Abstract
Global wind observations are fundamental for studying weather and climate dynamics and for operational forecasting. Most wind measurements come from atmospheric motion vectors (AMVs) by tracking the displacement of cloud or water vapor features. These AMVs generally rely on thermal infrared (IR) techniques for their height assignments, which are subject to large uncertainties in the presence of weak or reversed vertical temperature gradients near the planetary boundary layer (PBL) and tropopause folds. Stereo imaging can overcome the height assignment problem using geometric parallax for feature height determination. In this study we develop a stereo 3D-Wind algorithm to simultaneously retrieve AMV and height from geostationary (GEO) and low Earth orbit (LEO) satellite imagery and apply it to collocated Geostationary Operational Environmental Satellite (GOES) and Multi-angle Imaging SpectroRadiometer (MISR) imagery. The new algorithm improves AMV and height relative to products from GOES or MISR alone, with an estimated accuracy of <0.5 m/s in AMV and <200 m in height with 2.2 km sampling. The algorithm can be generalized to other LEO-GEO or LEO-LEO combinations for greater spatiotemporal coverage. The technique demonstrated with MISR and GOES has important implications for future high-quality AMV observations, for which a low-cost constellation of CubeSats can play a vital role.
Highlights
Atmospheric motion vectors (AMVs) derived by tracking cloud and water vapor features in satellite imagery have been one of the key observations used in numerical weather prediction (NWP) systems [1,2]
Accuracy of height assignment was assessed in a number of studies by comparing Moderate Resolution Imaging Spectroradiometer (MODIS) and geostationary satellite cloud top heights (CTHs) with CTHs from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and other independent measurements [4,5,6,7]
We demonstrate the benefits of our 3D-Winds algorithm using data from the Multi-angle Imaging SpectroRadiometer (MISR) instrument and the new Geostationary Operational Environmental Satellite series-R (GOES-R) satellites over the Contiguous United States (CONUS)
Summary
Atmospheric motion vectors (AMVs) derived by tracking cloud and water vapor features in satellite imagery have been one of the key observations used in numerical weather prediction (NWP) systems [1,2]. In addition to the tropics, Gelaro and Zhu [3] identified other regions where forecast error is most sensitive in observing system experiments (OSEs) These regions are often associated with strong atmospheric energetics and baroclinic instability where radiosonde data are sparse and NWP must depend heavily on satellite AMVs. One of the main uncertainties associated with the AMV measurements is the height assignment, which can prevent AMVs from being assimilated into NWP systems, especially in the presence of a strong vertical wind shear and complex thermal structures (e.g., tropopause folding, boundary layer inversion). Clouds may change shape or grow over the period of consecutive images but must not change shape so much as to be unrecognizable as the previous pattern This requirement may be limited by latitude-dependent cloud spatiotemporal variability. The results from this study highlight the potential of synergistic observations between advanced GEO imagers and a LEO constellation of CubeSat multi-angle sensors for future 3D global wind observations
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