Tropical Cyclone Forecasters Reference Guide 2. Tropical Climatology

Jeffries, Richard A.; Chu, Jan-Hwa; Miller, Ralph L.; Sampson, Charles R.

doi:10.21236/ada259922

Cited by 5 publications

(8 citation statements)

References 45 publications

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“…[24] The difference fields in moisture fluxes at 850 hPa between high-AAO years and low-AAO years (Figure 3) are comparable to the correlation map of SLP to that at 37.5°S, 142.5°E shown by Hines and Bromwich [2002]. It is noted that the present study utilizes the leading EOF mode (i.e., AAO index) in the SH, while Hines and 1965, 1972, 1982, 1987, 1991, 1997La Niña 1970, 1971, 1973, 1975, 1988, 1999High AAO July 1968, 1979, 1983, 1984, 1989August 1966, 1967, 1978, 1983, 1994September 1974, 1978, 1990, 1995Entire boreal summer 1969, 1985, 1993Low AAO July 1978, 1980, 1990, 1995August 1968, 1974, 1984, 1995September 1968, 1980, 1983, 1994Entire boreal summer 1977, 1981, 1996 El Niño (La Niña) periods are chosen when Niño 3.4 sea surface temperature is larger (smaller) than +0.5 (À0.5)°C. High (low) AAO periods are selected when the AAO index is ranked within the eight highest (lowe...…”

Section: Teleconnection Patterns In the Wnpmentioning

confidence: 80%

“…[17] High-and low-AAO periods can occur alternately even in the same year because of a strong month-to-month variation, e.g., 1968, 1974, 1978, 1983, 1984, 1990, 1994, 1995, 2001. On the other hand, some years maintain high and low values for the entire boreal summer, e.g., 1969, 1985, 1993for high AAO and 1977, 1981, 1996 for low AAO. In the following, all analyses are based on composites of the eight highest-AAO periods and the eight lowest- AAO periods.…”

Section: Aao Indexmentioning

confidence: 99%

“…Interestingly, there are three years each for the high AAO and the low AAO, which have consecutive months for the whole boreal summer. Using the 3 years' data sets (1969( , 1985( , and 1993( for high-AAO years and 1977( , 1981 for low-AAO years), the same calculations in Figure 4b are performed (figure not shown). Overall, the spatial distribution of the new calculations is comparable to the composite analysis in Figure 4b.…”

Section: Tc Passage Numbermentioning

confidence: 99%

“…[37] Concerning dynamic parameters, Figure 9 illustrates the distributions of climatology and differences (high-AAO years minus low-AAO years) in relative vorticity at 850 hPa and the vertical shear in the zonal wind at 200 and 850 hPa during August. As can be seen in Figure 9a [38] Jeffries and Miller [1993] documented three different mechanisms controlling the increase in low-level vorticity in the WNP: (1) an enhanced easterly trade wind due to a strengthening or equatorward movement of the subtropical ridge, (2) enhanced convection in the equatorial trough due to a cross-equatorial flow from the SH, and (3) a deepening of the monsoon depression over the Philippines and eastward advection associated with the low-level flow from the SH. As can be seen in the difference distribution of the 850-hPa moisture flux (Figure 3b), an anomalous anticyclone develops in the East China Sea and a cross-equatorial flow occurs over Indonesia.…”

Section: Large-scale Environments In Augustmentioning

confidence: 99%

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Possible influence of the Antarctic Oscillation on tropical cyclone activity in the western North Pacific

Kim

et al. 2005

J. Geophys. Res.

107

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[1] The present study investigates how large-scale atmospheric circulation in the Southern Hemisphere (SH) modulates tropical cyclone (TC) activity in the western North Pacific (WNP) during a typhoon season (July, August, and September; boreal summer). The variation of the SH circulation of interest is the Antarctic Oscillation (AAO). In the positive phase of AAO relative to its negative phase, two anomalous highs develop over the western Pacific in both hemispheres: a huge anticyclone in southeastern Australia and a relatively weak anticyclone in the East China Sea. These teleconnection patterns are examined and compared with previous analyses. Related to the AAO variations, a statistically significant alteration of TC activities is found over the WNP. The difference in the mean TC passage numbers over the East China Sea (120°-140°E, 20°-40°N) between the eight highest-AAO years and the eight lowest-AAO years is as large as 2, equivalent to a 50-100% increase from the climatology. This change is primarily a result of more TCs forming over the eastern Philippine Sea. On the other hand, TC passage numbers slightly decrease over the South China Sea. These changes in TC activity are predominant in August and are consistent with changes in low-level vorticity over the subtropical WNP. The influence of SH circulation variability on large-scale environments and tropical convection in the subtropical NH suggest a possible usage of AAO variation for long-range forecasting of TC activity over the WNP.

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Section: Teleconnection Patterns In the Wnpmentioning

confidence: 80%

Section: Aao Indexmentioning

confidence: 99%

Section: Tc Passage Numbermentioning

confidence: 99%

Section: Large-scale Environments In Augustmentioning

confidence: 99%

See 2 more Smart Citations

Possible influence of the Antarctic Oscillation on tropical cyclone activity in the western North Pacific

Kim

et al. 2005

J. Geophys. Res.

107

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“…The model's performance was compared with the performance of other numerical and statistical weather forecasting models. They reported a 12−hr forecast error of ≈ 100 km (62 mi) between the predicted and true cyclone-eye locations, which is comparable or marginally less than the statistical model used in [14] and a numerical model used in [15]. However, the NN model used in [13] was more accurate than the aforementioned statistical and dynamical models for long-term forecasts.…”

Section: Introductionmentioning

confidence: 96%

Forecasting the Evolution of North Atlantic Hurricanes: A Deep Learning Approach

Bose¹,

Pintar²,

Simiu³

2021

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Accurate prediction of storm evolution from genesis onwards may be of great importance considering that billions of dollars worth of property damage and numerous casualties are inflicted each year all over the globe. In the present work, two classes of Recurrent Neural Network (RNN) models for predicting storm-eye trajectory have been developed. These models are trained on input features available in or derived from the North Atlantic hurricane database maintained by the National Hurricane Center (NHC). The models utilize historical data to estimate probabilities of storms passing through any location. Furthermore, inputs to the models are such so that the model development methodology is applicable to any oceanic basin in any part of the globe. Model forecasting errors have been analyzed in detail. The error analysis shows that the Many-To-Many class of models are appropriate for long-term forecasting while Many-To-One prediction models perform comparably well for 6 − hr predictions. Application of these models to predicting more than 40 test storms in the North Atlantic basin shows that, at least for forecasts of up to 12 hours, they outperform all data-based storm trajectory prediction models cited in the paper. Apart from providing very fast predictions, the RNN models require less information than models used in current practice. Because the models presented herein emulate the trajectory trends of historical storms, they are currently being used for the simulation of synthetic storm tracks and features and the subsequent estimation of extreme wind speeds with various mean recurrence intervals.

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

Song

Fan

Xie

et al. 2022

Journal of Applied Meteorology and Climatology

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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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Tropical Cyclone Forecasters Reference Guide 2. Tropical Climatology

Cited by 5 publications

References 45 publications

Possible influence of the Antarctic Oscillation on tropical cyclone activity in the western North Pacific

Possible influence of the Antarctic Oscillation on tropical cyclone activity in the western North Pacific

Forecasting the Evolution of North Atlantic Hurricanes: A Deep Learning Approach

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

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