Improving Hurricane Intensity Forecasting through Data Assimilation: Environmental Conditions Versus the Vortex Initialization

Pu, Zhaoxia

doi:10.5772/14224

Cited by 1 publication

(2 citation statements)

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“…The relative trajectory prediction error results show that the orbital error in the MP test improves from 9% to 32% compared to the trajectory prediction error in the MI test, and improves from 4% to 30% compared to the trajectory prediction error in the PF test at most forecasting terms. This result may be due to the multiphysics technique (determining the error of the model due to the incomplete understanding of physical processes [4,9] has partly corrected the error of the model. So that the received background field has a significantly reduced error, and leads to a more accurate analysis field for the input of the model than the multiplicative inflation technique and considers the model perfect.…”

Section: Track Stormmentioning

confidence: 99%

“…However, this work depends heavily on the quality of the observed data (related to the error of the observed data) and the quality of the model's background guess data (related to the model's intrinsic error). The error related to the monitoring data belongs to the problem of quality control of professional monitoring; while the background field error is related to the model's internal errorserrors caused mainly by physical processes that are not fully understood [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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Forecasting the track and intensity Damrey storm in 2017 by the multi–physical ensemble Kalman filter

Pham¹,

Doan²,

Nguyen³

et al. 2022

VNJHM

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This study applicates the multi-physical method in ensemble Kalman filter determining error of WRF models to forecast the track and intensity of storm Damrey in 2017. The study run three experiments with assimilation of satellite data to forecast Damrey in 2017 at the beginning 00 UTC and 12 UTC November 1 st and 2 nd : (1) 21 ensemble members which are combinated from 11 physics options, no increase in error correlation (MP); (2) Using single set of physical model, 21 ensemble members, inflation factor λ = 6.5 (MI); (3) Using single set of physical model, 21 ensemble members without increase in error correlation (PF). Statistical results of track errors in MP test at the 24, 48, 72-hour is 12-32% reduction in compared with tests MI and PF. For storm intensity, absolute error of Pmin in the MP test at 24 and 72-hour is decreased from 30-47% in compared to the other two tests. And the absolute error of Vmax in the MP test at all forecasting terms is 13-26% reduction in compared with tests MI and PF. Thus, the multiphysical ensemble Kalman filter can forecast the track and intensity of storms affecting Vietnam.

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Section: Track Stormmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%