Cyclone Data Research Articles

DAMPAK TERJADINYA EL NINO/LA NINA TERHADAP INTENSITAS, MASA HIDUP DAN FREKUENSI SIKLON

Fenomena badai siklon adalah salah satu bencana hidrometeorologi yang paling merusak di Bumi. Siklon bisa terjadi di kawasan yang dipengaruhi aktivitas El Nino (La Nina). Siklon tropis maupun El Nino dapat menyebabkan kondisi cuaca atau iklim ekstrim di daerah tropis. Pada kajian ini, dilakukan kajian mengenai dampak fenomena El Nino (La Nina) terhadap besar kecilnya badai siklon (intensitas siklon) di sekitar wilayah Indonesia. Besar kecilnya badai siklon tersebut diklasifikasikan dengan menggunakan Skala Saffir-Simpson. Selain intensitas, dikaji pula hubungan El Nino (La Nina) dengan masa hidup dan frekuensi kemunculan fenomena siklon di sekitar wilayah Indonesia. Penelitian ini akan membandingkan data siklon pada tahun ketika terjadi peristiwa El Nino (La Nina) dengan data siklon pada tahun ketika tidak terjadi peristiwa El Nino (La Nina). Dalam kajian ini, digunakan data siklon selama 10 tahun (2007-2017) dari situs JAXA (Japan Aerospace Exploration Agency). Selain itu, digunakan juga data indeks El Nino dalam Nino 3.4 sebagai data aktivitas ENSO. Metode yang digunakan dalam penelitian ini menggunakan metode statistik. Dari hasil analisis tersebut, secara umum diperoleh bahwa El Nino (La Nina) memiliki pengaruh positif yang signifikan terhadap intensitas dan masa hidup siklon di sekitar wilayah Indonesia. Secara khusus, El Nino (La Nina) memiliki pengaruh positif yang signifikan terhadap intensitas dan masa hidup siklon di wilayah utara ekuator. Sementara itu, di selatan ekuator tidak menunjukan korelasi yang signifikan. Pengaruh El Nino (La Nina) terhadap frekuensi siklon belum dapat terlihat dengan jelas pada penelitian ini sehingga diperlukan penelitian lebih lanjut mengenai hal ini.

Intensity Estimation of Tropical Cyclones Using the Relevance Vector Machine From Infrared Satellite Image Data

In this study, tropical cyclone (TC) infrared geostationary satellite images obtained by FY-2 satellite scanning are used as reference points in the overall infrared geostationary satellite image, the deviation angle gradient co-occurrence matrix is constructed, and the statistical parameters of the co-occurrence matrix are extracted. Combined with the best track data of tropical cyclones provided by the China Meteorological Administration, the TC center is used as the origin point, and the satellite image is divided into nine 50 km scales, from the origin to a range of 450 km. Relevance vector machines are then used to construct the TC intensity estimation model for each scale. The experimental results show that the optimal radial scale is approximately 250 km. Based on these results, the proposed TC intensity estimation technique can be used for TC intensity prediction. This technique has the potential to improve the accuracy of TC intensity prediction and to provide technical support for disaster reduction.

Impacts of particulate matter on Gulf of Mexico tropical cyclones

ABSTRACTThis research analyzes the relationship between tropical cyclones and fine particulate matter (PM2.5) for landfalling Atlantic tropical cyclones from 2000 to 2015. Daily mean PM2.5 concentrations were collected from the United States Environmental Protection Agency. Tropical cyclone data were acquired from Tropical Prediction Center Best Track Reanalysis in Unisys weather. GRIdded Binary (GRIB formatted) data were downloaded from the Data Support Section of the Computational and Information Systems Laboratory at the National Center for Atmospheric Research (NCAR). Tracks of tropical cyclones were overlaid with the interpolated daily mean PM2.5 concentration value. Results suggest that, in general, tracks are distant from areas with the largest PM2.5 concentrations. To examine the cause-effect nature of this relationship, simulation using the Weather Research and Forecasting (WRF) model suggests that the intensity of Hurricane Lili was weakened only after passing the most PM2.5-polluted area in Louisiana. This result suggests that aerosol loading may weaken the intensity of tropical cyclones, at least in some cases.

Interactive visual analysis of spatiotemporal characteristics in tropical cyclone trajectory data

This paper presents an integrated visualization system that enables interactive visual analysis of trajectory data of tropical cyclones. This system couples together a novel visual exploring interface with a progressive-partition based trajectory clustering algorithm to enable users to interactively explore spatiotemporal patterns of tropical cyclone. The position, intensity change, and movement of cyclone can be observed vividly from the 2D spatial view. The occurrence frequencies, the lifetime, seasonality distribution, and other statistical features are computed on the fly during analysis and are inspected in multiple linked views. The experimental results with real cyclone data show the effectiveness of the proposed approach.

Return period and Pareto analyses of 45 years of tropical cyclone data (1970–2014) in the Philippines

The epiphenomena of tropical cyclones (TCs) such as landslides, storm surges, and floods cause the largest loss of life and property in the Philippines. In order to improve the disaster risk management efforts of the country, it is necessary to evaluate the return periods (RPs) or chance of occurrence (CoC) of TCs. Hence, this study generally aimed to investigate the relationship of the RPs/CoC, sea surface temperature (SST) anomaly associated with the Interdecadal Pacific Oscillation (IPO), and the cost of socio-economic damages and the number of deaths caused by TCs. The Weibull parametric models and the Pareto principle were utilised to achieve this overarching objective. Using the maximum sustained wind speed (v), forty-five (45) years of TC data (1970–2014) within the Philippines Area of Responsibility (PAR) were analysed by applying the stationary and non-stationary stochastic modelling techniques. The stationary Weibull probability density function revealed that TCs Rita (1978), Dot (1985) and Haiyan (2013) occupy the wind speed region of 61 ≥ v ≤ 64 m/s with a probability of 0.4% for any given year. On the other hand, the analysis of the cumulative distribution function revealed a 60% probability of TCs for the cumulative years with a maximum sustained wind speed of at most 38 m/s. This indicates the central estimate of the wind speed from 1970 to 2014 with TCs Ruby (1988) and Vicki (1998) as the observed cases. Furthermore, the probability values on the annual CoC maps depict the indicative positions of TCs, either singly, co-shared or cross-shared, that made landfall (or not) in the Philippines. Results from the non-stationary stochastic modelling revealed that the low probability values on the decadal CoC maps indicate the locations where extreme TC events are likely to occur within PAR; hence, showing the areas in the country that are more at-risk. The relationship of SST anomaly and CoC values disclosed that the TCs are intensified in the northern Philippines and south of West Philippines Sea during the positive(+) phase and the negative(-) phase of the IPO, respectively. Finally, the Pareto analysis revealed that 80% of the TC-related damage cost and the number of deaths are shared by three (3) different stationary and non-stationary RPs with TCs Ike (1984), Nina (1987), Fengshen (2008), Mike (1990), Parma (2009), and Haiyan (2013) as the observed extreme events. In the absence of accurate or updated cyclone risk models, the communities that are highly vulnerable to TCs can use the stationary and non-stationary stochastic CoC models as an early warning tool for disaster preparedness. Ultimately, the results of this study can provide significant insights to support the Philippines in their pursuit of improving cyclone resilience programs.

Variation Characteristics of Tropical Cyclones Making Landfall over China between 1951 and 2015 and Their Relationship with ENSO

Based on tropical cyclone (TC) data provided by the America Joint Typhoon Warning Center, this paper analyzes the variation characteristics of tropical cyclones making landfall over China during the 65-year period of 1951–2015 and their statistical relationship with the El Niño–Southern Oscillation (ENSO). The conclusions are as follows. (1) The landfall frequency during the period of 1951–2015 has strong inter-annual and inter-decadal variability characteristics, and an abrupt change in the landfall frequency occurred in 1988. However, from the long-term variation frequency trend, the landfall frequency decreased slowly from the late 1980s to 2015. (2) The landfall intensity increased rapidly from the late 1950s to the early 1960s, with an abrupt change occurring in 1996. In addition, there is a significant decreasing trend and then an increasing trend from the late 1960s to 2015. (3) There are significant negative correlations between the landfall frequency and the sea surface temperature (SST) in the ENSO-3.4 region. All the sliding correlation coefficients are negative from 1961 to 2002. A strong abrupt change in their correlation occurred in 1970, changing from a weak negative to a strong negative correlation. (4) There are no significant negative correlations between the landfall intensity and the SST in the ENSO-3.4 region, and their sliding correlation coefficient of inter-annual variability shows that there is an alternating appearance of positive and negative correlations. A strong abrupt change occurs in 1999, changing from a weak positive correlation to a strong negative correlation. The significant negative correlation occurred in the 2005. (5) The TC genesis locations move to the southeast in El Niño years and shift to the northeast in La Niña years. (6) TC frequency that influences the Chinese continental coast line in El Niño years is less than that which influences Japan. Meanwhile, in La Niña years, the TC frequency that influences Japan and its surrounding waters, the China Sea, the BohaiSea, and the South China Sea, increases, while it decreases in the TaiwanProvince.

COMPARATIVE STUDY OF CYCLONE SEVERITY LEVEL PREDICTION (CSLP) FOR CLASSIFICATION IN SPATIAL TEMPORAL DATA

The field of Data Mining has grown exponentially in the last decade. Spatial Temporal mining is a sub task, which deals with large set of dynamically varying spatial temporal data objects. Prediction of cyclone intensity level is very essential to take defensivemethods in the neighboring spatial regions. Cyclones are characterized by inward spiralling winds that rotate about a zone of low pressure. It is caused by atmospheric disturbances around a low-pressure area and is usually accompanied by violent storms and severe weather conditions. Differences in wind speed and wind shear keep cyclones effectively tearing the storms apart, before they reach a certain size. The wind patterns reduce wind shear and enables cyclone to grow twice as intense. Comparative study does experiments on various classification algorithms suitable to spatial temporal cyclone data and proposes a solution which removes outliers and accurately classifies cyclone intensity levels.

Projected Change in Wintertime Precipitation in California Using Projected Changes in Extratropical Cyclone Activity

Abstract Wintertime extratropical cyclones in the east Pacific region are the source of much of the precipitation over California. There is a lot of uncertainty in future projections of Californian precipitation associated with predicted changes in the jet stream and the midlatitude storm tracks. The question this work seeks to answer is how the changes in the frequency and the intensity of extratropical cyclones in the Pacific storm track influence future changes in Californian precipitation. The authors used an objective cyclone identification method applied to 25 CMIP5 models for the historical and RCP8.5 simulations and investigated the changing relationships between storm frequency, intensity and precipitation. Cyclone data from the historical simulations and differences between the historical and RCP8.5 simulations were used to “predict” the modeled precipitation in the RCP8.5 simulations. In all models, the precipitation predicted using historical relationships gives a lower future precipitation change than the direct model output. In the future, the relationship between track density and precipitation indicates that for the same number of tracks, more precipitation is received. The relationship between track intensity and precipitation (which is quite weak in the historical simulations) does not change in the future. This suggests that other sources, likely enhanced moisture availability, are more important than changes in the intensity of cyclones for the rainfall associated with the storm tracks.

Resolving Tropical Cyclone Intensity in Models

AbstractIn recent years, global weather forecast models and global climate models have begun to depict intense tropical cyclones, even up to category 5 on the Saffir‐Simpson scale. In light of the limitation of horizontal resolution in such models, the author performs calculations, using the extended Best Track data for Atlantic tropical cyclones, to estimate the ability of models with differing grid spacing to represent Atlantic tropical cyclone intensity statistically. Results indicate that, under optimistic assumptions, models with horizontal grid spacing of one fourth degree or coarser should not produce a realistic number of category 4 and 5 storms unless there are errors in spatial attributes of the wind field. Furthermore, the case of Irma (2017) is used to demonstrate the importance of a realistic depiction of angular momentum and to motivate the use of angular momentum in model evaluation.

Satellite Observations of Stratospheric Gravity Waves Associated With the Intensification of Tropical Cyclones

AbstractForecasting the intensity of tropical cyclones is a challenging problem. Rapid intensification is often preceded by the formation of “hot towers” near the eyewall. Driven by strong release of latent heat, hot towers are high‐reaching tropical cumulonimbus clouds that penetrate the tropopause. Hot towers are a potentially important source of stratospheric gravity waves. Using 13.5 years (2002–2016) of Atmospheric Infrared Sounder observations of stratospheric gravity waves and tropical cyclone data from the International Best Track Archive for Climate Stewardship, we found empirical evidence that stratospheric gravity wave activity is associated with the intensification of tropical cyclones. The Atmospheric Infrared Sounder and International Best Track Archive for Climate Stewardship data showed that strong gravity wave events occurred about twice as often for tropical cyclone intensification compared to storm weakening. Observations of stratospheric gravity waves, which are not affected by obscuring tropospheric clouds, may become an important future indicator of storm intensification.

Precipitation, Convective Clouds, and Their Connections With Tropical Cyclone Intensity and Intensity Change

AbstractIn this paper, satellite‐based precipitation, clouds with infrared (IR) brightness temperature (BT), and tropical cyclone (TC) data from 2000 to 2015 are used to explore the relationship between precipitation, convective cloud, and TC intensity change in the Western North Pacific Ocean. An IR BT of 208 K was chosen as a threshold for deep convection based on different diurnal cycles of IR BT. More precipitation and colder clouds with 208 K < IR BT < 240 K are found as storms intensify, while TC 24 h future intensity change is closely connected with very deep convective clouds with IR BT < 208 K. Intensifying TCs follow the occurrence of colder clouds with IR BT < 208 K with greater areal extents. As an indicator of very deep convective clouds, IR BT < 208 K is suggested to be a good predictor of TC intensity change. Based upon the 16 year analysis in the western North Pacific, TCs under the conditions that the mean temperature of very deep convective clouds is less than 201 K, and the coverage of this type of clouds is more than 27.4% within a radius of 300 km of the TC center, will more likely undergo rapid intensification after 24 h.

A METHOD OF RAPID CLASSIFICATION OF TROPICAL CYCLONE TRACKS OVER CHINA AND ITS APPLICATION

Using data of tropical cyclones making landfall in China between May and October each year during the 1951-2015 period from the Shanghai Typhoon Institute, China Meteorological Administration (CMA-STI) Tropical Cyclone (TC) Best Track Dataset, we developed a method of rapid classification of TC tracks based on their average movement velocities and noted three types of tracks: a westward type, a northwestward type, and a northward type. We compared the climate characteristics of the westward and northward types and discuss their corresponding causes. The results show that the westward and northward types account for more than 80% of all TCs making landfall in China. Their climate characteristics, such as the frequency, landfall intensity, duration over land, velocity over land, movement distance over land, and other changes, show both similarities and differences. Both TC types show significant increases in their over-land durations, indicating that the effects of these landfalling TCs are increasing. However, the causes of these two TC types are similar and different in certain respects. The changes in large-scale steering flows have significantly affected the frequencies and over-land velocities of the landfalling TCs of the westward and northward types. In addition, differences between the changes in formation locations of the westward and northward types may lead to significant difference in their landfall intensities.

English

Properly organized data is vital for appropriate statistics and theories. In this study, it was hypothesized that raw tropical cyclone (TC) data labeled with the current Gregorian time system, dampened the dominant signals and order in the data. Therefore, the objective of this study was to explore and reorganize the data, using the TianGan-DiZhi (T-D) calendar. All 6 h TC records in 60 sidereal years over the western North Pacific (WNP) were investigated after the data were transferred from the Gregorian to T-D calendar. TianGan and DiZhi, two collections of elements in the T-D calendar, were then quantified to conduct correlation analyses with different TC parameters. The results showed significant temporal and spatial correlation between 6 h TC records and variables in the T-D calendar over different timescales. Temporally, 6 h TC records in the T-D summer, generally from May 5 to August 6, of the 60 sidereal years were significantly correlated with the strength difference between yearly TianGan and yearly DiZhi for the sidereal years. Spatially, the longitudes and latitudes of 6 h TC records were also significantly correlated with daily variables in the T-D calendar. We conclude that, TC data over the WNP can be better interpreted using the quantified T-D calendar than the Gregorian calendar. Since this ancient time-labeling tool can provide properly organized data, it might be used to modify some inputs in current numerical models to improve forecasting power. Key words: Tropical cyclone, frequency, temporal, sidereal, Gan-Zhi, calendar.

A collection of experimental data for aerosol monitoring cyclones

ABSTRACTCyclone behavior is complex and difficult to model. Recent years have seen the development of new and better predictive models for cyclone performance, which are providing new insights into how cyclone performance is affected by cyclone geometry. Experimental data are essential for verification of such models. In this article we present a dataset of more than 250 experimental determinations of cyclone penetration. The dataset includes cyclones with a wide range of sizes and geometries, tested at a wide range of flow rates. We illustrate some empirical, semi-empirical and mathematical approaches to modeling these cyclone data. For our data, we show that mathematical modeling approaches developed for large gas-cleaning cyclones can also be applied to small aerosol monitoring cyclones, to diverse cyclone geometries, and laminar flow operating conditions.Copyright © Crown copyright

A New Variational Assimilation Method Based on Gradient Information from Satellite Data

With the development of meteorological observation technology, satellite data have found increasingly wide use in the numerical weather prediction field. However, there are various observational biases in satellite data, including a random bias brought about by complex weather systems and a systematic bias caused by the instrument itself, which greatly influence the quality of satellite data. A gradient information assimilation method is proposed in this paper to eliminate systematic bias. This method uses a gradient operator for gradient transformation between the model variable and observation variable and reaches the objective of eliminating systematic bias. An ideal experiment of variational data assimilation is conducted using the Community Radiative Transfer Model (CRTM) and Advanced Microwave Sounding Unit-A (AMSU-A) data, indicating that only assimilating gradient information can eliminate the smooth systematic bias in observation data. Then, a numerical simulation of tropical cyclone (TC) Megi and data assimilation experiment are conducted using the Weather Research Forecast (WRF) and WRF Data Assimilation (WRFDA) model as well as the Atmospheric Infrared Sounder (AIRS) data. The results show that the method of gradient information assimilation can improve the accuracy of TC tracks forecast and is also applicable for dealing with unreliable satellite data.

Wave Spectral Patterns during a Historical Cyclone: A Numerical Model for Cyclone Gonu in the Northern Oman Sea

The third generation wind-wave model Mike21-SW was used to study spectral characteristics of waves generated by the historical Cyclone Gonu in June 2007 along and off the Iranian coasts on the northern Oman Sea. The model was forced with the cyclone wind field generated using a Holland (1980) model based on cyclone data obtained from the Joint Typhon Warning Center (JTWC). The wave model was calibrated for the northern Oman Sea using bulk and spectral wave data at a station out of the Chabahar Bay. Evolution of directional-frequency spectra during the cyclone was investigated for two locations near the entrance and off the Chabahar Bay. At the offshore station, energy was contributed to the spectrum over an approximately 180 degree directional span that included different local and remotely generated waves. As the cyclone proceeded northwestward, all spectral directions continuously rotated in the clockwise direction at both locations. Frequency spectra at these locations were investigated for four different times corresponding to different locations of Cyclone’s eye and were justified using the sea growth parameter of the Joint North Sea Wave Project (JONSWAP) experiment. Using the modified JONSWAP parameters for hurricane conditions resulted in a frequency spectrum consistent with simulation results.

Modelling tropical cyclone hazards under climate change scenario using geospatial techniques

Tropical cyclones are a common and devastating natural disaster in many coastal areas of the world. As the intensity and frequency of cyclones will increase under the most likely future climate change scenarios, appropriate approaches at local scales (1-5 km) are essential for producing sufficiently detailed hazard models. These models are used to develop mitigation plans and strategies for reducing the impacts of cyclones. This study developed and tested a hazard modelling approach for cyclone impacts in Sarankhola upazila, a 151 km2 local government area in coastal Bangladesh. The study integrated remote sensing, spatial analysis and field data to model cyclone generated hazards under a climate change scenario at local scales covering < 1000 km2. A storm surge model integrating historical cyclone data and Digital Elevation Model (DEM) was used to generate the cyclone hazard maps for different cyclone return periods. Frequency analysis was carried out using historical cyclone data (1960--2015) to calculate the storm surge heights of 5, 10, 20, 50 and 100 year return periods of cyclones. Local sea level rise scenario of 0.34 m for the year 2050 was simulated with 20 and 50 years return periods. Our results showed that cyclone affected areas increased with the increase of return periods. Around 63% of study area was located in the moderate to very high hazard zones for 50 year return period, while it was 70% for 100 year return period. The climate change scenarios increased the cyclone impact area by 6-10 % in every return period. Our findings indicate this approach has potential to model the cyclone hazards for developing mitigation plans and strategies to reduce the future impacts of cyclones.

Risk and Adaptation: Evidence from Global Hurricane Damages and Fatalities

AbstractWe examine whether countries adapt to hurricanes. A spatially refined global tropical cyclone data set is created to test for adaptation. We find evidence of adaptation in most of the world by examining the effects of income, population density, and storm frequency on damage and fatalities. In contrast, there is no evidence of adaptation to damage in the United States, leading to a damage function which is 14 times higher than other developed (OECD) countries.

Observed trends and impacts of tropical cyclones in the Philippines

ABSTRACTAn analysis of tropical cyclone (TC) data from 1951 to 2013 in the Philippines revealed that an average of 19.4 TCs enter the Philippine Area of Responsibility (PAR) every year and nine TCs cross the country. Time series analysis of the TC datasets shows no significant trends in the annual number of TCs in PAR but a slightly decreasing trend in the number of landfalling TCs in the Philippines, particularly in the last two decades. However, while the analysis shows fewer typhoons (above 118 kph), more extreme TCs (above 150 kph) have affected the Philippines. The study also confirms that the Northern island of Luzon is most frequently hit by TCs, and that TC‐associated rainfall is greatest in this region compared to the southernmost part of the country. The impact of TCs shows a consistently increasing trend in economic losses and damages. Further understanding of past and future trends of TC activity in the Western North Pacific Basin, and the PAR, including the impacts associated with them, will provide valuable insights for climate change adaptation and disaster risk management.

Increased threat of tropical cyclones and coastal flooding to New York City during the anthropogenic era

In a changing climate, future inundation of the United States' Atlantic coast will depend on both storm surges during tropical cyclones and the rising relative sea levels on which those surges occur. However, the observational record of tropical cyclones in the North Atlantic basin is too short (A.D. 1851 to present) to accurately assess long-term trends in storm activity. To overcome this limitation, we use proxy sea level records, and downscale three CMIP5 models to generate large synthetic tropical cyclone data sets for the North Atlantic basin; driving climate conditions span from A.D. 850 to A.D. 2005. We compare pre-anthropogenic era (A.D. 850-1800) and anthropogenic era (A.D.1970-2005) storm surge model results for New York City, exposing links between increased rates of sea level rise and storm flood heights. We find that mean flood heights increased by ∼1.24 m (due mainly to sea level rise) from ∼A.D. 850 to the anthropogenic era, a result that is significant at the 99% confidence level. Additionally, changes in tropical cyclone characteristics have led to increases in the extremes of the types of storms that create the largest storm surges for New York City. As a result, flood risk has greatly increased for the region; for example, the 500-y return period for a ∼2.25-m flood height during the pre-anthropogenic era has decreased to ∼24.4 y in the anthropogenic era. Our results indicate the impacts of climate change on coastal inundation, and call for advanced risk management strategies.