TRMM Precipitation Radar reflectivity profiles compared to
high-resolution airborne and ground-based radar measurements

G. M. Heymsfield

NASA Goddard Space Flight Center

Greenbelt, Maryland

B. Geerts

Science Systems and Applications, Inc.

Lanham, Maryland

L. Tian

Universities Space Research Associates

Seabrook, Maryland

To be submitted to Journal of Applied Meteorology

*Corresponding author address:*

Gerald M. Heymsfield, NASA GSFC, Code 912, Greenbelt, MD 20771.

**ABSTRACT**

In this paper, TRMM Precipitation Radar (PR) products are evaluated by
means of simultaneous comparisons with data from the high-altitude ER-2
Doppler Radar (EDOP), as well as ground-based radars. The comparison is
aimed primarily at the vertical reflectivity structure, which is of key
importance in TRMM rain type classification and latent heating
estimation. The radars used in this study have considerably different
viewing geometries and resolutions, demanding non-trivial mapping
procedures in common earth-relative coordinates. Mapped vertical cross
sections and mean profiles the PR, EDOP, and ground-based radars are
compared for six cases. These cases cover a stratiform frontal rainband,
convective cells of various sizes and stages, and a hurricane.

For precipitating systems that are large relative to the PR footprint
size, PR reflectivity profiles compare very well to high-resolution
measurements thresholded to the PR minimum reflectivity, and derived
variables such as bright band height and rain types , even at high PR
incidence angles. It was found that for, the PR reflectivitycells small
rel Some of these differences can be explained by non-uniform beam
filling. For otr cases where strong reflectivity gradients occur within
a PR footprint, the reflectivity distribution is spread out due to
filtering by the PR antenna illumination pattern. In these cases, rain
type classification may err and be biased towards the stratiform type,
and the average reflectivity tends to be underestimated. The limited
sensitivit of he PR implies that the upper regions of precipitation
systems remain undetected and that the PR storm top height estimate is
unreliable, usually underestimating the actual storm top height. This
applies to all cases but the discrepancy is larger for smaller cells
where limited sensitivity is compounded by incomplete beam filling.
Users of level three TRMM PR products should be aware of this scale
dependency.

**1. Introduction**

The Tropical Rainfall Measuring Mission (TRMM) satellite carries a
spaceborne radar, providing real-time and climatological rainfall
estimation (Kummerow et al 1998). In 1998-99 several TRMM field
campaigns were held to validate TRMM radar reflectivity and passive
microwave data over tropical precipitation systems. 1999
\*\*\*\*\*ne\*\*\*\*)as-FLorida UNderflight) campaign focused on
springtime mesoscale convective systems (MCSs) mainly in southeastern
Texas. TEFLUN-B was conducted in August-September 1998 in central
Florida, in coordination with CAMEX-3 (Third Convection and Moisture
Experiment). The latter focused on hurricanes, especially during their
landfall, whereas TEFLUN-B concentrated on central Florida convection,
which is largely organized by sea breeze circulations. Finally, TRMM-LBA
(Land-Biosphere-Atmosphere interaction in the Amazon) took place during
the first two months of 1999 in the southwestern quadrant of the Amazon
Basin [^1]. All experiments wre amply supported by surface data, in
particular a network of raingauges and radiosondes, a ground-based
polarization radar, wind profilers, a cloud physics aircraft penetrating
the storms, and a high-altitude aircraft (NASA ER-2 and DC-8 \[TEFLUN-B
only\]), flying over the same storms. One of these aircraft, the ER-2,
was equipped with visible, infrared and microwave imagers, electric
field detectors, an interferometer, and the dual-antenna X-band ER-2
Doppler Radar (EDOP).

This study aims to assess how well the TRMM Precipitation Radar (PR)
measures the vertical structure of a variety of precipitating systems.
Of key importance to PR validation are TRMM-coincident aircraft flights
over and within precipitating clouds, especially if these clouds are
located within the network of ground-based instruments. Coordinated
airborne/surface radar measurements provide high spatial and temporal
coverage of precipitation systems covered by a single TRMM pass, thereby
improving our understanding of how well TRMM measures rainfall from
storms of various sizes, intensities and evolutionary stages. In
particular, the segregation between convective and stratiform
precipitation by means of TRMM-based criteria can be evaluated with
high-resolution data.

TRMM PR data are calibrated and geolocated, and reflectivities are
corrected for attenuation and partial beam filling. Furthermore, a range
of qualitative and quantitative attributes is derived from the PR
reflectivity profiles. The relative reliability of these data
corrections and derived products can only be assessed through detailed
validation efforts. One validation approach is to statistically compare
TRMM products to independent data sets, such as ground radar, rain
gauge, satellite IR, or sounding data. The statistical approach is
justified by the sparse sampling nature of the PR, both in space and in
time, making simultaneous comparisons too rare. Studies of this kind are
facilitated by the monthly-mean products (level 3) provided by the TRMM
Science Data and Information System (TSDIS). For instance, 3A-25 data
are gridded monthly-mean PR-based rainfall estimates for the global
tropics.ny statistical comparison comprise distinct precipitation
systems, but these \'individual\' differences become insignificant when
sufficiently large samples are compared. The availability of a
statistically large enough sample of PR data is questionable in some
regions and for some periods. More importantly, the data sets used in
statistical comparisons, in particular rain gauge data, are only
indirect measures of the PR measurements, thereby incorporating many
uncertainties which remain even when the averages match very well.

In this study we evaluate the TRMM PR products by means of simultaneous
comparisons against high-resolution reflectivity data in a small sample
of storms. Of particular importance are EDOP measurements. EDOP is a
non-scanning instrument with two antennas, one pointing to the nadir,
the other pointing 33.5° forward (Heymsfield et al. 1996). EDOP is an
excellent PR validation tool, because of its high vertical and
horizontal resolution, and also because, unlike ground-based radars, its
nadir antenna has essentially the same perspective as the PR (Figure 1).
The purpose of this paper is not to assess the accuracy of the PR
calibration. Calibration tests are routinely undertaken by the Japanese
Space Agency (NASDA) to evaluate sensor consistency and drift. Recent
tests concluded that the PR is consistent with a calibration accuracy
within 1 dBZ. EDOP data themselves underwent rigorous calibration tests,
before and after the field experiments, and \*erry test conclusion is
that the EDOP reflectivities shown in this paper are 1-3 dBZ higher than
that of the PR. But the calibration issue is not the topic of this
paper. Rather, the PR\'s vantage point, wavelength and other radar
characteristics are significantly different from those of EDOP (Table
1), and these differences lead to several important differences in radar
observations.

(1) *Horizontal resolution*. EDOP\'s beamwidth is \~3.0°, which in the
    nadir translates to \~0.5 km at 10 km altitude and \~1.0 km at sea
    level, when the ER-2 flies at 20 km altitude. Such resolution is
    sufficient to see most precipitating convective cells (e.g. LeMone
    and Zipser 1980). Shear-induced slopes in hydrometeor fallstreaks
    can often be seen, as well as mammata-like anvil protuberances. The
    PR footprint size is about 4.3 km throughout the troposphere.
    Convective precipitation often falls from isolated cells smaller
    than 4.3 km. Only about 5% of the convective updrafts (with at least
    0.5 m s^-1^) over tropical oceans have a diameter of at least 4.3 km
    (Jorgensen and LeMone 1989). Goldhirsh and Musiani (1986) found that
    the median convective cell size for summer storms near the
    mid-Atlantic coast of the United States is only 1.9 km. A minor
    related difference is that the EDOP sampling rate is 0.5 sec,
    resulting in an along-track sampling of about 100 m and an 80-90%
    overlap from one beam to the next. This yields higher beam-to-beam
    continuity and better resolution, since the pulse-volume averaged
    radar reflectivity represents a mean value at the center of the
    radar beam. No such oversampling occurs for the PR.

(2) *Sensitivity*. The TRMM PR\'s noise level (floor) is at \~-111 dBm
    (Bolen and Chandrasekar 1999); therefore the minimum detectable
    signal is approximately 18 dBZ. While this covers all rain rates
    down to about 0.4 mm hr^-1^ (assuming uniform beam filling), EDOP
    has a much higher sensitivity, allowing it to see the lightest rain,
    and most of the ice region of precipitating cloudsthe spatial
    variipitation sometimes is rar the cloud top. The effects of limited
    horizontal re, small storm cells from the PR\'s view. To be seen by
    the PR, a cell with a diameter of 1 km needs to have an average
    reflectivity of at least 33 dBZ (Figure 4 in Bolen and Chandrasekar
    1999). If the cell is located off-center in the PR footprint, the
    required reflectivity would be even higher, as will be discussed in
    Section 2b.

(3) *Vertical resolution*. The EDOP range resolution is 37.5 m, compared
    to 250 m for the PR. This implies that the PR vertical resolution is
    equally-distributed over 250 m at nadir, decreasing to a 1,580 m
    deep layer at the outer incidence angle (17^o^) where the radar
    pulse-volume (a slice of 4.3 km x 250 m) is slanted at 17^o^ from a
    level plane. As a consequence, detailed EDOP-derived bright band
    (denoted BB) profiles can be used to examine the ability of the PR
    to detect and characterize BBs at varying incidence angles.

(4) *Attenuation.* At 13.8 GHz the PR reflectivity profile suffers from
    significant attenuation in the lowest beam, both in convective and
    stratiform precipitation with peak reflectivities greater than about
    35 dBZ. This threshold decreases slightly with increasing depth of
    the high-reflectivity layer, e.g. the path-integrated attenuation
    (PIA) is 5 dB for a 5 km deep layer. Attenuation rate (dB per
    kilometer) at the EDOP frequency (9.6 GHz) is about a factor of two
    less than at the TRMM frequency; for many situations, EDOP has
    minimal attenuation for reflectivities below about 45 dBZ, or about
    40 dBZ if these values are sustained through a deep layer, as
    commonly occurs in tropical deep convection. In this study we use
    attenuation-corrected PR reflectivity data (2A25).

Given these differences, one can treat EDOP cross-sections as
high-resolution \'truth\' for the TRMM PR. This implies that EDOP data
can be \'degraded\' to a PR perspective, and that degraded EDOP data
from the various TRMM field campaigns can be used as a surrogate for the
PR. This argument was a key motivation for the high-altitude remote
sensing aircraft participation in the TRMM field campaigns (Zipser et
al. 1999). TRMM overpasses are relatively rare and do not document the
lifecycle of storms, therefore cloud microphysical modeling efforts
aimed at improving TRMM precipitation algorithms and derived latent
heating profiles will benefit from EDOP data as a complement to TRMM PR
data. Furthermore, PR-observed features can be extrapolated to finer
scales and to higher hydrometeor sensitivity by means of an inverted
degrading process, however such process is not unambiguous. One such
extrapolation is the estimation of the storm top height from PR data.

Of the four differences listed above, the first two are the most
important. There is some concern that non-uniform beam filling (NUBF)
has a systematic effect on PR reflectivity and hence rainfall and latent
heating estimates. This concern has been addressed both with theoretical
and observed echo patterns, however real TRMM data have not been used
until now. Durden et al (1998) used a scanning 13.8 GHz radar (the
Airborne Rain Mapping radar or ARMAR) aboard the NASA DC-8 to simulate
PR reflectivities in three dimensions. They found that degraded ARMAR
data of tropical oceanic convection tend to overestimate the
reflectivity near the cloud tops and underestimate the path-integrated
attenuation. Amayenc et al (1996) also found some biases due to NUBF
using nadir-looking airborne radar data of a rainstorm off the East
Coast of the USA. Kozu and Iguhi (1999) proposed a correction to PR
rainrate data due to NUBF, based on the local fine-scale rainfall
variability as observed using ship-based radar data in the western
equatorial Pacific. This variability can be correlated with a
PR-measurable quantity such as PIA, however this correlation is probably
not universally valid. In short, the de facto impact of sub-beam-scale
convection and sharp reflectivity gradients on PR rain estimation and
classification is not well understood and has not been analyzed by
comparing PR data to high-resolution data.

In this paper, comparisons are made between the PR, EDOP, and
ground-based radars for six TRMM overpasses during TEFLUN and TRMM-LBA.
The emphasis of this study is on comparison of the vertical pattEDOP and
PR reflectivities, whereas the ground radars provide an independent
check on the PR measurements. Other data, such as passive microwave
measurements from the TRMM Microwave Imager (TMI) and the ER-2 mounted
Advanced Microwave Precipitation Radiometer (AMPR) (Spencer et al.,
1984), are only used in the interpretation of the PR-EDOP comparison.
PR-derived products, such as BB characteristics and precipitation
classification, are assessed as well, but the key PR variable in most
other studies, i.e. surface rainrate, is not addressed here. Because of
the small size of some of the selected storms, and the different viewing
geometries and resolutions of the various radars, accur these data to a
common coordinate sy required. Section 2 describes the details of this
mapping methodology. In Section 3, six examples are presented, covering
a mainly stratiform frontal rainband), a convective cell in its decaying
stage, a small, growing convective cell, a small mesoscale convective
system (MCS), and a hurricane. Composite reflectivity profiles

**2. Methodology**

2a. Viewing geometry, resolution, and beamfilling effects

Comparison of the PR with EDOP and ground-based radars involves data
from drastically different viewing geometries (Figure 1). Both the PR
and EDOP have high vertical resolution but blur the horizontal
structure, while ground-based radars have excellent slant-range
resolution but blur the vertical structure. The ground radars
themselves, i.e. S-POL, TOGA, and WSR-88D, have somewhat different range
resolutions and beamwidths. Furthermore, the range gate values of
reflectivity from the different radars are located at different
locations in space and time. Comparison of data from these radars
requires interpolation to a common reference frame with high accuracy
geo-location. Two approaches are possible, each of which has merits. The
first approach is to degrade all the data sets to the lowest common
resolution volume. This volume has the horizontal dimensions of the PR
footprint and the range-dependent vertical depth of the beam of the
nearest ground radar. This allows for examination of differences between
data sets all on the same, lowest resolution scale. This approach is
ideally suited for calibration comparisons but it does not deal with the
NUBF problem. The second approach is to interpolate all the observations
to the coordinates of the highest resolution data (i.e., EDOP), in order
to examine what reflectivity structures are present in each data set
relative to the high-resolution 'truth'. The second approach is used in
this paper, i.e. PR and ground-based radar reflectivities are resampled
to a densethe PR\'s vertical resolution, which is maintained at its
nadir value (250 m). This approach is generally analogous to routine
meteorological interpolation of upper air and surface observations to a
grid for NWP model initialization. These data usually are widely-spaced
relative to grid intervals and thus the interpolation method can be
important in filtering and in reducing data aliasing (e.g., Trapp and
Doswell 1999). The technique to interpolate the PR and ground-based
radars to an EDOP section is described in Appendix A.

The largest cause of residual difference (i.e., not related to radar
characteristics) between correctly geo-interpolated radar data is the
non-simultaneity of the radar measurements. TRMM measurements of a storm
are essentially instantaneous, while ground-based radar volumes are
collected in 3-5 minutes and EDOP data collected in \~8 min/100 km
(Appendix B). During a time lag of a few minutes echo patterns can be
displaced significantly, such as in hurricane conditions, and/or they
can evolve, which is especially likely in small, short-lived convective
cells. A better match than the ones presented in this paper could be
obtained by correcting the data to a common time, i.e. the EDOP
observation time. Temporal correction advection is possible because of
the 3-D natur data, however advection veed, and evolution is the more
comences in all the hurriaeans of EDOP data, as discussed above. The
details of the rading process are de1. In essence EDOP data are gridded
to a vertical section and then degraded to the TRMM resolution by
sampling it with a one-dimensional (along-track) representation of the
PR antenna illumination function. This process accentuates differences
in horizontal resolution between the PR and EDOP.

2b. Radar beam filtering.

It is well known that the radar antenna main and side-lobes cause
distortion of a meteorological target (Donaldson 1964). This is
particularly true in sharp hydrometeor gradient regions viewed by
ground-based radars, or vertical edges of storms viewed by the PR and
EDOP. A more significant problem is the NUBF problems mentioned earlier.
Both of these problems can cause a significant misrepresentation of the
reflectivity. For simplicity, the filtering effects of a radar beam can
be calculated in the following manner. Assume the true and measured
reflectivity are defined by *Z* and *Z~m~*, respectively, and the
two-way antenna illumination function is represented by *I^2^*(*θ, φ*)
where *θ, φ* are the two-dimensional angles off the antenna boresight.
Additionally, the reflectivity variations along the slant range at range
*r* are assumed constant, the circular radiation pattern (*θ, φ*) can be
projected onto rectangular coordinates (*x,y*) in a beam-normal plane
through the relations *x-X=rθ*, and *y-Y=r φ*. Here (X,Y) are the
coordinates of the beam\'s center at range r, relative to the center of
a storm cell (where x=y=0). We define (*θ~ο~, φ~ο~*) = (2*x~ο~* ,
2*y~ο~)/r* as the 3 dB (half-power) points of the radar antenna.*  *
Then the effects of beam filtering of the true radar reflectivity field
may be given by:

$$\begin{array}{r}
Z_{m}(X,Y) = \int_{\text{-¥}}^{¥}{\int_{\text{-¥}}^{¥}{Z(x,y)I_{}^{2}}}(x - X,y - Y)\text{dxdy}\text{              }(1) \\
I_{}^{2} = \frac{4\text{ln}4}{{px}_{o}y_{o}}e^{-^{\left\lbrack \frac{(x - X)^{2}}{x_{o}^{2}\text{/ln}4} + \frac{(y - Y)^{2}}{y_{o}^{2}\text{/ln}4} \right\rbrack}}
\end{array}$$

where $I_{}^{2}$ is the normalized two-way antenna illumination
function. Clearly, this is a Gaussian distribution function, i.e. side
lobe effects are ignored for this simplistic representation. The above
relation is similar to Donaldson (1964) and others. Applying (1) to a
nadir PR beam with x~o~=2~o~=4.3 km, we assume atrue reflectivity field
*Z* (i.e. a small storm cell) t with a maximum reflectivity of 50
![](media/image1.pct){width="3.5770833333333334in"
height="0.5555555555555556in"}

w$\begin{array}{r}
Z_{m}(X,Y) = \text{AZ}_{o}e^{- \left\lbrack \frac{X^{2}}{\frac{r_{c}^{2}}{\text{ln}2}\left( 1 + \frac{x_{o}^{2}}{2r_{c}^{2}} \right)} + \frac{Y^{2}}{\frac{r_{c}^{2}}{\text{ln}2}\left( 1 + \frac{y_{o}^{2}}{2r_{c}^{2}} \right)} \right\rbrack}\text{                }(3) \\
A = \left\lbrack \sqrt{1 + \frac{x_{o}^{2}}{2r_{c}^{2}}}\sqrt{1 + \frac{y_{o}^{2}}{2r_{c}^{2}}} \right\rbrack^{- 1}\text{                                             }(4)
\end{array}$$$

hn

2A25 reflectivity profilved variables) and TMI data. All TRMM data are
processed ad algorithm are described in detail in NASDA (1999). 2A25
data , clutter rejectiourface, especially at high incidence angles and
over mountainous eferencing to an ellipsoidal representation of the
Earth's surface, attenuation correction and NUBF correction. A hybrid
betweeo ude the presence and height of a BB, the rain type clas

Rainfall is classified as stratiform if a BB exists (\'V method\')
and/or the horizontal echo variation is small (\'H method\'). he \'H
mehod\' is an adaptation of the method by Steiner etal (1995 to thePR
resoltion: a beam is convective if its maximum reflectivity (Z~max~)
exceeds 40 dBZ or if Z~max~ stands out above the ambient echo. In the
\'V method\' rain is classified as convective if no BB exists and if
Z~max~ 39 dBZ. learly the accuracy of the attenuation correction may
significantly impact the rain classification. Both methods yield three
outcomes (convective, stratiform, and inconclusive), and a combination
of the H and V methods allows rainfall characterization in a
probabilistic manner. For instance, if both H and V methods classify a
pixel as stratiform, the 2A23 rain type is \'stratiform certain\'. But
if there is no BB yet the H method suggests stratiform rain, then the
2A23 rain type is \'probably stratiform\' (Table 2). Sample tests
indicate that the likelihood of correct BB detection is abo

3.  **Comparison of TRMM with EDOP and ground radars**

ER-2 flights coincided withTRMM overpasses over various types of
precipitation systems. DA and B, CAME- \[check ???\] TRMM ovecurred whie
ER-2 fle PR swath o, but only six ofently data-rich aneous for dtor each
of the six ar echo pattern. 2 km altitude image from the TRMM PR is
shown together with a low level scan from the nearest ground radar in
Figure 3 for all cses, covering a mainly stratiform frontal rainband (a,
b), a convective cell in its decaying stage (c, d), a small, growing
convective cell (e, f), a small mesoscale convective system (MCS) (g,
h), and a hurricane (i-l). For this purpose, several ground-based radars
were used that collected data during the TRMM overpasses. The S-band
POLarizatioadar (S-POL) radar from the National Center for Atmospheric
Research (NCAR) participated in TEFLUN-B and TRMM-LBA. The TOGA radar
supported by the Wallops Flight Facility participated in TRMM-LBA.
Finally, the WSR-88D operationaladars at Fort Worth, TX (KFWS),
Melbourne, FL (KMLB), and (KLTX), were used when possible to provide
additional data for comparisons. All the ground-based radars were S-band
(3 GHz) Doppler radars with 1^o^ beamwidth antennas with the exception
of the TOGA radar with a 1.6^o^ beamwidth. The mapping procedures for
ground radar and PR data are described in Appendix A.2 and A.3
respectively. The temporal coincidences of all relevant radars are
listed in Table 3.

Comparison of vertical reflectivity cross-sections, which are the
emphasis of this paper, are presented in Figs. 4-9. The EDOP panel
provides nadir reflectivity mapped onto a cartesian (*x, z*) grid above
ground level (AGL). Each vertical column represents an EDa particular
surface latitude-longitude position. The vertical crd radar (panel b)and
PR (panel c) ae nstructpping procedures described in Appendix A. Figures
4-9 also show profiles of various derived qantities for each case. These
include: the brightness temperatures (0-85 GHz and 11 μm) from the TMI
and VIRS (panel e), storm top height and BB heightoduct) (panef), and PR
incidence angle together with rain type (also 2A23) (panel g). These
figures will be referred to in the subsequent discussion. The PR storm
top height product (2A25) is the height of the first (highest) echo
above the PR noise level. This height will be compared to the
EDOP-estimated storm top, practically defined as the 0 dBZ contour.
EDOP\'s sensitivitys well below 0 dBZ.

**[3a. Widespread]{.underline}** **[stratiform rain(21 April
1998)]{.underline}**

A broad rainban, associated with a well-defined cold front oriented
WSW-ENE, slowly propagates southeastward through central Texas on 21
April 1998. This rainband is over 700 km long, aligned with the cold
front, and largely stratiform. Its cloud tops and rain rates decrease
towards the northeast. The ER-2 flies along this rainband which also
coincides with the PR swath of the TRMM overpass at 634 UTC. (All times
hereafter are in UTC.) At this time there are three short convective
lines to the south and west of the ER-2 leg, most obvious in Figure 3a .
These lines are oriented normal to the broad rainband and move along it,
advected by strong westerly wind at 500 mb. They appear more vigorous in
the PR image than in the KFWS radar PPI. The convective lines are at
least 150 km from this WSR-88D radar, therefore their echo strength is
weakened by attenuation and partial beam filling.

The ER-2 was directed to start just to the east of the westernmost of
these three lines at 0624, thereby missing the two other lines to the
north. By the time of the TRMM overpass about 10 minutes later, the
westernmost line has moved into the ER-2 section. The PR reflectivity
pattern matches EDOP\'s very well east of this line, as PR-EDOP
coincidence improves to the east (Fig 4a). Some details are missed by
the PR such as the sloping fallstreaks evident in the EDOP section below
the freezing level. The fallstreaks are the result of the presence of a
25 ms^-1^ westerly shear between 0-5 km evident in the 00 sounding 6 h
earlier at Dallas Forth Worth. The storm top height in the PR section is
generally less than 1 km below the EDOP stormtop, however during TEFLUN
A the cloud top is likely higher than indicated since a EDOP sensitivity
was reduced due to a amplifier malfunction. The algorithm-derived
heights of the storm top and the BB (as shown in Figure 4f) verify well,
and the rain east of the convective line (at the left edge of the panels
in Figure 4) is classified correctly in the stratiform group. Compared
to tropical stratiform rainfall systems (e.g. in hurricanes, section 3e,
this rainband contains a large amount of ice, as evidenced by the 85 GHz
upwelling radiance. This is not because of high cloud tops (merely 8 km)
but because the freezing level (\~3 km altitude) is about 2 km lower
than in the tropics. Over much of the rainband the 85 GHz brightness
temperature, as measured by the TMI and AMPR, is below 220 K. McGaughey
et al (1996) find that 220 K is the minimum 85 GHz brightness
temperature associated with ice scattering in stratiform regions of
tropical oceanic systems. Passive microwave temperatures from lower
frequencies (especially 19 and 10 GHz) arely marginally depressed
(Figure 4e ).

**[3b.]{.underline} The trailing edge of a dissipating convective cell
(13 August 1998)**This case illustrates a borderline feature for the PR,
mainly in terms of sensitivitysensitivity, and there is a significant
reflectivity gradient across the EDOP section. The ambient wind is weak
at all levels on 13 August, and it is mainly westerly (\<8 m s^-1^)
below 10 km. Afternoon thunderstorms develop, mainly along outflow, sea
breeze and river breeze boundaries. About 15 km inland from the Banana
River, a sequence of short-lived thunderstorms builds discretely
southward and dissipates from the north. The ER-2 flies from west to
east across the northern edge of a storm cell (Figure 3c). The EDOP
reflectivity is low at all levels (Figure 5a), and there is a suggestion
of a weak BB. The PR, recording this storm 1 (right) to 4 (left) minutes
later, can see the storm cell, even some of its decaying anvil (Figure
5c). The PR storm top, just below 10 km, is about 2 km below the actual
storm top (Figure 5f). The PR can see the rain reaching the ground. This
rain is classified correctly as 'probably stratiform', because of the
H-method (the echo is too weak for it to \'stand out\'), not because a
BB is detected. The BB is not detected because it is quite weak and
because the PR incidence angle is fairly large (8°).

The PR only marginally detects the smaller but more int ( x\<10 in
Figure 5), about 4 minutes after the ER-2 passage. The AMPR brightness
temperatures at 10 and 19 GHz are lower than at 85 GHz for these cells,
suggesting that they contain very little ice. This PR detection failure
may be affected by NUBF, however the degraded EDOP image (Fig 5d)
suggests that the PR should still capture a clear signal. The more
likely cause is the rapid decay of these shallow, isolated cells. In the
2225 S-POL volume, shown in Figs 3d and 5b, volume (not shown). Another
factr may be the across-leg reflectivity gradient (i.e., nFig 3c .

**c. Smaebruary 1999)**

Many convective towers formed in the afternoon of 1 February 1999 over
Rondonia, Brazil, but they were generally small and short-lived. EDOP
recorded 12 cells with at least 40 dBZ at an altitude of 2 km during a 3
hour flight period (1730-2030). With the exception of a 30 km wide storm
overflown twice, the average diameter of these cells was 5 km (measured
between the 20 dB EDOP boundaries at 2 km), i.e. about the size of the
PR footprint. The sample is somewhat biased because the ER-2 targeted
the larger thunderstorms in the population. The tops of these storms
were not very high, but variable (5-9 km), and no spreading anvils nor
stratiform regions formed. The minimum 85 GHz brightness temperatures of
the first five of these cells was only 240-260 K (AMPR failed at 1842).
Organized deep convection did not develop on this day, probably because
of a lack of wind shear. Between 0-5 km, the wind was weak (less than 10
m s^-1^), mainly from the northeast. Easterly shear of about 25 m s^-1^
existed between 5-11 km, and this was evident in the shearing of the
tops of some taller cells.

At the time of the TRMM overpass, the ER-2 flew near the western edge of
a line of convective cells, about 40 km long and 5-10 km wide. This line
is captured both by the S-POL PPI and the PR CAPPI in Figure 3e-f. The
line is moving southward and growing in length, but the cell at 30 km
(visited twice by EDOP) is dissipating (Figure 6a) but still has a
narrow intense core with very few hydrometeors above 5 km and no anvil.
The S-POL section (Figure 6b) is almost identical to EDOP\'s, but with
lower resolution. The PR detects this cell, located close to the TRMM
nadir, about 6.6 min later (Figure 6c), but the maximum reflectivity is
about 30 dB (as opposed to 45 dB) and the storm top is near 5 km (as
opposed to 12 km). The degraded EDOP (Figure 6d) shows a stronger and
slightly deeper echo pattern than the PR. The difference may be partly
explained by cell evolution. When the ER-2 returns along this section
over this cell, about 6.8min after the TRMM passage (not shown), the
maximum reflectivity is still about 38 dB and the echo top about 10 km.
The minimum AMPR 85 GHz brightness temperature of this cell is about 240
K, but the TMI brightness temperature traces don\'t record any
significant disturbance over the cell (Figure 6e). The VIRS does see the
cell clearly, with a minimum IR temperature just below 225 K (i.e. a
storm top just above 11.5 km),. The main cell is classified as \'other\'
and \'probably stratiform\', not because of a BB, but because of low PR
maximum reflectivity. The size and strength of this feature, as revealed
in EDOP imagery, makes it clearly convective,.The weak cell to the north
of the main cell along this flight leg, near x=50 km in Fig 6a, offers a
nice example of detection failure due to the PR\'s sensitivity
threshold. The degraded EDOP image (Fig 6d) suggests that the PR should
just detect it. The PR does not see it, probably because at the time of
the TRMM overpass (5 min later), this feature has weakened further, as
suggested by the ER-2 return flight along this leg and S-POL imagery.
The only TRMM probe that records this weak feature is the VIRS (Fig 6e).
From a rainfall perspective, this weak feature is insignificant, since
most or all rain appears to evaporate before reaching the ground.

**3d. Small MCS (23 February 1999)**

A broken line of cells grew into a continuous line over 100 km long
between 1900 and 2000 on 23 February. This line was oriented NNW-SSE and
propagated southeastward at first but later stalled. Convection along
this line was most vigorous between 2000 and 2030, then weakened and a
trailing stratiform region formed to the west. By 2200 all convection
had disappeared and a \~3,000 km^2^ large area of stratiform rain
remained. This area expanded and intensified somewhat during the next
half-hour, and then dissipated during the next two hours. The ambient
wind below 5 km altitude was mostly northwesterly at 7-15 ms^-1^, an
easterly jet was f3 km. The easterly wind above 7 km probably supported
the formation of the westward-trailing stratiform region.

Figure 3g-h shows this line in a maturing stage. Convection is found
mostly to the south along the leading (eastern) side of the line, while
the northwestern portion develops into a stratiform region. The EDOP
section essentially runs along the leading line of convection. No BB is
present in this section (Fig. 7), except in the far south where some
remnants of the shorter-lived southern portion of the line can be seen.
The reflectivity generally drops off rapidly above the freezing level in
this section, and convection is not very deep. The AMPR data indicate
that much ice is transported to the west of the line (i.e. into the page
of Fig. 7a). The minimum AMPR 85 GHz brightness temperature is about 160
K in this section, but 135 K over the stratiform region. Higher altitude
CAPPIs from the PR (not shown) confirm that at 8 km the echoes are
strongest to the west of the ER-2 track, while at 2 km they are
strongest on the track or just to its east. This is consistent with the
observed growth of the stratiform region. The deepest echo in the EDOP
cross section ( Fig. 7a) are actually in the lee of an active cell to
the east of the line, so stratiform intensification processes (i.e.
mesoscale updraft and ice growth above the freezing level, Houze 1993)
may be active in this part of the cross section.

The ground-based radars (Figs. 7b,c), especially TOGA (which is closer),
match the EDOP section well. The height, intensity, and structure of the
PR echo also compares well to degraded EDOP echo pattern. The small
discrepancies are mainly due to across-line gradients (i.e. the third
dimension)..

PR rainfall is classified as 'certainly convective' to the south and
'probably stratiform' to the north (Fig. 7g). The classification as
stratiform is not due to a BB (neither the PR nor EDOP detect a BB) but
to weak reflectivities. This northern (right-hand) region appears
convective from an EDOP perspective, because of high reflectivities and
their gradients, and an absence of a BB. However this region is just to
the east of a large area which clearly is stratiform. The PR storm top
height (Fig. 7f) is close to ther radars, except to the south (x\<1here
stratiform and dissipating cloudve higher tops (EDOP, TOGA, and S-POL in
Figs. 7a-c) between 8-10 km. In fact even the PR (Fig 7d) has higher
topstel.

**3. Hurricane Bonnie Pass 1 (26 August 1998)**

EDOP data were collected during three TRMM passes over Hurricane Bonnie
on 26 August 1998. At the time of the overpasses (1137-1451), Bonnie\'s
central pressure was steady at \~ 965 mb, and its maximum sustained
surface winds were about 50 ms^-1^. Bonnie made landfall near Wilmington
NC around 0330 27 August. [^2]At the time of the EDOP observations,
Bonnie had one or more weak and ill-defined inner eyewalls and a
stronger and more continuous outer eyewall with a diameter of \~170 km.
Only the first and third TRMM passes are discussed here, because the PR
swath of the second one missed Bonnie\'s eye and only a short section of
high-incidence PR data coincided with the EDOP section. Note that the PR
algorithms correctly detect and place the BB even at high incidence
angles.

The first TRMM pass, at 1137, is almost exactly over Bonnie\'s eye (Fig.
3i-j). Several rain arcs can be seen in the region surrounded by the
outer eyewall, and the PR captures all but the finest features present
on the WSR-88D PPI, such as the shallow radial bands at the northwestern
margin of the storm. The EDOP section lags the TRMM section by 13-35
minutes.. The difference between the PR section (Fig. 8c) and the
corresponding degraded EDOP section (Fig. 8d) is largely due to this
time lag, and is most obvious inside of the outer eyewall, where echoes
are more transient. A much better could be obtained if advection of the
hurricane is accounted for, but this correction is of this discussion.

The EDOP section (Fig. 8a) displays some fine scale features which are
beyond the resolution or sensitivity of the PR. This includes: the
spreading of the anvil outward from the outer eyewall, thin rain columns
within the outer eyewall, and low-level radial confluence suggested by
the inwardly curved fall streaks in the rain layer on both sides of the
eye (which is near x= km). Otherwise the PR-EDOP comparison is excellent
for the outer eyewall, including its BB and outward slant.

In this section most rain is correctly classified as 'certainly' or
'probably stratiform' (Fig. 8g). The PR storm tops of the deeper
features are 2-4 km lower than EDOP heights. The TMI 85 GHz brightness
temperature associated with outer eyewall (near x=190 km) is as low as
240 K (compared to 220 K for AMPR), whereas the more shallow rain echoes
within the outer eyewall remain undetected at 85 GHz, both in TMI and
AMPR data. The low 85 GHz temperatures and low reflectivities aloft
implies an absence of significant ice scattering in the inner rainbands
This is common for tropical systems (ref. Zipser
???\*\*\*\*\*\*\*possibly my 92 paper).

**3f. Hurricane Bonnie Pass 3 (26 August 1998)**

At 1450 Bonnie\'s outer eyewall has contracted slightly, and the inner
rain arcs have weakened a little (Fig. 3k-l). While most of the
hurricane\'s rainfall field is northeast of the eye, the outer eyewall
is most intense towards the southwest. The ER-2 flies from NE to SW
across the eye, but the PR swath misses the northern part of the outer
eyewall. The EDOP cross section (Fig. 9a) confirms the asymmetry, with a
highly tilted yet weak northeast eyewall, a deeper southwest eyewall,
with the eye centered near x=150 km. Many hallow but intense echoes can
be seen within the outer eyewall in the EDOP pass. Ground radar and PR
CAPPIs (Figs. 3k-l) also indicate that these cells have echo tops that
are close to 6 km, however some cells inside the outer eyewall are much
deeper.

The PR section detects a deep cell (x=170 km in Fig. 9c) and when the
ER-2 flies overhead about 10 min later, this cell has largely been
advected out of the cross section. Therefore the degraded EDOP echo is
weaker than the PR echo; otherwise the PR-EDOP correspondence is quite
good. The PR BB height is similarly to EDOPs except that it is slightly
higher within the eye than in the eyewall (Fig. 9f). This elevated BB
height is due to a higher 0^o^C isotherm in the eyewall. This feature
can also be seen in pass 1 (Fig. 8f), but it is not as pronounced. The
cloud height derived from the PR is generally below the EDOP and actual
cloud height. This is apparent in the outer eyewall (x=170-200 km) where
the PR storm height is 2-4 km too low. Some fallstreaks below 5 km
altitude (120 \<x\<km))ximum reflectivity of \~25 dB are to thin
and/orseen, so no PR cloud height was assigned.

The rainfall classification scheme is excellent, even at high incidence
angles. Overall, aBB is detected correctly by the PR algorithm. Areas
classified \'certainly stratiform\' (x=170-190 km) aclearly as strform
in EDOP imagefor the two areas classified as convective, EDOP does not
reveal a BB. The 85 GHz brightness temperatures measured by the TMI are
only barely depressed by the shallow cells inside of the outer eyewall,
but they are as low as 205 K over the southwestern outer eyewall (Fig.
9e). These data correspond well to those of AMPR.

**4.0 Comparison of the statistics of the profiles.**

Mean height profiles of reflectivity were constructed (Fig. 10) for the
cross-sections shown in Figures 4-9 These profiles were obtained by
first converting relectivities in "dBZ" to linear units before averaging
across each height level (0.0375 m for EDOP and ground-based radars and
0.25 km intervals for the PR). To focus the comparison on the same
precipitation structures, reflectivity profiles from radars other than
the PR are thresholded to the PR\'s minimum detectable reflectivity of
17 dBZ. Mean profiles for ground-based radars had to be truncated near
the ground because the upward slanting of the lowest beam away from the
radar caused a bias in the averaging length at particular level. Much
information is lost in the reduction of a precipitating system to a mean
profile, but the comparison of the first moment only from various radars
is more feasible than that of CFADs (contoured frequency by altitude
diagram, Yuter and Houze 1995) or other distribution functions. The
comparison of mean profiles isseful for calibration purposes but the
focus in this study is on the details of the profiles. For instance, how
does the thickness and the strength of the BB in the PR compare to that
of EDOP and that of ground radars? How does reflectivity change with
height below the BB, and what cloud microphysical consequences can be
drawn ? How do profiles compare in the case of NUBF ?The vertical
filtering due to the PR\'s gate spacing and off-nadir viewing results in
a underestimation of the BB strength of 3-9 dBZ (Fig 10a and f). The PR
, but it should be cautioned thoften observes bright band thicknesses
much less than the 250 m pulse volume thickness. The effect of NUBF is
illustrated wel 10d: the PR estimate is - small or rapidly evolving
storms, a good temporal match is essential. Figure 10c shows the clorida
convection evidenced by the rapid eflectivity decre 22:19 as in Fig. 10c
are the result of the flight line crossing aprint is relatively large
and hence may sample this regently than discrepancies in thicane a
non-simultaneity; in this case advection mentiences. For all the cases
presented, the best time coismall MCS on 23 February 1. The
reflectvproffferene are largely duerences and also PR attion algorithms.
ClearlytePofile nicely matches the S-POL and EDOP traces
\[\*\*\*\*\*\*\*\*\* Gerry ....when 2.5dBZ is subtracted from the
latter\].

**5. Summary and Conclusions**

2A25 product to data from higher-resolution, more sensitive radars, i.e.
airborne (i.e., EDOP) anoundadars. Io an earth-relative cartesian grid,
as well as in the geo-locons covered by Ebeam.fferent beam geometries,
resolutions, earthrThisnot focused onfferences betweemphasis isn the
shape of the profilesic differences between them. The comparisoon
regions. However, in some regionsa significant portion of the rainfall
results from convecion that is small relative to the PR footprint. It is
well known that PR reflectivitlied operationally in obtaining the 2A2
PR-measured reflectivity, and hence rainrate, becomes increasingly
reduced not only as the e s in presented flived from the various
radargely due tneous samplinn). Six cases from TEFLUN (A and B), CAMEX-3
ad TRMy simultaneous and data-rich for a radar intercomparisons. These
cases represent various meteorological situations: a broad, mostly
stratiform rainband, a hurricane, a small MCS, a dissipating convective
storm, and a small yet active convective cell. Some preliminary
conclusions can be drawn from this small sample of EDOP-TRMM
coincidences:

-   resolution EDOP reflectivity sections show that the TRMM PR,
    gishold, captures most of the spectrum ofes of precipitating well,
    in tes of both 2A25 vertical reflectivity structure and deduced
    variables.The PR accurately detects bright bands, their depth and
    their height, in fact in this regard it outperforms WSR-88D radars
    at typical operating ranges. High-resolution rofiles from Ethe PR
    rainfall cEDOP (as well as other airborne radars such as ARMAR)
    reflectivities can be degraded to provideWhile the viewing
    geometries of the EDOP and AMPR are difo rovide nsight on the
    performance of TRMM during this situation.The vertical structure,
    intensiand rfootprint may be erroneoussub-PR footprint whhe tropics,
    underestimation of reflectivity and storm top heights can result
    inmis-classification of convective precipitation as stratiform.

-   

## **Acknowledgements**

This work was supported under NASAsby Dr. Ramesh Kakar at NASA
Headquarters. There are a number of key people in field campaign
operations that deserve special credit. Dr. Ed Zipser and Ms. Robbie
Hood worked jointly with the primary author in directing ER-2 flights
during the TEFLUN-B and CAMEX-3 campaigns, Dr. Steve Rutledge, Dr. Ed
Zipser and Walt Peterson direct TRMM-LBA ER-2 flights. Dr. Vivekanadan
Dr. Ed Brandes, and others, were key in the S-POL radar science
direction dDr. Steve Bidwell an Mr. Ed Zenker are greatly appreciated
for the superb the EDOP instrument. We are appreciative to Dr. Bob
Meneghini for many excellent science discussions, to Mr. Carlos Morales
and Dr. Liang for guidance on reading PR and other TRMM data setsadar
data and assistance was provided by Mr. Dave Wolff (TOGA) and Mr. Bob
Rilling at NCAR (S-POL). Many others answered the many small questions
th arose due to of the data sets used er.

**Appendix A: Mapping algorithms.**

An accurate interpolation of all the radar data sets into a common
(earth-relative) coordinate system is essential before the various data
sets can be compared. The coordinates of the EDOP flight line images are
chosen as this common frame of reference, as described in section 2. The
EDOP mapping is briefly described in Appendix A.1. Ground-based radar
and TRMM PR data are three-dimensional, an their mapping onto the EDOP
plane is described in Appendices A.2 and A.3 respectively. The
redistribution of two-dimensional (horizontal) TRMM data is described in
A.4.

**A.1 EDOP Coordinates and mapping.**

The ER-2 tracks presented in this paper are relatively linear although
occasionally there are small aircraft heading adjustments (such as the
26 August 1998 pass 1) or other more minor deviations in heading due to
cross wind variations at altitude. Thus to retain accuracy in the
mapping of other data sets, each beam of EDOP data is assumed to be
normal to the earth's surface and each gate has an associated position
*(δ, α, z*) where δ is latitude, α is longitude, and z is height above
the earth's surface. This assumption is quite good since the ER-2 is
relatively stable during flight with roll excursions of less than
0.25^o^ (i.e ±175 m on the ground) and pitch excursions of less than
1^o^The coordinates of gates in each beam at \~100 m intervals along the
flight track are then gridded in (*x, z*) such that pixels in a single
vertical column represent a single dwell of data, and the x axis
represents dwells along the flight line. In all cases presented here the
height of the earth surface is less than 200 m above mean sea level, so
EDOP heights are not corrected to represent altitude above sea level.

**A.2 Ground-based radar mapping.**

Ground-based radar data are collected in spherical coordinates \[(*r,
ϑ, φ*)  where r is range, *ϑ* is azimuth, and *φ* is elevation\] and are
mapped to EDOP vertical sections using the transformation equations
developed in Heymsfield et al. (1983). These "small range" equations are
applicable to distances less than about 200 km from the radar and can be
summarized as follows:

> $$\begin{array}{r}
> s = \frac{r\text{cos}f}{(1 + (\frac{r}{R}')\text{sin}f}\text{                                            }(\text{A1}) \\
> x = s\frac{R}{R'}\text{sin}(q)\text{                                                     }(\text{A2}) \\
> y = s\frac{R}{R'}\text{cos}(q)\text{                                                    }(\text{A3}) \\
> \alpha = \alpha_{s} + \frac{x}{R\text{cos}d}\text{                                                }(\text{A4}) \\
> d = d_{s} + \frac{y}{R} - \frac{x^{2}}{2R^{2}}\text{tan}d_{s}\text{                                     }(\text{A5})
> \end{array}$$

where *R* is the local radius of the earth at the radar station, the
effective earth radius *R'=4R/3*, (*x,y*) the radar-relative horizontal
location, and the subscript *s* refers to the radar location. These
approximations provide *(δ, α, z*) to within a few tenths of a km at 200
km from a radar. The topographic height of the radar above sea level is
ignored.

Using the above equations, the (*r, ϑ, φ*) location from a given radar
is calculated for each EDOP pixel. Then, for each pixel, a search is
performed over the radar volume scan for the 8 surrounding gates (4 each
from the elevation scans above and below *φ*).  Interpolation is
performed using trilinear interpolation, i.e., first the reflectivities
are interpolated bilinearly to (*r, ϑ*) on each elevation scan, and then
these values are linearly interpolated along *φ*.   It should be noted
that the reflectivites in "dBZ" are linearized before the interpolation
and converted back to dBZ after the interpolation. While better
interpolation schemes exist, the linear interpolation, also used in
Heymsfield et al. (1983), is simple to implement and provides reasonable
results. Trapp and Doswell (1999) address the ramifications of using
bilinear versus Cressman and Barnes interpolation which have more easily
understandable filtering responses.

The ground-based radar PPIscans shown in Figure 3 are constructed using
an almost identical interpolation approach to the above and in
Heymsfield et al. (1983). This approach uses equations A1-A5 and
interpolation to a regular latitude-longitude grid with intervals of
0.01^o^ in latitude and longitude.

# A.3 TRMM PR mapping

The PR reflectivity data used in this study is based on the 2A25 product
provided by TSDIS. This product includes attenuation-corrected
reflectivity and rain rate profiles, and geolocation information. The
reflectivity profiles have high vertical resolution (250 m) and coarser
(\~4 km) horizontal spacing. For simplicity, the PR profiles are assumed
vertically oriented even though they can be tilted up to about 17^o^
scan angle. This implies that an echo at the edge of the PR swath at 15
km altitude would be displaced about 4 km (i.e. one PR pixel spacing)
horizontally from the surface position of the profile, towards the TRMM
nadir position. In most cases, this is not a problem since the PR scan
angles are usually much smaller and the echo heights are less than 10
km. Thus, each range gate has a *(δ, α, z*) location.

Interpolation is performed as follows. For each EDOP profile which has
an associated *(δ, α, z)* coordinate, a search is performed on the TRMM
data for the four profiles surrounding this *(δ, α)* location are
identified. Then Cressman interpolation (Cressman, 1959) is applied to
these profiles level by level in the PR profile at 250 m intervals. The
resulting interpolation function is given by:

$$Z_{\text{edop}} = \sum_{i = 1}^{4}\frac{k^{2} - d^{2}}{k^{2} + d^{2}}Z_{\text{PR}}\text{                                                     }(\text{A6})$$

where *κ*=5.0 km is the influence radius, *d*, the distance from the
EDOP pixel location, and *Z~edo~*~p~ and *Z~PR~* are the EDOP and PR
reflectivities, respectively. This function is a relatively simple
objective analysis function, yet it captures most of the PR features
well in the interpolated vertical sections. This interpolation was
compared with using the nearest point to the EDOP pixel and the
interpolation approach was superior. The radius of influence was chosen
as the minimum value for which *d\<κ* for at least 4 PR pixels anywhere.
It is not much larger than the spacing between PR samples, so the
Cressman interpolation applied here is applying minimal smoothing of the
data. Discontinuities in the PR data displayed on the EDOP mesh arise
from the jump from one PR beam to another. These discontinuities are
much smaller than the \'degraded\' EDOP data because the latter
represent the PR resolution (Appendix B) and are not distributed on the
fine EDOP mesh.

The constant-altitude PR echo maps shown in Figure 3 are constructed
using a standard Delaunay triangulation scheme to map irregular gridded
points to a regular latitude-longitude grid with a grid mapping interval
of 0.02° in latititude and longitude.

# A.4 Mapping of two-dimensional TRMM parameters

Many of the TRMM parameters are located only by their latitude and
longitude, not by their altitude, for instance the TRMM Microwave Imager
(TMI) brightness temperatures (2B11), the Visible and Infrared Scanner
(VIRS) infrared temperatures (1B01), the path integrated attenuation
(2A21), rain type (2A25), normalized surface backscatter cross section
(2A21), etc. Some of these variables do have a physical altitude, e.g.
infrared tempertures are representative of the cloud top. Lateral
displacements due to off-nadir TRMM scanning angles are ignored, even
for the TMI which scans at a constant 53°. These quantities are
interpolated to the EDOP profiles in an identical fashion to the PR
data, as described in section A.3, using the Cressman weighting in (A6).
The exception to this are discrete variables such as the rain type
(2A25). For these quantities, the TRMM pixel nearest the EDOP profile is
used and no interpolation is performed.

## Appendix B: Simulating PR reflectivities using EDOP data

**B.1 Technique**

The simulation of spaceborne data using airborne radar data has been
discussed by Amayenc et al (1996) and Durden et al (1998), and a similar
technique is used here. First, EDOP data are corrected for aircraft
motion. In particular, changes in aircraft pitch renders the beams
non-equidistant. trictly speaking an inverse convolution of the EDOP
data is needed to remove the effects of EDOP range resolution, however
EDOP\'s resolution is high enough compared to that of the PR to treat
EDOP data as representative of a point. The next step in the simulation
involves a convolution of the EDOP data with a 1D (along-track) Gaussian
weighting function. Finally, a reflectivity threshold is applied,
reflecting the limited sensitivity of the PR. Differences in frequency
between the PR and EDOP lead to differences in attenuation. It is
assumed that the PR attenuation correction (Iguchi and Meneghini 1994)
is accurate and that EDOP reflectivities are not significantly
attenuated, in other words no further attenuation correction is
performed in the simulation process. Also, the effect of decreasing
vertical resolution of the PR with increasing scanning angle is ignored,
and the degraded EDOP data have a 250 m vertical resolution at any
incidence angle. The data are not convoluted with a Gaussian function in
the vertical.

# B.2 Limitations of degraded EDOP data as a surrogate for PR data

There are limitations to the representativness of degraded EDOP
reflectivities as surrogate PR data. A perfectly \'degraded\' EDOP
section will normally not perfectly match the corresponding PR section
for two reasons: lack of high-resolution information about the third
dimension (i.e. across the flight track of the ER-2), and
non-simultaneity of the observations. Simultaneous records will compare
poorly when reflectivity contours (on a map) are tightly packed along an
ER-2 flight leg, i.e. when the ER-2 flies along precipitation systems,
rather than across them. The radar maps in Figure 3, as well as passive
visible, infrared and microwave data from scanning instruments on the
ER-2 can be used to assess cross-track variability. Non-simultaneity is
often a more serious problem: for instance it takes the ER-2 about 8
minutes to sample a 100 km long storm, while it takes the TRMM satellite
about 14 seconds to travel the same distance (Table 1). Poor comparisons
can be expected from rapidly evolving storms, and when a high
reflectivity gradient is advected across an ER-2 flight leg. Small
thunderstorms are especially difficult to compare because of NUBF and
because they are typically short-lived. For larger (stratiform) systems
a larger time lag between EDOP and the PR is acceptable.

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**Table 1**. A comparison of some EDOP and TRMM PR parameters.

+----------------------------+-------------------------+--------------+
|                            | > EDOP                  | TRMM PR      |
+----------------------------+-------------------------+--------------+
| Frequency (GHz)            | > 9.6                   | 13.8         |
+----------------------------+-------------------------+--------------+
| Wavelength (cm)            | > 3.12                  | 2.17         |
+----------------------------+-------------------------+--------------+
| Antenna                    | > fixed, nadir and      | scanning to  |
|                            | > forward (34°)         | ±17°         |
+----------------------------+-------------------------+--------------+
| Footprint at 5 km altitude | > 0.76                  | 4.3          |
| (km)                       |                         |              |
+----------------------------+-------------------------+--------------+
|                            |                         |              |
+----------------------------+-------------------------+--------------+
| Range resolution (m)       | > 37.5                  | 250          |
+----------------------------+-------------------------+--------------+
| Time required to sample a  | > \~8 min               | \~14 s       |
| 100 km wide storm          |                         |              |
| (along-track)              |                         |              |
+----------------------------+-------------------------+--------------+
| Minimum detectable signal  | > -5                    | 18           |
| (dBZ) at 5 km altitude.    |                         |              |
+----------------------------+-------------------------+--------------+
| Number of indep. samples   | > \~300                 | 64           |
| per pixel                  |                         |              |
+----------------------------+-------------------------+--------------+

°.

**Table 2**. Definition of TRMM PR rain types as a combination of the
outcome of two tests, the H method and the V method. The order of the
listing is the same as that in the lower right corner of Figures 4-9.

  ------------------ ------------------ ---------------- ----------------
  PR rain type       stratiform         convective       inconclusive

  Strat cert         H,V                                 

  Strat cert         V                                   H

  Prob Strat         H                                   V

  Maybe Strat        V                  H                

  Convect cert                          H,V              

  Convect cert                          H                V

  Convect cert                          V                H

  Prob Convect                          H                V

  Maybe Convect      H                  V                

  Maybe Convect      V (BB not clear)   H                

  Others                                                 H,V
  ------------------ ------------------ ---------------- ----------------

**Table 3**. List of events with quasi-coincident TRMM-EDOP
observations. Times are in UTC. The EDOP times are the start/end of a
straight-and-level flight leg. The TRMM overpass time over the domains
shown in Figure 3 may take 10-30 seconds, but the time closest to the PR
coverage of the feature of interest is shown. The ground-based radar
time is the start time of a volume scan.

  -------------- -------------------- ------------ ---------- --------------
  Date           Type of Event        EDOP         TRMM       Ground Radar

  21 April \'98  frontal - mostly     06:24 -      06:33:45   KFWS 06:36:46
                 stratiform           06:45                   

  13 August \'98 decaying convective  22:25 -      22:29:52   S-POL 22::06
                 cell                 22:30                   

  1 February     growing convective   18:17 -      18:25:47   S-POL 18:16:00
  \'99           cell                 18:21                   

  23 February    small MCS            20:56 -      21:00:47   TOGA 21:00:19
  \'99                                21:03                   

  26 August \'99 Hurricane Bonnie     11:41 -      11:37:16   KLTX 11:36:10
                 (Pass 1)             12:13                   

  26 August \'99 Hurricane Bonnie     14:47 -      14:50:30   KLTX 14:55:03
                 (Pass 3)             15:12                   
  -------------- -------------------- ------------ ---------- --------------

[Figure Captions]{.underline}

Figure 1. A comparison of the PR, EDOP and ground radar geometries.

Figure 2. Scale dependency of PR reflectivity estimates. The curves show
the variation of PR-measured reflectivity as a function of the storm
cell size, for various cell locations relative to the center of the PR
footprint. The horizontal shape of the storm cell is assumed to be
bell-sh maximum refty of 50 dBZ. The PR beam illumination function is
alsoassmedbe Gaussian.

Figure 3. Reflectivities from TRMM PR and ground-based radar mapped to
common earth coordinates. The left panel for each case (3a, c, e, g, i,
and k) provides the 2 km altitude 2A25 product. The right panel (3b, d,
f, h, j, and l) displays the lowest clutter-free radar elevation scan
from the various radars. The ER-2 flight track with start and end times
(UTC) is indicated on the plot. Values exceeding 50 dBZ are shown white,
values less than 0 dBZ are black, and the background is gry. The \'X\'
denotes the location of the relevant ground radar.

Figure 4. Composite of vertical reflectivity sections from (a) EDOP, (b)
ground radar, and (c) the PR, for 21 April 1998, mapped to coordinates
of EDOP cross section (top panel). Panel (d) shows a \'degraded\' EDOP
section, simulating the PR (Appendix B). The time difference (Dt)
between the EDOP profile and the PR overpass is labeled below the EDOP
image in minutes, where positive numbers indicate that the EDOP vertical
profile is later than the TRMM image. Panels (e-g) show various TRMM
products corresponding to this cross section. TMI microwave brightness
temperatures at 10, 19, 35, and 85.5 GHz, together with the VIRS
infrared brightness temperature, are shown in panel (e). PR-derived
storm top and BB heights are shown in panel (f), and the rain type
classification is shown in panel (g) together with the PR incidence
angle.

Figure 5. As Fig 4, but for 13 August 1998.

Figure 6. As Fig 4, but for 1 February 1999.

Figure 7. As Fig 4, but for 23 February 1999. The TRMM simulation by
means of EDOP data is omitted, instead a reflectivity crosssection from
a second ground radar is shown.

Fig8. As Fig. 4,but for 26 August 1998 (pass 1).

Figure 9. As Fig 4, but for 26 August 1998 (pass 3).

Figure 10. Mean reflectivityprofiles derived from the vertical cross
sections in Figures 4-9. The averages are calculated over the entire
section, except were shown (i.e. for 13 August). Ground radar profiles
are identified also by the start time of the volume scans. (a) 21 April
1998; (b) and (c), 13 August 1998; (d) 1 February 1999; (e) 23 February
1999; and (f) 26 August 1998.

[^1]: ettp://www.cptec.inpe.b

[^2]: See the Preliminary Report by NHC at
    http://www.nhc.noaa.gov/1998bonnie.html.