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.

Extended Prediction of North Indian Ocean Tropical Cyclones

2012 ◽  
Vol 27 (3) ◽  
pp. 757-769 ◽  
Author(s):  
James I. Belanger ◽  
Peter J. Webster ◽  
Judith A. Curry ◽  
Mark T. Jelinek
Keyword(s):  
Tropical Cyclone ◽  
Indian Ocean ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Ensemble Prediction ◽  
Tropical Cyclone Genesis ◽  
Lead Times ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
The North

Abstract This analysis examines the predictability of several key forecasting parameters using the ECMWF Variable Ensemble Prediction System (VarEPS) for tropical cyclones (TCs) in the North Indian Ocean (NIO) including tropical cyclone genesis, pregenesis and postgenesis track and intensity projections, and regional outlooks of tropical cyclone activity for the Arabian Sea and the Bay of Bengal. Based on the evaluation period from 2007 to 2010, the VarEPS TC genesis forecasts demonstrate low false-alarm rates and moderate to high probabilities of detection for lead times of 1–7 days. In addition, VarEPS pregenesis track forecasts on average perform better than VarEPS postgenesis forecasts through 120 h and feature a total track error growth of 41 n mi day−1. VarEPS provides superior postgenesis track forecasts for lead times greater than 12 h compared to other models, including the Met Office global model (UKMET), the Navy Operational Global Atmospheric Prediction System (NOGAPS), and the Global Forecasting System (GFS), and slightly lower track errors than the Joint Typhoon Warning Center. This paper concludes with a discussion of how VarEPS can provide much of this extended predictability within a probabilistic framework for the region.

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).

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.

scholarly journals The AEMET-<i>γ</i>SREPS over the Antarctic Peninsula and the impact of kilometric-resolution EPS on logistic activities on the continent

2020 ◽  
Vol 17 ◽  
pp. 209-217
Author(s):  
Sergi Gonzalez ◽  
Alfons Callado ◽  
Mauricia Martínez ◽  
Benito Elvira
Keyword(s):  
Boundary Conditions ◽  
Antarctic Peninsula ◽  
Positive Impact ◽  
Austral Summer ◽  
Added Value ◽  
Ensemble Prediction ◽  
Prediction Systems ◽  
Nwp Model ◽  
The Antarctic ◽  
The Impact

Abstract. Kilometric-resolution Ensemble Prediction Systems (EPSs) will be the new state-of-the-art forecasting tools for short-range prediction in the following decade. Their value will be even greater in Antarctica due to the increasingly demanding weather forecasts for logistic services. During the 2018–2019 austral summer (1 December–31 March), coinciding with the Southern Hemisphere Special Observation Period of the Year of Polar Prediction (YOPP), the 2.5 km AEMET-γSREPS was operationally integrated over the Antarctic Peninsula. In particular, the Antarctic version of γSREPS comes up with crossing four non-hydrostatic convection-permitting NWP models at 2.5 km with three global NWP driving models as boundary conditions. The γSREPS forecasting system has been validated in comparison with ECMWF EPS. It is concluded that γSREPS has an added value to ECMWF EPS due to both its higher resolution and its multi-boundary conditions and multi-NWP model approach. γSREPS performance has a positive impact on logistic activities at research stations and its design may contribute to polar prediction research.

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 On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions

2008 ◽  
Vol 9 (6) ◽  
pp. 1301-1317 ◽  
Author(s):  
Guillaume Thirel ◽  
Fabienne Rousset-Regimbeau ◽  
Eric Martin ◽  
Florence Habets
Keyword(s):  
Short Range ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Streamflow Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Ensemble Streamflow Prediction ◽  
Prediction Systems ◽  
Set Up ◽  
The Impact

Abstract Ensemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d’Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins.

scholarly journals A Comparison of the Canadian Global and Regional Meteorological Ensemble Prediction Systems for Short-Term Hydrological Forecasting

2013 ◽  
Vol 141 (10) ◽  
pp. 3462-3476 ◽  
Author(s):  
Mabrouk Abaza ◽  
François Anctil ◽  
Vincent Fortin ◽  
Richard Turcotte
Keyword(s):  
Absolute Error ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Hydrological Models ◽  
Short Term ◽  
Ensemble Forecasts ◽  
Study Results ◽  
Prediction Systems ◽  
Set Up ◽  
Meteorological Models

Abstract Meteorological ensemble prediction systems (M-EPS) are generally set up at lower resolution than for their deterministic counterparts. Operational hydrologists are thus more prone to selecting deterministic meteorological forecasts for driving their hydrological models. Limited-area implementation of meteorological models may become a convenient way of providing the sought after higher-resolution meteorological ensemble forecasts. This study aims to compare the Canadian operational global EPS (M-GEPS) and the experimental regional EPS (M-REPS) for short-term operational hydrological ensemble forecasting over eight watersheds, for which performance and reliability was assessed. Higher-resolution deterministic forecasts were also available for the study. Results showed that both M-EPS provided better performance than their deterministic counterparts when comparing their mean continuous ranked probability score (MCRPS) and mean absolute error (MAE), especially beyond a 24-h horizon. The global and regional M-EPS led to very similar performance in terms of RMSE, but the latter produced a larger spread and improved reliability. The M-REPS was deemed superior to its operational global counterpart, especially for its ability to better depict forecast uncertainty.

An Objective Track Similarity Index and Its Preliminary Application to Predicting Precipitation of Landfalling Tropical Cyclones

2018 ◽  
Vol 33 (6) ◽  
pp. 1725-1742 ◽  
Author(s):  
Fumin Ren ◽  
Wenyu Qiu ◽  
Chenchen Ding ◽  
Xianling Jiang ◽  
Liguang Wu ◽  
...  
Keyword(s):  
Tropical Cyclones ◽  
South China ◽  
Prediction Models ◽  
Weather Prediction ◽  
Similarity Index ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Track Forecast ◽  
Area Index ◽  
Landfalling Tropical Cyclones

Abstract Combining dynamical model output and statistical information in historical observations is an innovative approach to predicting severe or extreme weather. In this study, in order to examine a dynamical–statistical method for precipitation forecasting of landfalling tropical cyclones (TC), an objective TC track similarity area index (TSAI) is developed. TSAI represents an area of the enclosed scope surrounded by two TC tracks and two line segments connecting the initiating and ending points of the two tracks. The smaller the TSAI value, the greater the similarity of the two TC tracks, where a value of 0 indicates that the two tracks overlap completely. The TSAI is then preliminarily applied to a precipitation forecast test of landfalling TCs over South China. Given the considerable progress made in TC track forecasting over past few decades, TC track forecast products are also used. Through this test, a track-similarity-based landfalling TC precipitation dynamical–statistical ensemble forecast (LTP_DSEF) model is established, which consists of four steps: adopting the predicted TC track, determining the TC track similarity, checking the seasonal similarity, and making an ensemble prediction. Its application to the precipitation forecasts of landfalling TCs over South China reveals that the LTP_DSEF model is superior to three numerical weather prediction models (i.e., ECMWF, GFS, and T639/China), especially for intense precipitation at large thresholds (i.e., 100 or 250 mm) in both the training (2012–14) and independent (2015–16) samples.

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.

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