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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Deepti: Deep-Learning-Based Tropical Cyclone Intensity Estimation System

Tropical cyclones are one of the costliest natural disasters globally because of the wide range of associated hazards. Thus, an accurate diagnostic model for tropical cyclone intensity can save lives and property. There are a number of existing techniques and approaches that diagnose tropical cyclone wind speed using satellite data at a given time with varying success. This paper presents a deep learning-based objective, diagnostic estimate of tropical cyclone intensity from infrared satellite imagery with 13.24 kt Root Mean Squared Error (RMSE). In addition, a visualization portal in a production system is presented that displays deep learning output and contextual information for end users, one of the first of its kind.

Manil Maskey↗
DOE OSTI2021

Deep Learning Experiments for Tropical Cyclone Intensity Forecasts

Reducing tropical cyclone (TC) intensity forecast errors is a challenging task that has interested the operational forecasting and research community for decades. To address this, we developed a deep learning (DL)-based multilayer perceptron (MLP) TC intensity prediction model. The model was trained using the global Statistical Hurricane Intensity Prediction Scheme (SHIPS) predictors to forecast the change in TC maximum wind speed for the Atlantic basin. In the first experiment, a 24-h forecast period was considered. To overcome sample size limitations, we adopted a leave one year out (LOYO) testing scheme, where a model is trained using data from all years except one and then evaluated on the year that is left out. When tested on 2010–18 operational data using the LOYO scheme, the MLP outperformed other statistical–dynamical models by 9%–20%. Additional independent tests in 2019 and 2020 were conducted to simulate real-time operational forecasts, where the MLP model again outperformed the statistical–dynamical models by 5%–22% and achieved comparable results as HWFI. The MLP model also correctly predicted more rapid intensification events than all the four operational TC intensity models compared. In the second experiment, we developed a lightweight MLP for 6-h intensity predictions. When coupled with a synthetic TC track model, the lightweight MLP generated realistic TC intensity distribution in the Atlantic basin. Therefore, the MLP-based approach has the potential to improve operational TC intensity forecasts, and will also be a viable option for generating synthetic TCs for climate studies.

58 GEOSCIENCES↗
NASA NTRS2016

Human Influence on Tropical Cyclone Intensity

Recent assessments agree that tropical cyclone intensity should increase as the climate warms. Less agreement exists on the detection of recent historical trends in tropical cyclone intensity.We interpret future and recent historical trends by using the theory of potential intensity, which predicts the maximum intensity achievable by a tropical cyclone in a given local environment. Although greenhouse gas-driven warming increases potential intensity, climate model simulations suggest that aerosol cooling has largely canceled that effect over the historical record. Large natural variability complicates analysis of trends, as do poleward shifts in the latitude of maximum intensity. In the absence of strong reductions in greenhouse gas emissions, future greenhouse gas forcing of potential intensity will increasingly dominate over aerosol forcing, leading to substantially larger increases in tropical cyclone intensities.

Human influence on climate↗
NASA NTRS2018

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning algorithm requiring a large number of training data. Since the archives of intensity data and tropical cyclone centric satellite images is openly available for use, the training data is easily created by combining the two. Results, case studies, prototypes, and advantages of this approach will be discussed.

tropical cyclone intensity↗
NASA NTRS2006

Changes in Tropical Cyclone Intensity Over the Past 30 Years: A Global and Dynamic Perspective

The hurricane season of 2005 was the busiest on record and Hurricane Katrina (2005) is believed to be the costliest hurricane in U. S. history. There are growing concerns regarding whether this increased tropical cyclone activity is a result of global warming, as suggested by Emanuel(2005) and Webster et al. (2005), or just a natural oscillation (Goldenberg et al. 2001). This study examines the changes in tropical cyclone intensity to see what were really responsible for the changes in tropical cyclone activity over the past 30 years. Since the tropical sea surface temperature (SST) warming also leads to the response of atmospheric circulation, which is not solely determined by the local SST warming, this study suggests that it is better to take the tropical cyclone activities in the North Atlantic (NA), western North Pacific (WNP) and eastern North Pacific (ENP) basins as a whole when searching for the influence of the global-scale SST warming on tropical cyclone intensity. Over the past 30 years, as the tropical SST increased by about 0.5 C, the linear trends indicate 6%, 16% and 15% increases in the overall average intensity and lifetime and the annual frequency. Our analysis shows that the increased annual destructiveness of tropical cyclones reported by Emanuel(2005) resulted mainly from the increases in the average lifetime and annual frequency in the NA basin and from the increases in the average intensity and lifetime in the WNP basin, while the annual destructiveness in the ENP basin generally decreased over the past 30 years. The changes in the proportion of intense tropical cyclones reported by Webster et a1 (2005) were due mainly to the fact that increasing tropical cyclones took the tracks that favor for the development of intense tropical cyclones in the NA and WNP basins over the past 30 years. The dynamic influence associated with the tropical SST warming can lead to the impact of global warming on tropical cyclone intensity that may be very different from our current assessments, which were mainly based on the thermodynamic theory of tropical cyclone intensity.

Wu, Liguang↗
NASA NTRS1981

Tropical cyclone intensity change. A quantitative forecasting scheme

One to two day future tropical cyclone intensity change from both a composite and an individual case point-of-view are discussed. Tropical cyclones occurring in the Gulf of Mexico during the period 1957-1977 form the primary data source. Weather charts of the NW Atlantic were initially examined, but few differences were found between intensifying and non-intensifying cyclones. A rawinsonde composite analysis detected composite differences in the 200 mb height fields, the 850 mb temperature fields, the 200 mb zonal wind and the vertical shears of the zonal wind. The individual cyclones which make up the composite study were then separately examined using this composite case knowledge. Similar parameter differences were found in a majority of individual cases. A cyclone intensity change forecast scheme was tested against independent storm cases. Correct predictions of intensification or non-intensification could be made approximately 75% of the time.

Dropco, K. M.↗
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↗
NASA NTRS1989

Upper-tropospheric influences on Western Pacific tropical cyclone intensity change as inferred from satellite ozone observations

The use of Nimbus-7 TOMS data to diagnose changes in tropical cyclone intensity is demonstrated. The relationship between upper-tropospheric environmental total ozone distribution and intensity for a tropical cyclone case study is examined. The climatological total ozone patterns are used to identify the upper-tropospheric features to differentiate between intensifying and nonintensifying Western Pacific tropical cyclones.

Rodgers, Edward B.↗
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↗
NASA NTRS2018

Deepti: Deep Learning-Based Tropical Cyclone Intensity Estimation

We present the development of a deep learning model for objective estimation of tropical cyclone intensity at a higher temporal frequency, deployment of the model in production, design and implementation of the tropical cyclone monitoring and intensity estimation system and development of an interactive portal for situational awareness and evaluation of intensity estimation.

Maskey, Manil↗
NASA NTRSDate not supplied

Objectively Identifying Transverse Cirrus Bands in Tropical Cyclones using a Convolutional Neural Network

Transverse cirrus bands (TCBs) are bands of upper-level clouds regularly seen in mesoscale and synoptic-scale weather systems. In tropical cyclones, their appearance has been subjectively linked to intensification and the diurnal cycle. However, these hypothesized relationships have not been rigorously tested due to the subjective nature of TCBs in satellite images. A machine learning technique that successfully identifies TCBs objectively in imagery from the GOES-16 Advanced Baseline Imager (ABI) has been developed to solve this problem. The technique uses a U-Net convolutional neural network (CNN) that assigns a probability to each pixel in an image based on the likelihood of the pixel being associated with a TCB. Using the U-Net CNN, a database of TCBs from 2019 to 2022 was developed for the Atlantic tropical cyclone basin by defining an appropriate probability threshold that defines the difference between TCB and non-TCB pixels. This threshold is where the Jaccard score, calculated using manually identified TCBs and model identified TCBs, is maximized. Statistics for TCB occurrence will also be presented, including the relationships between TCBs and storm relative motion, shear relative direction, cardinal direction, tropical cyclone intensity, tropical cyclone intensification rates, and time of day.

John Mark Mayhall↗
DOE OSTI2019

Prediction of Tropical Cyclone Intensity Change using Advance Machine Learning Techniques

A tropical cyclone (TC) is a rapidly rotating storm system characterized by a lowpressure center, a closed low-level atmospheric circulation, strong winds, and a spiral arrangement of thunderstorms that produce heavy rain or squalls. TC occurs when the sea temperatures are high, and the air above the warm ocean is heated and rises quickly, causing an area of very low pressure, and then drawing more warm moist air, leading to strong winds. The air cools and condenses when rising, so that cumulonimbus clouds formed and produce heavy rainfall. These high winds, excessive rainfall and flooding threaten lives and property.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Analyzing Relationships between Tropical Cyclone Intensity and Rain Rate over the Ocean Using Numerical Simulations

In this study, the relationship between tropical cyclone (TC) intensity and rain rate over the ocean is investigated using a full-physics numerical model (WRF) and a physics-based TC rainfall model (TCR). TC intensity is found to be nearly linearly correlated with the average rain rate in the inner core [~0.97 (mm h –1 m –2 )/(m s –1 )], while the correlation is weak at outer radii. This difference is induced because TC intensity is significantly correlated with both the vertical velocity and specific humidity in the inner core but is not significantly correlated with the vertical velocity in the outer radii. Further investigation shows that the intensity–rain-rate relationship at the outer radii is influenced by the TC evolution stage. The rain rate for the outer radii is positively correlated with TC intensity for nondecaying TCs, while this correlation is reduced for decaying TCs due to systematic downdrafts in the outer radii. In the context of climate change, the sensitivity of the TC rain rate to sea surface temperature (SST) is found to be +9% per 1 K increase of SST, roughly the product of the sensitivity of TC intensity to SST (+3%) and the Clausius-Clapeyron scaling (+7%). Coupled with synthetic storms, evolution of the TC rain rate over the twenty-first century under the SSP5-8.5 scenario is projected by the TCR (calibrated with the WRF simulations). The annual increase rates of averaged TC rain rate are 0.17% and 0.20% for the inner core and outer radii, respectively, larger than the annual increase rate of TC intensity (0.046%) but comparable to that of cube of intensity (0.18%).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1981

Monitoring tropical cyclone intensity using wind fields derived from short-interval satellite images

Rapid scan visible images from the Visible Infrared Spin Scan Radiometer sensor on board SMS-2 and GOES-1 were used to derive high resolution upper and lower tropospheric environmental wind fields around three western Atlantic tropical cyclones (1975-78). These wind fields were used to derive upper and lower tropospheric areal mean relative vorticity and their differences, the net relative angular momentum balance and upper tropospheric mass outflow. These kinematic parameters were shown by studies using composite rawinsonde data to be strongly related to tropical cyclone formation and intensity changes. Also, the role of forced synoptic scale subsidence in tropical cyclone formation was examined. The studies showed that satellite-derived lower and upper tropospheric wind fields can be used to monitor and possibly predict tropical cyclone formation and intensity changes. These kinematic analyses showed that future changes in tropical cyclone intensity are mainly related to the "spin-up" of the storms by the net horizontal transport of relative angular momentum caused by convergence of cyclonic vorticity in the lower troposphere and to a lesser extent the divergence of anticyclone vorticity in the upper troposphere.

Rodgers, E. B.↗
DOE OSTI2024

Poleward Migration of the Latitude of Maximum Tropical Cyclone Intensity—Forced or Natural?

Abstract Past studies have shown a significant observed poleward trend in the latitude at which tropical cyclones reach their lifetime maximum intensity (LMI), especially in the northwest Pacific basin. Given the brevity of the historical record, it remains difficult to separate the forced trend from internal variability of the climate system. A recently developed tropical cyclone downscaling model is used to downscale the Community Earth System Model, version 2 (CESM2), preindustrial control simulation. It is found that the observed trend in the latitude at which tropical cyclones reach their LMI in the northwest Pacific is very unlikely to be caused by internal variability. The same downscaling model is then used to downscale CESM2 simulations under historical forcing. The resulting trend distribution shows a significant poleward migration of tropical cyclone LMI even after regressing out both natural variability and the part of the forced warming pattern that projects onto natural variability. The results indicate that the observed poleward migration of the latitude at which tropical cyclones reach their LMI in the northwest Pacific basin is likely to be, at least in part, forced. However, the magnitude of the projected poleward trend in climate models can be significantly modulated by the simulated spatial pattern of ocean warming. This highlights how discrepancies between models and observations, with regard to projected changes to the equatorial zonal sea surface temperature gradient under anthropogenic forcing, can lead to large uncertainties in projected changes to the LMI latitude of tropical cyclones. Significance Statement Observations in the northwest Pacific basin show that the latitude at which tropical cyclones are at their most intense has been trending northward in the recent half century. These changes are important since tropical cyclones could bring hazardous weather to coastal areas that are poorly equipped to handle them. Here, we show that natural variations in Earth’s climate are very unlikely to explain the observed poleward trend in the latitude that tropical cyclone reach their maximum intensity. We find that it is much more likely that the observed trend is forced by human-related emissions, though the spatial pattern of warming in response to greenhouse emissions can have significant impacts on the magnitude of the trend.

Lin, Jonathan↗
NASA NTRS1985

Global view of the upper level outflow patterns associated with tropical cyclone intensity changes during FGGE

The characteristics of the upper tropospheric outflow patterns which occur with tropical cyclone intensification and weakening over all of the global tropical cyclone basins during the year long period of the First GARP Global Experiment (FGGE) are discussed. By intensification is meant the change in the tropical cyclone's maximum wind or central pressure, not the change of the cyclone's outer 1 to 3 deg radius mean wind which we classify as cyclone strength. All the 80 tropical cyclones which existed during the FGGE year are studied. Two-hundred mb wind fields are derived from the analysis of the European Center for Medium Range Weather Forecasting (ECMWF) which makes extensive use of upper tropospheric satellite and aircraft winds. Corresponding satellite cloud pictures from the polar orbiting U.S. Defense Meteorological Satellite Program (DMSP) and other supplementary polar and geostationary satellite data are also used.

Chen, L.↗