Geoscience Reference
In-Depth Information
Climatology [MOU 90] as well as the many theses written in France [TAB 89;
DUB 94; WAH 97] or in Brazil [LOM 89; MEN 95], where data produced by
remote sensing proved to be extremely useful. More recently, the initiative created
by the Research group Meteosat Second Generation of the French national center
for scientific research has meant more collaboration between different geographers
in order to produce and process Meteosat Second Generation images. The aim of
this chapter is to analyze the use of satellites, which have greatly improved our
knowledge of the spatialization of climate data over the past 30 years [KER 04]. An
explanation will be given as to why satellite data plays a decisive role when it comes
to increasing the density of geographical information for research that is carried out
in regions in which there are not many conventional climatological stations
available. For example, in Mato Grosso the rapid expansion of crops has led to an
increasing demand on the climate. After an overview of the available satellite data,
we will use examples of cases where geostationary data are used to estimate rainfall
levels, and where SPOT-vegetation satellite data are used to estimate the extension
of vegetation and crop cover.
3.2. The development phases of meteorological satellites
3.2.1.
Insufficient sources on the land
Up until the 1960s and the 1970s, the basic data used were measurements that
were taken on the ground. This involved observing and recording data taken in
stations that were located on the land itself and which was subject to strict rules
enforced all over the world by the World Meteorological Organization (WMO), by
Météo-France in France and by the National Institute of Meteorology in Brazil. The
data available are limited and extremely localized, and are treated with care over the
long term. In order for different datasets to be compared, these observations and
recordings need to be standardized (this is generally the case for any given country),
for example, the temperature recordings need to be measured at a specific level
(generally 2 m). The quality of the information available varies depending on the
type of material used and on the density of the network of climatological stations.
Using different climate data can also lead to the creation of certain problems
[ER30 81]. First, it is necessary to highlight the uneven quality of the observations:
reading errors, as well as problems with the climatological stations, are factors that
need to be considered whenever figures are analyzed. It is sometimes possible to
detect such errors and even correct them by using simple statistical methods.
However, one problem remains. The measuring equipment can sometimes be
unsuitable for use in turbulent atmospheric conditions, for example, rain gauges
tend to overflow during periods of heavy rain (this happened during a storm in
Nimes, Southern France, in 1988); anemometers are sometimes blown away by
strong gusts of winds (winds similar to hurricane force) and heavy storms, such as
that which occurred in Western France in 1987; these are examples of extreme
weather conditions that are not always considered when it comes to analyzing
climate data.
