scholarly journals A Direct Link between Feature Tracking and Height Assignment of Operational EUMETSAT Atmospheric Motion Vectors

2014 ◽  
Vol 31 (1) ◽  
pp. 33-46 ◽  
Author(s):  
Régis Borde ◽  
Marie Doutriaux-Boucher ◽  
Greg Dew ◽  
Manuel Carranza
Keyword(s):  
Feature Tracking ◽  
Cross Correlation ◽  
Motion Vectors ◽  
Tracking Process ◽  
Extraction Scheme ◽  
Atmospheric Motion ◽  
Image Pixels ◽  
The Individual ◽  
Height Assignment ◽  
Atmospheric Motion Vectors

Abstract Height assignment (HA) is currently the most challenging task in the operational atmospheric motion vectors’ (AMV) extraction scheme. Several sources of error are associated with the height assignment step, including the sensitivity of the HA methods to several atmospheric parameters. However, one of the main difficulties is to identify, for the HA calculation, the most significant image pixels used in the feature-tracking process. The most widely used method selects the coldest pixels in a representative target box (e.g., coldest 25%) to infer the height of the detected feature, irrespective of what was tracked. This paper presents a method based on a closer link between the pixels used for tracking and their HA. The individual contribution to the overall tracking cross-correlation coefficient is used to identify the most significant pixels contributing to the tracking. This approach has been implemented operationally at European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) to derive AMVs since September 2012. This paper details the method, gives specific examples, and provides a first glance at its performances and benefits for the operational AMV production.

scholarly journals GEO-GEO Stereo-Tracking of Atmospheric Motion Vectors (AMVs) from the Geostationary Ring

2020 ◽  
Author(s):  
James L. Carr ◽  
Dong L. Wu ◽  
Jaime Daniels ◽  
Mariel D. Friberg ◽  
Wayne Bresky ◽  
...  
Keyword(s):  
Deep Convection ◽  
Motion Vectors ◽  
Fire Smoke ◽  
Atmospheric Motion ◽  
Image Navigation ◽  
Boundary Layer Dynamics ◽  
Vantage Points ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Geostationary Ring

Height assignment is an important problem for satellite measurements of Atmospheric Motion Vectors (AMVs) that are interpreted as winds by forecast and assimilation systems. Stereo methods assign heights to AMVs from the parallax observed between observations from different vantage points in orbit while tracking cloud or moisture features. In this paper, we fully develop the stereo method to jointly retrieve wind vectors with their geometric heights from geostationary satellite pairs. Synchronization of observations between observing systems is not required. NASA and NOAA stereo-winds codes have implemented this method and we have processed large datasets from GOES-16, -17, and Himawari-8. Our retrievals are validated against rawinsonde observations and demonstrate the potential to improve forecast skill. Stereo winds also offer an important mitigation for the loop heat pipe anomaly on GOES-17 during times when warm focal plane temperatures cause infra-red channels that are needed for operational height assignments to fail. We also examine several application areas, including deep convection in tropical cyclones, planetary boundary layer dynamics, and fire smoke plumes, where stereo methods provide insights into atmospheric processes. The stereo method is broadly applicable across the geostationary ring where systems offering similar Image Navigation and Registration (INR) performance as GOES-R are deployed.

scholarly journals GEO-GEO Stereo-Tracking of Atmospheric Motion Vectors (AMVs) from the Geostationary Ring

2020 ◽  
Author(s):  
James L. Carr ◽  
Dong L. Wu ◽  
Jaime Daniels ◽  
Mariel D. Friberg ◽  
Wayne Bresky ◽  
...  
Keyword(s):  
Deep Convection ◽  
Motion Vectors ◽  
Fire Smoke ◽  
Atmospheric Motion ◽  
Image Navigation ◽  
Boundary Layer Dynamics ◽  
Vantage Points ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Geostationary Ring

Height assignment is an important problem for satellite measurements of Atmospheric Motion Vectors (AMVs) that are interpreted as winds by forecast and assimilation systems. Stereo methods assign heights to AMVs from the parallax observed between observations from different vantage points in orbit while tracking cloud or moisture features. In this paper, we fully develop the stereo method to jointly retrieve wind vectors with their geometric heights from geostationary satellite pairs. Synchronization of observations between observing systems is not required. NASA and NOAA stereo-winds codes have implemented this method and we have processed large datasets from GOES-16, -17, and Himawari-8. Our retrievals are validated against rawinsonde observations and demonstrate the potential to improve forecast skill. Stereo winds also offer an important mitigation for the loop heat pipe anomaly on GOES-17 during times when warm focal plane temperatures cause infra-red channels that are needed for operational height assignments to fail. We also examine several application areas, including deep convection in tropical cyclones, planetary boundary layer dynamics, and fire smoke plumes, where stereo methods provide insights into atmospheric processes. The stereo method is broadly applicable across the geostationary ring where systems offering similar Image Navigation and Registration (INR) performance as GOES-R are deployed.

Simulation of Atmospheric Motion Vectors for an Observing System Simulation Experiment

2020 ◽  
Vol 37 (3) ◽  
pp. 489-505 ◽  
Author(s):  
Ronald M. Errico ◽  
David Carvalho ◽  
Nikki C. Privé ◽  
Meta Sienkiewicz
Keyword(s):  
Water Vapor ◽  
Feature Tracking ◽  
Simulation Experiment ◽  
System Simulation ◽  
Simulation Experiments ◽  
Motion Vectors ◽  
Vertical Distributions ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors ◽  
Observing System Simulation Experiment

AbstractAn algorithm to simulate locations of atmospheric motion vectors for use in observing system simulation experiments is described and demonstrated. It is intended to obviate likely deficiencies in nature run data if used to produce images for feature tracking. The algorithm employs probabilistic functions that are tuned based on distributions of real observations and histograms of nature run fields. For distinct observation types, the algorithm produces geographical and vertical distributions, time-mean counts, and typical spacings of simulated locations that are, at least qualitatively, similar to those of real observations and are associated with nature run cloud and water vapor fields. It thus appears suitable for generating realistic atmospheric motion vectors for use in observing system simulation experiments.

scholarly journals Height Correction of Atmospheric Motion Vectors Using Airborne Lidar Observations

2013 ◽  
Vol 52 (8) ◽  
pp. 1868-1877 ◽  
Author(s):  
Martin Weissmann ◽  
Kathrin Folger ◽  
Heiner Lange
Keyword(s):  
North Pacific Ocean ◽  
Weather Prediction ◽  
Airborne Lidar ◽  
Discrete Level ◽  
Motion Vectors ◽  
Wind Speeds ◽  
Atmospheric Motion ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Lidar Observations

AbstractUncertainties in the height assignment of atmospheric motion vectors (AMVs) are the main contributor to the total AMV wind error, and these uncertainties introduce errors that can be horizontally correlated over several hundred kilometers. As a consequence, only a small fraction of the available AMVs are currently used in numerical weather prediction systems. For this reason, alternative approaches for the height assignment of AMVs are investigated in this study: 1) using collocated airborne lidar observations and 2) treating AMVs as layer winds instead of winds at a discrete level. Airborne lidar observations from a field campaign in the western North Pacific Ocean region are used to demonstrate the potential of improving AMV heights in an experimental framework. On average, AMV wind errors are reduced by 10%–15% when AMV winds are assigned to a 100–150-hPa-deep layer beneath the cloud top derived from nearby lidar observations. In addition, the lidar–AMV height correction is expected to reduce the correlation of AMV errors as lidars provide independent cloud height information. This suggests that satellite lidars may be a valuable source of information for the AMV height assignment in the future. Furthermore, AMVs are compared with dropsonde and radiosonde winds averaged over vertical layers of different depth to investigate the optimal height assignment for AMVs in data assimilation. Consistent with previous studies, it is shown that AMV winds better match sounding winds vertically averaged over ~100 hPa than sounding winds at a discrete level. The comparison with deeper layers further reduces the RMS difference but introduces systematic differences of wind speeds.

scholarly journals Estimation of Atmospheric Motion Vectors from Kalpana-1 Imagers

2009 ◽  
Vol 48 (11) ◽  
pp. 2410-2421 ◽  
Author(s):  
C. M. Kishtawal ◽  
S. K. Deb ◽  
P. K. Pal ◽  
P. C. Joshi
Keyword(s):  
Water Vapor ◽  
Motion Vector ◽  
Satellite System ◽  
Radiosonde Data ◽  
Motion Vectors ◽  
Vector Difference ◽  
Atmospheric Motion ◽  
The Difference ◽  
Height Assignment ◽  
Atmospheric Motion Vectors

Abstract The estimation of atmospheric motion vectors from infrared and water vapor channels on the geostationary operational Indian National Satellite System Kalpana-1 has been attempted here. An empirical height assignment technique based on a genetic algorithm is used to determine the height of cloud and water vapor tracers. The cloud-motion-vector (CMV) winds at high and midlevels and water vapor winds (WVW) derived from Kalpana-1 show a very close resemblance to the corresponding Meteosat-7 winds derived at the European Organisation for the Exploitation of Meteorological Satellites when both are compared separately with radiosonde data. The 3-month mean vector difference (MVD) of high- and midlevel CMV and WVW winds derived from Kalpana-1 is very close to that of Meteosat-7 winds, when both are compared with radiosonde. When comparing with radiosonde, the low-level CMVs from Kalpana-1 have a higher MVD value than that of Meteosat-7. This may be due to the difference in spatial resolutions of Kalpana-1 and Meteosat-7.

scholarly journals Height Correction of Atmospheric Motion Vectors Using Satellite Lidar Observations from CALIPSO

2014 ◽  
Vol 53 (7) ◽  
pp. 1809-1819 ◽  
Author(s):  
Kathrin Folger ◽  
Martin Weissmann
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Current Data ◽  
Motion Vectors ◽  
Deep Layers ◽  
Atmospheric Motion ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Lidar Observations

AbstractAtmospheric motion vectors (AMVs) provide valuable wind information for the initial conditions of numerical weather prediction models, but height-assignment issues and horizontal error correlations require a rigid thinning of the available AMVs in current data assimilation systems. The aim of this study is to investigate the feasibility of correcting the pressure heights of operational AMVs from the geostationary satellites Meteosat-9 and Meteosat-10 with cloud-top heights derived from lidar observations by the polar-orbiting Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. The study shows that the wind error of AMVs above 700 hPa is reduced by 12%–17% when AMV winds are assigned to 120-hPa-deep layers below the lidar cloud tops. This result demonstrates the potential of lidar cloud observations for the improvement of the AMV height assignment. In addition, the lidar correction reduces the “slow” bias of current upper-level AMVs and is expected to reduce the horizontal correlation of AMV errors.

Comparisons of Satellite-Derived Atmospheric Motion Vectors, Rawinsondes, and NOAA Wind Profiler Observations

2009 ◽  
Vol 48 (8) ◽  
pp. 1542-1561 ◽  
Author(s):  
Kristopher M. Bedka ◽  
Christopher S. Velden ◽  
Ralph A. Petersen ◽  
Wayne F. Feltz ◽  
John R. Mecikalski
Keyword(s):  
Boundary Layer ◽  
Prediction Models ◽  
Weather Prediction ◽  
Information Service ◽  
Wind Profiler ◽  
Motion Vectors ◽  
Processing Methods ◽  
Atmospheric Motion ◽  
The Individual ◽  
Atmospheric Motion Vectors

Abstract Geostationary satellite-derived atmospheric motion vectors (AMVs) have been used over several decades in a wide variety of meteorological applications. The ever-increasing horizontal and vertical resolution of numerical weather prediction models puts a greater demand on satellite-derived wind products to monitor flow accurately at smaller scales and higher temporal resolution. The focus of this paper is to evaluate the accuracy and potential applications of a newly developed experimental mesoscale AMV product derived from Geostationary Operational Environmental Satellite (GOES) imagery. The mesoscale AMV product is derived through a variant on processing methods used within the University of Wisconsin—Madison Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) AMV algorithm and features a significant increase in vector density throughout the troposphere and lower stratosphere over current NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) processing methods for GOES-12 Imager data. The primary objectives of this paper are to 1) highlight applications of experimental GOES mesoscale AMVs toward weather diagnosis and forecasting, 2) compare the coverage and accuracy of mesoscale AMVs with the NOAA/NESDIS operational AMV product, and 3) demonstrate the utility of 6-min NOAA Wind Profiler Network observations for satellite-derived AMV validation. Although the more conservative NOAA/NESDIS AMV product exhibits closer statistical agreement to rawinsonde and wind profiler observations than do the experimental mesoscale AMVs, a comparison of these two products for selected events shows that the mesoscale product better depicts the circulation center of a midlatitude cyclone, boundary layer confluence patterns, and a narrow low-level jet that is well correlated with subsequent severe thunderstorm development. Thus, while the individual experimental mesoscale AMVs may sacrifice some absolute accuracy, they show promise in providing greater temporal and spatial flow detail that can benefit diagnosis of upper-air flow patterns in near–real time. The results also show good agreement between 6-min wind profiler and rawinsonde observations within the 700–200-hPa layer, with larger differences in the stratosphere, near the mean top of the planetary boundary layer, and just above the earth’s surface. Despite these larger differences within select layers, the stability of the difference profile with height builds confidence in the use of 6-min, ∼404-MHz NOAA Wind Profiler Network observations to evaluate and better understand satellite AMV error characteristics.

scholarly journals GEO–GEO Stereo-Tracking of Atmospheric Motion Vectors (AMVs) from the Geostationary Ring

2020 ◽  
Vol 12 (22) ◽  
pp. 3779
Author(s):  
James L. Carr ◽  
Dong L. Wu ◽  
Jaime Daniels ◽  
Mariel D. Friberg ◽  
Wayne Bresky ◽  
...  
Keyword(s):  
Deep Convection ◽  
Motion Vectors ◽  
Fire Smoke ◽  
Atmospheric Motion ◽  
Image Navigation ◽  
Boundary Layer Dynamics ◽  
Vantage Points ◽  
Height Assignment ◽  
Atmospheric Motion Vectors ◽  
Geostationary Ring

Height assignment is an important problem for satellite measurements of atmospheric motion vectors (AMVs) that are interpreted as winds by forecast and assimilation systems. Stereo methods assign heights to AMVs from the parallax observed between observations from different vantage points in orbit while tracking cloud or moisture features. In this paper, we fully develop the stereo method to jointly retrieve wind vectors with their geometric heights from geostationary satellite pairs. Synchronization of observations between observing systems is not required. NASA and NOAA stereo-winds codes have implemented this method and we processed large datasets from GOES-16, -17, and Himawari-8. Our retrievals are validated against rawinsonde observations and demonstrate the potential to improve the forecast skill. Stereo winds also offer an important mitigation for the loop heat pipe anomaly on GOES-17 during times when warm focal plane temperatures cause infrared channels that are needed for operational height assignments to fail. We also examine several application areas, including deep convection in tropical cyclones, planetary boundary layer dynamics, and fire smoke plumes, where stereo methods provide insights into atmospheric processes. The stereo method is broadly applicable across the geostationary ring where systems offering similar image navigation and registration (INR) performance as GOES-R are deployed.

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