Hurricane Track Research Articles

An initialisation scheme for tropical cyclones in the South China Sea

AbstractWe propose an initialisation scheme for weak tropical cyclones (TCs) in the South China Sea (SCS) by combining composite and analysis vortices to improve forecasts of weak TCs in the region. The composite vortex is obtained from good samples of analyses of TCs in the SCS, and the analysis vortex is obtained from either European Centre for Medium‐Range Weather Forecasts (ECMWF) analysis data or an operational assimilation system. The new scheme has been applied to 57 cases of weak TCs, and the effect on the forecasting of four tropical storms has been studied in detail. It can be concluded that the proposed scheme can reduce track errors and intensity errors of weak TCs, compared with the control experiment. The possible reasons for these results are investigated in a case study for Linfa. Results of batch experiments show that the new method significantly improves TC track forecasting of tropical storms, severe tropical storms and typhoons, and demonstrates significant advantages in forecasting intensity of tropical storms, compared with the control experiment. It also performs better in predicting intensity of TCs in the SCS compared with ECMWF Atmospheric Model high resolution.

Statistical seasonal forecasting of tropical cyclones over the western North Pacific

Forecasting tropical cyclone (TC) activities has been a topic of great interest and research. Many studies and existing seasonal forecasting models have examined and predicted the number of TCs (including geneses and landfalls) mainly based on the environmental factors in the peak TC season. However, these predictions can be time-consuming, computationally expensive and uncertain, depending on the efficiency and predictability of the dynamical models. Therefore, here we propose an effective statistical seasonal forecasting model, namely the Sun Yat-sen University (SYSU) Model, for predicting the number of TCs (intensity at tropical storm or above) over the western North Pacific based on the environmental factors in the preseason. The nine categories comprising 103 candidate predictors in 1980–2015 (36 years) are systematically investigated. The best subset selection regression shows that the sea surface temperatures at the tropical North Atlantic and eastern North Pacific in April, the 500 hPa geopotential height difference between April and January at the open ocean southwest of Australia and the 700 hPa geopotential height at the North Pacific in April are the most significant predictors. The correlation coefficient between the modeled results and observations reaches 0.89. The model is successfully validated by leave-one-out, nine-fold cross-validations, and later 5 year (2016–2020) observations. The prediction of the SYSU Model exhibits a 95% hit rate in 1980–2020 (39 out of 41), suggesting an operational potential in the seasonal forecasting of TCs over the western North Pacific.

Tropical Cyclone Center Positioning Using Single Channel Microwave Satellite Observations of Brightness Temperature

Satellite observations of brightness temperature from the Advanced Technology Microwave Sounder (ATMS) and Microwave Humidity Sounder (MHS) humidity sounding channels can provide relatively high horizontal resolution information about cloud and atmospheric moisture in the troposphere, thus revealing the structures of tropical cyclones (TCs). There is usually a high brightness temperature in a TC eye region and low brightness temperature reflecting spiral rain bands. An azimuthal spectral analysis method is used as a center-fixing algorithm to determine the TC center objectively using the brightness temperature observations of the ATMS humidity-sounding channel 18 (183.31 ± 7.0 GHz) and MHS humidity-sounding channel 5 (190.31 GHz). The position in the brightness temperature field encompassing a TC that achieves the largest symmetric component is regarded as the TC center. Two Atlantic hurricanes in 2012, Hurricanes Sandy and Isaac, are first used to analyze the performance of the TC center-fixing technique. Compared with the National Hurricane Center best track, the root-mean-square differences of the center fixing results for Hurricanes Sandy and Isaac are less than 47.3 and 34.3 km, respectively. It is found that the uncertainty of the TC center-fixing algorithm and thus the difference from the best track increases when the brightness temperature distribution within a TC is significantly asymmetric. Then, the TC center-fixing technique is validated for all tropical storms and hurricanes over Northern Atlantic and Western Pacific in 2019. Compared with the best track data, the root-mean-square differences for tropical storms and hurricanes are 33.81 and 26.20 km, respectively. The demonstrated successful performance of the proposed TC center-fixing algorithm to use the single channel of microwave humidity sounders for TC positioning is important for vortex initialization in operational hurricane forecasts.

The Use of the Conway–Maxwell–Poisson in the Seasonal Forecasting of Tropical Cyclones

AbstractThe Conway–Maxwell–Poisson distribution improves the precision with which seasonal counts of tropical cyclones may be modeled. Conventionally the Poisson is used, which assumes that the formation and transit of tropical cyclones is the result of a Poisson process, such that their frequency distribution has equal mean and variance (“equi-dispersion”). However, earlier studies of observed records have sometimes found overdispersion, where the variance exceeds the mean, indicating that tropical cyclones are clustered in particular years. The evidence presented here demonstrates that at least some of this overdispersion arises from observational inhomogeneities. Once this is removed, and particularly near the coasts, there is evidence for equi-dispersion or underdispersion. To more accurately model numbers of tropical cyclones, we investigate the use of the Conway–Maxwell–Poisson as an alternative to the Poisson that represents any dispersion characteristic. An example is given for East China where using it improves the skill of a prototype seasonal forecast of tropical cyclone landfall.

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.

Correction to: Monitoring and prediction of hurricane tracks using GPS tropospheric products

Correction to: Monitoring and prediction of hurricane tracks using GPS tropospheric products

Predictive Agent-Based Modeling of Natural Disasters Using Machine Learning

Current applications of Agent-based Modeling (ABM) in natural phenomena like wildfire land suppression and hurricane forecasting are in monitoring emergent behavior patterns among large groups of people (Hilljegerdes 2018). However, current evacuation times and plans for natural disaster management leave underserved communities vulnerable to substantial financial and welfare losses, especially when false positives during current predictions continue to influence evacuation decisions. A Machine Learning ABM (ML-ABM) model of hurricane trajectories introduces a pioneering opportunity to capture complex physical processes associated with hurricanes while minimizing computational costs and errors, thereby providing more accurate real-time prediction of hurricanes for improved disaster management. This Hurricane Track Prediction ML-ABM model aims to quickly model and predict hurricane tracks in only a few minutes while retaining some of the complex physical process interactions of real storms through feature engineering and deep learning. This work focuses on the implementation of an RNN with bidirectional time-distributed Long-Short Term Memory cells, accounting for positive and negative time direction in time series forecasting. The observations and predictions were represented as a multi-agent system in NetLogo for further emergent pattern analysis in an expanded research by Arthur Drake et. al (2020). The model also evaluates benchmark comparisons against the National Hurricane Center’s 5-Year Average Forecast Errors and the BCD5 Error Model, a combined intensity and track prediction error model that utilizes best track input and models decay over land.

How Does the Arctic Sea Ice Affect the Interannual Variability of Tropical Cyclone Activity Over the Western North Pacific?

Although many studies have revealed that Arctic sea ice may impose a great impact on the global climate system, including the tropical cyclone (TC) genesis frequency over the western North Pacific (WNP), it is unknown whether the Arctic sea ice could have any significant effects on other aspects of TCs; and if so, what are the involved physical mechanisms. This study investigates the impact of spring (April-May) sea ice concentration (SIC) in the Bering Sea on interannual variability of TC activity in terms of the accumulated cyclone energy (ACE) over the WNP in the TC season (June-September) during 1981–2018. A statistical analysis indicates that the spring SIC in the Bering Sea is negatively correlated with the TC season ACE over the WNP. Further analyses demonstrate that the reduction of the spring SIC can lead to the westward shift and intensification of the Aleutian low, which strengthens the southward cold-air intrusion, increases low clouds, and reduces surface shortwave radiation flux, leading to cold sea surface temperature (SST) anomaly in the Japan Sea and its adjacent regions. This local cloud-radiation-SST feedback induces the persistent increasing cooling in SST (and also the atmosphere above) in the Japan Sea through the TC season. This leads to a strengthening and southward shift of the subtropical westerly jet (SWJ) over the East Asia, followed by an anomalous upper-level anticyclone, low-level cyclonic circulation anomalies, increased convective available potential energy, and reduced vertical wind shear over the tropical WNP. These all are favorable for the increased ACE over the WNP. The opposite is true for the excessive spring SIC. The finding not only has an important implication for seasonal TC forecasts but also suggests a strengthened future TC activity potentially resulting from the rapid decline of Arctic sea ice.

New Observation Operators for Cloud Liquid/Ice Water Path From ABI and Their Impact on Assimilation and Hurricane Forecasts

AbstractThe cloud liquid/ice water path (LWP/IWP) can provide valuable information about the cloud and precipitation. For the first time, the Gridpoint Statistical Interpolation (GSI) system is applied to examine the impact of the high spatial and temporal resolution LWP and IWP products derived from the Advanced Baseline Imager (ABI) on the new generation geostationary environmental satellites (GOES‐R series) in numerical weather prediction (NWP). To extend the GSI's capability to assimilate LWP and IWP products, a pair of new observation operators of LWP and IWP is developed to avoid the requirement for hydrometeor control variables. The new observation operators enable the direct use of hydrometeor background information from the NWP model and allow the vertical propagation of the water vapor increment according to the Jacobian distributions for the new LWP and IWP operators. The new LWP and IWP observation operators are evaluated by the single observation tests and the cycling assimilation and forecast experiments of Hurricane Irma (2017) and Hurricane Maria (2017). The results from single observation tests show that the assimilations of pseudo‐LWP and pseudo‐IWP can achieve positive water vapor increments, which also spread vertically according to the Jacobian distributions. The impact studies on two hurricanes demonstrate that the ABI LWP and IWP measurements can be successfully assimilated into the NWP model by the new observation operators, which enhances the water vapor analysis and further improves other atmospheric state variables in the forecast fields. The improved environmental fields lead to the overall forecast improvement for the two hurricanes, with the most notable track improvement.

Verification of forecasts for extreme rainfall, tropical cyclones, flood and storm surge over Myanmar and the Philippines

Within the humanitarian sector, there is a pressing need to scale up anticipatory action as climate change-related disasters increase. This article evaluates forecasts relating to extreme weather events – extreme rainfall, tropical cyclones, river flooding and storm surge – in Myanmar and the Philippines to assess the feasibility of using such forecasts to develop early warning systems and responses. To make best use of limited extant data, a variety of methods (reliability diagrams, hit rates, false alarm ratios, correlations) are employed. We review the skill of the European Centre for Medium-Range Weather Forecasts (ECMWF) tropical cyclone forecasts and find that whilst errors in cyclone position are relatively small, forecasting intensity is more difficult. When a tropical cyclone has formed, the probabilities provided in the ECMWF track forecast are highly reliable and only slightly overconfident. A tropical cyclone activity product is relatively reliable for forecasts less than a week ahead for North Indian Ocean cyclones affecting Myanmar, but becomes very overconfident beyond this. Looking at flood forecasting models, a comparison of the Global Flood Awareness System (GloFAS, produced by the ECMWF and the European Commission as part of the Copernicus Emergency Management Services) with the Global Flood Forecasting Information System (GLOFFIS, produced by Deltares) demonstrates that both GloFAS and GLOFFIS have difficulty simulating 1 in 2 year return period flows or higher, although GloFAS performance is better than GLOFFIS. GloFAS reforecasts show significantly overconfident probabilities over Myanmar where discharge observations are available, whilst the lack of a GLOFFIS reforecast prevents evaluation of this forecast system directly. Evaluation of the ten-day operational storm surge forecast (the Global Storm Surge Information System, GLOSSIS) produced by Deltares was attempted but lack of any data prevented assessment. These findings present valuable insights into how well forecasts perform, which is crucial information for establishing effective humanitarian action mechanisms.

Transformative Ocean Observing for Hurricane Forecasting, Readiness, and Response in the Caribbean Tropical Storm Corridor

Abstract The upper ocean in the Western Tropical Atlantic tropical storm corridor—including the Caribbean Sea—is under-sampled and climatologically warming (Figure 1). Regionally varying Essential Ocean Features impacting tropical cyclone dynamics include fresh water upper ocean layers, mesoscale eddies, high Upper Ocean Heat Content values, and inflows from the Subtropical and Equatorial Atlantic. Ongoing research indicates that hurricane intensity forecasts can be improved with expanded and sustained ocean data collection and utilization along the hurricane path.This proposed activity will build supporting physical and social infrastructure and conduct a long-term sampling program in this critical region using gliders, High Frequency Radars (HFR), and developing technologies to provide real-time information resulting in hurricane forecast improvement. Improved forecasts will support new generation of local storm surge/precipitation/wave and coastal impact models and guidance used to directly enhance resilience.The success of this project will depend on the merger of regional scale planning and management AND development of local-level partnerships for implementation. To be sustainable, operational, analytical, and actionable, capability has to exist at the multiple proposed regional system nodes. We will promote expanded education and workforce development using existing partner capabilities, and include an Ocean Observing for SIDS/Developing Economies component. Product and information delivery systems will have local interpretive support and will incorporate local knowledge and expertise.It is our hope that by 2030 our legacy would be successful program to—in the words of the Decade Action Framework—“sustain long-term high-quality observations of marine and coastal environments including human interactions and deliver forecast and decision-support tools.”

C-Band SAR Winds for Tropical Cyclone Monitoring and Forecast in the South-West Indian Ocean

Tropical cyclone (TC) monitoring and forecast in the South West Indian Ocean (SWIO) basin remain challenging, notably because of the lack of direct observations. During the 2018–2019 cyclone season, S-1 Sentinel SAR images were acquired, as part of the ReNovRisk-Cyclone research program, giving access to unprecedented detailed TC wind structure description without wind speed limitation. This paper assesses the quality of these data and the impact of their assimilation for TC forecasts. SAR observations are compared with analyses from a convection-permitting, limited area model AROME OI 3D-Var and with wind products used for operational TC monitoring. Their bias depends on the angle of incidence of the radar and the observation error is larger for extreme wind speed. The impact of SAR assimilation in AROME OI 3D-Var is assessed through two case studies. In the TC GELENA case, it leads to a better TC positioning and an improved representation of inner and outer vortex structures. The TC intensity reduction in the analysis propagates through subsequent analyses and it has an impact on forecasts for around 12 h. In the TC IDAI case, the 3D-Var does not manage to reproduce TC intensity captured by SAR. In both cases, the modification of the initial conditions has little influence on the intensification rate of the model forecasts. Sensitivity tests show that these results are robust to different observation errors and thinning.

Assimilation of Radial Velocity from Coastal NEXRAD into HWRF for Improved Forecasts of Landfalling Hurricanes

AbstractThe benefits of assimilating Next Generation Weather Radar (NEXRAD) radial velocity data for convective systems have been demonstrated in previous studies. However, impacts of assimilation of such high spatial and temporal resolution observations on hurricane forecasts have not been demonstrated with the National Centers for Environmental Prediction (NCEP) Hurricane Weather and Research Forecasting (HWRF) system. This study investigates impacts of NEXRAD radial velocity data on forecasts of the evolution of landfalling hurricanes with different configurations of data assimilation. The sensitivity of data assimilation results to influencing parameters within the data assimilation system, such as the maximum range of the radar data, superobservations, horizontal and vertical localization correlation length scale, and weight of background error covariances, are examined. Two hurricane cases, Florence and Michael, which occurred in the summer of 2018 are chosen to conduct a series of experiments. Results show that hurricane intensity, asymmetric structure of inland wind and precipitation, and quantitative precipitation forecasting are improved. Suggestions for implementation of operational configurations are provided.

2019 Atlantic Hurricane Forecasts from the Global-Nested Hurricane Analysis and Forecast System: Composite Statistics and Key Events

AbstractNOAA’s Hurricane Analysis and Forecast System (HAFS) is an evolving FV3-based hurricane modeling system that is expected to replace the operational hurricane models at the National Weather Service. Supported by the Hurricane Forecast Improvement Program (HFIP), global-nested and regional versions of HAFS were run in real time in 2019 to create the first baseline for the HAFS advancement. In this study, forecasts from the global-nested configuration of HAFS (HAFS-globalnest) are evaluated and compared with other operational and experimental models. The forecasts by HAFS-globalnest covered the period from July through October during the 2019 hurricane season. Tropical cyclone (TC) track, intensity, and structure forecast verifications are examined. HAFS-globalnest showed track skill superior to several operational hurricane models and comparable intensity and structure skill, although the skill in predicting rapid intensification was slightly inferior to the operational model skill. HAFS-globalnest correctly predicted that Hurricane Dorian would slow and turn north in the Bahamas and also correctly predicted structural features in other TCs such as a sting jet in Hurricane Humberto during extratropical transition. Humberto was also a case where HAFS-globalnest had better track forecasts than a regional version of HAFS (HAFS-SAR) due to a better representation of the large-scale flow. These examples and others are examined through comparisons with airborne tail Doppler radar from the NOAA WP-3D to provide a more detailed evaluation of TC structure prediction. The results from this real-time experiment motivate several future model improvements, and highlight the promise of HAFS-globalnest for improved TC prediction.

Monitoring and prediction of hurricane tracks using GPS tropospheric products

We have reconstructed integrated water vapor (IWV) using the zenith wet delays to track the properties of hurricanes and explore their spatial and temporal distributions estimated from 922 GPS stations. Our results show that a surge in GPS-derived IWV occurred at least six hours prior to the landfall of two major hurricanes (Harvey and Irma) that struck the Gulf and East Coasts of the USA in 2017. We observed enhanced IWV, in particular, for the two hurricanes landfall locations. The observed variations exhibit a correlation with the precipitation value constructed from GPM/IMERG satellite mission coinciding with hurricane storm front passage. We used GPS-IWV data as inputs for spaghetti line plots for our path predictions, helping us predict the paths of Hurricanes Harvey and Irma. Hence, a directly estimable zenith wet delay sourced from GPS that has not been previously reported can serve as an additional resource for improving the monitoring of hurricane paths.

Validation of Ensemble-Based Probabilistic Tropical Cyclone Intensity Change

Although there has been substantial improvement to numerical weather prediction models, accurate predictions of tropical cyclone rapid intensification (RI) remain elusive. The processes that govern RI, such as convection, may be inherently less predictable; therefore a probabilistic approach should be adopted. Although there have been numerous studies that have evaluated probabilistic intensity (i.e., maximum wind speed) forecasts from high resolution models, or statistical RI predictions, there has not been a comprehensive analysis of high-resolution ensemble predictions of various intensity change thresholds. Here, ensemble-based probabilities of various intensity changes are computed from experimental Hurricane Weather Research and Forecasting (HWRF) and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic (HMON) models that were run for select cases during the 2017–2019 seasons and verified against best track data. Both the HWRF and HMON ensemble systems simulate intensity changes consistent with RI (30 knots 24 h−1; 15.4 m s−1 24 h−1) less frequent than observed, do not provide reliable probabilistic predictions, and are less skillful probabilistic forecasts relative to the Statistical Hurricane Intensity Prediction System Rapid Intensification Index (SHIPS-RII) and Deterministic to Probabilistic Statistical (DTOPS) statistical-dynamical systems. This issue is partly alleviated by applying a quantile-based bias correction scheme that preferentially adjusts the model-based intensity change at the upper-end of intensity changes. While such an approach works well for high-resolution models, this bias correction strategy does not substantially improve ECMWF ensemble-based probabilistic predictions. By contrast, both the HWRF and HMON systems provide generally reliable predictions of intensity changes for cases where RI does not take place. Combining the members from the HWRF and HMON ensemble systems into a large multi-model ensemble does not improve upon HMON probablistic forecasts.

Performance of tropical cyclone forecasts in the western North Pacific in 2017

The forecasts of tropical cyclones (TC) in 2017 from five official guides, six global models, six regional models and six ensemble systems were assessed to study the current capability of track and intensity forecasts for the western North Pacific. The results show that the position errors for official agencies were under 100, 165, 265,335 and 425 km at the lead times of 24, 48, 72, 96 and 120 h, respectively. As the forecast lead times increased, the forecasted TCs propagated, on average, too slow for most official guides. It is encouraging to note that all the models had positive skill scores, there is an overall upward trend in the skill scores of the models during from 2010 to 2017. Furthermore, both global and regional models' intensity forecast skill was increasing year by year from 2010 to 2017. For the ensemble prediction systems (EPSs), ECMWF-EPS was the best forecast system for the lead time less than 72 h, beyond the 72 h, the best EPS belong to NCEP-GEFS.

Modulation of North Pacific and North Atlantic Tropical Cyclones by Tropical Transbasin Variability and ENSO during May–October

AbstractThis study highlights the distinct modulation of May–October tropical cyclones (TCs) in the western North Pacific (WNP), eastern North Pacific (ENP), and North Atlantic (NATL) Ocean basins by tropical transbasin variability (TBV) and ENSO. The pure TBV significantly modulates total TC counts in all three basins, with more TCs in the WNP and ENP and fewer TCs in the NATL during warm TBV years and fewer TCs in the WNP and ENP and more TCs in the NATL during cold TBV years. By contrast, the pure ENSO signal shows no impact on total TC count over any of the three basins. These results are consistent with changes in large-scale factors associated with TBV and ENSO. Low-level relative vorticity (VOR) is an important driver of WNP TC genesis frequency, with broad agreement between the observed spatial distribution of TC genesis and TBV/ENSO-associated VOR anomalies. TBV significantly affects ENP TC frequency as a result of changes in basinwide vertical wind shear and sea surface temperatures, whereas the modulation in TC frequency by ENSO is primarily caused by a north–south dipole modulation of large-scale atmospheric and oceanic factors. The pure TBV-related low-level VOR changes appear to be the most important factor modulating NATL TC frequency. Changes in large-scale factors compare well with the budget of synoptic-scale eddy kinetic energy. Possible physical processes associated with pure TBV and pure ENSO that modulate TC frequency are further discussed. This study contributes to the understanding of TC interannual variability and could thus be helpful for seasonal TC forecasting.

Evaluation of the Surface Wind Field over Land in WRF Simulations of Hurricane Wilma (2005). Part II: Surface Winds, Inflow Angles, and Boundary Layer Profiles

AbstractThis is the second of a two-part study that explores the capabilities of a mesoscale atmospheric model to reproduce the near-surface wind fields in hurricanes over land. The Weather Research and Forecasting (WRF) Model is used with two planetary boundary layer parameterizations: the Yonsei University (YSU) and the Mellor–Yamada–Janjić (MYJ) schemes. The first part presented the modeling framework and initial conditions used to produce simulations of Hurricane Wilma (2005) that closely reproduced the track, intensity, and size of its wind field as it passed over South Florida. This part explores how well these simulations can reproduce the winds at fixed points over land by making comparisons with observations from airports and research weather stations. The results show that peak wind speeds are remarkably well reproduced at several locations. Wind directions are evaluated in terms of the inflow angle relative to the storm center, and the simulated inflow angles are generally smaller than observed. Localized peak wind events are associated with vertical vorticity maxima in the boundary layer with horizontal scales of 5–10 km. The boundary layer winds are compared with wind profiles obtained by velocity–azimuth display (VAD) analyses from National Weather Service Doppler radars at Miami and Key West, Florida; results from these comparisons are mixed. Nonetheless the comparisons with surface observations suggest that when short-term hurricane forecasts can sufficiently predict storm track, intensity, and size, they will also be able to provide useful information on extreme winds at locations of interest.

Understanding the response of tropical cyclone structure to the assimilation of synthetic wind profiles

AbstractThis study examines how varying wind profile coverages in the tropical cyclone (TC) core, near-environment and broader synoptic environment affect the structure and evolution of a simulated Atlantic hurricane through data assimilation. Three sets of observing system simulation experiments (OSSEs) are examined in this paper. The first experiment establishes a benchmark for the case study specific to the forecast system used by assimilating idealized profiles throughout the parent domain. The second presents how TC analyses and forecasts respond to varying the coverage of swaths produced by polar-orbiting satellites of idealized wind profiles. The final experiment assesses the role of TC inner-core observations by systematically removing them radially from the center. All observations are simulated from a high-resolution regional “Nature Run” of a hurricane and the tropical atmosphere, assimilated an Ensemble Square-Root Kalman Filter and the Hurricane Weather and Research Forecast (HWRF) regional model. Results compare observation impact to the analyses, domain-wide and TC centric error statistics, and TC structural differences among the experiments. The study concludes that the most accurate TC representation is a result of the assimilation of collocated and uniform thermodynamic and kinematics observations. Intensity forecasts are improved with increased inner core wind observations, even if the observations are only available once daily. Domain-wide root-mean-square errors are significantly reduced when the TC is observed during a period of structural change, like rapid intensification. The experiments suggest the importance of wind observations and the role of inner-core surveillance when analyzing and forecasting realistic TC structure.