Accurate Real-time Navigation of AVHRR data
at high latitudes
Adam Dybbroe#, Pascal Brunel§, Anne Marsouin§, and Anke Thoss#
Adam.Dybbroe@smhi.se
#: Swedish Meteorological and Hydrological institute (SMHI), S-60176 Norrköping, Sweden
§: Centre de Météorologie Spatiale, Météo-France, BP 147, 22302 Lannion Cedex, France,
Norrköping, SMHI
Introduction
Smidbjerg, DMI
NOAA14
The position of an AVHRR footprint depends on
time, satellite position and velocity, satellite
attitude (its orientation) and radiometer viewing
geometry. The radiometer geometry is known
before launch. Time is usually available, e.g.
through the satellite time corrected from the
satellite clock error, or from an independent
clock. Satellite position and velocity may be
calculated by an orbit prediction model ingesting
daily bulletins (e.g. TBUS). The remaining
unknown is the satellite attitude, or in fact how
the actual attitude deviates from its nominal
value.
It is well known that without any kind of special
post-processing locally received NOAA AVHRR
data may have navigational errors causing the
AVHRR footprints to be displaced several
kilometres (not seldom around 10 or more).
These errors fluctuates in time (significant
changes from day to day is the normal) as can be
seen from figure 1, and are caused by deviations
between the nominal satellite attitude and the
actual, unknown, attitude.
Exact navigation may not be crucial when data
are used only for image display at the forecaster’s
desk. But inaccurate navigation may impose
serious problems when data are used as input to a
physical retrieval, like e.g. an objective cloud
classification scheme (see figure 2). Most
modern geophysical satellite retrievals rely on
other data sources like e.g. high-resolution land
Lannion, CMS
NOAA16
Figure 1: RMS distance error in km for NOAA14
(top panel) and NOAA 16 (lower panel) during April
and May 2001, using nominal attitude (red dashed)
and after having applied the navigation adjustment
(blue).
Figure 2: Nowcasting SAF Cloud Mask assuming zero attitude
(left) and with navigation adjustment using ANA (right) over
the Norwegean Sea and Lofoten (Norway) April 8, 2001, 11:05
UTC, NOAA16. Blue and green are cloud free sea and land
respectively, and white is either snow cover or sea ice. Clouds
are either grey (cloud contaminated pixels) or orange (cloud
filled pixels). Notice that the poor navigation causes an
artificial increase in cloud cover along the coast, and that snow
cover on land may be taken as sea-ice.
use, and without precise navigation of the satellite
data it is difficult to make full use of such data.
Some retrievals rely on data from several satellite
overpasses (e.g. snow cover mapping or SST’s),
and are thus particular sensitive to fluctuations in
navigation errors.
The actual satellite attitude can be estimated if an
adjustment is performed on the raw data
(navigated using the nominal attitude) using
known landmarks, as has been done operationally
at CMS, Météo-France since 1990, using their
Automatic Navigation Adjustment (ANA)
technique.
Attitude (Yaw, Roll and Pitch) errors during April and May 2001, as estimated by ANA
at SMHI Norrköping (red) and at CMS Lannion (blue):
NOAA 14 yaw error
NOAA 14 roll error
NOAA 14 pitch error
For the material discussed in this poster
we used NOAA HRPT data from these
three receiving stations. The DMI station
served as a backup for the station at
Norrköping during temporary antenna
failures in April and May 2001.
ANA at SMHI - Preliminary results
In January 2001 a first ANA users workshop was held at the Centre de
Météorologie Spatiale, Météo-France, in Lannion, with the participation of
SMHI, as well as the Danish and Norwegian Meteorological institutes. In
spring 2001 the ANA software was installed along side AAPP (version 2.8) in
a development environment at SMHI, and tested on locally received HRPT
data.
In our first tests we have been running ANA without the MAIA cloud mask.
This set-up proved still to be able to eliminate cloudy landmarks (no land-sea
mask or bad correlation) and the results showed to be in good agreement with
those at CMS. Except for the pitch error in the beginning of April, there are no
significant deviations in the attitude angles as estimated at SMHI and CMS.
The solar activity was very high in early April 2001 causing large errors in the
orbit prediction which show up as large pitch errors. From April 4 till April 8
the TBUS messages were not updated at SMHI, probably explaining the large
deviations between the CMS and SMHI estimates. It can be seen that there
seem to be a bias in the roll of NOAA 16 of around 0.5 to 1 mrad. The pitch
fluctuates a lot even during normal solar activity and it is quite large for
NOAA 16 (~5-10 mrad) during this period. It is, however, important to notice
that the pitch error also include other errors (orbit prediction and clock errors).
ANA is now installed in a semi-operational environment at SMHI for the
production of the prototype cloud and precipitation products of the Satellite
Application Facility (SAF) to support Nowcasting. See www.smhi.se/saf,
Dybbroe et al. (2000), Thoss et al. (2001) and Dybbroe et al. (2001) for a
description of the AVHRR & AMSU/MHS based cloud and precipitation
products and algorithms developed at SMHI.
Future plans
NOAA 16 yaw error
NOAA 16 roll error
NOAA 16 pitch error
The preliminary results during spring summer and autumn at high latitudes are
promising. However, a clear weakness of ANA is the nighttime land-sea
discrimination method (also used for twilight conditions) which depends on a
single Tb4-Tb5 histogram. Depending on the season, geographical area and
hour in the day, this method may fail to build the land-sea mask of a viewed
landmark, which reduce the number of images where the navigation can be
successfully adjusted.
The high-latitude winter season with little or no daylight, snow cover on the
ground and sea ice along the coasts, and cold land and water surfaces, is
particularly problematic. With the technique as implemented today ANA is
expected to fail on a major part of the satellite passes received at SMHI
Norrköping during the winter season (November-March).
Therefore, a small project has been set up at SMHI with support from the
Swedish National Space Board (SNSB) in order to improve on the performance
of ANA in general and at high latitudes in particular. Our focus will be on
trying to improve on the nighttime algorithm, making use of more image
features than just the current Tb4-Tb5.
Acknowledgement
What is ANA?
The Automatic Navigation Adjustment (ANA) technique was developed at
CMS Météo-France in the 1980ties, to provide highly accurate geo-located
AVHRR data. ANA combines a physical image deformation model and
automatic adjustment on coastal landmarks. The navigation adjustment is
done in satellite coordinates allowing interpreting the landmark navigation
errors in terms of satellite attitude: yaw, pitch and roll. In short the
adjustment involves the following six steps:
1) A generation of a set of coastal landmarks from the World
Vector Shoreline database (obtained freely with the Generic
Mapping Tools: http://gmt.soest.hawaii.edu/). Done only once for
your local receiving station.
2) Landmark location: Using the physical deformation model and
the nominal attitude the landmarks inside the swath are converted to
binary land-sea images expressed in satellite coordinates. Each such
reference window is associated with an actual window in the
AVHRR image, which is centred on the calculated landmark
position and fully containing the reference window.
3) Cloud mask: A cloud mask may be derived over every actual
window and those that are too cloudy are rejected. This step is
presently omitted at SMHI.
4) For each “cloud free” (cloud free or nealy cloud free as
determined by the cloud mask, e.g. MAIA) window a binary landsea image is constructed, using AVHRR channel 1 and 2 for
daytime conditions, and channel 4 and 5 at night.
5) A similarity coefficient is calculated for all possible
displacements. The displacement corresponding to the maximum
of the similarity coefficient gives the landmark navigation error
expressed in line and pixel numbers.
6) Attitude estimation: Assuming the attitude error is constant over
the whole image, it can be estimated by the rms resolution of a
system involving the measured landmarks position in the image
and their true latitudes and longitudes.
See Bordes et al. (1992) and Brunel and Marsouin (2000) for a detailed
description of ANA.
The ANA software has recently been made compatible with the
EUMETSAT ATOVS and AVHRR processing package, AAPP, version
3.0, which is becoming a standard also for processing AVHRR data. The
software is available on request, as such, without any commitment to
further maintenance. It is described in a User's Manual. Contact at CMS:
pascal.brunel@meteo.fr
The NOAA reception at SMHI Norrköping failed during major parts (27-30
April and 17 –31 May) of the study period discussed, due to severe problems
with the antenna. But thanks to DMI, and in particular Søren Andersen who
made their locally received NOAA HRPT level 0 data accessible to us we
managed to keep a continuous data series for research and development.
This work is part of a project sponsored by the Swedish National Space Board
and SMHI.
References
Bordes, P., Brunel, P. and Marsouin, A., 1992: Automatic Adjustment of AVHRR Navigation.
Journal of Atmospheric and Oceanic Technology, Vol. 9, No. 1, 15-27.
Brunel, P. and Marsouin, A., 2000: Operational AVHRR navigation results. Int. J. of Remote
Sensing, vol. 21, no. 5, 951-972.
Dybbroe, A., Thoss, A. and Karlsson K.-G., 2000: The AVHRR & AMSU/MHS products of the
Nowcasting SAF. Proceedings of the 2000 Eumetsat Meteorological Stallite Data Users’ Conference,
Bologna, Italy, pp. 729-736. ISBN: 92-9110-037-4.
Dybbroe, A., Thoss, A. and Karlsson, K.-G., 2001: Validation of Nowcasting SAF Polar platform
products. Proceedings of the 2001 Eumetsat Meteorological Satellite Data Users’ Conference,
October 1-5, Antalya, Turkey, pp. 344-451. ISBN: 92-9110-044-7.
Thoss, A., Dybbroe, A. and Bennartz, R., 2001: The Nowcasting
SAF Precipitating Clouds
Product. Proceedings of the 2001 Eumetsat Meteorological Satellite Data Users’ Conference, October
1-5, Antalya, Turkey, pp. 399-406. ISBN: 92-9110-044-7.