On the Prospects for Improved Tropical Cyclone Track Forecasts

AbstractThe success story of numerical weather prediction is often illustrated with the dramatic decrease of errors in tropical cyclone track forecasts over the past decades. In a recent essay, Landsea and Cangialosi, however, note a diminishing trend in the reduction of perceived positional error (PPE; difference between forecast and observed positions) in National Hurricane Center tropical cyclone (TC) forecasts as they contemplate whether “the approaching limit of predictability for tropical cyclone track prediction is near or has already been reached.” In this study we consider a different interpretation of the PPE data. First, we note that PPE is different from true positional error (TPE; difference between forecast and true positions) as it is influenced by the error in the observed position of TCs. PPE is still customarily used as a proxy for TPE since the latter is not directly measurable. As an alternative, TPE is estimated here with an inverse method, using PPE measurements and a theoretically based assumption about the exponential growth of TPE as a function of lead time. Eighty-nine percent variance in the behavior of 36–120-h lead-time 2001–17 seasonally averaged PPE measurements is explained with an error model using just four parameters. Assuming that the level of investments, and the pace of improvements to the observing, modeling, and data assimilation systems continue unabated, the four-parameter error model indicates that the time limit of predictability at the 181 nautical mile error level (n mi; 1 n mi = 1.85 km), reached at day 5 in 2017, may be extended beyond 6 and 8 days in 10 and 30 years’ time, respectively.

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Persistent northward North Atlantic tropical cyclone track migration over the past five centuries

Scientific Reports ◽

10.1038/srep37522 ◽

2016 ◽

Vol 6 (1) ◽

Cited By ~ 21

Author(s):

Lisa M. Baldini ◽

James U. L. Baldini ◽

Jim N. McElwaine ◽

Amy Benoit Frappier ◽

Yemane Asmerom ◽

...

Keyword(s):

Tropical Cyclone ◽

North Atlantic ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

The Past ◽

Atlantic Tropical Cyclone

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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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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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A Novel Deep Learning Approach for Tropical Cyclone Track Prediction Based on Auto-Encoder and Gated Recurrent Unit Networks

Applied Sciences ◽

10.3390/app10113965 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3965 ◽

Cited By ~ 2

Author(s):

Jie Lian ◽

Pingping Dong ◽

Yuping Zhang ◽

Jianguo Pan

Keyword(s):

Deep Learning ◽

Tropical Cyclone ◽

Weather Prediction ◽

Human Society ◽

Complex Data ◽

Strong Wind ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Gated Recurrent Unit ◽

Better Than

Under global climate change, the frequency of typhoons and their strong wind, heavy rain, and storm surge increase, seriously threatening the life and property of human society. However, traditional tropical cyclone track prediction methods have difficulties in processing large amounts of complex data in terms of prediction efficiency and accuracy. Recently, deep learning methods have shown a potential capability to process complex data efficiently and accurately. In this paper, we propose a novel data-driven approach based on auto-encoder (AE) and gated recurrent unit (GRU) models to forecast tropical cyclone landing locations using the historical tropical cyclone tracks and various meteorological attributes. This approach fuses a data preprocessing layer, an AE layer, and a GRU layer with a customized batch process. The model is trained on a real-world tropical cyclone dataset from the years 1945–2017. Through a comparison with existing forecasting methods, the results verified that our proposed model performed around 15%, 42%, and 56% better than the Numerical Weather Prediction model (NWP) in 24, 48, and 72 h forecasts, and 27%, 13%, 17%, and 17% better than RNN, AE-RNN, GRU, and LSTM, respectively, in 24 h forecasts, using the absolute position error. In addition, a comparison of the meteorological variables indicated that the variable maximum sustained wind speed had the most significant effect on tropical cyclone track prediction.

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Tropical cyclone track prediction by a high resolution limited area model using synthetic observations

MAUSAM ◽

10.54302/mausam.v48i3.4259 ◽

2021 ◽

Vol 48 (3) ◽

pp. 351-366

Author(s):

K. PRASAD ◽

Y.V. RAMA RAO ◽

SANJIB SEN

Keyword(s):

High Resolution ◽

Tropical Cyclone ◽

Tropical Cyclones ◽

Weather Prediction ◽

Forecast Model ◽

Limited Area ◽

Forecast Errors ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Humidity Field

ABSTRACT. Results of tropical cyclone track prediction experiments in die Indian seas by a high resolution limited area numerical weather prediction model (1° × 1° lat./long. grid) are presented. As the tropical cyclones form in data sparse regions of tropical oceans, and are, therefore, not well analysed in die initial fields, a scheme has been developed for generation of synthetic observations -based on die empirical structure of tropical cyclones, and their assimilation into the objective analysis, for preparing initial fields for running a forecast model. Experiments on track prediction have beat : conducted for die cyclones forming in the Bay of Bengal and Arabian Sea during the period 1990-95. Forecast errors of the model for 24 hr and 48 hr forecasts have been computed. A sensitivity experiment has been carried out to demonstrate the importance of initial humidity field on forecast model performance. The experiment brings out crucial important of the initial humidity field prescription in accurate track prediction by die forecast model.

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