@Article{SantekDNWBGGNCNSOLCDB:2019:20AtMo,
author = "Santek, David and Dworak, Richard and Nebuda, Sharon and Wanzong,
Steve and Borde, R{\'e}gis and Genkova, Iliana and
Garc{\'{\i}}a-Pereda, Javier and Negri, Renato Galante and
Carranza, Manuel and Nonaka, Kenichi and Shimoji, Kazuki and Oh,
Soo Min and Lee, Byung-Il and Chung, Sung-Rae and Daniels, Jaime
and Bresky, Wayne",
affiliation = "{University of Wisconsin-Madison} and {University of
Wisconsin-Madison} and {University of Wisconsin-Madison} and
{University of Wisconsin-Madison} and EUMETSAT and {National
Oceanic and Atmospheric Administration (NOAA)} and {Agencia
Estatal de Meteorolog{\'{\i}}a (AEMET)} and {Instituto Nacional
de Pesquisas Espaciais (INPE)} and EUMETSAT and {Japan
Meteorological Agency} and {Japan Meteorological Agency} and
{Seoul National University} and {Korea Meteorological
Administration (KMA)} and {Korea Meteorological Administration
(KMA)} and {National Oceanic and Atmospheric Administration
(NOAA)} and {National Oceanic and Atmospheric Administration
(NOAA)}",
title = "2018 Atmospheric Motion Vector (AMV) intercomparison study",
journal = "Remote Sensing",
year = "2019",
volume = "11",
number = "19",
month = "Oct.",
keywords = "Atmospheric Motion Vectors (AMVs), Intercomparison, Himawari,
CPTEC, INPE, EUMETSAT, JMA, KMA, NOAA, NWCSAF.",
abstract = "Atmospheric Motion Vectors (AMVs) calculated by six different
institutions (Brazil Center for Weather Prediction and Climate
Studies/CPTEC/INPE, European Organization for the Exploitation of
Meteorological Satellites/EUMETSAT, Japan Meteorological
Agency/JMA, Korea Meteorological Administration/KMA, Unites States
National Oceanic and Atmospheric Administration/NOAA, and the
Satellite Application Facility on Support to Nowcasting and Very
short range forecasting/NWCSAF) with JMA's Himawari-8 satellite
data and other common input data are here compared. The comparison
is based on two different AMV input datasets, calculated with two
different image triplets for 21 July 2016, and the use of a
prescribed and a specific configuration. The main results of the
study are summarized as follows: (1) the differences in the AMV
datasets depend very much on the 'AMV height assignment' used and
much less on the use of a prescribed or specific configuration;
(2) the use of the 'Common Quality Indicator (CQI)' has a
quantified skill in filtering collocated AMVs for an improved
statistical agreement between centers; (3) Among the six AMV
operational algorithms verified by this AMV Intercomparison, JMA
AMV algorithm has the best overall performance considering all
validation metrics, mainly due to its new height assignment
method: 'Optimal estimation method considering the observed
infrared radiances, the vertical profile of the Numerical Weather
Prediction wind, and the estimated brightness temperature using a
radiative transfer model'.",
doi = "10.3390/rs11192240",
url = "http://dx.doi.org/10.3390/rs11192240",
issn = "2072-4292",
language = "en",
targetfile = "santek_2018.pdf",
urlaccessdate = "29 mar. 2024"
}