Tropical Cyclone Research and Review最新文献

Forecasting wind structure of tropical cyclone (TC) is vital in assessment of impact due to high winds using Numerical Weather Prediction (NWP) model. The usual verification technique on TC wind structure forecasts are based on grid-to-grid comparisons between forecast field and the actual field. However, precision of traditional verification measures is easily affected by small scale errors and thus cannot well discriminate the accuracy or effectiveness of NWP model forecast. In this study, the Method for Object-Based Diagnostic Evaluation (MODE), which has been widely adopted in verifying precipitation fields, is utilized in TC's wind field verification for the first time. The TC wind field forecast of deterministic NWP model and Ensemble Prediction System (EPS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) over the western North Pacific and the South China Sea in 2020 were evaluated. A MODE score of 0.5 is used as a threshold value to represent a skillful (or good) forecast. It is found that the R34 (radius of 34 knots) wind field structure forecasts within 72 h are good regardless of DET or EPS. The performance of R50 and R64 is slightly worse but the R50 forecasts within 48 h remain good, with MODE exceeded 0.5. The R64 forecast within 48 h are worth for reference as well with MODE of around 0.5. This study states that the TC wind field structure forecast by ECMWF is skillful for TCs over the western North Pacific and the South China Sea.

Present research is an endeavour to scrutinise the spatio-temporal climatic characteristics of tropical cyclones (TCs) bustle in the Bay of Bengal basin, found in RSMC-IMD data all through 1971–2020. A large number of TCs, i.e. 121 with a decadal average of 35.2 TCs has been examined for the last 50 years where depression (D) and deep depression (DD) have not been taken into account as these are less violent in nature. During the study periods, inter-annual and inter-decadal variation in cyclogenesis, landfall, length, speed, track shape and sinuosity, energy metrics and damage profile have been perceived. The study is clearly showing TCs took the northward track during the pre-monsoon season and made their landfall across the coasts of Bangladesh and Myanmar, while post-monsoon TCs made their landfall directly on the coasts of Orissa and West Bengal. In the post-monsoon phase, VF, ACE and PDI are significantly higher than in the monsoon season in the case of TCs and higher in the pre-monsoon season than in the monsoon season in the case of TCs comparing the energy metrics in different seasons. TC activity is comparatively pronounced during La Niña and El Niño regimes respectively and the genesis position in the BoB is moves to the east (west) of 87° E. During the cold regime, the number of extreme TC above the VSCS category, increased intensely. It is believed that the research findings will help stakeholders of the nation to take accurate strides to combat such violent events with persistent intensification.

The cyclone tracks from 1877 to 2020 were analyzed to detect the spatial and temporal intensity. The tracks were gathered from previously published works. The previous articles' tracks were digitized and converted to shape files for analysis in Arc-GIS. A total 126 cyclone tracks were used to detect monthly and seasonal cyclone intensity and spatial variations. The fluctuations were examined over a 30-year period, which is believed to be the climate of a particular location. Tropical cyclones hit the Bay of Bengal's coast starting in May and lasting until December. In May and October, the number of cyclones is at its peak (26 nos in each month). From June through September, the number of cyclones fell. In October and November, the number of cyclones increased dramatically. The number of cyclones substantially fell in December, and no cyclones were observed from January through March. From 1939 through 1969, the highest number of cyclones (36) was recorded. In the mid- and late-twentieth century, there were a higher number of cyclones. The coastal region of Bangladesh suffered the fewest cyclones in history over the recent era (2001–2020). The western shore was particularly vulnerable from 1877 to 1907, and the entire coastal region was dangerous from 1908 to 2000. In the Post-monsoon (October to December) season, the number of cyclones is lower than in the Monsoon period (May to September). In the pre-monsoon season, 71 cyclones strike, while in the Monsoon season, 53 cyclones strike.

Based on the typhoon track and intensity data and the precipitation data of typhoon in China during 1961–2020, the overall characteristics of the rainstorm in Fujian caused by typhoon passing though Taiwan Island were studied. More than 80 percent of typhoons passing though the Taiwan Island can bring heavy rain to Fujian. There are 1.5 events of typhoon rainstorm in Fujian every year, and the average annual impact days are 3.0. In terms of spatial distribution, the frequency and intensity of cross-island typhoon rainstorm decrease rapidly from the coastal areas of Fujian to the inland areas, and Zherong, Changle and Jiu xianshan stations in the coastal areas are the high value centers. The typhoon paths of cross-island typhoon rainstorm in Fujian are mainly divided into three categories: landing-Fujian type (including landing-Fujian northeast turning, landing-Fujian middle northbound and landing-Fujian south westbound), landing-Guangdong and Zhejiang type and offshore turning type, among which landing-Fujian type typhoon has the most significant influence(only the landing-Fujian type appears the rainstorm of ≥50 mm·(24 h)⁻¹), and the rainstorm intensity, influence range and asymmetrical structure of the rainstorm are the strongest, the most extensive and the most significant in the landing-Fujian middle northbound path. Based on the NCEP reanalysis data, the comparative analysis of the environmental fields causing the difference of precipitation intensity between the two typhoons landing-Fujian middle northbound and landing-Fujian south westbound shows that: To the landing-Fujian middle northbound track, strong wind speed area on the north side of the typhoon center leads to strong onshore winds, in the role of mountain terrain, piedmont has better convergence and very strong deep vertical upward movement, with better moisture conditions, it can send low high-energy water vapor to the middle, the precipitation dynamics and water vapor conditions are significantly stronger than the landing-Fujian south westbound track, resulting in more typhoon heavy rain.

This study summarized the procedure for the seasonal predictions of tropical cyclones (TCs) over the western North Pacific (WNP), which is currently operating at the Korea Meteorological Administration (KMA), Republic of Korea. The methodology was briefly described, and its prediction accuracy was verified. Seasonal predictions were produced by synthesizing spatiotemporal evolutions of various climate factors such as El Niño–Southern Oscillation (ENSO), monsoon activity, and Madden–Julian Oscillation (MJO), using four models: a statistical, a dynamical, and two statistical–dynamical models. The KMA forecaster predicted the number of TCs over the WNP based on the results of the four models and season to season climate variations. The seasonal prediction of TCs is announced through the press twice a year, for the summer on May and fall on August. The present results showed low accuracy during the period 2014–2020. To advance forecast skill, a set of recommendations are suggested.

The Tropical Cyclone (TC) track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors, which is beneficial for giving the model guidance-based real-time cyclone warning advisories. This study has attempted to verify the Global Forecast System (GFS) model forecasted tropical cyclone track and intensity over the North Indian Ocean (NIO) for the years 2019 and 2020. GFS is one of the operational models in the India Meteorological Department (IMD), which provides the medium-range weather forecast up to 10 days. The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory (GFDL). A total of 13 tropical cyclones formed over the North Indian Ocean, eight during 2019 and five in 2020 have been considered in this study. The accuracy of the model predicted tracks and intensity is verified for five days forecasts (120 h) at 6-h intervals; the track errors are verified in terms of Direct Position Error (DPE), Along Track Error (ATE) and Cross-Track Error (CTE). The annual mean DPE over NIO during 2019 (51–331 km) is lower than 2020 (82–359 km), and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020. The positive ATE (76–332 km) indicates the predicted track movement is faster than the observed track during both years. The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years. The cyclone Intensity forecast for the maximum sustained wind speed (MaxWS) and central mean sea level pressure (MSLP) are verified in terms of mean error (ME) and root mean square error (RMSE). The errors are lead time independent. However, most of the time model under-predicted the cyclone intensity during both years. Finally, there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.

In this paper we introduce the convective vorticity vector and its application in the forecast and diagnosis of rainstorm. Convective vorticity vector is a parameter of vector field, different from scalar field, it contains more important information of physical quantities, so it could not be replaced. Considering the irresistible importance of vector field we will introduce the theory of vector field and its dynamic forecast method. With the convective vorticity vector and its vertical component's tendency equation, diagnostic analysis on the heavy-rainfall event caused by landfall typhoon “Morakot” in the year 2009 is conducted. The result shows that, the abnormal values of convective vorticity vector always changes with the development of the observed precipitation region, and their horizontal distribution is quite similar. Analysis reveals a certain correspondence between the convective vorticity vector and the observed 6-h accumulated surface rainfall, they are significantly related. The convective vorticity vector is capable of describing the typical vertical structure of dynamical and thermodynamic fields of precipitation system, so it is closely related to the occurrence and development of precipitation system and could have certain relation with the surface rainfall regions.

Water level forecasting according to rainfall is important for water resource management and disaster prevention. Existing hydrological analysis is accompanied by difficulties in water level forecasting analysis such as topographic data and model parameter optimization of the area. Recently, with the improvement of AI (Artificial Intelligence) technology, a research using AI technology in the water resource field is being conducted.

In this research, water level forecasting was performed using an AI-based technique that can capture the relationship between data. As the watershed for the study, the Seolmacheon catchment which has the rich historical hydrological data, was selected. SVM (Support Vector Machine) and a gradient boosting technique were used for AI machine learning. For AI deep learning, water level forecasting was performed using a Long Short-Term Memory (LSTM) network among Recurrent Neural Networks (RNNs) used for time series analysis.

The correlation coefficient and NSE (Nash-Sutcliffe Efficiency), which are mainly used forhydrological analysis, were used as performance indicators. As a result of the analysis, all three techniques performed excellently in water level forecasting. Among them, the LSTM network showed higher performance as the correction period using historical data increased.

When there is a concern about an emergency disaster such as torrential rainfall in Korea, water level forecasting requires quick judgment. It is thought that the above requirements will be satisfied when an AI-based technique that can forecast water level using historical hydrology data is applied.

Rainfall-runoff analysis is the most important and basic analysis in water resources management and planning. Conventional rainfall-runoff analysis methods generally have used hydrologic models. Rainfall-runoff analysis should consider complex interactions in the water cycle process, including precipitation and evapotranspiration. In this study, rainfall-runoff analysis was performed using a deep learning technique that can capture the relationship between a hydrological model used in the existing methodology and the data itself. The study was conducted in the Yeongsan River basin, which forms a large-scale agricultural area even after industrialization, as the study area. As the hydrology model, SWAT (Soil and Water Assessment Tool) was used, and for the deep learning method, a Long Short-Term Memory (LSTM) network was used among RNNs (Recurrent Neural Networks) mainly used in time series analysis. As a result of the analysis, the correlation coefficient and NSE (Nash-Sutcliffe Efficiency), which are performance indicators of the hydrological model, showed higher performance in the LSTM network. In general, the LSTM network performs better with a longer calibration period. In other words, it is worth considering that a data-based model such as an LSTM network will be more useful than a hydrological model that requires a variety of topographical and meteorological data in a watershed with sufficient historical hydrological data.