Journal of the Meteorological Society of Japan, Vol. 87A, pp. 237–253, 2009
DOI:10.2151/jmsj.87A.237
237
TRMM 2A12 Land Precipitation Product – Status and Future Plans
Nai-Yu WANG
Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, Maryland, USA
Chuntao LIU
Department of Meteorology, University of Utah, Salt Lake City, Utah, USA
Ralph FERRARO
NOAA/NESDIS, College Park, Maryland, USA
Dave WOLFF
NASA/GSFC, Greenbelt, Maryland, USA
Ed ZIPSER
Department of Meteorology, University of Utah, Salt Lake City, Utah, USA
and
Chris KUMMEROW
Department of Atmospheric Sciences, Colorado State University, Fort Collins, Colorado, USA
(Manuscript received 23 August 2008, in final form 24 December 2008)
Abstract
The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) 2A12 product consists
of unique components configured for land and oceanic precipitation retrievals. This design was based on
the vastly different physical characteristics of the retrieval, involving primarily emission over ocean and
entirely scattering over land. This paper describes the current status of the TRMM Version 6 (V6) 2A12
product over land and envisioned improvements for TRMM TMI V7 and GPM GMI V1.
On a global scale, the 2A12 land algorithm exhibits biases when compared with the TRMM 2A25
(Precipitation Radar (PR) based) and rain gauges. These range from 6 percent for GPCC to 20 percent
for 2A25. Closer comparison also reveals regional and seasonal biases, with the largest positive biases
found in warm-season convective zones and over semi-arid regions. Some negative biases are found in
warm-rain precipitation regimes where scattering at 85 GHz is unable to detect a precipitation signal.
On an instantaneous time scale, 2A12 land also produces a positive bias when compared with high-quality radar data from Melbourne, Florida, a TRMM ground validation site. The largest discrepancies occur
for rain rates of less than 2 mm h-1.
A number of known “anomalies” are highlighted, including overestimation of rainfall in deep convective systems, underestimation in warm-rain regimes, and a number of features associated with the
Corresponding author: Nai-Yu Wang, Earth System
Science Interdisciplinary Center, University of
Maryland, College Park, Maryland, USA
E-mail: [email protected]
©2009, Meteorological Society of Japan
238
Journal of the Meteorological Society of Japan
Vol. 87A
screening component of the algorithm (e.g., snow cover, deserts, etc.).
Future improvements for TRMM TMI V7 are described and include the use of ancillary data to
determine the underlying surface characteristics and the development of improved brightness-temperature to rain-rate relationships with a more robust data set of TMI and PR matchups, stratified by atmospheric parameters (i.e., surface temperature, atmospheric moisture, etc.) obtained from Numerical
Weather Prediction (NWP) model fields. Finally, the promise of an improved land algorithm through
the use of high-frequency microwave measurements is described. This will form the basis for the Global
Precipitation Measurement (GPM) Global Microwave Imager (GMI) V1 algorithm.
1. Introduction
The retrieval of rainfall over land from the TRMM
Microwave Imager (TMI) has undergone many changes from its original 1998 form. Imbedded within the
Goddard Profiling Algorithm (GPROF), originally
designed for oceanic retrievals and forming the basis for the TRMM 2A12 product, the land portion has
been improved substantially from its original form.
However, even though it is part of GPROF and utilizes
Bayesian retrieval, the land algorithm has several limitations; most notably, it has been “engineered” to simply match cloud profiles based primarily on the scattering signature at 85 GHz, irrespective of the actual
cloud microphysics being encountered. This creates
significant regional biases, especially in non-tropical
atmospheres, as the cloud profiles consist mainly of
tropical, oceanic precipitation systems that have been
simply transformed into land systems. Nonetheless,
the land algorithm has proven to be useful in a number of applications and serves as the cornerstone for
many of the multi-sensor precipitation products that
have emerged over the past several years, including
the TRMM 3B42RT products.
Another feature of the land algorithm is the set of
screens that is used to identify and remove anomalous surface types, such as desert and snow cover, that
have spectral characteristics similar to rainfall. These
screens were adopted from early work done for the
Special Sensor Microwave Imager (SSMI) and also
have limitations; they have remained virtually unchanged since the launch of TRMM.
The purpose of this paper is to present an overview
of the 2A12 land algorithm, describe its strengths and
weaknesses, and present some thoughts on the next
version of the algorithm as we enter into the Global
Precipitation Measurement (GPM) era. Section 2
provides details about the algorithm and its history,
Section 3 describes the performance of the current
product, Section 4 discusses future enhancements envisioned for the algorithm, and Section 5 presents a
summary.
2. 2A12 land background & history
The TMI precipitation algorithm (also known as
the Goddard Profiling Algorithm or GPROF) uses a
Bayesian approach to match observed brightness temperatures with those from a database of simulated
brightness temperatures through a radiative transfer
model coupled with hydrometeor profiles from cloudresolving model simulations (Kummerow et al. 2001).
Over-ocean precipitation retrievals make use of the
strong contrast between the warm, un-polarized emission from rainfall and the cold, polarized ocean surface. Both emission-based (Wilheit et al. 1977, 1991)
and scattering-based (Spencer et al. 1989; Grody 1991;
Ferraro and Marks 1995) methods are utilized. Over
land, precipitation retrievals are far more difficult
due to the relatively warm and un-polarized nature of
the land surface and the high and variable land-surface emissivity. Liquid hydrometeors may increase or
decrease the observed brightness temperatures, depending on the land-surface emissivity; this degrades
the usefulness of an emission-based algorithm. The
most useful signal from precipitation over land is the
brightness-temperature (Tb) depression due to scattering by ice or large raindrops, primarily at 85 GHz.
Because the land-surface emissivity varies with
changes in surface characteristics (i.e., vegetation,
snow cover, soil moisture, etc.) as well as surface temperature, a rain/no-rain Tb depression threshold has
been devised to alleviate the problem of varying surface characteristics. In addition, desert and snow surfaces cause the Tb to depress at 37 and 85 GHz and
can be confused with precipitation signatures. If these
surfaces are not properly screened, they can be mis-
March 2009
N.-Y. WANG et al.
interpreted as precipitation. Thus, the first step in the
retrieval is to determine whether rain is likely (rain
detection) before attempting to quantify the precipitation. This is the so-called “screening” procedure. This
screening method is by no means fool-proof. It may
fail to detect precipitation in the case of warm precipitation processes that do not produce significant icephase clouds, or may detect excessive precipitation in
the case of a strong ice-phase cloud that evaporates
before it reaches the ground as rain.
The basis of the current land-precipitation algorithm comes from the work of Grody (1991), who developed a global scattering index (SI) for SSMI. This
is a measure of the 85 GHz brightness-temperature
depression caused by the presence of ice. Ferraro et
al. (1994, 1998) made improvements over Grody’s
original SI to improve the rain detection sensitivity and to better screen anomalous scattering features
such as frozen surface and un-vegetated land. The basic algorithm uses the 19 and 21 GHz Tb to predict
the 85 GHz Tb under non-raining conditions, then
obtains a measure of the depression due to scattering by subtracting the observed 85 GHz Tb from the
predicted 85 GHz Tb. A threshold value for SI is then
determined to separate rain from no-rain. Subsequent
screens remove desert, snow-covered, and semiarid
land. To relate SI with the rainfall rate, Ferraro and
Marks (1995) calibrated SI with ground-based radar
and determined a rainfall rate and SI relationship appropriate for global applications. For Version 5 (V5)
of the TMI algorithms, it was decided to selectively
match the GPROF cloud-model database with the
ice-scattering and rain-rate algorithm (Ferraro 1997).
Between the 21 GHz and 85 GHz vertically polarized
brightness temperatures, a threshold of 8 Kelvins was
found to be appropriate for identifying pixels with
rain (McCollum and Ferraro 2003).
Some modifications have been made to the TMI
algorithm over land since V5. In V6, McCollum and
Ferraro (2003) utilized TMI and PR matchups to develop a probability of convection, and they now include both stratiform and convective rain rates in
the data base. Most recently, McCollum and Ferraro
(2005) refined the databases and provided coastline
improvement, all applicable to TMI, the Advanced
Microwave Scanning Radiometer-Earth (AMSR-E)
and SSMI.
3. Performance
There are several ways to illustrate the performance
of the 2A12 land algorithm: large-scale climatic pat-
239
terns, intercomparison with other precipitation products on various time scales, and comparisons with
ancillary data sets on an instantaneous time scale.
Additionally, the examination of specific storms on a
regional basis highlights certain known anomalies in
the retrieval caused by factors such as the underlying
surface conditions and the precipitation regime. All of
these aspects are discussed in this section.
3.1 Global comparisons
Two independent data sets are used in this comparison: The Global Precipitation Climatology Project
(GPCP) V2 gauges-only product, referred to as the
Global Precipitation Climatology Center (Rudolf,
1995) (GPCC) and the TRMM 2A25 PR rain product
(Iguchi et al. 2000). GPCC data is used here to provide independent, gauged-based precipitation data for
the global land surface in addition to satellite observations for comparison purposes. GPCC uses interpolated gauge observations and represents area-mean
monthly precipitation on grid cells. Caution must be
taken when interpreting GPCC over areas where there
are sparse or no gauges; however, this data set is widely used by the climate community and possesses one
of the most comprehensive collections of gauges yet
assembled. This section examines the overall similarities and differences between 2A12 and the other two
data sets, while a detailed examination is presented in
Section 3.3.
Figure 1 compares the 2A12 to the 2A25 global
rainfall distribution over land for December 1998
through May 2008. It shows the annual (1a) and seasonal (1b and 1c) differences. In general, 2A12 produces greater rainfall than 2A25, particularly over the
tropics and in the mid-latitude warm seasons. This
disagreement is likely attributable to the overestimation of the 85 GHz ice scattering-rain relationship in
convective precipitation regimes. However, there are
regions where 2A25 rainfall exceeds 2A12, particularly during the cold season in the subtropics. For example, over the eastern United States during December,
January, and February (DJF), 2A12 underestimates
relative to 2A25 (1b). Other examples of these areas
are in arid to semi-arid climates and coastal topographic areas where shallow warm rain systems that
the PR can detect but the TMI cannot may perhaps occur, or where rainfall is filtered out due to the screening employed within 2A12. Examples of the topographic effect are seen during the monsoon season in
June, July, and August (JJA) over the Western Ghats in
the Indian subcontinent and in the Arakan Mountains
240
Journal of the Meteorological Society of Japan
Vol. 87A
Fig. 1. Differences among 10-year mean unconditional monthly rainfall from TRMM PR (2A25), TMI
(2A12), and rain gauges (GPCC). Figures 1a to 1c represent the differences between 2A12 and 2A25 for
annual, DJF, and JJA, while Figs. 1d to 1f are for 2A12 and GPCC. Units are mm mon.-1
March 2009
N.-Y. WANG et al.
Fig. 2. Monthly time series of the mean land
(38°S to 38°N) rainfall anomalies for
2A12 and 2A25 (mm d-1). The anomalies
reflect departures from the mean values
of each data product for December 1998
through May 2008.
and the Bilauktang Range in the South Asian coastal
area, where 2A12 produces less rainfall than 2A25 or
GPCC (1c and 1f). The reason for this may be the low
level of humidity from southwest oceans, which does
not produce enough ice for 2A12 to detect rainfall.
Overall, the global mean land rainfall is 2.71 mm d-1
for 2A12 and 2.24 mm d-1 for 2A25.
Another interesting comparison of the behavior of
the two products is found when looking at interannual
variations on a monthly time scale (Fig. 2). Here the
mean is removed from each data set so that the comparison is strictly looking at the departures from the
overall mean of each data set. Overall, the two data
sets track closely to one another in terms of their magnitudes and positive/negative phases.
A similar analysis is performed for 2A12 vs. the
GPCC rain gauges (Figs. 1d–f and 3). In this case, the
bias pattern is somewhat similar to the previous comparison; however, there are notable differences. The
apparent overestimates in 2A12 remain when compared with the gauges, but now there are also pronounced underestimates in many locations. In particular, strong seasonal underestimates occur in South
America and Southeast Asia, perhaps attributable to
warm-rain processes that are unobservable by either
TMI or PR. Overall, the global mean land rainfall is
2.71 mm d-1 for 2A12 and 2.55 mm d-1 for the GPCC
rain gauges, or about a 6% difference, which is much
closer than 2A25. The interannual variations (Fig. 3)
241
Fig. 3. As in Fig. 2, but for 2A12 and GPCC
rain gauges over a common spatial domain (38°S to 38°N).
show much less agreement than was found with 2A25.
A summary of the correlation coefficients and rootmean-square errors from the monthly mean rainfall
for 2A12, 2A25, and GPCC is presented in Table 1.
3.2 Rain-rate distributions
The TRMM Ground Validation (GV) program produces quality-controlled rainfall products for four
primary sites: Darwin, Australia (DARW); Houston,
Texas (HSTN); Kwajalein, Republic of the Marshall
Islands (KWAJ); and Melbourne, Florida (MELB).
These sites were established during the pre-mission
phase of TRMM, providing researchers with a quasi-continuous, long-term time series of rainfall measurements at a higher spatio-temporal resolution than
can be observed with satellite sensors alone. The GV
data subsequently provides the empirical surface reference needed to independently verify the accuracy
of TRMM rainfall measurements (Thiele 1988). The
GV program is documented in Wolff et al. (2005,
2008), including site and product descriptions as well
as algorithms and data processing techniques. For this
study, the TRMM 2A53 instantaneous rain maps are
used. The 2A53 data provides instantaneous rain rates
at a resolution of 2 km x 2 km and covers a continuous region extending 150 km from a given GV radar.
Each rain map thus consists of a 151 x 151 pixel grid
with the GV radar located at the center pixel.
TRMM 2A12 and 2A25, along with the combined
2B31 (Haddad et al. 1997a, 1997b) instantaneous rain
rates, were matched at the scale of the TMI footprint
and were statistically compared to the 2A53 GV ra-
242
Journal of the Meteorological Society of Japan
Vol. 87A
Table 1. Summary of monthly mean rainfall correlation
coefficients (R) and root-mean-square errors (RMSE)
between TRMM 2A12, 2A25, and GPCC gauges
from 1998-2008.
Product
R
RMSE
mm day-1
2A12-2A25
0.78
0.30
2A12-GPCC
0.40
0.22
2A25-GPCC
0.53
0.26
dar rain-rate spectra at MELB. Six years of regional TRMM overpass data were used (1999 to 2004).
Though the TMI “footprint” used in this study provides a convenient scale for analysis, it does not represent a fundamental physical scale since the size is determined based on an empirical optimization of rain
information spanning a broad spectrum of physical
scales and with 13.9 km along-track sampling resolution (Kummerow et al. 1998; Olson et al. 2006). The
effective field of view at 10 GHz, for example, is 67
x 37 km2, whereas at 85 GHz the field of view is 7
x 5 km2. The 2A12 rain rate is derived from a combination of low-frequency channels with larger footprints and a higher (85 GHz) frequency channel with
a smaller footprint. The resultant 2A12 footprint is
then nominally about fourteen kilometers. Fourteen
kilometers subsequently represents a kind of maxi-
Fig. 4.
Probability distribution function
(PDF) and cumulative distribution function (CDF) of rain rates (mm h-1) for 2A12
vs. 2A53 (Melbourne, Florida GV site) for
1999 to 2004.
mum sample spacing; in other words, if one were to
lay down 14 km x 14 km boxes centered on each TMI
radiance footprint, they would be spatially contiguous
from scan to scan, but would overlap along the scan
line. To simplify the procedure, TMI and GV were
matched at the scale of the TMI footprint by considering a 7 km radius around the center of each TMI
Table 2. Summary of rain-rate statistics (mm h-1) for TRMM rainfall products 2A12, 2A25, and 2B31, compared with
2A53 (MELB) for 1999-2004.
Product
Mean
GV
Mean
Std.
Dev.
GV
Std. Dev.
RMS
Bias
Ratio
R
2A12
2.20
2.07
4.78
4.53
4.63
1.06
0.51
2A25
2.04
2.07
3.50
4.53
3.32
0.99
0.69
2B31
2.36
2.07
4.37
4.53
3.16
1.13
0.75
March 2009
N.-Y. WANG et al.
pixel location. Unconditional mean rain rates (i.e., R ≥
0) were then computed within each TMI footprint by
locating all of the pixels (rainy and non-rainy) found
within this circular region.
The results over land points are presented in Table
2. Melbourne is in a sub-tropical precipitation regime,
and the results are generally consistent with those of
the previous section: 2A12 has an apparent positive
bias compared with both 2A25 and the GV data. It
also has the highest Root Mean Square (RMS) error
and the lowest correlation.
Beyond comparing bulk statistics, an examination
of the probability distribution function (PDF) and cumulative distribution function (CDF) for the various
data types also reveals valuable information (Fig. 4).
In general, both the PDF and CDF are in very good
agreement for rain rates above 2 mm h-1, with 2A12
slightly overestimating medium rain rates (between
0.1 and 2 mm h-1).
3.3 Storm scale
Another useful approach to examining the performance of the 2A12 land algorithm is to examine individual storms. Figure 5 presents an example of a TMI
overpass on 30 August 2005, at approximately 0200
UTC that captures the remnants of Hurricane Katrina
over the United States. This example was chosen to
point out several characteristics of the retrieval. For
comparison, the National Oceanic and Atmospheric
Administration (NOAA) Stage IV (SIV) hourly precipitation product (a blend of radar and rain gauges) is
243
also presented.
There are several similarities and differences between the two rain fields (which have been gridded
to a common 0.25-degree grid). Evident is the spiral
rain feature associated with the remnants of Katrina,
with the core rain region depicted by both rain products along the northern portions of Mississippi and
Alabama. The SIV rain features appear to be more
continuous and realistic for a tropical cyclone, while
2A12 is more scattered in nature, although the TMI
reflects a “snapshot” of the rain field while SIV is an
hourly total, and this could explain these differences.
The maximum rain rates are near 15 mm h-1 for 2A12
and over 20 mm h-1 for SIV. Also presented in Fig. 5 is
a scatter plot of the rain rates from the two estimates
and the calculated statistics. The modest correlation of
0.71 is typical for matchups of this type. The scatter
plot also indicates a slight underestimation by 2A12 in
tropical rain systems whereas previous comparisons
indicated that in general, 2A12 overestimates warmseason convective rainfall. This again highlights the
regional and seasonal biases that are encountered in
satellite rainfall retrievals. Nonetheless, 2A12 is used
as a calibrating source of rainfall for several blended
rainfall schemes including the Climate Prediction
Center (CPC) morphing technique (CMORPH) (Joyce
et al. 2004).
3.4 Known anomalies in 2A12 land
To improve the current 2A12 rain-retrieval algorithm over land, it is important to evaluate the anom-
Fig. 5. 2A12 rain rates (left) and Stage IV rain rates (middle) over the United States at approximately 0200
UTC on August 30, 2005. Units are mm h-1. (right) Scatter plots between the two estimates; correlation=0.71, with 2A12 6% higher than the mean Stage IV value.
244
Journal of the Meteorological Society of Japan
alies in the retrieved product. However, it is difficult
to quantify these anomalies globally because there
is no single rainfall product that can represent the
ground truth. Products from rain gauges provide reliable estimates of rainfall only over areas with dense
rain-gauge networks. Rainfall retrieved from the
TRMM PR near-surface reflectivity has global coverage between 36°N and 36°S, but suffers from uncertain attenuation correction, problems over complex terrain, and the limit of the minimum detectable
signal (Iguchi et al. 2000). In this section, we begin
the discussion of the anomalies by further comparing
the rainfall estimates among products from TRMM
(2A12 and 2A25) and GPCC rain gauges.
As described in an earlier section, the differences
between the 10-year mean 2A12 and 2A25 unconditional monthly rainfall appear in Fig. 1a–c. The differences between mean 2A12 and unconditional rainfall from GPCC rain gauges (GPCC, Rudolf 1995)
are given in Fig. 1d–f. There are both similarities and
differences among 2A12, 2A25, and GPCC as seen in
Fig. 1a–f. These are related to the performance of the
algorithms in different weather regimes over different
types of land surfaces, and to various detection biases.
a. Biases in strong deep convective systems
The first most obvious anomaly in Fig. 1 is the
Vol. 87A
large overestimation of rainfall by 2A12 over central
Africa. There is a larger overestimation from 2A12
than 2A25 over GPCC. The sparse rain-gauge network over Africa may contribute to the uncertainty of
the GPCC estimates. However, even if GPCC is too
low, it is fair to ask why 2A12 has the highest rainfall
estimation over central Africa.
To answer this question, TMI Precipitation Features
(TPFs) are defined by grouping the 2A12 raining
area, using nine years of TRMM observations (Liu
et al. 2008). The total volumetric rainfall from 2A12
and 2A25, as well as the maximum height of the PR
20 dBZ echo inside each TPF, are then calculated
separately. Next, 2D histograms of the differences
between the 2A25 and 2A12 volumetric rainfall and
the minimum TMI 85 GHz Polarization Corrected
Temperature (PCT, Spencer et al. 1989) in TPFs with
size above 2000 km2 are generated over tropical land
and the ocean as seen in Fig. 6. There are large differences between the histograms over land and over the
ocean because of the different land and ocean 2A12
algorithms. Over the ocean, 2A12 retrieves more rainfall than 2A25 for raining systems with weak convection inferred from a relatively high minimum 85 GHz
PCT. However, over land, there are two peaks of large
overestimation of rainfall by 2A12 over 2A25. One
is for systems with weak convection; this is largely
Fig. 6. 2D histogram of rainfall differences between 2A12 and 2A25 and a minimum 85 GHz PCT in the precipitation features defined by a 2A12 raining area larger than 2000 km2 over tropical land and ocean.
March 2009
N.-Y. WANG et al.
caused by the absence of aloft ice in shallow systems
and 2A12 underestimating the warm rain (more details are in Section 3.4.b). The other peak is for systems with strong convection inferred from low minimum 85 GHz PCTs.
It is known that strong, deep convection lifts large
amounts of ice to high altitudes, causing strong scattering of the upwelling radiance from below by the ice
hydrometeors, leading to depression of the brightness
temperature at 85 GHz. Because the 2A12 land algorithm depends so strongly upon this ice-scattering signal, the algorithm derives high rainfall rates for these
cases. Central Africa is known to have the strongest
convective systems anywhere in the tropics (Liu and
245
Zipser 2005). Therefore, it is speculated that the overestimation of 2A12 over 2A25 and GPCC is caused
by an inappropriate 85 GHz ice scattering to rain rate
relationship. This may also explain the overestimation of rainfall by 2A12 over regions known for their
strong convection (Zipser et al. 2006), such as the
southern plains of the US in JJA (Figs. 1b and 1e), and
Argentina in DJF (Figs. 1c and 1f). A similar result
was noted by McCollum et al. (1999) when examining
biases in the GPCP blended rainfall product.
b. Underestimation of warm rainfall
Warm rain is defined as “rain formed from a
cloud having temperatures at all levels above 0°C,
Fig. 7. Rain detection differences (color fill) between TRMM 2A25 and TRMM 2A12 by ratios. GPCC unconditional rainfall (units are mm/month) is represented as contours. Note that a large area over northern
Australia is missing due to a lack of observations by the TRMM PR.
246
Journal of the Meteorological Society of Japan
and resulting from the droplet coalescence process”
(Glickman 2000; Schumacher and Houze 2003).
Because the current 2A12 land-precipitation algorithm depends on the ice-scattering signal to detect
rain, warm-cloud precipitation that does not produce
significant ice would likely escape detection. Liu and
Zipser (2008) show that over the east coasts of Brazil,
Madagascar, the Philippines, and Costa Rica in winter and the west coast of India in summer there is a
large amount of warm trade-wind rainfall. Also, over
the Amazon basin rainfall is produced by clouds that
do not produce a strong ice-scattering signature. It is
consistent that 2A12 shows less unconditional rainfall
than 2A25 over the corresponding regions in Figs. 1b
and 1c and also exhibits underestimation compared
with GPCC in Figs. 1e and 1f.
Vol. 87A
c. “False alarms” over arid land and snow surfaces
Properly distinguishing raining areas from surface areas that produce a microwave signature similar to that of precipitation has always been a challenge
(Grody 1991; Ferraro et al. 1998). The primary difficulty is the similar spectral characteristics of these
surface features in the frequency range between 18
and 90 GHz and the inability of passive microwave
sensors such as the TMI to uniquely separate them.
Additionally, the current 2A12 algorithm utilizes
screening techniques developed over a decade ago for
the Special Sensor Microwave Imager (SSMI), which
has a coarser spatial resolution and different instrument performance than TMI.
Two types of surface areas that are extremely difficult to identify: arid desert and snow. Figure 7 illustrates the rain-detection differences (color fill)
Fig. 8. Snow case over New Mexico on December 26, 2000. The NCEP surface temperature was near 269 K
over the region. The ice-scattering signature at 85 GHz from precipitation in the top left panel is consistent
with the near-surface reflectivity pattern from the PR in the top right panel. However, 2A12 (bottom left)
missed most of the precipitation region for this case.
March 2009
N.-Y. WANG et al.
between TRMM 2A25 rain occurrence and TRMM
2A12 rain occurrence in ratios. 2A12 (2A25) rain occurrence for each 1 x 1 latitude/longitude gird is calculated by dividing the total number of pixels with
2A12 surface (2A25 near-surface) rain with the total number of pixels sampled by TMI (PR). The
GPCC unconditional rainfall (units are mm/month) is
shown as contours. Note that a large area over northern Australia is missing due to lack of observations.
The 2A12 land algorithm estimates a more unrealistic raining area than 2A25 (red color in Fig. 7) in
the Sahel (Fig. 7b) and on the northern edge of the
Kalahari Desert (Fig. 7c) in Africa during the dry seasons. This difference can be caused by the 2A12 surface screening, which fails to detect sand. 2A12 also
detects large unrealistic raining areas over snow-covered high terrain, including eastern Tibet, Iran, and
247
the Rocky Mountains during boreal winter and over
the Andes and Altiplano during austral winter. This is
likely caused by the 2A12 screening failing to detect
surface snow. A similar result was also noted by Seto
et al. (2005) when examining snow and desert screening in 2A12. These detection anomalies can explain
the overestimation of rainfall by the 2A12 land algorithm over these regions in Fig. 1.
d. Other anomalies
One advantage of using a screening algorithm is
the elimination of false alarms of precipitation over a
snow-covered surface. However, this also prevents the
detection of actual precipitation in the form of snow.
For example, Fig. 8 illustrates a snow case over New
Mexico on December 26, 2000. The National Centers
for Environmental Prediction (NCEP) surface temper-
Fig. 9. Line of convection over New Mexico on August 6, 1999. The TRMM PR exhibits a typical structure
for a leading convective line and trailing stratiform as seen in the bottom left panel. This fine-scale structure could not be captured by the TMI due to its larger footprint. This leads to a relatively larger area with
a moderate rainfall rate in 2A12 retrievals (bottom right panel) compared to the heavy rainfall rate in the
convective region and lower rainfall rate in the stratiform region given by 2A25 (bottom middle panel).
248
Journal of the Meteorological Society of Japan
ature was 269 Kelvin over the area. There are clear
ice-scattering signals with Tb depressions at 85 GHz
and 37 GHz (Figs. 8a and 8f) due to precipitation in
the form of snow occupying a fairly deep layer, as
is also seen in the PR near-surface reflectivity (Fig.
8b) and the 2A25 near-surface rain rate (Fig. 8d).
However, these signals are not recognized as precipitation because the screening for surface snow uses 21
GHz (Fig. 8e) to filter out cold surfaces, so that 2A12
retrieves practically no precipitation for this snow case
(Fig. 8c). If a more relaxed screening were employed
in 2A12, large areas of false precipitation would be
detected in the process of bringing in precipitation
from systems similar to those depicted in Fig. 8.
Some of the differences between 2A12 to 2A25 are
due to differing horizontal resolution. As seen in Fig.
9, the fine structure of convection depicted by PR over
New Mexico, USA, on August 6, 1999, is smeared
by 2A12 due to using information from TMI lowfrequency channels with relatively larger footprints
(Kummerow et al. 1998).
Thick anvil clouds may be related to another possible anomaly in 2A12, since they can create sufficient
ice-scattering signals to satisfy the precipitation criteria in 2A12 over land. In some cases, large areas of
thick anvil exist for which the 2A25 algorithm shows
no surface rainfall because there is no detectable radar echo near the ground. However, 2A12 sometimes
reports large areas of rainfall under these thick anvil
clouds (not shown). These cases may partially explain
the higher rain detection by 2A12 than 2A25 over
central Africa (Fig. 7a–c), over Argentina in DJF (Fig.
7b), and over the southern plains of the US in JJA
(Fig. 7c).
In summary, this section presents several scenarios that may explain some of the important differences among 2A12, 2A25, and GPCC rainfall products.
However, there are still questions to be answered. For
example, why do both 2A12 and 2A25 report lower
rainfall than GPCC over the Bay of Bengal, Southeast
China, and southern Nigeria in JJA, and near the
mouth of the Amazon in DJF? Is this due to underestimation by both algorithms of systems dominated
by relatively shallow, mostly warm rain? What kind
of weak precipitation systems lead to higher or lower
2A12 rainfall than 2A25 as seen in Fig. 6? Further investigations are warranted.
4. Future direction
As was described in Section 3, there are several
deficiencies with the current 2A12 land algorithm,
Vol. 87A
some of which can be addressed in the TRMM TMI
Version 7 products. Improvements should also include
developing a physical model as a basis and hence to
move towards sensor-independent approaches as the
GPM GMI Version 1 algorithm is developed. These
concepts are described below.
4.1 TRMM TMI Version 7 (V7)
The spectral signature of rainfall is very similar to
that of other scattering surfaces such as arid land, desert sand, and snow cover. TMI alone cannot uniquely
identify each of these surfaces, including rainfall, on
a global basis with 100% accuracy. There is always
a tradeoff between proper rain detection and false
alarms; current algorithm philosophy has stressed
minimizing false alarms on a global basis, recognizing that this sacrifices regional accuracy. Although
the current 2A12 algorithm utilizes a series of tests
to identify these features (for details see Ferraro et
al. 1998), it is grossly outdated and the entire process needs to be revised. The reason for this is that
many of these tests stem from original work done for
the SSMI (Grody 1991; Ferraro et al. 1998) and are
not necessarily suited for use with TMI because of the
different sensor characteristics (e.g., footprint sizes,
different channels, etc.).
Rather than just relying on the sensor measurements
themselves, an improved approach should incorporate
ancillary data sets to make substantial progress in the
area of surface identification. At the most basic level,
a highly accurate land/water/coast data set specifically designed for passive microwave retrievals needs to
be developed and tailored for the TMI. A complicating factor is, at what resolution should this data base
be developed and how best to capture the water features? Presently, the data base used appears to identify too many regions as coast, i.e., the coastline is too
broad in many cases. This is necessary to minimize
land contamination in the 2A12 ocean component. In
the future, land information systems (e.g., Kumar et
al. 2006) that contain large inland water features and
island chains can be used to improve the land component in GPROF.
Another straightforward improvement in surface
identification would be through using operational data
products such as NOAA’s daily snow and ice products
and NWP data assimilation systems such as the Land
Data Assimilation System (LDAS). Such data sets are
supported 24 hours a day, seven days a week, and will
provide a reliable means to identify many of the surface types that are encountered by the land algorithm.
March 2009
N.-Y. WANG et al.
Additionally, this should improve upon the number
of “ambiguous” points that are flagged in the current
product and offer the potential to identify more valid
raining pixels over arid regions.
One last area for improvement in V7 is within the
land data base. As previously described, the current
set of profiles is centered primarily upon tropical
oceanic systems. This set was narrowed down, based
on the work of McCollum and Ferraro (2003) in V6
matching PR and TMI data to create a more realistic
Tb-to-surface-rainfall relationship. However, the actual hydrometeor profiles are not indicative of land precipitation processes. A near-term improvement would
build upon this method by incorporating ten years
of TMI and PR matchups and devising Tb and rainrate relationships as functions of rain regimes to account for geographical and seasonal variations. The
idea of using PR information for the TMI algorithm
was previously studied by Masunaga and Kummerow
(2005) and Grecu and Olson (2006) for over ocean
and by Kubota et al. (2007) for over land and ocean.
Additional environmental information such as surface
temperature and total precipitable water (obtained
from NWP model fields) can also be used. A set of
profiles stratified by such information can then be
assembled, which should extend the surface-rainfallto-Tb relationship over a wider range of precipitation
regimes, thus potentially reducing several of the regional biases that currently exist. In this manner, emphasis will be placed on the Tb-to-rain-rate relationship in different climate zones through the use of PR
2A25 and ancillary data rather than on cloud-resolving model simulations or ground-validation data.
4.2 GPM era
As we move from the TRMM to the Global
Precipitation Mission (GPM) satellite constellation
era, more focus will be placed on a larger range of
mid- and high-latitude precipitation regimes, including light rain, mixed-phase precipitation, and snowfall. In many parts of the world, these are the primary sources of annual precipitation, but most of these
have not been considered in the TRMM-era algorithms. The GPM core satellite will fly a high-resolution multichannel microwave imager (GMI) and
a dual-frequency Ku/Ka band radar (DPR) and will
cover up to 65° latitude. GMI will operate on all TMI
frequencies at 10.65, 18.7, 23.8, 36.5, and 89 GHz,
plus two additional high-frequency channels in the
166 GHz window (V-pol and H-pol) and two sounding channels near the 183 GHz water vapor line (183
249
± 3 and 183 ± 9). These new channels were selected
to improve the detection and estimation of light rain
and snowfall. The effects of precipitation on high-frequency channels have been examined through simulations and observations (Muller et al. 1994; Burns
et al. 1997; Bennartz and Petty 2001; SkofronickJackson et al. 2002; Bennartz and Bauer 2003; Zhao
and Weng 2002; Hong et al. 2005; Ferraro et al. 2005;
Qiu et al. 2005). These studies suggest that the impact of a Tb depression through scattering by hydrometeors increases with frequency. 166 GHz contains
significant information for identifying and retrieving precipitation over land because its sensitivity to
cloud-liquid water and ice is above that of 89 GHz by
about a factor of two. Furthermore, the influence of
surface emissivity at 166 GHz is much smaller than
at 89 GHz. In the 183 GHz water vapor channels, the
scattering signals from hydrometeors can be extracted due to the fact that the gaseous absorption at these
frequencies is well known, given the temperature and
water vapor profiles (Staelin et al. 2000). The surface
effect on the water vapor channels may be completely eliminated due to attenuation by the water vapor.
However, in spite of the aforementioned strengths of
the high-frequency channels, the relation between the
surface precipitation rate and the scattering-based
precipitation algorithm is complex and strongly dependent on the type of precipitation. For solid-phase
precipitation, the differences are caused by variations
in the ice-particle size distribution, shapes, and habits (Skofronkick-Jackson et al. 2000, 2002, and 2004;
Bennartz and Petty 2001). It is important for GPM algorithm development to understand the relationships
between ice-particle microphysics and the radiative
properties, and the relationships between the bulk radiative properties and the observed and calculated
passive microwave radiances and radar reflectivities.
One logical step to be taken prior to the launch
of a GPM mission is to use the data collected from
pre-launch GPM ground validation (GV) field campaigns, coupled with radiative transfer modeling, to
understand the snow microphysical-radiative relationships, and to develop and test snowfall-retrieval algorithms. In January 2007, GPM GV participated in the
snowfall-measurement component of the Canadian
CloudSat/Calipso Validation Project (C3VP) (Hudak
et al. 2006; Petersen et al. 2007). GPM’s participation was aimed at improving satellite-based snowfall
detection and retrieval algorithms. Intensive observations of snowfall using airborne and ground-based
instrumentation were conducted near Egber, Ontario,
250
Journal of the Meteorological Society of Japan
Vol. 87A
Fig. 10. Example of a synoptic snowfall event, observed by the King City radar (0135 UTC) and SSMIS on
board DMSP F16 (0138 UTC) on January 22, 2007. The co-located brightness temperatures from 91 and
150 GHz (y-axis) and the near-surface reflectivity (at 1 km height) (x-axis) indicate that the 91/150 GHz
brightness temperatures generally decrease with increasing radar reflectivity as the snowfall intensifies.
91 GHz exhibits higher variability than 150 GHz for a given radar reflectivity; this might be caused by the
higher influence of surface emissivity on 91 GHz than 150 GHz.
Canada (about 80 km north of Toronto). Emphasis was
placed on intensive ground-radar sampling of snow by
the scanning King City C-band radar (WKR), coordinated aircraft flight patterns through clouds, and coincident passive microwave satellite overpasses. Figure
10 depicts an example of a synoptic snowfall event
observed by the King City radar (0135 UTC) and the
Special Sensor Microwave Imager/Sensor (SSMIS)
on board Defense Meteorological Satellite Program
(DMSP) F16 (0138 UTC) on January 22, 2007. The
co-located brightness temperatures from 91 and 150
GHz (y-axis) and near-surface reflectivities (at 1 km
height) (x-axis) demonstrate that the 91/150 GHz
brightness temperatures generally decrease with increasing radar reflectivity as the snowfall intensifies.
91 GHz exhibits higher variability than 150 GHz for
a given radar reflectivity. This might be caused by the
higher influence of surface emissivity on 91 GHz than
150 GHz. The ability to estimate the surface emissivity is essential in understanding the snowfall signals
from passive, high-frequency microwave observations. Moreover, we are working with the GPM GV
team to utilize C3VP aircraft-snow microphysics and
ground-based data for radiative transfer modeling of
snow-scattering parameters and satellite-microwave
radiance.
5. Summary
We have presented an overview of the current 2A12
land algorithm and described its strengths and weaknesses through comparisons with other precipitation
products on various time and spatial scales. On the
global scale, the 2A12 exhibits a positive bias compared with TRMM 2A25 and GPCC. Closer examination reveals that there are also regional and seasonal biases, with the largest positive biases in the
warm-season convective zones and over semi-arid
regions. The positive bias is likely caused by an inadequate 85 GHz ice scattering to rain rate relationship and improper convective rainfall classification.
However, there are some exceptions in the subtropics
and during cold seasons where 2A25 produces higher
rainfall than 2A12. This negative bias is likely caused
by an inadequate 85 GHz ice scattering to rain rate relationship or an incorrect PR reflectivity to rain rate
relationship, or by attenuation correction in intensive
mid-latitude storms. Examination of cumulative probability distributions from 2A12 and TRMM GV radar in Melbourne reveals that the largest discrepancy
in rain occurrence is at rain rates of 2 mm h-1 or less.
Additionally, several 2A12 anomalies are highlighted
through examples from individual TRMM overpasses.
March 2009
N.-Y. WANG et al.
These include the overestimation of rainfall in strong
deep connective systems and underestimation in warm
rain, as well as anomalies associated with rain/no-rain
screening (surface snow and deserts). These comparison results strongly suggest that the rainfall estimate
biases vary as a function of the rain regime and that
future rainfall algorithm development therefore needs
to be conducted on a regional and seasonal basis. For
the near term (TRMM TMI V7), a larger set of TMI
and PR matchups will be utilized to develop Tb and
rain-rate relationships as functions of rain regime in
order to account for such variations. For the long term
(i.e. the pre- and post-GPM eras), when emphasis will
be placed on snowfall and light-rain algorithm development, the approach of using radiative transfer modeling coupled with high quality pre-GPM ground-validation campaign data will be adopted, and in fact is
already underway.
Acknowledgements
The authors would like to thank our colleagues on
the National Aeronautics and Space Administration
(NASA) Precipitation Measurement Missions’ Science
Team for their collaboration over the past several
years. We are also grateful for the support we received
from NASA (R. Kakar) and NOAA (G. Dittberner
and C. Miller). In addition, we would like to thank
the Climate Rainfall Data Center at Colorado State
University for the use of the data provided in Figs. 2
and 3. This work was partially supported through a
grant between NOAA and the University of Maryland/
Cooperative Institute for Climate Studies.
References
Adler, R. F., G. J. Huffman, A. Chang, R. Ferraro,
P. Xie, J. Janowiak, B. Rudolf, U. Schneider,
S. Curtis, D. Bolvin, A. Gruber, J. Susskind,
P. Arkin, and E. Nelkin, 2003: The version-2 Global Precipitation Climatology
Project (GPCP) monthly precipitation analysis
(1979-present). J. Hydrometeor., 4, 1147–1167.
Bennartz, R., and G. W. Petty, 2001: The sensitivity
of microwave remote sensing observations of
precipitation to ice particle size distribution, J.
Appl. Meteor., 40, 345–364.
Bennartz, R., and P. Bauer, 2003: Sensitivity of microwave radiance at 85-183 GHz to precipitating ice particles, Radio Sci., 33(4), 8075,
doi:10.1029/2002RS002626.
Burns, B. A., X. Wu, and G. R. Diak, 1997: Effects
of precipitation and cloud ice on brightness tem-
251
peratures in AMSU moisture channels, IEEE
Trans. Geo. Rem. Sens., 35, 1429–1437.
Ferraro, R. R., 1997: Special sensor microwave imager derived global rainfall estimates for climatological applications, J. Geophys. Res., 102,
16715–16735.
Ferraro, R. R., N. C. Grody, and G. F. Marks, 1994:
Effects of surface conditions on rain identification using the SSM/I, Remote Sens. Rev., 11,
195–209.
Ferraro, R. R., and G. F. Marks, 1995: The development of SSM/I rain-rate retrieval algorithms
using ground-based radar measurements. J.
Atmos. Oceanic Technol., 12, 755–770.
Ferraro, R. R., E. A. Smith, W. Berg, and G. J.
Huffman, 1998: A screening methodology for
passive microwave precipitation retrieval algorithms, J. Atmos. Sci., 55, 1583–1600.
Glickman, T. S., ed., 2000: Glossary of Meteorology.
2nd ed. Amer. Meteor. Soc., 855 pp.
Grecu, M., and W. S. Olson, 2006: Bayesian estimation of precipitation from satellite passive microwave observations using combined radar–radiometer retrievals. J. Appl. Meteor. Climatol.,
45, 416–433.
Grody, N. C., 1991: Classification of snow cover and precipitation using the Special Sensor
Microwave Imager, J. Geophys. Res., 96, 7423–
7435.
Haddad, Z. S., E. A. Smith, C. D. Kummerow, T.
Iguchi, M. R. Farrar, S. L. Durden, M. Alves,
and W. S. Olson, 1997a: The TRMM “Day-1'
radar/radiometer combined rain-profiling algorithm”, J. Meteor. Soc. Japan, 75, 799–809.
Haddad, Z. S., D. A. Short, S. L. Durden, E. Im, S.
Hensley, M. B. Grable, and R. A. Black, 1997b:
A new parametrization of the rain drop size distribution, IEEE Trans. Geosci. Rem. Sens., 35,
532–539.
Hong, G., G. Heyster, J. Miao, and K. Kunzi, 2005:
Sensitivity of microwave brightness temperatures to hydrometers in a tropical deep convective cloud system at 89-190 GHz, Radio Sci., 40,
RS4003, doi:10.1029/2004RS003129.
Hudak, D., H. Barker, P. Rodriguez, and D. Donovan,
2006: The Canadian CloudSat Validation
Project. 4th European Conf. on Radar in
Hydrology and Meteorology, Barcelona, Spain,
18-22 Sept., 2006, 609–612.
Huffman, G. J., R. F. Adler, D. T. Bolvin, G. Gu, E.
J. Nelkin, K. P. Bowman, Y. Hong, E. Stocker,
252
Journal of the Meteorological Society of Japan
and D. Wolff, 2007: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-global,
multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38–55.
Iguchi, T., T. Kozu, R. Meneghini, J. Awaka, and
K. Okamoto, 2000: Rain-profiling algorithm
for the TRMM Precipitation Radar. J. Appl.
Meteor., 39, 2038–2052.
Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie,
2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487–503.
Kubota, T., S. Shige, H. Hashizume, K. Aonashi, N.
Takahashi, S. Seto, M. Hirose, Y. N. Takayabu,
K. Nakagawa, K. Iwanami, T. Ushio, M. Kachi,
and K. Okamoto, 2007: Global precipitation
map using satellite-borne microwave radiometers by the GSMaP project: Production and validation, IEEE Trans. On Geoscience and Remote
Sensing, 45 (7), 2259–2275.
Kumar, S. V., C. D. Peters-Lidard, Y. Tian, P. R.
Houser, J. Geiger, S. Olden, L. Lighty, J. L.
Eastman, B. Doty, P. Dirmeyer, J. Adams, K.
Mitchell, E. F. Wood, and J. Sheffield, 2006:
Land information system: An interoperable
framework for high resolution land surface
modeling, Environ. Modelling Software, 21,
1402–1415.
Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and
J. Simpson, 1998: The Tropical Rainfall
Measuring Mission (TRMM) sensor package. J.
Atmos. Oceanic Technol., 15, 809–817.
Kummerow, C., Y. Hong, W. S. Olson, S. Yang, R. F.
Adler, J. McCollum, R. Ferraro, G. Petty, D.-B.
Shin, and T. T. Wilheit, 2001: The Evolution of
the Goddard Profiling Algorithm (GPROF) for
rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40, 1801–1820.
Liu, C., and E. J. Zipser, 2005: Global distribution of convection penetrating the tropical tropopause, J. Geophys. Res., 110,
doi:10.1029/2005JD00006063.
Liu, C., and E. J. Zipser, 2008: “Warm rain” in the
tropics: Seasonal and regional distribution based
on 9 years of TRMM data, J. Climate, (submitted).
Liu, C., E. J. Zipser, D. Cecil, S. W. Nesbitt, and
S. Sherwood, 2008: A cloud and precipitation feature database from 9 years of TRMM
observations, J. Appl. Meteor. Climatol.,
Vol. 87A
doi:10.1175/2008JAMC1890.1, (in press).
Masunaga, H., T. Iguchi, R. Oki, and M. Kachi, 2002:
Comparison of rainfall products derived from
TRMM microwave imager and Precipitation
Radar. J. Appl. Meteor., 41, 849–862.
Masunaga, H., and C. Kummerow, 2005: Combined
radar and radiometer analysis of precipitation
profiles for a parametric retrieval algorithm. J.
Atmos. Oceanic Technol., 22, 909–929.
McCollum, J. R., A. Gruber, and M. Ba, 1999:
Discrepancy between gauges and satellite estimates of rainfall in equatorial Africa. J. Appl.
Meteor., 39, 666–679.
McCollum, J. R., and R. R. Ferraro, 2003: The next
generation of NOAA/NESDIS SSM/I, TMI and
AMSR-E microwave land rainfall algorithms, J.
Geophys. Res., 108, 8382–8404.
McCollum, J. R., and R. R. Ferraro, 2005: Microwave
rainfall estimation over coasts. J. Atmos.
Oceanic Technol., 22, 497–512.
Muller, B. M., H. E. Fuelberg, and X. Xiang, 1994:
Simulations of the effects of water vapor, cloud
liquid water, and ice on AMSU moisture channel brightness temperatures. J. Appl. Meteor.,
33, 1133–1154.
Nesbitt, S. W., E. J. Zipser, and C. Kummerow, 2004:
An examination of version 5 rainfall estimates
from TRMM microwave imager, Precipitation
Radar, and rain gauges on global, regional, and
global scales. J. Appl. Meteor., 43, 1016–1036.
Olson, W. S., C. D. Kummerow, S. Yang, G. W. Petty,
W.-K. Tao, T. L. Bell, S. A. Braun, Y. Wang,
S. E. Lang, D. E. Johnson, and C. Chiu, 2006:
Precipitation and latent heating distributions
from satellite passive microwave radiometry.
Part I: Improved method and uncertainty estimates. J. Appl. Meteor. Climatol.,45, 702–720.
Petersen, W. A., D. Hudak, V. N. Bringi, P. Siqueira,
A. Tokay, V. Chandrasekar, L. F. Bliven, R.
Cifelli, T. Lang, S. Rutledge, G. SkofronickJackson, and M. Schwaller, 2007: NASA GPM/
PMM Participation in the Canadian CloudSat/
Calipso Validation Project (C3VP): Physical
process studies in snow. Preprints, 33rd Int.
Conf. on Radar Meteorology, Amer. Meteor.
Soc., 6-10 August 2007, Cairns, Australia.
Qiu, S., P. Pellegrino, and R. Ferraro, 2005: An evaluation of an improved AMSU rain rate algorithm
and its application to winter season rainfall in
the western United States. Wea. Forecasting,
20, 761–774.
March 2009
N.-Y. WANG et al.
Rudolf, B., 1995: Global Precipitation Climatology
Centre. In: WMO/UNEP, The global climate
system review, climate system monitoring June
1991 - November 1993. WMO-No. 819, Genf,
37.
Schumacher, C., and R. A. Houze Jr., 2003: The
TRMM Precipitation Radar’s view of shallow,
isolated rain, J. Appl. Meteor., 42, 1519–1524.
Seto, S., N. Takahashi, and T. Iguchi, 2005: Rain/norain classification methods for microwave observations over land using statistical information for brightness temperatures under no-rain
condition, J. Appl. Meteor., 44, 1243–1259.
Seto, S., and T. Iguchi, 2007: Rainfall-Induced changes in actual surface backscattering cross sections and effects on rain-rate estimates by spaceborne Precipitation Radar. J. Atmos. Oceanic
Technol., 24, 1693–1709.
Skofronick-Jackson, G. M., and J. R. Wang, 2000: The
estimation of hydrometer profiles from wideband microwave observations, J. Appl. Meteor.,
39, 1645–1657.
Skofronick-Jackson, G. M., A. J. Gasiewski, and J. R.
Wang, 2002: Influence of microphysical cloud
parameterizations on microwave brightness
temperatures. IEEE Trans. Geosci. Rem. Sens.,
40, 187–196.
Skofronick-Jackson, G. M., M.-J. Kim, J. A. Weinman,
and D.-E. Chang, 2004: A physical model to determine snowfall over land by microwave radiometry, IEEE Trans. Geosci. Remote Sens., 42,
5, 1047–1058.
Spencer, R. W., H. G. Goodman, and R. E. Hood,
1989: Precipitation retrieval over land and ocean
with the SSM/I: identification and characteristics of the scattering signal. J. Atmos. Oceanic
253
Technol., 6, 254–273.
Staelin, D. H., and F. W. Chen, 2000: Precipitation
observations near 54 and 183 GHz using the
NOAA-15 satellite, IEEE Trans. Geosci. Remote
Sens., 38, 2322–2332.
Thiele, O., 1988: Validating Space Observations
of Rainfall. Tropical Rainfall Measurements,
J. S. Theon and N. Fugono, Eds., A. Deepak
Publishing, 415–423.
Wilheit, T. T., A. T. C. Chang, M. S. V. Rao, E. B.
Rodgers, and J. S. Theon, 1977: A satellite technique for quantitatively mapping rainfall rates
over the ocean, J. Appl. Meteor., 16, 551–560.
Wilheit, T. T., A. T. C. Chang, and L. S. Chiu, 1991:
Retrieval of monthly rainfall indices from microwave radiometric measurement using probability distribution functions, J. Atmos. Oceanic
Technol., 8, 118–136.
Wolff, D. B., D. A. Marks, E. Amitai, D. S.
Silberstein, B. L. Fisher, A. Tokay, J. Wang, and
J. L. Pippitt, 2005: Ground validation for the
Tropical Rainfall Measuring Mission (TRMM).
J. Atmos. Oceanic Technol., 22, 365–380.
Wolff, D. B., and B. L. Fisher, 2008: Comparisons of
instantaneous TRMM ground validation and
satellite rain rate estimates at different spatial
scale. J. Appl. Meteor. Climatol., 47, 2215–
2237.
Zhao, L., and F. Weng, 2002: Retrieval of ice cloud
parameters using the Advanced Microwave
Sounding Unit (AMSU). J. Appl. Meteor., 41,
384–395.
Zipser, E., D. Cecil, C. Liu, S. W. Nesbitt, and S.
Yurter, 2006: where are the most intense thunderstorms on Earth?, Bull. Amer. Meteor. Res.,
87, 1057–1071.