A Parameter for Forecasting Tornadoes Associated with Landfalling Tropical Cyclones

2014 ◽  
Vol 29 (5) ◽  
pp. 1238-1255 ◽  
Author(s):  
Matthew J. Onderlinde ◽  
Henry E. Fuelberg
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Stepwise Regression ◽  
Atlantic Coast ◽  
Convective Available Potential Energy ◽  
Environmental Parameters ◽  
Time Of Day ◽  
Skill Scores ◽  
Landfalling Tropical Cyclones ◽  
Center Time

Abstract The authors develop a statistical guidance product, the tropical cyclone tornado parameter (TCTP), for forecasting the probability of one or more tornadoes during a 6-h period that are associated with landfalling tropical cyclones affecting the coastal Gulf of Mexico and the southern Atlantic coast. TCTP is designed to aid forecasters in a time-limited environment. TCTP provides a “quick look” at regions where forecasters can then conduct detailed analyses. The pool of potential predictors included tornado reports and tropical cyclone data between 2000 and 2008, as well as storm environmental parameters. The original pool of 28 potential predictors is reduced to six using stepwise regression and logistic regression. These six predictors are 0–3-km wind shear, 0–3-km storm relative helicity, azimuth angle of the tornado report from the tropical cyclone, distance from the cyclone’s center, time of day, and 950–1000-hPa convective available potential energy. Mean Brier scores and Brier skill scores are computed for the entire TCTP-dependent dataset and for corresponding forecasts produced by the Storm Prediction Center (SPC). TCTP then is applied to four individual cyclone cases to qualitatively and quantitatively assess the parameter and compare its performance with SPC forecasts. Results show that TCTP has skill at identifying regions of tornado potential. However, tornadoes in some tropical systems are overpredicted, but underpredicted in others. TCTP 6-h forecast periods provide slightly poorer statistical performance than the 1-day tornado probability forecasts from SPC, probably because the SPC product includes forecaster guidance and because their forecasts are valid for longer periods (24 h).

scholarly journals Forecasting Tropical Cyclone Formation in the Fiji Region: A Probit Regression Approach Using Bayesian Fitting

2011 ◽  
Vol 26 (2) ◽  
pp. 150-165 ◽  
Author(s):  
Savin S. Chand ◽  
Kevin J. E. Walsh
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Environmental Parameters ◽  
Model Parameters ◽  
Probit Regression ◽  
Relative Operating Characteristic ◽  
Verification Methods ◽  
Skill Scores ◽  
Regression Approach ◽  
Objective Methodology

Abstract An objective methodology for forecasting the probability of tropical cyclone (TC) formation in the Fiji, Samoa, and Tonga regions (collectively the FST region) using antecedent large-scale environmental conditions is investigated. Three separate probabilistic forecast schemes are developed using a probit regression approach where model parameters are determined via Bayesian fitting. These schemes provide forecasts of TC formation from an existing system (i) within the next 24 h (W24h), (ii) within the next 48 h (W48h), and (iii) within the next 72 h (W72h). To assess the performance of the three forecast schemes in practice, verification methods such as the posterior expected error, Brier skill scores, and relative operating characteristic skill scores are applied. Results suggest that the W24h scheme, which is formulated using large-scale environmental parameters, on average, performs better than that formulated using climatology and persistence (CLIPER) variables. In contrast, the W48h (W72h) scheme formulated using large-scale environmental parameters performs similar to (poorer than) that formulated using CLIPER variables. Therefore, large-scale environmental parameters (CLIPER variables) are preferred as predictors when forecasting TC formation in the FST region within 24 h (at least 48 h) using models formulated in the present investigation.

scholarly journals An Extended Model for the Potential Intensity of Tropical Cyclones

2012 ◽  
Vol 69 (2) ◽  
pp. 641-661 ◽  
Author(s):  
Thomas Frisius ◽  
Daria Schönemann
Keyword(s):  
Steady State ◽  
Wind Speed ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Convective Instability ◽  
Cloud Model ◽  
Convective Available Potential Energy ◽  
Extended Model ◽  
Flow Imbalance ◽  
Conditional Instability

Abstract Emanuel’s theory of hurricane potential intensity (E-PI) makes use of the assumption that slantwise convective instability vanishes in a steady-state vortex of a tropical cyclone. In the framework of an extended mathematical potential intensity model it is shown that relaxing this assumption and including an eye results in a larger maximum wind speed compared to that of the predictions made by E-PI. Previous studies by Bryan and Rotunno demonstrate that the effect of unbalanced flow considerably contributes to maximum winds in excess of E-PI (“superintensity”). The authors argue that the proposed mechanism induced by convective instability provides another possible explanation for simulated and observed tropical cyclones exceeding E-PI in addition to flow imbalance. Further evidence for the relevance of conditional instability in mature tropical cyclones to superintensity is given by the fact that convective available potential energy arises in numerical simulations of tropical cyclones. This is demonstrated by means of an axisymmetric cloud model that is in qualitative agreement with the analytical model. These simulations reveal a dependence of superintensity on the amount of CAPE outside the eyewall and also reproduce the decrease in superintensity with increased horizontal diffusion as found in previous studies.

Characteristics of Landfalling Tropical Cyclones in the United States and Mexico: Climatology and Interannual Variability

2005 ◽  
Vol 18 (8) ◽  
pp. 1247-1262 ◽  
Author(s):  
Joshua Larson ◽  
Yaping Zhou ◽  
R. Wayne Higgins
Keyword(s):  
United States ◽  
Tropical Cyclone ◽  
Climate Variability ◽  
Interannual Variability ◽  
Atmospheric Circulation ◽  
Tropical Cyclones ◽  
El Niño ◽  
El Nino ◽  
The Arctic ◽  
Landfalling Tropical Cyclones

Abstract The climatology and interannual variability of landfalling tropical cyclones and their impacts on precipitation in the continental United States and Mexico are examined. The analysis is based on National Hurricane Center 6-hourly tropical cyclone track data for the Atlantic and eastern Pacific basins and gridded daily U.S. precipitation data for the period August–October 1950–98. Geographic maps of total tropical cyclone strike days, and the mean and maximum percentage of precipitation due to tropical cyclones, are examined by month. To make the procedures objective, it is assumed that precipitation is symmetric about the storm’s center. While this introduces some uncertainty in the analysis, sensitivity tests show that this assumption is reasonable for precipitation within 5° of the circulation center. The relationship between landfalling tropical cyclones and two leading patterns of interannual climate variability—El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO)—are then examined. Relationships between tropical cyclone frequency and intensity and composites of 200-hPa geopotential height and wind shear anomalies are also examined as a function of ENSO phase and AO phase using classifications devised at the Climate Prediction Center. The data show that tropical cyclone activity in the Atlantic basin is modulated on both seasonal and intraseasonal time scales by the AO and ENSO and that impact of the two modes of climate variability is greater together than apart. This suggests that, during La Niña conditions, atmospheric circulation is more conducive to activity in the main development region during AO-positive conditions than during AO-negative ones and that, during El Niño conditions, atmospheric circulation appears even less conducive to tropical cyclone development during the negative phase of the AO than during the positive phase.

Spatial variations of North Atlantic landfalling tropical cyclone wind speed decay over the continental United States

2021 ◽  
Author(s):  
Yi-Jie Zhu ◽  
Jennifer M. Collins ◽  
Philip J. Klotzbach
Keyword(s):  
United States ◽  
Wind Speed ◽  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
East Coast ◽  
Gulf Coast ◽  
Spatial Variations ◽  
Decay Model ◽  
Weak Intensity ◽  
Landfalling Tropical Cyclones

AbstractUnderstanding tropical cyclone wind speed decay during the post-landfall stage is critical for inland hazard preparation. This paper examines the spatial variation of wind speed decay of tropical cyclones over the continental United States. We find that tropical cyclones making landfall over the Gulf Coast decay faster within the first 24 hours after landfall than those making landfall over the Atlantic East Coast. The variation of the decay rate over the Gulf Coast remains larger than that over the Atlantic East Coast for tropical cyclones that had made landfall more than 24 hours prior. Besides an average weaker tropical cyclone landfall intensity, the near-parallel trajectory and the proximity of storms to the coastline also help to explain the slower post-landfall wind speed decay for Atlantic East Coast landfalling tropical cyclones. Tropical cyclones crossing the Florida peninsula only slowly weaken after landfall, with an average of less than 20% post-landfall wind speed drop while transiting the state. The existence of these spatial variations also brings into question the utility of a uniform wind decay model. While weak intensity decay over the Florida peninsula is well estimated by the uniform wind decay model, the error from the uniform wind decay model increases with tropical cyclones making direct landfall more parallel to the Atlantic East Coast. The underestimation of inland wind speed by the uniform wind decay model found over the western Gulf Coast brings attention to the role of land-air interactions in the decay of inland tropical cyclones.

scholarly journals Simulation of Structure, Track and Landfall of Tropical Cyclone Bijli Using WRF-ARW Model

2015 ◽  
Vol 39 (2) ◽  
pp. 157-167 ◽  
Author(s):  
KM Zahir Rayhun ◽  
DA Quadir ◽  
MA Mannan Chowdhury ◽  
MN Ahasan ◽  
MS Haque
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Convective Available Potential Energy ◽  
Wrf Model ◽  
Relative Vorticity ◽  
Cumulus Parameterization ◽  
Arw Model ◽  
Boundary Layer Scheme ◽  
Cumulus Parameterization Schemes ◽  
Academy Of Sciences

An attempt was made to simulate the structure, track, landfall and a few dynamical aspects of the tropical cyclone Bijli that formed over the Bay of Bengal using WRF-ARW model. WRF model was run in a single domain using KF cumulus parameterization schemes with WSM 3 micro physics and YSU planetary boundary layer scheme. The ARW model was run for 24, 48, 72 and 96 hrs to simulate structure, track and landfall of tropical cyclones Bijli. The different simulated parameters viz. minimum sea level pressure, maximum wind speed, convective available potential energy and relative vorticity have been studied. The results showed that the model is capable to forecast the formation of the first depression 60 - 78 hrs in advance. This indicates the high and unique predictive power of ARW model for predicting the tropical cyclone formation. The model generates a realistic structure of the tropical cyclones with high spatial details. This was possible due to the higher spatial resolution of the regional model. One of the outstanding findings of the study is that the model was successfully predicted the tracks, recurvature and probable areas and time of landfall of the selected tropical cyclone Bijli with high accuracy even in the 96 hrs predictions.Journal of Bangladesh Academy of Sciences, Vol. 39, No. 2, 157-167, 2015

scholarly journals On the Use of Ensemble Predictions for Parametric Typhoon Insurance

Climate ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 174
Author(s):  
Kelvin S. Ng ◽  
Gregor C. Leckebusch ◽  
Qian Ye ◽  
Wenwen Ying ◽  
Haoran Zhao
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Hazard Assessment ◽  
Trigger Points ◽  
Added Value ◽  
Ensemble Prediction ◽  
Landfalling Tropical Cyclones ◽  
Prediction Systems ◽  
Set Up ◽  
Post Disaster

Parametric typhoon insurances are an increasingly used financial tool to mitigate the enormous impact of tropical cyclones, as they can quickly distribute much-needed resources, e.g., for post-disaster recovery. In order to optimise the reliability and efficiency of parametric insurance, it is essential to have well-defined trigger points for any post-disaster payout. This requires a robust localised hazard assessment for a given region. However, due to the rarity of severe, landfalling tropical cyclones, it is difficult to obtain a robust hazard assessment based on historical observations. A recent approach makes use of unrealised, high impact tropical cyclones from state-of-the-art ensemble prediction systems to build a physically consistent event set, which would be equivalent to about 10,000 years of observations. In this study, we demonstrate that (1) alternative trigger points of parametric typhoon insurance can be constructed from a local perspective and the added value of such trigger points can be analysed by comparing with an experimental set-up informed by current practice; (2) the estimation of the occurrence of tropical cyclone-related losses on the provincial level can be improved. We further discuss the potential future development of a general tropical cyclone compound parametric insurance.

Validation Schemes for Tropical Cyclone Quantitative Precipitation Forecasts: Evaluation of Operational Models for U.S. Landfalling Cases

2007 ◽  
Vol 22 (4) ◽  
pp. 726-746 ◽  
Author(s):  
Timothy Marchok ◽  
Robert Rogers ◽  
Robert Tuleya
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Forecast Error ◽  
Heavy Rain ◽  
Track Forecast ◽  
The Core ◽  
Quantitative Precipitation Forecasts ◽  
Operational Models ◽  
Landfalling Tropical Cyclones ◽  
The Impact

Abstract A scheme for validating quantitative precipitation forecasts (QPFs) for landfalling tropical cyclones is developed and presented here. This scheme takes advantage of the unique characteristics of tropical cyclone rainfall by evaluating the skill of rainfall forecasts in three attributes: the ability to match observed rainfall patterns, the ability to match the mean value and volume of observed rainfall, and the ability to produce the extreme amounts often observed in tropical cyclones. For some of these characteristics, track-relative analyses are employed that help to reduce the impact of model track forecast error on QPF skill. These characteristics are evaluated for storm-total rainfall forecasts of all U.S. landfalling tropical cyclones from 1998 to 2004 by the NCEP operational models, that is, the Global Forecast System (GFS), the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model, and the North American Mesoscale (NAM) model, as well as the benchmark Rainfall Climatology and Persistence (R-CLIPER) model. Compared to R-CLIPER, all of the numerical models showed comparable or greater skill for all of the attributes. The GFS performed the best of all of the models for each of the categories. The GFDL had a bias of predicting too much heavy rain, especially in the core of the tropical cyclones, while the NAM predicted too little of the heavy rain. The R-CLIPER performed well near the track of the core, but it predicted much too little rain at large distances from the track. Whereas a primary determinant of tropical cyclone QPF errors is track forecast error, possible physical causes of track-relative differences lie with the physical parameterizations and initialization schemes for each of the models. This validation scheme can be used to identify model limitations and biases and guide future efforts toward model development and improvement.

scholarly journals A case study for cyclone ‘Aila’ for forecasting rainfall using satellite derived rain rate data

MAUSAM ◽  
2022 ◽  
Vol 64 (1) ◽  
pp. 77-82
Author(s):  
HABIBURRAHAMAN BISWAS ◽  
P.K. KUNDU ◽  
D. PRADHAN
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Storm Track ◽  
Tropical Rainfall Measuring Mission ◽  
Tropical Rainfall ◽  
Cyclone Aila ◽  
Potential Trap ◽  
Landfalling Tropical Cyclones ◽  
Rain Rates

caxky dh [kkM+h esa cuus ,oa tehu ls Vdjkus okys pØokrh; rwQkuksa ds  ifj.kkeLo:i  Hkkjh o"kkZ dh otg ls if’pe caxky ds rV lesr Hkkjr ds iwohZ rV ds yksxksa dh tku eky dks dkQh [krjk jgrk gSA tehu ls Vdjkus okys m".kdfVca/kh; pØokrh rwQkuksa dh otg ls gksus okyh o"kkZ dh ek=k dk iwokZuqeku djuk cgqr dfBu gSA m".kdfVca/kh; pØokrh; rwQkuksa ds nk;js esa vkus okys o"kkZ okys {ks=ksa esa laHkkfor pØokrh; rwQku ls gksus okys o"kkZ lap;u dk iwokZuqeku djus ds fy, mixzg ls izkIr o"kkZ njksa dk mi;ksx fd;k tk ldrk gSA bl 'kks/k i= esa ‘vkbyk’ ds m".kdfVca/kh; o"kkZ ekiu fe’ku ¼Vh- vkj- ,e- ,e-½] mixzg o"kkZ nj vk¡dM+ksa rFkk rwQku ds ns[ks x, ekxZ dk mi;ksx djrs gq, m".kdfVca/kh; pØokr ‘vkbyk’ ds tehu ls Vdjkus ls 24 ?kVsa igys rVh; LVs’kuksa ij o"kkZ dk vkdyu djus dk iz;kl fd;k x;k gSA la;qDr jkT; vesfjdk esa fodflr lqifjfpr rduhd ds vk/kkj ij  m".kdfVca/kh; pØokr ‘vkbyk’ ds tehu ls Vdjkus ds 24 ?kaVs igys m".kdfVca/kh; o"kkZ foHko ¼Vh- vkj- ,- ih-½ iwokZuqeku fo’ks"k :i  ls rwQku dh fn’kk ds lkeus vkus okys rVh; {ks=ksa ds fy, vPNh o"kkZ dk iwokZuqeku miyC/k djkrk gSA Major threat to the life and property of people on the east coast of India, including West Bengal Coast, is due to very heavy rainfall from landfalling tropical cyclones originated over Bay of Bengal. Forecasting magnitude of rainfall from landfalling tropical cyclones is very difficult. Satellite derived rain rates over the raining areas of tropical cyclones can be used to forecast potential tropical cyclone rainfall accumulations. In the present study, an attempt has been made to estimate 24 hours rainfall over coastal stations before landfall of tropical Cyclone ‘Aila’ using Tropical Rainfall Measuring Mission (TRMM) satellite rain rates data and observed storm track of Aila. Forecast Tropical Rainfall Potential (TRaP), 24 hours prior to landfall for the tropical cyclone ‘Aila’ based on well known technique developed in USA, provides a good rainfall forecast especially for the coastal areas lying at the head of direction of the storm.

scholarly journals Pacific subsurface ocean temperature as a long-range predictor of South China tropical cyclone landfall

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Nathan Sparks ◽  
Ralf Toumi
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
South China ◽  
Southern Oscillation ◽  
Warm Pool ◽  
Ocean Temperature ◽  
Seasonal Forecasts ◽  
Subsurface Ocean ◽  
Landfalling Tropical Cyclones ◽  
Tropical Cyclone Landfall

Abstract Seasonal forecasts of the tropical cyclones which frequently make landfall along the densely populated South China coast are highly desirable. Here, we analyse observations of landfalling tropical cyclones in South China and of subsurface ocean temperatures in the Pacific warm pool region, and identify the possibility of forecasts of South China tropical cyclone landfall a year ahead. Specifically, we define a subsurface temperature index, subNiño4, and build a predictive model based on subNiño4 anomalies with a robust double cross-validated forecast skill against climatology of 23%, similar in skill to existing forecasts issued much later in the spring. We suggest that subNiño4 ocean temperatures precede the surface El Niño/Southern Oscillation state by about 12 months, and that the zonal shifts in atmospheric heating then change mid-level winds to steer tropical cyclones towards landfall in South China. We note that regional subsurface ocean temperature anomalies may permit atmospheric predictions in other locations at a longer range than is currently thought possible.

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