Low/Mid-Level Infrared AMV
Atmospheric Motion Vectors are derived using a sequence of three images.
Features are targeted in the second image (cirrus cloud edges, gradients in
water vapor, small cumulus clouds, etc.) are tracked within the first and
third images yielding two displacement vectors. These vectors are
averaged to derive the final wind vector.
Vector heights are assigned in a two-step process. The first utilizes the measured
radiances of the target and is based on the spectral response function of the
individual satellite and channel being sampled. The brightness temperature
of the target is derived from this radiance measurement. Once determined,
the brightness temperature is compared with a collocated numerical model
guess temperature profile, from which an initial height is estimated. The
final vector height is derived in the post-processing of the vector field.
CIMSS runs the raw winds through two quality control processes to assure
vector correctness and uniformity. The first process called "autoediting",
a two-stage, three-dimensional objective analysis of the wind field (Hayden and Pursor, 1995).
This scheme utilizes conventional data assimilation, neighboring wind "buddy"
checks, and numerical model analyses for wind vector editing and height
adjustments (Velden et al. 1997).
The second process is the EUMETSAT "Quality Indicator" (QI) methodology
(Holmlund et al., 2001). The QI is a statistically-based scheme which highlights
internal consistancy between vectors without use of a background numerical model.
Mid-level infrared AMVs are utilized to monitor the atmospheric
motion in the lower and middle troposphere (above the boundary layer),
typically between 500mb and 950mb
(explanation of heights) by
tracking cloud edges over a sequence of infrared imagery.
Low-level rotation of developing tropical waves and mid-level steering currents
can be identified by tropical cyclone (TC) forecasters
to help determine current atmospheric conditions that could affect TC development,
intensity change, and movement (Velden et al., 1998).
In addition, AMVs can be imported into regional and global numerical models to
provide information within data sparse regions, such as over the oceans and land regions
with little or no atmospheric monitoring capabilities. AMVs have been shown to have
a significant positive impact on the accuracies of numerical models
(Soden et al., 2001 and Goerss et al., 1998).
Holmlund, K., C. Velden, and M. Rohn, 2001: Enhanced Automated Quality Control
Applied to High-Density Satellite-Derived Winds. Mon. Wea. Rev., 129, 517-529.
Soden, B. J., C. Velden, and R. Tuleya, 2001: The Impact of Satellite Winds
on Experimental GFDL Hurricane Model Forecasts. Mon. Wea. Rev., 129, 835-852.
Velden, C. S., T. Olander, and S. Wanzong, 1998: The Impact of Multispectral
GOES-8 Wind Information on Atlantic Tropical Cyclone Track Forecasts in 1995.
Part 1: Dataset Methodology, Description and Case Analysis. Mon. Wea. Rev., 126, 1202-1218.
Goerss, J., C. Velden, and J. Hawkins, 1998: The Impact of Multispectral GOES-8
Wind Information on Atlantic Tropical Cyclone Track Forecasts in 1995. Part 2:
NOGAPS Forecasts. Mon. Wea. Rev., 126, 1219-1227.
Velden, C. S., C. Hayden, S. Nieman, W. Menzel, S. Wanzong, and J. Goerss, 1997:
Upper-Tropospheric Winds Derived from Geostationary Satellite Water Vapor
Observations. Bull. Amer. Meteor. Soc., 78, 173-195.
Hayden, C. M., and R. Purser, 1995: Recursive Filter Objective Analysis of
Meteorological Fields: Applications to NESDIS Operational Processing. J. Appl.
Meteor., 34, 3-15.
Additional AMV-related references available.
For more AMV information, please visit the
International Winds Working Group (IWWG)