Land_Ecosystems.pdf - S?TE

21 2 EDS SCIENCE PIN - CHAPVER S
ability of weather/climate forecasts. Precipitation clima
tology may come to be considered as important as are
temperature anomalies in the current debate about climate
change and its impact on critical human infrastructures
because of those systems’ sensitivity to rapid change
(Shuttleworth 1996).
There are significant physical contrasts between
snow- and non-snow-covered areas, but an assessment of
snow properties (such as snow water equivalent [SWE]
and depth) is difficult to achieve in topographically rough
terrain where typical patch-length scales may be on the
order of 100 m. Nonetheless, satellites have and will con
tinue to play a significant role in the real-time monitoring
of many cryospheric parameters. The frequency of ab
normal weather patterns coupled with the volatility of the
spring snow pack make cryospheric variables a sensitive
and critical parameter in the context of hydrologic sci
ence. More details of cryospheric factors that influence
the hydrological cycle can be found in Chapter 6.
Current capabilities: Until recently, data regard
ing rainfall and snow accumulation were obtained
primarily using networks of rain gauges and snow courses.
Advanced ground-based radar systems, such as the Next
Generation Weather Radar (NEXRAD) Weather Surveil
lance Radar (WSR)-88D in the U.S. (Crum et al. 1993),
now represent the state of the art in precipitation mea
surement, and techniques for compositing the radar data
with ground-level gauge data are under development.
These techniques still need improvement (Smith et al.
1996), particularly in mountainous terrains. Outside of
the most-developed countries, existing and projected
gauge and ground-based radar networks are unable to pro
vide adequate coverage. For this reason satellite methods
hold great promise.
EQS capabilities: Remote sensing of precipitation
is a key component of Earth Science Enterprise missions
such as TRMJvI and EOS PM-I. Several EOS sensors such
as TRMM Microwave Imager (TMI), Visible Infrared
Scanner (VIRS), Precipitation Radar (PR), AMSU, and
AMSR have been specifically designed for the collection
of data relating to precipitation and clouds. The TRMM
mission, launched in 1997 and conducted in cooperation
with Japan, measures the diurnal variation of precipita
tion in the tropics. Precipitation is measured at a spatial
scale of 43 km using a PR (13.8 GHz) between ±30°N
(220-km swath) and a 5-channel TMI (10-9 1 GHz, dual
polarization) between ±38°N (760-km swath). Further, a
5-channel visible/infrared imaging radiometer (VIRS)
provides data at a nominal 2-km resolution at nadir and
1500-km swath. The mission is expected to provide at
least three years of rainfall and climatological observa
tions for the tropics. These unique data Sets will facilitate
FIGuRE 5.9
A schematic diagram of components of the global hydrologic cycle.
detailed studies of the rainfall climatology in areas domi
nated by the Inter-Tropical Convergence Zone (ITCZ),
continental monsoons, hurricanes, and the El Niflo-South
em Oscillation (ENSO) cycle. In addition, because these
data will be collected simultaneously with multiple sen
sors they will facilitate analyses of sensor capabilities
leading to improvements in the rainfall estimation tech
niques that employ data from the geosynchronous Special
Sensor Microwaveflmager (SSM/J), the Geostationary
Operational Environmental Satellite (GOES), and the
NOAA series of satellites.
The EOS PM-I mission is expected to be equipped
with an AIRS (infrared sounder) and two microwave
sounders (AMSU and HSB). These three instruments will,
together, provide information regarding precipitation vol
umes, cloud thickness, and cloud water content.
IDS team contributions: Because precipitation
combines and interacts with the landscape, topography,
land use, and soil moisture with great variability, synop
tic and climatological-scale precipitation is of little use in
predicting the hydrologic response of the land. Local in
ferences cannot safely be drawn from regional
precipitation climatologies and data sets, regardless of the
methods used to build these data sets. Therefore, a top
priority of several IDS teams is to investigate methods of
distributing observed rainfall in both time and space.
The IDS team led by Kerr and Sorooshian has con
tributed to this by successfully using historical rainfall
climatologies and land-surface topography to distribute
subgrid-scale precipitation (Gao and Sorooshian 1994).
The lOS team led by Lau is participating in rainfall algo
rithm development in conjunction with the TRMM
mission, using both single and combined satellite sensors.
The three IDS teams, led respectively by Barron, Brewer,
and Dickinson, will be using the precipitation estimates
provided by AMSR and AIRS/AMSU in their modeling
studies. The 11)S team led by Dozier has been working on
estimating snow cover (Rosenthal and Dozier 1996) and