Impact of bogus vortex for track and intensity prediction of tropical cyclone

The initialization scheme designed to improve the representation of a tropical cyclone in the initial condition is tested during Orissa super cyclone (1999) over Bay of Bengal using the fifth-generation Pennsylvania State University — National Center for Atmospheric Research (Penn State — NCAR) Mesoscale Model (MM5). A series of numerical experiments are conducted to generate initial vortices by assimilating the bogus wind information into MM5. Wind speed and location of the tropical cyclone obtained from best track data are used to define maximum wind speed, and centre of the storm respectively, in the initial vortex. The initialization scheme produced an initial vortex that was well adapted to the forecast model and was much more realistic in size and intensity than the storm structure obtained from the NCEP analysis. Using this scheme, the 24-h, 48-h, and 72-h forecast errors for this case was 63, 58, and 46 km, respectively, compared with 120, 335, and 550 km for the non-vortex initialized case starting from the NCEP global analysis. When bogus vortices are introduced into initial conditions, the significant improvements in the storm intensity predictions are also seen. The impact of the vortex size on the structure of the initial vortex is also evaluated. We found that when the radius of maximum wind (RMW) of the specified vortex is smaller than that of which can be resolved by the model, the specified vortex is not well adapted by the model. In contrast, when the vortex is sufficiently large for it to be resolved on horizontal grid, but not so large to be unrealistic, more accurate storm structure is obtained.     

中国沿海登陆热带气旋活动特征分析

热带气旋是危害中国最严重的天气系统,分析和认识中国沿海登陆热带气旋活动的新特征对防灾减灾具有重要意义。依据近70年气象资料,采用统计学方法,对登陆中国沿海的热带气旋特征进行分析,研究发现:在气候变化的背景下,登陆中国的热带气旋发生了明显变化。近年台风登陆频数高于往年平均,其整体强度和最大值均呈增大趋势,年台风强度的不稳定性加剧;研究还发现台风强度越高,其生成地纬度带范围越窄且越靠近赤道;建立了高强度热带气旋（STY和SUPER TY）时间和纬度的关系"φ—m"。检验了台风季长与初旋日呈负相关且不受厄尔尼诺现象影响,台风季长符合正态分布并给出概率密度公式。     

Economic value of tropical cyclone conditions of readiness

Natural Hazards - U. S. Military bases are especially vulnerable to tropical cyclones (TCs) because they concentrate extremely expensive equipment in a small area. A TC hit at a base can result in...     

The effects of natural structure on estimated tropical cyclone surge extremes

The past 12 years have seen significant steps forward in the science and practice of coastal flood analysis. This paper aims to recount and critically assess these advances, while helping identify next steps for the field. This paper then focuses on a key problem, connecting the probabilistic characterization of flood hazards to their physical mechanisms. Our investigation into the effects of natural structure on the probabilities of storm surges shows that several different types of spatial-, temporal-, and process-related organizations affect key assumptions made in many of the methods used to estimate these probabilities. Following a brief introduction to general historical methods, we analyze the two joint probability methods used in most tropical cyclone hazard and risk studies today: the surface response function and Bayesian quadrature. A major difference between these two methods is that the response function creates continuous surfaces, which can be interpolated or extrapolated on a fine scale if necessary, and the Bayesian quadrature optimizes a set of probability masses, which cannot be directly interpolated or extrapolated. Several examples are given here showing significant impacts related to natural structure that should not be neglected in hazard and risk assessment for tropical cyclones including: (1) differences between omnidirectional sampling and directional-dependent sampling of storms in near coastal areas; (2) the impact of surge probability discontinuities on the treatment of epistemic uncertainty; (3) the ability to reduce aleatory uncertainty when sampling over larger spatial domains; and (4) the need to quantify trade-offs between aleatory and epistemic uncertainties in long-term stochastic sampling.     

Assessment of economic losses from tropical cyclone disasters based on PCA-BP

Tropical cyclones are the most common natural disasters in coastal regions and are the most costly in terms of economic losses. Economic loss assessment is the basis for disaster prevention and alleviation and for insurance indemnification. We use data from 1970 to 2008 for Zhejiang Province, China, in this study evaluating economic losses. We convert direct economic losses from tropical cyclone disasters in Zhejiang Province into indices of direct economic losses. To establish our assessment model, we process disaster-inducing assessment factors, disaster-formative environments and disaster-affected bodies using the principal component analysis method, and we abstract the principal component as the input of a BP neural network model. We found in the actual assessments of five tropical cyclones affecting Zhejiang Province in 2007 and 2008 that the post-disaster loss assessment values of tropical cyclones were higher than the actual losses, but that for more severe storms, the gap was smaller. This reflects the beneficial effect of efforts toward disaster prevention and alleviation for severe tropical cyclones. Pre-assessments based on relatively accurate forecast values of wind and precipitation at the start of a tropical cyclone have been in accordance with the post-disaster assessment values, while the pre-assessment results using less accurate forecast values have been unsatisfactory. Therefore, this model can be applied in the actual assessment of direct economic loss from tropical cyclone damage, but increasingly accurate forecasting of wind and precipitation remains crucial to improving the accuracy of pre-assessments.     

Impact of various synthetic vortices on cyclone track prediction

Sensitivity experiments are conducted for three cases of cyclones for investigating the impact of different vortex initialization schemes on the structure and track prediction of the cyclone using India Meteorological Department’s Limited Area Model. The surface wind and pressure profiles generated using Holland and Rankine initialization schemes differ from each other. These different generated profiles are compared with the actual data and the root mean square error (RMSE) was calculated between them. In case of the Holland vortex, ‘b’ is found to be equal to 1.5 and 2.0 respectively for two cases of very severe cyclonic storms in the Arabian Sea, namely 6–10 June 1998 and 16–20 May 1999 and 2.25 for the severe cyclonic storm in the Bay of Bengal. The ‘α’ parameter in Rankine’s scheme was found to be 0.5 for two cases and 0.4 for the third system. This shows that cyclones differ even if they attain the same intensity. The values of these parameters i.e. ‘b’ and ‘α’ are used for generating the synthetic wind data for individual cyclones and the same is used in the data assimilation system. The analysis and forecast generated for the above cases using the Holland scheme show that the simulated structure has characteristics closer to the actual storm; however, the Rankine scheme shows a weaker circulation. The mean track error for three cases in the Holland scheme is 93, 149, 257 and 307 km in 12-, 24-, 36- and 48-h forecast. The mean track errors for the Rankine scheme are 152, 274, 345 and 327 km, respectively, for the same period.     

Identification of clusters in tropical cyclone tracks of North Indian Ocean

Tropical cyclones are a key climate-related hazard in South Asia. Assessment of the risk of cyclone impacts requires a comprehensive characterization of historical cyclone climatology. This study analyzes the tracks of tropical cyclones in the North Indian Ocean. Based on their spatial characteristics, cyclone tracks appear to be grouped into five well-defined clusters. These clusters correspond to distinct regions of cyclonic activity and exhibit differences in characteristics such as genesis location, probability of landfall, duration, and maximum intensity. Some of the identified clusters appear particularly important with regard to impacts because events in these clusters have greater landfall probability and are more intense. The clustering approach is likely to provide useful insights for the characterization of cyclone risk.     

Multi-Physics ensemble prediction of tropical cyclone movement over Bay of Bengal

Ensemble prediction methodology based on variations in physical process parameterizations in tropical cyclone track prediction has been assessed. Advanced Research Weather Research and Forecasting model with 30-km resolution was used to make 5-day simulation of the movement of Orissa super cyclone (1999), one of the most intense tropical cyclones over the North Indian Ocean. Altogether 36 ensemble members with all possible combinations of three cumulus convection, two planetary boundary layer and six cloud microphysics parameterization schemes were produced. A comparison of individual members indicated that Kain–Fritsch cumulus convection scheme, Mellor–Yamada–Janjic planetary boundary layer scheme and Purdue Lin cloud microphysics scheme showed better performance. The best possible ensemble formulation is identified based on SPREAD and root mean square error (RMSE). While the individual members had track errors ranging from 96–240 km at 24 h to 50–803 km at 120 h, most of the ensemble predictions show significant betterment with mean errors less than 130 km up to 120 h. The convection ensembles had large spread of the cluster, and boundary layer ensembles had significant error disparity, indicating their important roles in the movement of tropical cyclones. Six-member ensemble predictions with cloud microphysics schemes of LIN, WSM5, and WSM6 produce the best predictions with least of RMSE, and large SPREAD indicates the need for inclusion of all possible hydrometeors in the simulation and that six-member ensemble is sufficient to produce the best ensemble prediction of tropical cyclone tracks over Bay of Bengal.     

Performance evaluation of HWRF model during post monsoon tropical cyclone Nilofar

An accurate tropical cyclone track and intensity forecast is very important for disaster management. Specialized numerical prediction models have been recently used to provide high-resolution temporal and special forecasts. Hurricane Weather Research and Forecast (HWRF) model is one of the emerging numerical models for tropical cyclone forecasting. This study evaluates the performance of HWRF model during the post monsoon tropical cyclone Nilofar on the north Indian Ocean basin. The evaluation uses the best track data provided by the Indian Meteorological Department (IMD) and the Joint Typhoon Warning Centre (JTWC). Cyclone track, central pressure, and wind speed are covered on this evaluation. Generally, HWRF was able to predict the Nilofar track with track error less than 230 km within the first 66 h of forecast time span. HWRF predicted more intense tropical cyclone. It predicted the lowest central pressure to be 922 hPa while it reached 950 hPa according to IMD and 937 hPa according to JTWC. Wind forecast was better as it predicted maximum wind speed of 122 kt while it reached 110 and 115 kt according to IMD and JTWC, respectively.     

Sensitivity of tropical cyclone intensification to boundary layer and convective processes

This study examines the role of the parameterization of convection, planetary boundary layer (PBL) and explicit moisture processes on tropical cyclone intensification. A high-resolution mesoscale model, National Center for Atmospheric Research (NCAR) model MM5, with two interactive nested domains at resolutions 90 km and 30 km was used to simulate the Orissa Super cyclone, the most intense Indian cyclone of the past century. The initial fields and time-varying boundary variables and sea surface temperatures were taken from the National Centers for Environmental Prediction (NCEP) (FNL) one-degree data set. Three categories of sensitivity experiments were conducted to examine the various schemes of PBL, convection and explicit moisture processes. The results show that the PBL processes play crucial roles in determining the intensity of the cyclone and that the scheme of Mellor-Yamada (MY) produces the strongest cyclone. The combination of the parameterization schemes of MY for planetary boundary layer, Kain-Fritsch2 for convection and Mixed-Phase for explicit moisture produced the best simulation in terms of intensity and track. The simulated cyclone produced a minimum sea level pressure of 930 hPa and a maximum wind of 65 m s⁻¹ as well as all of the characteristics of a mature tropical cyclone with an eye and eye-wall along with a warm core structure. The model-simulated precipitation intensity and distribution were in good agreement with the observations. The ensemble mean of all 12 experiments produced reasonable intensity and the best track.     

Genesis and rapid weakening of tropical cyclone Lehar (2013)

Natural Hazards - Tropical cyclogenesis and rapid weakening are subjects of considerable interest in the literature. This paper addresses the genesis and rapid weakening of a North Indian Ocean...     

Impacts of ENSO and IOD on tropical cyclone activity in the Bay of Bengal

Natural Hazards - The impacts of El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on tropical cyclone (TC) activity (intensity, frequency, genesis location, track and average...     

Sensitivity of high-resolution tropical cyclone intensity forecasts to surface flux parameterization

Surface flux parameterization schemes used in current dynamic models are primarily based upon measurements at low and moderate wind speeds. Recent studies show that these parameterization schemes may be incorrect at high wind speeds (e.g., tropical cyclone forecasts). Five high-resolution numerical model experiments are designed to assess the sensitivity of tropical cyclone intensity forecasts to changes in the surface flux parameterization. The sensitivity experiments are conducted by running 48 h forecasts of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) for six selected tropical cyclones with individual modifications to surface flux calculation that include: (1) limiting the surface stress for wind speeds greater than 33 m s⁻¹, or 64 knots (kt); (2) computing the stress at the top of the model bottom grid layer (MBGL) by averaging results from surface layer similarity and turbulence mixing parameterization for wind speeds greater than 33 m s⁻¹; (3) increasing the roughness lengths for heat and moisture transfer by a factor of ten; (4) setting the roughness lengths for heat and moisture transfer to 1/10 of the momentum roughness length; and (5) cooling the sea surface temperature (SST) by a prescribed rate at high winds. Averaged responses for the six storms to these sensitivity tests show that: (i) the limit on surface stress at high winds significantly increases the cyclone intensity in 48 h forecasts; (ii) the averaged surface layer stress at high winds increases the cyclone intensity but to a much lesser degree than limiting the surface stress; (iii) large increases in the roughness lengths for heat and moisture transfer are needed to significantly impact the intensity forecast; (iv) the different roughness length formula for surface transfer coefficients notably increases C _h/C _d ratio from 0.59 to 0.79 for 25 m s⁻¹ and 0.41 to 0.75 for 50 m s⁻¹ that significantly increases the predicted cyclone intensity; and (v) cooling of the SST by −5.8°C in 48 h reduces the maximum surface wind speed by −32 kt, or 16.5 m s⁻¹, at 48 h forecast. These results suggest that a surface flux parameterization scheme suitable for tropical cyclone intensity forecast must correctly model the leveling-off character of surface stress and C _h/C _d ratio at high winds. All modifications to surface flux calculation have little influence on 48 h track forecasts, even though they may significantly impact the intensity forecasts.

Cluster analysis of tropical cyclone tracks around Korea and its climatological properties

After the fuzzy clustering method (FCM) that analyzes the tracks of the tropical cyclones (TCs) struck the Korean peninsula (hereafter, K-TC) for a 60-year period (1951?C2010), it is found that both frequency and intensity of K-TC have been increased in recent years. In the order of the cluster number, both K-TC track pattern and its full-track pattern tended to shift southward. That is, while the passage frequency of TC in mainland China and the Manchurian regions decreased, it instead over the sea. Due to this decrease in the topographic effect on TC before reaching Korea, TC intensity around Korea became stronger. The vertical wind shear well reflected a TC intensity around Korea, which became weaker in mid-latitudes of East Asia. On the other hand, the peak month of K-TC frequency lags in the order of the cluster number. The two clusters that most TCs pass through the Korean Peninsula showed a stronger intensity and higher frequency before the 1970s. Meanwhile, another two clusters that most TCs pass through the Straits of Korea or the western region of the Japanese Islands showed those characteristics from the 1980s onward. Consequently, the changes in TC track, recurvature, frequency, and intensity around Korea were related to the southward shift of the western North Pacific high in the order of the cluster number.     

Assessing the influence of assimilating radar-observed radial winds on the simulation of a tropical cyclone

Observations by Doppler weather radar are crucial for nowcasting and short-time forecasting of severe weather events as they bring in refined information of the atmosphere. However, due to the inevitable noises and non-meteorological signals, they cannot be assimilated straightforwardly into a numerical model. In the present study, assimilation of the radial component of wind velocity observed by two Doppler radars is performed in the numerical simulation of Supertyphoon Rammasun (2014) just before its landfall. After several quality-control steps, the radar-observed radial velocities are de-aliased, noise-reduced and assimilated into the model to improve initial conditions for the high-resolution simulation. Results show that only when using global background error covariance matrix can the observational increment be properly assimilated into the model, correcting large-scale background steering flow and yielding a simulated track close to the observed one. However, little improvement is found in simulating the TC core-scale structures by the assimilation of radar velocity as compared to the radar-observed flow, primarily due to the insufficient spatial resolution of the model that may lead to the incorrect representation of the TC core structure and the rejection of some core-region observations during the data assimilation procedure. Moreover, assimilation-induced asymmetries consume a certain portion of mean kinetic energy, preventing the simulated Rammasun from axisymmetrization and thus intensification as compared with the non-assimilated experiment.