Tropical Cyclone Research and Review最新文献

This paper summarizes the progress and activities of tropical cyclone (TC) operational forecast centers during the last four years (2018–2021). It is part II of the review on TC intensity change from the operational perspective in the rapporteur report presented to the 10th International Workshop on TCs (IWTC) held in Bali, Indonesia, from Dec. 5–9, 2022. Part I of the review has focused on the progress of dynamical model forecast guidance. This part discusses the performance of TC intensity and rapid intensification forecasts from several operational centers. It is shown that the TC intensity forecast errors have continued to decrease since the 9th IWTC held in 2018. In particular, the improvement of rapid intensification forecasts has accelerated, compared with years before 2018. Consensus models, operational procedures, tools and techniques, as well as recent challenging cases from 2018 to 2021 identified by operational forecast centers are described. Research needs and recommendations are also discussed.

Southern Malawi is continuously affected by tropical cyclone-related floods (TCRFs), which have negative consequences on households' livelihoods, thereby displacing most households to neighbouring communities of Mozambique. The TCRFs have further threatened national, regional, community, and household food security agenda, which is already constrained by poverty, poor agricultural practices, low use of improved varieties, unaffordable inorganic fertilizers, and fragmenting landholding sizes. Accordingly, households have indigenously engineered resilience-based Sustainable Landscape Management (SLM) practices, like intercropping, agroforestry, cover cropping, and soil and water conservation practices, against the adverse effects of TCRFs on-farm productivity. Hence, this study examines the effect of TCRFs and SLM adoption on-farm productivity. While using rigorous endogenous switching regression econometric tools, the study finds TCRFs reducing farm productivity by 27 percent. After SLM adoption, the study observes farm productivity enhancement by 29–126 percent when households adopt at least one SLM practices under varying degrees of TCRFs. Despite the highlighted advantages of SLM adoption, female farmers are less likely to adopt SLM practices because they do not have access to productive resources. Hence, the study proposes the need of gender targeted extension services, accompanied by some seed capital for SLM adoption. Besides, there is need to sensitize farmers on the complementarities between inorganic fertilizer and SLM practices. Lastly, future studies should assess the effect of sustained SLM adoption or dis-adoption and input intensification on farm productivity.

Global climate change, climate extremes, and overuse of natural resources are all major contributors to the risk brought on by cyclones. In I West Bengal state of India, the Pathar Pratima Block frequently experiences a variety of risks that result in significant loss of life and livelihood. In order to govern coastal society, it is crucial to measure and map the multi-hazards risk status. To depict the multi-hazards vulnerability and risk status, no cutting-edge models are currently being applied. Predicting distinct physical vulnerabilities is possible using a variety of cutting-edge machine learning techniques. This study set out to precisely describe multi-hazard risk using powerful machine learning methods. This study involved the use of Analytic Hierarchical Analysis and two cutting-edge machine-learning algorithms - Random Forest and Artificial Neural Network, which are yet underutilized in this area. The multi-hazards risk was determined by taking into account six criteria. The southern and eastern regions of the research area are clearly identified by the multi-hazards risk maps as having high to extremely high hazards risk levels. Cyclonic hazards and embankment breaching are the main dominant factors among the multi-hazards. The machine learning approach is the most accurate model for mapping the multi-hazards risk where the ROC result of Random forest and artificial neural network is more than the conventional method AHP. Here RF is the most validated model than the other two. The effectiveness, root mean square error, true skill statistics, Friedman and Wilcoxon rank test, and area under the curve of receiver operating characteristic tests were used to evaluate the prediction capacity of newly constructed models. The RMSE values of 0.24 and 0.26, TSS values of 0.82 and 0.73, and AUC values of 88.20% and 89.10% as produced by RF and ANN models, respectively, were all excellent.

Using the 6-hourly reanalysis data of European Center ERA-Interim with horizontal resolution of 0.25° × 0.25° and hourly typhoon operational data provided by the CMA (China Meteorological Administration), a new high-level environmental field factor is derived, and its application during the recurvature period of No. 14 Typhoon Yagi and No. 18 Typhoon Rumbia in 2018 is compared and analyzed. According to the comparison study, there is always a clear positive abnormal value area of high-level environmental field factor on the northeast of Rumbia during its northward movement, implying an obvious alteration of $\nabla •\overrightarrow{u}$ and a big negative gradient of $\nabla \overrightarrow{u}$ on the northeast of Rumbia. With the eastward movement of the westerly trough and the strengthening of the subtropical westerly jet, Rumbia is expected to veer northeast. However, the change of high-level environmental field factor on the northeast of Yagi is not noticeable, and Yagi is far away from the upper-level jet stream, which is not conducive to Yagi's northeast recurvature.

This work use the global WRF model containing quadruply nesting with which the highest resolution reached 10 km to simulate the typhoons landed on the coast of China in 2011. The model is driven by the reanalysis data fnl with the resolution of 1° x 1°. The study assess the feasibility and applicability of the global WRF model in the 1–7 days prediction of Tropical Cyclone (TC) track by comparing it with the regional WRF model containing the same setting (physical scheme, dynamical frame, model resolution and nesting grid domain). The global model obtain a similar forecast accuracy to the regional model in 1–7 days, with a difference less than 50 km. The forecast accuracy of the global model for 1, 2, 3, 4, 5, 6 and 7 days is about 70 km, 120 km, 180 km, 240 km, 320 km, 400 km, and 500 km, respectively. The reason of the significant TC track errors in the forecast more than 3 or 4 days is analyzed, it is due to the poor representation of typhoon and its steering flow under the situation of binary typhoon system. The study show that the global WRF model can be exploited to proceed the high resolution TC simulation and make the TC track forecast up to 7 days but not in the case of multiple typhoon.

A typhoon passed through Hong Kong suddenly and unexpectedly on 18 September 1906 (the “Typhoon 1906”) and had a disastrous impact on Victoria Harbour and its surroundings in just a couple of hours. Since the year 1906 was the “Bingwu” year in the Chinese calendar, the typhoon is also known historically as the “Bingwu Typhoon”. Tremendous loss of lives and property resulted, and the shipping and fishing communities were devastated. Two mysteries arising from this calamitous typhoon have existed to date: 1) Why the Hong Kong Observatory was not able to provide any forewarning? 2) whether the storm surge reported in some contemporary records is entirely credible? This paper will focus on both of these.

In this paper, we re-analyse historical weather observations recorded in various historical documents and estimate the possible storm size, intensity and track of Typhoon 1906 using tropical cyclone models. Based on the re-analyses, the storm surges, storm tides and wave heights in Hong Kong are also estimated using storm surge and wave models. The results reveal that Typhoon 1906 was a midget typhoon, with a radius of maximum winds of 11 km or smaller, during its passage through Hong Kong. This explains why it was technically impossible for a forewarning to be given at that time when real-time weather observations from ships, meteorological satellites and radars were non-existent. We also estimate that the maximum storm surges (storm tides) in Hong Kong were not higher than 0.82 m (2.43 mCD) and 1.98 m (4.15 mCD) in Victoria Harbour and Tolo Harbour respectively. These figures are found to be limited by the intensity and the storm size of the typhoon. Therefore, we conclude that the previously documented storm surge figures are not supported by the present study.

The two most common types of disasters caused by natural hazards in the Asia-Pacific region are floods and storms, many of them associated with typhoon (tropical cyclone) related impacts. To improve the capacity of typhoon-related disaster risk reduction so as to maximum reduce the losses of people’s life and properties, the decision makers and the public are imminently demanding the information of the targeted impact caused by typhoon. As the front line in hydro-meteorological disaster prevention and mitigation against the typhoon-related disasters, National Meteorological and Hydrological Services (NMHSs) in TC Members have recognized that forecasting impact became more important than forecasting pure causing-disaster elements. Impact-based forecasting signals an evolution from “what the weather will be” to “what the weather will do”. Many things change as impact based forecasts evolve from previous weather forecasts. To enhance impact-based typhoon forecasting, the Typhoon Committee added it into the new updated Strategic Plan 2022–2026. This paper briefed generally the concept of impact based forecasting, introduced the implementation and progresses on typhoon impact based forecasting in TC Members in recent years, and initially discussed the measures and direction for enhancement of impact-based typhoon forecasting and early warning services in future.

The rotating-convection paradigm for tropical cyclone behaviour is shown to provide an attractive and consistent framework for interpreting the dynamics of formation and intensification of at least some medicanes. The ideas are illustrated by a case study of the medicane that formed over the eastern Mediterranean in mid-December 2020. This case study is based on analyses of data from the European Centre for Medium Range Weather Forecasts (ECMWF), imagery from the European geostationary meteorological satellite, Meteosat Second Generation, and output from a convection permitting numerical simulation of the event using the United Kingdom (UK) Met Office regional model with the RAL2 physics configuration. Limitations of the currently widely accepted interpretation of medicanes in terms of the so-called Wind-Induced Surface Heat Exchange (WISHE) intensification mechanism are discussed.

Spatial distribution of rainfall and wind speed forecast errors associated with landfalling tropical cyclones (TC) occur significantly due to incorrect location forecast by numerical models. Two major areas of errors are: (i) over-estimation over the model forecast locations and (ii) underestimation over the observed locations of the TCs. A modification method is proposed for real-time improvement of rainfall and wind field forecasts and demonstrated for the typical TC AMPHAN over the Bay of Bengal in 2020. The proposed method to improve the model forecasts is a relocation method through shifting of model forecast locations of TC to the real-time official forecast locations of India Meteorological Department (IMD). The modification is applied to the forecasts obtained from the operational numerical model, the Global Forecast System (GFS) of IMD. Application of the proposed method shows considerable improvement of both the parameters over both the locations. The rainfall forecast errors due to displacement are found to have improved by 44.1%–69.8% and 72.1%–85.2% over the GFS forecast locations and over the observed locations respectively for the respective forecast lead times 48 h, 72 h, and 96 h. Similarly, the wind speed forecasts have improved by 27.6%–56.0% and 63.7%–84.6% over the GFS forecast locations and over the observed locations respectively for the respective forecast lead times 60 h, 72 h, and 84 h. The results show that the proposed technique has capacity to provide improved spatial distributions of rainfall and wind speed forecasts associated with landfalling TCs and useful guidance to operational forecasters.