scholarly journals Improvements to MISR stereo motion vectors

2013 ◽  
Vol 118 (11) ◽  
pp. 5600-5620 ◽  
Author(s):  
Ákos Horváth
Keyword(s):  
Motion Vectors ◽  
Stereo Motion

Auroral event detection using spatiotemporal statistics of local motion vectors

2013 ◽  
Vol 24 (3) ◽  
pp. 175 ◽  
Author(s):  
Qian WANG ◽  
Jimin LIANG ◽  
Zejun HU
Keyword(s):  
Event Detection ◽  
Local Motion ◽  
Motion Vectors

A Spatiotemporally Variable Model for Nowcasting Storm Motion Vectors using Remotely Sensed Raster Data

2017 ◽  
Vol 17 (17th International Conference) ◽  
pp. 1-23
Author(s):  
Shaimaa El Sharkawy ◽  
Mona Safar ◽  
Mohamed Gad
Keyword(s):  
Remotely Sensed ◽  
Raster Data ◽  
Motion Vectors ◽  
Variable Model

Temporally Reliable Motion Vectors for Real‐time Ray Tracing

2021 ◽  
Vol 40 (2) ◽  
pp. 79-90
Author(s):  
Zheng Zeng ◽  
Shiqiu Liu ◽  
Jinglei Yang ◽  
Lu Wang ◽  
Ling‐Qi Yan
Keyword(s):  
Real Time ◽  
Ray Tracing ◽  
Motion Vectors

scholarly journals The Sentinel-3 SLSTR Atmospheric Motion Vectors Product at EUMETSAT

2021 ◽  
Vol 13 (9) ◽  
pp. 1702
Author(s):  
Kévin Barbieux ◽  
Olivier Hautecoeur ◽  
Maurizio De Bartolomei ◽  
Manuel Carranza ◽  
Régis Borde
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Forecast Model ◽  
Low Earth Orbit ◽  
Reference Image ◽  
Motion Vectors ◽  
Infrared Imager ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Leo Satellite

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.

Nowcasting with Data Assimilation: A Case of Global Satellite Mapping of Precipitation

2016 ◽  
Vol 31 (5) ◽  
pp. 1409-1416 ◽  
Author(s):  
Shigenori Otsuka ◽  
Shunji Kotsuki ◽  
Takemasa Miyoshi
Keyword(s):  
Data Assimilation ◽  
Space Time ◽  
Motion Vectors ◽  
The Past ◽  
Variational Techniques ◽  
Alternative Approach ◽  
Ensemble Transform Kalman Filter ◽  
Global Precipitation ◽  
Time Extrapolation ◽  
Satellite Mapping

Abstract Space–time extrapolation is a key technique in precipitation nowcasting. Motions of patterns are estimated using two or more consecutive images, and the patterns are extrapolated in space and time to obtain their future patterns. Applying space–time extrapolation to satellite-based global precipitation data will provide valuable information for regions where ground-based precipitation nowcasts are not available. However, this technique is sensitive to the accuracy of the motion vectors, and over the past few decades, previous studies have investigated methods for obtaining reliable motion vectors such as variational techniques. In this paper, an alternative approach applying data assimilation to precipitation nowcasting is proposed. A prototype extrapolation system is implemented with the local ensemble transform Kalman filter and is tested with the Japan Aerospace Exploration Agency’s Global Satellite Mapping of Precipitation (GSMaP) product. Data assimilation successfully improved the global precipitation nowcasting with the real-case GSMaP data.

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

2015 ◽  
Vol 8 (9) ◽  
pp. 3893-3901 ◽  
Author(s):  
S. Satheesh Kumar ◽  
T. Narayana Rao ◽  
A. Taori
Keyword(s):  
Meridional Wind ◽  
Noise Removal ◽  
Search Window ◽  
Motion Vectors ◽  
Good Correspondence ◽  
Cloud Motion ◽  
Cloud Height ◽  
Novel Approach ◽  
Wind Velocities ◽  
Cloud Tracking

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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