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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
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
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
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
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
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
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
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
Comparison of Optical Flow Derivation Techniques for Retrieving Tropospheric Winds from Satellite Image Sequences1. Introduction It is widely understood that accurate tropospheric wind observations are critical to understanding cloud and weather processes, aerosol and pollutant transport, and Earth’s global circulation ( Zeng et al. 2016 ). Wind observations are also routinely used to improve the initial state inputs for numerical weather prediction (NWP) models (e.g., Le Marshall et al. 2008 ; Wu et al. 2014 ). A key source of inferring these observations since the 1960s are atmospheric motion
1. Introduction It is widely understood that accurate tropospheric wind observations are critical to understanding cloud and weather processes, aerosol and pollutant transport, and Earth’s global circulation ( Zeng et al. 2016 ). Wind observations are also routinely used to improve the initial state inputs for numerical weather prediction (NWP) models (e.g., Le Marshall et al. 2008 ; Wu et al. 2014 ). A key source of inferring these observations since the 1960s are atmospheric motion
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
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
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
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
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
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
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
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
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
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
