Sensitivity of high-resolution tropical cyclone intensity forecasts to surface flux parameterization

Tropical cyclone (TC) intensity is controlled by both environmental factors and its internal structure. This paper investigates the influence of inner core features derived from satellite data on TC intensity changes and explores effective methods to improve TC intensity forecast accuracy. We construct models for forecasting TC intensity over the next 5 days using the light gradient boosting machine (LGBM) algorithm in the northwestern Pacific. The models integrate climatological and persistence predictors, large-scale environmental variables at the initial time, inner core features extracted from satellite data, and variables related to the forecasted TC location. The models are trained on TC data from 2005 to 2019, tested using best track data from 2020 to 2022 and real-time data in 2023. Results show that the inner core features derived from satellite data are helpful for intensity forecasts within 36 h, and variables related to the forecasted location are helpful for improving TC intensity forecast accuracy in most cases, especially for the forecasts beyond 48 h. Compared with the National Centers for Environmental Prediction (NCEP) and European Centre for Medium-Range Weather Forecasts (ECMWF) models, our models demonstrate 4%–48% and 19%–70% decreases in mean absolute errors (MAEs) in real-time intensity forecasts. The most important predictors identified include potential future intensity change, intensity changes during the previous 12 h, vertical wind shear, and sea–land ratio. The inner core feature extracted from satellite data is also among the top 10 important variables for short-time intensity forecasts. Significance Statement This study advances tropical cyclone (TC) intensity forecasting by integrating climatological and persistence predictors, large-scale environmental variables at the initial time, satellite-derived inner core features, and variables related to the forecasted TC location into the light gradient boosting machine (LGBM) models. Our models significantly improve forecast accuracy, reducing mean absolute errors by 4%–48% compared with the National Centers for Environmental Prediction (NCEP) and 19%–70% compared with the European Centre for Medium-Range Weather Forecasts (ECMWF). Results denote that the inner core features improve the intensity prediction in the short time, and variables related to the forecasted TC location improve the intensity forecasts in most cases, especially for the forecasts beyond 48 h. This study provides valuable insights for enhancing operational TC intensity forecasting.

This study examines the dependence of tropical cyclone (TC) intensity forecast errors on track forecast errors in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model. Using real-time forecasts and retrospective experiments during 2015–18, verification of TC intensity errors conditioned on different 5-day track error thresholds shows that reducing the 5-day track errors by 50%–70% can help reduce the absolute intensity errors by 18%–20% in the 2018 version of the COAMPS-TC model. Such impacts of track errors on the TC intensity errors are most persistent at 4–5-day lead times in all three major ocean basins, indicating a significant control of global models on the forecast skill of the COAMPS-TC model. It is of interest to find, however, that lowering the 5-day track errors below 80 n mi (1 n mi = 1.852 km) does not reduce TC absolute intensity errors further. Instead, the 4–5-day intensity errors appear to be saturated at around 10–12 kt (1 kt ≈ 0.51 m s−1) for cases with small track errors, thus suggesting the existence of some inherent intensity errors in regional models. Additional idealized simulations under a perfect model scenario reveal that the COAMPS-TC model possesses an intrinsic intensity variation at the TC mature stage in the range of 4–5 kt, regardless of the large-scale environment. Such intrinsic intensity variability in the COAMPS-TC model highlights the importance of potential chaotic TC dynamics, rather than model deficiencies, in determining TC intensity errors at 4–5-day lead times. These results suggest a fundamental limit in the improvement of TC intensity forecasts by numerical models that one should consider in future model development and evaluation.

India Meteorological Department (IMD) introduced the objective tropical cyclone (TC) intensity forecast valid for next 24 h over the north Indian Ocean (NIO) in 2003 and extended up to 72 h in 2009. In this study, an attempt is made to evaluate the TC intensity forecast issued by IMD during 2005–2011 (7 years) by calculating the absolute error (AE), root mean square error (RMSE) and skill in intensity forecast in terms of maximum sustained surface wind (MSW). The accuracy of TC intensity forecast has been analysed with respect to basin of formation (Bay of Bengal, Arabian Sea and NIO as whole), season of formation (pre-monsoon and post-monsoon seasons), intensity of TCs (cyclonic storm and severe cyclonic storm or higher intensities) and type of track of TCs (climatological/straight moving and recurving/looping type). The study shows that the average AE (RMSE) in intensity forecast is about 11(14), 14(19) and 20(26) knots, respectively, for 24-, 48- and 72-h forecasts over the NIO as a whole during 2009–2011. The skill of intensity forecast is about 44 %(48 %), 60 %(58 %) and 60 %(65 %) for 24-, 48- and 72-h forecasts during 2009–2011 with respect to AE (RMSE). There is no significant improvement in terms of reduction in AE and RMSE of MSW forecast over the NIO like that over the northwest Pacific and northern Atlantic Oceans during 2005–2011. However, the skill in intensity forecast compared to persistence method has significantly improved by about 6 %(10 %) and 9 %(8 %) per year, respectively, for 12- and 24-h forecasts considering the AE (RMSE) during 2005–2011. There is also significant increasing trend in percentage of 24-h intensity forecasts with error of 10 knots or less during 2005–2011.

An accurate tropical cyclone track and intensity forecast is very important for disaster management. Specialized numerical prediction models have been recently used to provide high-resolution temporal and special forecasts. Hurricane Weather Research and Forecast (HWRF) model is one of the emerging numerical models for tropical cyclone forecasting. This study evaluates the performance of HWRF model during the post monsoon tropical cyclone Nilofar on the north Indian Ocean basin. The evaluation uses the best track data provided by the Indian Meteorological Department (IMD) and the Joint Typhoon Warning Centre (JTWC). Cyclone track, central pressure, and wind speed are covered on this evaluation. Generally, HWRF was able to predict the Nilofar track with track error less than 230 km within the first 66 h of forecast time span. HWRF predicted more intense tropical cyclone. It predicted the lowest central pressure to be 922 hPa while it reached 950 hPa according to IMD and 937 hPa according to JTWC. Wind forecast was better as it predicted maximum wind speed of 122 kt while it reached 110 and 115 kt according to IMD and JTWC, respectively.

For typhoon warning centers, effective forecasting of tropical cyclone intensity is always required. The major difficulties and challenges in forecasting tropical cyclone intensity are the complex physical mechanism and the structure of tropical cyclones. The interaction between the tropical cyclone and its environment is also a complex process. In this paper, a model based on support vector machines is developed to yield the 12, 24, 36, 48, 72 h forecasts of tropical cyclone intensity. Furthermore, the forecasts resulting from the proposed model are compared with those from the Joint Typhoon Warning Center. Cross-validation tests are also applied to evaluate the accuracy and the robustness of the proposed model. The results confirm that the proposed model can provide accurate forecasts of tropical cyclone intensity, especially for a long lead-time. When the sample events are classified into five categories according to the Saffir-Simpson scale, the forecasts resulting from the proposed model have the best performance for events in categories 4 and 5. In addition, when a typhoon turns northward, although the water temperature drops rapidly, the proposed model still performs well. In conclusion, the proposed model is useful to improve the forecasts of tropical cyclones intensity.

Recent studies have shown that assimilating enhanced satellite-derived atmospheric motion vectors (AMVs) has improved mesoscale forecast of tropical cyclones (TC) track and intensity. The authors conduct data-denial experiments to understand where the TC analyses and forecasts benefit the most from the enhanced AMV information using an ensemble Kalman filter and the Weather Research and Forecasting Model. The Cooperative Institute for Meteorological Satellite Studies at the University of Wisconsin provides enhanced AMV datasets with higher density and temporal resolution using shorter-interval image triplets for the duration of Typhoon Sinlaku and Hurricane Ike (both 2008). These AMV datasets are then spatially and vertically subsetted to create six parallel cycled assimilation-forecast experiments for each TC: all AMVs; AMVs withheld between 100 and 350 hPa (upper layer), between 350 and 700 hPa (middle layer), and between 700 and 999 hPa (lower layer); and only AMVs within (interior) and outside (exterior) 1000-km radius of the TC center. All AMV subsets are found to be useful in some capacity. The interior and upper-layer AMVs are particularly crucial for improving initial TC position, intensity, and the three-dimensional wind structure along with their forecasts. Compared with denying interior or exterior AMVs, withholding AMVs in different tropospheric layers had less impact on TC intensity and size forecasts. The ensemble forecast is less certain (larger spread) in providing accurate TC track, intensity, and size when upper-layer AMVs or interior AMVs are withheld. This information could be useful to potential targeting scenarios, such as activating and focusing satellite rapid-scan operations, and decisions regarding observing system assessments and deployments.

In this study, the general impact of high-resolution moving nesting domains on tropical cyclone (TC) intensity and track forecasts was verified, for a total of 107 forecast cases of 33 TCs, using the Weather Research and Forecasting (WRF) model. The experiment, with a coarse resolution of 12 km, could not significantly capture the intensification process, especially for maximum intensities (>60 m s−1). The intense TCs were better predicted by experiments using a moving nesting domain with a horizontal resolution of 4 km. The forecast errors for maximum wind speed and minimum sea-level pressure decreased in the experiment with higher resolution; the forecast of lifetime maximum intensity was improved. For the track forecast, the experiment with a coarser resolution tended to simulate TC tracks deviating rightward to the TC motions in the best-track data; this erroneous deflection was reduced in the experiment with a higher resolution. In particular, the track forecast in the experiment with a higher resolution improved more frequently for intense TCs that were generally distributed at relatively lower latitudes among the test cases. The sensitivity of the track forecast to the model resolution was relatively significant for lower-latitude TCs. On the other hand, the track forecasts of TCs moving to the mid-latitudes, which were primarily influenced by large-scale features, were not sensitive to the resolution.

This study examines the potential limit in predicting tropical cyclone (TC) intensity under idealized conditions. Using the phase-space reconstruction method for TC intensity time series obtained from the CM1 idealized simulations, it is found that CM1 axisymmetric dynamics contain low-dimensional chaos at the maximum intensity equilibrium. Examination of several attractor invariants including the largest Lyapunov exponent, the Sugihara–May correlation, and the correlation dimension captures a consistent range of the chaotic attractor dimension between 4 and 5 for TC intensity at the maximum intensity equilibrium. In addition, the intensity error doubling time estimated from the largest Lyapunov exponent is roughly 1–3 h, which accords with the decay time obtained from the Sugihara–May correlation. Furthermore, the findings in this study reveal a relatively short TC intensity predictability limit for CM1, which is ∼3–9 h based on the maximum tangential wind but noticeably longer for the minimum central pressure (∼12–18 h) after reaching the mature stage. So long as the traditional metrics for TC intensity such as the maximum surface wind or the minimum central pressure is used for intensity forecast, our results support that TC intensity forecast errors will not be reduced indefinitely in any model, even in the absence of all model and observational errors. As such, the future improvement of TC intensity forecast should be based on different metrics beyond the absolute intensity errors that are currently used in real-time intensity verification. Significance Statement Using the phase-space reconstruction method for tropical cyclone (TC) intensity time series obtained from idealized axisymmetric simulations, we show that TC axisymmetric dynamics in CM1 possesses low-dimensional chaos at the maximum intensity equilibrium. This low-dimensional dynamics explains the long tradition of representing TC intensity by a few measures as in the current practice. The chaotic property of CM1 axisymmetric dynamics also suggests a relatively short predictability range for TC intensity at the maximum intensity equilibrium. The potential existence of low-dimensional chaos for TC intensity in CM1 idealized simulations as found in this study supports the use of different intensity verification metrics beyond the traditional absolute intensity errors currently used in operational model evaluation.

Regional models often face a chronic problem caused by inconsistencies between simulated model fields and large‐scale forcing data, which could limit predictability of regional models. Spectral nudging is a technique which can ameliorate this issue by providing large‐scale forcing in the regional model interior. In this study, spectral nudging was applied to tropical cyclone (TC) track and intensity forecasts, and its effects were investigated using a high‐resolution regional model. Case studies on Typhoon Neoguri and Vongfong showed that spectral nudging was effective at enhancing track forecast by improving large‐scale winds and the western North Pacific subtropical high. However, simulated TC intensity was underestimated without the optimization of spectral nudging. Sensitivity tests on spectral nudging options were performed to improve TC intensity while maintaining forecast track improvements. By reducing the nudging coefficient, intensity errors were reduced with a more reasonably developed TC structure. A total of 51 experiments for 18 TCs were conducted with revised spectral nudging options to investigate the general effects of spectral nudging on TC forecasting. The track forecast was generally improved by spectral nudging, especially after 96 hr, and forecast intensity was also improved as compared to experiments without nudging. Furthermore, spectral nudging was especially effective for TCs which occurred to the east of the western North Pacific and turned near Northeast Asia. Therefore, the impact of spectral nudging can be increased by selectively applying spectral nudging based on TC characteristics.

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 36:1-16 (2008) - DOI: https://doi.org/10.3354/cr00732 Regional climate model simulations of North Atlantic cyclones: frequency and intensity changes Tido Semmler1,*, Saji Varghese2, Ray McGrath1, Paul Nolan3, Shiyu Wang1, Peter Lynch3, Colin O’Dowd2 1Met Eireann, Glasnevin Hill, Dublin 9, Ireland 2National University of Ireland, Galway, University Road, Galway, Ireland 3University College Dublin, Belfield, Dublin 4, Ireland *Email: tido.semmler@met.ie ABSTRACT: Frequency and intensity of cyclones over the North Atlantic are investigated using 2 data sets from simulations with the Rossby Centre regional climate model RCA3. The model domain comprises large parts of the North Atlantic and the adjacent continents. RCA3 is driven by ECHAM5-OM1 general circulation model data for May to December from 1985 to 2000 and May to December from 2085 to 2100 assuming the SRES-A2 emission scenario. We apply an objective algorithm to identify and track tropical and extratropical cyclones, as well as extratropical transition. The simulation indicates increase in the count of strong hurricanes and extratropical cyclones. Contrasting, and generally weaker, changes are seen for the less extreme events. Decreases of 18% in the count of extratropical cyclones and 13% in the count of tropical cyclones with wind speeds of ≥18 m s-1 can be found. Furthermore, there is a pronounced shift in the tracks of hurricanes and their extratropical transition in November and December—more hurricanes are seen over the Gulf of Mexico, the Caribbean Sea and the western Sargasso Sea and less over the southern North Atlantic. KEY WORDS: Climate change · Tropical cyclone · Extratropical cyclone · Regional climate model · North Atlantic · Sea surface temperature · Static stability Full text in pdf format NextCite this article as: Semmler T, Varghese S, McGrath R, Nolan P, Wang S, Lynch P, O’Dowd C (2008) Regional climate model simulations of North Atlantic cyclones: frequency and intensity changes. Clim Res 36:1-16. https://doi.org/10.3354/cr00732 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 36, No. 1. Online publication date: March 13, 2008 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2008 Inter-Research.

share a focus on annual or multi-decadal mean SST estimates averaged over large ocean areas, such as the NAO Main Development Region (MDR), also referred to as the "Atlantic cyclogenic region," in which most hurricanes develop (here assumed to denote the region within 6° to 18° N latitude by 20° to 60° W longitude). Relatively elevated MDR SST during 1995 was hypothesized to increase the frequency of hurricane formation by transferring a relatively greater amount of heat to the usual number of easterly waves of wind (typically ~60) that enter the MDR each year between May and November after leaving the west coast of Africa, each acting to stimulate shallow cumulus convection that may evolve into cyclonic behavior (Saunders & Harris, 1997).The SST hypothesis has extended this frequency-specific observation, based on NAO data gathered in a single year, to pertain generally to cyclone intensity.However, the physical basis for this hypothesized extension involving local SST effects is dubious, in view of the great distances typically traversed by these storms.Over the last century, local NAO SST has varied in a large and predictable way along individual hurricane tracks, which typically extend many thousands of kilometers (km) from their points of origin (Fig. 1).The magnitude of this variation is sufficient to allow historical hurricane-activity records to serve as "natural experiments" that can be used to perform a direct test of the SST hypothesis, by examining the historical correlation between estimated hurricane intensities, and corresponding estimated historical changes in local SST (Smith & Reynolds, 2003, 2004), along individual hurricane tracks.To perform this test, we examined paired differences (Δv max ) in estimated local hurricane intensity v max , and corresponding differences (ΔSST) in local (i.e., year-, month-, latitude-, and longitudespecific) SST, over various non-overlapping intervals Δt = t i+1t i over time t for adjacent positions (indexed by i) along each hurricane track.Over all such intervals that occurred prior to attainment of each peak hurricane intensity, Max(v max ), the causal SST hypothesis requires that any detectable correlation between Δv max and ΔSST be positive.Below we describe methods and historical data on individual hurricane tracks that we used to implement this direct test of the SST hypothesis, and results we obtained that indicate that variation in SST is not causally linked to variation in hurricane intensities.If SST variations do not explain the historically observed pattern of hurricane intensities, what other factor(s) might explain this pattern?Statistical and trend analysis for hurricane activity traditionally has focused on annual frequency, landfall likelihood, storm duration, storm intensity (maximum sustained storm-specific wind speed v max ), or Saffir-Simpsonscale wind-intensity category (Fisher,

This study examines the influence of radiative heating on the prediction of tropical cyclone (TC) intensification in the Hurricane Weather Research and Forecasting (HWRF) model. Previous idealized modeling and observational studies demonstrated that radiative heating can substantially modulate the evolution of TC intensity. However, the relevance of this process under realistic conditions remains unclear. Here, we use observed longwave radiative heating to explore the performance of TC forecasts in HWRF simulations. The performance of TC intensity forecasts is then investigated in the context of radiative heating forecasts. In observations and HWRF forecasts, high clouds near the TC center increase the convergence of radiative fluxes. A sharp spatial gradient (≥60 W/m 2 ) in the flux convergence from the TC center outward toward the environment is associated with subsequent TC intensification. More accurate simulation of the spatial structure of longwave radiative heating is associated with more accurate TC intensity forecasts.

Intensity forecasts for tropical cyclones that undergo a period of rapid intensification are particularly susceptible to large errors. For those storms that interact with an upper-tropospheric trough, tropical cyclone intensity forecasts are complicated, as upper-tropospheric troughs can provide unique intensification mechanisms, but are often associated with unfavorable environmental conditions. Although tropical cyclones in environments with nearby upper-tropospheric troughs are associated with lesser intensification rates than tropical cyclones in environments devoid of upper-tropospheric troughs, some tropical cyclone--trough interactions are associated with a period of rapid intensification. This dissertation utilizes reanalysis output, satellite observations, and ensemble modeling simulations to understand whether rapid intensification episodes in the presence of upper-tropospheric troughs are associated with unique environmental, vortex, and convective characteristics. Furthermore, this dissertation seeks to identify whether newly formed tropical cyclones that interact with upper-tropospheric troughs are associated with different intensification mechanisms than more intense storms.

Although numerous studies have demonstrated that increasing model spatial resolution in free forecasts can potentially improve tropical cyclone (TC) intensity forecasts, studies on the impact of model resolution during data assimilation (DA) on TC prediction are lacking. In this study, using the ensemble-variational DA system for the Hurricane Weather Research and Forecasting (HWRF) Model, we investigated the individual impact of increasing the model resolution of first guess (FG) and background ensemble (BE) forecasts during DA on initial analyses and subsequent forecasts of Hurricane Patricia (2015). The impacts were compared between horizontal and vertical resolutions and also between the tropical storm (TS) and hurricane assimilation during Patricia. The results show that increasing the horizontal or vertical resolution in FG has a larger impact than increasing the resolution in BE on improving the analyzed TC intensity and structure for the hurricane stage. The result is reversed for the TS stage. These results are attributed to the effectiveness of increasing the FG resolution in intensifying the background vortex for the hurricane stage relative to the TS stage. Increasing the BE resolution contributes to improving the analyzed intensity through the better-resolved background correlation structure for both the hurricane and TS stages. Increasing horizontal resolution has an overall larger effect than increasing vertical resolution in improving the analysis at the hurricane stage and their effects are close for the analysis at the TS stage. Additionally, the more accurately analyzed primary circulation, secondary circulation, and warm-core structures via the increased resolution in DA lead to improved TC intensity forecasts.

Several key issues in the simple time-dependent theories of tropical cyclone (TC) intensification developed in recent years remain, including the lack of a closure for the pressure dependence of saturation enthalpy at sea surface temperature (SST) under the eyewall and the definition of environmental conditions, such as the boundary layer enthalpy in TC environment and the TC outflow-layer temperature. In this study, some refinements to the most recent time-dependent theory of TC intensification have been accomplished to resolve those issues. The first is the construction of a functional relationship between the surface pressure under the eyewall and the TC intensity, which is derived using the cyclostrophic wind balance and calibrated using full-physics axisymmetric model simulations. The second is the definition of TC environment that explicitly includes the air–sea temperature difference. The third is the TC outflow-layer temperature parameterized as a linear function of SST based on global reanalysis data. With these refinements, the updated time-dependent theory becomes self-contained and can give both the intensity-dependent TC intensification rate (IR) and the maximum potential intensity (MPI) under given environmental thermodynamic conditions. It is shown that the pressure dependence of saturation enthalpy at SST can lead to an increase in the TC MPI and IR by about half of that induced by dissipative heating due to surface friction. Results also show that both MPI and IR increase with increasing SST, surface enthalpy exchange coefficient, environmental air–sea temperature difference, and decreasing environmental boundary layer relative humidity, but the maximum IR is insensitive to surface drag coefficient. Significance Statement A new advancement in the recent decade is the development of simple time-dependent theories of tropical cyclone (TC) intensification, which can provide quantitative understanding of TC intensity change. However, several key issues in these simple time-dependent theories remain, including the lack of a closure for the pressure dependence of saturation enthalpy at sea surface temperature under the eyewall and the definition of environmental conditions. These are resolved in this study with several refinements, which make the most recent time-dependent theory of TC intensification self-contained and practical.