The Benefits of Using Short-Interval Satellite Images to Derive Winds for Tropical Cyclones

Rodgers, E. B.; Gentry, R. Cecil; Shenk, W. E.; Oliver, Vincent J.

doi:10.1175/1520-0493(1979)107<0575:tbousi>2.0.co;2

Cited by 24 publications

(7 citation statements)

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“…The impact of the image interval has been investigated since the very early days of AMV derivation. Schmetz et al (2000) provide a good summary of this work, which considered optimising coverage both around tropical cyclones (e.g., Rodgers et al, 1979) and more generally. For example, it was found that higher temporal resolution is very important for more rapidly evolving low-level cloud tracers (e.g., Shenk, 1991).…”

Section: Developments In the 1980s-1990smentioning

confidence: 99%

Assimilation of satellite data in numerical weather prediction. Part I: The early years

Eyre

English

Forsythe

2019

Quart J Royal Meteoro Soc

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Developments in the assimilation of satellite data in numerical weather prediction (NWP), from the first experiments in the late 1960s to the present day, are presented in a two‐part review article. This first part reviews the early years, up to about the year 2000. It includes summaries of the relevant satellite remote sensing technologies, the theoretical and practical challenges faced when assimilating their data within NWP systems, and the impacts on forecast skill. An important part of this story concerns developments in the assimilation of information on atmospheric temperature and humidity provided by data from passive infrared and microwave radiometers. Following early successes with the assimilation of retrieved temperature profiles, there followed a problematic period, as other aspects of NWP systems improved and the impacts of satellite sounding data declined. Positive impacts were re‐established in the 1990s through moves towards more direct assimilation of radiance information. Another important theme concerns developments in the assimilation of wind information via atmospheric motion vectors, which underwent a series of improvements during these years. Additional contributions were provided by information on ocean surface wind from scatterometers. Some contributions from other technologies during this period are also summarised.

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Section: Developments In the 1980s-1990smentioning

confidence: 99%

Assimilation of satellite data in numerical weather prediction. Part I: The early years

Eyre

English

Forsythe

2019

Quart J Royal Meteoro Soc

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“…TC cloud structures are typically fast-evolving due to deformation, evaporation, and vertical shear [41], [42], and cloud features are usually too short-lived to be tracked by routine imaging intervals of meteorological satellites (e.g., 30 min). The study in [43] demonstrates the significance and effectiveness of shorter imaging intervals (3-7.5 min) for improving the observation of rapidly changing features in TC cloud images. Obviously, improving AMV spatial and temporal resolution is essential to tracking cloud features in a TC system, especially in the high-speed zone of the eyewall [37], [41], [43], [44].…”

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confidence: 84%

Automatically Locate Tropical Cyclone Centers Using Top Cloud Motion Data Derived From Geostationary Satellite Images

Zheng

Guo

Yang

et al. 2019

IEEE Trans. Geosci. Remote Sensing

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This article presents a novel technique for automatically locating tropical cyclone (TC) centers based on top cloud motions in consecutive geostationary satellite images. The high imaging rate and spatial resolution images of the Gaofen-4 geostationary satellite enable us to derive pixel-wise top cloud motion data of TCs, and from the data, TC spiral centers can be accurately determined based on an entirely different principle from those based on static image features. First, a physical motion field decomposition is proposed to eliminate scene shift and TC migration in the motion data without requiring any auxiliary geolocation data. This decomposition does not generate the artifacts that appear in the results of the previously published motion field decomposition. Then, an algorithm of a motion direction-based index embedded in a pyramid searching structure is fully designed to automatically and effectively locate the TC centers. The test shows that the TC concentric motions are more clearly revealed after the proposed motion field decomposition and the located centers are in good agreement with the cloud pattern centers in a visual sense and also with the best track data sets of four meteorological agencies. Index Terms-Geostationary satellite, motion field decomposition, tropical cyclone (TC), typhoon eye. I. INTRODUCTIONT ROPICAL cyclones (TCs) are intense warm-cored cyclonic vortices, developed from low-pressure systems over the tropical oceans and driven by complex air-sea interaction [1]. Recent trend analyses based on both archive data and future projections consistently suggest an increase of the mean intensity of global TCs over coming decades [2]-[6], although there could be a decrease in TC frequency [7], [8].

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“…The images may then be received every three minutes rather than each 30 minutes and smaller cloud elements can be followed for 15 minutes to estimate the velocities. Rodgers et al (1978) were able to track about 6-10 times the usual number of clouds in this way, including some low-level cumulus between breaks in the cirrus overcast. This type of data helps fill the gap between the far-environment of the storm and the inner regions, which can only be sensed with aircraft reconnaissance.…”

Section: Initial Fieldsmentioning

confidence: 99%

Applications of Tropical Cyclone Models

Elsberry¹

1979

Bull. Amer. Meteor. Soc.

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The extensive research in tropical cyclone modeling during the 1960s and 1970s has resulted in a number of applications for real-time track prediction. A review of the characteristics of these 3-dimensional dynamical models is given, including a discussion of procedures for initializing and tracking the model storm. Some limited verifications of track forecasts are described. An outlook for the future is presented; both in terms of numerical model improvements, and for large-scale and inner-scale data required to implement the improved models. The entries in Table 1 suggest wide variances in model characteristics, and consequently it is not possible to evaluate directly the differences in the model predictions. The FNWC and JMA models have minimal 750

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The Benefits of Using Short-Interval Satellite Images to Derive Winds for Tropical Cyclones

Cited by 24 publications

References 0 publications

Assimilation of satellite data in numerical weather prediction. Part I: The early years

Assimilation of satellite data in numerical weather prediction. Part I: The early years

Automatically Locate Tropical Cyclone Centers Using Top Cloud Motion Data Derived From Geostationary Satellite Images

Applications of Tropical Cyclone Models

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