An Adaptive Multigrid Barotropic Tropical Cyclone Track Model

1999 ◽  
Author(s):  
Scott R. Fulton
Keyword(s):  
Tropical Cyclone ◽  
Cyclone Track ◽  
Tropical Cyclone Track

Experiments With a Self-Adaptive Multigrid Barotropic Tropical Cyclone Track Model

1999 ◽  
Author(s):  
Scott R. Fulton ◽  
Nicole M. Burgess ◽  
Brittany L. Mitchell
Keyword(s):  
Tropical Cyclone ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Self Adaptive

scholarly journals WRF-model sensitivity test and assimilation studies of Cempaka tropical cyclone

2021 ◽  
Vol 893 (1) ◽  
pp. 012029
Author(s):  
Fazrul Rafsanjani Sadarang ◽  
Fitria Puspita Sari
Keyword(s):  
Tropical Cyclone ◽  
Doppler Radar ◽  
Time Integration ◽  
Wrf Model ◽  
Radar Data ◽  
Control Model ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Parameterization Schemes ◽  
Assimilation Scheme

Abstract The WRF model was used to forecast the most intensive stage of Cempaka Tropical Cyclone (TC) on 27 - 29 November 2017. This study evaluates the combination of cumulus and microphysics parameterization and the efficiency of assimilation method to predict pressure values at the center of the cyclone, maximum wind speed, and cyclone track. This study tested 18 combinations of cumulus and microphysics parameterization schemes to obtain the best combination of both parameterization schemes which later on called as control model (CTL). Afterward, assimilation schemes using 3DVAR cycles of 1, 3, 6 hours, and 4DVAR, namely RUC01, RUC03, RUC06, and 4DV, were evaluated for two domains with grid size of each 30 and 10 km. GFS data of 0.25-degree and the Yogyakarta Doppler Radar data were used as the initial data and assimilation data input, respectively. The result of the parameterization test shows that there is no combination of parameterization schemes that constantly outperform all variables. However, the combination of Kain-Fritsch and Thompson can produce the best prediction of tropical cyclone track compared to other combinations. While, the RUC03 assimilation scheme was noted as the most efficient method based on the accuracy of track prediction and duration of model time integration.

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