Tropical cyclone track forecasts using JMA model with ECMWF and JMA initial conditions

2012 ◽  
Vol 39 (9) ◽  
pp. n/a-n/a ◽  
Author(s):  
Munehiko Yamaguchi ◽  
Tetsuo Nakazawa ◽  
Kazumasa Aonashi
Keyword(s):  
Tropical Cyclone ◽  
Initial Conditions ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Jma Model

Have We Reached the Limits of Predictability for Tropical Cyclone Track Forecasting?

2018 ◽  
Vol 99 (11) ◽  
pp. 2237-2243 ◽  
Author(s):  
Christopher W. Landsea ◽  
John P. Cangialosi
Keyword(s):  
Tropical Cyclone ◽  
Initial Conditions ◽  
Forecast Accuracy ◽  
The United States ◽  
Track Forecast ◽  
Synoptic Scale ◽  
Cyclone Track ◽  
Tropical Cyclone Track ◽  
Atmospheric Sciences ◽  
Steering Flow

AbstractThe tropical cyclone is the largest single-day-impact meteorological event in the United States and worldwide through its effects from storm surge, extreme winds, freshwater flooding, and embedded tornadoes. Fortunately, over the last three decades there have been incredible advances in forecast accuracy, especially for the track of the tropical cyclone’s center. Errors have been cut by two-thirds in just 25 years due to global modeling advances, data assimilation improvements, dramatic increases in observations primarily derived from satellite platforms, and use of ensemble forecast techniques. These four factors have allowed for highly accurate synoptic-scale atmospheric initial conditions and forecasts of the steering flow out through several days into the future. However, such improvements cannot continue indefinitely. It is well known in the atmospheric sciences that there exists an inherent “limit of predictability” because of errors at the smallest scales (microscale—meters and seconds) that eventually cascade up to the largest scales (synoptic scale—thousands of kilometers and several days). While there have been estimates of the limits of predictability for tropical cyclone track prediction in the past, our current capabilities have exceeded those somewhat pessimistic earlier outlooks. This essay discusses the current state of the art for tropical cyclone track prediction and reassesses whether reaching the “limit of predictability” is imminent. The ramifications of this eventual conclusion—whether in the short-term or still decades away—could be critical for all users of tropical cyclone track forecast information, including the emergency management community/governments, the media, the private sector, and the general public.

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.

Sign in / Sign up

Export Citation Format

Share Document