Development of a track-pattern-based medium-range tropical cyclone forecasting system for the western North Pacific

2021 ◽  
Author(s):  
Hung Ming Cheung ◽  
Chang-Hoi Ho ◽  
Minhee Chang ◽  
Dasol Kim ◽  
Jinwon Kim ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Correlation Coefficients ◽  
Machine Learning Techniques ◽  
Track Forecast ◽  
Dynamical Models ◽  
Pattern Correlation ◽  
Medium Range ◽  
Track Pattern

AbstractDespite tremendous advancements in dynamical models for weather forecasting, statistical models continue to offer various possibilities for tropical cyclone (TC) track forecasting. Herein, a track-pattern-based approach was developed to predict a TC track for a lead time of 6–8 days over the western North Pacific (WNP), utilizing historical tracks in conjunction with dynamical forecasts. It is composed of four main steps: (1) clustering historical tracks similar to that of an operational five-day forecast in their early phase into track patterns, and calculating the daily mean environmental fields (500-hPa geopotential height and steering flow) associated with each track; (2) deriving the two environmental variables forecasted by dynamical models; (3) evaluating pattern correlation coefficients between the two environmental fields from step (1) and those from dynamical model for a lead times of 6–8 days; and (4) producing the final track forecast based on relative frequency maps obtained from the historical tracks in step (1) and the pattern correlation coefficients obtained from step (3). TCs that formed in the WNP and lasted for at least seven days, during the 9-year period 2011–2019 were selected to verify the resulting track-pattern-based forecasts. In addition to the performance comparable to dynamical models under certain conditions, the track-pattern-based model is inexpensive, and can consistently produce forecasts over large latitudinal or longitudinal ranges. Machine learning techniques can be implemented to incorporate non-linearity in the present model for improving medium-range track forecasts.

Track-Pattern-Based Model for Seasonal Prediction of Tropical Cyclone Activity in the Western North Pacific

2012 ◽  
Vol 25 (13) ◽  
pp. 4660-4678 ◽  
Author(s):  
Hyeong-Seog Kim ◽  
Chang-Hoi Ho ◽  
Joo-Hong Kim ◽  
Pao-Shin Chu
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Correlation Coefficients ◽  
Seasonal Prediction ◽  
Track Density ◽  
Good Prediction ◽  
Seasonal Characteristics ◽  
Novel Approach ◽  
Track Pattern

Abstract Skillful predictions of the seasonal tropical cyclone (TC) activity are important in mitigating the potential destruction from the TC approach/landfall in many coastal regions. In this study, a novel approach for the prediction of the seasonal TC activity over the western North Pacific is developed to provide useful probabilistic information on the seasonal characteristics of the TC tracks and vulnerable areas. The developed model, which is termed the “track-pattern-based model,” is characterized by two features: 1) a hybrid statistical–dynamical prediction of the seasonal activity of seven track patterns obtained by fuzzy c-means clustering of historical TC tracks and 2) a technique that enables researchers to construct a forecasting map of the spatial probability of the seasonal TC track density over the entire basin. The hybrid statistical–dynamical prediction for each pattern is based on the statistical relationship between the seasonal TC frequency of the pattern and the seasonal mean key predictors dynamically forecast by the National Centers for Environmental Prediction Climate Forecast System in May. The leave-one-out cross validation shows good prediction skill, with the correlation coefficients between the hindcasts and the observations ranging from 0.71 to 0.81. Using the predicted frequency and the climatological probability for each pattern, the authors obtain the forecasting map of the seasonal TC track density by combining the TC track densities of the seven patterns. The hindcasts of the basinwide seasonal TC track density exhibit good skill in reproducing the observed pattern. The El Niño–/La Niña–related years, in particular, tend to show a better skill than the neutral years.

scholarly journals Dynamical Tropical Cyclone 96- and 120-h Track Forecast Errors in the Western North Pacific

2007 ◽  
Vol 22 (3) ◽  
pp. 520-538 ◽  
Author(s):  
Ryan M. Kehoe ◽  
Mark A. Boothe ◽  
Russell L. Elsberry
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Environmental Flows ◽  
Large Error ◽  
Laboratory Model ◽  
Track Forecast ◽  
Forecast Errors ◽  
Geophysical Fluid Dynamics Laboratory

Abstract The Joint Typhoon Warning Center has been issuing 96- and 120-h track forecasts since May 2003. It uses four dynamical models that provide guidance at these forecast intervals and relies heavily on a consensus of these four models in producing the official forecast. Whereas each of the models has skill, each occasionally has large errors. The objective of this study is to provide a characterization of these errors in the western North Pacific during 2004 for two of the four models: the Navy Operational Global Atmospheric Prediction System (NOGAPS) and the U.S. Navy’s version of the Geophysical Fluid Dynamics Laboratory model (GFDN). All 96- and 120-h track errors greater than 400 and 500 n mi, respectively, are examined following the approach developed recently by Carr and Elsberry. All of these large-error cases can be attributed to the models not properly representing the physical processes known to control tropical cyclone motion, which were classified in a series of conceptual models by Carr and Elsberry for either tropical-related or midlatitude-related mechanisms. For those large-error cases where an error mechanism could be established, midlatitude influences caused 83% (85%) of the NOGAPS (GFDN) errors. The most common tropical influence is an excessive direct cyclone interaction in which the tropical cyclone track is erroneously affected by an adjacent cyclone. The most common midlatitude-related errors in the NOGAPS tracks arise from an erroneous prediction of the environmental flow dominated by a ridge in the midlatitudes. Errors in the GFDN tracks are caused by both ridge-dominated and trough-dominated environmental flows in the midlatitudes. Case studies illustrating the key error mechanisms are provided. An ability to confidently identify these error mechanisms and thereby eliminate likely erroneous tracks from the consensus would improve the accuracy of 96- and 120-h track forecasts.

Technical note on a track-pattern-based model for predicting seasonal tropical cyclone activity over the western North Pacific

2013 ◽  
Vol 30 (5) ◽  
pp. 1260-1274 ◽  
Author(s):  
Chang-Hoi Ho ◽  
Joo-Hong Kim ◽  
Hyeong-Seog Kim ◽  
Woosuk Choi ◽  
Min-Hee Lee ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Technical Note ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity ◽  
Track Pattern

scholarly journals Performance of MPAS-A and WRF in predicting and simulating western North Pacific tropical cyclone tracks and intensities

2020 ◽  
Vol 143 (1-2) ◽  
pp. 505-520
Author(s):  
Yuk Sing Lui ◽  
Louis Kwan Shu Tse ◽  
Chi-Yung Tam ◽  
King Heng Lau ◽  
Jilong Chen
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Initial Conditions ◽  
Southern China ◽  
Wrf Model ◽  
Forecast Accuracy ◽  
Track Forecast ◽  
Variable Resolution ◽  
Parallel Simulations

AbstractPerformances of the Model for Prediction Across Scales-Atmosphere (MPAS-A) in predicting and the Weather Research and Forecasting (WRF) model in simulating western North Pacific (WNP) tropical cyclone (TC) tracks and intensities have been compared. Parallel simulations of the same historical storms that made landfall over southern China, namely, TCs Hope (1979), Gordon (1989), Koryn (1993), Imbudo (2003), Dujuan (2003), Molave (2009), Hato (2017) and Mangkhut (2018), were carried out using WRF and MPAS-A, with initial conditions (and, for WRF, lateral boundary conditions as well) taken from ERA-interim. For MPAS-A, the model was integrated using a standard 60-to-3-km variable-resolution global grid mesh and also on 160-to-2-km grids customized to cover the TC tracks with the highest resolution mesh. The WRF model was integrated using a 15-km/3-km nested domain. No TC bogus scheme was applied when initializing the MPAS-A and WRF simulations. It was found that while TC tracks were reasonably captured by the two models configured variously, the storm intensities were underestimated in general. Given MPAS-A runs were initial value predictions whereas WRF runs were dynamically downscaled from ERA-interim, the finding that MPAS-A has comparable (or slightly better) performance as (than) WRF is noteworthy. To further examine the sensitivity of the MPAS-A TC forecasts to the initial data, additional experiments were carried out for TCs Molave and Hope using ERA5 reanalysis as initial conditions. The ERA5 initialized runs showed significant (slight) improvement in intensity (track) evolution, suggesting that the underestimated TC intensity is likely related to inferior representation of storms in the ERA-interim initial fields. Furthermore, additional runs using another customized 60-to-2-km mesh showed a reasonable improvement in capturing the TC tracks, suggesting that the track forecast accuracy of MPAS-A in TC can be sensitive to the grid resolution in the coarsest part of the variable-resolution mesh used.

Dominant Influence of ENSO-Like and Global Sea Surface Temperature Patterns on Changes in Prevailing Boreal Summer Tropical Cyclone Tracks over the Western North Pacific

2020 ◽  
Vol 33 (22) ◽  
pp. 9551-9565
Author(s):  
Haikun Zhao ◽  
Philp J. Klotzbach ◽  
Shaohua Chen
Keyword(s):  
Tropical Cyclone ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Time Scales ◽  
North Pacific ◽  
Western North Pacific ◽  
Sea Surface ◽  
Track Pattern ◽  
Global Sea Surface Temperature

AbstractA conventional empirical orthogonal function (EOF) analysis is performed on summertime (May–October) western North Pacific (WNP) tropical cyclone (TC) track density anomalies during 1970–2012. The first leading EOF mode is characterized by a consistent spatial distribution across the WNP basin, which is closely related to an El Niño–Southern Oscillation (ENSO)-like pattern that prevails on both interannual and interdecadal time scales. The second EOF mode is represented by a tripole pattern with consistent changes in westward and recurving tracks but with an opposite change for west-northwestward TC tracks. This second EOF pattern is dominated by consistent global sea surface temperature anomaly (SSTA) patterns on interannual and interdecadal time scales, along with a long-term increasing global temperature trend. Observed WNP TC tracks have three distinct interdecadal epochs (1970–86, 1987–97, and 1998–2012) based on EOF analyses. The interdecadal change is largely determined by the changing impact of ENSO-like and consistent global SSTA patterns. When global SSTAs are cool (warm) during 1970–86 (1998–2012), these SSTAs exert a dominant impact and generate a tripole track pattern that is similar to the positive (negative) second EOF mode. In contrast, a predominately El Niño–like SSTA pattern during 1987–97 contributed to increasing TC occurrences across most of the WNP during this 11-yr period. These findings are consistent with long-term trends in TC tracks, with a tripole track pattern observed as global SSTs increase. This study reveals the potential large-scale physical mechanisms driving the changes of WNP TC tracks in association with climate change.

scholarly journals Improvement of Tropical Cyclone Track Forecast over the Western North Pacific Using a Machine Learning Method

2020 ◽  
Author(s):  
Kyoungmin Kim ◽  
Dong-Hyun Cha ◽  
Jungho Im
Keyword(s):  
Machine Learning ◽  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Numerical Models ◽  
Forecast Error ◽  
Selection Method ◽  
Tropical Storm ◽  
Track Forecast ◽  
Forecast Lead Time

<p> The accurate tropical cyclone (TC) track forecast is necessary to mitigate and prepare significant damage. TC has been predicted by the numerical models, statistical models, and machine learning methods in previous researches. However, those models are separately used for TC track forecast, and historical data with satellite images were used as input variables for machine learning without forecast data from numerical models. In this study, we corrected the TC track forecast of a numerical model by artificial neural network (ANN). TCs that occurred from 2006 to 2015 over the western North Pacific were hindcasted by the Weather Research and Forecasting (WRF) model, and all categories of TCs except for tropical depression (i.e., tropical storm, severe tropical storm, and typhoon) from June to November were included in this study. We evaluated the performance of TC track forecast in terms of duration, translation speed, and direction compared with the best track data. The simulated positions of TCs at 24-hour, 48-hour, and 72-hour forecast lead time were used as variables for training and testing ANN. To optimize the number of neurons in ANN, simulated TCs were divided into two parts; TCs in 2006-2014 for ANN optimization and those in 2015 for a blind test. Also, the output selection method based on the forecast error of the WRF was applied to exclude the outlier of ANN results. By applying the output selection, the forecast error of ANN was further reduced than that of the WRF. As a result, ANN with the output selection method could improve TC track forecast by about 15% compared to the WRF. Also, the effect of ANN tended to increase when the forecast error of the WRF was large. The output selection method was particularly effective by excluding outliers of ANN results when the forecast error of the WRF was small.</p><p>※ This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (NRF-2016M3C4A7952637).</p>

scholarly journals Are We Reaching the Limit of Tropical Cyclone Track Predictability in the Western North Pacific?

2021 ◽  
pp. 1-46
Author(s):  
Hui Yu ◽  
Guomin Chen ◽  
Cong Zhou ◽  
Wai Kin Wong ◽  
Mengqi Yang ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Lead Time ◽  
Western North Pacific ◽  
Current Trend ◽  
Forecast Accuracy ◽  
Exponential Model ◽  
Track Forecast ◽  
The Past ◽  
Position Errors

AbstractThe annual-mean position errors (PE) of tropical cyclone (TC) track forecasts from three forecast agencies (RSMC-Tokyo, CMA, and JTWC) are analyzed to document the past improvements and project future tendency in track forecast accuracy for TCs in the western North Pacific. An improvement of 48 h (2-day) in lead time has been achieved in the past thirty years, but with noticeable stepwise periods of improvements with superposed short-term fluctuations. The stepwise improvement features differ among the three forecast agencies, but are highly related to the development of objective forecast guidance and the application strategy. As demonstrated by an exponential model for the growth of PEs with lead time for TCs of tropical storm category and above, the improvements in the past ten years have mainly been due to the reduction in analysis errors rather than the reduction in the error growth rate. If the current trend continues, a further 2-day improvement in TC track forecast lead times may be projected for the coming fifteen years up to 2035, and we certainly have not reached yet the limit of TC track predictability in the western North Pacific.

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