Tropical Cyclone Cases Research Articles

Machine learning in calibrating tropical cyclone intensity forecast of ECMWF EPS

AbstractIntensity prediction of tropical cyclones (TC) has been one of the major challenges for the operational forecast and warning service, as well as consequential assessment of impacts including high winds, storm surge and heavy rainfall caused by TC. With the advances in global numerical weather prediction (NWP) modelling systems, TC track and intensity forecasts for medium range are available every 6 or 12 h, and ensemble prediction system (EPS) outputs provide various scenarios for producing probabilistic forecasts. The TC intensity forecast from the EPS of the European Centre for Medium‐Range Weather Forecasts (ECMWF) has shown systematic negative biases, although the performance is better than other global models in general. A machine learning model based on XGBoost, a decision‐tree‐based machine learning algorithm, is introduced in this paper to post‐process ECMWF EPS outputs and generate an improved forecast of TC intensity. The predictors such as selected percentiles of ensemble members in maximum wind and minimum pressure of previous TC cases were applied in the XGBoost model to generate a calibrated forecast for the maximum wind of TCs. Verification of the XGBoost model was made using TCs over the western North Pacific during 2016–2019. It is found that the negative biases of the intensity forecast from ECMWF EPS and HRES can be reduced with improvement in the overall accuracy.

Understanding the rapid intensification of tropical cyclone Titli using Hurricane WRF model simulations

Understanding Rapid Intensification (RI) is crucial for improving the Tropical Cyclone (TC) intensity forecast skill and TC induced disaster preparedness. The physical processes that lead to RI are not well studied over the North Indian Ocean (NIO) region. Two TC cases, i.e., Titli (exhibited RI) and Phethai (not exhibited RI) from the 2018 post-monsoon season are considered to understand the environmental conditions responsible for making RI happen. The Hurricane Weather Research and Forecasting (HWRF) model produced simulations for Titli and Phethai are analyzed to understand the intensification changes. The purpose of this study is to investigate the environmental and storm structure characteristics that led to the RI of TC Titli when compared to TC Phethai. The Phethai and Titli simulations are initially validated against the best estimations by India Meteorological Department (IMD) and compared in terms of the environmental vertical wind shear as this parameter is often negatively correlated with TC intensification. Area averages of the deep-layer and mid-layer vertical wind shear over the TC environment suggest that shear did not affect the intensification of the Titli simulation. Initially, Titli contained higher magnitudes of relative humidity throughout the troposphere within the storm’s circulation. This steered to a higher upward mass flux in the troposphere and rapid intensification of relative vorticity (circulation) in the middle troposphere prior to any significant change ensued at the surface. After a deep vortex was established, the mass flux (and lower-tropospheric convergence) continued to rise, leading to RI. These results offer a basis for future research to understand better and forecast the development of RI.

High Resolution Real-data WRF Modeling and Verification of Tropical Cyclone Tornadoes Associated with Hurricane Ivan 2004

This study used high-resolution real-data WRF simulations of Hurricane Ivan (2004) to document the structure of potentially tornadic supercells embedded in tropical cyclone (TC) rainbands. The simulated TC track and intensity matched well with observations post landfall, and the simulated TC structure closely replicated the observed shape and rainbands, as evident by an assessment of observed composite and simulated radar reflectivity. TC tornado surrogates (TCT-Ss) were identified and calibrated using thresholds based on percentile values of maximum updraft helicity (UHmax) and simulated radar reflectivity. Although the magnitude of UHmax generally decreased as the simulated TC moved inland, sensitivity testing revealed that a threshold based on the 99.95% percentile value of UHmax achieved optimal TCT-S coverage and agreement with observed TC tornado (TCT) tracks on domains with 3-km and 1-km grid spacings. Three cells were identified at three different stages of the TC inland evolution and were found to have hook appendages resembling supercells found in midlatitudes. The cells produced storm tops of 13 km, with rotating cores with depths of 4 km above ground level. Although results in this study are unique to Hurricane Ivan, the authors believe the use of convection-allowing models and UH-based surrogate methodologies can be applied to other TC cases.

An initialisation scheme for tropical cyclones in the South China Sea

AbstractWe propose an initialisation scheme for weak tropical cyclones (TCs) in the South China Sea (SCS) by combining composite and analysis vortices to improve forecasts of weak TCs in the region. The composite vortex is obtained from good samples of analyses of TCs in the SCS, and the analysis vortex is obtained from either European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis data or an operational assimilation system. The new scheme has been applied to 57 cases of weak TCs, and the effect on the forecasting of four tropical storms has been studied in detail. It can be concluded that the proposed scheme can reduce track errors and intensity errors of weak TCs, compared with the control experiment. The possible reasons for these results are investigated in a case study for Linfa. Results of batch experiments show that the new method significantly improves TC track forecasting of tropical storms, severe tropical storms and typhoons, and demonstrates significant advantages in forecasting intensity of tropical storms, compared with the control experiment. It also performs better in predicting intensity of TCs in the SCS compared with ECMWF Atmospheric Model high resolution.

Characteristics of the Long‐Lived Concentric Eyewalls in Tropical Cyclones

AbstractConcentric eyewalls (CEs) in tropical cyclones (TCs) in different basins were identified based on satellite imagery during 1997–2014. Their duration and structural parameters, including inner eyewall size, moat width, and outer eyewall width, were calculated. Differences in these parameters can best be distinguished by short‐lived and long‐lived CEs (i.e., CEs with durations shorter or longer than 20 h). A long‐lived CE tends to have a larger size, mainly contributed by a larger moat and a larger outer eyewall width. The inner eyewall size shows no significant difference between short‐lived and long‐lived CEs in the western North Pacific (WNP) but increases slightly and steadily with increasing CE durations in the Atlantic (ATL). Furthermore, the WNP has far more CEs than in ATL and in the eastern Pacific (EPAC) for all duration categories. Long‐lived CEs cover about 20% of all CEs and are associated with higher sea surface temperature and weaker vertical wind shear. In the WNP, the TC tracks associated with long‐lived CEs were with the less northward motion component. Furthermore, seven TCs with triple eyewalls in the WNP are identified during 1997–2014, with only one case in the EPAC and no ATL case. It is found that five cases of these triple eyewalls TC in the WNP evolved to long‐lived CEs. The results suggest that the CE internal dynamical process, the TC track during the CE period, and the environmental conditions are all critical to the CE size and duration.

Five-day track forecast skills of WRF model for the western North Pacific tropical cyclones

AbstractIn this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF model) are verified. We utilize 12 km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. One hundred and twenty-five forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moves northward in subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.

Major Floods and Tropical Cyclones over Bangladesh: Clustering from ENSO Timescales

The present study analyzed major floods and tropical cyclones (TCs) over Bangladesh on El Niño-Southern Oscillation (ENSO) timescales. The geographical location, low and almost flat topography have introduced Bangladesh as one of the most vulnerable countries of the world. Bangladesh is highly vulnerable to the extreme hazard events like floods and cyclones which are impacted by ENSO. ENSO is mainly a tropical event, but its impact is global. El Niño (La Niña) represents the warm (cold) phase of the ENSO cycle. Rainfall and cyclonic disturbances data have been used for the period of 70 years (1948–2017) and compared with the corresponding observations of the Southern Oscillation Index. Result shows that major flood events occurred during the monsoon period, and most of them are during the La Niña condition, consistent with the historical archives of flood events in Bangladesh. Synoptic conditions of these events are well matched during La Niña condition. On the other hand, the major TC cases are in the period of either pre-monsoon or post-monsoon season. The pre-monsoon cases are under neutral (developing La Niña) or El Niño and the post-monsoon cases are under La Niña, consistent with climatology studies that La Niña is favorable to have more intense TCs over the Bay of Bengal.

Which Features of the SST Forcing Error Most Likely Disturb the Simulated Intensity of Tropical Cyclones?

Among all of the sources of tropical cyclone (TC) intensity forecast errors, the uncertainty of sea surface temperature (SST) has been shown to play a significant role. In the present study, we determine the SST forcing error that causes the largest simulation error of TC intensity during the entire simulation period by using the WRF model with time-dependent SST forcing. The SST forcing error is represented through the application of a nonlinear forcing singular vector (NFSV) structure. For the selected 12 TC cases, the NFSV-type SST forcing errors have a nearly coherent structure with positive (or negative) SST anomalies located along the track of TCs but are especially concentrated in a particular region. This particular region tends to occur during the specific period of the TCs life cycle when the TCs present relatively strong intensity, but are still intensifying just prior to the mature phase, especially within a TC state exhibiting a strong secondary circulation and very high inertial stability. The SST forcing errors located along the TC track during this time period are verified to have the strongest disturbing effect on TC intensity simulation. Physically, the strong inertial stability of TCs during this time period induces a strong response of the secondary circulation from diabatic heating errors induced by the SST forcing error. Consequently, this significantly influences the subsidence within the warm core in the eye region, which, in turn, leads to significant errors in TC intensity. This physical mechanism explains the formation of NSFV-type SST forcing errors. According to the sensitivity of the NFSV-type SST forcing errors, if one increases the density of SST observations along the TC track and assimilates them to the SST forcing field, the skill of TC intensity simulation generated by the WRF model could be greatly improved. However, this adjustment is most advantageous in improving simulation skill during the time period when TCs become strong but are still intensifying just prior to reaching full maturity. In light of this, the region along the TC track but in the time period of TC movement when the NFSV-type SST forcing errors occur may represent the sensitive area for targeting observation for SST forcing field associated with TC intensity simulation.

Tropical Cyclone Intensity Estimation From Geostationary Satellite Imagery Using Deep Convolutional Neural Networks

In this study, a set of deep convolutional neural networks (CNNs) was designed for estimating the intensity of tropical cyclones (TCs) over the Northwest Pacific Ocean from the brightness temperature data observed by the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. We used 97 TC cases from 2015 to 2018 to train the CNN models. Several models with different inputs and parameters are designed. A comparative study showed that the selection of different infrared (IR) channels has a significant impact on the performance of the TC intensity estimate from the CNN models. Compared with the ground truth Best Track data of the maximum sustained wind speed, with a combination of four channels of data as input, the best multicategory CNN classification model has generated a fairly good accuracy (84.8%) and low root mean square error (RMSE, 5.24 m/s) and mean bias (−2.15 m/s) in TC intensity estimation. Adding attention layers after the input layer in the CNN helps to improve the model accuracy. The model is quite stable even with the influence of image noise. To reduce the side-effect of the very unbalanced distribution of TC category samples, we introduced a focal_loss function into the CNN model. After we transformed the multiclassification problem into a binary classification problem, the accuracy increased to 88.9%, and the RMSE and the mean bias are significantly reduced to 4.62 and −0.76 m/s, respectively. The results show that our CNN models are robust in estimating TC intensity from geostationary satellite images.

Improved Prediction of Landfalling Tropical Cyclone in China Based on Assimilation of Radar Radial Winds with New Super-Observation Processing

AbstractThis work explores the impact of assimilating radial winds from the Chinese coastal Doppler radar on track, intensity, and quantitative precipitation forecasts (QPF) of landfalling tropical cyclones (TCs) in a numerical weather prediction model, focusing mainly on two aspects: 1) developing a new coastal radar super-observation (SO) processing method, namely, an evenly spaced thinning method (ESTM) that is fit for landfalling TCs, and 2) evaluating the performance of the radar radial wind data assimilation in QPFs of landfalling TCs with multiple TC cases. Compared to a previous method of generating SOs (i.e., the radially spaced thinning method), in which the density of SOs is equal within the radial space of a radar scanning volume, the SOs created by ESTM are almost evenly distributed in the horizontal grids of the model background, resulting in more observations located in the TC inner-core region being involved in SOs. The use of SOs from ESTM leads to more cyclonic wind innovation, and larger analysis increments of height and horizontal wind in the lower level in an ensemble Kalman filter data assimilation experiment with TC Mujigae (2015). Overall, forecasts of a TC’s landfalling position, intensity, and QPF are improved by radar data assimilation for all cases, including Mujigae and the other eight TCs that made landfall on the Chinese mainland in 2017. Specifically, through assimilation, TC landing position error and intensity error are reduced by 33% and 25%, respectively. The mean equitable threat score of extreme rainfall [>80 mm (3 h)−1] forecasts is doubled on average over all cases.

A case study of Chlorophyll a response to tropical cyclone Wind Pump considering Kuroshio invasion and air-sea heat exchange

New evidences provided that the tropical cyclone (TC) Linfa in 2015 induced looping path of Kuroshio invasion into the northeastern South China Sea (NESCS) through the northwestern Luzon Strait (LS), based on the in-situ measurements, satellite data and model output data. This TC-enhanced Kuroshio invasion with low nutrients and denser waters suppressed the TC “Wind Pump” induced upwelling and nutrients uptake, and therefore inhibited the Chlorophyll a concentration (Chl-a) increase from surface to ~50 m in the open ocean of the NESCS. The TC-induced Kuroshio invasion promoted the generation of the strong cyclonic eddy to its left side where weak Ekman Pumping Velocity was observed. This enhancing cyclonic eddy then dominated the nutrients uplift and increased the surface and subsurface (0–50 m) Chl-a through eddy pumping rather than Ekman Pumping. The TC-declined anti-cyclonic eddy, which shoaled the Mixed Layer Depth (MLD), benefited to the nutrient uptake through TC-induced upwelling and thereby increased the surface Chl-a and raised the Chl-a Maximum Layer (CML) to ~20 m over the southwestern LS. The temporal Chl-a variations were also influenced by TC intensities and biochemical processes. The air-sea heat budget analysis indicated that, the air-sea heat exchange contributed to nearly 80% of the sea surface cooling (SST cooling) over the northwestern LS with Kuroshio invasion, while eddy-induced upwelling dominated the SST cooling over the western LS, and the wind-driven upwelling (and mixing) controlled the SST cooling over the southwestern LS. These different formations of SST cooling then played important role in Chl-a variations. This study is the first case of TC “Wind Pump” induced Chl-a variations considering air-sea heat exchange, Kuroshio invasion and mesoscale eddies over LS, which would help to evaluate the influence of TCs over the other major heat transport arteries of the world ocean: The Gulf Stream area.

Dynamical downscaling of a multimodel ensemble prediction system: Application to tropical cyclones

AbstractThis study attempts dynamical downscaling to improve north Indian ocean (NIO) tropical cyclone prediction from a global multimodel ensemble prediction system using weather research and forecasting (WRF) model. A total of 16 ensembles are used in the WRF simulations, these ensembles are bias‐corrected prior to downscaling for model climatological errors. The ensemble mean constructed from the output of all downscaled ensembles is analyzed for added value to global predictions. This mean is also compared against observation as well as high‐resolution (12 km) deterministic forecast from global forecast system (GFS). Two devastating NIO tropical cyclone cases of year 2017 which were not reliably predicted by global systems have been selected for this study. The results show that downscaled predictions well simulate the intensity and spatial distribution of the rainfall and relative vorticity associated with these cyclonic storms. The wind and temperature vertical profiles during cyclone mature stage are also captured more accurately than raw prediction and high‐resolution global deterministic forecast. The study affirms the adequacy of dynamical downscaling in predicting the cyclonic storms over global real‐time weather forecasting system.

Assimilation of radar radial velocity data with the WRF hybrid 4DEnVar system for the prediction of hurricane Ike (2008)

Four dimensional ensemble-variation data assimilation (4DEnVar) is the method that considers the flow dependent background error covariance (BEC) and asynchronous observations throughout the assimilation window, which avoids the maintenance of the adjoint model. The impacts of assimilation of radial velocity (Vr) data using hybrid-4DEnVar for the analyses and forecasts of hurricane Ike are investigated using Weather Research and Forecasting and Data Assimilation model (WRFDA). 4DEnVar is coupled with Ensemble Transform Kalman Filter (ETKF) by updating the ensemble mean by the hybrid scheme and the ensemble perturbations are updated by the ETKF. Single observation tests for typical Jet cast and tropical cyclone (TC) case are conducted before the real hurricane Ike (2008) case. It is found that the analysis increment moves downstream by the end of the assimilation window. The linear propagation represented by the 4DEnVar method is close to the full nonlinear model integration. For the real IKE case, it is found that positive and spiral temperature increments, best track and intensity forecast are found in 4DEnVar experiment, indicating a more realistic thermal structure of hurricane Ike. 3DEnVar and 3DVar-FGAT are limited due to the lack of the BEC description spatially and temporally. 3DVar experiment produces much smoother and weaker increments with cold temperature increments at the hurricane vortex center at lower levels.

Subseasonal to Seasonal Prediction of Weather to Climate with Application to Tropical Cyclones

AbstractDemands are growing rapidly in the operational prediction and applications communities for forecasts that fill the gap between daily weather forecasts and seasonal climate outlooks. Recent scientific advances have identified sources of predictability on this time range, and modeling advances are leading to better forecasts. However, much remains to be done to further improve their skill and to develop new climate service forecast products to help countries and sectorial decision makers better manage weather risks and extremes and to adapt to climate change. This paper reviews the history and describes the main challenges and opportunities for the modeling and forecast‐applications communities to improve subseasonal to seasonal (S2S) forecasts and products, along with current developments catalyzed by the World Weather Research Programme and World Climate Research Programme's joint Sub‐Seasonal to Seasonal Prediction Project. The case of tropical cyclones is highlighted as an illustrative example of the points discussed.

Some observations of low level wind shear at the Hong Kong International Airport in association with tropical cyclones

AbstractIn summer, Hong Kong is affected by tropical cyclones moving northwestwards over the northern part of the South China Sea. They may bring strong southeasterly airflow to the territory. The wind is disturbed by complex terrain to the south of the Hong Kong International Airport, resulting in turbulent airflow in the airport area and leading to the occurrence of low level wind shear and turbulence. This study documents the observations of wind shear at the airport in some recent cases of tropical cyclones in Hong Kong. The alerts from three operational wind shear algorithms are described, based on anemometers, the light detection and ranging system and microwave radar. The situations at the maximum wind shear magnitudes are presented. This is followed by a statistical analysis of the occurrences of wind shear against the wind shear magnitude and sign. A logarithmic relation between the two is found, and it is applicable for all tropical cyclone cases under study and different wind shear algorithms. However, there is no uniform equation for the logarithmic distribution, apart from the case of laser radar wind shear alerts for headwind loss. Finally, the accuracy of wind shear alerts of one tropical cyclone case is presented by comparison with pilot wind shear reports. It is hoped that the documentation in this study could serve as a useful reference to aviation weather forecasters at other airports with similar wind shear situations, as well as contributing to an improved understanding of the statistical behaviour of the wind shear phenomenon.

Sensitivity to Tendency Perturbations of Tropical Cyclone Short-range Intensity Forecasts Generated by WRF

The present study uses the nonlinear singular vector (NFSV) approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting (WRF) model for tropical cyclone (TC) intensity forecasts. For nine selected TC cases, the NFSV-tendency perturbations of the WRF model, including components of potential temperature and/or moisture, are calculated when TC intensities are forecasted with a 24-hour lead time, and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts. The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time, and their dominant energies concentrate in the middle layers of the atmosphere. Moreover, such structures do not depend on TC intensities and subsequent development of the TC. The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs, makes larger contributions to the TC intensity forecast uncertainty. Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model, even if using a WRF with coarse resolution.

Monthly variation and spatial distribution of quadrant tropical cyclone size in the Western North Pacific

AbstractWith the tropical cyclone (TC) size parameter defined as the radius of 17‐m·s−1 oceanic surface wind, 225 TC cases were recorded in the western North Pacific during 2000–2009 based on the QuikSCAT near‐surface wind vector database and the best‐track dataset. In accordance with the symmetry index (the ratio of minimum and maximum quadrant sizes), the TCs were classified into symmetric and asymmetric structures. The asymmetric TCs were divided into four types: the northeast, southeast, southwest, and northwest. The spatio‐temporal characteristics of these TC types were further investigated. The monthly variation and the spatial distribution of maximum quadrant TC size exhibited significant differences among the four types. By contrast, the quadrant size of the symmetric TCs from June to November showed few changes (2.6°–2.7° latitude). It was found that the TC lifetime is an important factor affecting the quadrant TC size because of its close relationship with the activities of the western Pacific subtropical high. In addition, the climatological mean circulation also has a notable influence on the quadrant TC size through superposition of prominent background wind. Symmetric TCs are more likely to occur in the oceanic region where the background low‐level wind is the weakest.

Effects of the outer size on tropical cyclone track forecasts

AbstractThe track forecast of a tropical cyclone (TC) located near the neutral point of a split subtropical high pressure system under the influence of a nearby trough is difficult and can lead to a large forecast spread among different global numerical weather prediction models. Three TCs with large forecast spread under such an environmental flow pattern have been identified: Hagupit (2008), Lupit (2009) and Nida (2009). Simulations using a bogus vortex of different radii of 15 m·s−1 (R15) with the same environmental flow for each TC case are conducted to study the effect of TC size on the forecast track of the TC. With a nearby strong monsoon trough, a TC with a smaller R15 merges with the cyclonic circulation within the trough leading to a larger outer circulation. After removing the symmetric component of the TC from the total wind field, an extensive cyclonic asymmetric gyre is found and its circulation steers the TC to move more westward. The TC thus shifts northward within the neutral point. With a weak monsoon trough further away from the TC, a TC with larger R15 with a larger outer circulation interacts with the monsoon trough, causing the TC to move more westward. With the presence of a westerly trough, a TC with larger R15 and outer circulation interacts with the trough such that the TC moves more northeastward. The importance of a correct representation of TC size in a numerical weather prediction model in predicting the TC track is discussed.

Evaluation of parametric precipitation models in reproducing tropical cyclone rainfall patterns

Current flood mapping practices in the United States model both storm surge and riverine flooding in coastal regions. In regions where the hazards overlap, independence and non-concurrence are generally assumed, although recent literature has noted several cases of tropical cyclones (TCs) where storm surge and rainfall-runoff interacted nonlinearly, violating this assumption. In order to evaluate the effects of the nonlinear interactions on flood risk statistics using traditional methods, i.e. by using simulation suites of physically-based flood hazard models with varied TC characteristics, it is necessary to couple the rainfall-runoff and storm surge components into a single simulation framework. In order to accomplish this efficiently, use of a parametric TC rainfall model is proposed. Four such models were obtained by the authors: R-CLIPER (Rainfall Climatology and Persistence), IPET (Interagency Performance Evaluation Task Force Rainfall Analysis), PHRaM (Parametric Hurricane Rainfall Model), and P-CLIPER (PDF Precipitation-Climatology and Persistence). The objective of the current study is to compare precipitation fields produced by four existing parametric TC rainfall models and then to select the most appropriate model for inclusion in future combined flooding studies. Meteorological skill metrics were used to evaluate rainfall models for 67 Atlantic TCs affecting the United States from 2004 through 2017. Of the four models evaluated, the highly simplified IPET rainfall model demonstrated the most skill at reproducing storm-total precipitation for thresholds above 75 mm (3 in.); however, the skill metrics obtained indicate that none of the models is suitable for direct use in combined flooding studies at this time.

Improved Calculation of Turbulence Parameters Based on Six Tropical Cyclone Cases: Implication to Wind Turbine Design in Typhoon-Prone Areas

In view of the absence or insufficiency of tropical cyclone (TC) turbulence parameters in current design standards of wind turbines, in this paper, TC turbulence parameter models with roughness length involved are developed based on six landfall TCs observed from meteorological towers located on various underlying surfaces, so as to provide references for the wind turbine design under TC conditions. Firstly, the roughness length values are examined in order to reduce the effect on turbulence parameters of the various underlying surfaces. On this basis, the reference turbulence intensity is normalized by the roughness length. The related turbulence parameters are parameterized, including the turbulence standard deviation and the turbulence spectrum; and the turbulence parameters available under TC conditions for turbulence turbine design are presented finally. Comparisons of the wind parameter models presented in this paper with those used in current turbine design standards suggest that the former can represent TC characteristics more accurately. In order to withstand TCs, we suggest that the turbulence parameter models recommended in this paper be included in future wind turbine design standards under TC conditions.