Hurricane Intensity Research Articles

Impacts of climate change on future hurricane induced rainfall and flooding in a coastal watershed: A case study on Hurricane Harvey

The warming climate is likely to increase hurricane-associated extreme rainfall and lead to sea-level rise (SLR). Thus, how the floods induced by intense hurricanes respond to these potential changes is of great concern. This study investigates the future warmer climate impacts on hurricane-induced extreme rainfall, and—more importantly—the subsequent compound flooding at the watershed scale (from an event-based analysis perspective). To this goal, a modeling framework is designed based on the Distributed Hydrology Soil Vegetation Model (DHSVM), the Two-Dimensional fvand the Regional Community Earth System Model (R-CESM). The framework was applied to Hurricane Harvey (2017) at the Clear Creek watershed (a coastal watershed in the southern Houston) as a case study. The results show that the projected maximum rainfall totals over the watershed would be exacerbated by 17.7 % and 49.7 % in the 2050s and 2090s (respectively) under Representative Concentration Pathway 8.5 (RCP 8.5). This means a 16.1 % increase in Harvey rainfall over the watershed per degree Celsius increase in Mean Surface Temperature over the Gulf of Mexico region (18°∼31° N, 77°∼98° W). Meanwhile, the increases in maximum inundation extent would be 11.0 % (2050s) and 19.5 % (2090s). Furthermore, considerable increases in maximum inundation depth and duration in regions along the middle and downstream of Clear Creek (and also those around Clear Lake) are expected. The projected SLR will have little effect on the maximum inundation depth and extent if storm surge changes are not taken into account; meanwhile, it will influence the inundation duration at downstream locations. This modeling framework can be also applied at other coastal watersheds to evaluate the projected climate change impacts on the compound flooding induced by extreme climate events.

Relative Timing of the Ends of Hurricane Intensification and Contraction of the Radius of Maximum Wind in the North Atlantic and Eastern North Pacific

AbstractThe statistical relationship between the ending times of hurricane intensification and contraction of the radius of maximum wind (RMW) over the North Atlantic (NA) and Eastern North Pacific (EP) is assessed using the Extended Best Track and Tropical Cyclone Observations‐Based Structure databases during 1999–2014. Results show that in more than half of hurricanes the end of RMW contraction precedes the end of intensification, and those hurricanes generally have lower relative intensity (intensity normalized by the corresponding maximum potential intensity) and larger curvature of the tangential wind profile at the RMW. The preceding time tends to be longer with lower relative intensity and lower intensification rate and on average is longer in the NA (∼19 hr) than in the EP (∼12 hr) due to the overall lower intensification rate in the former. These findings can help improve our understanding of hurricane structure and intensity changes.

Intensification of a distant hurricane by warm‐core eddies in the Gulf Stream in boreal fall

AbstractThis study investigated how warm‐core eddies (WCEs) in the Gulf Stream (GS) modulated the intensity of a distant tropical cyclone (TC) approaching the current in October. We performed cloud‐resolving regional simulations including a control run with the observed WCEs and a sensitivity run excluding the WCEs. These simulations found that the WCEs played a favorable role in the development of the distant TC. The WCEs affected the synoptic‐scale thermodynamic environments over the North Atlantic through the enhanced heat and moisture supply from the GS, increasing the moisture imports toward the distant TC. The WCEs‐enhanced moisture influx created very moist environments in the inner core. This inner‐core moistening was favorable for deep eyewall convection and an associated TC secondary circulation, leading to TC development. This result indicates the importance of WCEs in facilitating the remote process leading to TC development that previous studies have proposed.

Coastal erosion narratives in the Gulf of Mexico: implications for climate change governance

This article presents a study of coastal erosion narratives by the Mexican government, scientists, and local fishers in coastal communities in the Gulf of Mexico. It shows how plans to enroll fishing communities into programs to adapt to or to slow coastal erosion are based on simplified environmental narratives that rely on global climate change as the main cause of coastal erosion. They discount local processes and local explanations, as well as scientific studies that outline complex multi-scalar explanations for coastal erosion. Government narratives frame global climate change, as manifested in increased frequency and intensity of hurricanes and other hydrometereological extreme events and sea level rise, as the main causes of changes in coastal environments, including coastal erosion They fail to acknowledge other causes including the environmental degradation caused by the influential oil industry. In contrast, fishers' more complex and locally-embedded narratives are shaped by their long-term struggles against the state-owned oil company, whom they hold primarily responsible for coastal erosion in their communities. Scientists similarly emphasize the importance of local and regional processes, with climate change understood primarily as having significant impacts in the future, but less so in the recent past. Differences in temporal and geographical scaling among these narratives highlight the importance of considering how the translation of climate change adaptation programming from the global to diverse local situations would ideally consider site-specific power relations as well as community-based perspectives.

A Comparative Study on the Performances of Spectral Nudging and Scale-Selective Data Assimilation Techniques for Hurricane Track and Intensity Simulations

It is a common practice to use a buffer zone to damp out spurious wave growth due to computational error along the lateral boundary of limited-area weather and climate models. Although it is an effective technique to maintain model stability, an unintended side effect of using such buffer zones is the distortion of the data passing through the buffer zone. Various techniques are introduced to enhance the communication between the limited-area model’s inner domain and the outer domain, which provides lateral boundary values for the inner domain. Among them, scale-selective data assimilation (SSDA) and the spectral nudging (SPNU) techniques share similar philosophy, i.e., directly injecting the large-scale components of the atmospheric circulation from the outer model domain into the interior grids of the inner model domain by-passing the lateral boundary and the buffer zone, but the two methods are taking different implementation approaches. SSDA utilizes a 3-dimensional variational data assimilation procedure to accomplish the data injection objective, whereas SPNU uses a nudging process. In the present study, the two approaches are evaluated comparatively for simulating hurricane track and intensity in a pair of cases: Jeanne (2004) and Irma (2017) using the Weather Research and Forecasting (WRF) model. The results indicate that both techniques are effective in improving tropical cyclone intensity and track simulations by reducing the errors of the large-scale circulation in the inner model domain. The SSDA runs produced better simulations of temperature and humidity fields which are not directly nudged. The SSDA runs also produced more accurate storm intensities in both cases and more realistic structure in Hurricane Jeanne’s case than those produced by the SPNU runs. It should be noted, however, that extending these case study results to more general situations requires additional studies covering a large number of additional cases.

The National Hurricane Center Tropical Cyclone Model Guidance Suite

Abstract The National Hurricane Center (NHC) uses a variety of guidance models for its operational tropical cyclone track, intensity, and wind structure forecasts, and as baselines for the evaluation of forecast skill. A set of the simpler models, collectively known as the NHC guidance suite, is maintained by NHC. The models comprising the guidance suite are briefly described and evaluated, with details provided for those that have not been documented previously. Decay-SHIFOR is a modified version of the Statistical Hurricane Intensity Forecast (SHIFOR) model that includes decay over land; this modification improves the SHIFOR forecasts through about 96 h. T-CLIPER, a climatology and persistence model that predicts track and intensity using a trajectory approach, has error characteristics similar to those of CLIPER and D-SHIFOR but can be run to any forecast length. The Trajectory and Beta model (TAB), another trajectory track model, applies a gridpoint spatial filter to smooth winds from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model. TAB model errors were 10%–15% lower than those of the Beta and Advection model (BAM), the model it replaced in 2017. Optimizing TAB’s vertical weights shows that the lower troposphere’s environmental flow provides a better match to observed tropical cyclone motion than does the upper troposphere’s, and that the optimal steering layer is shallower for higher-latitude and weaker tropical cyclones. The advantages and disadvantages of the D-SHIFOR, T-CLIPER, and TAB models relative to their earlier counterparts are discussed. Significance Statement This paper provides a comprehensive summary and evaluation of a set of simpler forecast models used as guidance for NHC’s operational tropical cyclone forecasts, and as baselines for the evaluation of forecast skill; these include newer techniques that extend forecasts to 7 days and beyond.

Does ERA5 Mark a New Era for Resolving the Tropical Cyclone Environment?

Abstract The synoptic environment around tropical cyclones plays a significant role in vortex evolution. To capture the environment, the operational and research communities calculate diagnostic quantities. To aid with applications and research, the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) combines disparate data sources. A key part of TC PRIMED is the environmental context. Often, environmental diagnostics come from multiple sources. However, TC PRIMED uses the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ERA5) product to provide a more complete representation of the storm environment from a single source. Reanalysis products usually poorly resolve tropical cyclones and their surrounding environment. To understand the uncertainty of large-scale diagnostics, ERA5 is compared to the Statistical Hurricane Intensity Prediction Scheme developmental dataset and the National Oceanic and Atmospheric Administration Gulfstream IV-SP dropwindsondes. This analysis highlights biases in the ERA5 environmental diagnostic quantities. Thermodynamic fields show the largest biases. The boundary layer exhibits a cold temperature bias that limits the amount of convective instability; also, the upper troposphere contains temperature biases and shows a high relative humidity bias. However, the upper-troposphere large-scale kinematic fields and derived metrics are low biased. In the lower troposphere, the temperature gradient and advection calculated from the thermal wind suggest that the low-level wind field is not representative of the observed distribution. These diagnostics comparisons provide uncertainty so that users of TC PRIMED can assess the implications for specific research and operational applications.

A Simple Bias and Uncertainty Scheme for Tropical Cyclone Intensity Change Forecasts

Abstract To better forecast tropical cyclone (TC) intensity change and understand forecast uncertainty, it is critical to recognize the inherent limitations of forecast models. The distributions of intensity change for statistical–dynamical models are too narrow, and some intensity change forecasts are shown to have larger errors and biases than others. The Intensity Bias and Uncertainty Scheme (IBUS) is developed in an intensity change framework, which estimates the bias and the standard deviation of intensity forecast errors. The IBUS is developed and applied to the Decay Statistical Hurricane Intensity Prediction Scheme (DSHP), the Logistic Growth Equation Model (LGEM), and official National Hurricane Center (NHC) forecasts (OFCL) separately. The analysis uses DSHP, LGEM, and OFCL forecasts from 2010 to 2019 in both the Atlantic and east Pacific basins. Each IBUS contains both a bias correction and forecast uncertainty estimate that is tested on the training dataset and evaluated on the 2020 season. The IBUS is able to reduce intensity biases and improve forecast errors beyond 120 h in each model and basin relative to the original forecasts. The IBUS is also able to communicate forecast uncertainty that explains ∼7%–11% of forecast variance at 48 h for DSHP and LGEM in the Atlantic. Better performance is found in the east Pacific at 96 h where the IBUS explains up to 30% of the errors in DSHP and 14% of the errors for LGEM. The IBUS for OFCL explains 9%–13% of the 48-h forecast uncertainty in the Atlantic and east Pacific with up to 30% variance explained for east Pacific forecasts at 96 h. IBUS for OFCL has the capability to provide intensity forecast uncertainty similar to the “cone of uncertainty” for track forecasts.

Benefits of and strategies to update premium rates in the US National Flood Insurance Program under climate change.

The United States' National Flood Insurance Program (NFIP) has accumulated over $20 billion in debt to the US Treasury since 2005, partly due to discounted premiums on homes in flood-prone areas. To address this issue, FEMA introduced Risk Rating 2.0 in October 2021, which is able to assess and charge more accurate and equitable rates to homeowners. However, rates must be continually updated to account for increasing flood damage caused by sea level rise and more intense hurricanes due to climate change. This study proposes a strategy to adopt updated premium rates that account for climate change effects and address affordability and risk mitigation issues with a means-tested voucher program. The strategy is tested in a coastal community, Ortley Beach, NJ, by projecting its future flood risk under sea level rise and storm intensification. Compared with using static rates for all the properties in Ortley Beach, the proposed strategy is shown to reduce the NFIP's potential losses to the community from 2020 to 2050 by half (from $4.6 million to $2.3 million), improve the community's flood resistance, and address affordability concerns. Sensitivity analysis of varying incomes, loan interest rates, and conditions for a voucher indicates that the strategy is feasible and effective under a wide range of scenarios. Thus, the proposed strategy can be applied to various communities along the US coastline as an effective way of updating risk-based premiums while addressing affordability and resilience concerns.

Hurricane wind disaster assessment methods on coastal structures based on area and radial distribution integration

Early studies have shown that hurricane disaster assessment indicators that only consider the intensity-related single factor cannot well represent the actual degree of damage caused by hurricanes to coastal structures. In the current study, for hurricane wind damage, in addition to the three wind disaster index formulas (corresponding to the wind resistance characteristics of infrastructures in developed, developing, and underdeveloped regions, respectively) that are related to hurricane intensity, the spatial scale characteristics of hurricanes are also considered. Thus, two kinds of hurricane wind disaster assessment methods by combing the wind disaster index and spatial scale of hurricanes are carried out, area and radial integration, respectively. Compared with the traditional single-factor evaluation index, the method based on area integral can more accurately characterize the degree of hurricane wind disaster. However, the peripheral small wind speed of the hurricane has a greater impact on wind disaster assessment; for medium-strength and intense hurricanes, the highest proportion is up to 61.5–67.7%; and that of weak hurricanes is up to 85.7–94.1%. To solve the challenging problems of hurricanes with small wind speed but the large size and large wind speed but small size, the area integral method with the concept of cut-in wind speed is introduced. The impact of small wind speed on wind disaster assessment is significantly reduced; the highest proportion of medium-strength and intense hurricanes is 14.5–22.5%, and that of weak hurricanes is 27.5–33.3%. Because the three-dimensional wind field is not easy to capture, the structure along the coastline is the most severely damaged, and the linear radial integral is less sensitive to small wind speeds than the quadratic area integral, a hurricane wind disaster assessment method based on radial integral is proposed. The results show that the method has good spatial region insensitivity. Both methods show that the numerical results can accurately represent the hurricane wind disaster when the horizontal grid size is 185 m or 555 m. However, the wind disaster assessment trends of the two methods are not linear because of the asymmetry of the hurricane structure. The two hurricane wind disaster assessment methods proposed in this study can better reflect the degree of disaster. For the comprehensive hurricane wind disaster assessment for large-scale wind farms in the deep sea, the area-based method can be used; for the infrastructure structures along the coastline, the radial-based method is more suitable. Since the measured meteorological data contain different characteristic radius and the corresponding wind speed of hurricanes, the method based on radial integral is more practical.

Increasing Hurricane Intensification Rate Near the US Atlantic Coast

AbstractHurricanes often cause severe damage and loss of life, and storms that intensify close to the coast pose a particularly serious threat. While changes in hurricane intensification and environment have been examined at basin scales previously, near‐coastal changes have not been adequately explored. In this study, we address this using a suite of observations and climate model simulations. Over the 40‐year period of 1979–2018, the mean 24‐hr hurricane intensification rate increased by ∼1.2 kt 6‐hr−1 near the US Atlantic coast. However, a significant increase in intensification did not occur near the Gulf coast over the same period. The enhanced hurricane intensification along the Atlantic coast is consistent with an increasingly favorable dynamic and thermodynamic environment there, which is well simulated by climate models over the historical period. Further, multi‐model projections suggest a continued enhancement of the storm environment and hurricane intensification near the Atlantic coast in the future.

The Potential Value of Combining Assimilation of Water Vapor Radiances and Cloud Liquid/Ice Water Path From ABI on Analyses and Forecasts for Hurricane Irma (2017)

AbstractAll‐sky assimilation of infrared (IR), near‐IR, and visible band measurements are beneficial for forecasting cloud‐covered tropical cyclones, but numerous challenges remain. This study uses the combined assimilation of clear‐sky water vapor radiances and the cloud liquid/ice water path (CWP) retrieved from the Advanced Baseline Imager on the new generation geostationary environmental satellites. The two measurements are spatial complementary to one another. Microphysics schemes, cumulus parameterizations, and dynamic CWP observation errors are optimized using sensitivity tests. The cycling assimilations and subsequent forecasts of Hurricane Irma (2017) are investigated to assess the added value of the combined assimilation and its impacts on hurricane forecasts. Comparisons with ERA5 and dropsonde observations from the National Hurricane Operations Plan show that the combined assimilation can improve the prognostic variables of Hurricane Irma (2017) for both the large‐scale circumstances and the inner‐core area, resulting in a clear improvement in hurricane track and intensity predictions. The detailed diagnostics show that the improved track predictions can be attributed to the more precise steering flow produced by the combined assimilation. Moreover, the additional CWP assimilation allows for a more accurate initialization of water vapor in the hurricane's inner‐core area when the model produces dry simulated water vapor bias. Then the subsequent phase changes of water vapor improve the hurricane intensity forecasting by enhancing the feedback between heating and vortex circulation. The combined assimilation shows the potential value of better utilization of all‐sky satellite measurements to improve hurricane initialization and forecast.

Future Hurricanes Will Increase Palm Abundance and Decrease Aboveground Biomass in a Tropical Forest

AbstractHurricanes are expected to intensify throughout the 21st century, yet the impact of frequent major hurricanes on tropical ecosystems remains unknown. To investigate tropical forest damage and recovery under different hurricane regimes, we generate a suite of scenarios based on Coupled Model Intercomparison Project Phase 6 climate projections and increased hurricane recurrence and intensity for the Luquillo Experimental Forest, Puerto Rico. We then use the Ecosystem Demography model to predict changes in carbon stocks, forest structure and composition. Our results indicate that frequent hurricane disturbances in the future would decrease the overall aboveground biomass, decrease the dominance of late‐successional species, but increase the dominance of palm species. Warmer climates with increased CO2 would have little effect on the functional‐type composition but increase the aboveground biomass. However, the predicted climate and CO2 fertilization effects would not compensate for the biomass loss due to more frequent severe‐hurricane disturbances.

Hurricane Ida (2021): Rapid Intensification Followed by Slow Inland Decay

Abstract Hurricane Ida recently became one of the strongest hurricanes to hit Louisiana on record, with an estimated landfalling maximum sustained wind of 130 kt (1 kt ≈ 0.51 m s−1). Although Hurricane Ida made landfall at a similar time of year and landfall location as Hurricane Katrina (2005), Ida’s postlandfall decay rate was much weaker than Hurricane Katrina. This manuscript includes a comparative analysis of pre- and postlandfall synoptic conditions for Hurricane Ida and other historical major landfalling hurricanes (category 3+ on the Saffir–Simpson hurricane wind scale) along the Gulf Coast since 1983, with a particular focus on Hurricane Katrina. Abundant precipitation in southeastern Louisiana prior to Ida’s landfall increased soil moisture. This increased soil moisture along with extremely weak overland steering flow likely slowed the storm’s weakening rate postlandfall. Offshore environmental factors also played an important role, particularly anomalously high nearshore sea surface temperatures and weak vertical wind shear that fueled the rapid intensification of Ida just before landfall. Strong nearshore vertical wind shear weakened Hurricane Katrina before landfall, and moderate northward steering flow caused Katrina to move inland relatively quickly, aiding in its relatively fast weakening rate following landfall. The results of this study improve our understanding of critical factors influencing the evolution of the nearshore intensity of major landfalling hurricanes in the Gulf of Mexico. This study can help facilitate forecasting and preparation for inland hazards resulting from landfalling hurricanes with nearshore intensification and weak postlandfall decay.

Stakeholders’ Perceptions of Nature-Based Solutions for Hurricane Risk Reduction Policies in the Mexican Caribbean

Nature-based solutions (NbSs) have long recognized the value of coastal and marine ecosystem management and associated ecosystem services as useful tools for climate change mitigation (e.g., blue carbon) and adaptation (e.g., coastal protection against flooding and storm surges). However, NbSs remain poorly acknowledged and mostly absent from coastal planning for disaster risk reduction policies in the Caribbean, as well as from ex-post disaster reconstruction funds. With the increasing frequency and intensity of hurricanes in the region, NbSs are now more needed than ever. Taking Mexico as a representative case study for the wider Caribbean, we here seek to identify and analyze the barriers and opportunities perceived by relevant stakeholders for mainstreaming coastal-marine NbSs into coastal management and disaster risk reduction policies (e.g., mangroves as green infrastructure) to protect coastal societies and national economies against hurricanes. We conduct semi-structured, in-depth interviews with twenty stakeholders covering academic, governmental, tourism, NGO, coastal planning, and financial domains. Among the twenty-three identified barriers, governance, institutional, financial, and human-capacity aspects are the most dominant perceptions behind the current lack of NbS implementation. Future action for the policy integration of NbSs requires widespread political will and better quantification of both the provision of ecosystem services and their economic benefits under conventional markets.

Bat Species Diversity and Abundance of Trophic Guilds after a Major Hurricane along an Anthropic Disturbance Gradient

The frequency and intensity of hurricanes have increased with climate change, and their effects on most taxa are not known. We analyzed a species diversity of bats in three locations with different regimes of anthropic disturbance. We assessed the effect of the season and post-hurricane time on the abundance of trophic guilds in coastal Jalisco, México, during the two years following Hurricane Patricia (category 4). During a sampling effort of 15,629.76 m2 of netting, we captured 790 bats of 21 species. The species diversity was higher in the site with the highest proportion of primary tropical deciduous forest and was higher in 2016 than in 2017; the species composition did not differ greatly between the two years. The abundance of bats in various trophic guilds varied relative to the four climatic seasons. The general abundance of bats, frugivores-omnivores, and insectivores showed a significant increasing trend over time after the hurricane, which may indicate a recovery of the ecosystem or an abundance of early-successional fruiting plants. The results also confirm that species diversity recovers faster in a conserved forest. Thus, it is important to conserve natural areas to mitigate the effects of major disturbances.

An Analysis of Tropical Cyclone Vortex and Convective Characteristics in Relation to Storm Intensity Using a Novel Airborne Doppler Radar Database

Abstract This analysis introduces a novel airborne Doppler radar database, referred to as the Tropical Cyclone Radar Archive of Doppler Analyses with Re-centering (TC-RADAR). TC-RADAR comprises over 900 analyses from 273 flights into TCs in the North Atlantic, eastern North Pacific, and central North Pacific basins between 1997 and 2020. This database contains abundant sampling across a wide range of TC intensities, which facilitated a comprehensive observational analysis on how the three-dimensional, kinematic TC inner-core structure is related to TC intensity. To examine the storm-relative TC structure, we implemented a novel TC center-finding algorithm. Here, we show that TCs below hurricane intensity tend to have monopolar radial profiles of vorticity and a wide range of vortex tilt magnitudes. As TC intensity increases, vorticity becomes maximized within an annulus inward of the peak wind, the vortex decays more slowly with height, and the vortex tends to be more aligned in the vertical. The TC secondary circulation is also strongly linked to TC intensity, as more intense storms have shallower and stronger lower-tropospheric inflow as well as larger azimuthally averaged ascent. The distribution of vertical velocity is found to vary with TC intensity, height, and radial domain. These results—and the capabilities of TC-RADAR—motivate multiple avenues for future work, which are discussed. Significance Statement Acquiring observations of the inner core of tropical cyclones (TCs) is a challenge due to the hazardous conditions inherent to the storm. A proven method of sampling the TC core region is the use of airborne radar. This study presents a novel database comprising over 900 airborne radar analyses collected in storms between 1997 and 2020, which is freely available to the research community. Here we demonstrate the utility of the database by examining how the three-dimensional structure of the TC core region changes depending upon the intensity of the storm. By identifying how the baseline TC vortex structure varies with TC intensity, this work provides the foundation for multiple future research avenues and model evaluation efforts.

Canopy arthropod responses to repeated canopy opening in a wet tropical forest

AbstractWith predictions of increased frequency of intense hurricanes, it is increasingly crucial to understand how biotic and abiotic components of forests will be affected. This study describes canopy arthropod responses to repeated experimental and natural canopy opening at the Luquillo Experimental Forest Long‐term Ecological Research Site (LTER) in Puerto Rico. The canopy trimming experiment (CTE1) treatments were started in 2004, and a second trimming (CTE2) was conducted in 2014, to study effects of increased hurricane frequency at the site. Paired disturbed plots with canopy trimmed (trim) and undisturbed plots with no trimming (no trim) were replicated in three experimental blocks. Arthropods were sampled by bagging branches on seven representative early and late successional overstory and understory tree species annually from 2004 to 2009 for CTE1 and 2015 to 2019 for CTE2. In addition to the experimental manipulation, the CTE site was disturbed by Hurricane Maria (Category 4) in September 2017, providing an additional natural canopy opening to the experiment. We evaluated the effect of the second experimental trimming, compared canopy arthropod responses to the three canopy‐opening events, and compared the effects of experimental trimming and natural canopy opening by Hurricane Maria. The second experimental canopy trimming produced canopy arthropod responses consistent with hurricane disturbances, with sap‐sucking herbivores increasing in abundance on the trimmed plots, whereas other functional groups generally declined in abundance in disturbed plots. Responses to the first and second trimmings were generally similar. However, Hurricane Maria exacerbated the responses, indicating the likely effect of increased hurricane frequency and intensity.

The Consequences of Surface-Exchange Coefficient Uncertainty on an Otherwise Highly Predictable Major Hurricane

Abstract In addition to initial conditions, uncertainty in model physics can also influence the practical predictability of tropical cyclones. In this study, the influence that various magnitudes of uncertainty in the surface exchange coefficients of momentum (Cd) and enthalpy (Ck) can have on an otherwise highly predictable major hurricane (Hurricane Patricia) is compared with that resulting from climatological environmental initial condition uncertainty and the intrinsic limit for this case. As the systematic uncertainty in Cd and Ck is reduced from 40% to 1%, the simulated uncertainty in the intensity and structure is substantially reduced and approaches the intrinsic limit when uncertainty is reduced to 1%. In addition, the forecasted intensity and structure uncertainty only becomes less than that resulting from climatological environmental initial condition uncertainty once the systematic uncertainty in Cd and Ck is reduced to ∼10%, highlighting the strong influence of model error in limiting TC predictability. If Cd and Ck are perturbed stochastically, instead of systematically, it is shown that the influence on the simulated intensity and structure is negligible and nearly identical to the intrinsic limit, regardless of the magnitude of the stochastic Cd and Ck perturbations. While the magnitude of the stochastic Cd and Ck perturbations are comparable to the systematic perturbations, the stochastic perturbations are shown to not substantially perturb the time-integrated inner-core fluxes of momentum or enthalpy that predominantly determine simulated tropical cyclone intensity. Last, it is shown that the kinetic energy error growth behavior varies with the radius, azimuthal wavenumber, and ensemble design. Significance Statement The air–sea energy exchange beneath hurricanes is highly uncertain but strongly influences intensity. In this study, the influences of different magnitudes of surface-exchange coefficient uncertainty on the simulated intensity of an intense hurricane is compared with that resulting from environmental initial condition uncertainty and the intrinsic predictability limit. The main takeaway is that current surface-exchange coefficient uncertainties result in larger intensity uncertainty than environmental initial condition uncertainty, and substantial improvements in predictions are possible if current surface-exchange coefficient uncertainties are reduced. Furthermore, it is shown that randomly perturbing the surface-exchange coefficients at each point in space and time is not the best approach to account for the influences of this uncertain physical process on hurricane prediction because it has minimal influence on the simulated intensity.

ASSESSING LAND COVER CHANGE AROUND BAYOU PEROT-LITTLE LAKE, NEW ORLEANS USING SENTINEL 2 SATELLITE IMAGERY

Abstract. Global climate change has affected the rate of rising sea level, the frequency, intensity, timing, and distribution of hurricanes and tropical storms which threatens coastal ecosystems such as Bayou Perot, Little Lake in New Orleans along the Gulf of Mexico. The impact of hurricanes could include wetland and coastal land loss. This paper compared the land cover changes around Bayou-Perot- Little Lake, New Orleans, USA following Hurricanes Ida (August 26, 2021 to August 28, 2021). Two high-resolution Sentinel 2 imagery dated before and after Hurricane Ida was compared to assess the impacts of the hurricane on the land cover around Bayou Perot. A Random Forest classification (RF) algorithm in Google Earth Engine was used to produce maps and identify areas that have experienced conversions in land use or land cover change after the hurricane. This method of classification has the advantages of high classification accuracy and the ability to measure variable importance in land-cover mapping. In addition to random classification algorithm, other analysis such as the Normalized Difference Vegetation Index (NDVI) was be used to gain a better perspective of the overall changes in vegetation across the landscape. Five main classes were considered after the classification which included water, vegetation, bare soil, built up and marsh area. The results of the land cover change showed exposed old coastal marsh, valuable dune habitat providing storm protection to estuaries, wetlands, and the coastal population destroyed.