Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 29, issue 4
Articles | Volume 29, issue 4
https://doi.org/10.5194/npg-29-329-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-29-329-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 4
Research article
 | 
06 Oct 2022
Research article |  | 06 Oct 2022

Using a hybrid optimal interpolation–ensemble Kalman filter for the Canadian Precipitation Analysis

Dikraa Khedhaouiria, Stéphane Bélair, Vincent Fortin, Guy Roy, and Franck Lespinas

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Bachmann, K., Keil, C., and Weissmann, M.: Impact of radar data assimilation and orography on predictability of deep convection, Q. J. Roy. Meteor. Soc., 145, 117–130, https://doi.org/10.1002/qj.3412, 2019. a
Bonavita, M., Hamrud, M., and Isaksen, L.: EnKF and Hybrid Gain Ensemble Data Assimilation. Part II: EnKF and Hybrid Gain Results, Mon. Weather Rev., 143, 4865–4882, https://doi.org/10.1175/MWR-D-15-0071.1, 2015. a
Brown, J. D., Seo, D.-J., and Du, J.: Verification of Precipitation Forecasts from NCEP's Short-Range Ensemble Forecast (SREF) System with Reference to Ensemble Streamflow Prediction Using Lumped Hydrologic Models, J. Hydrometeorol., 13, 808–836, https://doi.org/10.1175/JHM-D-11-036.1, 2012. a
Buizza, R.: Ensemble forecasting and the need for calibration, in: Statistical Postprocessing of Ensemble Forecasts, edited by: Vannitsem, S., Messner, J. W., and Wilks, D. S., Elsevier, 15–48, ISBN: 978-0-12-812372-0, 2019. a, b
Caron, J.-F., Milewski, T., Buehner, M., Fillion, L., Reszka, M., Macpherson, S., and St-James, J.: Implementation of Deterministic Weather Forecasting Systems Based on Ensemble–Variational Data Assimilation at Environment Canada. Part II: The Regional System, Mon. Weather Rev., 143, 2560–2580, 2015. a, b
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This study introduces a well-known use of hybrid methods in data assimilation (DA) algorithms that has not yet been explored for precipitation analyses. Our approach combined an ensemble-based DA approach with an existing deterministically based DA. Both DA scheme families have desirable aspects that can be leveraged if combined. The DA hybrid method showed better precipitation analyses in regions with a low rate of assimilated surface observations, which is typically the case in winter.
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