Tropical Cyclone Research and Review最新文献

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.

Countries in Asia and the Pacific are more prone to natural disasters than those in other parts of the world. Because of this, there is an urgent need to continue developing effective, end-to-end early warning systems that lead to an effective response by emergency managers and people at risk. ESCAP/WMO Typhoon Committee (TC), in cooperation with WMO/ESCAP Panel on Tropical Cyclones (PTC), conducted a regional cooperation project on Synergized Standard Operating Procedures for Coastal Multi-Hazards Early Warning System (SSOP) with fund support from ESCAP Multi-Donor Trust Fund for Tsunami, Disaster and Climate Preparedness in Indian Ocean and South East Asia. SSOP project was conducted successfully and achieved its proposed goals. Its results and achievements greatly benefit the Members not only in the region but also in all other regions of WMO. The paper reviewed its implementation process, strategy and activities; briefed its main achievements including SSOP Manual, capacity building and cooperation mechanism between TC and PTC; summarized the experiences and lessons from project implementation; and highlighted its sustainability. The paper also suggested the approaches to enhance the sustainability of SSOP results and the cooperation between two regional bodies TC and PTC.

The Advanced Dvorak Technique (ADT) is used by tropical cyclone prediction centres around the world to accurately evaluate the intensity of tropical cyclones (TCs) from meteorological operational satellites. The algorithm development team has introduced new improvements to the objective ADT to further extend its capabilities and accuracy. A study has therefore undergone to evaluate the new edition of ADT (9.0) based on all the North Indian Ocean Tropical cyclones during 2018, 2019 and 2020 (Total 15 No.). It is found that ADT (9.0) performed well with the conformity of IMD’s best track T. No estimates. ADT is reasonably good in estimating the intensity for T ≥ 4.0 (VSCS to SuCS) and overestimate the intensity for T ≤ 3.5(CS/SCS).

Widespread catastrophic fog episodes in polluted northern India have been attributed to tropical cyclone activity in the Bay of Bengal & Arabian Sea; however, limited studies have been conducted on the effect of tropical cyclone intensity (‘T’ Numbers) on different fog characteristics in Indo Gangetic Basin, Northern India. In this study, different characteristics, including persistence, intensity, and areal extension, were analyzed at the Indira Gandhi International Airport, New Delhi during 1998–99, 2013–14, and 2016–17. A high-intensity tropical cyclone (Severe to Very Severe Cyclonic Storm) has been found to significantly increase the persistence, intensity, and areal extension of fog by inducing strong subsidence over the IGI Airport/Indo-Gangetic Basin. This knowledge is vital for improving the short-term forecasting of fog in the Indo-Gangetic Basin of Northern India and will further support the Government agencies to take preventive safety measures and planning well in advance time.

Although tropical cyclone (TC) track forecast errors (TFEs) of operational warning centres have substantially decreased in recent decades, there are still many cases with large TFEs. The International Grand Global Ensemble (TIGGE) data are used to study the possible reasons for the large TFE cases and to compare the performance of different numerical weather prediction (NWP) models. Forty-four TCs in the western North Pacific during the period 2007–2014 with TFEs (+24 to +120 h) larger than the 75th percentile of the annual error distribution (with a total of 93 cases) are identified.

Four categories of situations are found to be associated with large TFEs. These include the interaction of the outer structure of the TC with tropical weather systems, the intensity of the TC, the extension of the subtropical high (SH) and the interaction with the westerly trough. The crucial factor of each category attributed to the large TFE is discussed.

Among the TIGGE model predictions, the models of the European Centre for Medium-Range Weather Forecasts and the UK Met Office generally have a smaller TFE. The performance of different models in different situations is discussed.