Aircraft Observations of Tropical Cyclones

Aberson, Sim D.; Cione, Joseph J.; Wu, Chueh‐Yu; Bell, Michael M.

doi:10.1142/9789814293488_0008

Cited by 6 publications

(6 citation statements)

References 67 publications

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“…Though aircrafts can provide precise information about the development, intensity change, and track of a TC, these observations are mainly available in the Atlantic basin (Aberson et al, 2010;Gray, Neumann, & Tsui, 1991;Martin & Gray, 1993).…”

Section: Data Collectionmentioning

confidence: 99%

“…Even with multiple forecasting techniques in operation, TC forecasting centers are still struggling to handle this difficulty. Considering the operational forecasting techniques' limitations in TC intensity forecasting (Aberson et al, 2010;DeMaria et al, 2007;Rogers et al, 2006), as well as the inefficiencies of the current cyclone early warning system in Bangladesh (Akhand, 2003;U. Haque et al, 2012), new techniques, equally effective in TC track and intensity forecasting need to be explored.…”

mentioning

confidence: 99%

“…While the factors governing TC motion work at synoptic (large) scale, and are relatively obvious and not overly complex (Bin, Elsberry, Yuqing, & Liguang, 1999;, the factors responsible for TC intensity change operate on multiple scales and are often difficult to observe. As a result, exactly which physical processes govern TC intensity and how they contribute to TC intensity change are not well-understood still today (Aberson et al, 2010;Rogers et al, 2006).The factors governing TC motion include:1. Large-scale environmental circulation is the most important factors that determines TC motion (Chan & Kepert, 2010;Neumann, 1992).…”

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confidence: 99%

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An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting

Roy

2016

Linköping Studies in Science and Technology. Dissertations

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Introduction: Tropical Cyclones (TCs) inflict considerable damage to life and property every year. A major problem is that residents often hesitate to follow evacuation orders when the early warning messages are perceived as inaccurate or uninformative. The root problem is that providing accurate early forecasts can be difficult, especially in countries with less economic and technical means. Aim:The aim of the thesis is to investigate how cyclone early warning systems can be technically improved. This means, first, identifying problems associated with the current cyclone early warning systems, and second, investigating if biologically based Artificial Neural Networks (ANNs) are feasible to solve some of the identified problems.Method: First, for evaluating the efficiency of cyclone early warning systems, Bangladesh was selected as study area, where a questionnaire survey and an in-depth interview were administered. Second, a review of currently operational TC track forecasting techniques was conducted to gain a better understanding of various techniques' prediction performance, data requirements, and computational resource requirements. Third, a technique using biologically based ANNs was developed to produce TC track and intensity forecasts. Systematic testing was used to find optimal values for simulation parameters, such as feature-detector receptive field size, the mixture of unsupervised and supervised learning, and learning rate schedule. Five types of 2D data were used for training. The networks were tested on two types of novel data, to assess their generalization performance. Results:A major problem that is identified in the thesis is that the meteorologists at the Bangladesh Meteorological Department are currently not capable of providing accurate TC forecasts. This is an important contributing factor to residents' reluctance to evacuate. To address this issue, an ANN-based TC track and intensity forecasting technique was developed that can produce early and accurate forecasts, uses freely available satellite images, and does not require extensive computational resources to run. Bidirectional connections, combined supervised and unsupervised learning, and a deep hierarchical structure assists the parallel extraction of useful features from five types of 2D data. The trained networks were tested on two types of novel data: First, tests were performed with novel data covering the end of the lifecycle of trained cyclones; for these test data, the forecasts produced by the networks were correct in 91-100% of the cases. Second, the networks were tested with data of a novel TC; in this case, the networks performed with between 30% and 45% accuracy (for intensity forecasts). Conclusions:The ANN technique developed in this thesis could, with further extensions and up-scaling, using additional types of input images of a greater number of TCs, improve the efficiency of cyclone early warning systems in countries with less economic and technical means. The thesis work also creates opportunities for furthe...

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Section: Data Collectionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting

Roy

2016

Linköping Studies in Science and Technology. Dissertations

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“…Supplementary dropsonde data gathered during tropical cyclone reconnaissance missions and ingested into operational numerical weather prediction (NWP) systems have, in most cases, resulted in reduced tropical cyclone track forecast errors. In particular, data gathered in the hurricane environment have provided improved depiction of hurricane steering winds and the large-scale troughs and ridges that highly influence hurricane tracks (e.g., Aberson et al 2010;Majumdar et al 2013;Brennan et al 2015). Moreover, for dropsondes deployed in the vicinity of extratropical and tropical cyclones in midlatitudes during the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX), Schäfler et al (2018) and Schindler et al (2020) have shown positive downstream impact.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Dropsonde Data on the Performance of the NCEP Global Forecast System during the 2020 Atmospheric Rivers Observing Campaign. Part I: Precipitation

Lord

Tallapragada

et al. 2023

Weather and Forecasting

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The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical precipitation forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the NCEP Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble-variational (4DEnVar) data assimilation system. The control run (CTRL) used all the routinely assimilated data and included ARR dropsonde data, whereas the denial run (DENY) excluded the dropsonde data. There were 17 Intensive Observing Periods (IOPs) totaling 46 Air Force C-130 and 16 NOAA G-IV missions to deploy dropsondes over targeted regions with potential for downstream high-impact weather associated with the ARs. Data from a total of 628 dropsondes were assimilated in the CTRL. The dropsonde data impact on precipitation forecasts over U.S. West Coast domains is largely positive, especially for day 5 lead time, and appears driven by different model variables on a case-by-case basis. These results suggest that data gaps associated with ARs can be addressed with targeted ARR field campaigns providing vital observations needed for improving U.S. West Coast precipitation forecasts.

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“…In extensive studies prior to this period, TCs were examined with aircraft, but, with limited exception, those studies will not be covered here. The interested reader is referred to [19,20] for a comprehensive assessment of these earlier studies that documented TC structure and intensity from aircraft observations. Intensity change, as defined here, includes a range of intensification rates, including the RI of tropical storms to major hurricanes; slower (but still substantial) intensification of TCs with initial intensities ranging from tropical depressions to hurricane strength; the organization of tropical depressions into tropical storms that subsequently undergo RI; and the formation of tropical depressions from disorganized tropical cloud clusters.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Our Understanding of Tropical Cyclone Intensity Change Processes from Airborne Observations

Rogers

2021

Atmosphere

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Recent (past ~15 years) advances in our understanding of tropical cyclone (TC) intensity change processes using aircraft data are summarized here. The focus covers a variety of spatiotemporal scales, regions of the TC inner core, and stages of the TC lifecycle, from preformation to major hurricane status. Topics covered include (1) characterizing TC structure and its relationship to intensity change; (2) TC intensification in vertical shear; (3) planetary boundary layer (PBL) processes and air–sea interaction; (4) upper-level warm core structure and evolution; (5) genesis and development of weak TCs; and (6) secondary eyewall formation/eyewall replacement cycles (SEF/ERC). Gaps in our airborne observational capabilities are discussed, as are new observing technologies to address these gaps and future directions for airborne TC intensity change research.

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Aircraft Observations of Tropical Cyclones

Abstract: Nine different types of aircraft are currently in use to observe tropical cyclones and their environments for operations and research. The following is a description of those aircraft, their instrumentation, and the field programs with which they have been involved.

Cited by 6 publications

References 67 publications

An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting

An Informed System Development Approach to Tropical Cyclone Track and Intensity Forecasting

The Impact of Dropsonde Data on the Performance of the NCEP Global Forecast System during the 2020 Atmospheric Rivers Observing Campaign. Part I: Precipitation

Recent Advances in Our Understanding of Tropical Cyclone Intensity Change Processes from Airborne Observations

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