Prediction Of Tropical Cyclone Activity Research Articles

Dynamical Seasonal Prediction of Tropical Cyclone Activity Using the FGOALS-f2 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.

Monthly prediction of tropical cyclone activity over the South China Sea using the FGOALS-f2 ensemble prediction system

The monthly prediction skill for tropical cyclone (TC) activity in the South China Sea (SCS) during the typhoon season (July to November) was evaluated using the FGOALS-f2 ensemble prediction system. Specifically, the prediction skill of the system at a 10-day lead time for monthly TC activity is given based on 35-year (1981–2015) hindcasts with 24 ensemble members. The results show that FGOALS-f2 can capture the climatology of TC track densities in each month, but there is a delay in the monthly southward movement in the area of high track densities of TCs. The temporal correlation coefficient of TC frequency fluctuates across the different months, among which the highest appears in October (0.59) and the lowest in August (0.30). The rank correlation coefficients of TC track densities are relatively higher (R > 0.6) in July, September, and November, while those in August and October are relatively lower (R within 0.2 to 0.6). For real-time prediction of TCs in 2020 (July to November), FGOALS-f2 demonstrates a skillful probabilistic prediction of TC genesis and movement. Besides, the system successfully forecasts the correct sign of monthly anomalies of TC frequency and accumulated cyclone energy for 2020 (July to November) in the SCS.摘要本研究基于新一代FGOALS-f2动力集合预测系统35年 (1981–2015年) 的热带气旋历史回报试验对南海台风季 (7–11月) 热带气旋活动超前10天的月预测技巧进行评估, 并对2020年南海台风季热带气旋活动进行了实时月预测尝试. 结果表明: FGOALS-f2能较好地预测南海热带气旋路径密度演变特征, 预测的热带气旋生成个数与实际观测结果的相关系数在0.2–0.6之间. 2020年实时预测方面, FGOALS-f2较为准确地给出了南海台风季热带气旋生成位置与移动路径的概率预测, 且对南海热带气旋生成个数和强度异常的确定性预测结果与观测接近.

Assessing the potential predictability of tropical cyclone activity in the Philippines on weekly timescales

Assessing the potential predictability of tropical cyclone activity in the Philippines on weekly timescales

Superiority of Mega‐ENSO Index in the Seasonal Prediction of Tropical Cyclone Activity Over the Western North Pacific

AbstractIn this study, we compared the performance of two potential predicators, that is, El Niño–Southern Oscillation (ENSO) index (Niño‐3.4) and mega‐ENSO index, in the seasonal forecast of tropical cyclone (TC) activity and its spatial distribution over the western North Pacific (WNP) during the extended TC season, which is of public concern. Our results clearly show that, although both mega‐ENSO and Niño‐3.4 indices in the preceding May are important predictors for the seasonal predication, the relative‐sea surface temperature (SST)‐dependent mega‐ENSO exhibits a higher skill in the seasonal forecasting compared with the absolute‐SST‐dependent ENSO. Further results show that, despite of stronger destructiveness of TCs in high mega‐ENSO (El Niño) years than in low mega‐ENSO (La Niña) years, more attention should be paid to the TCs in low mega‐ENSO years, which are more likely to occur in coastal areas compared with the TCs in high mega‐ENSO years. Due to the responses of TC genesis, TC potential intensity, and large‐scale flow to the SST change in low mega‐ENSO years, the WNP TCs tend to originate in the northwestern quadrant and intensify at high latitudes and then turn northwestward over the TC prevailing region, which contributes to the northwestward migration of the WNP TC exposure in terms of track density and destructiveness density and thus imposes more risks in the coastal areas in low mega‐ENSO years. In addition, despite the significant predication skill in forecasting TC activity when using mega‐ENSO/Niño‐3.4 as a single predicator, it is still far to predict reliable WNP activity, especially its spatial distribution, without considering other predictors.

Large-scale environmental controls on the seasonal statistics of rapidly intensifying North Atlantic tropical cyclones

This study is concerned with the connections between the large-scale environment and the seasonal occurrence of rapid intensification (RI) of North Atlantic tropical cyclones. Physically-motivated statistical analysis using observations and reanalysis products suggests that for tropical cyclones over the open tropical North Atlantic, the interannual variability of the probability of storms undergoing RI is influenced by seasonal large-scale atmospheric and oceanic variables, but not so for storms over the Gulf of Mexico and western Caribbean Sea. We suggest that this differentiated response is due to the former region exhibiting a strong negative correlation between the seasonal anomalies of vertical wind shear and potential intensity. Differences in the mean climatology and subseasonal variations of the large-scale environment in these regions appear to play an insignificant role in the distinctive seasonal environmental controls on RI. We suggest that the interannual correlation of vertical wind shear and potential intensity is an indicator of seasonal predictability of tropical cyclone activity (including RI) across the tropics .

Dynamical Seasonal Prediction of Tropical Cyclone Activity: Robust Assessment of Prediction Skill and Predictability

AbstractImproving the seasonal prediction of tropical cyclone (TC) activity demands a robust analysis of the prediction skill and the inherent predictability of TC activity. Using the resampling technique, this study analyzes a state‐of‐the‐art prediction system and offers a robust assessment of when and where the seasonal prediction of TC activity is skillful. We found that uncertainties of initial conditions affect the predictions and the skill evaluation significantly. The sensitivity of predictions to initial conditions also suggests that landfall and high‐latitude activity are inherently harder to predict. The lower predictability is consistent with the relatively low prediction skill in these regions. Additionally, the lower predictability is largely related to the atmospheric environment rather than the sea surface temperature, at least for the predictions initialized shortly before the hurricane season. These findings suggest the potential for improving the seasonal TC prediction and will help the development of the next‐generation prediction systems.

Near-Future Prediction of Tropical Cyclone Activity over the North Atlantic

Prediction of tropical cyclone (TC) activity is essential to better prepare for and mitigate TC-induced disasters. Although many studies have attempted to predict TC activity on various time scales, very few have focused on near-future predictions. Here a decrease in seasonal TC activity over the North Atlantic (NA) for 2016–30 is shown using a track-pattern-based TC prediction model. The TC model is forced by long-term coupled simulations initialized using reanalysis data. Unfavorable conditions for TC development including strengthened vertical wind shear, enhanced low-level anticyclonic flow, and cooled sea surface temperature (SST) over the tropical NA are found in the simulations. Most of the environmental changes are attributable to cooling of the NA basinwide SST (NASST) and more frequent El Niño episodes in the near future. The consistent NASST warming trend in the projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests that natural variability is more dominant than anthropogenic forcing over the NA in the near-future period.

Characteristics and Impacts of Extratropical Rossby Wave Breaking during the Atlantic Hurricane Season

This study investigates the characteristics of extratropical Rossby wave breaking (RWB) during the Atlantic hurricane season and its impacts on Atlantic tropical cyclone (TC) activity. It was found that RWB perturbs the wind and moisture fields throughout the troposphere in the vicinity of a breaking wave. When RWB occurs more frequently over the North Atlantic, the Atlantic main development region (MDR) is subject to stronger vertical wind shear and reduced tropospheric moisture; the basinwide TC counts are reduced, and TCs are generally less intense, have a shorter lifetime, and are less likely to make landfalls. A significant negative correlation was found between Atlantic TC activity and RWB occurrence during 1979–2013. The correlation is comparable to that with the MDR SST index and stronger than that with the Niño-3.4 index. Further analyses suggest that the variability of RWB occurrence in the western Atlantic is largely independent of that in the eastern Atlantic. The RWB occurrence in the western basin is more closely tied to the environmental variability of the tropical North Atlantic and is more likely to hinder TC intensification or reduce the TC lifetime because of its proximity to the central portion of TC tracks. Consequently, the basinwide TC counts and the accumulated cyclone energy have a strong correlation with western-basin RWB occurrence but only a moderate correlation with eastern-basin RWB occurrence. The results highlight the extratropical impacts on Atlantic TC activity and regional climate via RWB and provide new insights into the variability and predictability of TC activity.

Seasonal prediction of tropical cyclone activity over the north Indian Ocean using three artificial neural networks

Three artificial neural network (ANN) methods, namely, multilayer perceptron (MLP), radial basis function (RBF) and generalized regression neural network (GRNN) are utilized to predict the seasonal tropical cyclone (TC) activity over the north Indian Ocean (NIO) during the post-monsoon season (October, November, December). The frequency of TC and large-scale climate variables derived from NCEP/NCAR reanalysis dataset of resolution 2.5° × 2.5° were analyzed for the period 1971–2013. Data for the years 1971–2002 were used for the development of the models, which were tested with independent sample data for the year 2003–2013. Using the correlation analysis, the five large-scale climate variables, namely, geopotential height at 500 hPa, relative humidity at 500 hPa, sea-level pressure, zonal wind at 700 hPa and 200 hPa for the preceding month September, are selected as potential predictors of the post-monsoon season TC activity. The result reveals that all the three different ANN methods are able to provide satisfactory forecast in terms of the various metrics, such as root mean-square error (RMSE), standard deviation (SD), correlation coefficient (r), and bias and index of agreement (d). Additionally, leave-one-out cross validation (LOOCV) method is also performed and the forecast skill is evaluated. The results show that the MLP model is found to be superior to the other two models (RBF, GRNN). The (MLP) is expected to be very useful to operational forecasters for prediction of TC activity.

A Track Pattern–Based Seasonal Prediction of Tropical Cyclone Activity over the North Atlantic

Abstract A seasonal prediction model of tropical cyclone (TC) activities for the period August–October over the North Atlantic (NA) has been developed on the basis of TC track patterns. Using the fuzzy c-means method, a total of 432 TCs in the period 1965–2012 are categorized into the following four groups: 1) TCs off the U.S. East Coast, 2) TCs over the Gulf of Mexico, 3) TCs that recurve into the open ocean of the central NA, and 4) TCs that move westward in the southern NA. The model is applied to predict the four TC groups separately in conjunction with global climate forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2). By adding the distributions of the four TC tracks with precalculated weighting factors, this seasonal TC forecast model provides the spatial distribution of TC activities over the entire NA basin. Multiple forecasts initialized in six consecutive months from February to July are generated at monthly intervals to examine the applicability of this model in operational TC forecasting. Cross validations of individual forecasts show that the model can reasonably predict the observed TC frequencies over NA at the 99% confidence level. The model shows a stable spatial prediction skill, proving its advantage for forecasting regional TC activities several months in advance. In particular, the model can generate reliable information on regional TC counts in the near-coastal regions as well as in the entire NA basin.

Seasonal prediction of tropical cyclone activity over the north Indian Ocean using the neural network model

A neural network (NN) model is developed to predict the seasonal number of tropical cyclones (TCs) formed over the north Indian Ocean during the post-monsoon season (October, November, December). The frequency of TCs and the large scale climate variables derived from the NCEP/NCAR reanalysis dataset of resolution 2.5°×2.5° have been analyzed for the period 1971-2013. Data for the years 1971-2002 have been used for the development of the model, which is tested with independent sample data for the years 2003-2013. Applying correlation analysis, five large-scale climate variables, namely geopotential height at 500 hPa, relative humidity at 500 hPa, sea level pressure, and zonal wind at 700 hPa and 200 hPa for the antecedent month September are selected as predictors. Based on some performance parameter statistics, the performance of the NN model is evaluated and the results are compared with the multiple linear regression (MLR) model. From the results it is inferred that the predicted tropical cyclone count by both models is very close to the actual counts for both periods. However, the NN model is found to be superior to the MLR model. This tropical cyclone prediction technique may be useful for operational prediction purposes.

An analysis of tropical cyclone occurrence in the Southern Hemisphere derived from a new satellite-era data set

Satellite remote sensing is vital to accurately estimate tropical cyclone (TC) parameters such as position and intensity. A TC archive for the Southern Hemisphere (SH) has been prepared for the ‘satellite era’, and it now consists of best track data, covering name (and/or unique identification number), position and intensity (in terms of central pressure). Based on these best track data, TC climatologies for the South Indian Ocean (SIO) and South Pacific Ocean (SPO) are investigated. Variability in TC activity is examined depending on different phases of the El Niño-Southern Oscillation (ENSO). The outcomes demonstrate prospects for improving the regional seasonal prediction of TC activity. The growth in the availability of satellite data is found to have a significant influence on the quantity and quality of available TC data, particularly in relation to the intensity of TCs. To provide a means of accessing detailed information and data on historical TCs in the SH, a specialized website for disseminating results and data was developed using the OpenLayers platform. This allows dynamic map navigation of detailed information for user-selected regions.

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

AbstractSkillful 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.

Prospects for Improving the Operational Seasonal Prediction of Tropical Cyclone Activity in the Southern Hemisphere

Tropical cyclones (TCs) are the most destructive weather phenomena to impact on tropical regions, and reliable predicttion of TC seasonal activity is important for preparedness of coastal communities in the tropics. In investigating prospects for improving the skill of TC seasonal prediction in the South Indian and South Pacific Oceans, including the Australian Region, we used linear regression to model the relationship between the annual number of cyclones and three indices (SOI, NI?O3.4 and 5VAR) describing the strength of the El Ni?o-Southern Oscillation (ENSO). The correlation between the number of Australian Region (90?E - 160?E) TCs and the indices was strong (3-month 5VAR ?0.65, NI?O3.4 ?0.62 and SOI +0.64), and a cross-validation assessment demonstrated that the models which used July-August-September indices and the temporal trend as the predictors performed well. The predicted number of TCs in the Australian Region for 2010/2011 and 2011/2012 seasons was 14 (11 recorded) and 12, respectively. We also found that the correlation between the numbers of TCs in the western South Indian region (30?E to 90?E) and the eastern South Pacific region (east of 170?E) and the indices was weak, and it is therefore not sensible to build linear regression forecast models for these regions. We conclude that for the Australian Region, the new statistical model provides prospects for improvement in forecasting skill compared to the statistical model currently employed at the National Climate Centre, Australian Bureau of Meteorology. The next step towards improving the skill of TC seasonal prediction in the various regions of the Southern Hemisphere will be undertaken through analysis of outputs from the dynamical climate model POAMA (Predictive Ocean-Atmosphere Model for Australia).

Climate Prediction of Tropical Cyclone Activity in the Vicinity of Taiwan Using the Multivariate Least Absolute Deviation Regression Method

In this study, a multivariate linear regression model is applied to predict the seasonal tropical cyclone (TC) count in the vicinity of Taiwan using large-scale climate variables available from the preceding May. Here the season encompasses the five-month period from June through October, when typhoons are most active in the study domain. The model is based on the least absolute deviation so that regression estimates are more resistant (i.e., not unduly influenced by outliers) than those derived from the ordinary least square method. Through lagged correlation analysis, five parameters (sea surface temperature, sea level pressure, precipitable water, low-level relative vorticity, and vertical wind shear) in key locations of the tropical western North Pacific are identified as predictor datasets. Results from cross-validation suggest that the statistical model is skillful in predicting TC activity, with a correlation coefficient of 0.63 for 1970 - 2003. If more recent data are included, the correlation coefficient reaches 0.69 for 1970 - 2006. Relative importance of each predictor variable is evaluated. For predicting higher than normal seasonal TC activity, warmer sea surface temperatures, a moist troposphere, and the presence of a low-level cyclonic circulation coupled with low-latitude westerlies in the Philippine Sea in the antecedent May appear to be important.