The Impacts of Assimilating Fengyun-4A Atmospheric Motion Vectors on Typhoon Forecasts

Chen, Keyi; Guan, Peigen

doi:10.3390/atmos14020375

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…The satellite-derived winds or atmospheric motion vectors (AMVs) offer tropospheric wind with high levels of spatial and temporal coverage that radar radial velocity or conventional data cannot observe. AMVs are mainly used to identify synoptic scale airflows, but AMVs can also be used to identify mesoscale winds induced by convective clouds [24]. It is important for the mesoscale flow fields to be resolved for high-impact, convective-scale forecast events [25].…”

Section: Introductionmentioning

confidence: 99%

Improving Radar Data Assimilation Forecast Using Advanced Remote Sensing Data

2023

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Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (±250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced.

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Section: Introductionmentioning

confidence: 99%

Improving Radar Data Assimilation Forecast Using Advanced Remote Sensing Data

2023

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Tropical Cyclone Monitoring and Analysis Techniques: A Review

Qian,

Li,

et al. 2024

J Meteorol Res

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Performance of a Hybrid Gain Ensemble Data Assimilation Scheme in Tropical Cyclone Forecasting with the GRAPES Model

et al. 2023

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Hybrid data assimilation (DA) methods have received extensive attention in the field of numerical weather prediction. In this study, a hybrid gain data assimilation (HGDA) method that combined the gain matrices of ensemble and variational methods was first applied in the mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso). To evaluate the performance of the HGDA method in the GRAPES_Meso model, different DA schemes, including the three-dimensional variational (3DVAR), local ensemble transform Kalman filter (LETKF), and HGDA schemes, were compared across eight tropical cyclone (TC) cases, and FY-4A atmospheric motion vectors were assimilated. The results indicated that the HYBRID scheme outperformed the 3DVAR and LETKF schemes in TC position forecasting, and with ensemble forecasting techniques, the HYBRID scheme promoted the accuracy of the prediction TC intensity. The threat score (TS) values for the light and medium precipitation forecasts obtained in the HYBRID experiment were higher than those for the forecasts obtained in the 3DVAR and LETKF experiments, which may be attributed to the forecasting accuracy for the TC position. Regarding heavy and extreme rainfall, the HYBRID scheme achieved a more stable effect than those of the 3DVAR and LETKF schemes. The results demonstrated the superiority of the HGDA scheme in TC prediction with the GRAPES_Meso model.

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The Impacts of Assimilating Fengyun-4A Atmospheric Motion Vectors on Typhoon Forecasts

Cited by 4 publications

References 13 publications

Improving Radar Data Assimilation Forecast Using Advanced Remote Sensing Data

Improving Radar Data Assimilation Forecast Using Advanced Remote Sensing Data

Tropical Cyclone Monitoring and Analysis Techniques: A Review

Performance of a Hybrid Gain Ensemble Data Assimilation Scheme in Tropical Cyclone Forecasting with the GRAPES Model

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