Beautiful Visualizations Slain by Ugly Facts: Redesigning the National Hurricane Center’s ‘Cone of Uncertainty’ Map

Common Misconceptions

The Track Forecast Cone, commonly known as the “cone of uncertainty”, is the most popular hurricane and tropical storm forecast product that the National Hurricane Center produces. However, it is often misinterpreted by non-experts. In this study we first explored the most common misconceptions about the cone and produced two alternative redesigns that we expected to be more attractive to and easier to understand by non-expert readers. Our results were mixed, but reveal promising paths for future efforts.

Atlantic Hurricane Season of 2006

Monthly Weather Review ◽

10.1175/2007mwr2377.1 ◽

2008 ◽

Vol 136 (3) ◽

pp. 1174-1200 ◽

Cited By ~ 28

Author(s):

James L. Franklin ◽

Daniel P. Brown

Keyword(s):

Tropical Cyclones ◽

The United States ◽

Tropical Storm ◽

Track Forecast ◽

Forecast Errors ◽

Atlantic Hurricane ◽

Hurricane Season ◽

First Time ◽

Abstract The 2006 Atlantic hurricane season is summarized and the year’s tropical cyclones are described. A verification of National Hurricane Center official forecasts during 2006 is also presented. Ten cyclones attained tropical storm intensity in 2006. Of these, five became hurricanes and two became “major” hurricanes. Overall activity was near the long-term mean, but below the active levels of recent seasons. For the first time since 2001, no hurricanes made landfall in the United States. Elsewhere in the basin, hurricane-force winds were experienced in Bermuda (from Florence) and in the Azores (from Gordon). Official track forecast errors were smaller in 2006 than during the previous 5-yr period (by roughly 15%–20% out to 72 h), establishing new all-time lows at forecast projections through 72 h. Since 1990, 24–72-h official track forecast errors have been reduced by roughly 50%.

LESSONS LEARNED FROM RAPID DEPLOYMENT OF WAVE GAGES AND CAMERAS DURING HURRICANE IRMA

Coastal Engineering Proceedings ◽

10.9753/icce.v36.waves.62 ◽

2018 ◽

pp. 62

Author(s):

Navid H. Jafari ◽

Qin J. Chen ◽

Cody Johnson ◽

Jack Cadigan ◽

Brian Harris

Keyword(s):

Tropical Storm ◽

Lessons Learned ◽

Wind Speeds ◽

Hurricane Wind ◽

Tropical Wave ◽

Run Up ◽

Rapid Deployment ◽

The U.S ◽

Hurricane Irma

Hurricane Irma was a category 5 hurricane on the Saffir-Simpson hurricane wind scale. Irma developed from a tropical wave around the Cape Verde Islands. The National Hurricane Center started monitoring it on August 26, and it was classified as a tropical storm named Irma on August 30. Moving across the Atlantic Ocean, Irma increased in strength. On September 5, Irma was classified as a category 5 hurricane with wind speeds up to 175 mph (280 km/h). Irma made landfall in the U.S. on Cudjoe Key (near Big Pine and Summerland Keys) in the morning of September 10, still being a category 4 hurricane, and made a second landfall on Marco Island, south of Naples, on the same day as a category 3 hurricane. This paper describes the lessons learned by the authors when deploying wave gages and cameras to observe the wave run-up.

Prediction of Consensus Tropical Cyclone Track Forecast Error

Monthly Weather Review ◽

10.1175/mwr3390.1 ◽

2007 ◽

Vol 135 (5) ◽

pp. 1985-1993 ◽

Cited By ~ 39

Author(s):

James S. Goerss

Keyword(s):

Tropical Cyclone ◽

Regression Models ◽

Average Distance ◽

Forecast Error ◽

Dependent Data ◽

Track Forecast ◽

Consensus Model ◽

Cyclone Track ◽

Tropical Cyclone Track ◽

Abstract The extent to which the tropical cyclone (TC) track forecast error of a consensus model (CONU) routinely used by the forecasters at the National Hurricane Center can be predicted is determined. A number of predictors of consensus forecast error, which must be quantities that are available prior to the official forecast deadline, were examined for the Atlantic basin in 2001–03. Leading predictors were found to be consensus model spread, defined to be the average distance of the member forecasts from the consensus forecast, and initial and forecast TC intensity. Using stepwise linear regression and the full pool of predictors, regression models were found for each forecast length to predict the CONU TC track forecast error. The percent variance of CONU TC track forecast error that could be explained by these regression models ranged from just over 15% at 48 h to nearly 50% at 120 h. Using the regression models, predicted radii were determined and were used to draw circular areas around the CONU forecasts that contained the verifying TC position 73%–76% of the time. Based on the size of these circular areas, a forecaster can determine the confidence that can be placed upon the CONU 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.

Weighted Analog Technique for Intensity and Intensity Spread Predictions of Atlantic Tropical Cyclones

Weather and Forecasting ◽

10.1175/waf-d-15-0030.1 ◽

2015 ◽

Vol 30 (5) ◽

pp. 1321-1333 ◽

Cited By ~ 5

Author(s):

Hsiao-Chung Tsai ◽

Russell L. Elsberry

Keyword(s):

Tropical Cyclones ◽

Bias Correction ◽

Western North Pacific ◽

Probability Of Detection ◽

Track Forecast ◽

Sample Mean ◽

Prediction Technique ◽

Intensity Spread ◽

Joint Typhoon Warning Center

Abstract A situation-dependent intensity and intensity spread prediction technique for the Atlantic called the Weighted Analog Intensity Atlantic (WAIA) is developed using the same procedures as for a similar technique for the western North Pacific that is operational at the Joint Typhoon Warning Center. These simple techniques are based on rankings of the 10 best historical track analogs to match the official track forecast and current intensity. A key step is the development of a bias correction to eliminate an overforecast bias. The second key step is a calibration of the original intensity spread among the 10 analogs to achieve a probability of detection of about 68% at all forecast intervals, which it is proposed would be an appropriate intensity spread for the National Hurricane Center (NHC) official intensity forecasts. The advantages of WAIA as an operational intensity forecast product for Atlantic tropical cyclones are described in terms of mean absolute errors, sample-mean biases, and geographic distributions of WAIA versus various guidance products available at NHC. Specific attention is given to the four guidance products that are included in the intensity consensus (ICON) technique that is the most skillful of all the products. Evidence is given that WAIA would be an independent, and more likely skillful at longer forecast intervals, technique to include in ICON. Consequently, WAIA would likely lead to improved NHC intensity forecasts at 4–5-day intervals.

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

Environmental Sciences Proceedings ◽

10.3390/ecas2021-10332 ◽

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 ◽

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.

The Sanders Barotropic Tropical Cyclone Track Prediction Model (SANBAR)

Meteorological Monographs ◽

10.1175/0065-9401-33.55.233 ◽

2008 ◽

Vol 55 ◽

pp. 233-240 ◽

Cited By ~ 1

Author(s):

Robert W. Burpee

Keyword(s):

Tropical Cyclone ◽

Prediction Model ◽

Large Scale ◽

Track Forecast ◽

Forecast Errors ◽

The North ◽

Regional Area ◽

Wind Input ◽

Large Scale Flow ◽

Abstract Sanders designed a barotropic tropical cyclone (TC) track prediction model for the North Atlantic TC basin that became known as the Sanders barotropic (SANBAR) model. It predicted the streamfunction of the deeplayer mean winds (tropical circulation vertically averaged from 1000 to 100 hPa) that represents the vertically averaged tropical circulations. Originally, the wind input for the operational objective analysis (OA) consisted of winds measured by radiosondes and 44 bogus winds provided by analysis at the National Hurricane Center (NHC), which corresponded to the vertically averaged flow over sparsely observed tropical, subtropical, and midlatitude oceanic regions. The model covered a fixed regional area and had a grid size of ~ 154 km. It estimated the initial storm motion solely on the basis of the large-scale flow from the OA, not taking into account the observed storm motion. During 1970, the SANBAR model became the first dynamical TC track model to be run operationally at NHC. Track forecasts of SANBAR were verified from the 1971 TC season when track model verifications began at NHC until its retirement after the 1989 Atlantic TC season. The average annual SANBAR forecast track errors were verified relative to Climatology and Persistence (CLIPER), the standard no-skill track forecast. Comparison with CLIPER determines the skill of track forecast methods. Verifications are presented for two different versions of the SANBAR model system used operationally during 1973–84 and 1985–89. In homogeneous comparisons (i.e., includes only forecasts for the same initial times) for the former period, SANBAR's track forecasts were slightly better than CLIPER at 24–48-h forecast intervals; however, from 1985 to 1989 the average SANBAR track forecast errors from 24–72 h were ~10% more skillful than homogeneous CLIPER track forecasts.

A Bayesian Representation of the Storm Approach Probability Based on Operational Track Forecast Errors

Weather and Forecasting ◽

10.1175/waf-d-20-0040.1 ◽

2020 ◽

Vol 35 (5) ◽

pp. 2025-2032

Author(s):

Namyoung Kang

Keyword(s):

Normal Distribution ◽

Posterior Distribution ◽

Marginal Distribution ◽

Bivariate Normal Distribution ◽

Wishart Distribution ◽

Tropical Storm ◽

Track Forecast ◽

Forecast Errors ◽

Bivariate Normal ◽

Specific Interpretation

AbstractThis study provides a statistical review on the forecast errors of tropical storm tracks and suggests a Bayesian procedure for updating the uncertainty about the error. The forecast track errors are assumed to form an axisymmetric bivariate normal distribution on a two-dimensional surface. The parameters are a mean vector and a covariance matrix, which imply the accuracy and precision of the operational forecast. A Bayesian method improves quantifying the varying parameters in the bivariate normal distribution. A normal-inverse-Wishart distribution is employed to determine the posterior distribution (i.e., the weights on the parameters). Based on the posterior distribution, the predictive probability density of track forecast errors is obtained as the marginal distribution. Here, “storm approach” is defined for any location within a specified radius of a tropical storm. Consequently, the storm approach probability for each location is derived through partial integration of the marginal distribution within the forecast storm radius. The storm approach probability is considered a realistic and effective representation of storm warning for communicating the threat to local residents since the location-specific interpretation is available on a par with the official track forecast.

Is Tropical Cyclone Intensity Guidance Improving?

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-12-00240.1 ◽

2014 ◽

Vol 95 (3) ◽

pp. 387-398 ◽

Cited By ~ 180

Author(s):

Mark DeMaria ◽

Charles R. Sampson ◽

John A. Knaff ◽

Kate D. Musgrave

Keyword(s):

Tropical Cyclone ◽

North Pacific ◽

Absolute Error ◽

Track Forecast ◽

Forecast Errors ◽

Cyclone Intensity ◽

The Past ◽

Forecast Models ◽

Combine Information ◽

The mean absolute error of the official tropical cyclone (TC) intensity forecasts from the National Hurricane Center (NHC) and the Joint Typhoon Warning Center (JTWC) shows limited evidence of improvement over the past two decades. This result has sometimes erroneously been used to conclude that little or no progress has been made in the TC intensity guidance models. This article documents statistically significant improvements in operational TC intensity guidance over the past 24 years (1989–2012) in four tropical cyclone basins (Atlantic, eastern North Pacific, western North Pacific, and Southern Hemisphere). Errors from the best available model have decreased at 1%–2% yr−1 at 24–72 h, with faster improvement rates at 96 and 120 h. Although these rates are only about one-third to one-half of the rates of reduction of the track forecast models, most are statistically significant at the 95% level. These error reductions resulted from improvements in statistical–dynamical intensity models and consensus techniques that combine information from statistical–dynamical and dynamical models. The reason that the official NHC and JTWC intensity forecast errors have decreased slower than the guidance errors is because in the first half of the analyzed period, their subjective forecasts were more accurate than any of the available guidance. It is only in the last decade that the objective intensity guidance has become accurate enough to influence the NHC and JTWC forecast errors.

Improvement of Tropical Cyclone Track Forecast over the Western North Pacific Using a Machine Learning Method

10.5194/egusphere-egu2020-13648 ◽

2020 ◽

Author(s):

Kyoungmin Kim ◽

Dong-Hyun Cha ◽

Jungho Im

Keyword(s):

Machine Learning ◽

Tropical Cyclone ◽

North Pacific ◽

Western North Pacific ◽

Numerical Models ◽

Forecast Error ◽

Selection Method ◽

Tropical Storm ◽

Track Forecast ◽

Forecast Lead Time

<p>&#160;The accurate tropical cyclone (TC) track forecast is necessary to mitigate and prepare significant damage. TC has been predicted by the numerical models, statistical models, and machine learning methods in previous researches. However, those models are separately used for TC track forecast, and historical data with satellite images were used as input variables for machine learning without forecast data from numerical models. In this study, we corrected the TC track forecast of a numerical model by artificial neural network (ANN). TCs that occurred from 2006 to 2015 over the western North Pacific were hindcasted by the Weather Research and Forecasting (WRF) model, and all categories of TCs except for tropical depression (i.e., tropical storm, severe tropical storm, and typhoon) from June to November were included in this study. We evaluated the performance of TC track forecast in terms of duration, translation speed, and direction compared with the best track data. The simulated positions of TCs at 24-hour, 48-hour, and 72-hour forecast lead time were used as variables for training and testing ANN. To optimize the number of neurons in ANN, simulated TCs were divided into two parts; TCs in 2006-2014 for ANN optimization and those in 2015 for a blind test. Also, the output selection method based on the forecast error of the WRF was applied to exclude the outlier of ANN results. By applying the output selection, the forecast error of ANN was further reduced than that of the WRF. As a result, ANN with the output selection method could improve TC track forecast by about 15% compared to the WRF. Also, the effect of ANN tended to increase when the forecast error of the WRF was large. The output selection method was particularly effective by excluding outliers of ANN results when the forecast error of the WRF was small.</p><p>&#8251; This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT (NRF-2016M3C4A7952637).</p>

Eastern North Pacific Hurricane Season of 2009

Monthly Weather Review ◽

10.1175/2010mwr3497.1 ◽

2011 ◽

Vol 139 (6) ◽

pp. 1657-1672 ◽

Cited By ~ 1

Author(s):

Todd B. Kimberlain ◽

Michael J. Brennan

Keyword(s):

North Pacific ◽

Normal Activity ◽

Tropical Storm ◽

Property Damage ◽

Hurricane Season ◽

Center Track ◽

Abstract The 2009 eastern North Pacific hurricane season had near normal activity, with a total of 17 named storms, of which seven became hurricanes and four became major hurricanes. One hurricane and one tropical storm made landfall in Mexico, directly causing four deaths in that country along with moderate to severe property damage. Another cyclone that remained offshore caused an additional direct death in Mexico. On average, the National Hurricane Center track forecasts in the eastern North Pacific for 2009 were quite skillful.