Environmental Engineering Reference
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areas, points to satellite observation as the only viable mean for global-scale rainfall
monitoring. Over the ocean, marine observations by ships and buoys have been the
major source of rainfall observations. Efforts to document and analyze these marine
observations have pointed to lack of standards of measurement, inadequate sam-
pling as major sources of uncertainty. Major efforts, such as the International
Comprehensive Ocean-Atmosphere Data Set (ICOADS), have been undertaken to
collect, document, and quality control these observations (Woodruff et al. 1987 ,
2011 ). The advent of satellite and sensor technology that began in the late 1960s
ushered in a new era of geophysical monitoring techniques for operational and
climate applications (Acker et al. 2002 ).
Early work of oceanic rainfall relies on visible and infrared observations of
cloud type and extent (see Barrett and Martin 1981 ; Acker et al. 2002 ; Chiu 2011 ).
During the Global Atmospheric Research Experiment (GARP) Atlantic Tropical
Experiment (GATE) conducted in 1974, Arkin ( 1979 ) found a tight relation
between the total areal rainfall as estimated from shipborne radar data taken and
the area of high clouds within the observation area. He developed a GOES Precipi-
tation Index (GPI). The GPI expresses the total space/time rainfall as the total areas
of high clouds (with cloud top temperature of
235 K) multiplied by a constant rain
rate of 3 mm/day. This technique has been extended to other geosynchronous
satellites and has proven to work well in the tropics if high non-raining cirrus
clouds are excluded (Chiu et al. 1993 ). This observation is consistent with the so-
called area-time integral in radar rainfall estimation and the threshold techniques in
estimating space/time rainfall (Lovejoy and Austin 1979 ; Inoue 1987 ; Chiu 1988 ;
Chiu and Kedem 1990 ). Follow-on development includes the partitioning of the
cloud areas into convective and stratiform rain, technique to discriminate non-
raining high cirrus, and their merging with microwave rainfall measurements to
improve the space/time sampling (Acker et al. 2002 ; Chiu 2011 ; Chokngamwong
and Chiu 2009 ; Huffman et al. 1997 ).
Microwave remote sensing of rain is especially suited over the ocean. In the
microwave regime, the emissivity of the sea surface decreases with temperature;
hence, the sea surface acts as a fairly constant dark background against which
highly emissive raining hydrometeors can be distinguished. Since the first launch of
the Electrically Scanning Microwave Radiometer onboard NASA's NIMBUS 5
satellite (Wilheit et al. 1977 ), our understanding on the use of microwave in rainfall
estimation has greatly improved. This is propelled by a long record of the Special
Sensor Microwave Imager (SSM/I) data taken on board the Defense Meteorological
Satellite Program (DMSP) satellites and a focused international effect of the
Tropical Rainfall Measuring Mission (TRMM, Kummerow et al. 2000 ).
While satellite observations provide snapshots of the raining conditions, the revisit
time tends to be long compared to the timescale of rain cells. These small-scale rain
events are likely to be under-sampled, thus leading to a bias in the estimation of
space/time rainfall. This chapter describes a technique to estimate space/time oceanic
rainfall frommicrowave radiometry that takes account of the interactions between the
microwave radiation and the falling hydrometeors and the characteristics of the rain
fields. The microwave emission-based brightness temperature histogram technique,
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