Stochastic simulation model for tropical cyclone tracks,with special emphasis on landfall behavior

We consider a spatial stochastic model for the simulation of tropical cyclone tracks, which has recently been introduced. Cyclone tracks are represented as labeled polygonal lines, which are described by the movement directions, translational speeds, and wind speeds of the cyclones in regular 6-h intervals. In the present paper, we compare return levels for wind speeds of historically observed cyclone tracks with those generated by the simulator, where a mismatch is shown for most of the considered coastal regions. To adjust this discrepancy, we develop a stochastic algorithm for acceptance and rejection of simulated cyclone tracks with landfall. It is based on the fact that the locations, translational speeds, and wind speeds of cyclones at landfall constitute three-dimensional Poisson point processes, which are a basic model type in stochastic geometry. Due to that, a well-known thinning property of Poisson processes can be applied. This means that to each simulated cyclone, an acceptance probability is assigned, which is higher for cyclones with suitable landfall characteristics and lower for implausible ones. More intuitively, the algorithm comprises the simulation of a more comprehensive cyclone event set than needed and the random selection of those tracks that best match historical observations at landfall. A particular advantage of our algorithm is its applicability to multiple landfalls, i.e., to cyclones that successively make landfall at two geographically distinct coastlines, which is the most relevant case in applications. It turns out that the extended simulator provides a much better accordance between landfall characteristics of historical and simulated cyclone tracks.     

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.     

Temporal variation of tropical cyclones in the North Atlantic Basin

A total of 269 tropical storms and hurricanes originated in the North Atlantic basin from 1960–1989. Of these, 76 made landfall on the continental United states. This study divides the 76 tropical storms into their month of formation. Seasonal shifts in the principal areas of tropical cyclone formation over the Atlantic basin have been recognized for many decades. The results of the study suggest that the early and late season tropical cyclones develop in areas which are first affected by the position of the sun, resulting in an increase in water temperatures. These cyclones normally make landfall along the Gulf Coast and usually are of low intensity. Formation areas shift eastward in mid-summer with a slight increase in intensity. By late August and early September, the formation areas have extended to the Cape Verde Islands. These storms tend to strike the east coast of the US and are normally more intense. By the end of the hurricane season, the primary formation area has shifted back to the Gulf of Mexico, with low intensity storms affecting the Gulf Coast.     

A fast intensity simulator for tropical cyclone risk analysis

Robust estimates of tropical cyclone risk can be made using large sets of storm events synthesized from historical data or from physics-based algorithms. While storm tracks can be synthesized very rapidly from statistical algorithms or simple dynamical models (such as the beta-and-advection model), estimation of storm intensity by using full-physics models is generally too expensive to be practical. Although purely statistical intensity algorithms are fast, they may not be general enough to encompass the effects of natural or anthropogenic climate change. Here we present a fast, physically motivated intensity algorithm consisting of two coupled ordinary differential equations predicting the evolution of a wind speed and an inner core moisture variable. The algorithm includes the effects of ocean coupling and environmental wind shear but does not explicitly simulate spatial structure, which must be handled parametrically. We evaluate this algorithm by using it to simulate several historical events and by comparing a risk analysis based on it to an existing method for assessing long-term tropical cyclone risk. For simulations based on the recent climate, the two techniques perform comparably well, though the new technique does better with interannual variability in the Atlantic. Compared to the existing method, the new method produces a smaller increase in global tropical cyclone frequency in response to global warming, but a comparable increase in power dissipation.     

Climatology of landfalling tropical cyclones in Bangladesh 1877–2003

Bangladesh is highly susceptible to tropical cyclones. Unfortunately, there is a dearth of climatological studies on the tropical cyclones of Bangladesh. The Global Tropical Cyclone Climatic Atlas (GTCCA) lists historical storm track information for all the seven tropical cyclone ocean basins including the North Indian Ocean. Using GIS, tropical cyclones that made landfall in Bangladesh during 1877–2003 are identified and examined from the climatological perspective. For the convenience of study, the coast of Bangladesh is divided into five segments and comparisons are made among the coastal segments in terms of cyclone landfall and vulnerability. There is a large variability in the year-to-year occurrence of landfalling tropical cyclones in Bangladesh. Most of the tropical cyclones (70%) hit in the months of May–June and October–November generally show the well-known pattern of pre- and post-monsoon cyclone seasons in that region.     

Advances in surrogate modeling for storm surge prediction: storm selection and addressing characteristics related to climate change

This paper establishes various advancements for the application of surrogate modeling techniques for storm surge prediction utilizing an existing database of high-fidelity, synthetic storms (tropical cyclones). Kriging, also known as Gaussian process regression, is specifically chosen as the surrogate model in this study. Emphasis is first placed on the storm selection for developing the database of synthetic storms. An adaptive, sequential selection is examined here that iteratively identifies the storm (or multiple storms) that is expected to provide the greatest enhancement of the prediction accuracy when that storm is added into the already available database. Appropriate error statistics are discussed for assessing convergence of this iterative selection, and its performance is compared to the joint probability method with optimal sampling, utilizing the required number of synthetic storms to achieve the same level of accuracy as comparison metric. The impact on risk estimation is also examined. The discussion then moves to adjustments of the surrogate modeling framework to support two implementation issues that might become more relevant due to climate change considerations: future storm intensification and sea level rise (SLR). For storm intensification, the use of the surrogate model for prediction extrapolation is examined. Tuning of the surrogate model characteristics using cross-validation techniques and modification of the tuning to prioritize storms with specific characteristics are proposed, whereas an augmentation of the database with new/additional storms is also considered. With respect to SLR, the recently developed database for the US Army Corps of Engineers’ North Atlantic Comprehensive Coastal Study is exploited to demonstrate how surrogate modeling can support predictions that include SLR considerations.     

Characteristics of tropical cyclones in China and their impacts analysis

This paper discusses the characteristics of tropical cyclones (TCs) based on available data from 1951 to 2008, including the frequency of TC generation in the Western North Pacific (WNP) and those which make landfall in China. The impacts of TCs on both human and economic losses for the period 1983–2008 are also discussed. Examination of the frequency indicates a decreasing trend in the generation of TCs in the WNP since the 1980s, but the number of TCs making landfall has remained constant or shown only a slight decreasing trend. The number of casualties caused by TCs in China appears to show a slight decreasing trend while the value of economic loss is increasing significantly. These results can be attributed to increased natural disaster prevention and mitigation efforts by the Chinese government in recent years, and also reflect the rapid economic development in China particularly in TC-prone areas.     

Landfalling tropical cyclones on the Pacific coast of Mexico: 1850-2010

Historical documents and newspapers from Mexican Pacific states (north of 14° N) were reviewed to determine the incidence of landfalling tropical cyclones from 1850 to 1949, prior to the start of the United States National Hurricane Center database. The reviewed documents are only found in Mexican repositories at national, state and municipal level and the systematic search embarked upon in this study yielded valuable information that cannot be found elsewhere. Atime series of landfall was reconstructed back to 1850, indicating active and quiet periods. An average of 1.8 ± 1.6 landfalls per year is determined from the time series for 1850-2010. When the series is limited to 1880-2010, eliminating the first 30 years that may have some undercounting, the average increases to 2.1 ± 1.6 cases per year. Spectral and wavelet analysis of the 161 years of landfalling tropical cyclones indicates that the Pacific Decadal Oscillation (PDO) modulates the activity. The influence of El Niño/Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) on the landfall frequency may be present throughout the reconstruction period but both oscillations have lower correlations compared to that from the PDO.     

Experimental superensemble forecasts of tropical cyclones over the Bay of Bengal

This study entails the implementation of an experimental real time forecast capability for tropical cyclones over the Bay of Bengal basin of North Indian Ocean. This work is being built on the experience gained from a number of recent studies using the concept of superensemble developed at the Florida State University (FSU). Real time hurricane forecasts are one of the major components of superensemble modeling at FSU. The superensemble approach of training followed by real time forecasts produces the best forecasts for tracks and intensity (up to 5 days) of Atlantic hurricanes and Pacific typhoons. Improvements in track forecasts of about 25–35% compared to current operational forecast models has been noted over the Atlantic Ocean basin. The intensity forecasts for hurricanes are only marginally better than the best models. In this paper, we address tropical cyclone forecasts over the Bay of Bengal for the years 1996–2000. The main result from this study is that the position and intensity errors for tropical cyclone forecasts over the Bay of Bengal from the multimodel superensemble are generally less than those of all of the participating models during 1- to 3-day forecasts. Some of the major tropical cyclones, such as the November 1996 Andhra Pradesh cyclone and October 1999 Orissa super cyclone were well handled by this superensemble approach. A conclusion from this study is that the proposed approach may be a viable way to construct improved forecasts of Bay of Bengal tropical cyclone positions and intensity.     

Customization of WRF-ARW model with physical parameterization schemes for the simulation of tropical cyclones over North Indian Ocean

The convection and planetary boundary layer (PBL) processes play significant role in the genesis and intensification of tropical cyclones (TCs). Several convection and PBL parameterization schemes incorporate these processes in the numerical weather prediction models. Therefore, a systematic intercomparison of performance of parameterization schemes is essential to customize a model. In this context, six combinations of physical parameterization schemes (2 PBL Schemes, YSU and MYJ, and 3 convection schemes, KF, BM, and GD) of WRF-ARW model are employed to obtain the optimum combination for the prediction of TCs over North Indian Ocean. Five cyclones are studied for sensitivity experiments and the out-coming combination is tested on real-time prediction of TCs during 2008. The tracks are also compared with those provided by the operational centers like NCEP, ECMWF, UKMO, NCMRWF, and IMD. It is found that the combination of YSU PBL scheme with KF convection scheme (YKF) provides a better prediction of intensity, track, and rainfall consistently. The average RMSE of intensity (13?hPa in CSLP and 11?m?s^?1 in 10-m wind), mean track, and landfall errors is found to be least with YKF combination. The equitable threat score (ETS) of YKF combination is more than 0.2 for the prediction of 24-h accumulated rainfall up to 125?mm. The vertical structural characteristics of cyclone inner core also recommend the YKF combination for Indian seas cyclones. In the real-time prediction of 2008 TCs, the 72-, 48-, and 24-h mean track errors are 172, 129, and 155?km and the mean landfall errors are 125, 73, and 66?km, respectively. Compared with the track of leading operational agencies, the WRF model is competing in 24?h (116?km error) and 72?h (166?km) but superior in 48-h (119?km) track forecast.     

Progresses and Prospects for North Tropical Atlantic Mode Interannual Variability

North Tropical Atlantic Mode (NTAM) is the leading variability of the boreal spring sea surface temperature anomalies over the North Tropical Atlantic at interannual timescale. It is also known as the northern pole of the Atlantic Meridional Mode (AMM). NTAM shows significant impact on the shift of Intertropical Convergence Zone, the precipitation of the surrounding countries, the quasi-biennial oscillation of El Nino-Southern Oscillation (ENSO), and the recent global warming hiatus. Despite its distinct influence on global climate, NTAM has not received equivalent attention as other tropical variability (e.g. ENSO). By revisiting previous studies, this paper summarized the triggers and mechanisms responsible for the evolution and development of NTAM, including remote forcing from ENSO, south tropical Atlantic as well as North Atlantic Oscillation (NAO), local air-sea coupling, and the interactions among different triggers. Also, this paper detailedly introduced the ability of CMIP5 (The fifth phase of the Coupled Model Intercomparison Project) model simulation. The prominent model biases over the equatorial Atlantic significantly limit the study of NTAM. Finally, a future prospective of NTAM interannual variability was presented.     

Blueprints for Medieval hydroclimate

According to tree ring and other records, a series of severe droughts that lasted for decades afflicted western North America during the Medieval period resulting in a more arid climate than in subsequent centuries. A review of proxy evidence from around the world indicates that North American megadroughts were part of a global pattern of Medieval hydroclimate that was distinct from that of today. In particular, the Medieval hydroclimate was wet in northern South America, dry in mid-latitude South America, dry in eastern Africa but with strong Nile River floods and a strong Indian monsoon. This pattern is similar to that accompanying persistent North American droughts in the instrumental era. This pattern is compared to that associated with familiar climate phenomena. The best fit comes from a persistently La Niña-like tropical Pacific and the warm phase of the so-called Atlantic Multidecadal Oscillation. A positive North Atlantic Oscillation (NAO) also helps to explain the Medieval hydroclimate pattern. Limited sea surface temperature reconstructions support the contention that the tropical Pacific was cold and the subtropical North Atlantic was warm, ideal conditions for North American drought. Tentative modeling results indicate that a multi-century La Niña-like state could have arisen as a coupled atmosphere–ocean response to high irradiance and weak volcanism during the Medieval period and that this could in turn have induced a persistently positive NAO state. A La Niña-like state could also induce a strengthening of the North Atlantic meridional overturning circulation, and hence warming of the North Atlantic Ocean, by (i) the ocean response to the positive NAO and by shifting the southern mid-latitude westerlies poleward which (ii) will increase the salt flux from the Indian Ocean into the South Atlantic and (iii) drive stronger Southern Ocean upwelling.     

The occurrence laws of campus stampede accidents in China and its prevention and control measures

This research appraises how residential built environment growth influences coastal exposure and how this component of societal vulnerability contributes to tropical cyclone impact and disaster potential. Historical housing unit data and future demographic projections from a high-resolution, spatial allocation model illustrate that the area within 50 km of the US Atlantic and Gulf Coastlines has the greatest housing unit density of any physiographic region in the USA, with residential development in this region outpacing non-coastal areas. Tropical cyclone exposure for six at-risk metropolitan statistical areas (MSAs) along the US Atlantic and Gulf Coasts are assessed. All six MSAs evaluated are distinct in their development character, yet all experienced significant growth from 1940 through the contemporary period; projections from the model under various socioeconomic pathways reveal that this growth is anticipated to continue during the twenty-first century. Using a worst-case scenario framework, the historical and future residential data for the six MSAs are intersected with synthetic hurricane wind swaths generated from contemporary landfalling events. The New York City MSA contains the greatest residential built environment exposure, but Miami is the most rapidly changing MSA and has the greatest potential for hurricane disaster occurrence based on the juxtaposition of climatological risk and exposure. A disaster potential metric illustrates that all six MSAs will experience significant increases in disaster probability during the twenty-first century. This analysis facilitates a detailed spatiotemporal assessment of US coastal region vulnerability, providing decision makers with information that may be used to evaluate the potential for tropical cyclone disasters, mitigate tropical cyclone hazard impacts, and build community resilience for these and other hazards in the face of environmental and societal change.     

Interannual variability of tropical cyclone activity along the Pacific coast of North America

The interannual variability of near-coastal eastern North Pacific tropical cyclones is described using a data set of cyclone tracks constructed from U.S. and Mexican oceanic and atmospheric reports for the period 1951-2006. Near-coastal cyclone counts are enumerated monthly, allowing us to distinguish interannual variability during different phases of the May-November tropical cyclone season. In these data more tropical cyclones affect the Pacific coast in May-July, the early months of the tropical cyclone season, during La Niña years, when equatorial Pacific sea surface temperatures are anomalously cool, than during El Niño years. The difference in early season cyclone counts between La Niña and El Niño years was particularly pronounced during the mid-twentieth century epoch when cool equatorial temperatures were enhanced as described by an index of the Pacific Decadal Oscillation. Composite maps from years with high and low near-coastal cyclone counts show that the atmospheric circulation anomalies associated with cool sea surface temperatures in the eastern equatorial Pacific are consistent with preferential steering of tropical cyclones northeastward toward the west coast of Mexico.     

Numerical simulation of tropical cyclone thane: role of boundary layer and surface drag parameterization schemes

In the recent times, several advanced numerical models are utilized for the prediction of the intensity, track and landfall time of a cyclone. Still there are number of issues concerning their prediction and the limitation of numerical models in addressing those issues. The most pertinent question is how intensive a cyclone can become before it makes a landfall and where the cyclone moves under the ambient large-scale flow. In this paper, detailed study has been carried out using Weather Research Forecast model with two boundary schemes to address the above question by considering a recent tropical cyclone in Bay of Bengal region of North Indian Ocean. In addition, the impact of the surface drag effect on the low-level winds and the intensity of the cyclone are also studied. The result reveals that large differences are noted in the ocean surface fluxes between YSU and MYJ with MYJ producing relatively higher fluxes than YSU. It is found that the YSU scheme produced a better simulation for the THANE cyclone in terms of winds, pressure distribution and cloud fractions. Comparison with available observations indicated the characteristics of horizontal divergence, vorticity and vector track positions produced by YSU experiment are more realistic than with MYJ and other experiments. However, when the drag coefficient is changed as 0.5 or 2.0 from the default values, appreciable changes in the surface fluxes are not noticed. A maximum precipitation is reported in YSU as compared to the MYJ PBL scheme for the tropical cyclone THANE.     

CMIP5和CMIP6模式在历史试验下对AMO和PDO的模拟评估

利用Hadley中心的观测海温资料,以及耦合模式比较计划的第五阶段和耦合模式比较计划的第六阶段历史试验的模式资料,分析和评估了2个最为重要的年代际尺度模态,北大西洋年代际振荡、太平洋年代际振荡在耦合模式比较计划的第五阶段和耦合模式比较计划的第六阶段中的模拟能力。通过对比多模式集合发现,在空间模态方面,耦合模式比较计划的第五阶段和耦合模式比较计划的第六阶段都能模拟出北大西洋年代际振荡在北大西洋地区的信号,但耦合模式比较计划的第六阶段的模拟更好,对于太平洋年代际振荡模态而言,都能模拟出在北太平洋地区的信号,而太平洋年代际振荡在热带太平洋地区的信号,耦合模式比较计划的第六阶段模拟的振幅明显更接近观测。在周期的模拟方面,耦合模式比较计划的第五阶段和耦合模式比较计划的第六阶段结果相似,都能模拟出北大西洋年代际振荡存在60~70年的周期,以及太平洋年代际振荡存在20年和60~70年的双周期。整体而言,耦合模式比较计划的第六阶段相比于耦合模式比较计划的第五阶段在空间特征模拟方面有一定进步,但是对于周期的模拟能力,没有明显进步。     

Predicting bathymetry from Geosat-ERM and shipborne profiles in the South Atlantic Ocean

Portions of two Geosat-ERM altimeter tracks and corresponding suborbital shipboard gravity and bathymetry profiles in the South Atlantic Ocean were analyzed: one across the Walvis Ridge (about 1100 km long) and the other in the Brazil Basin (about 2300 km long). Together, these profiles sample those types of sea-floor topography which dominate the gravity signature at wavelengths of 20 to 300 km. The Walvis Ridge is a massive aseismic ridge and the Brazil Basin profile crosses both an old seamount (emplaced at the time the crust was young) and a very young mid-plate volcano. Both profiles cross fracture zones. After the gravity and bathymetry profiles were split into subprofiles, various cross-spectral characteristics could be determined by FFT techniques. Analysis showed that observed admittance is not well constrained by either an Airy-type or flexural compensation models across the Walvis Ridge, but those over the Brazil Basin can be readily explained by an Airy-type model with a mean crustal thickness of about 20 km.

A theoretical filter was then designed, based on a priori geological knowledge, and used to predict bathymetry from the high-passed gravity/geoid anomalies.

Not surprisingly, the predicted bathymetry shows more detailed and correct short-wavelength (20–300 km) features than those predicted from the historical data base, as represented e.g., by the DBDB5 gridded bathymetric model. For areas where historical shipboard bathymetry measurements are widely spaced (longer than about 10 km for single-beam data) but where some regional geologic information is available (such as the relative ages of mid-plate volcanoes and crust), bathymetry predicted from altimetric data can be used to upgrade regional bathymetrie data bases, on which regional geologic/geophysical understanding depends.     

Climate and solar signals in property damage losses from hurricanes affecting the United States

The authors show that historical property damage losses from US hurricanes contain climate signals. The methodology is based on a statistical model that combines a specification for the number of loss events with a specification for the amount of loss per event. Separate models are developed for annual and extreme losses. A Markov chain Monte Carlo procedure is used to generate posterior samples from the models. Results indicate the chance of at least one loss event increases when the springtime north–south surface pressure gradient over the North Atlantic is weaker than normal, the Atlantic ocean is warmer than normal, El Ni?o is absent, and sunspots are few. However, given at least one loss event, the magnitude of the loss per annum is related only to ocean temperature. The 50-year return level for a loss event is largest under a scenario featuring a warm Atlantic Ocean, a weak North Atlantic surface pressure gradient, El Ni?o, and few sunspots. The work provides a framework for anticipating hurricane losses on seasonal and multi-year time scales.     

Decadal Prediction Skill of the Global Sea Surface Temperature in the BCC_CSM1.1 Climate Model

This study assesses retrospective decadal prediction skill of Sea Surface Temperature (SST) variability in initialized climate prediction experiments (INT) with the Beijing Climate Center Climate System Model (BCC_CSM1.1). Ensemble forecasts were evaluated using observations, and compared to an ensemble of uninitialized simulations (NoINT). The results show as follows: ①The warming trend of global mean SST simulated by the INT runs is closer to the observation than that in the NoINT runs.②The INT runs show high SST prediction skills over broad regions of tropical Atlantic, western tropical Pacific and tropical Indian Oceans. ③ In the North Pacific and the east-central tropical Pacific Ocean, the prediction skills are very weak, and there are few improvements coming from the initialization in the INT runs. ④ In the southern Indian Ocean, the prediction skills of the INT runs are significantly larger than that of the NoINT runs, with the maximum skill at the 3~6 and 4~7 years lead time. The above-mentioned conclusions are similar to the results of other climate models. However, the prediction skill in the North Atlantic Ocean is much lower than that of other models, especially in the subpolar region. The low skills in the Atlantic Ocean may be attributed to the misrepresentation of the lead-lag relationship between the Atlantic meridional heat transport and the SST in the BCC_CSM1.1.     

Sensitivity of inland decay of North Atlantic tropical cyclones to soil parameters

Using the HURDAT best track analysis of track and intensity of tropical cyclones that made landfall over the continental United States during the satellite era (1980?C2005), we analyze the role of land surface variables on the cyclone decay process. The land surface variables considered in the present study included soil parameters (soil heat capacity and its surrogate soil bulk density), roughness, topography and local gradients of topography. The sensitivity analysis was carried out using a data-adaptive genetic algorithm approach that automatically selects the most suitable variables by fitting optimum empirical functions that estimates cyclone intensity decay in terms of given observed variables. Analysis indicates that soil bulk density (soil heat capacity) has a dominant influence on cyclone decay process. The decayed inland cyclone intensities were found to be positively correlated with the cube of the soil bulk density (heat capacity). The impact of the changes in soil bulk density (heat capacity) on the decayed cyclone intensity is higher for higher intensity cyclones. Since soil bulk density is closely related to the soil heat capacity and inversely proportional to the thermal diffusivity, the observed relationship can also be viewed as the influence of cooling rate of the land surface, as well as the transfer of heat and moisture underneath a land-falling storm. The optimized prediction function obtained by statistical model processes in the present study that predicts inland intensity changes during 6-h interval showed high fitness index and small errors. The performance of the prediction function was tested on inland tracks of eighteen hurricanes and tropical storms that made landfall over the United States between 2001 and 2010. The mean error of intensity prediction for these cyclones varied from 1.3 to 15.8 knots (0.67?C8.12?m?s^?1). Results from the data-driven analysis thus indicate that soil heat flux feedback should be an important consideration for the inland decay of tropical cyclones. Experiments were also undertaken using Weather Research Forecasting (WRF) Advanced Research Version (ARW ver 3.3) to assess the sensitivity of the soil parameters (roughness, heat capacity and bulk density) on the post-landfall structure of select storms. The model was run with 1-km grid spacing, limited area single domain with boundary conditions from the North American Regional Reanalysis. Of different experiments, only the surface roughness change and soil bulk density (heat capacity) change experiments showed some sensitivity to the intensity change. The WRF results thus have a low sensitivity to the land parameters (with only the roughness length showing some impact). This calls for reassessing the land surface response on post-landfall characteristics with more detailed land surface representation within the mesoscale and hurricane modeling systems.