The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008
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Figure 3. K-index, précipitable water and cloud analysis image
Figure 4. Multi Sensor Precipitation Estimate
The multi sensor precipitation estimate (MPE) is directly
retrieved from the Eumetsat Website, so even organizations not
operating a ground receiving station can utilize this information,
as this important parameter is affecting people in a very direct
way. To derive the MPE use is made of the relationship
between cloud temperature and rainfall intensity, as colder
clouds are likely to produce more rainfall. The MPE uses a
statistical matching algorithm in temporal and geographical
windows to correlate the SSM/I instrument derived rain rates
with Meteosat IR brightness temperature images. The obtained
relationship is lateron converted to the full Meteosat-9 temporal
resolution and a MPE product is generated each 15 minutes.
The algorithm performs well for the tropical and subtropical
convection areas. The relationship is based upon SSM/I -
Meteosat co-located pixels from 40 degree North to 40 degree
South (Heinemann et al, 2002). With the DMSP program (two
satellites in polar-orbit array), a given location on Earth is
revisited every 6 hours, allowing 4 brigthness temperature
versus rain rate calibration events on a 24 hr basis. The MPE
example presented in figure 4 is of the same day-time as those
given for figures 1, 2 and 3.
Figure 4: The multi sensor precipitation estimate
Atmospheric motion vectors are available at a lower temporal
resolution as the product is generated by applying a correlation
algorithm to a sequence of images. By tracking of the
movement of the cloud field or humidity structures, winds can
be extracted. Height is determined from the infrared
temperature and converted to pressure
(http://www.eumetsat.int). The para-meters extracted from the
BUFR file are longitude, latitude, pressure, wind direction,
wind speed and temperature. The wind direction is classified
into three classes using pressure thresholds (< 35000 Pa, 35000-
65000 Pa and > 65000 Pa) for the high, medium and low wind
vectors which are displayed in red, green and blue respectively.
The approximate elevation in meter (z) is derived according to
an equation as given by Lunde (1980) (z = -
1/0.00001184*(ln(Pa/l01325)) assuming normal pressure at sea
level. For visualization a predefined ILWIS MapView is used,
containing the country boundaries and the classified wind
direction vectors are shown, scaled according to the wind speed.
Figure 5. Atmospheric motion vectors
Figure 6 shows the extracted Fire product. The header lines in
the original ascii file are removed and the space delimited
columns are imported into an ILWIS table, which subsequently
is transformed into a point map an visualized with the country
boundaries. A different symbol size is used for the possible and
probable fires given in the table. The temporal resolution of
this product is also 15 minutes, which makes it ideal for a fire
monitoring system.