Improving Flood Forecasting Skill with the Ensemble Kalman Filter

Humberto Vergara; Yang Hong; Jonathan Gourley

doi:10.18270/rt.v13i1.1294

Autores/as

Humberto Vergara Universidad del Bosque
Yang Hong University of Oklahoma
Jonathan Gourley University of Oklahoma

DOI:

https://doi.org/10.18270/rt.v13i1.1294

Palabras clave:

Ensemble flood forecasting, Sequential data assimilation.

Resumen

The purpose of this particular work was to explore the benefits and drawbacks of sequential state updating for flood forecasting and identify factors or mechanisms affecting the updating process and thus controlling its performance. The Ensemble Kalman filter was employed to assimilate hourly streamflow observations into a simple but widely used conceptual rainfall-runoff model for flood prediction purposes. Ensembles were constructed by perturbing model forcing and parameters. Parametric perturbations were obtained from multiple model calibrations with an optimization algorithm. Errors in streamflow observations were characterized through an innovative yet simple empirical model. A sensitivity analysis was performed to evaluate the improvement of the first guess forecast. Additionally, the forecast skill was assessed as a function of lead-time. It was found that the improvement is mainly reflected in runoff volume, while the peak time
can be deteriorated as a trade-off of the assimilation process. Overall, ensemble-based models with sequential data assimilation outperformed the best-calibrated deterministic models for lead times of at least 1.5 days.

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Biografía del autor/a

Humberto Vergara, Universidad del Bosque

Received the B.Sc. degree in environmental engineering from El Bosque University, Colombia, and the M.Sc. degree in water resources engineering from the University of Oklahoma, Norman, OK, USA. He is currently working toward the Ph.D. degree in the Department of Civil Engineering and Environmental Science at the University of Oklahoma, Norman. He is currently a Graduate Research Assistant at the Hydrometeorology and Remote Sensing (HyDROS; hydro.ou.edu) Laboratory and the National Severe Storms Laboratory (NSSL; http://www.nssl.noaa.gov) in Norman, Oklahoma. His primary field of study is hydrological modeling for flood forecasting. He focuses on model development, ensemble forecasting and data assimilation.

Yang Hong, University of Oklahoma

Received the B.S. and M.S. degrees in geosciences and environmental sciences from Peking University, Beijing, China, and the Ph.D. degree, major in hydrology and water resources and minor in remote sensing and spatial analysis, from the University of Arizona, Tucson, AZ, USA. Following a postdoctoral appointment in the Center for Hydrometeorology and Remote Sensing, University of California, Irvine, CA, USA, he joined the National Aeronautics and Space Administration Goddard Space Flight Center, Greenbelt, MD, USA, in 2005. He is currently an Associate Professor with the School of Civil Engineering and Environmental Sciences and the School of Meteorology, University of Oklahoma, Norman, OK, USA, where he is also directing the Remote Sensing Hydrology research group (http://hydro.ou.edu). He also serves as the Co-director of the Water Technologies for Emerging Regions Center (http://water. ou.edu) and an affiliated Faculty Member with the Atmospheric Radar Research Center (http://arrc. ou.edu). He has served in the editorial boards of the International Journal of Remote Sensing, the Natural Hazards journal, and the Landslides journal. His primary research interests are in remotesensing retrieval and validation, hydrology and water resources, natural hazard prediction, land surface modeling, and data assimilation systems for water resource planning under changing climate. Dr. Hong is currently the American Geophysical Union Precipitation Committee Chair.

Jonathan Gourley, University of Oklahoma

Received the B.S. and M.S. degrees in meteorology with a minor in hydrology and the Ph.D. degree in civil engineering and environmental science from the University of Oklahoma, Norman, OK, USA. He is currently a Research Hydrometeorologist with NOAA’s National Severe Storms Laboratory, is an affliate Associate Professor with the School of Meteorology, University of Oklahoma, and Director of the National Weather Center’s seminar series. His research focuses on rainfall observations from remote sensing platforms with an emphasis on ground-based radars and implementing these highresolution observations into hydrologic models. He completed a postdoctoral study with researchers in Paris, France, to demonstrate the capabilities of dual-polarimetric radar in improving data quality, microphysical retrievals, and precipitation estimation. MeteoFrance has subsequently upgraded several of their operational radars with polarimetric technology. Dr. Gourley received the Department of Commerce Silver Medal Award in 1999 “For developing an important prototype Warning Decision Support System for weather forecasters and making significant enhancements to the NEXRAD system, resulting in more timely and reliable warnings.” He also received an Honorable Mention in 2004 from the Universities Council on Water Resources Dissertation Awards Committee.

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¦ Revista de Tecnología ¦ Journal of Technology ¦ Volumen 13 ¦ Número 1 ¦ Págs. 9-27

Humberto Vergara, Yang Hong, Jonathan J. Gourley.

Los Autores

Humberto J. Vergara

Received the B.Sc. degree in environmental engineering from El Bosque University, Colombia, and

the M.Sc. degree in water resources engineering from the University of Oklahoma, Norman, OK,

USA. He is currently working toward the Ph.D. degree in the Department of Civil Engineering and

Environmental Science at the University of Oklahoma, Norman. He is currently a Graduate Research

Assistant at the Hydrometeorology and Remote Sensing (HyDROS; hydro.ou.edu) Laboratory and

the National Severe Storms Laboratory (NSSL; http://www.nssl.noaa.gov) in Norman, Oklahoma.

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Improving Flood Forecasting Skill with the Ensemble Kalman Filter

Autores/as

DOI:

Palabras clave:

Resumen

Descargas

Biografía del autor/a

Humberto Vergara, Universidad del Bosque

Yang Hong, University of Oklahoma

Jonathan Gourley, University of Oklahoma

Referencias bibliográficas

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