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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Method to Design and Optimize Axial Flow Cyclones for Gas–Liquid Separation

This article provides a detailed design guide, optimization, and performance assessment for air–water separation of an axial flow cyclone. Axial flow cyclones (also known as swirl tube demisters, mist eliminators, or Austin–Write cyclones) have a range of applications in several different industries. This method of gas–liquid separation offers many benefits. Among these are high liquid separation efficiencies (near 99%) and an inline design that allows them to be more easily fitted into existing piping structures. Despite these benefits, there are several design parameters that have not been optimized for performance in wastewater purification applications. This research fills the gap in the literature by quantifying the effect of new design parameters on water collection efficiency, η water collection , and the air bypass efficiency, η air bypass , defined as the ratio of the air mass flowrate exiting through the desired air outlet over the inlet air mass flowrate. A set of wide-ranging experiments were conducted to study the effects of gas–liquid flow rates, tube geometry, and relative injection angles to optimize the water collection and air bypass efficiencies. The water collection efficiency exceeded 99.8% when the liquid streamline came in direct contact with the water drainage exit. An empirical correlation was developed to predict the swirl pitch as a function of the above design parameters. Predictions from the correlation were within 10% of the experimental results. The correlation can be used to design highly efficient in-line gas–liquid separators.

Engineering↗
DOE OSTI2023

Measurements and Prediction of Ash Deposition in a Cyclone-Fired Boiler Operating under Variable Load Conditions

Measurements of ash deposition rates were made between the secondary superheater and reheater sections of a 450 MW cyclone-fired lignite boiler as the operational load varied from 33 to 100%. Significant reductions in deposition rates with a decrease in operational load were observed. To uncover the causative mechanisms behind these observations, operational data from the power plant were used to carry out computational fluid dynamic (CFD) simulations of the boiler. After ascertaining that the gas temperatures and velocities at various sections within the boiler were being represented adequately, decoupled simulations of the ash deposition process on the deposit probe were carried out using a finely resolved boundary layer mesh. Fly ash particle size distribution (PSD) and its concentration for the decoupled calculations were determined from stand-alone cyclone barrel simulations. The ash partitioning (mass %) between the fly ash and slag was found to be ~50:50, which was in line with previous field observations, and it did not vary significantly across different cyclone loads. The predicted PSD of the deposit ash was concentrated in the size range 10–30 microns, which was in agreement with cross-sectional images of the deposit obtained from the measurements. At lower loads, sharp variations in the deposition rates were predicted in the gas temperature range 950–1150 K. The particle kinetic energy—particle viscosity-based capture methodology utilized in this study in conjunction with appropriate ash compositions, ash viscosity models and gas temperature estimates can help estimate slagging propensities at different loads reasonably well in these systems.

Krishnamoorthy, Gautham (ORCID:0000000255205092)↗
DOE OSTI2021

Southwest Pacific tropical cyclone development classification utilizing machine learning and synoptic composites

This study evaluates the ability of machine learning algorithms to classify tropical depressions (TDs) and tropical storms (TSs) in the western region of the southwest Pacific Ocean (SWPO). Decision rules are generated to predict the environment required for a depression to fully develop into a mature storm, and the most influential predictors in the classification decision are ranked. TD and TS are discriminated based on a maximum sustained wind speed threshold (≥17 ms -1 ). Various aerosol, thermodynamic, and dynamic parameters are extracted closest to the initiation point of each non-developing and developing sample. The covariates associated with each labelled sample are used to train a decision tree and random forest model. Results using a testing dataset suggest the random forest approach more accurately distinguishes between non-developing and developing samples. The classification accuracy of the decision tree and random forest are 72% and 91%, respectively. Random forest outperformed the decision tree by providing higher accuracy in test data. The most important variables for binary classification are sea salt aerosol optical depth (AOD), 1,000 mb relative humidity, and sea surface temperature. AOD is a quantitative estimate of the aerosols presents in the air through the extinction of a ray of light as it passes through the atmosphere. Mean composite maps constructed in an unsupervised manner have been created for the most important variables identified by the random forest classifier during TD and TS events to highlight the difference in geophysical and aerosol variables' climatology during the two different classifications. This work will advance the risk management strategies for northeastern Australia and other SWPO basin islands to control their tropical cyclone related losses through prioritizing forecasting variables that are the strongest predictors of the strengthening of tropical depressions into tropical cyclones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Subseasonal Tropical Cyclone Prediction and Modulations by MJO and ENSO in CESM2

Subseasonal tropical cyclone (TC) reforecasts from the Community Earth System Model version 2 (CAM6) subseasonal prediction system are examined in this study. Here, we evaluate the modeled TC climatology and the probabilistic forecast skill of basin-wide TC genesis at weekly temporal resolution. Prediction skill is calculated using the Brier skill score relative to a constant annual mean climatology and to a monthly varying seasonal climatology during TC season. The model captures the observed basin-wide climatological TC seasonality and spatial distributions at weeks 1–6, but TC genesis is largely underestimated from Week 2 onward. For some basins and lead times, the predicted TC genesis is primarily controlled by the number of TC “seeds” and the mean-state climate condition. The model has good prediction skill relative to the constant climatology across all the basins and lead times, but is only skillful in the eastern Pacific, North Indian Ocean, and Southern Hemisphere at Week 1 when compared to the seasonal climatology, indicating limited skill in predicting deviations from the seasonal cycle. We find strong modulations of the predicted TC genesis at up to 3 weeks of forecast lead time by the Madden-Julian Oscillation. The interannual variability of predicted TC genesis and accumulated cyclone energy are skillfully predicted in the North Atlantic and the Northwestern Pacific, with a strong modulation by the El Nino-Southern Oscillation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Can socio-economic indicators of vulnerability help predict spatial variations in the duration and severity of power outages due to tropical cyclones?

Abstract Tropical cyclones are the leading cause of major power outages in the U.S., and their effects can be devastating for communities. However, few studies have holistically examined the degree to which socio-economic variables can explain spatial variations in disruptions and reveal potential inequities thereof. Here, we apply machine learning techniques to analyze 20 tropical cyclones and predict county-level outage duration and percentage of customers losing power using a comprehensive set of weather, environmental, and socio-economic factors. Our models are able to accurately predict these outage response variables, but after controlling for the effects of weather conditions and environmental factors in the models, we find the effects of socio-economic variables to be largely immaterial. However, county-level data could be overlooking effects of socio-economic disparities taking place at more granular spatial scales, and we must remain aware of the fact that when faced with similar outage events, socio-economically vulnerable communities will still find it more difficult to cope with disruptions compared to less vulnerable ones.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Metrics for evaluating tropical cyclones in climate data

This manuscript describes a software suite that can be used for objective evaluation of tropical cyclones (TCs) in gridded climate data. Using cyclone trajectories derived from 6-hourly data, a comprehensive set of metrics is defined to systematically compare and contrast products to one another. In addition to annual TC climatologies, attention is paid to spatial and temporal patterns of storm occurrence and intensity. Assessment can be performed either on the global scale or regional domains. Simple to visualize ‘scorecards’ allow for rapid credibility assessment. We showcase three key findings enabled by this suite. First, we compare the representation of TCs in seven current-generation global reanalyses and conclude that higher resolution models and those with TC-specific assimilation contain more accurate storm climatologies. Second, using a free-running Earth system model (ESM) we find that full basin refinement is required in variable-resolution configurations to adequately simulate North Atlantic TC frequency. Furthermore, upstream refinement over northern Africa offers little benefit in simulating storm occurrence but spatial genesis patterns are improved. Finally, we show that TCs simulated by ESMs can be highly sensitive to individual parameterizations in climate models, with North Atlantic TC metrics varying greatly depending on version of the Morrison-Gettelman microphysics package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Stronger Tropical Cyclone–Induced Ocean Cooling in Near-Coastal Regions Compared to the Open Ocean

Abstract Tropical cyclones (TC) often induce strong mixing in the upper ocean that generates a trail of cooler sea surface temperature (Twake) in their wakes. The Twake can affect TC intensity, so its prediction is important, especially in coastal regions where TCs can make landfall. Coastal Twakes are often more complex than those in the open ocean due to the influences of coastline geometry, highly variable water depth, continental runoff, and shelf processes. Using observational data since 2002, here we show a significantly stronger global mean Twake in coastal regions compared to offshore regions. Temperature stratification is the main driver of stronger coastal Twakes in the North Atlantic and east Pacific. In the northwest Pacific and north Indian Ocean, the differences between coastal and offshore Twakes are smaller due to compensation between TC forcings and ocean stratification. The north Indian Ocean is unique in the Northern Hemisphere because salinity stratification plays a major role on the spatial distribution of Twake. In the South Pacific Ocean, TC intensity and translation speed are crucial for explaining coastal–offshore Twake differences, while ocean stratification and mixed layer depth are more important for the coastal–offshore Twake differences in the south Indian Ocean. These findings suggest that coastal–offshore differences in ocean stratification need to be properly represented in models in order to capture changes in TC-induced ocean cooling as storms approach landfall. Significance Statement Landfalling tropical cyclones (TCs) often cause considerable damage in coastal regions with dense human populations. Understanding TC–ocean interaction and how it differs between coastal and offshore regions can help predict TC intensity prior to landfall. Sea surface cooling after TC passage is an important proxy for TC–ocean interaction. A global evaluation of coastal TC-induced cooling has not been conducted. Using data covering two decades, we show significantly stronger TC-induced surface cooling in coastal regions compared to offshore regions at the global scale and in all basins except the northwest Pacific and north Indian Ocean. The difference is driven mainly by upper-ocean conditions in the North Atlantic, east Pacific, and south Indian Ocean, and by TC characteristics in the South Pacific.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Extratropical Pathway for the Madden–Julian Oscillation’s Influence on North Atlantic Tropical Cyclones

Abstract This study investigates the combined impacts of the Madden–Julian oscillation (MJO) and extratropical anticyclonic Rossby wave breaking (AWB) on subseasonal Atlantic tropical cyclone (TC) activity and their physical connections. Our results show that during MJO phases 2–3 (enhanced Indian Ocean convection) and 6–7 (enhanced tropical Pacific convection), there are significant changes in basinwide TC activity. The MJO and AWB collaborate to suppress basinwide TC activity during phases 6–7 but not during phases 2–3. During phases 6–7, when AWB occurs, various TC metrics including hurricanes, accumulated cyclone energy, and rapid intensification probability decrease by ∼50%–80%. Simultaneously, large-scale environmental variables, like vertical wind shear, precipitable water, and sea surface temperatures become extremely unfavorable for TC formation and intensification, compared to periods characterized by suppressed AWB activity during the same MJO phases. Further investigation reveals that AWB events during phases 6–7 occur in concert with the development of a stronger anticyclone in the lower troposphere, which transports more dry, stable extratropical air equatorward, and drives enhanced tropical SST cooling. As a result, individual AWB events in phases 6–7 can disturb the development of surrounding TCs to a greater extent than their phases 2–3 counterparts. The influence of the MJO on AWB over the western subtropical Atlantic can be attributed to the modulation of the convectively forced Rossby wave source over the tropical eastern Pacific. A significant number of Rossby waves initiating from this region during phases 5–6 propagate into the subtropical North Atlantic, preceding the occurrence of AWB events in phases 6–7.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Future North Atlantic tropical cyclone intensities in thermodynamically modified historical environments

Tropical cyclones (TCs) rank as the deadliest and most financially crippling natural disasters in the United States for the last half-century. It is imperative to assess potential shifts in TC intensity within the paradigm of an evolving climate. In this study, we have modeled the intensities of 620 historical TC events in the North Atlantic Basin using the Risk Analysis Framework for Tropical Cyclones (RAFT)'s deep learning intensity model. By applying a thermodynamic warming signal extrapolated from Global Climate Models, we rerun historical events under eight different future climate scenarios, providing a spectrum of potential TC intensity outcomes. One of the future simulations indicates a staggering 43% increase in the number of major hurricanes, underscoring the critical impact of climate change on TC intensity. Additionally, an interactive dashboard has been created to enable users to explore individual storm simulations and understand the influence of future climate signals on environmental conditions of TC development and resulting TC intensities. This dataset and the user-friendly tool offer invaluable resources for systematic exploration of the discrete effects that changes in the air-sea thermodynamic state have on the intensities of TCs.

Climate Change↗
DOE OSTI2025

Future North Atlantic tropical cyclone intensities in thermodynamically modified historical environments

Tropical cyclones (TCs) have ranked as the deadliest and most financially crippling natural disasters in the United States. It is imperative to assess potential shifts in TC intensity within the paradigm of an evolving climate. In this study we apply a fixed-constraint storyline approach that holds storm tracks and initial conditions constant to probe future TC intensity in the North Atlantic Basin. First, we simulate 618 historical TC events using the Risk Analysis Framework for Tropical Cyclones (RAFT)'s deep-learning intensity model. Next, we apply warming signals derived from eight CMIP6 climate scenarios and rerun each event to explore how intensities respond across scenarios. Finally, we develop an interactive dashboard that allows users to explore individual storm simulations and the scenario-modified environmental drivers. Together, this dataset and tool provide a clear, illustrative way to investigate how TC intensity responds to changes in air-sea state.

Climate Change↗
DOE OSTI2021

Tropical Cyclone Frequency

Abstract The frequency with which tropical cyclones (TCs) occur controls all other aspects of tropical cyclone risk since a storm that does not occur can do no harm. Yet this frequency is poorly understood. There is no accepted theory that explains the average number of TCs that occur each year on the Earth, nor how that number will change with global warming. Arguments based on global budgets of heat or moisture do not yet appear helpful, nor does a detailed understanding of the physical processes of TC genesis. Empirical indices that predict TC frequency as a function of large‐scale environmental variables can explain some of its relative variations in space and time, but not its absolute value. Global numerical models with horizontal grid spacings on the order of 25–50 km have allowed much improved simulations of TC activity, however. Many such models project a decrease in frequency with warming, but some project an increase. Idealized simulations, including those at higher resolutions, offer promise by allowing a systematic, deductive investigation of the roles of individual environmental factors. In addition to the larger‐scale environmental modulation of genesis likelihood, precursor disturbances, or “seeds”, may exert an independent influence on TC frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Trends in Global Tropical Cyclone Activity: 1990–2021

Abstract This study investigates global tropical cyclone (TC) activity trends from 1990 to 2021, a period marked by largely consistent observational platforms. Several global TC metrics have decreased during this period, with significant decreases in hurricane numbers and Accumulated Cyclone Energy (ACE). Most of this decrease has been driven by significant downward trends in the western North Pacific. Globally, short‐lived named storms, 24‐hr intensification events of ≥50 kt day −1 , and TC‐related damage have increased significantly. The increase in short‐lived named storms is likely due to technological improvements, while rapidly intensifying TC increases may be fueled by higher potential intensity. Damage increases are largely due to increased coastal assets. The significant decrease in hurricane numbers and global ACE are likely due to the trend toward a more La Niña‐like base state from 1990 to 2021, favoring North Atlantic TC activity and suppressing North and South Pacific TC activity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Tropical Oceanic Influences on Observed Global Tropical Cyclone Frequency

Abstract The global tropical cyclone (TC) number has historically been relatively constant from year‐to‐year, however, the reason remains unknown. Furthermore, climate projections are inconclusive regarding future global TC frequency changes. Here, we investigated years in which observed global TC activity deviated from the mean and potential links to ocean drivers from 1980 to 2021. We found that the annual global number of named storm days and accumulated cyclone energy (ACE) were significantly linked with El Niño–Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM). La Niña and positive AMM are associated with the bottom percentiles of both TC metrics, and vice versa for El Niño and negative AMM. The ENSO Longitude Index explains variability in annual global named storm days and ACE as well as the Niño 3.4 index. This research reveals that reliable future projections of ENSO are necessary, but not sufficient, to understand future changes in global TC frequency.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Isopycnal Submesoscale Stirring Crucially Sustaining Subsurface Chlorophyll Maximum in Ocean Cyclonic Eddies

Abstract Mesoscale and submesoscale processes have crucial impacts on ocean biogeochemistry, importantly enhancing the primary production in nutrient‐deficient ocean regions. Yet, the intricate biophysical interplay still holds mysteries. Using targeted high‐resolution in situ observations in the South China Sea, we reveal that isopycnal submesoscale stirring serves as the primary driver of vertical nutrient transport to sustain the dome‐shaped subsurface chlorophyll maximum (SCM) within a long‐lived cyclonic mesoscale eddy. Density surface doming at the eddy core increased light exposure for phytoplankton production, while along‐isopycnal submesoscale stirring disrupted the mesoscale coherence and drove significant vertical exchange of tracers. These physical processes play a crucial role in maintaining the elevated phytoplankton biomass in the eddy core. Our findings shed light on the universal mechanism of how mesoscale and submesoscale coupling enhances primary production in ocean cyclonic eddies, highlighting the pivotal role of submesoscale stirring in structuring marine ecosystems.

Cao, Haijin↗
DOE OSTI2024

A North Atlantic synthetic tropical cyclone track, intensity, and rainfall dataset

Tropical Cyclones (TCs) cause significant socio-economic damages to the US and Caribbean coastal regions annually, making it important to understand TC risk at the local-to-regional scales. However, the short length of the observed record and the substantial computational expense associated with high-resolution climate models make it difficult to assess TC risk using either approach. To overcome these challenges, we developed a database of synthetic TCs using the Risk Analysis Framework for Tropical Cyclones (RAFT). The database includes 40,000 synthetic TC tracks, along-track intensities and storm-induced precipitation. TC tracks generated in RAFT are in reasonable agreement with the observed spatial distribution of TC tracks and basin-scale TC statistics. Specifically, along the coast, spatial variations in TC crossing probability and extreme winds upon landfall are well-reproduced by RAFT with R-squared values of 0.81 and 0.73, respectively. In summary, the synthetic TC database constructed with RAFT provides a reasonable pathway for the robust assessment of North Atlantic TC wind and rainfall risks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Near-term tropical cyclone risk and coupled Earth system model biases

Most current climate models predict that the equatorial Pacific will evolve under greenhouse gas–induced warming to a more El Niño-like state over the next several decades, with a reduced zonal sea surface temperature gradient and weakened atmospheric Walker circulation. Yet, observations over the last 50 y show the opposite trend, toward a more La Niña-like state. Recent research provides evidence that the discrepancy cannot be dismissed as due to internal variability but rather that the models are incorrectly simulating the equatorial Pacific response to greenhouse gas warming. This implies that projections of regional tropical cyclone activity may be incorrect as well, perhaps even in the direction of change, in ways that can be understood by analogy to historical El Niño and La Niña events: North Pacific tropical cyclone projections will be too active, North Atlantic ones not active enough, for example. Other perils, including severe convective storms and droughts, will also be projected erroneously. While it can be argued that these errors are transient, such that the models’ responses to greenhouse gases may be correct in equilibrium, the transient response is relevant for climate adaptation in the next several decades. Given the urgency of understanding regional patterns of climate risk in the near term, it would be desirable to develop projections that represent a broader range of possible future tropical Pacific warming scenarios—including some in which recent historical trends continue—even if such projections cannot currently be produced using existing coupled earth system models.

Science & Technology - Other Topics↗
DOE OSTI2022

Quantifying Heavy Precipitation throughout the Entire Tropical Cyclone Life Cycle

Abstract Tropical cyclones (TCs) and their associated precipitation can have devastating impacts on the areas affected, with outcomes ranging from mudslides to inland flash flooding. Previous studies have used a fixed radius around the TC to isolate storm-related precipitation. One previous study instead used a dynamic radius of 8 m s −1 winds, but the wind field of the TC can deteriorate or shift quickly after landfall or the onset of extratropical transition (ET). This study uses a dynamical radius derived from the 500-hPa geopotential height in and around the TC to define TC- and post-tropical cyclone (PTC)-related heavy precipitation, allowing for the analysis of precipitation with tropical origins after the official demise of the original TC. Climatologies are constructed, indicating a maximum in TC- and PTC-related heavy precipitation in the west North Pacific and a secondary maximum in the east North Pacific. PTC-related heavy precipitation accounts for as much as 40% of the annual heavy precipitation in the northwest portion of the west North Pacific basin and 3.13% of heavy precipitation globally. We observe that the major hurricane stage contributes on average 2.6% of the global TC- and PTC-related precipitation, while the less intense but more common tropical storm stages of the TC life cycle contribute 85.7% of this observed precipitation. This analysis framework can be further extended to assess model biases and climate projections of TC and PTC precipitation.

Meteorology & Atmospheric Sciences↗
DOE OSTI2023

A North Atlantic synthetic tropical cyclone tracks, intensity, and rainfall dataset

Tropical Cyclones (TCs) cause significant socio-economic damages to the US and Caribbean coastal regions annually, making it important to understand TC risk at the local-to-regional scales where their impacts are most prominent. However, the short length of the observed record and the substantial computational expense associated with high-resolution climate models make it difficult to assess TC risk using either approach. To overcome these challenges, we developed a database of synthetic TCs using the Risk Analysis Framework for Tropical Cyclones (RAFT). The database includes 50,000 synthetic TC tracks, along-track intensities and storm-induced precipitation. TC tracks generated in RAFT are in reasonable agreement with observations for spatial distribution of TC tracks and basin-scale distributions of TC translation speeds, lifetime maximum intensities and intensification rates. Also, spatial variations in coastal frequency and precipitation for landfalling TCs are well-reproduced in RAFT. In summary, the synthetic TC database based on RAFT provides a reasonable pathway for robust assessment of TC wind and rainfall risk for the US coastal regions and other areas affected by Atlantic TCs.

Xu, Wenwei↗