Tropical Cyclone Gale Wind Radii Estimates, Forecasts, and Error Forecasts for the Western North Pacific

Abstract In 2016, the Joint Typhoon Warning Center extended forecasts of gale-force and other wind radii to 5 days. That effort and a thrust to perform postseason analysis of gale-force wind radii for the “best tracks” (the quality controlled and documented tropical cyclone track and intensity estimates released after the season) have prompted requirements for new guidance to address the challenges of both. At the same time, operational tools to estimate and predict wind radii continue to evolve, now forming a quality suite of gale-force wind radii analysis and forecasting tools. This work provides an update to real-time estimates of gale-force wind radii (a mean/consensus of gale-force individual wind radii estimates) that includes objective scatterometer-derived estimates. The work also addresses operational gale-force wind radii forecasting in that it provides an update to a gale-force wind radii forecast consensus, which now includes gale-force wind radii forecast error estimates to accompany the gale-force wind radii forecasts. The gale-force wind radii forecast error estimates are computed using predictors readily available in real time (e.g., consensus spread, initial size, and forecast intensity) so that operational reliability and timeliness can be ensured. These updates were all implemented in operations at the Joint Typhoon Warning Center by January 2018, and more updates should be expected in the coming years as new and improved guidance becomes available.

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GPCE-AX: An Anisotropic Extension to the Goerss Predicted Consensus Error in Tropical Cyclone Track Forecasts

Weather and Forecasting ◽

10.1175/2010waf2222410.1 ◽

2011 ◽

Vol 26 (3) ◽

pp. 416-422 ◽

Cited By ~ 4

Author(s):

James A. Hansen ◽

James S. Goerss ◽

Charles Sampson

Keyword(s):

Tropical Cyclone ◽

Forecast Error ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Consensus Forecasts ◽

The Mean ◽

Along Track ◽

National Hurricane Center ◽

Joint Typhoon Warning Center ◽

Initial Intensity

Abstract A method to predict an anisotropic expected forecast error distribution for consensus forecasts of tropical cyclone (TC) tracks is presented. The method builds upon the Goerss predicted consensus error (GPCE), which predicts the isotropic radius of the 70% isopleth of expected TC track error. Consensus TC track forecasts are computed as the mean of a collection of TC track forecasts from different models and are basin dependent. A novel aspect of GPCE is that it uses not only the uncertainty in the collection of constituent models to predict expected error, but also other features of the predicted storm, including initial intensity, forecast intensity, and storm speed. The new method, called GPCE along–across (GPCE-AX), takes a similar approach but separates the predicted error into across-track and along-track components. GPCE-AX has been applied to consensus TC track forecasts in the Atlantic (CONU/TVCN, where CONU is consensus version U and TVCN is the track variable consensus) and in the western North Pacific (consensus version W, CONW). The results for both basins indicate that GPCE-AX either outperforms or is equal in quality to GPCE in terms of reliability (the fraction of time verification is bound by the 70% uncertainty isopleths) and sharpness (the area bound by the 70% isopleths). GPCE-AX has been implemented at both the National Hurricane Center and at the Joint Typhoon Warning Center for real-time testing and evaluation.

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Prediction of Consensus Tropical Cyclone Track Forecast Error

Monthly Weather Review ◽

10.1175/mwr3390.1 ◽

2007 ◽

Vol 135 (5) ◽

pp. 1985-1993 ◽

Cited By ~ 39

Author(s):

James S. Goerss

Keyword(s):

Tropical Cyclone ◽

Regression Models ◽

Average Distance ◽

Forecast Error ◽

Dependent Data ◽

Track Forecast ◽

Consensus Model ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

National Hurricane Center

Abstract The extent to which the tropical cyclone (TC) track forecast error of a consensus model (CONU) routinely used by the forecasters at the National Hurricane Center can be predicted is determined. A number of predictors of consensus forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic basin in 2001–03. Leading predictors were found to be consensus model spread, defined to be the average distance of the member forecasts from the consensus forecast, and initial and forecast TC intensity. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the CONU TC track forecast error. The percent variance of CONU TC track forecast error that could be explained by these regression models ranged from just over 15% at 48 h to nearly 50% at 120 h. Using the regression models, predicted radii were determined and were used to draw circular areas around the CONU forecasts that contained the verifying TC position 73%–76% of the time. Based on the size of these circular areas, a forecaster can determine the confidence that can be placed upon the CONU forecasts. Independent data testing yielded results only slightly degraded from those of dependent data testing, highlighting the capability of these methods in practical forecasting applications.

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Investigating the Utility of Using Cross-Oceanic Training Sets for Superensemble Forecasting of Eastern Pacific Tropical Cyclone Track and Intensity

Weather and Forecasting ◽

10.1175/2007waf2007016.1 ◽

2008 ◽

Vol 23 (3) ◽

pp. 516-522 ◽

Cited By ~ 1

Author(s):

Mark R. Jordan ◽

T. N. Krishnamurti ◽

Carol Anne Clayson

Keyword(s):

Tropical Cyclone ◽

Real Time ◽

Tropical Cyclones ◽

Eastern Pacific ◽

Cyclone Track ◽

Training Set ◽

Tropical Cyclone Track ◽

Time Model ◽

Model Biases ◽

Training Sets

Abstract This paper examines how combining training-set forecasts from two separate oceanic basins affects the resulting tropical cyclone track and intensity forecasts in a particular oceanic basin. Atlantic and eastern Pacific training sets for 2002 and 2003 are combined and used to forecast 2004 eastern Pacific tropical cyclones in a real-time setting. These experiments show that the addition of Atlantic training improves the 2004 eastern Pacific forecasts. Finally, a detailed study of training-set and real-time model biases is completed in an effort to determine why cross-oceanic training may have helped in this instance.

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A Novel Deep Learning Model by BiGRU with Attention Mechanism for Tropical Cyclone Track Prediction in Northwest Pacific

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-20-0291.1 ◽

2021 ◽

Author(s):

Tao Song ◽

Ying Li ◽

Fan Meng ◽

Pengfei Xie ◽

Danya Xu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Tropical Cyclone ◽

Attention Mechanism ◽

Northwest Pacific ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Deep Learning Model ◽

Joint Typhoon Warning Center ◽

Unit Network

Abstract Tropical cyclones are amongst the most powerful and destructive meteorological systems on earth. In this paper, we propose a novel deep learning model for tropical cyclone track prediction method. Specifically, the track task is regarded as a time series predicting challenge, and then a deep learning framework by Bi-directional Gate Recurrent Unit network (BiGRU) with attention mechanism is developed for track prediction. This proposed model can excavate the effective information of the historical track in a deeper and more accurate way. Data exepriments are conducted on tropical cyclone best track data provided by the Joint Typhoon Warning Center (JTWC) from 1988 to 2017 in the Northwest Pacific. As results, our model performs well in tracks of 6h, 12h, 24h, 48h and 72h in the future. The prediction results show that our proposed combined model are superior to state-of-the-art deep learning models, include Recurrent Neural Network (RNN), Long Short-Term Memory neural network (LSTM), Gate Recurrent Unit network (GRU) and BiGRU without the use of attention mechanism. Compared with China Meteorological Administration (CMA), Japan Meteorological Agency (JMA) and Joint Typhoon Warning Center (JTWC), our method has obvious advantage in the mid- to long-term track forecasting, especially in the next 72 hours.

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