Ensemble Tropical Cyclone Research Articles

Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

AbstractEnsemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1,000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index to estimate peak surge heights along the central coast of Japan. We show that Pareto‐optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios. These solutions exemplify a diversity of trade‐off surge outcomes across diverse coastal locations, reflecting variations in coastal geometry, including bathymetry. For example, some of the Pareto‐optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in‐depth evaluation of Pareto‐optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multiscenario assessment methods such as those used by JMA/National Hurricane Center in the United States.

Evaluation of Independent Stochastically Perturbed Parameterization Tendency (iSPPT) Scheme on HWRF-Based Ensemble Tropical Cyclone Intensity Forecasts

Abstract Tropical cyclone (TC) intensity has been shown to have limited predictability in numerical weather prediction models; therefore, ensemble forecasting may be critical. An ensemble prediction system (EPS) should ideally cover all sources of uncertainty; however, most meso- and convective-scale EPSs typically consider initial-condition uncertainty alone, with limited treatment of model uncertainty, even though the evolution of mesoscale features is highly dependent on uncertain parameterization schemes. The role of stochastic treatment of model error in the Hurricane Weather Research and Forecasting (HWRF) EPS is evaluated by applying independent stochastically perturbed parameterization (iSPPT) scheme to individual parameterization schemes for four TCs from 2017 to 2018. Experiments with Hurricane Irma (2017) indicate that TC intensity ensemble standard deviation is most sensitive to the amplitude of the stochastic perturbation field, with smaller impact from adjusting the decorrelation time scale and spatial length scale. Results from all four TC cases show that stochastic perturbations to the turbulent mixing scheme can increase the ensemble standard deviation in intensity metrics over a 72-h simulation without introducing significant differences in mean error or bias. By contrast, stochastic perturbations to the microphysics, radiation, and cumulus tendencies have negligible effects on intensity standard deviation.

A global evaluation of multi‐model ensemble tropical cyclone track probability forecasts

AbstractAt the Met Office, dynamic ensemble forecasts from the Met Office Global and Regional Ensemble Prediction System (MOGREPS‐G), the European Centre for Medium‐Range Weather Forecasts Ensemble (ECMWF ENS) and National Centers for Environmental Prediction Global Ensemble Forecast System (NCEP GEFS) global ensemble forecast models are post‐processed to identify and track tropical cyclones. The ensemble members from each model are also combined into a 108‐member multi‐model ensemble. Track probability forecasts are produced for named tropical cyclones showing the probability of a location being within 120 km of a named tropical cyclone at any point in the next 7 days, and also broken down into each 24‐hour forecast period. This study presents the verification of these named‐storm track probabilities over a two‐year period across all global tropical cyclone basins, and compares the results from basin to basin. The combined multi‐model ensemble is found to increase the skill and value of the track probability forecasts over the best‐performing individual ensemble (ECMWF ENS), for both overall 7‐day track probability forecasts and 24‐hour track probabilities. Basin‐based and storm‐based verification illustrates that the best performing individual ensemble can change from basin to basin and from storm to storm, but that the multi‐model ensemble adds skill in every basin, and is also able to match the best performing individual ensemble in terms of overall probabilistic forecast skill in several high‐profile case‐studies. This study helps to illustrate the potential value and skill to be gained if operational tropical cyclone forecasting can continue to migrate away from a deterministic‐focused forecasting environment to one where the probabilistic situation‐based uncertainty information provided by the dynamic multi‐model ensembles can be incorporated into operational forecasts and warnings.

Evaluation of Atmosphere and Ocean Initial Condition Uncertainty and Stochastic Exchange Coefficients on Ensemble Tropical Cyclone Intensity Forecasts

Tropical cyclone (TC) intensity forecasts are impacted by errors in atmosphere and ocean initial conditions and the model formulation, which motivates using an ensemble approach. This study evaluates the impact of uncertainty in atmospheric and oceanic initial conditions, as well as stochastic representations of the drag Cd and enthalphy Ck exchange coefficients on ensemble Advanced Hurricane WRF (AHW) TC intensity forecasts of multiple Atlantic TCs from 2008 to 2011. Each ensemble experiment is characterized by different combinations of either deterministic or ensemble atmospheric and/or oceanic initial conditions, as well as fixed or stochastic representations of Cd or Ck. Among those experiments with a single uncertainty source, atmospheric uncertainty produces the largest standard deviation in TC intensity. While ocean uncertainty leads to continuous growth in ensemble standard deviation, the ensemble standard deviation in the experiments with Cd and Ck uncertainty levels off by 48 h. Combining atmospheric and oceanic uncertainty leads to larger intensity standard deviation than atmosphere or ocean uncertainty alone and preferentially adds variability outside of the TC core. By contrast, combining Cd or Ck uncertainty with any other source leads to negligible increases in standard deviation, which is mainly due to the lack of spatial correlation in the exchange coefficient perturbations. All of the ensemble experiments are deficient in ensemble standard deviation; however, the experiments with combinations of uncertainty sources generally have an ensemble standard deviation closer to the ensemble-mean errors.

Typhoon-Position-Oriented Sensitivity Analysis. Part I: Theory and Verification

Abstract A new sensitivity analysis method is proposed for the ensemble prediction system in which a tropical cyclone (TC) position is taken as a metric. Sensitivity is defined as a slope of linear regression (or its approximation) between state variable and a scalar representing the TC position based on ensemble simulation. The experiment results illustrate important regions for ensemble TC track forecast. The typhoon-position-oriented sensitivity analysis (TyPOS) is applied to Typhoon Shanshan (2006) for the verification time of up to 48 h. The sensitivity field of the TC central latitude with respect to the vorticity field obtained from large-scale random initial perturbation is characterized by a horizontally tilted pattern centered at the initial TC position. These sensitivity signals are generally maximized in the middle troposphere and are far more significant than those with respect to the divergence field. The results are consistent with the sensitivity signals obtained from existing methods. The verification experiments indicate that the signals from TyPOS quantitatively reflect an ensemble-mean position change as a response to the initial perturbation. Another experiment with Typhoon Dolphin (2008) demonstrates the long-term analysis of forecast sensitivity up to 96 h. Several additional tests have also been carried out to investigate the dependency among ensemble members, the impacts of using different horizontal grid spacing, and the effectiveness of ensemble-Kalman-filter-based perturbations.

Evaluation of a Wind-Wave System for Ensemble Tropical Cyclone Wave Forecasting. Part II: Waves

Abstract A wind-wave forecast system, designed with the intention of generating unbiased ensemble wave forecasts for extreme wind events, is assessed. Wave hindcasts for 12 tropical cyclones (TCs) are forced using a wind analysis produced from a combination of the North American Regional Reanalysis (NARR) and a parametric wind model. The default drag parameterization is replaced by one that is more in line with recent studies where a cap at weak-to-moderate wind speeds is applied. Quadrant-based significant wave height (Hs) statistics are composited in a storm-relative reference frame and stratified by the radius of maximum wind, storm speed, and storm intensity. Improvements in Hs are gleaned from both downscaling the NARR winds and tuning the wave model. However, the paradigm whereby the drag coefficient depends solely on the wind speed is limiting. Results indicate that Hs is biased low in the right quadrants (for all statistical subcategories). Conversely, Hs is high biased in the left-rear quadrant even though the analysis wind field is underforecast there. At radii less than 100 nautical miles, the model peak wave direction is offset from the observed, with the model (buoy) peak more in line with (to the left of) the direction of the tropical cyclone motion. As a result, the predominant storm-relative wind direction, which is northwesterly in the left-rear quadrant, opposes that of the buoy peak wave direction, while the model peak is more crosswise with respect to the wind. This will likely reduce the magnitude of the wind stress in the model.

Evaluation of a Wind-Wave System for Ensemble Tropical Cyclone Wave Forecasting. Part I: Winds

Abstract A computationally efficient method of producing tropical cyclone (TC) wind analyses is developed and tested, using a hindcast methodology, for 12 Gulf of Mexico storms. The analyses are created by blending synthetic data, generated from a simple parametric model constructed using extended best-track data and climatology, with a first-guess field obtained from the NCEP–NCAR North American Regional Reanalysis (NARR). Tests are performed whereby parameters in the wind analysis and vortex model are varied in an attempt to best represent the TC wind fields. A comparison between nonlinear and climatological estimates of the TC size parameter indicates that the former yields a much improved correlation with the best-track radius of maximum wind rm. The analysis, augmented by a pseudoerror term that controls the degree of blending between the NARR and parametric winds, is tuned using buoy observations to calculate wind speed root-mean-square deviation (RMSD), scatter index (SI), and bias. The bias is minimized when the parametric winds are confined to the inner-core region. Analysis wind statistics are stratified within a storm-relative reference frame and by radial distance from storm center, storm intensity, radius of maximum wind, and storm translation speed. The analysis decreases the bias and RMSD in all quadrants for both moderate and strong storms and is most improved for storms with an rm of less than 20 n mi. The largest SI reductions occur for strong storms and storms with an rm of less than 20 n mi. The NARR impacts the analysis bias: when the bias in the former is relatively large, it remains so in the latter.

On the relative benefits of a multi‐centre grand ensemble for tropical cyclone track prediction in the western North Pacific

AbstractIn this study, ensemble tropical cyclone (TC) track predictions were examined using the THORPEX Interactive Grand Global Ensemble (TIGGE) dataset. The main goal was to investigate the relative benefits of a multi‐centre grand ensemble (MCGE) over a single‐model ensemble (SME) from both deterministic and probabilistic perspectives. Here, the SME was composed of all ensemble members of the ensemble prediction system (EPS) at a certain numerical weather prediction (NWP) centre, while the MCGE was composed of all ensemble members of all or selected SMEs. Nine NWP centres participating in the TIGGE project were considered, and 58 TCs in the western North Pacific from 2008 to 2010 were verified.In the verification of TC strike probability, the Brier skill score of the MCGE was larger than that of the best SME, which was the ECMWF EPS, on a medium‐range time‐scale, although this was not true on a short‐ to medium‐range scale. In addition, the reliability was improved by the MCGE, especially in the high‐probability range. Moreover, the MCGE reduced the number of missed events by about one tenth compared with the best SME. In the verification of confidence information, the MCGE was successful in extracting confidence information across all prediction times from 1 to 5 days. The relative benefits of the MCGE over the SME were seen in cases where the ensemble spread was extremely small. In such cases, the position errors of the MCGE were generally smaller than those of the best SME, indicating that, when multiple SMEs simultaneously predict a low uncertainty, the confidence level increases and the probability of a large position error decreases. In the verification of deterministic track predictions, the ensemble mean TC track predictions by the combination of the ECMWF, JMA, and UKMO EPSs were found on average to be slightly more accurate for 5‐day predictions than those of the best SME, although the differences in the errors were not statistically significant. Copyright © 2012 Royal Meteorological Society

Critical time requirements for operational use of deterministic and ensemble tropical cyclone track forecasts

Tropical cyclone track forecasts have been improved, and forecast intervals have been extended to five days, owing to improved global and regional numerical model guidance. Critical time requirements that must be met for operational use of the deterministic model track forecasts are summarized for the U.S. and other selected non-U.S. tropical cyclone warning centers. One of the most accurate deterministic model forecasts from the European Center for Medium-range Weather Forecasts arrives too late to be used with other models at the + 6 h warning time, and thus is at least 12 h old before it can be operationally used. The time-critical nature of the tropical cyclone warning system is a major obstacle to operational use of single-model, or proposed multi-model, ensemble prediction system (EPS) mean and spread information, which is 12 h (or 18 h) delayed. This EPS mean and spread must also be superior to the mean and spread of the consensus of deterministic models that are available six hours earlier. These requirements must be met before the EPS tropical cyclone tracks will be operationally useful in specifying the uncertainty in the official track forecasts, which is the next challenge in tropical cyclone track warnings.