Rapid Intensification Forecast Research Articles

Improvement in Forecasting Short-Term Tropical Cyclone Intensity Change and Their Rapid Intensification Using Deep Learning

Abstract Forecasting the intensity of a tropical cyclone (TC) remains challenging, particularly when it undergoes rapid changes in intensity. This study aims to develop a convolutional neural network (CNN) for 24-h forecasts of the TC intensity changes and their rapid intensifications over the western Pacific. The CNN model, the DeepTC, is trained using a unique loss function, an amplitude focal loss, to better capture large intensity changes, such as those during rapid intensification (RI) events. We showed that the DeepTC outperforms operational forecasts, with a lower mean absolute error (8.9%–10.2%) and a higher coefficient of determination (31.7%–35%). In addition, the DeepTC exhibits a substantially better skill at capturing RI events than operational forecasts. To understand the superior performance of the DeepTC in RI forecasts, we conduct an occlusion sensitivity analysis to quantify the relative importance of each predictor. Results revealed that scalar quantities such as latitude, previous intensity change, initial intensity, and vertical wind shear play critical roles in successful RI prediction. Additionally, the DeepTC utilizes the three-dimensional distribution of relative humidity to distinguish RI cases from non-RI cases, with higher dry–moist moisture gradients in the mid-to-low troposphere and steeper radial moisture gradients in the upper troposphere showed during RI events. These relationships between the identified key variables and intensity change were successfully simulated by the DeepTC, implying that the relationship is physically reasonable. Our study demonstrates that the DeepTC can be a powerful tool for improving RI understanding and enhancing the reliability of TC intensity forecasts.

An assessment of model capability on rapid intensification prediction of tropical cyclones in the South China Sea

The absence of robust quantitative evaluation methods has led to insufficient knowledge of models capability on the rapid intensification (RI) prediction of tropical cyclones (TCs). In this study, we propose a method and define some indicators aiming to evaluate model capability on predicting RI in a more accurate manner. An assessment of model predictive capability on RI of TCs based on 10 years of operational forecasts has been conducted using different RI criteria. The Tropical Regional Atmosphere Model for the South China Sea of China Meteorological Administration (CMA-TRAMS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) high resolution (HRES) operational forecasts were used. Analysis results revealed that the criterion of 6-hour sea level pressure (SLP) change is more appropriate to be used in RI operational forecast. The maximum lead time (MLT) of CMA-TRAMS and HRES was 72 and 78 h, and the maximum deviation of RI occurrence time of CMA-TRAMS and HRES was 48 h delay and 24 h ahead, respectively. Overall results suggest that the model predictive capability of RI is currently limited, and both models have inadequate capability in providing sufficient heat and energy to support RI in the long run. A tendency of CMA-TRAMS to have a lag in RI occurrence time was also demonstrated due to an air-sea interaction lag resulting from the fixed skin sea surface temperature used. Results of the present study provide insights and could be the basis for future efforts on improving parametrization schemes for properly describing RI process of TCs.

Predicting Short-Term Intensity Change in Tropical Cyclones Using a Convolutional Neural Network

Abstract This study details a two-method, machine learning approach to predict current and short-term intensity change in global tropical cyclones (TCs), “D-MINT” and “D-PRINT.” D-MINT and D-PRINT use infrared imagery and environmental scalar predictors, while D-MINT also employs microwave imagery. Results show that current TC intensity estimates from D-MINT and D-PRINT are more skillful than three established intensity estimation methods routinely used by operational forecasters for North Atlantic and eastern and western North Pacific TCs. Short-term intensity predictions are validated against five operational deterministic guidances at 6-, 12-, 18-, and 24-h lead times. D-MINT and D-PRINT are less skillful than NHC and consensus TC intensity predictions in North Atlantic and eastern North Pacific TCs, but are more skillful than the other guidances for at least half of the lead times. In western North Pacific, north Indian Ocean, and Southern Hemisphere TCs, D-MINT is more skillful than the JTWC and other individual TC intensity forecasts for over half of the lead times. When probabilistically predicting TC rapid intensification (RI), D-MINT is more skillful in North Atlantic and western North Pacific TCs than three operationally used RI guidances, but less skillful for yes–no RI forecasts. In addition, this work demonstrates the importance of microwave imagery, as D-MINT is more skillful than D-PRINT. Since D-MINT and D-PRINT are convolutional neural network models interrogating two-dimensional structures within TC satellite imagery, this study also demonstrates that those features can yield better short-term predictions than existing scalar statistics of satellite imagery in operational models. Finally, a diagnostics tool is revealed to aid the attribution of the D-MINT/D-PRINT intensity predictions. Significance Statement This study develops a method to predict current and short-term forecasts of tropical cyclone (TC) intensity using artificial intelligence. The resultant models use a convolutional neural network (CNN) that can identify two-dimensional features in satellite imagery that are indicative of TC intensity and future intensity change. The performance results indicate that in several TC basins, the CNN approach is generally more skillful than alternative satellite-based estimates of TC intensity as well as operational short-term forecasts of deterministic intensity change and of similar skill to probabilistic rapid intensification forecasts.

Deterministic Rapid Intensity Forecast Guidance for the Joint Typhoon Warning Center’s Area of Responsibility

Abstract Intensity consensus forecasts can provide skillful overall guidance for intensity forecasting at the Joint Typhoon Warning Center as they provide among the lowest mean absolute errors; however, these forecasts are far less useful for periods of rapid intensification (RI) as guidance provided is generally low biased. One way to address this issue is to construct a consensus that also includes deterministic RI forecast guidance in order to increase intensification rates during RI. While this approach increases skill and eliminates some bias, consensus forecasts from this approach generally remain low biased during RI events. Another approach is to construct a consensus forecast using an equally-weighted average of deterministic RI forecasts. This yields a forecast that is generally among the top performing RI guidance, but suffers from false alarms and a high bias due to those false alarms. Neither approach described here is a prescription for forecast success, but both have qualities that merit consideration for operational centers tasked with the difficult task of RI prediction.

The Development of a Consensus Machine Learning Model for Hurricane Rapid Intensification Forecasts with Hurricane Weather Research and Forecasting (HWRF) Data

Abstract This study focused on developing a consensus machine learning (CML) model for tropical cyclone (TC) intensity-change forecasting, especially for rapid intensification (RI). This CML model was built upon selected classical machine learning models with the input data extracted from a high-resolution hurricane model, the Hurricane Weather Research and Forecasting (HWRF) system. The input data contained 21 or 34 RI-related predictors extracted from the 2018 version of HWRF (H218). This study found that TC inner-core predictors can be critical for improving RI predictions, especially the inner-core relative humidity. Moreover, this study emphasized the importance of performing resampling on an imbalanced input dataset. Edited nearest-neighbor and synthetic minority oversampling techniques improved the probability of detection (POD) by ∼10% for the RI class. This study also showed that the CML model has satisfactory performance on RI predictions compared to the operational models. CML reached 56% POD and 46% false alarm ratio (FAR), while the operational models had only 10%–30% POD but 50%–60% FAR. The CML performance on the non-RI classes was comparable to the operational models. The results indicated that, with proper and sufficient training data and RI-related predictors, CML has the potential to provide reliable probabilistic RI forecasts during a hurricane season.

Lightning-Based Tropical Cyclone Rapid Intensification Guidance

Abstract With several seasons of Geostationary Lightning Mapper (GLM) data, this work revisits incorporating lightning observations into operational tropical cyclone rapid intensification guidance. GLM provides freely available, real-time lightning data over the central and eastern North Pacific and North Atlantic Oceans. A long-term lightning dataset is needed to use GLM in a statistical–dynamical operational application to capture the relationship between lightning and the rare occurrence of rapid intensification. This work uses the World Wide Lightning Location Network (WWLLN) dataset from 2005 to 2017 to develop lightning-based predictors for rapid intensification guidance models. The models mimic the operational Statistical Hurricane Intensity Prediction Scheme Rapid Intensification Index and Rapid Intensification Prediction Aid frameworks. The frameworks are averaged to form a consensus as a means to isolate the impact of the lightning predictors. Two configurations for lightning predictors are assessed: a spatial configuration with 0–100-km inner core and 200–300-km rainband area for the preceding 6-h predictors and a temporal configuration with an inner core only for the preceding 0–1, 0–6, and 6–12 h. When tested on the 2018–21 seasons, the temporal configuration adds skill primarily to the 12–48-h forecasts when compared to the no-lightning version and rapid intensification operational consensus. When WWLLN is replaced with GLM, minor changes to the prediction are observed suggesting that this approach is suitable for operational applications and provides a new baseline for tropical cyclone lightning-based rapid intensification aids. Significance Statement The forecasting of rare, yet critical, tropical cyclone rapid intensification events continues to be challenging. The current operational tools to anticipate rapid intensity changes use a combination of numerical weather prediction–derived environmental conditions and satellite-based cloud top temperature variations of deep convection. Here, we use freely available Geostationary Lightning Mapper data, which provide independent information about convection, in similar intensity guidance frameworks using temporal and spatial aspects of lightning variability. Our results show an improvement in short-term (12–48 h) rapid intensification forecasts by using temporal lightning information, and our investigation highlights that users of Geostationary Lightning Mapper lightning information should be cognizant of the influence and impact of land on these observations.

A Rapid Intensification Deterministic Ensemble (RIDE) for the Joint Typhoon Warning Center’s Area of Responsibility

Abstract The Rapid Intensification Deterministic Ensemble (RIDE) is an operational method used to estimate the probability of tropical cyclone rapid intensification in the Joint Typhoon Warning Center’s area of responsibility. Inputs to RIDE are current intensity, storm latitude, intensity change forecasts from seven routinely available operational deterministic models of intensity change, and the number of those models exceeding their individual 90th percentile of intensity change. Deterministic model inputs come from four numerical weather prediction models, two statistical–dynamical models, and one purely statistical model. In RIDE, logistic regression combines the deterministic inputs to form a probabilistic rapid intensification forecast model. RIDE then also generates deterministic intensity forecasts from these probabilistic forecasts that serve as forecaster guidance and as input to intensity consensus aids. Results based on a year of independent verification suggest good reliability and discrimination with a general tendency to underpredict rapid intensification events, but with few false alarms. Significance Statement An operational tropical cyclone forecaster makes a forecast with deterministic and probabilistic intensity guidance tools at their disposal. These models have a varying degree of abilities for predicting both intensity change and rapid intensification. The forecaster faces a dilemma in how to combine this disparate guidance to anticipate rapid intensification events. Here, the RIDE model provides probability forecasts associated with rapid intensification at 12-, 24-, 36-, 48-, and 72-h lead times and associated deterministic forecasts. RIDE provides skillful rapid intensification forecasts and helps rectify this forecast dilemma.

A review of recent advances (2018–2021) on tropical cyclone intensity change from operational perspectives, part 2: Forecasts by operational centers

This paper summarizes the progress and activities of tropical cyclone (TC) operational forecast centers during the last four years (2018–2021). It is part II of the review on TC intensity change from the operational perspective in the rapporteur report presented to the 10th International Workshop on TCs (IWTC) held in Bali, Indonesia, from Dec. 5–9, 2022. Part I of the review has focused on the progress of dynamical model forecast guidance. This part discusses the performance of TC intensity and rapid intensification forecasts from several operational centers. It is shown that the TC intensity forecast errors have continued to decrease since the 9th IWTC held in 2018. In particular, the improvement of rapid intensification forecasts has accelerated, compared with years before 2018. Consensus models, operational procedures, tools and techniques, as well as recent challenging cases from 2018 to 2021 identified by operational forecast centers are described. Research needs and recommendations are also discussed.

A Forecast Cycle–Based Evaluation for Tropical Cyclone Rapid Intensification Forecasts by the Operational HWRF Model

Abstract An evaluation framework for tropical cyclone rapid intensification (RI) forecasts is introduced and applied to evaluate the performance of RI forecasts by the operational Hurricane Weather Research and Forecasting (HWRF) Model. The framework is based on the performance of each 5-day forecast cycle, while the conventional RI evaluation is based on the statistics of successful or false RI forecasts at individual lead times. The framework can be used to compare RI forecasts of different cycles, which helps model developers and forecasters to characterize RI forecasts under different scenarios. It also can provide the evaluation of statistical performance in the context of 5-day forecast cycles. The RI forecast of each cycle is assessed using a modified probability-based approach that takes the absolute errors in intensity changes into account. The overall performance of RI forecasts during a given period is assessed based on the fractions of the individual forecast cycles during which RI events are successfully or falsely predicted. The framework is applied to evaluate the performance of RI forecasts by the HWRF Model for the whole life cycle of a single hurricane, as well as for each of the hurricane seasons from 2009 to 2021. The metric based on the probabilities of detection and false alarm rate of RI is compared with that based on the absolute errors in the intensity and intensity change during RI events. Significance Statement An evaluation framework for tropical cyclone rapid intensification (RI) forecasts is introduced, focusing on the performance of RI forecasts in each 5-day forecast cycle. The cycle-based approach can help to characterize RI forecasts under different conditions such as certain synoptic scenarios, initial conditions, or vortex structures. It also can be used to assess the overall performance of RI forecasts in terms of the percentages of individual forecast cycles that successfully or falsely predict RI events.

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

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.

Development of objective forecast guidance on tropical cyclone rapid intensity change

AbstractForecasting tropical cyclone (TC) intensity and rapid intensification (RI) are major challenges for numerical weather prediction models. In the present study, a model post‐processing technique, namely the TC intensity guidance on rapid intensification (TINT‐RI), has been developed for the western North Pacific basin (South China Sea excluded). It combines logistic regression and the naïve Bayes classifier to provide RI forecasts up to the next 48 hr. Predictors describing physical and environmental conditions are taken from the outputs of the European Centre for Medium‐Range Weather Forecasts model and the National Oceanic and Atmospheric Administration tropical cyclone heat potential field. Several observed TC characteristics such as persistence (change of past 12 hr intensity) are also included in the algorithm. To further reduce the false alarm ratio, the TINT‐RI makes reference to the intensity forecasts from a previously developed statistical‐dynamical TC intensity forecast model and the change of model vertical wind shear. Verification for TCs with RI reveals that the TINT‐RI is significantly more skilful than the direct model outputs from major global numerical weather prediction models and climatology. A case study on extreme RI of Super Typhoon Hato (1713) during its substantial intensification over the Luzon Strait before entering the northern part of the South China Sea is presented to discuss the performance of the TINT‐RI. Potential ways to improve the TINT‐RI such as using sea surface temperature and its anomaly as the oceanic predictor are discussed.

Forecast Uncertainty of Rapid Intensification of Typhoon Dujuan (201521) Induced by Uncertainty in the Boundary Layer

Using ensemble forecast experiments generated by the weather research and forecasting model, the forecast uncertainties of intensity and its rapid intensification (RI) induced by the uncertainty occurring in the boundary layer are investigated for Typhoon Dujuan (201521). The results show that the uncertainty in the boundary layer in the typhoon area, compared with that in other areas of the model domain, not only leads to a much larger forecast uncertainty of the typhoon intensity but also considerably perturbs the RI forecast uncertainty. Particularly, the uncertainty in the gale area in the boundary layer, compared with that in the inner-core and other areas, makes a much larger contribution to the forecast uncertainty of typhoon intensity, with the perturbations including moisture component being most strongly correlated with the occurrence of RI. Further analyses show that such perturbations increase the maximum tangential wind in the boundary layer and enhance the vorticity in the eyewall, which then facilitate the spin-up of the inner-core and induce the occurrence of RI. It is inferred that more observations, especially those associated with the moisture, should be preferentially assimilated in the gale area within the boundary layer of a tropical cyclone, which will help improve the forecast skill of the RI. These results also tell us that the boundary layer parameterization scheme should be further developed to improve the forecast skill of tropical cyclone intensity and its RI behavior.

Understanding Error Distributions of Hurricane Intensity Forecasts during Rapid Intensity Changes

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.

Applying Satellite Observations of Tropical Cyclone Internal Structures to Rapid Intensification Forecast With Machine Learning

AbstractTropical cyclone (TC) intensity change is controlled by both environmental conditions and internal storm processes. We show that TC 24‐hr subsequent intensity change (DV24) is linearly correlated with the departures in satellite observations of inner‐core precipitation, ice water content, and outflow temperature from respective threshold values corresponding to neutral TCs of nearly constant intensity. The threshold values vary linearly with TC intensity. Using machine learning with the inner‐core precipitation and the predictors currently employed at the National Hurricane Center (NHC) for probabilistic rapid intensification (RI) forecast guidance, our model outperforms the NHC operational RI consensus in terms of the Peirce Skill Score for RI in the Atlantic basin during 2009–2014 by 37%, 12%, and 138% for DV24 ≥ 25, 30, and 35 kt, respectively. Our probability of detection is 40%, 60%, and 200% higher than the operational RI consensus, while the false alarm ratio is only 4%, 7%, and 6% higher.

Evaluation of Two Initialization Schemes for Simulating the Rapid Intensification of Typhoon Lekima (2019)

Two different initialization schemes for tropical cyclone (TC) prediction in numerical models are evaluated based on a case study of Typhoon Lekima (2019). The first is a dynamical initialization (DI) scheme where the axisymmetric TC vortex in the initial conditions is spun up through the 6-h cycle runs before the initial forecast time. The second scheme is a bogussing scheme where the analysis TC vortex is replaced by a synthetic Rankine vortex. Results show that although both initialization schemes can help improve the simulated rapid intensification (RI) of Lekima, the simulation employing the DI scheme (DIS) reproduces better the RI onset and intensification rate than that employing the bogussing scheme (BOG). Further analyses show the cycle runs of DI help establish a realistic TC structure with stronger secondary circulation than those in the control run and BOG, leading to fast vortex spinup and contraction of the radius of maximum wind (RMW). The resultant strong inner-core primary circulation favors precession of the midlevel vortex under the moderate vertical wind shear (VWS) and thus helps vortex alignment, contributing to an earlier RI onset. Afterwards, the decreased vertical shear and the stronger convection inside the RMW support the persistent RI of Lekima in DIS. In contrast, the reduced VWS is not well captured and the inner-core convection is weaker and resides farther away from the TC center in BOG, leading to slower intensification. The results imply that the DI effectively improves the prediction of the inner-core process, which is crucial to the RI forecast.

Development and Evaluation of an Evolutionary Programming-Based Tropical Cyclone Intensity Model

AbstractA statistical–dynamical tropical cyclone (TC) intensity model is developed from a large ensemble of algorithms through evolutionary programming (EP). EP mimics the evolutionary principles of genetic information, reproduction, and mutation to develop a population of algorithms with skillful predictor combinations. From this evolutionary process the 100 most skillful algorithms as determined by root-mean square error on validation data are kept and bias corrected. Bayesian model combination is used to assign weights to a subset of 10 skillful yet diverse algorithms from this list. The resulting algorithm combination produces a forecast superior in skill to that from any individual algorithm. Using these methods, two models are developed to give deterministic and probabilistic forecasts for TC intensity every 12 h out to 120 h: one each for the North Atlantic and eastern and central North Pacific basins. Deterministic performance, as defined by MAE, exceeds that of a “no skill” forecast in the North Atlantic to 96 h and is competitive with the operational Statistical Hurricane Intensity Prediction Scheme and Logistic Growth Equation Model at these times. In the eastern and central North Pacific, deterministic skill is comparable to the blended 5-day climatology and persistence (CLP5) track and decay-SHIFOR (DSHF) intensity forecast (OCD5) only to 24 h, after which time it is generally less skillful than OCD5 and all operational guidance. Probabilistic rapid intensification forecasts at the 25–30 kt (24 h)−1 thresholds, particularly in the Atlantic, are skillful relative to climatology and competitive with operational guidance when subjectively calibrated; however, probabilistic rapid weakening forecasts are not skillful relative to climatology at any threshold in either basin. Case studies are analyzed to give more insight into model behavior and performance.

Impact of Assimilating Upper-Level Dropsonde Observations Collected during the TCI Field Campaign on the Prediction of Intensity and Structure of Hurricane Patricia (2015)

Abstract The dropsondes released during the Tropical Cyclone Intensity (TCI) field campaign provide high-resolution kinematic and thermodynamic measurements of tropical cyclones within the upper-level outflow and inner core. This study investigates the impact of these upper-level TCI dropsondes on analyses and prediction of Hurricane Patricia (2015) during its rapid intensification (RI) phase using an ensemble–variational data assimilation system. In the baseline experiment (BASE), both kinematic and thermodynamic observations of TCI dropsondes at all levels except the upper levels are assimilated. The upper-level wind and thermodynamic observations are assimilated in additional experiments to investigate their respective impacts. Compared to BASE, assimilating TCI upper-level wind observations improves the accuracy of outflow analyses verified against independent atmospheric motion vector (AMV) observations. It also strengthens the tangential and radial wind near the upper-level eyewall. The inertial stability within the upper-level eyewall is enhanced, and the maximum outflow is more aligned toward the inner core. Additionally, the analyses including the upper-level thermodynamic observations produce a warmer and drier core at high levels. Assimilating both upper-level kinematic and thermodynamic observations also improves the RI forecast. Compared to BASE, assimilating the upper-level wind induces more upright and inward-located eyewall convection, resulting in more latent heat release closer to the warm core. This process leads to stronger inner-core warming. Additionally, the initial warmer upper-level inner core produced by assimilating TCI thermodynamic observations also intensifies the convection and latent heat release within the eyewall, thus further contributing to the improved intensity forecasts.

Effects of Parameterized Boundary Layer Structure on Hurricane Rapid Intensification in Shear

Abstract This study investigates the role of the parameterized boundary layer structure in hurricane intensity change using two retrospective HWRF forecasts of Hurricane Earl (2010) in which the vertical eddy diffusivity Km was modified during physics upgrades. Earl undergoes rapid intensification (RI) in the low-Km forecast as observed in nature, while it weakens briefly before resuming a slow intensification at the RI onset in the high-Km forecast. Angular momentum budget analysis suggests that Km modulates the convergence of angular momentum in the boundary layer, which is a key component of the hurricane spinup dynamics. Reducing Km in the boundary layer causes enhancement of both the inflow and convergence, which in turn leads to stronger and more symmetric deep convection in the low-Km forecast than in the high-Km forecast. The deeper and stronger hurricane vortex with lower static stability in the low-Km forecast is more resilient to shear than that in the high-Km forecast. With a smaller vortex tilt in the low-Km forecast, downdrafts associated with the vortex tilt are reduced, bringing less low-entropy air from the midlevels to the boundary layer, resulting in a less stable boundary layer. Future physics upgrades in operational hurricane models should consider this chain of multiscale interactions to assess their impact on model RI forecasts.

Real-Time Forecasts of Typhoon Rapid Intensification in The North Western Pacific Basin with the NCEP Operational HWRF Model

ABSTRACT This study presents the real-time performance of the United States (US) National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecast (HWRF) model in predicting rapid intensification (RI) of typhoons in the North Western Pacific (WPAC) basin in 2013. Examination of all RI cases in WPAC during 2013 shows that the HWRF model captures a consistent vortex structure at the onset of all RI as seen in previous idealized studies with HWRF. However, HWRF has issues with predicting RI when the model vortex is initialized with intensity greater than hurricane strength. Further verification of the probability of detection (POD) and the false alarm rate (FAR) of RI forecasts shows that the HWRF model outperforms all other models used by the US Navy’s Joint Typhoon Warning Center, possessing highest POD and lowest FAR in 2013. Examination of the intensity change forecasts at different forecast lead times also confirms that the HWRF model has superior performance, particularly at the 72-h lead time with the POD index ~ 0.91 and the FAR index ~ 0.33. Such unique performance of the HWRF model demonstrates its role in helping operational agencies improve their official intensity (and RI) forecasts for tropical cyclones in the WPAC basin.

Development of NWP Based Objective Cyclone Prediction System (CPS) for North Indian Ocean Tropical Cyclones — Evaluation of Performance

ABSTRACT An objective NWP based Cyclone Prediction System (CPS) has been developed and implemented at IMD for operational cyclone forecasting over the north Indian Ocean (NIO). The five forecast components of CPS are (a) Genesis Potential Parameter (GPP), (b) Multi-Model Ensemble (MME) technique for track prediction, (c) Statistical Cyclone Intensity Prediction (SCIP), (d) rapid intensification and (e) decay model to forecast intensity after the landfall. Verification shows GPP had higher probability of detection (0.98) and lower false alarm ratio (0.27) with higher critical success index (0.72). Mean track error of MME ranged from 74 km at 12 h to 200 km at 72 h and reduced by 27% to 52% for 36 h to 72 h forecast during 2009-2013. The mean forecast errors of landfall position ranged from 56 km at 24 h to 137 km at 72 h and landfall time error ranged from 3.6 h at 24 h to 6.1 h at 72 h. Mean intensity errors of SCIP ranged from 5.4 kt at 12 h to 16.9 kt at 72 h. The probabilistic rapid intensification forecast was skillful compared to climatology. The 6-hourly decaying intensity (after landfall) errors ranged from 3 kt to 4.9 kt. Results demonstrate the potential of CPS for operational cyclone forecast over the NIO.