scholarly journals A GRAPES ‐based mesoscale ensemble prediction system for tropical cyclone forecasting: configuration and performance

2018 ◽  
Vol 144 (711) ◽  
pp. 478-498 ◽  
Author(s):  
Xubin Zhang
Keyword(s):  
Tropical Cyclone ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
And Performance

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

scholarly journals Improvements in tropical cyclone forecasting through ensemble prediction system at NCMRWF in India

2020 ◽  
Vol 9 (2) ◽  
pp. 106-116
Author(s):  
Anumeha Dube ◽  
Raghavendra Ashrit ◽  
Sushant Kumar ◽  
Ashu Mamgain
Keyword(s):  
Tropical Cyclone ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System

scholarly journals Bias Correction of Tropical Cyclone Parameters in the ECMWF Ensemble Prediction System in Australia

2019 ◽  
Vol 147 (11) ◽  
pp. 4261-4285
Author(s):  
Saima Aijaz ◽  
Jeffrey D. Kepert ◽  
Hua Ye ◽  
Zhendong Huang ◽  
Alister Hawksford
Keyword(s):  
Tropical Cyclone ◽  
Bias Correction ◽  
Oil And Gas ◽  
Surface Wind ◽  
Oil And Gas Industry ◽  
Ensemble Prediction ◽  
Linear Regression Method ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Correction Technique

Abstract Global ensemble prediction systems have considerable ability to predict tropical cyclone (TC) formation and subsequent evolution. However, because of their relatively coarse resolution, their predictions of intensity and structure are biased. The biases arise mainly from underestimated intensities and enlarged radii, in particular the radius of maximum winds. This paper describes a method to reduce this limitation by bias correcting TCs in the ECMWF Ensemble Prediction System (ECMWF-EPS) for a region northwest of Australia. A bias-corrected TC system will provide more accurate forecasts of TC-generated wind and waves to the oil and gas industry, which operates a large number of offshore facilities in the region. It will also enable improvements in response decisions for weather sensitive operations that affect downtime and safety risks. The bias-correction technique uses a multivariate linear regression method to bias correct storm intensity and structure. Special strategies are used to maintain ensemble spread after bias correction and to predict the radius of maximum winds using a climatological relationship based on wind intensity and storm latitude. The system was trained on the Australian best track TC data and the ECMWF-EPS TC data from two cyclone seasons. The system inserts corrected vortices into the original surface wind and pressure fields, which are then used to estimate wind exceedance probabilities, and to drive a wave model. The bias-corrected system has shown an overall skill improvement over the uncorrected ECMWF-EPS for all TC intensity and structure parameters with the most significant gains for the maximum wind speed prediction. The system has been operational at the Australian Bureau of Meteorology since November 2016.

scholarly journals Dynamical Seasonal Prediction of Tropical Cyclone Activity Using the FGOALS-f2 Ensemble Prediction System

2021 ◽  
Vol 36 (5) ◽  
pp. 1759-1778
Author(s):  
Jinxiao Li ◽  
Qing Bao ◽  
Yimin Liu ◽  
Guoxiong Wu ◽  
Lei Wang ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Real Time ◽  
Large Scale ◽  
Southern Oscillation ◽  
Correlation Coefficients ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System

AbstractThere is a distinct gap between tropical cyclone (TC) prediction skill and the societal demand for accurate predictions, especially in the western Pacific (WP) and North Atlantic (NA) basins, where densely populated areas are frequently affected by intense TC events. In this study, seasonal prediction skill for TC activity in the WP and NA of the fully coupled FGOALS-f2 V1.0 dynamical prediction system is evaluated. In total, 36 years of monthly hindcasts from 1981 to 2016 were completed with 24 ensemble members. The FGOALS-f2 V1.0 system has been used for real-time predictions since June 2017 with 35 ensemble members, and has been operationally used in the two operational prediction centers of China. Our evaluation indicates that FGOALS-f2 V1.0 can reasonably reproduce the density of TC genesis locations and tracks in the WP and NA. The model shows significant skill in terms of the TC number correlation in the WP (0.60) and the NA (0.61) from 1981 to 2015; however, the model underestimates accumulated cyclone energy. When the number of ensemble members was increased from 2 to 24, the correlation coefficients clearly increased (from 0.21 to 0.60 in the WP, and from 0.18 to 0.61 in the NA). FGOALS-f2 V1.0 also successfully reproduces the genesis potential index pattern and the relationship between El Niño–Southern Oscillation and TC activity, which is one of the dominant contributors to TC seasonal prediction skill. However, the biases in large-scale factors are barriers to the improvement of the seasonal prediction skill, e.g., larger wind shear, higher relative humidity, and weaker potential intensity of TCs. For real-time predictions in the WP, FGOALS-f2 V1.0 demonstrates a skillful prediction for track density in terms of landfalling TCs, and the model successfully forecasts the correct sign of seasonal anomalies of landfalling TCs for various regions in China.

Study of perturbing method in regional BGM ensemble prediction system

2012 ◽  
Vol 4 (1) ◽  
pp. 65
Author(s):  
Xiao Yu-Hua ◽  
He Guang-Bi ◽  
Chen Jing ◽  
Deng Guo
Keyword(s):  
Ensemble Prediction ◽  
Prediction System ◽  
Ensemble Prediction System
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