ATSC5210
Kyoko Taniguchi
Precipitation Climatology
Precipitation is the main concern of people when they talk about the weather.
Precipitation affects people’s activities in so many ways in fact. Draughts damage
agricultural products, which leads to food shortage. On the other hand, a concentrated
heavy rain causes disasters, such as flash floods and land sliding. To minimize the
damages from precipitation-related disasters, understanding the precipitation processes is
necessary.
Fig. 1 is the annual mean precipitation of the U.S. The pattern is influenced more
by topography than by latitude. Also, more precipitation is observed over the regions that
are close to oceans. In other words, the ocean-land relationship plays an important role in
precipitations over the U.S. However, those are not the only factors of precipitation. Since
precipitation is part of the hydrological cycle, the temporal variation influences
precipitation pattern. In fact, an inverse relationship is found between the Himalayan snow
depth at the end of May and summer monsoon precipitation amount in India (Zhu et al,
2005). Interestingly, this kind of relationship over the U.S. is not as clear as over the
Himalaya-India region. Therefore, various factors impact the precipitation pattern, and the
monitoring is essential to understand this pattern.
Figure 1: The average annual precipitation over the United States of America (Edlund,
2005).
There are several methods to monitor precipitation, but they can be classified into
two categories: the ground based observation and the remote sensing observation. The
ground based observation has been operated for more than 100 years. One of the common
instruments to measure rain is the rain gauge. The rain gauge can be found in various
fashions, but the basic mechanism is a tipping bucket. As precipitation fills up the bucket,
the bucket would tip over to empty it. The gauge counts the tipping to estimate the rain
amount and rain rate for the location. However, all instruments contain errors, and the rain
gauge is not an exception. The errors can be significant for heavier rain due to the amount
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of rain and the capacity of the bucket of the rain gauges. Conversely, light precipitation
can cause error due to evaporation. This is also a main concern for snow measurement
since some of the instruments melt snow to measure the water equivalent amount, and it
causes some of water to evaporate. Also, wind and turbulence lead to some measurement
errors, especially for snow. Even so, the data is considered as more reliable because the
measurements are taken directly. This observation system is distributed not only over the
land, but also over the oceans. Unfortunately, the distribution is highly heterogeneous,
especially over the oceans. To improve the coverage of oceans, some instruments are
developed, and Automotive Temperature Line Acquisition System, ATLAS, is one
example (Serra et al, 2001). ATLAS is a self-siphoning rain gauge that built on a buoy,
and obtains daily mean precipitation as well as other meteorological measurements, such
as temperature, relative humidity etc. Although ATLAS is certainly collecting valuable
information, the surface observation network is not dense enough to achieve precipitation
climatology.
To fill the gap, the remote sensing method is employed. The biggest advantage of
the remote sensing method is the vast area coverage. Within the remote sensing methods,
two approaches can be made. One is direct measurement, such as radar. In the direct
measurement, an instrument detects signals from the hydrometeors, such as raindrops and
snowflakes. Radar is an active remote sensing system, so it measures the backscattering
from the hydrometeors. Even though some hydrometeors are detected better than others,
radar provides fairly good monitoring. Especially the radar facilities over the U.S. provide
fair coverage (Fig.2). Unfortunately, the range is reduced by limitations of the radar, such
as the fact that signals detected by radar contain higher noise the farther the hydrometeors
are. Therefore, radar is not a way to monitor precipitation over the oceans because radar
facilities are mainly located on the ground.
Figure 2: Radar coverage over the U.S. (NOAA, 2005a)
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To overcome the coverage obstacle, satellite data can be used. Microwave (MI)
measurement is one example. Since all matter emits MI, MI detection is passive. To
estimate the precipitation, hydrometeors’ emission needs to be highlighted from the
background. MI calculation is based on Planck’s radiation law (NASA, 2005b). It is a
function of temperature and emissivity (NASA, 2005a). Over the ocean, MI emission is
nearly constant, and its emissivity is about 0.5. In contrast, raindrops have unity
emissivity, so they appear to be warmer over the ocean, which is then easier to treat as
background. Therefore, the moist atmosphere seems more outstanding over oceans.
Unfortunately, the emission over land varies due to the surface characteristics. Hence, it is
relatively harder to identify precipitation over land. Ferraro et al presented algorithms to
enhance MI detection over land (2000). The algorithm utilizes brightness temperature (Tb)
from several MI channels. With these Tb values, it is possible to detect precipitation over
relatively difficult regions, such as snow covered grounds (emissivity of 0.4 to 0.8,
roughly) or desert and semi-arid regions (emissivity around 0.95). Also, the scattering
index (SI), which is the Tb difference between two channels, can be used to separate two
different types of rain system (stratiform and convective) which allows improving
accuracy of the estimation. Unfortunately, MI detection requires larger antenna that can be
carried only on polar orbiting satellites. Although the Tropical Rainfall Measuring
Mission, TRMM, offers more frequent MI images over tropic and sub-tropic regions
(NASA, 2005b), data is still poorly sampled in time and space to use global precipitation
detection (Fig. 3).
Figure 3: Precipitation rate with microwave measurement from NOAA-17 (ascending)
(NOAA, 2005b). Notice that time differences exist within the image.
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Unlike direct measurement, indirect measurement is given by estimated
precipitation from probable conditions of precipitation based on observations. In this
method, the estimation is less accurate than the direct measurements, but it can provide
better temporal samples. One of the accepted algorithms is the GPI (GOES Precipitation
Index). As the name indicates, this algorithm uses the IR data from geostationary satellite
to obtain the fractional area which satisfies a threshold temperature. With the fractional
area, precipitation amount is estimated based on the set rate of 3 mm/hour. GPI is used to
estimate the precipitation. However, some studies show that the threshold temperature,
235K, is not suitable for all regions. According to Arkin and Meisner (1987), GPI method
works best with 220K as a threshold temperature in extratropic regions while it works fine
with 235K in tropics. In addition, Negri and Adler indicated unsuitableness of the GPI
method around Japanese islands (1993). This is due to the characteristics of the method.
Originally, this method was developed specifically for the tropical oceans. Therefore, the
method is most sensitive to convective precipitation. However, frontal system and/or
orographic effects also play a role in higher latitudes. Precipitation is a combination of
these mechanisms, especially around Japanese islands. As a result, some errors are
introduced. In fact, larger errors are observed over land. Hence, the GPI method requires
calibrations for various continental area and seasons based on climatology (Kebe et al,
2005).
To take advantage of both direct and indirect remote sensing methods, combination
precipitation detection is also developed. With radar reflectivity (Z), the Area-Time
Integral, ATI, method estimates rain rate based on the Z-R relation:
Z = aRb
where a and b are coefficients and R is rain rate. Since numerous relations for specific
conditions have been established over the last 60 years, a and b can be easily found in the
literature (Sauvageot, 1992). This method is now going beyond as it is applied to
Geostational satellite data via GPI method (Kebe et al, 2005). In the study, this ATI
method is proved to be sensitive enough to detect precipitation even in low annual rainfall
regions, i.e. arid climate, as well as tropical climate over both ocean and land.
In the Auto-estimator method, radar measurement and other parameters from
observation modify GOES data based calculation (Vincente et al, 1998). It delivers
reliable real-time products, which are suitable for flash floods prediction. However, this
method is not appropriate for long-term precipitation estimation, particularly stratiform
cloud systems and wintertime.
Another approach is MI measurement to improve GPI, known as NexSat (NexSat,
2005). By co-locating both detection data, GOES data based estimation employs better
threshold temperature to estimate precipitation for each location. Hence, the estimation is
more locally accurate and still close to real time products can be obtained. This method
provides two kinds of products: instantaneous precipitation rate and accumulation amount.
The rate product is beneficial to improve the forecast, while the accumulation product is
effective not only for hydrological studies, but also for disaster managements. Both are
available between 60ºN/S latitude every 3 hours. Products within 40ºN/S latitude are
highly especially reliable due to the TRMM coverage.
In Global Precipitation Climatology Project, GPCP, several data types are
combined to provide mean precipitation of various length periods (Huffman et al, 1997).
The estimation from MI data is calculated with different algorithms for ocean and land.
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Emission estimation is performed over ocean based on the Tb, while scattering estimation
is over land with SI. The MI result is combined with GPI estimation of IR GOES to
produce multi-satellite (MS) products. With some adjustments, the MS products and
gauge data are put together in satellite/gauge (SG) product (Fig.4). The SG products show
fair agreement with the climatology, so that complete global coverage with extended data
period is awaited.
Figure 4: GPCP satellite/gauge Global Precipitation analysis based on data from 1979 to
2002 (NASA, 2005c).
As indicated above, every method has advantages and disadvantages. To improve
the satellite measurements, Algorithm Intercomparison Projects (AIP) and Precipitation
Intercomparison Projects (PIP) series are operated (Smith et al, 1998). Accordingly those
studies, the most favorable product is different for each application. To help selecting the
product for a specific application, the Climate Rainfall Data Center, CRDC, provides
several services online (Berg and Kummerow, 2005). CRDC not only offers the
information for individual products, but also allows comparing between the available
products because none of the data product can represent the “absolute truth.” Support from
product experts is available in order to gain technical knowledge as well.
Currently, more improvement has been made to enhance precipitation systems e.g.
additional channels, precipitation radar (NASA, 2005b), etc.
In addition to the
intercomparison projects, the improvements of instruments will polish the precipitation
detection and the precipitation climatology.
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Reference
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