Fully Automated Cloud-Drift Winds in NESDIS Operations

Nieman, Steven J.; Menzel, W. Paul; Hayden, Christopher M.; Gray, Donald G.; Wanzong, Steve; Velden, Christopher S.; Daniels, Jaime

doi:10.1175/1520-0477(1997)078<1121:facdwi>2.0.co;2

Cited by 134 publications

(78 citation statements)

References 12 publications

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“…At this time, AMVs largely provide the only source of upper-level wind observations over the oceanic areas. The winds are derived by tracking targets such as clouds or water-vapor structures across image sequences (e.g., Nieman et al 1997;Velden et al 1997;Schmetz et al 1993;Holmlund 2000). An estimate of the appropriate height or pressure level is also provided by the wind producers, and this estimate is mostly based on an estimate of the cloud top (for high-level winds) or the cloud base (for low-level winds).…”

Section: Introductionmentioning

confidence: 99%

Atmospheric Motion Vectors from Model Simulations. Part I: Methods and Characterization as Single-Level Estimates of Wind

Bormann

Hernandez-Carrascal

Borde

et al. 2014

Journal of Applied Meteorology and Climatology

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The objective of this study is to improve the characterization of satellite-derived atmospheric motion vectors (AMVs) and their errors to guide developments in the use of AMVs in numerical weather prediction. AMVs tend to exhibit considerable systematic and random errors that arise in the derivation or the interpretation of AMVs as single-level point observations of wind. One difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. This study uses instead a simulation framework: geostationary imagery for Meteosat-8 is generated from a high-resolution simulation with the Weather Research and Forecasting regional model, and AMVs are derived from sequences of these images. The forecast model provides the ''truth'' with a sophisticated description of the atmosphere. The study considers infrared and water vapor AMVs from cloudy scenes. This is the first part of a two-part paper, and it introduces the framework and provides a first evaluation in terms of the brightness temperatures of the simulated images and the derived AMVs. The simulated AMVs show a considerable global bias in the height assignment (60-75 hPa) that is not observed in real AMVs. After removal of this bias, however, the statistics comparing the simulated AMVs with the true model wind show characteristics that are similar to statistics comparing real AMVs with short-range forecasts (speed bias and root-mean-square vector difference typically agree to within 1 m s 21 ). This result suggests that the error in the simulated AMVs is comparable to or larger than that in real AMVs. There is evidence for significant spatial, temporal, and vertical error correlations, with the scales for the spatial error correlations being consistent with estimates for real data.

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Section: Introductionmentioning

confidence: 99%

Atmospheric Motion Vectors from Model Simulations. Part I: Methods and Characterization as Single-Level Estimates of Wind

Bormann

Hernandez-Carrascal

Borde

et al. 2014

Journal of Applied Meteorology and Climatology

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“…87% of these modules meet ΔWV≥0.21g/cm 2 , as shown in Fig.1b. Thus more than 87% of the cloud free modules in this case can be tracked by FY-2E for AMVs because the threshold ΔWV is less than 0.20 g/cm 2 in Mid-latitude Summer as shown in Table 1. …”

Section: A Case For Wv Distributionmentioning

confidence: 89%

“…In the past several decades, the technology to obtain atmospheric motion vectors (AMVs) from geostationary satellite images has been developed from manual to fully automatic [1,2], and the product has become good supplements in the areas where lack of stations such as desert and ocean [3,4,5]. The current techniques for AMVs derivation from satellite images are mainly concentrated on clouds in IR window imagery and water vapor (WV) in WV imagery respectively [6,7,8] .…”

Section: Introductionmentioning

confidence: 99%

The significance analysis of FY-2E split window data for "clear region" AMVs derivation

Wang¹,

Zhan²,

Zhang³

et al. 2013

AIP Conference Proceedings

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Abstract. The current cloud-tracing technique for atmospheric motion vector derivation would fail in "clear" regions in IR window imagery. This paper makes analysis on brightness temperature sensitivity in thermal IR window channels with respect to water vapor and aerosol contents which might be used as tracers for deriving atmospheric motion vectors in cloud free regions. The data for sensitivity analysis based on the radiation transfer model MODTRAN includes MODIS Atmospheric Aerosol products and FY-2E Atmospheric Total Precipitation Water products. The results from the analysis show that water vapor and aerosol signals greater than the T B sensitivity threshold of FY-2E-borne IR radiometer exist under certain conditions in clear region, and therefore the possibility significantly exists to derive atmospheric motion vectors in clear regions from thermal infrared images obtained with FY-2E satellite.

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“…A detailed qualitative and quantitative comparison of these two methods is given in [7]. Fully automated cloud drift wind estimation is reported by NESDIS [17]. The NESDIS operational system includes tracer selection, height assignment, tracking, and a quality control step.…”

Section: Introductionmentioning

confidence: 99%

Cloud tracking by scale space classification

Mukherjee

Acton

2002

IEEE Trans. Geosci. Remote Sensing

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Abstract-The problem of cloud tracking within a sequence of geo-stationary satellite images has direct relevance to the analysis of cloud life cycles and to the detection of cloud motion vectors (CMVs). The proposed approach first identifies a homogeneous consistent cloud mass for tracking and then establishes motion correspondence within an image sequence. In contrast to the crosscorrelation based approach as adopted in automatic CMV detection analysis, a scale space classifier is designed to detect cloud mass in the source image taken at time t and the destination image at time + . Boundaries of the extracted cloud segments are matched by computing a correspondence between high curvature points. This shape based method is capable of tracking in the cases of rotation, scaling, and shearing, while the correlation technique is limited to translational motion. The final tracking results provide motion magnitude and direction for each contour point, allowing reliable estimation of meteorological events and wind velocities aloft. With comparable computational expense, the scale space classification technique exceeds the performance of the traditional correlation-based approach in terms of reduced localization error and false matches.

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Fully Automated Cloud-Drift Winds in NESDIS Operations

Cited by 134 publications

References 12 publications

Atmospheric Motion Vectors from Model Simulations. Part I: Methods and Characterization as Single-Level Estimates of Wind

Atmospheric Motion Vectors from Model Simulations. Part I: Methods and Characterization as Single-Level Estimates of Wind

The significance analysis of FY-2E split window data for "clear region" AMVs derivation

Cloud tracking by scale space classification

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