scholarly journals Operational Forecasting of Tropical Cyclone Rapid Intensification at the National Hurricane Center

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 683
Author(s):  
Mark DeMaria ◽  
James L. Franklin ◽  
Matthew J. Onderlinde ◽  
John Kaplan
Keyword(s):  
Tropical Cyclone ◽  
Absolute Error ◽  
Baseline Period ◽  
Probability Of Detection ◽  
Rapid Intensification ◽  
Improvement Program ◽  
Deterministic Models ◽  
Operational Forecasting ◽  
National Hurricane Center ◽  
Made In

Although some recent progress has been made in operational tropical cyclone (TC) intensity forecasting, the prediction of rapid intensification (RI) remains a challenging problem. To document RI forecast progress, deterministic and probabilistic operational intensity models used by the National Hurricane Center (NHC) are briefly reviewed. Results show that none of the deterministic models had RI utility from 1991 to about 2015 due to very low probability of detection, very high false alarm ratio, or both. Some ability to forecast RI has emerged since 2015, with dynamical models being the best guidance for the Atlantic and statistical models the best RI guidance for the eastern North Pacific. The first probabilistic RI guidance became available in 2001, with several upgrades since then leading to modest skill in recent years. A tool introduced in 2018 (DTOPS) is currently the most skillful among NHC’s probabilistic RI guidance. To measure programmatic progress in forecasting RI, the Hurricane Forecast Improvement Program has introduced a new RI metric that uses the traditional mean absolute error but restricts the sample to only those cases where RI occurred in the verifying best track or was forecast. By this metric, RI forecasts have improved by ~20–25% since the 2015–2017 baseline period.

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).

scholarly journals Is Tropical Cyclone Intensity Guidance Improving?

2014 ◽  
Vol 95 (3) ◽  
pp. 387-398 ◽  
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 ◽  
National Hurricane Center

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.

scholarly journals An Advanced Artificial Intelligence System for Investigating Tropical Cyclone Rapid Intensification with the SHIPS Database

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 484
Author(s):  
Yijun Wei ◽  
Ruixin Yang
Keyword(s):  
Artificial Intelligence ◽  
Tropical Cyclone ◽  
Parameter Tuning ◽  
Probability Of Detection ◽  
Feature Engineering ◽  
Rapid Intensification ◽  
Model Structure ◽  
Artificial Intelligence System ◽  
Intelligence System ◽  
Future Work

Currently, most tropical cyclone (TC) rapid intensification (RI) prediction studies are conducted based on a subset of the SHIPS database using a relatively simple model structure. However, variables (features) in the SHIPS database are built upon human expertise in TC intensity studies based on hard and subjective thresholds, and they should be explored thoroughly to make full use of the expertise. Based on the complete SHIPS data, this study constructs a complicated artificial intelligence (AI) system that handles feature engineering and selection, imbalance, prediction, and hyper parameter-tuning, simultaneously. The complicated AI system is used to further improve the performance of the current studies in RI prediction, and to identify other essential SHIPS variables that are ignored by previous studies with variable importance scores. The results outperform most of the earlier studies by approximately 21–50% on POD (Probability Of Detection) with reduced FAR (False Alarm Rate). This study built a baseline for future work on new predictor identification with more complicated AI techniques.

scholarly journals Advancements in Objective Multisatellite Tropical Cyclone Center Fixing

2016 ◽  
Vol 55 (1) ◽  
pp. 197-212 ◽  
Author(s):  
Anthony J. Wimmers ◽  
Christopher S. Velden
Keyword(s):  
Tropical Cyclone ◽  
Near Infrared ◽  
Alternative Methods ◽  
Accuracy And Precision ◽  
Operational Forecasting ◽  
Median Error ◽  
National Hurricane Center ◽  
Tropical Cyclone Center ◽  
Infrared Window

AbstractAn improved version of the Automated Rotational Center Hurricane Eye Retrieval (ARCHER) tropical cyclone (TC) center-fixing algorithm, introduced here as “ARCHER-2,” is presented with a characterization of its accuracy and precision and a comparison with alternative methods. The algorithm is calibrated for 37- and 85–92-GHz microwave imagers; geostationary imagery at visible, near-infrared, and longwave infrared window channels; and scatterometer ambiguities. In addition to a center fix, ARCHER-2 produces a quantitative estimate of expected error that can be used automatically or manually to evaluate the suitability of a result. The median center-fix error ranges from 24 (using scatterometer) to 49 (using infrared window) km relative to the National Hurricane Center best track. Multisatellite, multisensor results can also be used together to produce a TC-track estimate that selects from the best of all of the available imagery in the ancillary “ARCHER-Track” product. The median error of ARCHER-Track varies between 17 and 38 km, depending on TC intensity and data latency. The bias of the product’s expected error varies between 0% and 12%, which translates to an average of only 4 km. When compared with operational, subjective center-fix estimates, the ARCHER-Track approach improves on 29%–43% of these cases at the tropical-depression and tropical-storm stages, at which further assistance is typically sought. This result demonstrates that ARCHER-2 and ARCHER-Track can complement and accelerate operational forecasting where needed and can furnish other automated TC-analysis methods with well-characterized center-fix information.

scholarly journals Is the Number of Tropical Cyclone Rapid Intensification Events in the Western North Pacific Increasing?

SOLA ◽  
2020 ◽  
Vol 16 (0) ◽  
pp. 1-5 ◽  
Author(s):  
Udai Shimada ◽  
Munehiko Yamaguchi ◽  
Shuuji Nishimura
Keyword(s):  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Rapid Intensification

scholarly journals Are Special Processes at Work in the Rapid Intensification of Tropical Cyclones?

2015 ◽  
Vol 143 (3) ◽  
pp. 878-882 ◽  
Author(s):  
Roman Kowch ◽  
Kerry Emanuel
Keyword(s):  
Tropical Cyclone ◽  
Tropical Cyclones ◽  
Internal Variability ◽  
Open Ocean ◽  
Rapid Intensification ◽  
Exponential Distributions ◽  
Frequency Distributions ◽  
Fat Tail ◽  
Dissipation Rates ◽  
Special Factors

Abstract Probably not. Frequency distributions of intensification and dissipation developed from synthetic open-ocean tropical cyclone data show no evidence of significant departures from exponential distributions, though there is some evidence for a fat tail of dissipation rates. This suggests that no special factors govern high intensification rates and that tropical cyclone intensification and dissipation are controlled by statistically random environmental and internal variability.

scholarly journals Evaluation of Mekong River commission operational flood forecasts, 2000–2012

2014 ◽  
Vol 18 (7) ◽  
pp. 2645-2656 ◽  
Author(s):  
T. C. Pagano
Keyword(s):  
Protective Action ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Mekong River ◽  
Average Error ◽  
Wet Season ◽  
Phnom Penh ◽  
Mekong River Commission ◽  
Flood Level ◽  
Skill Scores

Abstract. This study created a 13-year historical archive of operational flood forecasts issued by the Regional Flood Management and Mitigation Center (RFMMC) of the Mekong River Commission. The RFMMC issues 1- to 5-day daily deterministic river height forecasts for 22 locations throughout the wet season (June–October). When these forecasts reach near flood level, government agencies and the public are encouraged to take protective action against damages. When measured by standard skill scores, the forecasts perform exceptionally well (e.g., 1 day-ahead Nash–Sutcliffe > 0.99) although much of this apparent skill is due to the strong seasonal cycle and the narrow natural range of variability at certain locations. Five-day forecasts upstream of Phnom Penh typically have 0.8 m error standard deviation, whereas below Phnom Penh the error is typically 0.3 m. The coefficients of persistence for 1-day forecasts are typically 0.4–0.8 and 5-day forecasts are typically 0.1–0.7. RFMMC uses a series of benchmarks to define a metric of percentage satisfactory forecasts. As the benchmarks were derived based on the average error, certain locations and lead times consistently appear less satisfactory than others. Instead, different benchmarks were proposed and derived based on the 70th percentile of absolute error over the 13-year period. There are no obvious trends in the percentage of satisfactory forecasts from 2002 to 2012, regardless of the benchmark chosen. Finally, when evaluated from a categorical "crossing above/not-crossing above flood level" perspective, the forecasts have a moderate probability of detection (48% at 1 day ahead, 31% at 5 days ahead) and false alarm rate (13% at 1 day ahead, 74% at 5 days ahead).

Pathways to the Production of Precipitating Hydrometeors and Tropical Cyclone Development

2016 ◽  
Vol 144 (6) ◽  
pp. 2395-2420 ◽  
Author(s):  
J.-W. Bao ◽  
S. A. Michelson ◽  
E. D. Grell
Keyword(s):  
Tropical Cyclone ◽  
Wrf Model ◽  
Cloud Microphysics ◽  
Weather Research And Forecasting ◽  
Rapid Intensification ◽  
Latent Heating ◽  
Vertical Distributions ◽  
Double Moment ◽  
Microphysical Processes ◽  
Definition Of

Abstract Pathways to the production of precipitation in two cloud microphysics schemes available in the Weather Research and Forecasting (WRF) Model are investigated in a scenario of tropical cyclone intensification. Comparisons of the results from the WRF Model simulations indicate that the variation in the simulated initial rapid intensification of an idealized tropical cyclone is due to the differences between the two cloud microphysics schemes in their representations of pathways to the formation and growth of precipitating hydrometeors. Diagnoses of the source and sink terms of the hydrometeor budget equations indicate that the major differences in the production of hydrometeors between the schemes are in the spectral definition of individual hydrometeor categories and spectrum-dependent microphysical processes, such as accretion growth and sedimentation. These differences lead to different horizontally averaged vertical profiles of net latent heating rate associated with significantly different horizontally averaged vertical distributions and production rates of hydrometeors in the simulated clouds. Results from this study also highlight the possibility that the advantage of double-moment formulations can be overshadowed by the uncertainties in the spectral definition of individual hydrometeor categories and spectrum-dependent microphysical processes.

scholarly journals Precipitation Properties Observed during Tropical Cyclone Intensity Change

2015 ◽  
Vol 143 (11) ◽  
pp. 4476-4492 ◽  
Author(s):  
George R. Alvey III ◽  
Jonathan Zawislak ◽  
Edward Zipser
Keyword(s):  
Tropical Cyclone ◽  
Passive Microwave ◽  
Intensity Change ◽  
Rapid Intensification ◽  
Relative Importance ◽  
Tropical Cyclone Intensity ◽  
Cyclone Intensity ◽  
East Pacific ◽  
Intensity Changes ◽  
Strong Convection

Abstract Using a 15-yr (1998–2012) multiplatform dataset of passive microwave satellite data [tropical cyclone–passive microwave (TC-PMW)] for Atlantic and east Pacific storms, this study examines the relative importance of various precipitation properties, specifically convective intensity, symmetry, and area, to the spectrum of intensity changes observed in tropical cyclones. Analyses are presented not only spatially in shear-relative quadrants around the center, but also every 6 h during a 42-h period encompassing 18 h prior to onset of intensification to 24 h after. Compared to those with slower intensification rates, storms with higher intensification rates (including rapid intensification) have more symmetric distributions of precipitation prior to onset of intensification, as well as a greater overall areal coverage of precipitation. The rate of symmetrization prior to, and during, intensification increases with increasing intensity change as rapidly intensifying storms are more symmetric than slowly intensifying storms. While results also clearly show important contributions from strong convection, it is concluded that intensification is more closely related to the evolution of the areal, radial, and symmetric distribution of precipitation that is not necessarily intense.

scholarly journals Entropy-Based Method for Temporal Downscaling of Precipitation to Improve Sediment Delivery Ratio Assessment

2020 ◽  
Author(s):  
Pedro Alencar ◽  
Eva Paton ◽  
José de Araújo
Keyword(s):  
Maximum Entropy ◽  
Sediment Yield ◽  
Absolute Error ◽  
Reanalysis Data ◽  
Delivery Ratio ◽  
Sediment Delivery ◽  
Precipitation Characteristics ◽  
Erosion Modelling ◽  
Novel Method ◽  
Made In

Scarcity of precipitation data is still a problem in erosion modelling, especially when working in remote and data-scare areas. While much effort was made in the past to use remote sensing or reanalysis data, they are still considered to be not completely reliable, notably for sub-daily measures such as duration and intensity. A way forward are statistical analyses, which can help modellers to obtain sub-daily precipitation characteristics by using daily totals. In this paper, we propose a novel method (Maximum Entropy Distribution of Rainfall Intensity and Duration - MEDRID) to assess the duration and intensity of sub-daily rainfalls relevant for the modelling of sediment delivery ratios. We use the generated data to improve the sediment yield assessment in seven catchments with areas varying from 10 to 10 km and a broad timespan of measured data (1 to 81 years). The best probability density function derived from MEDRID to reproduce sub-daily duration is the generalised gamma distribution (NSE = 0.98), whereas for rain intensity it is the uniform (NSE = 0.87). The MEDRID method coupled with the SYPoME model (Sediment Yield using the Principle of Maximum Entropy) represents a significant improvement over empirically-based SDR models, given its average absolute error of 21% and a Nash Sutcliffe Efficiency of 0.96, (rather than 105% and -4.49, respectively).

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