Tropical Cyclone Research and Review最新文献

Nowadays, with the continual development of the science and technology applied in data observation, monitoring and collection, human has more and more means and channels to obtain various data, consequently, the amount of collected and stored data is also getting bigger and bigger. In recent years, hydro-meteorological data have multiplied in some Typhoon Committee (TC) Members. Data-based advanced technology applications in TC, such as application of Artificial Intelligent (AI) and impact-based typhoon disaster forecasting and early warning, has emerged one after another. A consistent and integrated data quality management system is crucial for ensuring accurate hydrological and meteorological analysis and prediction. Considering the importance and urgent necessary, TC working group on hydrology (WGH) conducted a cooperation project on data quality management in the past years with the major objective of improving the capacity of TC Members on integrated data quality control and processing. Despite the significant improvements, the uncertainties and difficulties in processing the full-elements of hydro-meteorological data still persist in hydro-meteorological data. To tackle these challenges and further enhance the data quality management system, the integration of AI technology shows great promise. By examining the data quality management system at World Meteorological Organization (WMO) as a starting point, this paper explored how related organizations in China, Japan, Malaysia, Philippines and Republic of Korea, manage the quality of hydro-meteorological data; reviewed the current status of hydro-meteorological data quality control in TC Members, and discussed the potential areas to be enhanced in future.

Based on the best-track dataset from the Shanghai Typhoon Institute/China Meteorological Administration, the paper provides a comprehensive summary and analysis of tropical cyclone (TC) activities in the Western North Pacific (WNP) and the South China Sea (SCS) for 2022. Using the historical climatology from 1951 to 2020, the anomalous conditions during 2022 in TC frequency, origin locations, tracks, intensity, and duration for the entire ocean basin as well as landfall events in China are examined. Results show that the overall TC frequency is slightly lower than normal, but the multiple TC events have a very high frequency of occurrence. Origin locations of TCs, which mark the starting points of their paths, show a large westward and northward deviation from climatology. Around 40% of the named TCs exhibit a shift in their direction of movement from westerly to easterly. Additionally, comparisons of the means, medians, upper and lower quartiles all indicate that the intensity of TCs in 2022 is generally lower than the climatology, with the duration of TCs at tropical storm intensity or above being shorter than usual. A notable observation is the fewer incidence of TC landfalls in China, but with a geographical concentration in Guangdong Province. These anomalous annual TC activities are influenced by related atmospheric and oceanic environmental conditions modulated by multi-scale climate variability. The findings provide useful information for enhancing disaster mitigation strategies in the Asia-Pacific region.

This study examines the track and intensity forecasts of two typical Bay of Bengal tropical cyclones (TC) ASANI and MOCHA. The analysis of various Numerical Weather Prediction (NWP) model forecasts [ECMWF (European Centre for Medium range Weather Forecast), NCEP (National Centers for Environmental Prediction), NCUM (National Centre for Medium Range Weather Forecast-Unified Model), IMD (India Meteorological Department), HWRF (Hurricane Weather Research and Forecasting)], MME (Multi-model Ensemble), SCIP (Statistical Cyclone Intensity Prediction) model, and OFCL (Official) forecasts shows that intensity forecasts of ASANI and track forecasts of MOCHA were reasonably good, but there were large errors and wide variation in track forecasts of ASANI and in intensity forecasts of MOCHA. Among all model forecasts, the track forecast errors of IMD model and MME were least in general for ASANI and MOCHA respectively. Also, the landfall point forecast errors of IMD were least for ASANI, and the MME and OFCL forecast errors were least for MOCHA. No model is found to be consistently better for landfall time forecast for ASANI, and the errors of ECMWF, IMD and HWRF were least and of same order for MOCHA. The intensity forecast errors of OFCL and SCIP were least for ASANI, and the forecast errors of HWRF, IMD, NCEP, SCIP and OFCL were comparable and least for MOCHA up to 48 h forecast and HWRF errors were least thereafter in general. The ECMWF model forecast errors for intensity were found to be highest for both the TCs. The results also show that although there is significant improvement of track forecasts and limited or no improvement of intensity forecast in previous decades but challenges still persists in real time forecasting of both track and intensity due to wide variation and inconsistency of model forecasts for different TC cases.

To investigate the multiscale interaction characteristics of Landfall Typhoon Lekima (2019), this study analyzed the characteristics of the different scale vortex structure and interactions among different scales based on vorticity equation diagnosis. The analysis is based on the simulation results of the WRF model which has been thoroughly verified. The main results are as follows: the original vorticity dominated by the meso-α scale vorticity increases with height and then decreases, with maximum vorticity distributed at 900 hPa. The meso-β scale vorticity varies significantly with altitude, while the meso-γ scale vorticity field exhibits obvious positive vorticity below 850 hPa. The meso-α scale vorticity tendency primarily maintains negative, contributing significantly to the overall reduction in the original vorticity field over time. The increase in mid-to-upper-level (above 550 hPa) original vorticity is mainly related to the variations in the meso-β and meso-γ scale vorticity fields. The original vorticity dominated by the meso-α scale vorticity increases with height and then decreases, and the whole layer vorticity decreases over time. The meso-β scale vorticity varies significantly with altitude and time, while the meso-γ scale vorticity field consistently exhibits significant positive vorticity below 850 hPa. The vorticity equation diagnosis revealed that the primary source terms of the vorticity tendencies are the twisting and stretching terms, and the main sink terms being horizontal and vertical vorticity transport terms below 900 hPa. The source terms and sink terms exchange above 850 hPa. Scale separation results show that the primary contributions of all impact factors originate from the meso-α and meso-γ scale fields (accounting for over 80% of the total), with the contribution of the meso-α scale being less than that of the meso-γ scale and a notable contribution over 35.5% of the interactions between different scales.

One of the most important parameters in meteorology is the mean wind profile in the tropical cyclone boundary layer. The vertical profile of wind speed and wind direction were measured during the period of the Nisarga cyclone from May 31st, 2020, to June 5th, 2020, using the newly installed Phased Array Doppler Sodar system at the Center for Space and Atmospheric Science (CSAS), Sanjay Ghodawat University, Kolhapur (16.74° N, 74.37° E; near India's western coast). Our analysis revealed that the maximum mean wind speed was 17 m/s on June 3, 2020, at 10:00 IST. It also shows the change in wind direction from southwest to southeast on June 2 and 3, 2020. Daily high-resolution reanalysis data in the domain, 0–25°N, 65–110°E, during the period from May 31st to June 5th, 2020, revealed the variation of the atmospheric pressure of the Nisarga cyclone from 1000 to 1008 hPa, sea surface temperature (SST) between 30 °C and 31 °C, outgoing longwave radiation (OLR) between 100 and 240 Wm^-2, wind speed between 3 and 15 m/s, and low values of vertical wind shear (VWS) were observed to the north of Nisarga track. These observations may provide more insights for the study of boundary layer turbulence during cyclonic activities.

In this study, we identified the moisture sources for the precipitation associated with tropical cyclones (TCs) during the rapid intensification (RI) process from 1980 to 2018 by applying a Lagrangian moisture source diagnostic method. We detected sixteen regions on a global scale for RI events distributed as follows: four in the North Atlantic (NATL), two in the Central and East Pacific Ocean (NEPAC), the North Indian Ocean (NIO) and South Indian Ocean (SIO), and three in the South Pacific Ocean (SPO) and the Western North Pacific Ocean (WNP). The moisture uptake (MU) mostly was from the regions where TCs underwent RI. The Western NATL, tropical NATL, Caribbean Sea, the Gulf of Mexico and the Central America and Mexico landmass supported ∼85.4% of the precipitating moisture in the NATL, while the latter source and the eastern North Pacific Ocean provided the higher amount of moisture in NEPAC (∼84.3%). The Arabian Sea, the Bay of Bengal and the Indian Peninsula were the major moisture sources in NIO, contributing approximately 81.3%. The eastern and western parts of the Indian Ocean supplied most of the atmospheric humidity in SIO (∼83.8%). The combined contributions (∼87.9%) from the western and central SPO and the Coral Sea were notably higher in SPO. Meanwhile, TCs in the WNP basin mostly received moisture from the western North Pacific Ocean, the Philippine Sea and the China Sea, accounting for 80.1%. The remaining moisture support in each basin came from the summed contributions of the remote sources. Overall, RI TCs gained more moisture up to 2500 km from the cyclone centre than those slow intensification (SI) and the total MU was approximately three times higher during RI than SI. Finally, the patterns of the MU differences respond to the typical pathways of moisture transport in each basin.

The report highlights the significant progress over various regions with respect to understanding of coastal hazards, numerical modeling techniques and the generation & dissemination of coastal hazard warnings and products. The developments over various regions in the globe during 2014–18 have been discussed in this report as presented during 10th Session of International Workshop on Tropical Cyclones (IWTC-X) at Bali, Indonesia. More specifically, various regions have started to confront the uncertainty that cannot be removed from TC analyses and forecasts and further communicate those hazards within the context of risk [probabilistic] based information. Progress also includes impact-based forecasts such as communicating coastal inundation information relative to total water level instead of storm surge, specifically (i.e., anomaly from astronomical tide and waves). Lastly, updates to model grid configuration, model resolution, and coupled dynamical systems continue to resolve the costal hazards more effectively. Those approaches have likely helped reduce loss of life relative to historical standards. However, regions agree that the generation and dissemination of coastal hazard information still need to be improved in view of growing population along the coast and thus increased exposure of life to coastal hazard.

The observation-reanalysis difference method (OMR) and wind profile fitting method were used to analyze the influence urbanization has on the near-surface wind speed in Shanghai during tropical cyclone events. The data used include daily wind speed data from the Shanghai Meteorological Observatory Station collected from 1991 to 2020, hourly wind speed data from 100 m high wind towers from 2017 to 2019, and reanalysis data that covered the same period. The results indicated that during tropical cyclone occurrence, the wind speed of the OMR in the central urban region was significantly lower than that in the suburban area, and the OMR declined more obviously over the year, down to −8 m/s in 2020. Urbanization leads to the increase of average wind weakening rate to be higher than the maximum wind weakening rate, causing the maximum weakening rate of the central urban region to the average wind over 80%, while maximum wind speed was less than 80%. The weakening rate of both the average and maximum wind speeds during tropical cyclone impacts is greater than the rate that the annual average wind speed was reduced. A logarithmic variation was visible in the wind profile of the island station during tropical cyclone occurrence, with an average friction velocity U∗ of 0.9389 m/s and an average rough length Z₀ of 0.4915 m. The wind speed during tropical cyclone events was higher than the three-year average wind speed within each layer. The suburban stations show a linear variation, and every hundred meters of height results in an increase of 5–6 m/s in the wind speed change rate. At 100 m of altitude, the wind speed in the suburban region is reduced by approximately 40%.

A comparative analysis of the rapid intensification (RI) of super cyclonic storms Chapala (2015) and Kyarr (2019) in the Arabian Sea is conducted using the North Indian Ocean tropical cyclone data, microwave sounding images, the NOAA OISST data and the ERA5 reanalysis data. Results show that the subtropical westerly jet stream and the Southern Hemisphere anticyclonic circulation led to the formation of an obvious double-channel outflow from the northern and southern sides of the two storm centers, and the substantial inflow appeared at the eastern boundary layer of both storms. These promoted the vertical ascent motion and release of the latent heat of condensation. A warm sea surface is a necessary but not dominant factor for the RI of cyclonic storms in the Arabian Sea. During the RI of Chapala and Kyarr, the deep vertical wind shear was less than 10 m s⁻¹; moreover, the mid-level humidity conditions favored the RI of the two cyclonic storms. Chapala had a single warm core, whereas Kyarr had double warm cores in the vertical direction. The impacts of the latent heat of fusion is more obvious for Chapala, and the potential vorticity in its inner core increases from 4.4 PVU to 8.8 PVU, whereas the potential vorticity and vorticity in the inner core of Kyarr do not change significantly. Microwave detection images show that both Chapala and Kyarr were accompanied by the formation of eyewalls during the RI phase, and the radius of maximum wind decreased and the maximum wind speed increased during the eyewall-thinning process. Both Chapala and Kyarr passed through a positive anomaly region of maximum potential intensity during the RI phase, which increases the possibility to develop to higher intensity after genesis.