a period of hindcasts common to all global models) and the area of the Czech Republic. E-OBS datasets of
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Hydrologic Ensemble Forecast Service"

1

Liguori, Giovanni, and Nadia Pinardi. Evaluation of Extreme Forecast Indices (WP5+6). EuroSea, 2023. http://dx.doi.org/10.3289/eurosea_d4.11.

Full text
Abstract:
While originally developed for weather forecasting, the Extreme Forecast index (EFI) concept has found utility in diverse fields. This study marks the inaugural application of EFI principles to numerical ocean forecasting. EFI offers a metric to gauge the forecast's deviation from historical norms specific to the location and time of year. A heightened EFI value signifies that the forecast falls beyond the usual range of variability, signifying a higher probability of extreme conditions. This novel use of EFI stands to benefit oceanographers by identifying significant oceanic events, aiding de
APA, Harvard, Vancouver, ISO, and other styles
2

Alves, Jose-Henrique, Roberto Padilla-Hernandez, Deanna Spindler, et al. Development of a wave model component in the first coupled Global Ensemble Forecast System at NOAA. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49784.

Full text
Abstract:
We describe the development of the wave component in the first global-scale coupled operational forecast system using the Unified Forecasting System at NOAA, part of the U.S. National Weather Service operational forecasting suite. The operational implementation of the atmosphere–wave coupled Global Ensemble Forecast System, version 12, was a critical step in NOAA’s transition to the broader community based UFS framework. GEFSv12 represents a significant advancement, extending forecast ranges and empowering the NWS to deliver advanced weather predictions with extended lead times for high-impact
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Hydrologic Ensemble Forecast Service' for particular source types:
Journal articles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography