Preprints
https://doi.org/10.5194/npg-2022-10
https://doi.org/10.5194/npg-2022-10
 
04 Apr 2022
04 Apr 2022
Status: a revised version of this preprint was accepted for the journal NPG and is expected to appear here in due course.

Using a Hybrid Optimal Interpolation-Ensemble Kalman Filter for the Canadian Precipitation Analysis

Dikraa Khedhaouiria1, Stéphane Bélair2, Vincent Fortin2, Guy Roy1, and Franck Lespinas1 Dikraa Khedhaouiria et al.
  • 1Meteorological Service of Canada, Environment and Climate Change Canada, Dorval, QC, Canada
  • 2Meteorological Research Division, Environment and Climate Change Canada, Dorval, QC, Canada

Abstract. Several data assimilation (DA) approaches exist to generate consistent and continuous precipitation fields valuable for hydrometeorological applications and land data assimilation. Usually, DAs are based on either static or dynamic approaches. Static methods rely on deterministic forecasts to estimate background error covariance matrices, while dynamic ones use ensemble forecasts. Associating the two methods is known as hybrid DA and has proven beneficial for different applications as it combines the advantages of both approaches. The present study intends to explore hybrid DA for the 6-hour Canadian Precipitation Analysis (CaPA). Based on optimal interpolations (OI), CaPA blends forecasts and observations from surface stations and ground-based radar datasets to provide precipitation fields over the North American domain. The application of hybrid DA to CaPA consisted of finding the optimal linear combination between i) an OI based on the Regional Deterministic Prediction System (RDPS) and ii) an Ensemble Kalman Filter (EnKF) based on the 20-member Regional Ensemble Prediction System (REPS). The results confirmed the known effectiveness of the hybrid approach when low-density observation networks are assimilated. Indeed, the experiments conducted for the summer without radar datasets and for the winter (characterized by very few observations in CaPA) showed that attributing a relatively high weight of the EnKF (50 and 70 % for summer and winter, respectively) gave better analysis skills and a reduction of false alarms than the OI method. A deterioration of the moderate to high-intensity precipitation bias was, however, observed during summer. Reducing to 30 % the weight attributed to the EnKF permitted alleviating the bias deterioration while improving skill compared to the OI-based CaPA.

Dikraa Khedhaouiria et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2022-10', Anonymous Referee #1, 02 May 2022
    • AC1: 'Reply on RC1', Dikraa Khedhaouiria, 08 Jul 2022
  • RC2: 'Comment on npg-2022-10', Anonymous Referee #2, 12 May 2022
    • AC2: 'Reply on RC2', Dikraa Khedhaouiria, 08 Jul 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2022-10', Anonymous Referee #1, 02 May 2022
    • AC1: 'Reply on RC1', Dikraa Khedhaouiria, 08 Jul 2022
  • RC2: 'Comment on npg-2022-10', Anonymous Referee #2, 12 May 2022
    • AC2: 'Reply on RC2', Dikraa Khedhaouiria, 08 Jul 2022

Dikraa Khedhaouiria et al.

Dikraa Khedhaouiria et al.

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Short summary
This study introduces a well-known use of hybrid methods in data assimilation (DA) algorithms yet not 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.