Global Precipitation Monitoring - Satellite-based Applications on Climate Change - page 84

Fig. 6.2 NRL passive MW composite, overlaid on the global IR composite that was presented in
Fig. 6.1 . The various blue color shades indicate the intensity of rainfall derived from the array of
MW sensors used in this composite (e.g., SSM/I, AMSU, TMI, and AMSR-E)
the TRMM satellite is the first instrument designed to measure rain from space
(Kummerow et al. 1998 ; Iguchi et al. 2000 ). Although an excellent source of
rainfall information directly, its primary purpose is to be used in conjunction with
the more widely available satellites that contain only PMW sensors. With coinci-
dent TMI and PR data, significant advances have been made over the TRMM
mission life to improve the current state of passive MW retrievals through the
better understanding of precipitation and cloud microphysics (Hirose et al. 2009 ;
Gopolan et al. 2010 ; Wang et al. 2009 ).
Launched in April 2006, CloudSat is the first millimeter wavelength cloud radar
in space (Stephens et al. 2002 ). The Cloud Profiling Radar (CPR) aboard CloudSat
operates at 94 GHz as opposed to 13.8 GHz on PR; thus, it is sensitive to clouds and
light precipitation rates, including snowfall (e.g., Matrosov 2007 ; L'Ecuyer and
Stephens 2002 ; Liu 2008 ). CloudSat has recently been instrumental in advancing
the understanding of cold season precipitation and our ability to retrieve it from
PMW sensors.
6.3 Multisensor Global Rainfall Products
The sensors discussed so far each has their limitations which make them unsuitable
for use in certain situations. For example, PMW estimates over the ocean might be
more accurate than GEO-IR estimates, but the latter are better suited for studies of
the diurnal cycle due to the superior sampling obtained from a GEO satellite. On the
other hand, most GEO-IR sensors only cover a limited geographic domain, whereas
the LEO essentially covers the entire globe. By optimizing the strengths and
weaknesses of each data source and combining them with surface measurements
where they are deemed to be the most reliable, combined datasets can be superior to
estimates from individual sensors. This section will describe some of these
“merged” rainfall datasets and present some examples of their utility for global
climate monitoring.