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NASA NTRS2024

Celebrating 10 Years of the Sub-Seasonal to Seasonal Prediction Project and Looking to the Future

The conference clearly demonstrated the increasing interest and growth of the scientific community working on the development and application of sub-seasonal to seasonal prediction since the start of the World Weather Research Programme (WWRP)/World Climate Research Programme (WCRP) sub-seasonal to seasonal (S2S) prediction project in 2013. The conference, which was held at the University of Reading (United Kingdom), was organized into three main themes as briefly summarized below, with eleven invited talks, 74 oral contributed talks, and 101 posters. The conference also included a two-hour breakout session, wherein eight groups discussed the current state and prospect for S2S prediction, and an early career researcher event. A summary of these discussions and recommendations is presented below. The conference web page (https://research.reading.ac.uk/s2s-summit2023/) is archived at the University of Reading. Introductory comments by representatives of the World Meteorological Organization (WMO) WWRP and WCRP emphasized the importance of the weather–climate linkage, targeted by S2S forecasts (from 2 weeks to a season ahead), addressing the challenges of creating “end-to-end” forecasts that encompass the entire climate-services chain from the prediction science and forecast, to the development and issuing of forecast products tailored to informing user-decisions. They also emphasized the efficacy of multi-model ensemble efforts and databases to foster collaborations internationally and between operational centres and academia. Although the WWRP/WCRP S2S project comes to an end in 2023, S2S prediction will remain an important focus for WWRP and WCRP. In WWRP, a new project called SAGE (Sub-seasonal to seasonal predictions for Agriculture and Environment) will start in 2024. Another important legacy of the S2S project will be the maintenance of the S2S database (Vitart et al. 2017) and the establishment of a WMO Lead Center for sub-seasonal prediction multi-model ensemble (LC-SSPMME) which will provide real-time multi-model S2S climate information. In two keynote presentations, Prof. Brian Hoskins (University of Reading) and Dr. Gilbert Brunet (Australian Bureau of Meteorology) discussed the potential of S2S predictability and the ongoing journey for understanding and improving these predictions. This conference was a sequel to the International Conference on Sub-seasonal to Seasonal Prediction (Robertson et al., 2014) which took place in College Park (Maryland, USA) in February 2014 to celebrate the start of the WWRP/WCRP S2S project, and to WCRP and WWRP conferences in Boulder, USA, in 2018 (Merryfield et al., 2020). A significant development compared to the previous S2S conferences was the large number of presentations on research to operation (R2O) and S2S applications and on the use of artificial intelligence and machine learning (AI/ML) methods for S2S prediction. Some of these methods provide empirical S2S forecasts which are competitive with state-of-the-art dynamical models. Other presentations demonstrated that AI/ML can provide alternative calibration of dynamical model outputs to traditional methods. Several talks and posters highlighted the increasing use of AI/ML, including deep learning, in S2S forecast post-processing and using AI to identify higher flow-dependent skill. Finally, some presentations demonstrated the value of AI/ML methods for a better understanding of S2S sources of predictability and attribution of extreme events.

S. J. Woolnough↗
DOE OSTI2022

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Abstract The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Advancing an integrated understanding of land–ocean connections in shaping the marine ecosystems of coastal temperate rainforest ecoregions

Land and ocean ecosystems are strongly connected and mutually interactive. As climate changes and other anthropogenic stressors intensify, the complex pathways that link these systems will strengthen or weaken in ways that are currently beyond reliable prediction. In this review we offer a framework of land–ocean couplings and their role in shaping marine ecosystems in coastal temperate rainforest (CTR) ecoregions, where high freshwater and materials flux result in particularly strong land–ocean connections. Using the largest contiguous expanse of CTR on Earth—the Northeast Pacific CTR (NPCTR)—as a case study, we integrate current understanding of the spatial and temporal scales of interacting processes across the land–ocean continuum, and examine how these processes structure and are defining features of marine ecosystems from nearshore to offshore domains. We look ahead to the potential effects of climate and other anthropogenic changes on the coupled land–ocean meta-ecosystem. Finally, we review key data gaps and provide research recommendations for an integrated, transdisciplinary approach with the intent to guide future evaluations of and management recommendations for ongoing impacts to marine ecosystems of the NPCTR and other CTRs globally. In the light of extreme events including heatwaves, fire, and flooding, which are occurring almost annually, this integrative agenda is not only necessary but urgent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Role of Renewables in Grid Resilience

This chapter focuses on revealing the impact of renewable energies, especially distributed energy resources, on modern power system resilience. It is widely recognized that the increasing adoption of renewable energy is bringing more uncertainty and variability, posing new challenges to grid resilience. However, both supply and demand are becoming more active in grid resilience management. The communities themselves would benefit from the utilization of local resources and flexible management to gain resilience during the extreme events.

grid resilience↗
DOE OSTI2020

Projected changes in the terrestrial and oceanic regulators of climate variability across sub-Saharan Africa

Future changes in the sign and intensity of ocean–land–atmosphere interactions have been insufficiently studied, despite implications for regional climate change projections, extreme event statistics, and seasonal climate predictability. In response to this deficiency, the present study focuses on projected responses to the enhanced greenhouse effect in: (1) the mean state of the atmosphere and land surface; (2) oceanic and terrestrial drivers of sub-Saharan climate variability; and (3) total seasonal climate predictability of sub-Saharan Africa, a region known for its pronounced land–atmosphere coupling. Analysis focuses on output from 23 Earth System Models in the Coupled Model Intercomparison Project Phase Five for the late twentieth and twenty-first centuries. It is projected that the greatest warming across sub-Saharan Africa will occur over the Sahel, the monsoon season will become more persistent into late summer and autumn, short rains in the Horn of Africa (HOA) will intensify, and leaf area index will increase across the HOA. Stepwise Generalized Equilibrium Feedback Assessment, i.e. a multivariate statistical approach, is applied to the model output over sub-Saharan Africa in order to explore the oceanic and terrestrial drivers of regional climate. The models indicate that the study region’s climate variability is dominated by oceanic drivers, with secondary contributions from soil moisture and very modest impacts from vegetation. Overall, the general model consensus of future projections indicates a concerning diminished seasonal predictability of sub-Saharan African regional climate based on key oceanic and terrestrial predictors and an elevated role of the land surface (associated with soil moisture anomalies) compared to oceanic drivers in regulating regional climate variability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Dynamics and drivers of land use and land cover changes in Bangladesh

Abstract Bangladesh has undergone dramatic land use and land cover changes (LULCC) in recent years, but no quantitative analysis of LULCC drivers at the national scale exists so far. Here, we quantified the drivers of major LULCC in combination with biophysical and socioeconomic observations at the sub-district level. We used Landsat satellite data to interpret LULCC from 2000 to 2010 and employed a Global Surface Water Dataset to account for the influences of water seasonality. The results suggest that major LULCC in Bangladesh occur between agricultural land and waterbodies and between forest and shrubland. Exclusion of seasonal waterbodies can improve the accuracy of our LULCC results and driver analysis. Although the gross gain and loss of agricultural land are large on the local scale, the net change (gross gain minus gross loss) at a country scale is almost negligible. Climate dynamics and extreme events and changes in urban and rural households were driving the changes from forest to shrubland in the southeast region. The conversion from agricultural land to standing waterbodies in the southwest region was mainly driven by urban household dynamics, population growth, distance to cities and major roads, and precipitation dynamics. This study, which is the first effort accounting for water seasonality and quantifying biophysical and socioeconomic drivers of LULCC at the national scale, provides a perspective on overall LULCC and underlying drivers over a decadal time scale and national spatial scale and can serve as a scientific basis for developing land policies in Bangladesh.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A treatment-effect model to quantify human dimensions of disaster impacts: the case of Hurricane Maria in Puerto Rico

Here, we propose a supervised learning approach using publicly available panel data to statistically quantify the specific manifestations of human impacts of an extreme event, such as changes number of suicides, substance abuse, excess mortality, and unemployment. This allows us to conceptually focus our framework on human impacts and how by attributing them to disaster events along widely accepted psychological, economic, and social dimensions. Our modified treatment-effect model allows counterfactual baseline conditions to be posited for each manifestation from which an aggregated quantitative multi-faceted measure of human impacts can be determined. The developed statistical methodology could be beneficial to policymakers who must allocate scarce resources to those communities in greater need. We illustrate the applicability of our approach using annual and monthly panel data from 2012 to 2018 encompassing the 2017 Hurricane Maria event across various municipalities in Puerto Rico. Our statistical modeling methodology stands apart since (i) it explicitly and more realistically captures the effect of different human-oriented manifestations of an actual event and (ii) it is flexible enough to accommodate individual preferences of various stakeholders in how they assign importance to multiple manifestations of human impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A reforecasting-based dynamic reserve estimation for variable renewable generation and demand uncertainty

The installed capacity of renewables-based energy sources has been increasing in traditional power systems. In order to accommodate the increased variability and uncertainty associated with the deeper penetration of renewable sources like solar and wind, adjusted amounts of dynamic reserve are needed. Although probabilistic dynamic reserve estimation methods have been previously developed, most of them consider the uncertainty to be represented by parametric density functions that tend to perform poorly under extreme events and, moreover, neglect uncertainty introduced by the forecasting model itself. Toward addressing these limitations, this work presents, for the first time, a dynamic reserve estimation method for flexibility that incorporates nonparametric density estimation and a machine learning based reforecasting to provide a day-ahead prediction of the mean and spread of uncertainty around the base forecast. The prediction is, in turn, used to estimate the up and down reserve relative to the base forecast. Here, the present method takes various endogenous and exogenous features, including the calendar variables, as input to estimate the day-ahead reserve. Using a combination of reforecasting and dynamic reserve estimation techniques, the method is shown to adjust better to the dynamic nature of reserve requirements providing only what is needed to accommodate the expected deviations. Considering California Independent System Operator (CAISO) solar, wind and load data over an 18 month period, up to 67% reduction in the amount of reserve capacity needed for a one day reserve and reserve penalty for solar uncertainty is demonstrated. Additionally, the risk of reserve insufficiency in meeting the net demand is reduced by 20% with the proposed method.

14 SOLAR ENERGY↗
DOE OSTI2020

Mercury linked to Deccan Traps volcanism, climate change and the end-Cretaceous mass extinction

Mercury (Hg) anomalies linked to Large Igneous Provinces (LIP) volcanism have been identified in sediments across all five major mass extinctions in Earth's history. This study tests whether Hg in marine sediments is a reliable proxy linking Deccan Traps volcanic eruptions to late Maastrichtian global climate warming and the mass extinction at the Cretaceous-Paleogene boundary (KPB). Our primary test site is the Elles section in Tunisia, the auxiliary Global Stratotype Section and Point (GSSP) to El Kef. Elles has the most complete marine sedimentary record and a high average sedimentation rate of ~4.7 cm/ky. Here, we chose the Hor Hahar section in Israel to corroborate the geographic distribution of Hg fallout from Deccan volcanism. Reliability of the Hg proxy over the last 550 ky of the Maastrichtian to early Danian was evaluated based on high-resolution age control (orbital cyclostratigraphy), stable isotope climate record, Hg concentrations, biotic turnover and mass extinction. These results were correlated with the pulsed Deccan eruptive history constrained previously by U-Pb zircon geochronology. Our results support Hg as robust proxy for Deccan volcanism with large Hg spikes marking “extreme event” (EE) pulsed eruptions correlative with climate warming peaks separated by steady, less intense eruptions. Long-term global climate warming began near ~350 ky pre-KPB, reached maximum warming (3–4 °C) between 285 and 200 ky pre-KPB, followed by gradual cooling and rapid temperature drop between 45 and 25 ky pre-KPB. During the last 25 ky before the KPB, multiple Hg EE eruptions correlate with hyperthermal warming that culminated in the rapid mass extinction at Elles during ≤1000 years of the Cretaceous. These latest Cretaceous Hg peaks may correlate with massive, distal, Deccan-sourced lava flows (>1000 km long) that traversed the Indian subcontinent and flowed into the Bay of Bengal, bracketing the mass extinction. These results support Deccan volcanism as a primary driver of the end-Cretaceous mass extinction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Guest Editorial: Special issue on flexible and resilient urban energy systems

This guest editorial summarizes the topics and the papers selected for the Special Issue on Flexible and Resilient Urban Energy Systems. After rigorous reviewing process, 21 papers are accepted for publication. These 21 accepted papers cover various aspects of urban energy systems and are distributed as following: situational awareness of urban energy systems (2 papers), quantification metrics of flexibility and resilience of urban energy systems (3 papers), vulnerability modeling of urban energy systems under various extreme events (3 papers), planning of flexible and resilient urban energy systems (4 papers), robust and resilient operation and control of urban energy systems (4 papers), recovery and restoration strategy of urban energy systems (2 papers), and coordination and interoperability of interconnected energy systems (3 papers). The Guest Editorial Board hopes this Special Issue can provide a valuable information for future research and advancements in the field of flexible and resilient urban energy systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Statistical evaluation of microscale stress conditions leading to void nucleation in the weak shock regime

Here, we investigate the heterogeneity of the stress state driven by anisotropic deformation response at the single crystal level through five statistical volume element (SVE) calculations of polycrystalline BCC tantalum. This work focuses on grain boundaries as a prominent material defect type prone to void nucleation based upon experimental observations of predominantly intergranular void nucleation in this material. The SVEs are constructed to be statistically representative of larger volumes of material and are meshed such that mean and standard deviation of grain size and orientation information is reconstructed. The computational meshes feature hexahedral (brick) elements and smooth conformal grain boundaries where significant stress concentration is known to occur, a tail effect of interest in the extreme events process of dynamic ductile damage. An existing micromechanical crystallographic plasticity model shown to capture the single crystal behavior of BCC tantalum well is used to perform the polycrystal calculations. The model includes representation of the non-Schmid effect of non-planar screw dislocation kinetics in tantalum. A three-dimensional stress state time profile predicted by damage modeling of a flyer plate impact experiment is applied as boundary conditions to each SVE. Resulting grain boundary stress state statistics are strongly non-Gaussian. Significant structural evolution is observed within the compressive hold before unloading into tension in the stress profile. Strong angular dependence of grain boundary traction magnitude with shock direction is observed. Non-Schmid effects continue to suggest their influence on propensity of microstructural defect types to nucleate voids. A general void nucleation criterion is proposed using probability theory. The general framework is specified to polycrystalline BCC tantalum in the weak shock regime to include the SVE calculations and literature molecular dynamics calculations of grain boundary void nucleation strength. Probability density functions (PDFs) are used to describe the interaction between the local stress state heterogeneity and the distributed grain boundary void nucleation strength state. A causation entropy maximization procedure removes the requirement for ad hoc selection of a PDF functional form and provides a rigorous procedure for data-based PDF determination. The resulting physically informed PDF describes the spatial appearance frequency of nucleated voids as a function of applied macroscale pressure. Lower length scale physics are thus packaged in a precise and computationally efficient way to provide computational plasticity insight to macroscale dynamic ductile damage models.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment

This study presents a framework to quantify changes in soil saturation in response to flooding caused by extreme hydrologic perturbation on coastal ecosystems at the interfaces and transition between terrestrial and aquatic systems. Subsurface heterogeneity limits the use of in situ measurements to quantify subsurface flow during flooding due to the spatial discontinuity in the measured data. While geophysical methods, including time-lapse electrical resistivity imaging (ERI), are increasingly used to monitor soil hydrological processes, their abilities to parameterize flow models have been underutilized. This study combines background ERI, ground penetrating radar (GPR), time-lapse ERI, soil characterization, and a numerical flow model developed using an Advanced Terrestrial Simulator (ATS) code to quantify the infiltration pathway and describe the hydrological dynamics during a simulated flooding experiment. We assessed the use of two conceptual models developed using [1] ERI and GPR data that described the stratigraphic distribution, and time-lapse ERI that mapped permeability contrast, and [2] information from a national soil database for capturing changes in saturation. Combining the ERI and GPR results with soil core data revealed the stratigraphic heterogeneity at the site with a silty clay layer from 1 to 2 m between an overlying loamy topsoil and an underlying saturated silty sand. This silty clay layer could restrict deep infiltration. The time-lapse ERI showed up to a 35% decrease in resistivity, which correlated with soil moisture data (R 2 value > 0.53) and revealed preferential infiltration zones used to inform the flow model. Numerical simulation results from both the geophysics- and soil database-informed models quantified changes in soil saturation with calculated soil moistures that agreed with field data. The geophysics-informed model captured more of the system’s variability, reflective of shallow subsurface heterogeneities. The framework presented will serve as a precursor for a robust ecohydrological model that can describe the impacts of extreme events induced by climate change on coastal ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Building and grid system benefits of demand flexibility and energy efficiency

The global electricity system is undergoing a dramatic transformation. Technology advancements in clean energy generation (e.g., solar photovoltaics and wind) and electrified demand (e.g., transportation and building heating) are providing a cost-competitive pathway to mitigate the urgent and severe consequences associated with a changing climate. In addition, as extreme events continue to increase in frequency and magnitude, solutions are needed for adapting to the current climate. Through energy efficiency and demand flexibility, buildings play a prominent role in meeting these challenges; recently in Joule, a paper by Langevin et al. provides clear insight into the magnitude of buildings’ technical potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Validation of CFD simulations of the moored DeepCwind offshore wind semisubmersible in irregular waves

This article examines the use of computational fluid dynamics (CFD) simulations to predict the response of a floating wind platform to irregular-wave excitation. This work was conducted as part of the Reproducible CFD Joint Industry Project for Floating Offshore Wind Applications and involved verification across several participants and modeling tools and validation against experiments. The authors pay special attention to the uncertainties in both CFD results and experimental measurements. In this work, we perform detailed comparisons of the incident waves and the motion of a moored structure. The nonlinear, low-frequency resonance motion is of particular interest because it potentially drives the mooring and tower-base loads. The verification and validation study is partially successful in that the CFD simulations capture the low-frequency slow-drift motion well but underpredict the low-frequency pitch resonance. This underprediction can be attributed in part to the CFD incident waves, which showed some discrepancies with the experimental waves, especially around extreme events. The effects of the wave discrepancies are also estimated and investigated using a mid-fidelity OpenFAST model. Overall, the present study increases our confidence in using CFD simulations to predict the global performance of offshore wind platforms in irregular waves and to produce data for the calibration of lower-fidelity models.

17 WIND ENERGY↗
DOE OSTI2020

Analysis of Electric and Magnetic Lightning-Generated Wave Amplitudes Measured by the Van Allen Probes

Herein we provide a statistical analysis of both electric and magnetic field wave amplitudes of very low frequency lightning-generated waves (LGWs) based on the equivalent of 11.5 years of observations made by the Van Allen Probes encompassing ~24.6 × 10 6 survey mode measurements. We complement this analysis with data from the ground-based World Wide Lightning Location Network to explore differences between satellite and ground-based measurements. LGW mean amplitudes are found to be low compared with other whistler mode waves (1 ± 1.6 pT and 19 ± 59 μV/m). Extreme events (1/5,000) can reach 100 pT and contributes strongly to the mean power below $L$ = 2. We find excellent correlations between World Wide Lightning Location Network-based power and wave amplitudes in space at various longitudes. We reveal strong dayside ionospheric damping of the LGW electric field. LGW amplitudes drop for $L$ < 2, contrary to the Earth's intense equatorial lightning activity. We conclude that it is difficult for equatorial LGW to propagate and remain at $L$ < 2.

Electric wave power↗
DOE OSTI2022

The Data Synergy Effects of Time-Series Deep Learning Models in Hydrology

When fitting statistical models to variables in geoscientific disciplines such as hydrology, it is a customary practice to stratify a large domain into multiple regions (or regimes) and study each region separately. Traditional wisdom suggests that models built for each region separately will have higher performance because of homogeneity within each region. However, each stratified model has access to fewer and less diverse data points. Here, through two hydrologic examples (soil moisture and streamflow), we show that conventional wisdom may no longer hold in the era of big data and deep learning (DL). We systematically examined an effect we call data synergy, where the results of the DL models improved when data were pooled together from characteristically different regions. The performance of the DL models benefited from modest diversity in the training data compared to a homogeneous training set, even with similar data quantity. Moreover, allowing heterogeneous training data makes eligible much larger training datasets, which is an inherent advantage of DL. A large, diverse data set is advantageous in terms of representing extreme events and future scenarios, which has strong implications for climate change impact assessment. The results here suggest the research community should place greater emphasis on data sharing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Connecting Large‐Scale Meteorological Patterns to Extratropical Cyclones in CMIP6 Climate Models Using Self‐Organizing Maps

Abstract Extratropical cyclones (ETCs) are responsible for the majority of cool‐season extreme events in the northeastern United States (NEUS), often leading to high‐impact weather conditions that can have wide‐ranging socioeconomic impacts. Evaluating the ability of climate models to adequately simulate ETC dynamics is essential for improving model performance and increasing confidence in future projections used by stakeholders and policymakers. ETCs are traditionally studied using techniques such as case studies and synoptic typing, however, these approaches can be time‐consuming, require subjective analysis, and do not necessarily identify the coincident large‐scale meteorological patterns (LSMPs). Here, we apply self‐organizing maps (SOMs) as an automated machine‐learning approach to characterize the LSMPs and associated frequency and intensity of discrete ETC events over NEUS. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during the last four decades. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity (ACA) associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMPs and ETC events identified in the SOM. Our results identify a robust bias toward more zonal patterns, with models struggling to reproduce the more amplified patterns typically associated with the highest cyclone activity. While model resolution has some impact on simulation credibility, model configuration appears to be more important in LSMP representation. The vast majority of CMIP6 models produce too few ETCs, although model errors are distributed around historical reanalyses when ACA is normalized by storm frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Revisiting the Definition of Rapid Intensification of Tropical Cyclones by Clustering the Initial Intensity and Inner‐Core Size

Abstract Rapid intensification (RI) of tropical cyclones (TCs) provides a great challenge in operational forecasting and contributes significantly to the development of major TCs. RI is commonly defined as an increase in the maximum sustained surface wind speed of at least a certain threshold within 24 hr. The most widely used threshold is 30 kt (15.4 m/s), which was determined statistically. Here we propose a new definition for RI by objectively clustering TCs using the intensification rate, initial intensity, and radius of the maximum wind speed (RMW). A group of 770 samples is separated at a threshold of 45 kt (23.2 m/s). The threshold is 40 kt (20.6 m/s) for the western North Atlantic, where TC size measurements are more reliable. Monte Carlo experiments demonstrate that the proposed threshold is robust even considering the uncertainty in RMW of as high as 30 km. We show that, when a TC undergoes RI, its maximum wind speed is approximately 60 ± 15 kt (30.9 ± 7.7 m/s) and the RMW is 45 ± 20 km. The new threshold outperforms the conventional threshold of 30 kt/24 hr in describing the bimodal distribution of lifetime maximum intensity and explaining the annual count of Category 5 TCs. This new definition provides a more physically based threshold and describes a more reliable representation of extreme events. Although more comparisons are needed for operational application, it is likely to be desirable for case‐based process studies and could provide a more valuable metric for TC intensification classification and research.

Li, Yi↗