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- Author or Editor: Anthony Wimmers x
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Abstract
This paper introduces a method of image filtering for viewing gravity waves in satellite imagery, which is particularly timely to the advent of the next-generation Advanced Himawari Imager (AHI) and the Advanced Baseline Imager (ABI). Applying a “high pass” filter to the upper-troposphere water vapor channel reveals sub-Kelvin-degree variations in brightness temperature that depict an abundance of gravity wave activity at the AHI/ABI sensitivity. Three examples demonstrate that this high-pass product can be exploited in a forecasting setting to identify possible varieties of turbulence-prone gravity waves that either 1) move roughly orthogonally to the apparent background flow or 2) produce interference as separate wave packets pass through the same location.
Abstract
This paper introduces a method of image filtering for viewing gravity waves in satellite imagery, which is particularly timely to the advent of the next-generation Advanced Himawari Imager (AHI) and the Advanced Baseline Imager (ABI). Applying a “high pass” filter to the upper-troposphere water vapor channel reveals sub-Kelvin-degree variations in brightness temperature that depict an abundance of gravity wave activity at the AHI/ABI sensitivity. Three examples demonstrate that this high-pass product can be exploited in a forecasting setting to identify possible varieties of turbulence-prone gravity waves that either 1) move roughly orthogonally to the apparent background flow or 2) produce interference as separate wave packets pass through the same location.
Abstract
This study develops a probabilistic model based on a convolutional neural network to predict rapid intensification (RI) in both North Atlantic and eastern North Pacific tropical cyclones (TCs). Coined “I-RI,” an advantage of using a convolutional neural network to predict RI is that it is designed to learn from spatial fields, like two-dimensional satellite imagery, as well as scalar features. The resulting model RI probability output is validated against two operational RI guidances—an empirical and a deterministic method—to assess skill at predicting RI over 12-, 24-, 36-, 48-, and 72-h lead times. Results indicate that in North Atlantic TCs, AI-RI is more skillful at predicting RI over 12- and 24-h lead times compared to both operational RI guidances. In eastern North Pacific TCs, AI-RI is more skillful than the empirical operational RI guidance at most RI thresholds, but less skillful than the deterministic RI guidance at all thresholds. For TCs north of 15°N, where the deterministic skill was lower, AI-RI was more skillful than the deterministic operational guidance for over half of the RI thresholds. It is also found that AI-RI struggles to reach the higher RI probabilities produced by both of the operational RI guidances in both basins. This work demonstrates that the two-dimensional structures within the satellite imagery of TCs and the evolution of these structures identified using the difference in satellite images, captured by a convolutional neural network, yield better 12–24-h indicators of RI than existing scalar assessments of satellite brightness temperature.
Significance Statement
The purpose of this study is to develop a method to predict tropical cyclone rapid intensification using artificial intelligence. The developed model uses a convolutional neural network, which can identify features in satellite imagery that are indicative of rapid intensification. The results suggest that, compared with current operational rapid intensification models, a convolutional neural network approach is generally more skillful at predicting rapid intensification.
Abstract
This study develops a probabilistic model based on a convolutional neural network to predict rapid intensification (RI) in both North Atlantic and eastern North Pacific tropical cyclones (TCs). Coined “I-RI,” an advantage of using a convolutional neural network to predict RI is that it is designed to learn from spatial fields, like two-dimensional satellite imagery, as well as scalar features. The resulting model RI probability output is validated against two operational RI guidances—an empirical and a deterministic method—to assess skill at predicting RI over 12-, 24-, 36-, 48-, and 72-h lead times. Results indicate that in North Atlantic TCs, AI-RI is more skillful at predicting RI over 12- and 24-h lead times compared to both operational RI guidances. In eastern North Pacific TCs, AI-RI is more skillful than the empirical operational RI guidance at most RI thresholds, but less skillful than the deterministic RI guidance at all thresholds. For TCs north of 15°N, where the deterministic skill was lower, AI-RI was more skillful than the deterministic operational guidance for over half of the RI thresholds. It is also found that AI-RI struggles to reach the higher RI probabilities produced by both of the operational RI guidances in both basins. This work demonstrates that the two-dimensional structures within the satellite imagery of TCs and the evolution of these structures identified using the difference in satellite images, captured by a convolutional neural network, yield better 12–24-h indicators of RI than existing scalar assessments of satellite brightness temperature.
Significance Statement
The purpose of this study is to develop a method to predict tropical cyclone rapid intensification using artificial intelligence. The developed model uses a convolutional neural network, which can identify features in satellite imagery that are indicative of rapid intensification. The results suggest that, compared with current operational rapid intensification models, a convolutional neural network approach is generally more skillful at predicting rapid intensification.
Abstract
Several simple and computationally inexpensive machine learning models are explored that can use advanced Dvorak technique (ADT)-retrieved features of tropical cyclones (TCs) from satellite imagery to provide improved maximum sustained surface wind speed (MSW) estimates. ADT (version 9.0) TC analysis parameters and operational TC forecast center best track datasets from 2005 to 2016 are used to train and validate the various models over all TC basins globally and select the best among them. Two independent test sets of TC cases from 2017 to 2018 are used to evaluate the intensity estimates produced by the final selected model called the “artificial intelligence (AI)” enhanced advanced Dvorak technique (AiDT). The 2017–18 MSW results demonstrate a global RMSE of 7.7 and 8.2 kt (1 kt ≈ 0.51 m s−1), respectively. Basin-specific MSW RMSEs of 8.4, 6.8, 7.3, 8.0, and 7.5 kt were obtained with the 2017 dataset in the North Atlantic, east/central Pacific, northwest Pacific, South Pacific/south Indian, and north Indian Ocean basins, respectively, with MSW RMSE values of 8.9, 6.7, 7.1, 10.4, and 7.7 obtained with the 2018 dataset. These represent a 30% and 23% improvement over the corresponding ADT RMSE for the 2017–18 datasets, respectively, with the AiDT error reduction significant to 99% in both sets. The AiDT model represents a notable improvement over the ADT performance and also compares favorably to more computationally expensive and complex machine learning models that interrogate satellite images directly while still preserving the operational familiarity of the ADT.
Abstract
Several simple and computationally inexpensive machine learning models are explored that can use advanced Dvorak technique (ADT)-retrieved features of tropical cyclones (TCs) from satellite imagery to provide improved maximum sustained surface wind speed (MSW) estimates. ADT (version 9.0) TC analysis parameters and operational TC forecast center best track datasets from 2005 to 2016 are used to train and validate the various models over all TC basins globally and select the best among them. Two independent test sets of TC cases from 2017 to 2018 are used to evaluate the intensity estimates produced by the final selected model called the “artificial intelligence (AI)” enhanced advanced Dvorak technique (AiDT). The 2017–18 MSW results demonstrate a global RMSE of 7.7 and 8.2 kt (1 kt ≈ 0.51 m s−1), respectively. Basin-specific MSW RMSEs of 8.4, 6.8, 7.3, 8.0, and 7.5 kt were obtained with the 2017 dataset in the North Atlantic, east/central Pacific, northwest Pacific, South Pacific/south Indian, and north Indian Ocean basins, respectively, with MSW RMSE values of 8.9, 6.7, 7.1, 10.4, and 7.7 obtained with the 2018 dataset. These represent a 30% and 23% improvement over the corresponding ADT RMSE for the 2017–18 datasets, respectively, with the AiDT error reduction significant to 99% in both sets. The AiDT model represents a notable improvement over the ADT performance and also compares favorably to more computationally expensive and complex machine learning models that interrogate satellite images directly while still preserving the operational familiarity of the ADT.
Abstract
This study details a two-method, machine-learning approach to predict current and short-term intensity change in global tropical cyclones (TCs), ‘D-MINT’ and ‘D-PRINT’. D-MINT and D-PRINT use infrared imagery and environmental scalar predictors, while D-MINT also employs microwave imagery.
Results show that current TC intensity estimates from D-MINT and D-PRINT are more skillful than three established intensity estimation methods routinely used by operational forecasters for North Atlantic, and eastern and western North Pacific TCs.
Short-term intensity predictions are validated against five operational deterministic guidances at 6-, 12-, 18-, and 24-hour lead times. D-MINT and D-PRINT are less skillful than NHC and consensus TC intensity predictions in North Atlantic and eastern North Pacific TCs, but are more skillful than the other guidances for at least half of the lead times. In western North Pacific, North Indian Ocean, and Southern Hemisphere TCs, D-MINT is more skillful than the JTWC and other individual TC intensity forecasts for over half of the lead times. When probabilistically predicting TC rapid intensification (RI), D-MINT is more skillful in North Atlantic and western North Pacific TCs than three operationally-used RI guidances, but less skillful for yes-no RI forecasts.
In addition, this work demonstrates the importance of microwave imagery, as D-MINT is more skillful than D-PRINT. Since D-MINT and D-PRINT are convolutional neural network models interrogating two-dimensional structures within TC satellite imagery, this study also demonstrates that those features can yield better short-term predictions than existing scalar statistics of satellite imagery in operational models. Finally, a diagnostics tool is revealed to aid the attribution of the D-MINT/D-PRINT intensity predictions.
Abstract
This study details a two-method, machine-learning approach to predict current and short-term intensity change in global tropical cyclones (TCs), ‘D-MINT’ and ‘D-PRINT’. D-MINT and D-PRINT use infrared imagery and environmental scalar predictors, while D-MINT also employs microwave imagery.
Results show that current TC intensity estimates from D-MINT and D-PRINT are more skillful than three established intensity estimation methods routinely used by operational forecasters for North Atlantic, and eastern and western North Pacific TCs.
Short-term intensity predictions are validated against five operational deterministic guidances at 6-, 12-, 18-, and 24-hour lead times. D-MINT and D-PRINT are less skillful than NHC and consensus TC intensity predictions in North Atlantic and eastern North Pacific TCs, but are more skillful than the other guidances for at least half of the lead times. In western North Pacific, North Indian Ocean, and Southern Hemisphere TCs, D-MINT is more skillful than the JTWC and other individual TC intensity forecasts for over half of the lead times. When probabilistically predicting TC rapid intensification (RI), D-MINT is more skillful in North Atlantic and western North Pacific TCs than three operationally-used RI guidances, but less skillful for yes-no RI forecasts.
In addition, this work demonstrates the importance of microwave imagery, as D-MINT is more skillful than D-PRINT. Since D-MINT and D-PRINT are convolutional neural network models interrogating two-dimensional structures within TC satellite imagery, this study also demonstrates that those features can yield better short-term predictions than existing scalar statistics of satellite imagery in operational models. Finally, a diagnostics tool is revealed to aid the attribution of the D-MINT/D-PRINT intensity predictions.
Abstract
The probabilistic prediction of tropical cyclone (TC) rapid intensification (RI) in the Atlantic and eastern Pacific Ocean basins is examined here using a series of logistic regression models trained on environmental and infrared satellite-derived features. The environmental predictors are based on averaged values over a 24-h period following the forecast time. These models are compared against equivalent models enhanced with additional TC predictors created from passive satellite microwave imagery (MI). Leave-one-year-out cross validation on the developmental dataset shows that the inclusion of MI-based predictors yields more skillful RI models for a variety of RI and intensity thresholds. Compared with the baseline forecast skill of the non-MI-based RI models, the relative skill improvements from including MI-based predictors range from 10.6% to 44.9%. Using archived real-time data during the period 2004–13, evaluation of simulated real-time models is also carried out. Unlike in the model development stage, the simulated real-time setting involves using Global Forecast System forecasts for the non-satellite-based predictors instead of “perfect” observational-based predictors in the developmental data. In this case, the MI-based RI models still generate superior skill to the baseline RI models lacking MI-based predictors. The relative improvements gained in adding MI-based predictors are most notable in the Atlantic, where the non-MI versions of the models suffer acutely from the use of imperfect real-time data. In the Atlantic, relative skill improvements provided from the inclusion of MI-based predictors range from 53.5% to 103.0%. The eastern Pacific relative improvements are less impressive but are still uniformly positive.
Abstract
The probabilistic prediction of tropical cyclone (TC) rapid intensification (RI) in the Atlantic and eastern Pacific Ocean basins is examined here using a series of logistic regression models trained on environmental and infrared satellite-derived features. The environmental predictors are based on averaged values over a 24-h period following the forecast time. These models are compared against equivalent models enhanced with additional TC predictors created from passive satellite microwave imagery (MI). Leave-one-year-out cross validation on the developmental dataset shows that the inclusion of MI-based predictors yields more skillful RI models for a variety of RI and intensity thresholds. Compared with the baseline forecast skill of the non-MI-based RI models, the relative skill improvements from including MI-based predictors range from 10.6% to 44.9%. Using archived real-time data during the period 2004–13, evaluation of simulated real-time models is also carried out. Unlike in the model development stage, the simulated real-time setting involves using Global Forecast System forecasts for the non-satellite-based predictors instead of “perfect” observational-based predictors in the developmental data. In this case, the MI-based RI models still generate superior skill to the baseline RI models lacking MI-based predictors. The relative improvements gained in adding MI-based predictors are most notable in the Atlantic, where the non-MI versions of the models suffer acutely from the use of imperfect real-time data. In the Atlantic, relative skill improvements provided from the inclusion of MI-based predictors range from 53.5% to 103.0%. The eastern Pacific relative improvements are less impressive but are still uniformly positive.
Abstract
Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind intensity and passive satellite microwave imagery and is named “M-PERC” for Microwave-Based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.
Abstract
Eyewall replacement cycles (ERCs) in tropical cyclones (TCs) are generally associated with rapid changes in TC wind intensity and broadening of the TC wind field, both of which can create unique forecasting challenges. As part of the NOAA Joint Hurricane Testbed Project, a new model was developed to provide operational probabilistic guidance on ERC onset. The model is based on the time evolution of TC wind intensity and passive satellite microwave imagery and is named “M-PERC” for Microwave-Based Probability of Eyewall Replacement Cycle. The model was initially developed in the Atlantic basin but is found to be globally applicable and skillful. The development of M-PERC and its performance characteristics are described here, as well as a new intensity prediction model that extends previous work. Application of these models is expected to contribute to a reduction of TC intensity forecast error.
Abstract
Intense thunderstorms threaten life and property, impact aviation, and are a challenging forecast problem, particularly without precipitation-sensing radar data. Trained forecasters often look for features in geostationary satellite images such as rapid cloud growth, strong and persistent overshooting tops, U- or V-shaped patterns in storm-top temperature (and associated above-anvil cirrus plumes), thermal couplets, intricate texturing in cloud albedo (e.g., “bubbling” cloud tops), cloud-top divergence, spatial and temporal trends in lightning, and other nuances to identify intense thunderstorms. In this paper, a machine-learning algorithm was employed to automatically learn and extract salient features and patterns in geostationary satellite data for the prediction of intense convection. Namely, a convolutional neural network (CNN) was trained on 0.64-μm reflectance and 10.35-μm brightness temperature from the Advanced Baseline Imager (ABI) and flash-extent density (FED) from the Geostationary Lightning Mapper (GLM) on board GOES-16. Using a training dataset consisting of over 220 000 human-labeled satellite images, the CNN learned pertinent features that are known to be associated with intense convection and skillfully discriminated between intense and ordinary convection. The CNN also learned a more nuanced feature associated with intense convection—strong infrared brightness temperature gradients near cloud edges in the vicinity of the main updraft. A successive-permutation test ranked the most important predictors as follows: 1) ABI 10.35-μm brightness temperature, 2) ABI GLM flash-extent density, and 3) ABI 0.64-μm reflectance. The CNN model can provide forecasters with quantitative information that often foreshadows the occurrence of severe weather, day or night, over the full range of instrument-scan modes.
Abstract
Intense thunderstorms threaten life and property, impact aviation, and are a challenging forecast problem, particularly without precipitation-sensing radar data. Trained forecasters often look for features in geostationary satellite images such as rapid cloud growth, strong and persistent overshooting tops, U- or V-shaped patterns in storm-top temperature (and associated above-anvil cirrus plumes), thermal couplets, intricate texturing in cloud albedo (e.g., “bubbling” cloud tops), cloud-top divergence, spatial and temporal trends in lightning, and other nuances to identify intense thunderstorms. In this paper, a machine-learning algorithm was employed to automatically learn and extract salient features and patterns in geostationary satellite data for the prediction of intense convection. Namely, a convolutional neural network (CNN) was trained on 0.64-μm reflectance and 10.35-μm brightness temperature from the Advanced Baseline Imager (ABI) and flash-extent density (FED) from the Geostationary Lightning Mapper (GLM) on board GOES-16. Using a training dataset consisting of over 220 000 human-labeled satellite images, the CNN learned pertinent features that are known to be associated with intense convection and skillfully discriminated between intense and ordinary convection. The CNN also learned a more nuanced feature associated with intense convection—strong infrared brightness temperature gradients near cloud edges in the vicinity of the main updraft. A successive-permutation test ranked the most important predictors as follows: 1) ABI 10.35-μm brightness temperature, 2) ABI GLM flash-extent density, and 3) ABI 0.64-μm reflectance. The CNN model can provide forecasters with quantitative information that often foreshadows the occurrence of severe weather, day or night, over the full range of instrument-scan modes.
