scholarly journals Reducing Operational Hurricane Intensity Forecast Errors during Eyewall Replacement Cycles

2016 ◽  
Vol 31 (2) ◽  
pp. 601-608 ◽  
Author(s):  
James P. Kossin ◽  
Mark DeMaria
Keyword(s):  
Forecast Error ◽  
Intensity Change ◽  
Forecast Errors ◽  
Hurricane Intensity ◽  
Intensity Forecast ◽  
Prediction Scheme ◽  
Hurricane Prediction ◽  
Eyewall Replacement Cycles ◽  
National Hurricane Center ◽  
Intensity Evolution

Abstract Eyewall replacement cycles (ERCs) are fairly common events in tropical cyclones (TCs) of hurricane intensity or greater and typically cause large and sometimes rapid changes in the intensity evolution of the TC. Although the details of the intensity evolution associated with ERCs appear to have some dependence on the ambient environmental conditions that the TCs move through, these dependencies can also be quite different than those of TCs that are not undergoing an ERC. For example, the Statistical Hurricane Prediction Scheme (SHIPS), which is used in National Hurricane Center operations and provides intensity forecast skill that is, on average, equal to or greater than deterministic numerical model skill, typically identifies an environment that is not indicative of weakening during the onset and subsequent evolution of an ERC. Contrarily, a period of substantial weakening does typically begin near the onset of an ERC, and this disparity can cause large SHIPS intensity forecast errors. Here, a simple model based on a climatology of ERC intensity change is introduced and tested against SHIPS. It is found that the application of the model can reduce intensity forecast error substantially when applied at, or shortly after, the onset of ERC weakening.

scholarly journals Understanding Error Distributions of Hurricane Intensity Forecasts during Rapid Intensity Changes

2020 ◽  
Vol 35 (6) ◽  
pp. 2219-2234
Author(s):  
Benjamin C. Trabing ◽  
Michael M. Bell
Keyword(s):  
Inner Core ◽  
Forecast Error ◽  
Vertical Wind Shear ◽  
Probability Of Detection ◽  
Intensity Change ◽  
Forecast Errors ◽  
Hurricane Intensity ◽  
East Pacific ◽  
Intensity Forecast ◽  
Error Distributions

AbstractThe characteristics of official National Hurricane Center (NHC) intensity forecast errors are examined for the North Atlantic and east Pacific basins from 1989 to 2018. It is shown how rapid intensification (RI) and rapid weakening (RW) influence yearly NHC forecast errors for forecasts between 12 and 48 h in length. In addition to being the tail of the intensity change distribution, RI and RW are at the tails of the forecast error distribution. Yearly mean absolute forecast errors are positively correlated with the yearly number of RI/RW occurrences and explain roughly 20% of the variance in the Atlantic and 30% in the east Pacific. The higher occurrence of RI events in the east Pacific contributes to larger intensity forecast errors overall but also a better probability of detection and success ratio. Statistically significant improvements to 24-h RI forecast biases have been made in the east Pacific and to 24-h RW biases in the Atlantic. Over-ocean 24-h RW events cause larger mean errors in the east Pacific that have not improved with time. Environmental predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) are used to diagnose what conditions lead to the largest RI and RW forecast errors on average. The forecast error distributions widen for both RI and RW when tropical systems experience low vertical wind shear, warm sea surface temperature, and moderate low-level relative humidity. Consistent with existing literature, the forecast error distributions suggest that improvements to our observational capabilities, understanding, and prediction of inner-core processes is paramount to both RI and RW prediction.

scholarly journals SHIPS-MI Forecast Analysis of Hurricanes Claudette (2003), Isabel (2003), and Dora (1999)

2007 ◽  
Vol 22 (4) ◽  
pp. 689-707 ◽  
Author(s):  
Thomas A. Jones ◽  
Daniel J. Cecil
Keyword(s):  
Inner Core ◽  
Forecast Accuracy ◽  
Precipitation Forecast ◽  
Intensity Change ◽  
Forecast Errors ◽  
Positive Contribution ◽  
Hurricane Intensity ◽  
Intensity Prediction ◽  
Prediction Scheme ◽  
Dependent Sample

Abstract Three hurricanes, Claudette (2003), Isabel (2003), and Dora (1999), were selected to examine the Statistical Hurricane Intensity Prediction Scheme with Microwave Imagery (SHIPS-MI) forecast accuracy for three particular storm types. This research was conducted using model analyses and tropical cyclone best-track data, with forecasts generated from a dependent sample. The model analyses and best-track data are assumed to be a “perfect” representation of the actual event (e.g., perfect prog assumption). Analysis of intensity change forecasts indicated that SHIPS-MI performed best, compared to operational SHIPS output, for tropical cyclones that were intensifying from tropical storm to hurricane intensity. Passive microwave imagery, which is sensitive to the intensity and coverage of precipitation, improved intensity forecasts during these periods with a positive intensity change contribution resulting from above normal inner-core precipitation. Forecast improvement was greatest for 12–36-h forecasts, where the microwave contribution to SHIPS-MI was greatest. Once a storm reached an intensity close to its maximum potential intensity, as in the case of Isabel and Dora, both SHIPS and SHIPS-MI incorrectly forecast substantial weakening despite the positive contribution from microwave data. At least in Dora’s case, SHIPS-MI forecasts were slightly stronger than those of SHIPS. Other important contributions to SHIPS-MI forecasts were examined to determine their importance relative to the microwave inputs. Inputs related to sea surface temperature (SST) and persistence–climatology proved to be very important to intensity change forecasts, as expected. These predictors were the primary factor leading to the persistent weakening forecasts made by both models for Isabel and Dora. For Atlantic storms (Claudette and Isabel), the contribution from shear also proved important at characterizing the conduciveness of the environment toward intensification. However, the shear contribution was often small as a result of multiple offsetting shear-related predictors. Finally, it was observed that atmospheric parameters not included in SHIPS, such as eddy momentum flux, could substantially affect the intensity, leading to large forecast errors. This was especially true for the Claudette intensity change forecasts throughout its life cycle.

scholarly journals Recent Progress in Tropical Cyclone Intensity Forecasting at the National Hurricane Center

2020 ◽  
Vol 35 (5) ◽  
pp. 1913-1922 ◽  
Author(s):  
John P. Cangialosi ◽  
Eric Blake ◽  
Mark DeMaria ◽  
Andrew Penny ◽  
Andrew Latto ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Forecast Error ◽  
Forecast Errors ◽  
Atlantic Basin ◽  
Cyclone Intensity ◽  
The Past ◽  
Intensity Forecast ◽  
Past Half Century ◽  
National Hurricane Center ◽  
Past Half

AbstractIt has been well documented that the National Hurricane Center (NHC) has made significant improvements in Atlantic basin tropical cyclone (TC) track forecasting during the past half century. In contrast, NHC’s TC intensity forecast errors changed little from the 1970s to the early 2000s. Recently, however, there has been a notable decrease in TC intensity forecast error and an increase in intensity forecast skill. This study documents these trends and discusses the advancements in TC intensity guidance that have led to the improvements in NHC’s intensity forecasts in the Atlantic basin. We conclude with a brief projection of future capabilities.

Prediction of Consensus Tropical Cyclone Intensity Forecast Error

2014 ◽  
Vol 29 (3) ◽  
pp. 750-762 ◽  
Author(s):  
James S. Goerss ◽  
Charles R. Sampson
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Regression Models ◽  
Forecast Error ◽  
Absolute Intensity ◽  
Intensity Change ◽  
Forecast Errors ◽  
Cyclone Intensity ◽  
Intensity Forecast ◽  
Consensus Models

Abstract The extent to which the tropical cyclone (TC) intensity forecast error of IVCN and S5YY, consensus models routinely used by forecasters at the National Hurricane Center and the Joint Typhoon Warning Center, respectively, can be predicted is determined. A number of predictors of consensus intensity forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic and eastern North Pacific basins for 2008–11 and the western North Pacific basin for 2012. Leading predictors were found to be forecast TC intensity and intensity change, initial intensity and latitude of the TC, and consensus model spread, defined to be the average of the absolute intensity differences between the member forecasts and the consensus forecast. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the IVCN and S5YY TC intensity forecast errors. Using the regression models, intervals were determined centered on the IVCN and S5YY forecasts that contained the verifying TC intensity about 67% of the time. Based on the size of these intervals, a forecaster can determine the confidence that can be placed upon the IVCN or S5YY forecasts. Independent data testing yielded results only slightly degraded from those of dependent data testing, highlighting the capability of these methods in practical forecasting applications.

scholarly journals A Deterministic Rapid Intensification Aid

2011 ◽  
Vol 26 (4) ◽  
pp. 579-585 ◽  
Author(s):  
Charles R. Sampson ◽  
John Kaplan ◽  
John A. Knaff ◽  
Mark DeMaria ◽  
Chris A. Sisko
Keyword(s):  
Real Time ◽  
Arithmetic Mean ◽  
Forecast Errors ◽  
Rapid Intensification ◽  
Intensity Forecast ◽  
Probability Threshold ◽  
Forecast Time ◽  
National Hurricane Center ◽  
Made In

Abstract Rapid intensification (RI) is difficult to forecast, but some progress has been made in developing probabilistic guidance for predicting these events. One such method is the RI index. The RI index is a probabilistic text product available to National Hurricane Center (NHC) forecasters in real time. The RI index gives the probabilities of three intensification rates [25, 30, and 35 kt (24 h)−1; or 12.9, 15.4, and 18.0 m s−1 (24 h)−1] for the 24-h period commencing at the initial forecast time. In this study the authors attempt to develop a deterministic intensity forecast aid from the RI index and, then, implement it as part of a consensus intensity forecast (arithmetic mean of several deterministic intensity forecasts used in operations) that has been shown to generally have lower mean forecast errors than any of its members. The RI aid is constructed using the highest available RI index intensification rate available for probabilities at or above a given probability (i.e., a probability threshold). Results indicate that the higher the probability threshold is, the better the RI aid performs. The RI aid appears to outperform the consensus aids at about the 50% probability threshold. The RI aid also improves forecast errors of operational consensus aids starting with a probability threshold of 30% and reduces negative biases in the forecasts. The authors suggest a 40% threshold for producing the RI aid initially. The 40% threshold is available for approximately 8% of all verifying forecasts, produces approximately 4% reduction in mean forecast errors for the intensity consensus aids, and corrects the negative biases by approximately 15%–20%. In operations, the threshold could be moved up to maximize gains in skill (reducing availability) or moved down to maximize availability (reducing gains in skill).

Predicting Hurricane Intensity and Associated Hazards: A Five-Year Real-Time Forecast Experiment with Assimilation of Airborne Doppler Radar Observations

2015 ◽  
Vol 96 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Fuqing Zhang ◽  
Yonghui Weng
Keyword(s):  
Inner Core ◽  
Doppler Radar ◽  
Surface Wind ◽  
Forecast Errors ◽  
Hurricane Intensity ◽  
Radar Observations ◽  
Intensity Forecast ◽  
Probabilistic Forecasts ◽  
Atlantic Hurricanes ◽  
Airborne Doppler Radar

Abstract Performance in the prediction of hurricane intensity and associated hazards has been evaluated for a newly developed convection-permitting forecast system that uses ensemble data assimilation techniques to ingest high-resolution airborne radar observations from the inner core. This system performed well for three of the ten costliest Atlantic hurricanes: Ike (2008), Irene (2011), and Sandy (2012). Four to five days before these storms made landfall, the system produced good deterministic and probabilistic forecasts of not only track and intensity, but also of the spatial distributions of surface wind and rainfall. Averaged over all 102 applicable cases that have inner-core airborne Doppler radar observations during 2008–2012, the system reduced the day-2-to-day-4 intensity forecast errors by 25%–28% compared to the corresponding National Hurricane Center’s official forecasts (which have seen little or no decrease in intensity forecast errors over the past two decades). Empowered by sufficient computing resources, advances in both deterministic and probabilistic hurricane prediction will enable emergency management officials, the private sector, and the general public to make more informed decisions that minimize the losses of life and property.

scholarly journals Performance Evaluation of Numerical Tools for Hurricane Forecast (NTHF) System during 2020 North Atlantic Tropical Cyclones Season

2021 ◽  
Vol 8 (1) ◽  
pp. 22
Author(s):  
Albenis Pérez-Alarcón ◽  
José C. Fernández-Alvarez ◽  
Alfo J. Batista-Leyva
Keyword(s):  
Tropical Cyclones ◽  
North Atlantic ◽  
Track Forecast ◽  
Forecast Errors ◽  
Prediction Scheme ◽  
Numerical Tools ◽  
Marginal Improvement ◽  
National Hurricane Center ◽  
Basic Input ◽  
Better Than

This study evaluates the performance of the Numerical Tools for Hurricane Forecast (NTHF) system during the 2020 North Atlantic (NATL) tropical cyclones (TCs) season. The system is configured to provide 5-day forecasts with basic input from the National Hurricane Center (NHC) and the Global Forecast System. For the NTHF validation, the NHC operational best track was used. The average track errors for 2020 NATL TCs ranged from 62 km at 12 h to 368 km at 120 h. The NTHF track forecast errors displayed an improvement over 60% above the guidance Climatology and Persistence (CLIPER) model from 36 h to 96 h, although the NTHF was better than the CLIPER in all forecast periods. The forecast errors for the maximum wind speed (minimum central pressure) ranged between 20 km/h and 25 km/h (4 hPa to 8 hPa), but the NTHF model intensity forecasts showed only marginal improvement of less than 20% after 78 h over the baseline Decay Statistical Hurricane Intensity Prediction Scheme (D-SHIPS) model. Nevertheless, the NTHF’s ability to provide accurate intensity forecasts for the 2020 NATL TCs was higher than the NTHF’s average ability during the 2016–2019 period.

scholarly journals Toward the Probabilistic Simulation of Storm Surge and Inundation in a Limited-Resource Environment

2010 ◽  
Vol 138 (7) ◽  
pp. 2953-2974 ◽  
Author(s):  
Justin R. Davis ◽  
Vladimir A. Paramygin ◽  
David Forrest ◽  
Y. Peter Sheng
Keyword(s):  
Storm Surge ◽  
Forecast Error ◽  
Limited Resource ◽  
Forecast Errors ◽  
Research Association ◽  
Probabilistic Simulation ◽  
Hurricane Charley ◽  
Storm Surge Modeling ◽  
National Hurricane Center ◽  
Resource Environment

Abstract To create more useful storm surge and inundation forecast products, probabilistic elements are being incorporated. To achieve the highest levels of confidence in these products, it is essential that as many simulations as possible are performed during the limited amount of time available. This paper develops a framework by which probabilistic storm surge and inundation forecasts within the Curvilinear Hydrodynamics in 3D (CH3D) Storm Surge Modeling System and the Southeastern Universities Research Association Coastal Ocean Observing and Prediction Program’s forecasting systems are initiated with specific focus on the application of these methods in a limited-resource environment. Ensemble sets are created by dividing probability density functions (PDFs) of the National Hurricane Center model forecast error into bins, which are then grouped into priority levels (PLs) such that each subsequent level relies on results computed earlier and has an increasing confidence associated with it. The PDFs are then used to develop an ensemble of analytic wind and pressure fields for use by storm surge and inundation models. Using this approach applied with official National Hurricane Center (OFCL) forecast errors, an analysis of Hurricane Charley is performed. After first validating the simulation of storm surge, a series of ensemble simulations are performed representing the forecast errors for the 72-, 48-, 24-, and 12-h forecasts. Analysis of the aggregated products shows that PL4 (27 members) is sufficient to resolve 90% of the inundation within the domain and appears to represent the best balance between accuracy and timeliness of computed products for this case study. A 5-day forecast using the PL4 set is shown to complete in 83 min, while the intermediate PL2 and PL3 products, representing slightly less confidence, complete in 14 and 28 min, respectively.

The Variability and Predictability of Axisymmetric Hurricanes in Statistical Equilibrium

2013 ◽  
Vol 70 (4) ◽  
pp. 993-1005 ◽  
Author(s):  
Gregory J. Hakim
Keyword(s):  
Environmental Variability ◽  
Initial Conditions ◽  
Forecast Error ◽  
Statistical Equilibrium ◽  
Intensity Change ◽  
Order Estimate ◽  
Forecast Errors ◽  
Initial Structure ◽  
Axisymmetric Solution ◽  
Maximum Wind

Abstract The variability and predictability of axisymmetric hurricanes are determined from a 500-day numerical simulation of a tropical cyclone in statistical equilibrium. By design, the solution is independent of the initial conditions and environmental variability, which isolates the “intrinsic” axisymmetric hurricane variability. Variability near the radius of maximum wind is dominated by two patterns: one associated primarily with radial shifts of the maximum wind, and one primarily with intensity change at the time-mean radius of maximum wind. These patterns are linked to convective bands that originate more than 100 km from the storm center and propagate inward. Bands approaching the storm produce eyewall replacement cycles, with an increase in storm intensity as the secondary eyewall contracts radially inward. A dominant time period of 4–8 days is found for the convective bands, which is hypothesized to be determined by the time scale over which subsidence from previous bands suppresses convection; a leading-order estimate based on the ratio of the Rossby radius to band speed fits the hypothesis. Predictability limits for the idealized axisymmetric solution are estimated from linear inverse modeling and analog forecasts, which yield consistent results showing a limit for the azimuthal wind of approximately 3 days. The optimal initial structure that excites the leading pattern of 24-h forecast-error variance has largest azimuthal wind in the midtroposphere outside the storm and a secondary maximum just outside the radius of maximum wind. Forecast errors grow by a factor of 24 near the radius of maximum wind.

scholarly journals Understanding Biases in Tropical Cyclone Intensity Forecast Error

2018 ◽  
Vol 33 (1) ◽  
pp. 129-138 ◽  
Author(s):  
Wei Na ◽  
John L. McBride ◽  
Xing-Hai Zhang ◽  
Yi-Hong Duan
Keyword(s):  
Tropical Cyclone ◽  
Forecast Error ◽  
Correlation Coefficients ◽  
Rate Of Change ◽  
Intensity Change ◽  
Forecast Errors ◽  
Second Derivative ◽  
Rapid Intensification ◽  
Time Curve ◽  
Large Intensity

Abstract The characteristics of 24-h official forecast errors (OFEs) of tropical cyclone (TC) intensity are analyzed over the North Atlantic, east Pacific, and western North Pacific. The OFE is demonstrated to be strongly anticorrelated with TC intensity change with correlation coefficients of −0.77, −0.77, and −0.68 for the three basins, respectively. The 24-h intensity change in the official forecast closely follows a Gaussian distribution with a standard deviation only ⅔ of that in nature, suggesting the current official forecasts estimate fewer cases of large intensity change. The intensifying systems tend to produce negative errors (underforecast), while weakening systems have consistent positive errors (overforecast). This asymmetrical bias is larger for extreme intensity change, including rapid intensification (RI) and rapid weakening (RW). To understand this behavior, the errors are analyzed in a simple objective model, the trend-persistence model (TPM). The TPM exhibits the same error-intensity change correlation. In the TPM, the error can be understood as it is exactly inversely proportional to the finite difference form of the concavity or second derivative of the intensity–time curve. The occurrence of large negative (positive) errors indicates the intensity–time curve is concave upward (downward) in nature during the TC’s rapid intensification (weakening) process. Thus, the fundamental feature of the OFE distribution is related to the shape of the intensity–time curve, governed by TC dynamics. All forecast systems have difficulty forecasting an accelerating rate of change, or a large second derivative of the intensity–time curve. TPM may also be useful as a baseline in evaluating the skill of official forecasts. According to this baseline, official forecasts are more skillful in RW than in RI.

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