Hurricane Track Research Articles

Differentiated Impacts of Central and Eastern Atlantic Niño on Hurricane Activity in the Tropical North Atlantic

AbstractRecent research highlights the influence of the Atlantic Niño on the likelihood of strong hurricanes forming in the tropical Atlantic. This phenomenon increases the risk of hurricanes impacting the Caribbean islands and the United States. A recent study identifies two distinct types of the Atlantic Niño, with warming concentrated in the central (CA) and eastern (EA) equatorial Atlantic, respectively. By analyzing observational and reanalysis data, we investigated how these two types of the Atlantic Niño affect hurricane activity. The findings reveal that the CA Niño enhances hurricane frequency south of 20°N, while the CA Niña promotes hurricanes north of 20°N. The CA Niño exerts a more significant influence on hurricanes than the EA Niño, primarily by affecting wind shear, relative vorticity, and vertical velocity. In contrast, the EA Niño mainly impacts relative humidity and African Easterly Waves. These insights could improve the accuracy of seasonal hurricane forecasts.

Sea surface temperature error under tropical cyclone in the western North Pacific.

Tropical cyclone forecasting relies on accurate sea surface temperature, yet the errors in available sea surface temperature products remain unclear. Here, I evaluate the sea surface temperature errors under tropical cyclones in the western North Pacific based on surface drifters across eight widely-used datasets, including satellite, atmospheric and oceanic analysis-or-reanalysis products, spanning from 2014 to 2023. The ensemble mean bias and root-mean-squared-error are firstly quantified. Both satellite and oceanic analysis sea surface temperatures exhibit a cold bias, while atmospheric analysis-or-reanalysis sea surface temperature shows a warm bias. Specifically, atmospheric analysis-or-reanalysis products consistently show a warm bias in the right-rear quadrant of the tropical cyclone coordinate system. Regarding root-mean-squared-error, for most products, the right side consistently exhibits higher errors than the left side. SST bias significantly impacts tropical cyclone intensity change as indicated by empirical relationship. These results provide direct insights for operational tropical cyclone forecasting and research.

Multi-season evaluation of hurricane analysis and forecast system (HAFS) quantitative precipitation forecasts

Quantitative precipitation forecasts (QPF) from numerical weather prediction models need systematic verification to enable rigorous assessment and informed use, as well as model improvements. The United States (US) National Oceanic and Atmospheric Administration (NOAA) recently made a major update to its regional tropical cyclone modeling capabilities, introducing two new operational configurations of the Hurricane Analysis and Forecast System (HAFS). NOAA performed multi-season retrospective forecasts using the HAFS configurations during the period that the Hurricane Weather and Forecasting (HWRF) model was operational, which was used to assess HAFS performance for key tropical cyclone forecast metrics. However, systematic QPF verification was not an integral part of the initial evaluation. The first systematic QPF evaluation of the operational HAFS version 1 configurations is presented here for the 2021 and 2022 season re-forecasts as well as the first HAFS operational season, 2023. A suite of techniques, tools, and metrics within the enhanced Model Evaluation Tools (METplus) software suite are used. This includes shifting forecasts to mitigate track errors, regridding model and observed fields to a storm relative coordinate system, as well as object oriented verification. The HAFS configurations have better performance than HWRF for equitable threat score (ETS), but larger over forecast biases than HWRF. Storm relative and object oriented verification show the HAFS configurations have larger precipitation areas and less intense precipitation near the TC center as compared to observations and HWRF. HAFS QPF performance decreased for the 2023 season, but the general spatial patterns of the model QPF were very similar to 2021-2022.

A method to automatically ascertain the identities of tropical cyclones in tropical cyclone warning messages

In the operational forecasting of tropical cyclones (TCs), decoding TC warning messages from global centers, along with extracting, organizing, and storing useful track observations and forecasts, are fundamental tasks. The technical core lies in accurately identifying distinct TC individuals through automated programming methods. Based on the statistical characteristics of historical distances between TC individuals, this study designs a novel method for automatic identification of TC individuals and establishes a database of TC track observations and forecasts by integrating the persistent features from various elements in TC warning messages. This method accurately identifies each TC individual and assigns it a unique database number through a two-step process: initially, through the 'Same Center same Number Comparison(SCNC)' identification method, followed by the 'Spatio-Temeporal Distance Comparison(STDC)' identification method.On this basis, we obtain a well-organized and comprehensive dataset that covers entire TC life time. Over the past decade,the operational practice has demonstrated that this method is accurate and efficient, providing solid data support for the TC forecasting operation, the assessment of TC forecasting accuracy, the compilation of TC yearbook, and TC-related research.

New Rankine Vortex Models Developed Based on SMAP Measurements

Abstract The L-band passive microwave radiometer on board the NASA Soil Moisture Active Passive (SMAP) satellite can measure brightness temperature to retrieve sea surface wind speed under tropical cyclone (TC) conditions without being affected by rainfall or signal saturation caused by high wind speeds. Based on this advantage, this paper used the SMAP wind products for parameterizing the key decay exponent α of the Rankine vortex model (a traditional parametric model of the TC wind field) and finally developed new Rankine models. The SMAP dataset included 67 TC cases. Through data statistics, we examined the relationship between α and the maximum wind speed Um, the relationship between α and the radius of maximum wind speed (Rm), and the relationship between Rm and the averaged radius of 17 m s−1 (R17). Results showed that the three relationships were both positive correlations, indicating that α can be parameterized in three empirical ways. The first way is to calculate solely with Um. The second way is to calculate solely with Rm. The third way is to calculate Um and Rm together. The three ways correspond to three new models: the SMAP Rankine Model-1 (SRM-1), the SMAP Rankine Model-2 (SRM-2), and the SMAP Rankine Model-3 (SRM-3). Finally, comparisons were made between the new models and three existing Rankine models, according to the model simulations and the Advanced Microwave Scanning Radiometer 2 measurements of 49 TC cases. Results showed that the SRM-3 performed best overall. Significance Statement Ocean surface wind fields are important driving factors for numerical models to guide evolution forecasting and risk assessment of tropical cyclones. The purpose of this study is to utilize the SMAP products to modify the traditional Rankine vortex model for tropical cyclone surface wind field estimation. Rankine decay exponent was found between 0.3 and 0.9 and positively dependent upon maximum wind speed and its radius. Based on these characteristics, the proposed new Rankine models could calculate the decay exponent and further the TC surface wind field symmetrically with high accuracy, simply using maximum wind and its radius. This paper shows a practical use of SMAP measurements on TC wind field monitoring, analyzing, and modeling.

Analysing the atmospheric-oceanic conditions driving the sustained long track and intensity of Tropical Cyclone Freddy

During February-March 2023, the record-breaking tropical cyclone (TC) Freddy caused widespread flooding and damages across southeastern Africa. While <5% of TCs make landfall into southern Africa, TC Freddy made landfall twice and is the only TC in the past two decades that has tracked over 8000 km across the entire southern Indian Ocean. To understand why TC Freddy was so unique, this study investigated the evolution, track and atmospheric-oceanic mechanisms driving TC Freddy using the ERA5, CFSv2, OSTIA, NCEP-NCAR datasets and track data from various sources. It was found that SSTs were >27 °C during TC Freddy’s lifetime, while TC Dingani and a split Mascarene High played a role in steering TC Freddy across the southern Indian Ocean. Leading up to the development of TC Freddy, conditions were favourable for TC genesis, as indicated by the levels of the Genesis Potential Parameter (GPP) and its modified version (GPPI), the tropical cyclone heat potential levels, and elevated SSTs. Ridging subtropical anticyclones and the Mascarene High alongside favourable steering flow and GPP (and GPPI) conditions resulted in Freddy’s double landfall in Mozambique. In assessing the tracks, it was found that there are discrepancies in the track of the commonly used IBTrACS when compared to ERA5 and RSMC tracks, which has implications for impact studies due to the underestimation of landfall considerations. This study reveals the unique characteristics and atmospheric-oceanic mechanisms driving TC Freddy, emphasising the importance of accurate representation of favourable conditions and track data for enhancing TC forecasting and impact assessments.

Hurricane surge and inundation in the Bahamas, part 2: Flood risk assessment

AbstractA hurricane surge and inundation risk assessment has been carried out for Grand Bahama and Eleuthera in The Bahamas. 10,000 years of synthetic hurricane tracks were generated based on a statistical analysis of historical hurricanes from 1979 to 2020 inclusive. The surge and inundation were modelled using a hydrodynamic TELEMAC‐2D model covering sea around The Bahamas and the land of the two islands. Predictions of flood extents and depths were mapped for return periods of up to 1000 years for present day conditions and three climate change scenarios to 2100. The flooding experienced over Grand Bahama during Hurricane Dorian in 2019, was estimated to have a return period of up to approximately 450 years. Under the climate change scenarios the likelihood of flooding similar to Hurricane Dorian was estimated to be approximately 1.7 times more likely in 2050 and up to 3.4 times as likely in 2100 under a high greenhouse gas emissions scenario. The storm surge maps can be used by the Bahamas Department of Meteorology and other government agencies for emergency management, planning of infrastructure and building resilience in response to climate change.

A Dynamic Smagorinsky Model for Horizontal Turbulence Parameterization in Tropical Cyclone Simulation

AbstractThe horizontal turbulence parameterization is vital for the intensity and structure forecasting of tropical cyclone (TC) in numerical weather prediction (NWP) models. The default two‐dimensional (2D) standard Smagorinsky model with a single universal constant in Weather and Research Forecasting (WRF) model has been proven to be over dissipative for TC, leading to underprediction of TC intensity. This study provides the first attempt to implement the physically based 2D dynamic Smagorinsky model (DSM) for horizontal turbulence parameterization in WRF model for TC forecasts. The DSM dynamically computes the Smagorinsky coefficient as a function of the resolved flow during the simulation, avoiding the need to prescribe the coefficient a prior. The test results of the DSM in a TC NWP model show that the DSM can significantly improve the wind intensity forecasts compared to the standard Smagorinsky model.

Spatial Memory of Notable Hurricane Tracks and Their Geophysical Hazards

Previous research has shown that people use a benchmark hurricane as part of their preparation and evacuation decision-making process. While hurricanes are a common occurrence along the Gulf Coast, research on personal memories of past storms is lacking. Particularly, how well do people remember the track and geophysical hazards (wind speed, storm surge, and total rainfall) of past storms? The accurate or inaccurate recollection and perception of previous storm details can influence personal responses to future storms, such as the decision to evacuate or take other life-saving actions. Survey responses of residents in Alabama and Mississippi were studied to determine if people were accurately able to recall a notable storm’s name when seeing an image of the storm’s track. Those who were able to identify the storm by its track were also asked if they could remember the storm’s maximum reported rainfall, maximum sustained winds, and storm surge at landfall. Results showed that there were statistically significant differences between the levels of accurate recall for different storms, with Hurricanes Katrina and Michael having the most correct responses. Regardless of the storm, most people struggled to remember geophysical hazards. The results of this study are important as they can inform broadcast meteorologists and emergency managers on forecast elements of the storm to better emphasize in future communication in comparison to the actual values from historical benchmark storms.

Deep learning approaches in predicting tropical cyclone tracks: An analysis focused on the Northwest Pacific Region

In this study, we conducted a comprehensive and integrated test of tropical cyclone track prediction using deep learning technologies, aiming to enhance the efficiency and accuracy of the prediction methods. We employed the Best Track dataset from the China Meteorological Administration's Tropical Cyclone Data Center, which covers the Northwest Pacific region from 1949 to 2023. This dataset provides comprehensive coverage, encompassing critical tropical cyclone details like time, latitude, longitude, and wind speed. Our focus was on evaluating and comparing different deep learning models, including Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Gated Recurrent Units (GRU), for their effectiveness in handling complex time series data. Through detailed analysis of various model configurations, including factors such as input-output lengths, hidden size, the number of layers, the implementation of bi-directional networks, and attention mechanisms, we discovered that LSTM and GRU models significantly outperform traditional RNN models in dealing with long-term dependencies and enhancing prediction accuracy. Moreover, the LSTM model, used to forecast key tropical cyclones during the 2023 Pacific tropical cyclone season, achieved mean errors of 21.84 km, 37.56 km, and 26.12 km for Typhoons Mawar, Doksuri, and Saola, respectively. This method also demonstrated high efficiency in rapid response to extreme weather changes, processing each tropical cyclone's forecast in just about 8 s. The results not only illustrate the practical utility of deep learning in tropical cyclone track prediction but also provide new, effective tools for disaster prevention and mitigation efforts.

The impact of coupling a dynamic ocean in the Hurricane Analysis and Forecast System

Coupling a three-dimensional ocean circulation model to an atmospheric model can significantly improve forecasting of tropical cyclones (TCs). This is particularly true of forecasts for TC intensity (maximum sustained surface wind and minimum central pressure), but also for structure (e.g., surface wind-field sizes). This study seeks to explore the physical mechanisms by which a dynamic ocean influences TC evolution, using an operational TC model. The authors evaluated impacts of ocean-coupling on TC intensity and structure forecasts from NOAA’s Hurricane Analysis and Forecast System v1.0 B (HFSB), which became operational at the NOAA National Weather Service in 2023. The study compared existing HFSB coupled simulations with simulations using an identical model configuration in which the dynamic ocean coupling was replaced by a simple diurnally varying sea surface temperature model. The authors analyzed TCs of interest from the 2020–2022 Atlantic hurricane seasons, selecting forecast cycles with small coupled track-forecast errors for detailed analysis. The results show the link between the dynamic, coupled ocean response to TCs and coincident TC structural changes directly related to changing intensity and surface wind-field size. These results show the importance of coupling in forecasting slower-moving TCs and those with larger surface wind fields. However, there are unexpected instances where coupling impacts the near-TC atmospheric environment (e.g., mid-level moisture intrusion), ultimately affecting intensity forecasts. These results suggest that, even for more rapidly moving and smaller TCs, the influence of the ocean response to the wind field in the near-TC atmospheric environment is important for TC forecasting. The authors also examined cases where coupling degrades forecast performance. Statistical comparisons of coupled versus uncoupled HFSB further show an interesting tendency: high biases in peak surface winds for the uncoupled forecasts contrast with corresponding low biases, contrary to expectations, in coupled forecasts; the coupled forecasts also show a significant negative bias in the radii of 34 kt winds relative to National Hurricane Center best track estimates. By contrast, coupled forecasts show very small bias in minimum central pressure compared with a strong negative bias in uncoupled. Possible explanations for these discrepancies are discussed. The ultimate goal of this work will be to enable better evaluation and forecast improvement of TC models in future work.

Impacts of the new UM convection scheme, CoMorph-A, over the Indo-Pacific and Australian regions

A new convection scheme, ‘CoMorph-A’, has been introduced into the latest UK Met Office coupled (GC4) and atmosphere-only (GA8) models. In this study, the impact of CoMorph-A is assessed in atmosphere-only Atmospheric Model Intercomparison Project simulations, as well as in sets of initialised 28-day forecasts with both the coupled and uncoupled models. Initial results show improvements over the Indo-Pacific and northern Australian regions, as well as improvements in the rainfall bias, Madden–Julian Oscillation simulation and prediction, tropical cyclone forecasts and the diurnal cycle of rainfall over the Maritime Continent. The improvements are mostly consistent across the initialised forecasts and the climate simulations, indicating the effectiveness of the new scheme across applications. The use of this new convection scheme is promising for future model configurations, and for improving the simulation and prediction of Australian weather and climate. The UK Met Office is continuing to develop CoMorph and will soon release version B.

Risk-Based Visualization of Hurricane Forecasts

The research presented here tests user comprehension of a new kind of risk-based visualization of hurricane forecasts versus other common representations: a cone of uncertainty (with and without a centerline) and an ensemble representation. Our landfall heat maps use intensity hues and a filled black circle with size representing hurricane category at the time of forecast, rather than representing the hurricane path. In two online studies we compare how different representations of the same scenarios affect viewers’ (a) judgments of the risk that a location is likely to be hit by a hurricane, (b) their likelihood of issuing an evacuation order for that location (if they were to be in a role of issuing evacuation orders), and (c) their confidence in their judgment. Findings contribute to developing a toolkit of visual materials that designers of hurricane visualizations can select from based on empirical evidence of user responses to their elements.

Enhanced Atlantic Meridional Mode predictability in a high-resolution prediction system.

Accurate prediction of sea surface temperatures (SSTs) in the tropical North Atlantic on multiyear timescales is of paramount importance due to its notable impact on tropical cyclone activity. Recent advances in high-resolution climate predictions have demonstrated substantial improvements in the skill of multiyear SST prediction. This study reveals a notable enhancement in high-resolution tropical North Atlantic SST prediction that stems from a more realistic representation of the Atlantic Meridional Mode and the associated wind-evaporation-SST feedback. The key to this improvement lies in the enhanced surface wind response to changes in cross-equatorial SST gradients, resulting from Intertropical Convergence Zone bias reduction when atmospheric model resolution is increased, which, in turn, amplifies the positive feedback between latent and sensible surface heat fluxes and SST anomalies. These advances in high-resolution climate prediction hold promise for extending tropical cyclone forecasts at multiyear timescales.

The Role of Turbulence in an Intense Tropical Cyclone: Momentum Diffusion, Eddy Viscosities, and Mixing Lengths

Abstract Improved representation of turbulent processes in numerical models of tropical cyclones (TCs) is expected to improve intensity forecasts. To this end, the authors use a large-eddy simulation (with 31-m horizontal grid spacing) of an idealized category 5 TC to understand the role of turbulent processes in the inner core of TCs and their role on the mean intensity. Azimuthally and temporally averaged budgets of the momentum fields show that TC turbulence acts to weaken the maximum tangential velocity, diminish the strength of radial inflow into the eye, and suppress the magnitude of the mean eyewall updraft. Turbulent flux divergences in both the vertical and radial directions are shown to influence the TC mean wind field, with the vertical being dominant in most of the inflowing boundary layer and the eyewall (analogous to traditional atmospheric boundary layer flows), while the radial becomes important only in the eyewall. The validity of the downgradient eddy viscosity hypothesis is largely confirmed for mean velocity fields, except in narrow regions which generally correspond to weak gradients of the mean fields, as well as a narrow region in the eye. This study also provides guidance for values of effective eddy viscosities and vertical mixing length in the most turbulent regions of intense TCs, which have rarely been measured observationally. A generalized formulation of effective eddy viscosity (including the Reynolds normal stresses) is presented. Significance Statement This study uses a turbulence-resolving simulation of a category 5 tropical cyclone to understand the role of turbulence in intense storms. Results show that turbulence clearly modulates storm structure and intensity. This study provides guidance for the values of turbulent quantities (which are usually parameterized in comparatively coarse operational TC forecast models) in scarcely observed regions of intense storms. Furthermore, a complete formulation of the effective eddy viscosities is proposed, incorporating contributions from typically ignored Reynolds normal stress terms.

Improving Hurricane Intensity and Streamflow Forecasts in Coupled Hydrometeorological Simulations by Analyzing Precipitation and Boundary Layer Schemes

Abstract Hurricanes have been the most destructive and expensive hydrometeorological event in U.S. history, causing catastrophic winds and floods. Hurricane dynamics can significantly impact the amount and spatial extent of storm precipitation. However, the complex interactions of hurricane intensity and precipitation and the impacts of improving hurricane dynamics on streamflow forecasts are not well established yet. This paper addresses these gaps by comprehensively characterizing the role of vertical diffusion in improving hurricane intensity and streamflow forecasts under different planetary boundary layer, microphysics, and cumulus parameterizations. To this end, the Weather Research and Forecasting (WRF) atmospheric model is coupled with the WRF hydrological (WRF-Hydro) model to simulate four major hurricanes landfalling in three hurricane-prone regions in the United States. First, a stepwise calibration is carried out in WRF-Hydro, which remarkably reduces streamflow forecast errors compared to the U.S. Geological Survey (USGS) gauges. Then, 60 coupled hydrometeorological simulations were conducted to evaluate the performance of current weather parameterizations. All schemes were shown to underestimate the observed intensity of the considered major hurricanes since their diffusion is overdissipative for hurricane flow simulations. By reducing the vertical diffusion, hurricane intensity forecasts were improved by ∼39.5% on average compared to the default models. These intensified hurricanes generated more intense and localized precipitation forcing. This enhancement in intensity led to ∼16% and ∼34% improvements in hurricane streamflow bias and correlation forecasts, respectively. The research underscores the role of improved hurricane dynamics in enhancing flood predictions and provides new insights into the impacts of vertical diffusion on hurricane intensity and streamflow forecasts. Significance Statement Despite significant recent improvements, numerical weather prediction models struggle to accurately forecast hurricane intensity and track due to many reasons such as inaccurate physical parameterization for hurricane flows. Furthermore, the performance of existing physics schemes is not well studied for hurricane flood forecasting. This study bridges these knowledge gaps by extensively evaluating different physical parameterizations for hurricane track, intensity, and flood forecasts using an atmospheric model coupled with a hydrological model. Then, a reduced diffusion boundary layer scheme is developed, making remarkable improvements in hurricane intensity forecasts due to the overdissipative nature of the considered schemes for major hurricane simulations. This reduced diffusion model is shown to significantly enhance hurricane flood forecasts, indicating the significance of hurricane dynamics on its induced precipitation.

A Hybrid Machine Learning/Physics‐Based Modeling Framework for 2‐Week Extended Prediction of Tropical Cyclones

AbstractPrediction of tropical cyclones (TCs) beyond a week is challenging but of great importance for disaster prevention and mitigation. We propose a hybrid machine learning (ML)/physics‐based modeling framework to extend TC forecasts to 2 weeks. This framework integrates a recently launched ML‐based global weather prediction model (Pangu) and the high‐resolution physics‐based regional weather research and forecasting (WRF) model. The Pangu model shows promise in enhancing the accuracy of predictions for large‐scale circulation and TC tracks, while the high‐resolution WRF model is capable of capturing the core processes underlying TC evolution. To capitalize on the complementary strengths of both the Pangu and WRF models in predicting TCs, the framework comprises three key components: downscaling the Pangu model using the WRF model, adjusting large‐scale circulation through spectral nudging driven by the Pangu model forecasts, and updating sea surface temperature using an ocean mixed‐layer model. These components also ensure the framework's feasibility for real‐time TC forecasting. The prediction skill of the framework has been demonstrated for five long‐lived TCs across various basins from 2018 to 2023. Results indicate that the hybrid ML/physics‐based modeling framework decreased the 2‐week mean TC track and intensity errors by 59% and 32% compared to the global numerical weather prediction models, by 2% and 59% compared to the ERA5‐driven Pangu model, and by 32% and 23% compared to the ERA5‐driven WRF model, respectively. This implies that the framework has great potential to be used for 2‐week extended prediction of TCs.

Cloud Height Distributions and the Role of Vertical Mixing in the Tropical Cyclone Eye Derived From Compact Raman Lidar Observations

AbstractThe distribution of tropical cyclone (TC) eye cloud heights is documented for the first time using compact Raman lidar (CRL) measurements with high spatial resolution. These cloud heights act as tracers for low‐level vertical mixing in the eye region. Cloud height distributions using all available data from nine Atlantic TCs in 2021 and 2022 show significant vertical variance, dispelling the notion of a flat stratiform eye cloud deck. Eye cloud widths are multiscale, with shallow convective clouds dominating CRL returns. Data from Hurricane Sam (2021) highlight the evolution of shallow convective clouds in the TC eye and their associated temperature inversions. The frequent appearance of convective eye clouds, along with observed vertical wind fluctuations, suggests that vertical mixing from the boundary layer frequently occurs in the TC eye, even beneath strong inversions. This strong vertical mixing should be accurately portrayed by TC simulations and forecasts.

Spatial and conventional verifications of hurricanes Dorian and Fiona using the Canadian precipitation analysis & integrated multi-satellite retrievals for GPM products

Reanalysis and satellite-based rainfall packages are useful for monitoring hydroclimatic extremes. These advanced tools can be used as an early-warning system for decision-making during extreme events. Hurricane Dorian and Fiona impacted the North Atlantic from September 6–9, 2019 and September 22–25, 2022, respectively. This study evaluated the Canadian Precipitation Analysis (CaPA) in conjunction with integrated multi-satellite retrievals for the Global Precipitation Measurement Mission (GPM) in capturing these events at Early, Late and Final run stages (IMERG-E, IMERG-L & IMERG-F, respectively). Statistical verification was conducted through continuous, categorical and spatial methods to answer multiple research questions. The results show that CaPA outperformed all the IMERG products across various intensities throughout the duration of the extremes compared to station observations; therefore, CaPA can be a proxy for gauged observations. When CaPA was used as the reference for spatial verification, IMERG products correctly captured the spatial evolution of the hurricane from day to day, indicating they are reliable for hydroclimatic extreme applications. In addition, there are no striking statistical differences between the IMERG products despite the fact that IMERG-E and IMERG-L had no adjustment or gauge observation. In general, this study shows that IMERG-E has many potential applications such as CaPA assimilation, a hazard early warning system, and hurricane tracking and prediction. Further, including IMERG-E into CaPA will leverage its high resolution and latency. This study will benefit those involved in hydroclimatic studies and decision-making in the North Atlantic.

WMO Typhoon Landfall Forecast Demonstration Project (2010–22): A Decade of Transition from Track Forecasts to Impact Forecasts

Abstract The Typhoon Landfall Forecast Demonstration Project (TLFDP) (2010–22) was an international cooperative scientific project conducted under the framework of the WMO. The primary objectives of the TLFDP were to enhance the capability of tropical cyclone (TC) forecasters and support related decision-makers in effective utilization of the most advanced forecasting techniques for the ultimate purpose of reducing and preventing disasters associated with TC landfall. Forty agencies/organizations/projects globally participated in the activities of the TLFDP following its inception in 2010, although the primary focus was on landfalling TCs in the western North Pacific. The TLFDP facilitated collaborations and workshops that realized notable achievements in four key areas: 1) the collection, production, and sharing of TC data; 2) the development and application of TC forecast verification metrics; 3) research on TC forecast skill; and 4) development of new techniques for TC forecasting. An obvious outcome was the shift from prediction of TC features, including track and intensity, toward prediction of TC impacts with more probabilistic conception. The final years of the project also promoted increasing application of artificial intelligence and machine learning techniques in various techniques for analysis and forecasting of TCs. Although the TLFDP ended in 2022, its core activities have continued to be extended through new WMO projects and regional cooperative initiatives.