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

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.

2013 ◽  
Vol 122 (1-2) ◽  
pp. 55-64 ◽  
Author(s):  
Du Duc Tien ◽  
Thanh Ngo-Duc ◽  
Hoang Thi Mai ◽  
Chanh Kieu

2018 ◽  
Vol 7 (3.29) ◽  
pp. 272 ◽  
Author(s):  
P Janardhan Saikumar ◽  
T Ramashri

The very severe Tropical Cyclone Vardah caused huge damage to property and life in south India during December 2016. The sensitivity of numerical simulations of the very severe tropical cyclone Vardah to different physics parameterization schemes is carried out to determine the best microphysics and cumulus physics parameterization schemes. The WRF Numerical weather prediction model configured with two nested domains. The horizontal resolution of domain-1is 27 km and domain-2 is 9 km. The tropical cyclone Vardah simulated track results were compared with the best track data given by the Indian Meteorological Department (IMD). WRF model Simulations were carried out using different microphysics (mp) parameterization schemes by fixing convective cumulus physics (cu) option to Grell-3D ensemble scheme and boundary layer option to updated Yonsei University scheme. The Vardah Cyclone track well simulated using WRF Single Moment-3 (WSM3) microphysics scheme in combination with G3D cumulus physics scheme. The cumulus physics and microphysics parameterization schemes influence the cyclone track prediction skill.  


2018 ◽  
Vol 33 (6) ◽  
pp. 1567-1585
Author(s):  
Yuxuan Yang ◽  
Lifeng Zhang ◽  
Bin Zhang ◽  
Wei You ◽  
Mingyang Zhang ◽  
...  

Abstract The sensitivity of the proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation (4DVar) method (referred to as POD-4DEnVar) to cumulus and microphysics schemes was investigated using the Weather Research and Forecasting (WRF) Model for heavy rainfall in South China. Results show that the choice of the cumulus and microphysics schemes for ensemble samples significantly impacts precipitation prediction and that Doppler radar data assimilation using POD-4DEnVar is sensitive to the parameterization schemes used for the ensemble samples. The cumulus and microphysics schemes primarily affect the vertical velocity and rainwater mixing ratio of the ensemble forecasts. Variations in the ensemble samples caused by different parameterization schemes are introduced into the four-dimensional ensemble variational assimilation by the radar data observation operator. These variations affect the analysis fields and result in variations in precipitation prediction. To obtain the optimal result (smallest forecast error), three methods are designed based on the physical ensemble technique, which can filter out the effects of different parameterization schemes for the ensemble samples through averaging. The results show that the precipitation forecasts from the three assimilation experiments are improved compared with a control experiment, but each physical ensemble method leads to a unique precipitation forecast.


1999 ◽  
Author(s):  
Scott R. Fulton ◽  
Nicole M. Burgess ◽  
Brittany L. Mitchell

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