**An Automated Radar Technique for the Identification of Warm Rain
Processes**

Xiaoyong Xu^1^, Kenneth Howard^2^, and Jian Zhang^1^

*^1^*Cooperative Institute for Mesoscale Meteorological Studies, The
University of Oklahoma, and *^1,2^*NOAA/OAR National Severe Storms
Laboratory, Norman, Oklahoma

Submitted to

*Journal of Hydrometeorology*

July 2007

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_

Corresponding author address: Xiaoyong Xu, NSSL/WRDD, 120 David L Boren
Blvd., Norman, OK 73072. Email: <Xiaoyong.Xu@noaa.gov>

**ABSTRACT**

A radar-based automated technique for the identification of warm rain
process was developed to improve quantitative precipitation estimation
during extreme rainfall events. The technique uses vertical profiles of
reflectivity to identify the presence of warm rain (i.e. tropical
rainfall) microphysics and delineates the tropical rainfall region to
which the tropical *Z-R* relationship is applied. The performance of the
algorithm is examined based on case studies of five storms that produced
extreme precipitation in the United States. Results demonstrate relative
improvements in radar based quantitative precipitation estimation
through the identification and dynamic adjustment of *Z-R* relationships
to account for warm rain processes.

**1. Introduction**

Radar based quantitative precipitation estimation (QPE) is crucial in
timely and accurate flood and flash flood identification and warnings.
Unfortunately, radar rainfall estimates are affected by source errors
such as reflectivity calibration difference, inappropriate radar
reflectivity--rainfall rate (*Z-R*) relationship, range degradation,
hail and bright band contaminations (see, e.g., Wilson and Brandes 1979;
Zawadzki 1984; Joss and Waldvogel 1990; Hunter 1996; Baeck and Smith
1998). There are principally three major contributors to radar rainfall
underestimation based on reflectivity observations. The first two are
related to the low echo centroid structure of the storm (see, e.g.,
Smith et al. 1996). Low-level growth of rainfall from the top of the
beam to the bottom of the beam, which was sampled by the radar, and from
the bottom of the beam to the surface, which was not, contributed to
underestimation of rainfall depth. The second reason for underestimation
resulted from inappropriate *Z--R* parameters. The drop-size
distribution (DSD), a component in *Z--R* relationship, differs markedly
as a function of rainfall intensity (see Willis and Tattelman 1989). The
third major source of error is introduced in using a threshold
reflectivity value to avoid significant overestimates of rainfall due to
hail contamination. A number of efforts have been focused on correcting
precipitation underestimates by radar (e.g., Wood 1997; Anagnostou and
Krajewski 1998; Vieux and Bedient 1998; Vignal et al. 2000; Germann and
Joss 2002). In this paper, attention is paid to the underestimation by
radar due to using inappropriate *Z-R* relationship with tropical
rainfall. The standard WSR-88D *Z-R* parameters (Z=300R^1.4^) are on
occasion inappropriate for heavy rain, thus leading to the occurrence of
significant underestimates of rainfall (e.g., Baeck and Smith 1998;
Vieux and Bedient 1998). These conditions are most prevalent in tropical
rainfall regimes where a deep warm cloud layer exists and warm rain
processes prevail. To compensate for this, a tropical *Z-R* relationship
can and should be utilized to enhance the warm cloud rainfall estimation
in place of the standard *Z-R*. Wood (1997) used the tropical *Z-R*
relationship to improve precipitation estimates during a heavy rain
event in southeast Texas. In a flash flood study, Vieux and Bedient
(1998) reduced the underestimates significantly with the tropical *Z-R*
relationship. Aiken (2000) discussed the performance of the Raleigh
WSR-88D during Hurricanes Fran and Floyd and suggested that the
precipitation algorithms where the tropical *Z-R* was used, performed
exceptionally well during these hurricanes. These studies applied an
uniform tropical *Z-R* to totally replace the uniform standard *Z-R.*
However, there is usually coexistence of different precipitation types
within a rainfall event. The uniform tropical *Z-R* probably results in
error in radar rainfall estimate due to its inappropriateness to other
precipitation classes (e.g., convective or stratiform) than warm rain.
Hence a potentially more accurate and physically correct approach would
be to apply different *Z-R* relationships based on real-time
precipitation echo classification.

The objective of this paper is to show improved accuracy in radar based
QPE through the identification and dynamic adjustment of *Z-R*
relationships to account for warm rain processes.

**2. Algorithm description**

**2.1 Identification of tropical rainfall based on VPRs**

Vertical profiles of reflectivity (VPRs) can provide important
information for radar QPE applications such as the delineation of the
bright band layer (e.g., Zhang et al, 2007, Z07 hereafter), the
identification of potential warm rain processes and the adjustment for
the precipitation efficiency below cloud base. The shape of VPRs is a
potential indicator of the precipitation microphysical process (Houze
1993). Examination of VPRs characteristics where heavy rainfall was
being significantly underestimated when using convective *Z-R* were
found to have reflectivity increases monotonically below 0 °C height
with a maximum reflectivity at the lowest level. This concurs with
Zipser and Lutz (1994) who argued that the tropical oceanic profile have
a maximum reflectivity at the lowest level and a very rapid decrease in
reflectivity with height beginning just above the freezing level.
Further, Kucera et al. (1996) found that the volume average reflectivity
profiles in TOGA COARE (Tropical Ocean Global Atmosphere Coupled
Ocean-Atmosphere Response Experiment) increased slightly from the 2km
altitude to the surface.

In this research, the identification (or diagnosis) of warm cloud
processes or tropical rainfall was based on the hourly averaged
stratiform VPRs and the 0 °C height derived from a background
temperature profile. The temperature data are obtained from hourly
analyses of the Rapid Update Cycle (RUC, Benjamin et al. 2004) model.
For each radar volume scan, two mean VPRs are computed from base level
reflectivity data near the radar, one for convective and the other for
stratiform precipitation (Z07). A running hourly average is applied to
the volume scan mean VPRs to obtain hourly averaged VPRs. Two classes of
VPR structures are considered to be tropical VPRs that reflect the
presence of warm rain microphysics: 1) reflectivity increases as the
decrease of height with a maximum at the lowest level (e.g., Fig.1a),
and 2) there exists a bright band and reflectivity increases (Fig. 1b)
or remains constant (Fig. 1c) with the decrease of height below the
bottom of the bright band. It is possible for tropical VPR to contain a
bright band. Several studies (Houze 1989; Tokay and Short 1996) have
strongly suggested the coexistence of different microphysical processes
within tropical rainfall. The warm-rain processes of nucleation,
condensation, and coalescence provide the major source of water for
raindrops via continuation of the coalescence process. Microphysical
processes such as freezing and riming might occur when updrafts carry
liquid hydrometeors well above the freezing level where ice nucleation
processes, wind shear, detrainment, and gravitational settling create
the stratiform environment, as characterized by ice-crystal growth, and
aggregation above the 0^0^C isotherm and melting and evaporation below.
Those hydrometeors that return to the surface as precipitation are
almost always in the liquid phase, especially over the tropical oceans.
Similar VPR structures can be seen in Figs. 8-11 of Tokay et al. (1999).

The automated identification scheme for tropical VPRs can be summarized
as a sequence of the following processing steps.

1)  Look for the local maximum reflectivity *Z~lmax~* near the freezing
    level in the VPR. The search starts from 500m above the 0°C height
    at the radar site and continues downward. A 500 m cushion is used to
    account for uncertainties in the model 0°C height due to infrequent
    and sparse upper air sounding observations (e.g., Z07). The VPR is
    identified as the "tropical VPR" if the *Z~lmax~* is located at the
    bottom of the VPR.

2)  If the *Z~lmax~* is not located at the bottom of the VPR, determine
    if there exists a bright band in the VPR using the method developed
    by Z07.

3)  If there is not a bright band, search for the global minimum
    reflectivity *Z~gmin\ ~*below the level that the *Z~lmax~* is
    located at. Once the *Z~gmin\ ~*is found, the following criterion is
    checked: *Z~lmax~* - *Z~gmin~* \< α. Here the parameter α is an
    adaptable threshold (default α= 0.8 dB). α can be relaxed to a
    larger value (e.g., 1.6 dB) when the *Z~gmin~* is not lower than the
    reflectivity at the start height in step 1). If the aforementioned
    criterion is not satisfied, the VPR is labels as the "non-tropical
    VPR". If the criterion is met, search for the new *Z~lmax~* below
    the original *Z~lmax~* and for the new *Z~gmin~* below the new
    *Z~lmax~*. This process is repeated until the bottom of the VPR is
    reached. If the bottom of the VPR can be reached, the VPR is
    identified as the "tropical VPR".

4)  If there is a bright band, search for the new *Z~lmax~* below the
    bottom of the bright band. The rest of the process is the same as
    step 3).

Several small adjustments are made in determining the bright band bottom
in order to account for the VPR variability. A flowchart of the tropical
VPR identification scheme is given in Fig. 2.

**2.2 Delineation of tropical rainfall region**

Ideally, the *Z-R* relation is chosen based on the DSD observation to
minimize the inappropriate *Z-R* interference. That, however, is
difficult to fulfill in real-time operational application due to the
shortage of the DSD data. As alternatives, various techniques were
developed to classify precipitation types based on three-dimensional
radar reflectivity observations (Steiner et al. 1995; Z07) and
atmospheric environmental data (Z07). Different *Z-R* relationships were
applied based on precipitation types and more accurate QPE was obtained
(Steiner et al. 1995). In the current study, the Z07 precipitation
typing technique was used to delineate convective and stratiform
precipitation and a new tropical precipitation delineation scheme is
added. The combined technique produces a two-dimensional field in which
each grid cell has a precipitation type flag.

The new tropical precipitation delineation is based on tropical VPRs and
the two-dimensional radar reflectivity grid that will be used for
rainfall estimation. The 2-D reflectivity field is basically the
un-blocked radar reflectivity observations closest to the ground (namely
"hybrid scan reflectivity", see Fulton et al., 1998). A grid cell is
assigned a precipitation flag marked as tropical precipitation if the
following three criteria are all satisfied: 1) the grid is covered by
the tropical VPR influencing area whose centre is positioned at the
radar site with a radius of 250 km; 2) the hybrid scan reflectivity is
greater than 30 dBZ; 3) the surface wet bulb temperature is higher than
2 ^0^C to assure that the surface precipitation is not snow. Grid cells
outside influence areas of the tropical rainfall VPRs are also
identified as tropical precipitation if they adjoin those that are
already labeled as tropical precipitation and meet the criteria #2 and
#3. The search starts from the grid cells on the boundary of the
tropical VPRs' influences regions and proceeds outwards until boundaries
of the analysis domain are reached. The grid cells labeled as tropical
precipitation compose the tropical rainfall region. A flowchart of the
delineation of the tropical precipitation region is given in Fig. 3.

**2.3 Rainfall classification-based *Z-R* transformation**

Based on the precipitation type classification, different *Z-R*
relations are used to compute rain-rate from different microphysical
processes. In the new algorithm developed in this study, the formula
Z=230R^1.25^ is utilized for the tropical rainfall. For convective echo,
the relationship used is Z=300^1.4^, and for stratiform echo,
Z=200R^1.6^ is applied. The current study does not consider other *Z-R*
relations such as snow.

> **3. Case studies**

Five cases (see Table 1) were examined to assess the improvement of the
new tropical rain identification in comparison over just a convective
and stratiform. Two sets of QPEs were generated for each case, one uses
two precipitation types (convective and stratiform, or "CS" hereafter)
and the associated *Z-R* relationships (see section 2.3) and the other
uses convective, stratiform and the new tropical precipitation types
("CST" hereafter) and the associated *Z-R* relationships (see section
2.3).

The performances of two configurations are assessed by comparing their
gridded rainfall accumulations with corresponding gauge rainfall
reports. The gauge rainfall reports come from the Hydrometeorological
Automated Data System (HADS, http://www.nws.noaa.gov/oh/hads/). The
performance of each configurations in rainfall estimation is quantified
base on the following statistics: 1) the mean bias, Bias =
\<A~R~\>/\<A~G~\> and 2) the radar--gauge root-mean-square error, RMSE =
\<\|A~R~-A~G~\|^2^\>^1/2^, where A~R~ and A~G~ represent radar and gauge
accumulations for any given radar-gauge (R-G) pair, and brackets mean
averaging over all such pairs. A bias greater (less) than 1.0 indicates
that the radar has overestimated (underestimated) the rainfall assuming
the gauge report was accurate. In this work, a valid R-G pair is defined
as a pair in which the gauge accumulation value is 0.03 inches or more.
Smaller accumulations are omitted in order to reduce errors caused by
the minimum precipitation threshold of the rain gauge (e.g., Klazura et
al. 1999; Anagnostou and Krajewski 1999). The performance of the CST is
compared against that of the CS. These comparisons are used to
demonstrate improvements with using the CST configuration over using the
CS configuration.

> **a. Hurricane Isabel on September 18-19, 2003**

Hurricane Isabel was considered one of the most significant tropical
cyclones to affect North Carolina and Virginia. Isabel made landfall in
eastern North Carolina around 01:00 UTC 09/18/2003 and produced heavy
rains between 01:00UTC 09/18/2003 - 05:00 UTC 09/19/2003. Fig.4 presents
example VPR plots that were identified as representative of the tropical
rainfall for this case. Fig.5 depicts the radar precipitation type
classification result using the CST at the corresponding time to Fig.4.
Figs. 4 and 5 indicate that warm rain microphysical processes played an
important role in this hurricane event. These warm rain microphysical
processes are successfully captured by the automated identification
logics of tropical VPRs. Underestimates of rainfall by radar take place
almost over the whole domain of interest when using the CS (left panel
in Fig. 6). After applying the CST, the underestimation area is reduced
with a better consistence between radar estimations and gauge (right
panel in Fig.6). The improvements coming from the CST with respect to
the CS are also illustrated by the changes in radar--rain gauge
difference statistics. From the upper panel in Fig. 7, it can be seen
that the Bias from the CS and the CST are 0.75 and 0.99, respectively,
in the 24-hr rain accumulation estimates for 01:00 UTC 9/18/2003 --
01:00UTC 9/19/2003. The corresponding RMSE are 0.85 and 0.53 inches,
respectively. For the accumulation from 05:00 UTC 9/18/2003 to 05:00UTC
9/19/2003 (bottom panel in Fig.7), the Biases produced by the two
schemes are 0.72 and 0.95, respectively. The corresponding RMSE are 0.90
and 0.53 inches, respectively. The CST performs better than does the CS
for this event, potentially indicating that the tropical rainfall
identification component in the CST would improve rainfall estimate for
hurricanes and other similar precipitations in which the efficient warm
rain processes dominate.

**b. Texas rainfall event during March 26-27, 2007**

The rainfall rates in central Texas on March 26-27 were extraordinary
and resulted in wide spread flooding. The use of CS alone resulted in
significant underestimation in observed rainfall mainly from the
inappropriate *Z-R* to areas with warm rain process (left panel in
Fig.10). The warm rain microphysical processes were successfully
separated from other microphysical processes based on the identification
of tropical VPRs (Fig.8 and Fig.9). From Fig.10, the use of the CST
leads to a Bias (1.01) closer to 1 and a reduced RMSE (0.43 inches)
compared to the Bias (0.70) and RMSE (0.56 inches) by the CS. Overall an
improved consistence between radar estimation and gauge was introduced
after applying the tropical rainfall identification module. However,
must considerable underestimations remains with this case even with the
tropical identification. The significant underestimations in central
Texas event are potentially a result of 1) VPRs not being computed at
KDYX from 12:00 to 16:45 UTC of 3/36/2007, 2) reflectivity data dropouts
from KDYX during the period of 12:00-18:00 UTC of 3/36/2007, 3) the KGRK
radar running considerably colder in comparison to adjacent radars, and
4) the current tropical *Z-R* relationship does not represent the full
efficiency of this precipitation microphysics.

**c. Tropical Storm Barry during June 1-3, 2007**

The Tropical Storm Barry resulted in copious amounts of rainfall across
the southeastern US. The VPRs associated with TS Barry depict a classic
transition from convective to tropical precipitation processes (Fig.11).
Fig.12 shows the scatterplot of radar versus gauge for this case. For
the radar 24-hr accumulation ending at 12:00 UTC of 6/3/207 by the CS,
the average bias and RMSE are 0.62 and 0.76 inches, respectively. While
with CST there remained significant underestimation in some areas,
overall an improved radar-gauge consistence was realized. The Bias and
RMSE are improved to 0.79 and 0.51 inches, respectively. The possible
reasons for the remaining underestimations are: 1) reflectivity data
dropouts during part of the event; 2) the current tropical Z-R
relationship is not fully representative of the actual rainfall process
similar to the central Texas event.

**d. Heavy Rainfall in NE Oklahoma on June 11, 2007**

The overnight rainfall event in NE Okalahoma and SE Kansas on June 11,
2007 was very impressive with rain-rates approaching 1 inch per hour.
The VPRs at radars KINX and KTLX are indicative of warm rain
microphysics (Figures not shown). Figs. 13 and 14 show the improvement
of the consistence of radar-estimated rainfall and the rain gauge
accumulations when the CST is used. Using the CST removed biases lower
than 1 in most of radar-gauge pairs, especially for the gauges with high
amounts. After using the CST scheme, biases near 1.0 dominate in the
region of interest (right panel in Fig.13). For 1-hr accumulation
between 09:00-10:00UTC, the Bias is improved to a high degree from 0.57
by the CST to 1.02 by the CS. The corresponding RMSE is lowered from
0.33 inches to 0.15 inches. A very substantial improvement in terms of
Bias and RMSE is also indicated for the 1-hr accumulation for 12:00 --
13:00UTC and 6-hr amount from 09:00 -- 15:00UTC.

> **e. North Texas Flooding on June 18, 2007**

During this event, 8 inches of rain was reported at Gainesville Texas in
a 12 hour period. The rainfall rates were consistent with warm rain
processes and as evident from examining the VPR at KFWS (Fig. 15). The
CS grossly underestimates the precipitation (left panel in Fig.16). The
Bias by the CS in the 12-hr accumulation for 01:00 -- 13:00 UTC is as
low as 0.65. The corresponding RMSE reaches 0.83 inches. With the CST,
the Bias is improved to 1.05 and the RMSE is reduced to 0.59 inches. For
the extreme accumulation (point A in of Fig. 16), radar estimate agreed
well with the gauge when the CST configuration was employed. However, a
significant increase in the rainfall amounts and a better radar-gauge
correspondence was not obtained in other locations near the KFWS radar
(Points B and C in Fig.16). As with previous cases missing data likely
contributed to the poor correspondence. The impact of missing data on
the CST performance is being further investigated.

**4. Summary**

A radar-based fully automated technique for the identification of warm
rain process was developed to improve quantitative precipitation
estimation during extreme rainfall events. The technique uses vertical
profiles of reflectivity to identify the presence of warm rain (e.g.,
tropical rainfall) microphysics and delineates the tropical rainfall
region to which the tropical *Z-R* relationship is applied. The
technique comprises of three modules: 1) identification of tropical
rainfall based on VPRs; 2) delineation of tropical rainfall region; and
3) *Z-R* transformation based on precipitation typing.

The performance of the algorithm was examined based on case studies of
five storms that produced extreme precipitation. Table 2 summarizes the
performances of radar rainfall estimates before (CS configuration) and
after (CST configuration) the new tropical rainfall identification
scheme is applied. The results demonstrate relative improvements in
radar based quantitative precipitation estimation through the
identification and dynamic adjustment of *Z-R* relationships to account
for warm rain processes. The improvement is operationally significant
given that extreme rainfall events often associated with flash floods
and hurricanes landfall are associated with high efficient warm rain
processes.

**Acknowledgments**

Major funding for this research was provided through the collaboration
with the Central Weather Bureau of Taiwan, Republic of China and partial
funding was provided under NOAA-University of Oklahoma Cooperative
Agreement #NA17RJ1227, U.S. Department of Commerce.

The authors also thank Wenwu Xia and Carrie Langston for assembling and
processing much of the data.

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TABLE 1. Extreme rainfall case studies

  -------------------------------------------------- --------------------
  Storm                                              Date

  1: Hurricane Isabel, North Carolina and Virginia   18-19 Sep, 2003

  2: Texas heavy rainfall, Texas State               26-27 Mar, 2007

  3: Tropical Storm Barry, Southeastern US           1-3 Jun, 2007

  4: Northeast Oklahoma heavy rainfall               11 Jun, 2007

  5: North Texas Flooding, Gainesville Texas         18 Jun, 2007
  -------------------------------------------------- --------------------

TABLE 2. Summary of Gauge-radar comparisons using the two schemes

  ------- ------------------------------ -------- ---------- -------- ----------
  Storm   Study time period              CS                  CST      
                                         scheme              scheme   

                                         Bias     RMSE (in)  Bias     RMSE (in)

  1       24-hr accumulation ending at   0.75     0.85       0.99     0.53
          01:00 UTC on 19 Sep, 2003                                   

          24-hr accumulation ending at   0.72     0.90       0.95     0.53
          05:00 UTC on 19 Sep, 2003                                   

                                                                      

  2       24-hr accumulation ending at   0.70     0.56       1.01     0.43
          12:00 UTC on 27 Mar, 2007                                   

                                                                      

  3       24-hr accumulation ending at   0.62     0.76       0.79     0.51
          12:00 UTC on 3 Jun, 2007                                    

                                                                      

  4       1-hr accumulation ending at    0.57     0.33       1.02     0.15
          10:00 UTC on 11 Jun, 2007                                   

          1-hr accumulation ending at    0.58     0.25       0.99     0.11
          13:00 UTC on 11 Jun, 2007                                   

          6-hr accumulation ending at    0.59     0.88       1.02     0.36
          15:00 UTC on 11 Jun, 2007                                   

                                                                      

  5       12-hr accumulation ending at   0.65     0.83       1.05     0.59
          13:00 UTC on 18 Jun, 2007                                   
  ------- ------------------------------ -------- ---------- -------- ----------

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> Fig.1 Conceptual models of tropical VPRs reflecting the presence of
> warm rain processes: reflectivity increases as the decreases of height
> with a maximum at the lowest level (a); and there exists a bright band
> and reflectivity increases (b) or remains constant (c) with the
> decrease of height below the bottom of the bright band. The solid line
> is VPR. The dashed line is the 0 ^0^C isotherm.
>
> Fig.2 A flowchart of the automated scheme to identify tropical
> rainfall VPRs.
>
> Fig.3 A flowchart of the delineation of tropical rainfall regions
> based on tropical VPRs.

![](media/image5.png){width="2.95in" height="2.3979166666666667in"}
![](media/image6.png){width="2.95in" height="2.3979166666666667in"}

![](media/image7.png){width="2.95in" height="2.3979166666666667in"}
![](media/image8.png){width="2.95in" height="2.3979166666666667in"}

![](media/image9.png){width="2.95in" height="2.3979166666666667in"}
![](media/image10.png){width="2.95in" height="2.3979166666666667in"}

> Fig. 4 Hourly averaged stratiform VPRs (bold black curves): a), b) and
> c) are from KAKQ, KMHX and KRAX radars, respectively, at 16:00UTC; d),
> e) and f) are from KAKQ, KMHX and KRAX radars, respectively, at
> 18:00UTC on 18 September 2003. The locations of the radars are shown
> in Fig.5. The bold grey horizontal lines represent the heights of 20,
> 10, 0, -10, -20°C temperatures at the radar sites. The thin grey bars
> around the mean VPR represent standard deviations of reflectivity
> values that went into the mean VPR.

![](media/image11.png){width="2.9833333333333334in"
height="2.2888888888888888in"}![](media/image12.png){width="2.975in"
height="2.2888888888888888in"}

![](media/image13.png){width="2.975in"
height="2.2888888888888888in"}![](media/image14.png){width="2.9833333333333334in"
height="2.2895833333333333in"}

> Fig. 5 Hybrid Scan Reflectivity (left) and precipitation flag (right)
> fields for Hurricane Isabel at 16:00UTC (top) and 18:00 UTC (bottom)
> on 18 September 2003. For the precipitation flag, the green area is
> identified as the tropical rainfall; the red and purple areas
> represent convective rainfall; the light green and yellow areas stand
> for the stratiform precipitation.

![](media/image15.png){width="2.975in"
height="2.2888888888888888in"}![](media/image16.png){width="2.975in"
height="2.2888888888888888in"}

![](media/image17.png){width="2.9833333333333334in"
height="2.2888888888888888in"}![](media/image18.png){width="2.9833333333333334in"
height="2.2888888888888888in"}

> Fig.6 Distribution of bias (= A~R~/A~G~) of individual R-G pair for
> 24-hr accumulations ending at 01:00UTC (top) and at 05:00UTC (bottom)
> on 9/19/2003. Radar estimations used in the left and right panels are
> from the CS and CST schemes, respectively. The warm colors
> (orange/red) are for biases less than 1 (radar underestimate). The
> cold colors (blue/purple) are for biases greater than 1 (radar
> overestimate). The white colors are for biases very close to 1, i.e.
> the radar agrees well with gauge.

![](media/image19.wmf){width="2.98125in"
height="2.9791666666666665in"}![](media/image20.wmf){width="2.9805555555555556in"
height="2.98125in"}

![](media/image21.wmf){width="2.9805555555555556in"
height="2.9791666666666665in"}![](media/image22.wmf){width="2.9805555555555556in"
height="2.98125in"}

> Fig. 7 Scatterplot of radar vs gauge for Hurricane Isabel Case in
> 24-hr accumulations ending at 01:00UTC (top) and at 05:00UTC (bottom)
> on 9/19/2003. Radar estimations used in the left and right panels are
> from the CS and CST schemes, respectively. The one-to-one line (dashed
> line) is the line of perfect correlation. The R-G pairs (data points)
> are all of the radar-gauge pairs in which the gauge accumulations are
> greater than 0.03 inches. Linear fit (solid line) is the linear
> regression line.

![](media/image23.png){width="2.9833333333333334in"
height="2.2888888888888888in"}![](media/image24.png){width="2.9833333333333334in"
height="2.2888888888888888in"}

> Fig.8 a) Hybrid Scan Reflectivity (HSR) and b) precipitation flag for
> Texas Rainfall Case at 23:00UTC on 26 March 2007.

![](media/image25.png){width="2.95in"
height="2.3979166666666667in"}![](media/image26.png){width="2.95in"
height="2.3979166666666667in"}

![](media/image27.png){width="2.95in"
height="2.3979166666666667in"}![](media/image28.png){width="2.95in"
height="2.3979166666666667in"}

> Fig. 9 Same as Fig.4 but for hourly averaged stratiform VPRs valid at
> 23:00UTC on 26 March 2007: a) and b), from KEWX and KGRK radars,
> respectively, are identified as tropical; c) and d), from KMAF and
> KDFX radars, respectively, are non-tropical. The locations of the
> radars are shown in Fig.8.

![](media/image29.wmf){width="2.9805555555555556in"
height="2.977777777777778in"}![](media/image30.wmf){width="2.9805555555555556in"
height="2.98125in"}

> Fig.10 As in Fig.7, except for Texas Rainfall Case in 24-hr
> accumulations ending at 12:00 UTC on March 27, 2007.

![](media/image31.png){width="2.95in" height="2.3979166666666667in"}
![](media/image32.png){width="2.95in" height="2.3979166666666667in"}

![](media/image33.png){width="2.95in" height="2.3979166666666667in"}

> Fig.11 Same as Fig.4 but for hourly averaged stratiform VPRs from KBYX
> radar valid at 01:00UTC (convective, a), 16:00 UTC (tropical, b) on 1
> June 2007, and 14:00 UTC on 2 June 2007 (tropical, c).

![](media/image34.wmf){width="2.9805555555555556in"
height="2.98125in"}![](media/image35.wmf){width="2.977777777777778in"
height="2.979861111111111in"}

> Fig.12 As in Fig.7, except for Tropical Strom Barry Case in 24-hr
> accumulations from 6/02/07 12:00 -- 6/03/07 12:00 UTC.

![](media/image36.png){width="2.9833333333333334in"
height="2.21875in"}![](media/image37.png){width="2.975in"
height="2.21875in"}![](media/image38.png){width="2.9833333333333334in"
height="2.21875in"}![](media/image39.png){width="2.9833333333333334in"
height="2.21875in"}![](media/image40.png){width="2.9833333333333334in"
height="2.21875in"}![](media/image41.png){width="2.9833333333333334in"
height="2.21875in"}

> Fig. 13 As in Fig.6, except for NE Oklahoma Heavy Rain Case in 1-hr
> accumulations ending at 10:00 UTC (top), at 13:00 UTC (middle), and in
> 6-hr accumulations ending at 15:00 UTC (bottom) on 11 June 2007.

![](media/image42.wmf){width="2.9805555555555556in"
height="2.977777777777778in"}![](media/image43.wmf){width="2.9805555555555556in"
height="2.98125in"}

![](media/image44.wmf){width="2.9805555555555556in"
height="2.977777777777778in"}![](media/image45.wmf){width="2.9805555555555556in"
height="2.98125in"} ![](media/image46.wmf){width="2.9805555555555556in"
height="2.977777777777778in"}![](media/image47.wmf){width="2.9805555555555556in"
height="2.98125in"}

> Fig.14 As in Fig.7, except for NE Oklahoma Heavy Rain Case in 1-hr
> accumulations ending at 10:00 UTC (top), at 13:00 UTC (middle), and in
> 6-hr accumulations ending at 15:00 UTC (bottom) on 11 June 2007.

![](media/image48.png){width="2.95in"
height="2.3979166666666667in"}![](media/image49.png){width="2.95in"
height="2.3979166666666667in"}![](media/image50.png){width="2.95in"
height="2.3979166666666667in"}

> Fig. 15 Same as Fig.4 but for hourly averaged stratiform VPRs from
> KFWS radar valid at 05:00UTC (a), 06:00 UTC (b), and 07:00 UTC (c) on
> 18 June 2007. They are all identified as tropical.

![](media/image51.wmf){width="2.9805555555555556in"
height="2.977777777777778in"}![](media/image52.wmf){width="2.9805555555555556in"
height="2.98125in"}

> Fig.16 As in Fig.7, except for Gainesville North Texas Case in 12-hr
> accumulations from 01:00 - 13:00 UTC on 18 June 2007