Tropical Cyclone Intensity Prediction Research Articles

Impact of Assimilating Himawari‐8‐Derived Layered Precipitable Water With Varying Cumulus and Microphysics Parameterization Schemes on the Simulation of Typhoon Hato

AbstractUnderstanding moisture information ahead of tropical cyclone (TC) convection is very important for predicting TC track, intensity, and precipitation. The advanced Himawari imager onboard the Japanese Himawari‐8/‐9 satellite can provide high spatial and temporal resolution moisture information. Three‐layered precipitable water (LPW) with its three water vapor absorption infrared bands can be assimilated to generate better understanding and prediction of TC evolution. The impacts of LPW assimilation in the Weather Research and Forecasting model with nine combinations of physical parameterization schemes, including three cumulus parameterization (CP) and three microphysics parameterization (MP) schemes on TC prediction, have been comprehensively analyzed using Typhoon Hato as a case study. The results indicate that LPW assimilation reduces the average track error and speed up TC movement by better adjustment of the atmospheric circulation fields via changing the vertical structure of moisture and thermal profile. The track forecasts retain sensitivity to CP schemes after LPW assimilation. Also, LPW assimilation improves TC intensity prediction because the latent heat release process is accurately adjusted. It has been revealed that LPW assimilation can weaken the intensity sensitivity to MP schemes more than to CP schemes. Skill scores were used to evaluate precipitation forecasts after Hato's landfall. The results indicate that heavy precipitation forecasts are more sensitive to the choice of MP schemes. After LPW assimilation, the equitable threat scores among different results become similar and all forecast skills are increased. In addition, group statistic results with different initial time show the same conclusions.

Intensity Estimation of Tropical Cyclones Using the Relevance Vector Machine From Infrared Satellite Image Data

In this study, tropical cyclone (TC) infrared geostationary satellite images obtained by FY-2 satellite scanning are used as reference points in the overall infrared geostationary satellite image, the deviation angle gradient co-occurrence matrix is constructed, and the statistical parameters of the co-occurrence matrix are extracted. Combined with the best track data of tropical cyclones provided by the China Meteorological Administration, the TC center is used as the origin point, and the satellite image is divided into nine 50 km scales, from the origin to a range of 450 km. Relevance vector machines are then used to construct the TC intensity estimation model for each scale. The experimental results show that the optimal radial scale is approximately 250 km. Based on these results, the proposed TC intensity estimation technique can be used for TC intensity prediction. This technique has the potential to improve the accuracy of TC intensity prediction and to provide technical support for disaster reduction.

Tropical cyclone intensity prediction over the North Indian Ocean - An NWP based objective approach

Tropical cyclone intensity prediction over the North Indian Ocean - An NWP based objective approach

Predictability of Tropical Cyclone Intensity over the Western North Pacific Using the IBTrACS Dataset

Abstract The predictability limits of tropical cyclone (TC) intensity over the western North Pacific (WNP) are investigated using TC best track data. The results show that the predictability limit of the TC minimum central pressure (MCP) is ~102 h, comparable to that of the TC maximum sustained wind (MSW). The spatial distribution of the predictability limit of the TC MCP over the WNP is similar to that of the TC MSW, and both gradually decrease from the eastern WNP (EWNP) to the South China Sea (SCS). The predictability limits of the TC MCP and MSW are relatively high over the southeastern WNP where the modified accumulated cyclone energy (MACE) is relatively large, whereas they are relatively low over the SCS where the MACE is relatively small. The spatial patterns of the TC lifetime and the lifetime maximum intensity (LMI) are similar to that of the TC MACE. Strong and long-lived TCs, which have relatively long predictability, mainly form in the southwestern WNP. In contrast, weak and short-lived TCs, which have relatively short predictability, mainly form in the SCS. In addition to the dependence of the predictability limit on genesis location, the predictability limits of TC intensity also evolve in the TC life cycle. The predictability limit of the TC MCP (MSW) gradually decreases from 102 (108) h at genesis time (00 h) to 54 (84) h 4 days after TC genesis.

Hurricane Kinetic Energy Spectra from In Situ Aircraft Observations

Abstract Since the pioneering paper by Nastrom and Gage on aircraft-derived power spectra, significant progress has been made in understanding the wavenumber distribution of energy in Earth’s atmosphere and its implications for the intrinsic limits of weather forecasting. Improvements in tropical cyclone intensity predictions have lagged those of global weather forecasting, and limited intrinsic predictability may be partially responsible. In this study, we construct power spectra from aircraft data of over 1200 missions carried out by the National Oceanic and Atmospheric Administration (NOAA) and Air Force Reserve Command (AFRC) Hurricane Hunters. Each mission is parsed into distinct flight legs, and legs meeting a specified set of criteria are used for spectral analysis. Here, we produce power spectra composites for each category of the Saffir–Simpson scale, revealing a systematic relationship between spectral slope and storm intensity. Specifically, as storm intensity increases, we find that 1) spectral slope becomes steeper across scales from 10 to 160 km and 2) the transition zone where spectral slope begins to steepen shifts downscale.

Close linkage between quasi‐biweekly oscillation and tropical cyclone intensification over the western North Pacific

Forecasting tropical cyclone (TC) intensity is challenging, particularly at the subseasonal scale. In this study, we found that compared to the 30−90‐day Madden–Julian oscillation (MJO), the activity of 10−30‐day quasi‐biweekly oscillation (QBWO) has a closer connection with the intensity change of TCs over the tropical western North Pacific. Both the TC growth rate (24‐hr increases in maximum sustained wind speed) and the probability of TC intensification during the active phases of QBWO increase significantly compared to those during the inactive QBWO phases. The effects of QBWO on TC intensification take place mainly through alteration of the large‐scale dynamic conditions. During the active convection phase of QBWO, the 10–30‐day cyclonic vorticity and convergence in the lower troposphere, divergence in the upper troposphere, and the circulation‐induced moistening anomalies provide a favorable environment for TC growth. The thermodynamic conditions of atmospheric instability, sea surface temperature (SST), and ocean heat potential show negative but insignificant contributions to TC intensification at the quasi‐biweekly time scale. These results not only aid our understanding of the influences of QBWO‐related environmental conditions on TC intensification, but also suggest the potential predictability of TC intensity at an extended range (10–30 days) once the QBWO has been successfully predicted.

Dependence of Tropical Cyclone Intensification Rate on Sea Surface Temperature, Storm Intensity, and Size in the Western North Pacific

Abstract This study extends the statistical analysis on the dependence of tropical cyclone (TC) intensification rate (IR) on sea surface temperature (SST), storm initial intensity (maximum sustained surface wind speed Vmax), and storm size, in terms of the radius of maximum wind (RMW), the radius of 34-kt (AR34; 1 kt = 0.51 m s−1) wind, and the outer-core wind skirt parameter DR34 (= AR34 − RMW), for North Atlantic TCs to western North Pacific (WNP) TCs during 1982–2015. Results show that the relationship between the TC maximum potential intensification rate (MPIR) and SST also exists in the WNP. TC IR depends strongly on TC intensity and structure, consistent with the findings for North Atlantic TCs. TC IR is positively (negatively) correlated with storm intensity when Vmax is below (above) 70 kt and negatively correlated with the RMW. Rapid intensification (RI) occurs only in a relatively narrow range of parameter space in storm intensity and both inner- and outer-core sizes, with the highest IR appearing for Vmax = 70 kt, RMW ≦ 40 km, AR34 = 150 km, and DR34 = 100 km. The highest frequency of occurrence of intensifying TCs occurs for Vmax ~ 40–60 kt, RMW ~ 20–60 km, AR34 = 200 km, and DR34 = 120 km. Overall, these values are very similar to those for TCs in the North Atlantic. These results suggest the need for the realistic initialization of TC structure in numerical models and the inclusion of size parameters in statistical TC intensity prediction schemes.

Statistical–Dynamical Typhoon Intensity Predictions in the Western North Pacific Using Track Pattern Clustering and Ocean Coupling Predictors

Abstract A statistical–dynamical model for predicting tropical cyclone (TC) intensity has been developed using a track-pattern clustering (TPC) method and ocean-coupled potential predictors. Based on the fuzzy c-means clustering method, TC tracks during 2004–12 in the western North Pacific were categorized into five clusters, and their unique characteristics were investigated. The predictive model uses multiple linear regressions, where the predictand or the dependent variable is the change in maximum wind speed relative to the initial time. To consider TC-ocean coupling effects due to TC-induced vertical mixing and resultant surface cooling, new potential predictors were also developed for maximum potential intensity (MPI) and intensification potential (POT) using depth-averaged temperature (DAT) instead of sea surface temperature (SST). Altogether, 6 static, 11 synoptic, and 3 DAT-based potential predictors were used. Results from a series of experiments for the training period of 2004–12 using TPC and DAT-based predictors showed remarkably improved TC intensity predictions. The model was tested on predictions of TC intensity for 2013 and 2014, which are not used in the training samples. Relative to the nonclustering approach, the TPC and DAT-based predictors reduced prediction errors about 12%–25% between 24- and 96-h lead time. The present model is also compared with four operational dynamical forecast models. At short leads (up to 24 h) the present model has the smallest mean absolute errors. After a 24-h lead time, the present model still shows skill that is comparable with the best operational models.

Impact of wave whitecapping on land falling tropical cyclones

Predicting tropical cyclone structure and evolution remains challenging. Particularly, the surface wave interactions with the continental shelf and their impact on tropical cyclones have received very little attention. Through a series of state-of-the-art high-resolution, fully-coupled ocean-wave and atmosphere-ocean-wave experiments, we show here, for the first time, that in presence of continental shelf waves can cause substantial cooling of the sea surface. Through whitecapping there is a transfer of momentum from the surface which drives deeper vertical mixing. It is the waves and not just the wind which become the major driver of stratified coastal ocean ahead-of-cyclone cooling. In the fully-coupled atmosphere-ocean-wave model a negative feedback is found. The maximum wind speed is weaker and the damaging footprint area of hurricane-force winds is reduced by up to 50% due to the strong wave induced ocean cooling ahead. Including wave-ocean coupling is important to improve land falling tropical cyclone intensity predictions for the highly populated and vulnerable coasts.

Tropical Cyclone Intensity Prediction in the Western North Pacific Basin Using SHIPS and JMA/GSM

Tropical Cyclone Intensity Prediction in the Western North Pacific Basin Using SHIPS and JMA/GSM

A Consensus Technique for Tropical Cyclone Intensity Prediction over the Western North Pacific

A Consensus Technique for Tropical Cyclone Intensity Prediction over the Western North Pacific

Global assessment of tropical cyclone intensity statistical–dynamical hindcasts

This paper assesses the characteristics of linear statistical models developed for tropical cyclone (TC) intensity prediction at global scale. To that end, multilinear regression models are developed separately for each TC‐prone basin to estimate the intensification of a TC given its initial characteristics and environmental parameters along its track. We use identical large‐scale environmental parameters in all basins, derived from a 1979–2012 reanalysis product. The resulting models display comparable skill to previously described similar hindcast schemes. Although the resulting mean absolute errors are rather similar in all basins, the models beat persistence by 20–40% in most basins, except in the North Atlantic and northern Indian Ocean, where the skill gain is weaker (10–25%). A large fraction (60–80%) of the skill gain arises from the TC characteristics (intensity and its rate of change) at the beginning of the forecast. Vertical shear followed by the maximum potential intensity are the environmental parameters that yield most skill globally, but with individual contributions that strongly depend on the basin. Hindcast models built from environmental predictors calculated from their seasonal climatology perform almost as well as using real‐time values. This has the potential to considerably simplify the implementation of operational forecasts in such models. Finally, these models perform poorly to predict intensity changes for Category 2 and weaker TCs, while they are 2–4 times more skilful for the strongest TCs (Category 3 and above). This suggests that these linear models do not properly capture the processes controlling the early stages of TC intensification.

The Relationship between Tropical Cyclones at Genesis and Their Maximum Attained Intensity

Despite recent improvements made to tropical cyclone intensity predictions, this study investigates a different approach than those attempted thus far. Here, the overall environmental setup at genesis is evaluated to determine whether it predisposes a storm to reach its future maximum intensity. Variables retrieved from ERA-Interim are used to generate storm-centered composites at the time of genesis for Atlantic basin, main development region TCs from 1979–2015. Composites are stratified by their maximum attained intensity: tropical depressions (GTD), tropical storms (GTS), minor hurricanes (GMN), or major hurricanes (GMJ). A multiple-parameter linear regression is then used to associate the eventual attained intensity of tropical cyclone to the obtained variables at genesis. The regression has an adjusted r2 of 0.39, which indicates that a statistical relationship is present. Regression coefficients, along with the spatial distribution of variables in the storm-centered composites, indicate that storms that reach higher intensities are associated at genesis with stronger, more compact, low-level vortices, better-defined outflow jets, a more compact region of high midlevel relative humidity, and higher atmospheric water vapor content.

Global 7 km mesh nonhydrostatic Model Intercomparison Project for improving TYphoon forecast (TYMIP-G7): experimental design and preliminary results

Abstract. Recent advances in high-performance computers facilitate operational numerical weather prediction by global hydrostatic atmospheric models with horizontal resolutions of ∼ 10 km. Given further advances in such computers and the fact that the hydrostatic balance approximation becomes invalid for spatial scales < 10 km, the development of global nonhydrostatic models with high accuracy is urgently required. The Global 7 km mesh nonhydrostatic Model Intercomparison Project for improving TYphoon forecast (TYMIP-G7) is designed to understand and statistically quantify the advantages of high-resolution nonhydrostatic global atmospheric models to improve tropical cyclone (TC) prediction. A total of 137 sets of 5-day simulations using three next-generation nonhydrostatic global models with horizontal resolutions of 7 km and a conventional hydrostatic global model with a horizontal resolution of 20 km were run on the Earth Simulator. The three 7 km mesh nonhydrostatic models are the nonhydrostatic global spectral atmospheric Double Fourier Series Model (DFSM), the Multi-Scale Simulator for the Geoenvironment (MSSG) and the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). The 20 km mesh hydrostatic model is the operational Global Spectral Model (GSM) of the Japan Meteorological Agency. Compared with the 20 km mesh GSM, the 7 km mesh models reduce systematic errors in the TC track, intensity and wind radii predictions. The benefits of the multi-model ensemble method were confirmed for the 7 km mesh nonhydrostatic global models. While the three 7 km mesh models reproduce the typical axisymmetric mean inner-core structure, including the primary and secondary circulations, the simulated TC structures and their intensities in each case are very different for each model. In addition, the simulated track is not consistently better than that of the 20 km mesh GSM. These results suggest that the development of more sophisticated initialization techniques and model physics is needed to further improve the TC prediction.

Influence of Equatorial Waves on the Genesis of Super Typhoon Haiyan (2013)

Abstract The influence of equatorial wave disturbances on the genesis of Super Typhoon Haiyan (2013) is investigated through spectral, composite, and ensemble sensitivity analysis of various observational datasets in combination with predictions from an operational ensemble. Under the favorable large-scale environmental conditions of the Asian monsoon combined with the Madden–Julian oscillation (MJO), the incipient Haiyan develops from a cyclonic disturbance that originates from a train of westward-propagating mixed Rossby–gravity (MRG) waves. Haiyan eventually develops in the monsoon trough region at the leading edge of the moist MJO phase that has strong low-level convergence, high moisture content, and weak shear, along with high sea surface temperature. These favorable environmental conditions promote the intensification of deep moist convection that facilitates the development of the cyclonic disturbance from an MRG wave into a tropical depression, which later intensifies rapidly into Super Typhoon Haiyan, one of the world’s strongest and most destructive tropical storms ever recorded. Results from ensemble sensitivity analyses are consistent with this finding and further show that the uncertainties in tropical waves and their interactions can impact the large-scale environment surrounding Haiyan’s precursor and therefore limit the predictability of tropical cyclone formation and intensity. The better-performing members tend to have a stronger initial MRG wave disturbance, which provides a stronger initial seed for the later development of the storm, as well as a stronger moist MJO wave in the tropical region, which not only promotes deep convection near the precursor location, but also reduces the environmental vertical wind shear by strengthening the tropical westerlies.

Advances in Tropical Cyclone Intensity Forecasts

AbstractNOAA established the 10-year Hurricane Forecast Improvement Project (HFIP) to accelerate the improvement of forecasts and warnings of tropical cyclones and to enhance mitigation and preparedness by increased confidence in those forecasts. Specific goals include reducing track and intensity errors by 20% in 5 years and 50% in 10 years and extending the useful range of hurricane forecasts to 7 days. Under HFIP, there have been significant improvements to NOAA's operational hurricane prediction model resulting in increased accuracy in the numerical guidance for tropical cyclone intensity predictions. This paper documents many of the improvements that have been accomplished over the last 5 years, as well as some future research directions that are being pursued.

Predictability of tropical cyclone intensity: scale‐dependent forecast error growth in high‐resolution stochastic kinetic‐energy backscatter ensembles

A systematic study of the intrinsic predictability of tropical cyclone (TC) intensity is conducted using a set of cloud‐resolving model ensembles of Hurricane Earl (2010). The ensembles are perturbed with a stochastic kinetic‐energy backscatter scheme (SKEBS) and started from identical initial conditions. Scale‐dependent error growth is investigated by imposing stochastic perturbations with various spatial scales on the TC and its environment. Predictability limits (upper bound) are determined by computing the error magnitude associated with each component of the Fourier‐decomposed TC wind fields at forecast times up to 7 days. Three SKEBS ensembles with different perturbation scales are used to investigate the effects of small‐scale, mesoscale and large‐scale uncertainties on the predictability of TC intensity. In addition, the influence of the environmental flow is investigated by perturbing the lateral boundary conditions. It is found that forecast errors grow rapidly on small scales (azimuthal wave numbers > 20), which saturate within 6–12 h in all four ensembles, regardless of perturbation scale. Errors grow relatively slower on scales corresponding to rain bands (wave numbers 2–5), limiting the predictability of these features to 1–5 days. Earl's mean vortex and the wave number‐1 asymmetry are comparatively resistant to error growth and remain predictable for at least 7 days. Forecast uncertainty of the mean vortex and wave number‐1 asymmetry is greater in the large‐scale perturbation and perturbed lateral boundary condition ensembles. The results from this case indicate that the predictability of the mean vortex and wave number‐1 asymmetry is predominately associated with the predictability of the large‐scale environment, which is generally much longer than that of convective‐scale processes within the TC.

Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

Abstract. The tropical cyclone (TC) track and intensity predictions over Bay of Bengal (BOB) using the Advanced Research Weather Research and Forecasting (ARW) model are evaluated for a number of data assimilation experiments using various types of data. Eight cyclones that made landfall along the east coast of India during 2008–2013 were simulated. Numerical experiments included a control run (CTL) using the National Centers for Environmental Prediction (NCEP) 3-hourly 0.5 × 0.5° resolution Global Forecasting System (GFS) analysis as the initial condition, and a series of cycling mode variational assimilation experiments with Weather Research and Forecasting (WRF) data assimilation (WRFDA) system using NCEP global PrepBUFR observations (VARPREP), Atmospheric Motion Vectors (VARAMV), Advanced Microwave Sounding Unit (AMSU) A and B radiances (VARRAD) and a combination of PrepBUFR and RAD (VARPREP+RAD). The impact of different observations is investigated in detail in a case of the strongest TC, Phailin, for intensity, track and structure parameters, and finally also on a larger set of cyclones. The results show that the assimilation of AMSU radiances and Atmospheric Motion Vectors (AMV) improved the intensity and track predictions to a certain extent and the use of operationally available NCEP PrepBUFR data which contains both conventional and satellite observations produced larger impacts leading to improvements in track and intensity forecasts. The forecast improvements are found to be associated with changes in pressure, wind, temperature and humidity distributions in the initial conditions after data assimilation. The assimilation of mass (radiance) and wind (AMV) data showed different impacts. While the motion vectors mainly influenced the track predictions, the radiance data merely influenced forecast intensity. Of various experiments, the VARPREP produced the largest impact with mean errors (India Meteorological Department (IMD) observations less the model values) of 78, 129, 166, 210 km in the vector track position, 10.3, 5.8, 4.8, 9.0 hPa deeper than IMD data in central sea level pressure (CSLP) and 10.8, 3.9, −0.2, 2.3 m s−1 stronger than IMD data in maximum surface winds (MSW) for 24, 48, 72, 96 h forecasts respectively. An improvement of about 3–36 % in track, 6–63 % in CSLP, 26–103 % in MSW and 11–223 % in the radius of maximum winds in 24–96 h lead time forecasts are found with VARPREP over CTL, suggesting the advantages of assimilation of operationally available PrepBUFR data for cyclone predictions. The better predictions with PrepBUFR could be due to quality-controlled observations in addition to containing different types of data (conventional, satellite) covering an effectively larger area. The performance degradation of VARPREP+RAD with the assimilation of all available observations over the domain after 72 h could be due to poor area coverage and bias in the radiance data.

A Statistical Analysis on the Dependence of Tropical Cyclone Intensification Rate on the Storm Intensity and Size in the North Atlantic

AbstractThe dependence of tropical cyclone (TC) intensification rate IR on storm intensity and size was statistically analyzed for North Atlantic TCs during 1988–2012. The results show that IR is positively (negatively) correlated with storm intensity (the maximum sustained near-surface wind speed Vmax) when Vmax is below (above) 70–80 knots (kt; 1 kt = 0.51 m s−1), and negatively correlated with storm size in terms of the radius of maximum wind (RMW), the average radius of gale-force wind (AR34), and the outer-core wind skirt parameter DR34 (=AR34 − RMW). The turning point for Vmax of 70–80 kt is explained as a balance between the potential intensification and the maximum potential intensity (MPI). The highest IR occurs for Vmax = 80 kt, RMW ≤ 40 km, and AR34 = DR34 = 150 km. The high frequency of occurrence of intensifying TCs occurs for Vmax ≤ 80 kt and RMW between 20 and 60 km, AR34 ≤ 200 km, and DR34 ≤ 150 km. Rapid intensification (RI) often occurs in a relatively narrow parameter space in storm intensity and both inner- and outer-core sizes. In addition, a theoretical basis for the intensity dependency has also been provided based on a previously constructed simplified dynamical system for TC intensity prediction.

Forecasting a Large Number of Tropical Cyclone Intensities around Japan Using a High-Resolution Atmosphere–Ocean Coupled Model

Abstract This work quantifies the benefits of using a high-resolution atmosphere–ocean coupled model in tropical cyclone (TC) intensity forecasts in the vicinity of Japan. To do so, a large number of high-resolution calculations were performed by running the Japan Meteorological Agency (JMA) nonhydrostatic atmospheric mesoscale model (AMSM) and atmosphere–ocean coupled mesoscale model (CMSM). A total of 281 3-day forecasts were compiled for 34 TCs from April 2009 to September 2012 for each model. The performance of these models is compared with the JMA global atmospheric spectral model (GSM) that is used for the operational TC intensity guidance. The TC intensities are better predicted by CMSM than the other models. The improvement rates in CMSM relative to GSM and AMSM generally increase with increasing forecast time (FT). CMSM is better than GSM and AMSM by 27.4% and 21.3% at FT = 48 h in terms of minimum sea level pressure, respectively. Regarding the maximum wind speed, CMSM is better than GSM and AMSM by 12.8% and 19.5% at FT = 48 h, respectively. This is due to smaller initial intensity errors and sea surface cooling consistent with in situ observations that suppress erroneous TC intensification. Thus, a high-resolution coupled model is promising for TC intensity prediction in the area surrounding Japan, where most of the TCs are in a decay stage. In contrast, coupling to the upper-ocean model yields only a negligible difference in the TC track forecast skill on average.