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Wayne C. Bresky, Jaime M. Daniels, Andrew A. Bailey, and Steven T. Wanzong

1. Introduction Cloud-tracked winds produced from satellite-image sequences have long exhibited a pronounced slow speed bias at mid- (400–700 hPa) and upper (100–400 hPa) levels of the atmosphere when compared with rawinsonde profiles and other wind measurements (e.g., Schmetz et al. 1993 , their Fig. 4; Bormann et al. 2002 ; von Bremen et al. 2008 ; Rohn et al. 2001 ). Monitoring statistics from the Met Office that compare atmospheric motion vectors (AMV) from several global AMV data

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Ákos Horváth, Olivier Hautecoeur, Régis Borde, Hartwig Deneke, and Stefan A. Buehler

combination with Earth’s rotation over 50 min produces a minimum imagery overlap between the tandem of approximately one-half of a swath at low latitudes. Atmospheric motion vectors (AMVs)—hereinafter also referred to as “winds” although they are only a proxy measure of air motion—are derived day and night by tracking clouds in 10.8- μ m infrared (IR) images, utilizing either the swath overlap between consecutive orbits of one of the MetOp satellites or that between the pair. EUMETSAT provides three

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Kevin J. Mueller, Dong L. Wu, Ákos Horváth, Veljko M. Jovanovic, Jan-Peter Muller, Larry Di Girolamo, Michael J. Garay, David J. Diner, Catherine M. Moroney, and Steve Wanzong

1. Introduction Atmospheric motion vectors (AMVs), a proxy measure of wind, are indispensable to regional and global numerical weather prediction (NWP) models and analyses. Derived by tracking cloud or water vapor features in satellite imagery, AMVs mitigate critical data gaps in regions that are otherwise observation poor (e.g., the Arctic, Antarctic, and global oceans). However, most AMVs rely on radiometric techniques for height assignment that have large uncertainties, particularly for

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Sebastian S. Harkema, Emily B. Berndt, John R. Mecikalski, and Alana Cordak

identify and track cloud, or moisture, features to estimate atmospheric motion at different levels. These ABI atmospheric motion vector winds can depict synoptic and mesoscale motions, and those associated with wintertime midlatitude cyclones (i.e., synoptic conveyor belts; Bedka et al. 2009 ). A need for a robust cloud tracking algorithm plays a pivotal role in understanding ABI characteristics associated with snowfall and TSI. Hanna et al. (2008) demonstrated the importance of the cloud-top T B

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Derek J. Posselt, Longtao Wu, Kevin Mueller, Lei Huang, Fredrick W. Irion, Shannon Brown, Hui Su, David Santek, and Christopher S. Velden

the slow bias, as a large target window has the tendency to smooth the instantaneous wind field. On the other hand, the tracked wind speed is greater than the true wind primarily at low true wind speeds ( Fig. 6b ) and may be due to the emergence of features associated with processes other than horizontal advection (e.g., vertical motion or cloud processes). We now examine the effect of vertical smoothing of the water vapor fields on tracked winds. We utilize the 4-km grid spacing WRF domain for

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Wei-Liang Chuang, Chien-Ben Chou, Kuang-An Chang, Yu-Cheng Chang, and Hsin-Lung Chin

1. Introduction Atmospheric motion vectors (AMVs) are crucial information not only for the quantitative description of atmospheric circulations, but also for the improvement of weather forecasts through providing wind field data for assimilation. That is especially true in the oceanic region where radiosondes are sparse ( Gelaro et al. 2012 ). Many researchers have shown that assimilating AMVs has a significant impact on the prediction of a hurricane’s track with different numerical weather

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Matthew A. Lazzara, Richard Dworak, David A. Santek, Brett T. Hoover, Christopher S. Velden, and Jeffrey R. Key

value of 1.0), the lower the expected observational error. The primary purpose of assigning a QI to each AMV is to give end users a confidence estimate in the quality of the observation, and also as a potential aid for determining observational weights in data assimilation. c. Modifications to composite image and AMV generation For composite LEO–GEO AMV generation, the targeting and tracking of cloud features are constrained to be poleward of 50° latitude in both hemispheres. This overlaps both the

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Joaquin E. Blanco, David S. Nolan, and Brian E. Mapes

topography. It will be shown that zonal CC propagation is determined by low- to midlevel wind, and that the oscillations of such zonal wind, linked to CCKW dynamics, have specific characteristics: the westward to eastward transitions in CC motion occurs quite fast, in a front-like manner. Two distinct but complementary approaches are applied. The first, an objective CC-tracking algorithm in the time–longitude space, permits the quantification of CC motion based on precipitation thresholds, and for the

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Ryo Oyama

advances ( Berger et al. 2011 ; Bessho et al. 2016 ; Line et al. 2016 ). Satellite observations at time intervals as short as 5–15 min, often referred to as rapid scans, are useful for deriving spatially and temporally dense atmospheric motion vectors (AMVs; Velden et al. 2005 ; Oyama 2015 ). AMVs are wind products that are identified by tracking clouds and water vapor patterns in successive geostationary satellite images, and they are used not only for numerical weather prediction ( Warrick 2016

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Alessandro Battaglia and Pavlos Kollias

-by-component error analysis of retrieving along-track wind velocities in cloud media using different configurations of a millimeter-wave scanning Doppler stereoradar with polarization diversity conceived for a space platform in low-Earth orbit (LEO). A considerable number of cross-track Doppler measurements are included to ensure extended sampling and coverage. The error analysis is conducted using a state-of-the art spaceborne radar forward radiation model in combination with the Weather Research and

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