Impact of Wind Guess on the Tracking of Atmospheric Motion Vectors

Borde, Régis; García-Pereda, Javier

doi:10.1175/jtech-d-13-00105.1

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…This means that it is difficult to track the target when the target box sizes are excessively small or large. These results are in good agreement with those reported in previous research [19,20,38]. However, the validation scores with AMVs of QIF > 80 were almost independent of the target box size regardless of the channel.…”

Section: Validation and Optimization Of The Gk-2a Amv Algorithmsupporting

confidence: 92%

Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

Borde

Carranza

et al. 2019

Remote Sensing

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We derived an atmospheric motion vector (AMV) algorithm for the Geostationary Korea Multipurpose Satellite (GEO-KOMPSAT-2A; GK-2A) launched on 4 December 2018, using the Advanced Himawari Imager (AHI) onboard Himawari-8, which is very similar to the Advanced Meteorological Imager onboard GK-2A. This study clearly describes the main steps in our algorithm and optimizes it for the target box size and height assignment methods by comparing AMVs with numerical weather prediction (NWP) and rawinsonde profiles for July 2016 and January 2017. Target box size sensitivity tests were performed from 8 × 8 to 48 × 48 pixels for three infrared channels and from 16 × 16 to 96 × 96 pixels for one visible channel. The results show that the smaller box increases the speed, whereas the larger one slows the speed without quality control. The best target box sizes were found to be 16 × 16 for CH07, 08, and 13, and 48 × 48 pixels for CH03. Height assignment sensitivity tests were performed for several methods, such as the cross-correlation coefficient (CCC), equivalent blackbody temperature (EBBT), infrared/water vapor (IR/WV) intercept, and CO 2 slicing methods for a cloudy target as well as normalized total contribution (NTC) and normalized total cumulative contribution (NTCC) for a clear-air target. For a cloudy target, the CCC method is influenced by the quality of the cloud's top pressure. Better results were found when using EBBT and IR/WV intercept methods together rather than individually. Furthermore, CO 2 slicing had the best statistics. For a clear-air target, the combined use of NTC and NTCC had the best statistics. Additionally, the mean vector difference, root-mean-square (RMS) vector difference, bias, and RMS error (RMSE) between GK-2A AMVs and NWP or rawinsonde were smaller by approximately 18.2% on average than in the case of the Communication, Ocean and Meteorology Satellite (COMS) AMVs. In addition, we verified the similarity between GK-2A and Meteosat Third Generation (MTG) AMVs using the AHI of Himawari-8 from 21 July 2016. This similarity can provide evidence that the GK-2A algorithm works properly because the GK-2A AMV algorithm borrows many methods of the MTG AMV algorithm for geostationary data and inversion layer corrections. The Pearson correlation coefficients in the speed, direction, and height of the prescribed GK-2A and MTG AMVs were larger than 0.97, and the corresponding bias/RMSE were0.07/2.19 m/s, 0.21/14.8 • , and 2.61/62.9 hPa, respectively, considering common quality indicator with forecast (CQIF) > 80.

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Section: Validation and Optimization Of The Gk-2a Amv Algorithmsupporting

confidence: 92%

Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

Borde

Carranza

et al. 2019

Remote Sensing

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“…It reduces computing time drastically and improves the robustness of AMV tracking. The impacts of the use of the guess are explained by Borde et Garcia Pereda [10]. The forecasted wind is retrieved at the location of the centre of the target, and at the height where the atmospheric temperature equals the average brightness temperature of the 20 % coldest pixels in the target.…”

Section: Motion Trackingmentioning

confidence: 99%

The Sentinel-3 SLSTR Atmospheric Motion Vectors Product at EUMETSAT

Barbieux

Hautecœur²,

Bartolomei

et al. 2021

Remote Sensing

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Atmospheric Motion Vectors (AMVs) are an important input to many Numerical Weather Prediction (NWP) models. EUMETSAT derives AMVs from several of its orbiting satellites, including the geostationary satellites (Meteosat), and its Low-Earth Orbit (LEO) satellites. The algorithm extracting the AMVs uses pairs or triplets of images, and tracks the motion of clouds or water vapour features from one image to another. Currently, EUMETSAT LEO satellite AMVs are retrieved from georeferenced images from the Advanced Very-High-Resolution Radiometer (AVHRR) on board the Metop satellites. EUMETSAT is currently preparing the operational release of an AMV product from the Sea and Land Surface Temperature Radiometer (SLSTR) on board the Sentinel-3 satellites. The main innovation in the processing, compared with AVHRR AMVs, lies in the co-registration of pairs of images: the images are first projected on an equal-area grid, before applying the AMV extraction algorithm. This approach has multiple advantages. First, individual pixels represent areas of equal sizes, which is crucial to ensure that the tracking is consistent throughout the processed image, and from one image to another. Second, this allows features that would otherwise leave the frame of the reference image to be tracked, thereby allowing more AMVs to be derived. Third, the same framework could be used for every LEO satellite, allowing an overall consistency of EUMETSAT AMV products. In this work, we present the results of this method for SLSTR by comparing the AMVs to the forecast model. We validate our results against AMVs currently derived from AVHRR and the Spinning Enhanced Visible and InfraRed Imager (SEVIRI). The release of the operational SLSTR AMV product is expected in 2022.

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“…The target cloud is then identified from the cleaned image (after the removal of noisy structures). In the present study, the target cloud is identified as the cloud that have the highest gradient value and number of pixels (Borde and García-Pereda, 2014) and at the same time should be isolated and persists for some time (at least in the next image). Further, priority is given to that cloud (if more than one cloud satisfies the above criteria) which is at the center of the image.…”

Section: Image Processing and Target Cloud Identificationmentioning

confidence: 99%

“…Any error in the estimation of cloud base height will lead to significant errors in both pixel width and velocity of cloud (Park et al, 2012;Borde and García-Pereda, 2014 and references therein). Since height information is very crucial, a lidar, which provides the cloud information at a resolution of 30 m, is employed in the present study.…”

Section: Estimation Of Cloud Height and Pixel Widthmentioning

confidence: 99%

A novel approach for the extraction of cloud motion vectors using airglow imager measurements

Kumar¹,

Rao²,

Taori³

2015

Atmos. Meas. Tech.

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Abstract. The paper explores the possibility of implementing an advanced photogrammetric technique, generally employed for satellite measurements, on airglow imager, a ground-based remote sensing instrument primarily used for upper atmospheric studies, measurements of clouds for the extraction of cloud motion vectors (CMVs). The major steps involved in the algorithm remain the same, including image processing for better visualization of target elements and noise removal, identification of target cloud, setting a proper search window for target cloud tracking, estimation of cloud height, and employing 2-D cross-correlation to estimate the CMVs. Nevertheless, the implementation strategy at each step differs from that of satellite, mainly to suit airglow imager measurements. For instance, climatology of horizontal winds at the measured site has been used to fix the search window for target cloud tracking. The cloud height is estimated very accurately, as required by the algorithm, using simultaneous collocated lidar measurements. High-resolution, both in space and time (4 min), cloud imageries are employed to minimize the errors in retrieved CMVs. The derived winds are evaluated against MST radar-derived winds by considering it as a reference. A very good correspondence is seen between these two wind measurements, both showing similar wind variation. The agreement is also found to be good in both the zonal and meridional wind velocities with RMSEs < 2.4 m s −1 . Finally, the strengths and limitations of the algorithm are discussed, with possible solutions, wherever required.

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Impact of Wind Guess on the Tracking of Atmospheric Motion Vectors

Cited by 9 publications

References 9 publications

Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

Development and Intercomparison Study of an Atmospheric Motion Vector Retrieval Algorithm for GEO-KOMPSAT-2A

The Sentinel-3 SLSTR Atmospheric Motion Vectors Product at EUMETSAT

A novel approach for the extraction of cloud motion vectors using airglow imager measurements

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