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Tong, M., Sippel, J.A., Tallapragada, V., Liu, E., Kieu, C., Kwon, I.-H. Wang, W., Liu, Q., Ling, Y. and Zhang, B. (2018) Impact of Assimilating Aircraft Reconnaisse Observations on Tropical Cyclone Initialization and Prediction Using Operational HWRF and GSI Ensemble-Variational Hybrid Data Assimilation. Monthly Weather Review, 146, 4155-4173.
https://doi.org/10.1175/MWR-D-17-0380.1

has been cited by the following article:

• TITLE: Evaluation of Multiplicative Weight of Covariance Matrix on Hybrid Data Assimilation Schemes

  AUTHORS: Pedro M. González-Jardines, Maibys Sierra-Lorenzo, Adrián L. Ferrer-Hernández

  KEYWORDS: SisPI, WRFDA, Hybrid-Methods, Covariance Weights

  JOURNAL NAME: Atmospheric and Climate Sciences, Vol.13 No.2, April 27, 2023

  ABSTRACT: This research develops a comparative study between different multiplicative weights that are assigned to the covariance matrix that represents the background error in two hybrid assimilation schemes: 3DEnVAR and 4DEnVAR. These weights are distributed between the static and time-invariant matrix and the matrix generated from the perturbations of a previous ensemble. The assigned values are 25%, 50%, and 75%, always having as a reference the ensemble matrix. The experiments are applied to the short-range Prediction System (SisPI) that works operationally at the Institute of Meteorology. The impact of Tropical Storm Eta on November 7 and 8, 2020 was selected as a study case. The results suggest that by giving the main weight to the ensemble matrix more realistic solutions are achieved because it shows a better representation of the synoptic flow. On the other hand, it is observed that 3DEnVAR method is more sensitive to multiplicative weight change of the first guess. More realistic results are obtained with 50% and 75% relations with 4DEnVAR method, whereas with 3DEnVAR a weight of 75% for the ensemble matrix is required.