Strong Tropical Cyclones Research Articles

Effects of single- and double-moment microphysics schemes on the intensity of super typhoon Sarika (2016)

Abstract The Weather Research and Forecasting (WRF) Double Moment (DM) 6-class (WDM6)/WRF DM 5-class (WDM5) and WRF Single Moment (SM) 6-class (WSM6)/ WRF SM 5-class (WSM5) microphysics schemes were selected to conduct four simulation experiments for super tropical cyclone (TC) Sarika (2016) using the WRF model. Both the SM and DM schemes had little effect on TC track, but there were significantly difference in TC intensity simulations. TC simulated by the SM schemes are stronger than that by the DM ones and are also closer to the observed. The SM and DM schemes affect TC intensity through its dynamic and thermal structures. TC simulated by the SM schemes has a smaller eye, a smaller radius of maximum wind (RMW), stronger tangential wind, closer latent heating area to TC center, higher positive temperature deviation in about TC center, and thus stronger TC. The latent heating rate of condensation in the SM schemes is higher than those of DM ones. The higher latent heating areas of condensation in the SM schemes are in the inner side of the eyewall and are significantly closer to the TC center and strengthen TC. On the contrary, the higher latent heating areas of condensation in the DM schemes exist within in the outer side of the eyewall and TC weakened. For the DM schemes, since equations predicting cloud water and rain water number concentrations are added, the production rates for autoconversion and accretion are also changed. The DM schemes have distinctly different cloud water and rain water distribution characteristics when compared with the SM ones.

Autumn Tropical Cyclones over the Western North Pacific during 1949-2016: A Statistical Study

We used tropical cyclone (TC) best track data for 1949–2016, provided by the Shanghai Typhoon Institute, China Meteorological Administration (CMA-STI), and a TC size dataset (1980-2016) derived from geostationary satellite infrared images to analyze the statistical characteristics of autumn TCs over the western North Pacific (WNP). We investigated TC genesis frequency, location, track density, intensity, outer size, and landfalling features, as well as their temporal and spatial evolution characteristics. On average, the number of autumn TCs accounted for 42.1% of the annual total, slightly less than that of summer TCs (42.7%). However, TCs classified as strong typhoons or super typhoons were more frequent in autumn than in summer. In most years of the 68-yr study period, there was an inverse relationship between the number of autumn TCs and that of summer TCs. The genesis of autumn TCs was concentrated at three centers over the WNP: the first is located near (14°N, 115°E) over the northeastern South China Sea and the other two are located in the vast oceanic area east of the Philippines around (14°N, 135°E) and (14°N, 145°E), respectively. In terms of intensity, the eight strongest TCs during the study period all occurred in autumn. It is revealed that autumn TCs were featured with strong typhoons and super typhoons, with the latter accounting for 28.1% of the total number of autumn TCs. Statistically, the average 34-knot radius (R34) of autumn TCs increased with TC intensity. From 1949 to 2016, 164 autumn TCs made landfall in China, with an average annual number of 2.4. Autumn TCs were most likely to make landfall in Guangdong Province, followed by Hainan Province and Taiwan Island.

Impact of Tropical Cyclone Initialization on Its Convection Development and Intensity: A Case Study of Typhoon Megi (2010)

AbstractThis study investigates the impact of tropical cyclone (TC) initialization methods on TC intensity prediction under a framework coupling the Weather Research and Forecasting Model with the TC Centered-Local Ensemble Transform Kalman Filter (WRF TCC-LETKF). While the TC environments are constrained by assimilating the same environmental observations, two different initialization strategies, assimilating real dropsonde observations (the DP experiment) and synthetic axisymmetric surface wind structure (the VT experiment), are employed to construct the TC inner-core structure. These two experiments have distinct results on predicting the rapid intensification (RI) of Typhoon Megi (2010), which can be attributed to their different convective burst (CB) development. In DP, the assimilation of the dropsondes helps establish a realistic TC structure with asymmetry information, leading to scattered CB distribution and persistent RI with abundant moisture supply. In VT, assimilating the axisymmetric surface wind structure spins up the TC efficiently. However, the initially excessive CB coverage causes a too-early high-level warm core, and the reduced moisture supply hinders RI. The forecast results imply that if the TC structure is initialized using a scheme considering only the axisymmetric vortex structure, the RI potential can possibly be underestimated due to the inability to represent the realistic asymmetric structure. Finally, assimilation of both the real and synthetic data can be complementary, giving a strong TC initially that undergoes a longer RI period.

A Novel Data-Driven Tropical Cyclone Track Prediction Model Based on CNN and GRU With Multi-Dimensional Feature Selection

Strong tropical cyclones have made a drastic effect on human life and natural environment. As large amounts of meteorological data and monitoring data continue to accumulate, traditional methods for predicting tropical cyclone tracks face numerous challenges regarding their prediction efficiency and accuracy. Deep learning methods recently have been proven to be able to learn both spatial and temporal features from a large amount of dataset and be extremely efficient and accurate for forecasting data in complex structures. In this paper, we propose a novel data-driven deep learning model to predict tropical cyclone tracks using the spatial locations and multiple meteorological factors. This model comprises a multi-dimensional feature selection layer, a CNN layer and a GRU layer. The proposed model was trained using a dataset of real-world tropical cyclones from the years 1945 to 2017. Through the comparison experiments, the results verify that the proposed model outperforms the traditional forecasting methods, including a climatologically aware forecasting technique, the Sanders Barotropic technique and a numerical weather prediction (NWP) model. In addition, the proposed model has better accuracy than some deep learning methods, including RNN, GRU, CNN, AE-RNN, CNN-RNN, and CNN-GRU without the proposed feature selection layer.

Impacts of Horizontal and Vertical Resolutions on the Microphysical Structure and Boundary Layer Fluxes of Typhoon Hato (2017)

We set four sets of simulation experiments to explore the impacts of horizontal resolution (HR) and vertical resolution (VR) on the microphysical structure and boundary layer fluxes of tropical cyclone (TC) Hato (2017). The study shows that higher HR tends to strengthen TC. Increasing VR in the upper layers tends to weaken TC, while increasing VR in the lower layers tends to strengthen TC. Simulated amounts of all hydrometeors were larger with higher HR. Increasing VR at the upper level enhanced the mixing ratios of cloud ice and cloud snow, while increasing VR at the lower level elevated the mixing ratios of graupel and rainwater. HR has greater impact on the distributions of hydrometeors. Higher HR has a more complete ring structure of the eyewall and more concentrated hydrometeors along the cloud wall. Increasing VR at the lower level has little impact on the distribution of TC hydrometeors, while increasing VR at the upper level enhances the cloud thickness of the eyewall area. Surface latent heat flux (SLHF) is influenced greatly by resolution. Higher HR leads to larger water vapor fluxes and larger latent heat, which would result in a stronger TC. A large amount of false latent heat was generated when HR was too high, leading to an extremely strong TC, VR has a smaller impact on SLHF than HR. But increasing VR at the upper-level reduces the SLHF and weakens TC, and elevating VR at the lower-level increases the SLHF and strengthens TC. The changes in surface water vapor flux and SLHF were practically identical and the simulation results were improved when HR and VR were more coordinated. The friction velocity was greater with higher VR. Enhancing VR at the lower level increased the friction velocity, while increasing VR at the upper level reduced it.

A statistical comparison of the potential intensity index for tropical cyclones over the Western North Pacific

AbstractPotential intensity (PI), an upper thermodynamic limit of tropical cyclone (TC) intensity, is a useful index in TC statistical forecasting. This study evaluated the relationship between the current intensity of TCs and four PI indexes calculated with different sea surface indicators over the Western North Pacific (WNP) from 2009 to 2016. To mirror the degree of sea cooling caused by TCs, three sea surface temperature (SST) indicators (pre‐SST, 80 m depth mean sea temperature, and dynamic mixed mean sea temperature) were compared with real‐time SST obtained from HYbrid Coordinate Ocean Model (HYCOM) data. The results showed that four versions of PI all overestimated the current intensity of developing TCs. In contrast, when a TC at its strongest or in its weakening stages in a favorable atmospheric environment, pre‐sea conditions were strongly correlated with TC intensity. For strong TCs, the ocean‐coupled PI index was best able to describe TC intensity.

Reexamination of the Tropical Cyclone Wind–Pressure Relationship Based on Pre-1987 Aircraft Data in the Western North Pacific

Abstract The wind–pressure relationship (WPR) for tropical cyclones (TCs) in the western North Pacific is reexamined based on aircraft data, TC best track data, and daily reanalysis data during 1957–87. Minimum sea level pressure (MSLP) was estimated from aircraft reconnaissance, and maximum surface wind speeds (MSWs) were adjusted from the maximum wind speed at flight level. The mean MSLP was found to be higher during 1957–64 than during 1965–87, presumably due to the change in reconnaissance instrumentation and technology, which results in a systematic MSW bias (too high) before 1965 in the China Meteorological Administration (CMA) dataset. Further analyses found that the WPR used in the CMA dataset is more accurate for strong TCs, while the WPR in the Tokyo Regional Specialized Meteorological Center (RSMC) dataset is better for weak TCs after the MSW-RSMC converted by the Dvorak conversion table (1984) and when using the aircraft datasets as a baseline. Several prevailing operational WPRs used in the western North Pacific are reexamined. Results show that the WPR of Knaff and Zehr explains 71% of the variance with a MAE of 9.22 hPa, which represents a significant improvement over other WPRs. Utilizing data after 1965 (a total of 1874 samples), the effects of TC center latitude, size, translation speed, intensification trend, and environmental pressure on the WPRs were examined. Results show that faster-traveling TCs, smaller in size, and located in a higher environmental pressure at lower latitudes, exhibited a higher MSLP for a given MSW. Meanwhile, the latitude, translational speed, and the environmental pressure produces additional improvement, but the TC size and intensity change added only a little skill to the WPR equation.

Simulation of Chemical Transport by Typhoon Mireille (1991)

AbstractThis study examines the transport of chemical species to the upper troposphere/lower stratosphere (UTLS) by Typhoon Mireille (1991). We follow an integrated research approach, using in situ flight data when available and results from a high‐resolution chemical transport model. The Weather Research and Forecasting model coupled with chemistry (WRF‐Chem) was used with an innermost grid spacing of 3 km to explicitly resolve the convection being studied. Mireille was well simulated based on comparisons between the simulated fields and in situ chemical measurements from NASA's Pacific Exploratory Mission‐West A field project. Results from the simulated fields show that pollution from distant sources is ingested by Mireille and subsequently lofted by eyewall convection to the upper troposphere, enhancing concentrations in this region. Flux calculations suggest that a strong tropical cyclone (TC), such as Mireille, can impact UTLS chemistry as much as a continental middle‐latitude cyclone. Furthermore, overshooting convective cells in Mireille produced values of chemical flux density at tropopause level that are as much as 15–30 times greater than that of theTC as a whole. Although overshooting tops comprise only a small area of the simulated total TC, they transport large quantities of gaseous species to the upper troposphere because of their strong updrafts.

Strong Disturbance Impact of Tropical Cyclone Lionrock (2016) on Korean Pine-Broadleaved Forest in the Middle Sikhote-Alin Mountain Range, Russian Far East

Tropical cyclones (hurricanes and typhoons) cause large-scale disturbances in forest ecosystems all over the world. In the summer of 2016, a strong tropical cyclone named Lionrock created windthrow patches in the area of more than 400 km2 on the forested eastern slopes of the Sikhote-Alin Range, in the Russian Far East. Such large-scale forest destruction by wind had never been recorded in the area prior to this event. We examined the tropical cyclone impact upon the forest composition, structure and tree mortality rates on two study sites (1 ha and 0.5 ha in size)—a contiguous windthrow patch site, and a site with partial canopy damage. Korean pine (Pinus koraiensis Siebold and Zucc.), Manchurian fir (Abies nephrolepis Trautv.) and Dahurian larch (Larix cajanderi Mayr.) were the primary tree species represented in the affected forest communities. Combined with the partial canopy damage, 7.7% of trees were blown down by the disturbance event. We determined that this one event mortality rate nearly equaled the average mortality rate for a ten year period for these forests (8.5 ± 4.0%) under normal conditions (no large-scale disturbances). Within a contiguous windthrow patch, tree mortality was determined to be 52.6%, which is significantly higher than the cumulative tree loss for the previous 50 years (42.4%). A substantial portion of thinner-stemmed trees (DBH (diameter measured at breast height) < 30 cm) were wind snapped, and those with larger diameters (DBH > 60 cm) were uprooted. Our results indicate that the probability of tree loss due to catastrophic wind loads increases as a result of the decrease in local density. We believe that tree loss estimates should include the impacts within contiguous patches of windthrows, as well as the patches with only partial tree canopy damage. Strong wind impact forecasting is possible with accounting for species composition within the stand sites and their spatial structure.

Rainfall Contribution of Tropical Cyclones in the Bay of Bengal between 1998 and 2016 using TRMM Satellite Data

In the Bay of Bengal (BoB) area, landfalling Tropical Cyclones (TCs) often produce heavy rainfall that results in coastal flooding and causes enormous loss of life and property. However, the rainfall contribution of TCs in this area has not yet been systematically investigated. To fulfil this objective, firstly, this paper used TC best track data from the Indian Meteorological Department (IMD) to analyze TC activity in this area from 1998 to 2016 (January–December). It showed that on average there were 2.47 TCs per year generated in BoB. In 1998, 1999, 2000, 2005, 2008, 2009, 2010, 2013, and 2016 there were 3 or more TCs; while in 2001, 2004, 2011, 2012, and 2015, there was only 1 TC. On a monthly basis, the maximum TC activity was in May, October, and November, and the lowest TC activity was from January to April and in July. Rainfall data from the Tropical Rainfall Measurement Mission (TRMM) were used to estimate TC rainfall contribution (i.e., how much TC contributed to the total rainfall) on an interannual and monthly scale. The result showed that TCs accounted for around 8% of total overland rainfall during 1998–2016, and with a minimum of 1% in 2011 and a maximum of 34% in 1999. On the monthly basis, TCs’ limited rainfall contribution overland was found from January to April and in July (less than 14%), whereas the maximum TC rainfall contribution overland was in November and December (16%), May (15%), and October (14%). The probability density functions showed that, in a stronger TC, heavier rainfall accounted for more percentages. However, there was little correlation between TC rainfall contribution and TC intensity, because the TC rainfall contribution was also influenced by the TC rainfall area and frequency, and as well the occurrence of other rainfall systems.

A Comparison Between Ray-Traced GFS/WRF/ERA and GNSS Slant Path Delays in Tropical Cyclone Meranti

Tropical cyclone (TCs) prediction has improved in the last decade, but studying their structure and dynamics remains a challenging task due to limited in situ observations. The global navigation satellite system (GNSS) technique enables retrieving the state of the troposphere with high accuracy in all weather conditions. In this article, we use GNSS slant total delays (STDs) to verify the quality of the weather models during the passage of the TC Meranti, which was the strongest TC in 2016. STDs observed from 28 GNSS stations in Taiwan are compared with the STDs reconstructed from three meteorological data sets: the weather research and forecasting (WRF) model, the global forecast system (GFS), and the ERA-Interim (ERA) using a 2-D ray-tracing technique. Furthermore, two strategies were tested—including and excluding the delays due to liquid and ice water content. The results reveal a good consistency between the GNSS and the ray-traced STDs. The best agreement was found for the WRF, with a mean difference of −0.5 mm and a standard deviation of 29.4 mm when the hydrometeor delay was included. The mean hydrometeor contribution reached up to 2.8 mm for the WRF, while for the ERA and GFS, the contribution to the total delay was negligible. The recorded absolute percent differences were greater for the northern HENC station (mostly within 0.8%) than for the southern GOLI station (on average below 0.5%) due to a closer location to the passing Meranti. The most significant biases were seen in the ERA (around 7%) and the GFS (exceeding −3%) for the PLIM station, located in the central mountain range valley.

Climatology of tropical cyclone tornadoes in China from 2006 to 2018

We surveyed the occurrence of tropical cyclone (TC) tornadoes in China from 2006 to 2018. There were 64 cataloged TC tornadoes, with an average of five per year. About one-third of the landfalling TCs in China were tornadic. Consistent with previous studies, TC tornadoes preferentially formed in the afternoon shortly before and within about 36 h after landfall of the TCs. These tornadoes mainly occurred in coastal areas with relatively flat terrains. The maximum number of TC tornadoes occurred in Jiangsu and Guangdong provinces. Most of the TC tornadoes were spawned within 500 km of the TC center. Two notable characteristics were found: (1) TC tornadoes in China mainly occurred in the northeast quadrant (Earth-relative coordinates) rather than the right-front quadrant (TC motion-relative coordinates) of the parent TC circulation; and (2) most tornadoes were produced by TCs with a relatively weak intensity (tropical depressions/storms), in contrast with the United States where most tornadoes are associated with stronger TCs. Further analyses showed that TC tornadoes in China tend to be spawned in an environment with large low-level storm relative helicity and large convective available potential energy taking entrainment effects into account. TC tornadoes were particularly active in 2018, with 24 reported tornadoes accounting for 37.5% of the total surveyed samples. The first recorded tornado outbreak in the modern history of China occurred in the envelope of TC Yagi (2018), in which 11 tornadoes were reported in association with significant midlevel intrusions of dry air and the interaction of Yagi with an approaching midlatitude midlevel trough.

Factors Affecting the Variability of Maximum Potential Intensity (MPI) of Tropical Cyclones Over the North Atlantic

AbstractContributions of atmospheric factors to the variability of the calculated theoretical maximum potential intensity (MPI) of tropical cyclones (TCs) over the North Atlantic are explored using the 6‐hourly atmospheric reanalysis and TC best track data from 1980 to 2015. The results show that for a given sea surface temperature (SST), the calculated theoretical MPI between the medians of top 10% and bottom 10% samples can vary by as large as 10–15 m/s, which accounts for 20–25% of the median of the MPI. It is shown that the drier (moister) and colder (warmer) environment favors higher (lower) MPI, and the TC‐MPI is more sensitive to atmospheric temperature at lower SSTs but more sensitive to atmospheric humidity at higher SSTs. Results from sensitivity experiments show that the tropospheric temperature and humidity profiles and the outflow layer temperature are all responsible for the MPI variability, but their relative importance vary with SST. The atmospheric humidity accounts for 12–13 (7–11) m/s at SSTs over (below) 28 °C, the tropospheric temperature accounts for about 7–12 (5–6) m/s at SSTs below (above) 28 °C, and the outflow temperature accounts for 7–8 m/s almost independent of SST. These results strongly suggest that the modulation of MPI by synoptic variability needs to be considered when MPI is calculated and used as a predictor/parameter in operational TC intensity prediction schemes, especially for strong TCs. Some other implications of the results are also discussed.

Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific

Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015), and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.

Estimation of biophysical properties of areas in the Western North Pacific with a high frequency of tropical cyclones

Tropical cyclones (TCs) are among the most important factors that influence the upper oceanic environment of the Western North Pacific (WNP). To better understand the effects of TCs on the changes in the physical and biological processes of the WNP, two regions with similar properties but different TC frequencies were studied. Unlike previous studies, the long-term effects of TCs on the biophysical properties of the WNP throughout the TC season were discussed. The biophysical properties of both regions during the TC season were compared using satellite-derived measurements and Argo float data during 2003–2017. During the TC season, short-term rapid enhancement of the wind during TC passages had a direct effect on the cooling of the sea surface temperature and the deepening of the mixed-layer depth. Moreover, surface nitrate concentrations in the TC-prone region were significantly higher than those in the region not heavily influenced by TCs. As a result, the mean surface chlorophyll-a (Chl-a) concentration in the region with a high TC frequency was significantly higher than that with a low TC frequency. The Chl-a concentrations also tended to be higher during the El Niño years because of more strong TC events in both regions, while they were generally lower during the La Niña years when there were fewer strong TCs. Based on a conservative estimate, new primary production in TC season caused by TCs in the region with a high TC frequency accounted for approximately 1–7% of the annual amount in this region during the study years. These findings suggest that the TCs play an important role in the enhancement of biological carbon fixation in parts of the WNP.

Effect of dynamical downscaling to cyclone simulation: a study case for Haiyan typhoon

The capacity of a global gridded climate data in describing tropical cyclone (TC) activity might be still less adequate due to its coarse resolution or the TC intensity itself. Grid refinement method such as a dynamical downscaling is one way to add values of the climate attributes in the data. However, the performance of the dynamical downscaling itself still con-tains many uncertainties. This paper aims to evaluate the effect of the dynamical downscaling model on the climate simulation during a strong tropical cyclone Haiyan. The snapshots and the best track data from the historical satellite imagery will be used for validation. In general the downscaled cyclone simulation does not give a significant change to the driven data, but it results in more finer resolution of wind field and improves the time and the position of the eye of the cyclone.

Near-future tropical cyclone predictions in the western North Pacific: fewer tropical storms but more typhoons

This study presents forecasts of near-future tropical cyclone (TC) activities over the western North Pacific (WNP) using a TC track-pattern-based prediction model in conjunction with long-term free-run simulations from the National Centers for Environmental Prediction Climate Forecast System. The prediction shows that the East Asian coastal area will be affected by fewer TC landfalls. However, the number of stronger TC landfalls may increase in the near future (2016–2030) compared to the present-day period (2002–2015). The potential candidates for modulating the near-future TC activity are three large-scale climate variability: El Nino–Southern Oscillation (ENSO), the North Pacific sea surface temperature (NPSST) variation, and basin-wide warming of the Pacific. More frequent El Nino episodes in the near future may contribute to the activation of TC formations in the far-eastern tropical ocean, which generates a favorable influence on TC intensification due to longer distances and development periods over the ocean. NPSST variations have contrasting effects, i.e., they have unfavorable effects on TC genesis due to weak vorticity, while creating favorable conditions for TC intensification by providing more humid environments in the subtropics. The impact of overall Pacific warming on the changes in TC activities is low compared to that of the other two factors. Our results physically demonstrate the contributions of three WNP sea surface temperature variability on near-future TC activity, emphasizing the critical roles of ENSO and NPSST rather than the near-term warming effect.

Study of upper ocean parameters during passage of tropical cyclones over Indian seas

ABSTRACTThe primary energy supply for tropical cyclones is the upward latent heat fluxes that are directly related to Sea Surface Temperatures (SST). The strong winds induce a negative SST anomaly in the tropical cyclone wake. This is usually referred to as the ‘cold wake’. Many studies have suggested that the cold wake results in a significant reduction of upward latent heat fluxes that supply energy to the tropical cyclone, and hence provides a negative feedback on its intensity. The cold-wake feedback on the intensity of tropical cyclones is a strong motivation to understand the oceanic response to tropical cyclones. A recent study of re-examining the mechanisms controlling the cold wake has shown the importance of vertical Ekman pumping. Under the core of tropical cyclones (typically over a disk of 100 km radius), vertical pumping is responsible for a cooling of the entire water column, while surface heat fluxes and vertical mixing both contribute to the surface cooling during the cyclone passage. Farther away from the cyclone core, vertical mixing generally overwhelms the effect of vertical pumping, and also the effect of heat fluxes in the case of strong tropical cyclones. The study examines the Ekman pumping during the passage of few cyclonic storms in the Arabian Sea (AS) and Bay of Bengal (BOB) during last decade using different datasets such as Satellite (QuikScat) and reanalysis (ERA-Interim) data and calls up the value for satellite deliverables. Surface currents and subsurface temperature structure is examined further and for different Ekman pumping velocity (EPV), variable subsurface response is illustrated. The changes in ocean surface temperature, sea level and heat content obtained from the remote sensing data products, in the wake of cyclone are also reported and thus the usefulness of satellite products to study the coupled tropical system is demonstrated.

Simulation of Ocean Responses to an Idealized Landfalling Tropical Cyclone Using a Coupled Atmosphere-Wave-Ocean Modeling System

ABSTRACT Oceanic responses to a hypothetical landfalling tropical cyclone (TC) are studied by using a coupled atmosphere-wave-ocean modeling system (CAWOMS). A set of experiments are conducted to compare the effects of atmosphere-wave-ocean interaction on ocean responses in coastal and deep waters. The results show that in a three-way coupled atmosphere-wave-ocean system, the response to a tropical cyclone is considerably different in coastal water and deep water. In a three-way coupled system, air-sea interactions tend to increase coastal storm surge, inundation, significant wave heights and ocean currents in shallow coastal areas as a result of wave-enhanced air-sea heat and moisture fluxes. But the change is little in sea surface temperature and mixed-layer structure due to the well-mixed nature in the coastal zone. In contrast, in a three-way coupled system, air-sea interactions enhance sea surface cooling, increase mixed layer depth in deep waters largely due to the tendency of a wave-enhanced TC to induce strong mixing and entrainment in the upper ocean. A stronger TC also strengthens the surface currents and significant wave height in the offshore waters. The inclusion of waves in air-sea interactions fundamentally changes the dynamic and thermodynamic coupling between tropical cyclone and the underlying ocean. In the absence of TC-wave consideration, a negative feedback between the TC and the upper ocean mixed layer results in a weakening of the TC system and a cooling in the offshore upper ocean and therefore reduces coastal storm surge, flooding areas, significant wave height and ocean currents. Only in a TC-wave-ocean three-way coupled system, air-sea interaction may correspond to a stronger TC due to wave-induced air-sea heat and moisture fluxes which compensate the effect of negative feedback between the TC and the upper ocean. In coastal waters, the negative feedback between the TC and the ocean mixed layer is fairly weak. Air-sea interaction is dominated by the positive TC-wave feedback. As a result, air-sea interaction increases coastal storm surge, inundation, currents and significant wave height.

The Performance of Global Models in TC Track Forecasting Over the Western North Pacific from 2010 to 2012

ABSTRACT Forecasts of tropical cyclone (TC) tracks from six global models during 2010 and 2012 were assessed to study the current capability of track forecast guidance over the western North Pacific. To measure the performance of the global model forecasts, a series of statistical evaluations of track forecasts up to 120 h were carried out, including the mean, median, percentile distribution, regional distribution, relative position, correlation analysis, and binned analysis. Results showed that certain improvements have been made for the six global models in their prediction accuracy and stability in the past three years. Remarkably, stepped decreases in the values of each quantile were found at all lead time levels from 2010 to 2012 for NCEP-GFS. an analysis of the regional distribution of position errors showed that a high-latitude region, low-latitude region (which covers mostly the TC genesis region) and the South China Sea are the three main areas within which large errors tend to concentrate. The majority of the models show their own respective characteristics of systematic bias at each lead time, as established through the relative position analysis results. Only the results of NCEP-GFS and CMA-T639 did not show any obvious systematic bias in the three-year study period. Binned analyses indicated that the prediction accuracy and stability of most of the models were better for strong TCs than for weak TCs at short lead time levels. It was also found that the models tend to perform better for initially large TCs, or for those with weak vertical wind shear at lead times shorter than 48 h. The results demonstrate the heavy reliance of forecast errors upon the initial characteristics of a TC or its environmental conditions.