Impact of the Assimilation of Non-Precipitating Echoes Reflectivity Data on the Short-Term Numerical Forecast of SisPI

Hernández, Adrian Luis Ferrer; Jardines, Pedro Manuel González; Lorenzo, Maibys Sierra; Figueroa, Darielis de la Caridad Aguiar

doi:10.3390/ecas2022-12845

Cited by 1 publication

(2 citation statements)

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“…Taking into account the current needs of the national meteorological service, the SisPI project (Short-range Forecast System, acronym in Spanish) evaluated the implementation of a robust and efficient data assimilation design that would improve the ability of short-term forecasts while adjusting to current technological capabilities. In this way, it is obtained that the hybrid assimilation schemes constitute the option that allows for obtaining more realistic results [3] [4].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, in 2021 [3] [4] hybrid assimilation methods are evaluated in SisPI, for the first time in Cuba. The results of this study suggest that the 4DEnVAR (4-Dimensional Ensemble-Variational) scheme is the most robust but at the same time the most computationally expensive, while 3DEnVAR (3-Dimensional Ensemble-Variational) tends to be unstable in performance since it can lead to very realistic solutions or others similar to 3DVAR, showing a discrete modification of the background field and therefore, very close to the version of the model without data assimilation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Multiplicative Weight of Covariance Matrix on Hybrid Data Assimilation Schemes

González-Jardines¹,

Lorenzo²,

Ferrer-Hernández³

2023

ACS

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%