TY - JOUR
T1 - Vientos—A New Satellite Mission Concept for 3D Wind Measurements by Combining Passive Water Vapor Sounders with Doppler Wind Lidar
AU - Zeng, Xubin
AU - Su, Hui
AU - Hristova-Veleva, Svetla
AU - Posselt, Derek J.
AU - Atlas, Robert
AU - Brown, Shannon T.
AU - Dixon, Ross D.
AU - Fetzer, Eric
AU - Galarneau, Thomas J.
AU - Hardesty, Michael
AU - Jiang, Jonathan H.
AU - Kangaslahti, Pekka P.
AU - Ouyed, Amir
AU - Pagano, Thomas S.
AU - Reitebuch, Oliver
AU - Roca, Remy
AU - Stoffelen, Ad
AU - Tucker, Sara
AU - Wilson, Anna
AU - Wu, Longtao
AU - Yanovsky, Igor
N1 - Publisher Copyright:
© 2024 American Meteorological Society. All rights reserved.
PY - 2024/2
Y1 - 2024/2
N2 - It is challenging to accurately characterize the three-dimensional distribution of horizontal wind vectors (known as 3D winds). Feature-matching satellite cloud top or water vapor fields have been used for decades to retrieve atmospheric motion vectors, but this approach is mostly limited to a single and uncertain pressure level at a given time. Satellite wind lidar measurements are expected to provide more accurate data and capture the line-of-sight wind for clear skies, within cirrus clouds, and above thick clouds, but only along a curtain parallel to the satellite track. Here we propose Vientos—a new satellite mission concept that combines two or more passive water vapor sounders with Doppler wind lidar to measure 3D winds. The need for 3D wind observations is highlighted by inconsistencies in reanalysis estimates, particularly under precipitating conditions. Recent studies have shown that 3D winds can be retrieved using water vapor observations from two polar-orbiting satellites separated by 50min, with the help of advanced optical flow algorithms. These winds can be improved through the incorporation of a small number of collocated higher-accuracy measurements via machine learning. The Vientos concept would enable simultaneous measurements of 3D winds, temperature, and humidity, and is expected to have a significant impact on scientific research, weather prediction, and other applications. For example, it can help better understand and predict the preconditions for organized convection. This article summarizes recent results, presents the Vientos mission architecture, and discusses implementation scenarios for a 3D wind mission under current budget constraints.
AB - It is challenging to accurately characterize the three-dimensional distribution of horizontal wind vectors (known as 3D winds). Feature-matching satellite cloud top or water vapor fields have been used for decades to retrieve atmospheric motion vectors, but this approach is mostly limited to a single and uncertain pressure level at a given time. Satellite wind lidar measurements are expected to provide more accurate data and capture the line-of-sight wind for clear skies, within cirrus clouds, and above thick clouds, but only along a curtain parallel to the satellite track. Here we propose Vientos—a new satellite mission concept that combines two or more passive water vapor sounders with Doppler wind lidar to measure 3D winds. The need for 3D wind observations is highlighted by inconsistencies in reanalysis estimates, particularly under precipitating conditions. Recent studies have shown that 3D winds can be retrieved using water vapor observations from two polar-orbiting satellites separated by 50min, with the help of advanced optical flow algorithms. These winds can be improved through the incorporation of a small number of collocated higher-accuracy measurements via machine learning. The Vientos concept would enable simultaneous measurements of 3D winds, temperature, and humidity, and is expected to have a significant impact on scientific research, weather prediction, and other applications. For example, it can help better understand and predict the preconditions for organized convection. This article summarizes recent results, presents the Vientos mission architecture, and discusses implementation scenarios for a 3D wind mission under current budget constraints.
KW - Convective storms
KW - Dynamics
KW - Machine learning
KW - Satellite observations
KW - Wind
KW - Wind shear
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U2 - 10.1175/BAMS-D-22-0283.1
DO - 10.1175/BAMS-D-22-0283.1
M3 - Article
AN - SCOPUS:85186124126
SN - 0003-0007
VL - 105
SP - E357-E369
JO - Bulletin of the American Meteorological Society
JF - Bulletin of the American Meteorological Society
IS - 2
ER -