Tropical Cyclone Research and Review最新文献

The official website of the Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA) said more tropical cyclones (TCs) enter the Philippine Area of Responsibility (PAR) than anywhere else in the world. With the average of 20 TCs per year, about eight (8) or nine (9) of them are crossing the Philippines. The peak of the typhoon season is July through October, when nearly 70% of all typhoons develop (http://bagong.pagasa.dost.gov.ph/climate/tropical-cyclone-information). Based on the report of the Asian Disaster Reduction Center (ADRC), five of the typhoons that visit the country are destructive and being situated in the “Pacific Ring of Fire” makes the country vulnerable to frequent earthquakes and volcanic eruptions. Its geographical location and physical environment also contribute to its high susceptibility to tsunami, sea-level rise, storm surges, landslides, flash/flood/flooding, and drought (https://www.adrc.asia/nationinformation.php?NationCode = 608&Lang = en). For the past years, some typhoons that visited the country brought serious damages and kill many Filipinos by floods and landslides. The researcher comes up with the idea of assessing the aftermath of 2020 typhoons that visited the country. The data used by the researcher were collected from different sources, namely NDRRMC (National Disaster Risk Reduction and Management Council), PAGASA, social media and other websites. The result of the study reveals that the most destructive typhoon in 2020 that caused huge damage on the infrastructure and agriculture is Ulysses followed by Rolly, Quinta, Ambo, Vicky, Pepito, Ofel, and Marce. Most of the affected areas are those nearer to water bodies, surrounded by mountains with few trees to absorb a huge amount of water and situated in the low-lying areas.

The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery, weather charts, objectively reanalyzed data, the observational data and station sounding data were analyzed to investigate a sea fog event occurred over the Yellow and East China Seas on March 17, 2014. The sounding profiles, weather situations and the related meteorological factors during the development and dissipation stages of this sea fog event were documented. Weather Research Forecast (WRF) model was applied to simulate this sea fog case. The simulated horizontal atmospheric visibility, cloud water, humidity, and vertical wind profile during the different stages of this fog event were analyzed. During the development stage of this sea fog, a southerly lower-jet with 16–18 ms-1, an inversion layer and a cold center over the Yellow Sea were detected. The relative humidity in the fog area was above 95%. The specific humidity over the East China Sea was higher than that over the Yellow Sea. Southerly was dominated in fog area. However, during the dissipation stage of this sea fog, westerly replaced the southerly and at the lower level, southerly jet disappeared. A dry air area formed over the Shandong Peninsula and moved eastwards. Moreover, the WRF modeling result showed that the simulated atmospheric horizontal visibility and cloud water were approximately consistent with the MODIS satellite imagery. Most of cloud water concentrated below 200–400 m, and the cloud water in the southern part of fog area extended to a higher height than the northern part. While both of air temperature and dew-point temperature were close to sea surface temperature.

This article provides a review of tropical cyclone (TC) surface wind estimation from an operational forecasting perspective. First, we provide a summary of operational forecast center practices and historical databases. Next, we discuss current and emerging objective estimates of TC surface winds, including algorithms, archive datasets, and individual algorithm strengths and weaknesses as applied to operational TC surface wind forecast parameters. Our review leads to recommendations about required surface coverage – an area covering at least 1100 km from center of TC at a 2-km resolution in the inner-core, and at a frequency of at least once every 6 h. This is enough coverage to support a complete analysis of the TC surface wind field from center to the extent of the 34-kt (17 m s⁻¹) winds at 6-h intervals. We also suggest future designs of TC surface wind capabilities include funding to ensure near real-time data delivery to operators so that operational evaluation and use are feasible within proposed budgets. Finally, we suggest that users of archived operational wind radii datasets contact operational organizations to ensure these datasets are appropriate for their needs as the datasets vary in quality through time and space, even from a single organisation.

The unexpectedly outbreak of COVID-19 in early 2020, as a public emergency, has impacted the way of human behavior deeply and widely in today's society, including the countermeasures of typhoon-related disaster risk reduction and preparedness in the Members of Typhoon Committee (TC). This paper briefed the impacts due to COVID-19 pandemic on activities of the Committee in 2020; introduced the countermeasures took in National Hydrological and Meteorological Services (NHMS) of TC Members during typhoon season in 2020 for coping with the crisis caused by COVID-19 pandemic; summarized the innovative strategies and countermeasures for dealing with the crisis of special or emergency public situation for typhoon-related disaster risk prevention, preparedness and reduction in future based on the review and analysis of the experiences from Members and international/regional agencies, and the outcomes from TC Integrated Workshop and Annual Session, including strengthening meteorological and hydrological services and value of preparedness; enhancing multi-sectoral coordination mechanisms; promoting the mobile-based data transmission and information dissemination; and increasing installation of home-based hydro-meteorological monitoring stations. The paper also discussed the impact-based forecasting and the application of big-data and AI technology in typhoon-related disaster risk reduction as two new key areas to be taken into consideration in TC updating Strategic Plan 2022–2026.

Forecasting flooding hazards is a very effective non-engineering measure for flood control. Presently, the commonly used forecasting method in simulating flash flood events is through a watershed hydrological model. Many Asia-Pacific countries like the Philippines are prone to frequent hydrometeorological hazards such as tropical cyclones, resulting in frequent heavy rainfall events. However, most rivers in the many basins lack water flow observation data, which makes it challenging to use lumped and data-driven models for flash flood forecasting. With the continuous progress of remote sensing (RS) and geographic information system (GIS) techniques, the physically-based distributed hydrological model (PBDHMs) has rapidly advanced. PBDHMs can directly determine the model parameters according to the underlying surface characteristics from remotely-sensed data, which makes it possible for flood forecasting in areas with little to virtually no data. In this study, the Matina River basin in Davao City was selected as a case study in simulating a small data-poor basin in the region. The Liuxihe model was used to formulate a forecasting scheme and simulated the past flash flood events. The results show that there is a good correspondence between the past heavy rainfall events and their corresponding simulated river discharges. The results conform to the hydrological regularities, which can be used for flood forecasting and can serve as a baseline for the development of a flood forecasting system in the rivers of Davao City and elsewhere.

China experiences one of the most frequent flood disasters in the world. Establishing accurate and reliable flood prediction program is the key to deal with flood disasters. Nanshui Reservoir Basin, in southern China, belongs to subtropical monsoon climate, with more rain in spring, concentrated rainstorm in summer and typhoon storm in autumn. Floods at dam site are mostly small and medium-sized floods with steep rise and slow fall as typical mountain flood. In order to explore the applicability of Liuxihe model in flood prediction of Nanshui Reservoir, this paper builds up Liuxihe model for Nanshui Reservoir based on DEM, land use and soil type data, and selects a typical flood event to optimize the parameters using particle swarm optimization (PSO) algorithm and verifies the accuracy of the model by simulating the other floods. Liuxihe model established in this paper indicates a satisfactory performance for flood prediction for Nanshui Reservoir, which can meet the accuracy requirement of flood prediction. Finally, the effects of different river grading and PSO algorithm on flood prediction are discussed. The results show that the PSO algorithm can obviously improve the accuracy of the Liuxihe model for flood forecast in Nanshui Reservoir. The simulation based on four-level channel grading has better results than that based on three-level channel, which indicates increased peak flood value, delayed peak time and closer simulation to the measured value.

Urbanization has been a worldwide development trend, which regulates river courses, impervious surfaces and drainage systems. Urbanization causes hydrological effects, including increased runoff volumes, peak discharges and flow concentrations. This manuscript selects the Malaysian Sungai Pinang watershed as a case study to illustrate these land use, channel and flooding changes of Asian coastal cities. The Landsat satellite remote sensing images were first used to estimate the land use/land cover changes of the Sungai Pinang watershed by using SVM algorithm, and the results shows the urbanization was very rapid in the past decades, with the urbanization rate reached 46.41% in 2018 based on the build area rate. River channel characteristics also changed significantly, from natural river to concrete channel. Some flood resilience measures for coastal cities experiencing urbanization are also proposed for development and flood mitigation. Moreover, a flood forecasting model of the Sungai Pinang watershed is established herein. The simulation results of the Liuxihe model constructed in this study conforms to hydrological regularities and can provide a technical reference for flood control and disaster reduction. However, it is necessary to pay attention to the uncertainty of the forecast results.

Heavy precipitation induced by typhoons is the main driver of catastrophic flooding, and studying precipitation patterns is important for flood forecasting and early warning. Studying the space-time characteristics of heavy precipitation induced by typhoons requires a large range of observation data that cannot be obtained by ground-based rain gauge networks. Satellite-based estimation provides large domains of precipitation with high space-time resolution, facilitating the analysis of heavy precipitation patterns induced by typhoons. In this study, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) satellite data were used to study the temporal and spatial features of precipitation induced by Typhoon Hato, which was the strongest typhoon of 2017 to make landfall in China. The results show that rainfall on the land lasted for six days from the typhoon making landfall to disappearing, reaching the maximum when the typhoon made landfall. Hato produced extremely high accumulated rainfall in South China, almost 300 mm in Guangdong Province and Guangxi Zhuang Autonomous Region and 260 mm in Hainan Province. The rainfall process was separated into three stages and rainfall was the focus in the second stage (5 h before making landfall to 35 h after making landfall).

To understand structural changes and forecast error, a case study of binary typhoons in the western North Pacific (WNP) of 2018 was investigated using best track and reanalysis data. Soulik was generated on August 16 and Cimaron was generated on August 18, respectively. The 19th typhoon Soulik and 20th typhoon Cimaron co-existed from August 18 to 24 and approached each other. Soulik was located on the western side and Cimaron was located on the eastern side of the WNP. They were located approximately 1300 km from each other at 00 UTC August 22. The Soulik structure began changing around August 22 and became weak and slow, while Cimaron maintained its intensity, size, and moving speed. This observational evidence is likely caused by the binary interaction between two typhoons within a certain distance and environmental steering flow, such as the location of the North Pacific high and strong jet stream of the northern flank of the North Pacific high.

Soulik was initially forecasted to make landfall and reach Seoul; however, its track changed from northward to northeastward from August 21 to 23 according to both official guidance and unified model (UM). Four global numerical weather prediction models forecasted different tracks of Soulik. UM and JGSM forecasted a northward track whereas ECMWF and GFS showed a northeastward track for 12 UTC August 21 through 12 UTC August 24. The latter models were similar to the best track. The track forecast error and spread of Soulik were larger than those of Cimaron. The mean absolute error of the maximum wind speed of Soulik was similar to the average of total typhoons in 2018.