Articles | Volume 25, issue 9
Research article
29 Sep 2021
Research article |  | 29 Sep 2021

Ensemble streamflow data assimilation using WRF-Hydro and DART: novel localization and inflation techniques applied to Hurricane Florence flooding

Mohamad El Gharamti, James L. McCreight, Seong Jin Noh, Timothy J. Hoar, Arezoo RafieeiNasab, and Benjamin K. Johnson

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Revised manuscript under review for HESS
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Cited articles

Abbaszadeh, P., Moradkhani, H., and Yan, H.: Enhancing hydrologic data assimilation by evolutionary particle filter and Markov chain Monte Carlo, Adv. Water Resour., 111, 192–204, 2018. a, b, c
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Avellano, A.: The data assimilation research testbed: A community facility, B. Am. Meteorol. Soc., 90, 1283–1296, 2009. a, b
Anderson, J. L.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642, 2003. a, b
Anderson, J. L.: A non-Gaussian ensemble filter update for data assimilation, Mon. Weather Rev., 138, 4186–4198, 2010. a
Ashman, M. S., Socolow, R. S., Zanca, J. L., and Bonito, M. V.: One Hundred Years of Streamflow Measurements in Massachusetts and Rhode Island, US Department of the Interior, US Geological Survey, 2004. a
Short summary
The article introduces novel ensemble data assimilation (DA) techniques for streamflow forecasting using WRF-Hydro and DART. Model-related biases are tackled through spatially and temporally varying adaptive prior and posterior inflation. Spurious and physically incorrect correlations, on the other hand, are mitigated using a topologically based along-the-stream localization. Hurricane Florence (2018) in the Carolinas, USA, is used as a test case to investigate the performance of DA techniques.