# Current State-of-the-Art Anomalous Propagation

#  and Ground Clutter Mitigation Schemes:

### Implications for next generation schemes within 

### The Weather and Radar Processor (WARP)

### Christopher W. Porter

National Severe Storms Laboratory/Cooperative Institute for Mesoscale
Meteorological Studies

**TABLE OF CONTENTS**

Chapter 1.
**Introduction**..................................................................
1

###### 

###### Chapter 2. **AP mitigation efforts at MIT/Lincoln Labs**............................ 6

1.  Multi-Sensor Approach within ITWS...........................\......
    6

2.  The Layered Composite Reflectivity Map -- Anomalous........\... 11

> Propagation Removed Product

Chapter 3. **Current WSR-88D Clutter Suppression and AP
Removal**......... 15

3.1 Clutter Suppression within the WSR-88D RDA.................... 15

3.2 AP removal within the Precipitation Processing Subsystem...\... 19

Chapter 4. **Quantitative Precipitation Estimation at
NSSL**....................... 21

Chapter 5. **Next Generation Clutter Suppression and AP
Mitigation**........... 28

Chapter 6. **University of Washington's Mesoscale Group Radar
Quality**...\... 41

**Control Algorithm**

Chapter 7. **Goddard Space Flight Center Radar Quality Control
Algorithm**. 45

Chapter 8. **Princeton University Testing of Radar Data Quality
Control**...\.... 47

**Algorithm**

Chapter 9. **Importance of Radar Data Parameters in Removing
AP/GC**...\... 52

Chapter 10. **Summary and Implications for WARP's AP
Mitigation**........... 54

> **Technique**

Chapter 11.
**References**...................................................................
62

**1. Introduction**

Radars receive power returns from both meteorological and
non-meteorological objects. Non-meteorological signals can result from
scatterers within the atmosphere such as dust, insects, and birds all of
which can produce "clear-air" returns of up to 30 dBZ (Doviak and Zrnic
1993). Additionally, ground clutter can exist when the radar signal
reflects from permanent structures such as buildings, trees, and the
surface of the earth.

The vertical structure of the planetary boundary layer can also provide
a favorable condition for non-meteorological returns when the radar beam
is refracted back towards the earth resulting in significant
reflectivity return when intercepting the ground. Electromagnetic waves
in free space travel in straight lines at the speed of light because the
dielectric permittivity *ε~o~* and magnetic permeability *μ~o~* are
constants and related to the speed of light, *c*, by

## *c* = (μ~o~ε~o~) ^-1/2^ (1)

However, when considering the earth's atmosphere, the atmospheric
permittivity, *ε*, is larger than *ε~o~* and vertically stratified
resulting in the bending or refraction of electromagnetic waves. This
also reduces the velocity of propagation (*v*) within the earth's
atmosphere. The variation of the velocity of propagation with altitude,
or the index of refraction, *n*, is defined as the velocity of
propagation in free space to that in the medium of question (earth's
atmosphere),

*n* = *c* / *v .* (2)

The refractivity of the atmosphere (*N*) is defined to be

## *N* = (*n*-1) \* 10^6^ (3)

and within the Earth's atmosphere can be expressed as a function of
temperature (*T*), pressure (*P*) and the partial pressure of water
vapor (*e*) (Skolnik 1980):

$N = (\text{77}\text{.}\frac{6 \ast p}{T})\begin{matrix}
 \\

\end{matrix}$ (4)

The refractivity of the atmosphere decreases with height. However, when

dN/dh \< -157 km^-1^ ,

trapping or ducting of electromagnetic beams occurs as energy
propagating at shallow angles with respect to the earth's surface is
refracted back towards the surface of the earth. This condition is also
referred to as anomalous propagation (AP). According to equation (4), AP
is produced as a result of strong vertical temperature and moisture
gradients. These conditions are met, for example, in regions of strong
nocturnal temperature inversions, thunderstorm outflows, or when warm,
dry air is advected over a cool, moist layer.

Attempts to remove non-meteorological returns have been underway for
many years. Steiner and Smith (1996, Appendix B) presents an exhaustive
overview of causes of anomalous propagation and ground clutter (AP/GC)
and methods derived to remove radar echoes they produce. AP/GC can be
removed in several different manners including placement of the radar
site, choice of radar characteristics (wavelength, antenna, scan
strategy, polarization), signal processing prior to data archiving, or
through processing of archived data.

The Weather and Radar Processor (WARP) is the primary tool used by en
route air traffic management personnel to obtain, process and display
weather information. WARP will collect, generate and disseminate a
mosaic of multiple WSR-88D images for display along with aircraft
targets. WSR-88D images must be free of non-meteorological echoes that
can obfuscate true atmospheric conditions resulting in confusing and
muddled displays used by en route controllers. WARP will utilize
narrowband, Level III WSR-88D radar data to produce mosaicked images.
Therefore, within this study, AP/GC mitigation efforts that use archived
radar data within operational applications similar to WARP will be
examined. Strengths and weaknesses of these techniques are evaluated and
techniques producing more effective and efficient results are noted.
Additionally, an examination of techniques that will be available in the
near future is also included.

Currently, several major efforts are being conducted to remedy problems
associated with AP/GC returns that are relevant to the removal strategy
used within WARP. AP/GC removal procedures exist at Massachusetts
Institute of Technology/Lincoln Labs (MIT/LL), the Radar Operations
Center (ROC) formerly known as the Operational Support Facility, the
National Center for Atmospheric Research (NCAR), the National Severe
Storms Laboratory (NSSL), the University of Washington (UW), Goddard
Space Flight Center (GSFC), and Princeton University (PU).

MIT/LL has been involved with FAA-funded work throughout the 1990's, and
has developed two different schemes to remove AP/GC. One scheme has been
incorporated in the Weather Surveillance Radar 1988 Doppler (WSR-88D)
system as part of the Layered Composite Reflectivity Maximum - Anomalous
Propagation Removal Product (LRM-APR) (section 2). The other technique
has been incorporated into the Integrated Terminal Weather System
(ITWS), which is designed to aid terminal air traffic controllers. In
addition to the LRM-APR product, the ROC has a different AP mitigation
scheme associated with the Precipitation Processing Subsystem (PPS),
which is used to estimate rainfall amounts using the WSR-88D (section
3). NSSL has developed a Quantitative Precipitation Estimation and
Segregation Using Multiple Sensors (QPE-SUMS) technique to accurately
estimate precipitation types and rates, especially within the
Intermountain West. QPE-SUMS contains an AP/GC removal scheme
containing, in part, a method using satellite infrared temperature
information (section 4). NCAR is currently being funded by the ROC to
produce a next generation AP mitigation scheme. The NCAR system is a
large and somewhat complex effort to provide a technique to
automatically classify radar echoes and apply clutter filters and AP
removal schemes without requiring interaction from a WSR-88D operator
(section 5). The UW has produced an AP/GC automated technique designed
to remove at least 95% of non-meteorological echoes from radar data that
is routinely archived (section 6). GSFC uses a radar data quality
control (QC) algorithm to remove unwanted radar echoes from the Tropical
Rainfall Measuring Mission (TRMM) radar database in order to accurately
provide radar rainfall estimates (section 7). PU has been involved in a
comprehensive and longstanding effort not only to build a database of
AP/GC contaminated cases that have been manually corrected but to also
house multiple AP/GC removal schemes (section 8). The database allows
for testing of different techniques by using common AP/GC cases. PU is
also building a removal technique aimed at combining the strengths of
previously formulated algorithms.

**2. AP mitigation efforts at MIT/Lincoln Labs**

2.1 *Multi-Sensor Approach within ITWS*

ITWS is a fully automated system designed to improve the safety,
efficiency, and capacity of terminal area aviation operations. ITWS
acquires data from FAA and National Weather Service (NWS) sensors as
well as from aircraft in flight in the terminal area. ITWS is designed
to provide Air Traffic personnel a suite of products that are
immediately usable without further meteorological interpretation.

ITWS ingests radar data from three different types of radars: 1) the
Airport Surveillance Radar (i.e., ASR-9 and ASR-11), 2) the Terminal
Doppler Weather Radar (TDWR), and 3) the WSR-88D. Controllers use data
from the ASR-9 to manage aircraft within the Terminal Radar Approach
Control (TRACON) area. In addition to aircraft detection capabilities,
the radar also has a weather channel that can provide information on the
location and intensity of precipitation in the terminal area. The ASR-9
and ASR-11 radars are just as susceptible to AP problems as any other.

In an effort to mitigate AP for the ASR-9, MIT/LL uses a multiple radar
approach employing precipitation products from the ASR-9 and WSR-88D
radars. This multi-sensor approach can be advantageous since each radar
can view radar echoes within the "cone of silence" of the other radars.
Furthermore, AP regions within each radar's reflectivity returns will
not be co-located as long as the radar beams originate from different
locations (i.e., the radars are not all co-located).

Within ITWS, a more conservative approach to AP removal is taken to
protect against the chance that valid precipitation echoes may be
reduced or removed altogether, which could present a safety hazard to
aircraft. Thus, identification of AP is accomplished by comparing an
ASR-9 reflectivity image to the WSR-88D Composite Maximum Reflectivity
(comprefl) product. ASR-9 reflectivity data is given in Video Integrator
Processor (VIP) Level 1-6 rather than a dBZ value, as in the WSR-88D. In
order to compare the reflectivity values, a lookup table is used that
equates VIP levels with dBZ values (Table 1). An AP mask is produced
upon receipt of the comprefl product and is used to edit subsequent
ASR-9 scans until a new comprefl is received.

ASR-9 data are collected as close as possible in time to the middle of
the WSR-88D volume scan and are compared to the comprefl to create an AP
mask. At first, the comprefl is searched to determine if any storms are
present. If no contiguous area of 9 square kilometers containing at
least 17 dBZ is found within the comprefl, then any corresponding radar
echoes from the ASR-9 are considered non-meteorological in origin and
removed. If precipitation exists within comprefl, a 7 km x 7 km region
within the 88D data centered on each ASR-9 pixel is produced. If less
than 10 pixels of reflectivity above the corresponding dBZ value
relative to the ASR-9 VIP level are found, then the candidate ASR-9
pixel is considered AP. Once the target is considered AP, it receives an
AP mask value (or VIP level) equivalent to the WSR-88D dBZ value.

It is important to note that ASR-9 radar echoes considered as AP are
edited and not necessarily removed. The ASR-9 echoes are edited to make
them agree with the comprefl product. The term "AP mask" is probably an
unfortunate choice of words because an "AP mask" is created whenever
comprefl and ASR-9 radar echoes values do not agree. Thus, an "AP mask"
can be created when precipitation echoes from both products are not of
the same value. When a true AP radar echo exists within the ASR-9 radar
data, the magnitude of the AP echo is modified, not removed, in order to
match the reflectivity magnitude of precipitation echoes within the
comprefl. An underlying assumption is made that the comprefl product is
devoid of AP echoes.

This AP mitigation scheme has been evaluated and performance metrics
applied to three different types of cases: 1) All AP, 2) all weather,
and 3) mixed AP and weather. An ASR-9 pixel has to be at least VIP Level
2 or greater to be scored (ITWS user's group deemed VIP Level 1 to be
operationally insignificant). The performance metrics for the AP-edit
algorithm are PEAP (probability of editing AP) and PEW (probability of
editing weather). PEAP is given by the number of AP pixels correctly
edited divided by the total number of AP pixels. PEW is given by the
number of weather pixels edited divided by the total number of weather
pixels. The goal is to obtain a high PEAP and a low PEW. These
statistics can be further categorized by the level of radar return
(e.g., Level 3 and greater, Level 2 and greater).

  ----------------------- ----------------------- -----------------------
  **ASR-9 VIP Level**     **WSR-88D Value (dBZ)** **AP Mask Value**

  6                       ≥ 42                    6

  6                       ≥ 37                    5

  6                       ≥ 32                    4

  6                       ≥ 27                    3

  6                       ≥ 22                    2

  6                       ≥ 17                    1

  6                       \< 17                   BADVAL

  5                       ≥ 37                    5

  5                       ≥ 32                    4

  5                       ≥27                     3

  5                       ≥ 22                    2

  5                       ≥ 17                    1

  5                       \< 17                   BADVAL

  4                       ≥ 32                    4

  4                       ≥ 27                    3

  4                       ≥ 22                    2

  4                       ≥ 17                    1

  4                       \< 17                   BADVAL

  3                       ≥ 27                    3

  3                       ≥ 22                    2

  3                       ≥ 17                    1

  3                       \< 17                   BADVAL

  2                       ≥ 22                    2

  2                       ≥ 17                    1

  2                       \< 17                   BADVAL

  1                       ≥ 17                    1

  1                       \< 17                   BADVAL
  ----------------------- ----------------------- -----------------------

Table 1: ASR-9, WSR-88D, and AP Mask look-up table values used in the
AP-edit algorithm within ITWS. Reproduce from Klingle-Wilson et al.
(1995).

An analysis for 9 days in Memphis (6 hours from 3 all-AP days, 8 hours
from 3 all-weather days, and 4 hours from 3 mixed days) in 1994 resulted
in the correct modification of 97% of AP pixels for VIP Level 2 or
greater. In mixed weather cases the number of correctly modified pixels
drops to 80%. For VIP Level 3, the statistics are 98% and 91%,
respectively. Only in the mixed case are any real meteorological returns
edited. In this case, 1% of VIP Level 2 and above returns are
incorrectly modified.

Boorman et al. (1999) changes the scoring system slightly to account for
high reflectivity clear-air echoes greater than 17 dBZ within the
WSR-88D. Because WSR-88D clear-air reflectivity values can exceed 17
dBZ, the possibility exists that some ASR-9 radar echoes are compared
against clear air return rather than real VIP 1 Level precipitation.
Thus, the WSR-88D values in Table 1 are all raised 5 dBZ so that VIP
Level 1 corresponds to ≥22 dBZ. As a result, all ASR-9 VIP Level 1
returns are then compared against precipitation echoes and not clear air
returns. In addition, the number of WSR-88D points required to have a
certain threshold of reflectivity within a 7 square mile area centered
on the ASR-9 pixel is increased. Both of these modifications improve the
AP mitigation scheme's ability to remove AP regions as identified by
experienced radar meteorologists. Boorman et al. (1999) examined nine
mixed weather cases where 97.6% of pixels containing AP were edited (VIP
Level reduced) to some degree. And only 2.4% of the precipitation echo
pixels were incorrectly modified.

2.2 *The Layered Composite Reflectivity Map - Anomalous Propagation
Removed Product*

The multi-sensor approach to AP mitigation within the ITWS system can be
successful as long as WSR-88D and ASR-9 radars are not co-located. If
they are, then regions of AP/GC are likely to exist in the same regions
of reflectivity return. In an effort to resolve this problem, MIT/LL
developed and tested an algorithm to remove AP from the WSR-88D
reflectivity data (Isaminger et al. 1997). This algorithm has since been
added to the Layered Composite Reflectivity MaximumWSR-88D Level III
product.

In this scheme, AP removal is made possible through the use of
reflectivity, velocity, and spectrum width data. The technique separates
the atmosphere surrounding a radar into three regions based on range and
elevation scan (Figure 1). Within these different regions, different
applications of the same AP removal technique are made (Figure 2). The
first region is referred to as the "Omit All" volume located within 45
km of the radar and throughout the lowest 1 km above ground level. All
meteorological returns are considered clutter within this region. The
second region, "Accept If" is located from 45 to 103 km in range, at 0.5
degrees and below 3 km in altitude, and not within the "Omit All"
region. A target within this region will only be accepted as a
meteorological return if its velocity is $$ 1.0 ms^-1^ ***and*** its
spectrum width is $$ 0.5 ms^-1^. A target in this region is still
considered likely to be clutter. However, if it meets the velocity and
spectrum width thresholds, it will be accepted as meteorological in
origin. The third region is a "Reject if" region within 230 km of the
radar and below 5 degrees in elevation while not being in the "Omit All"
or

![](media/image1.png){width="4.463888888888889in"
height="2.9097222222222223in"}

Figure 1: Regions of varying conditional thresholds used in the LRM-APR
WSR-88D product. Reproduced from Isaminger et al. (1997).

"Accept If" regions. Targets within this region are considered
meteorological returns if they possess a velocity $$ 1.0 ms^-1^ ***or***
a spectrum width $$ 0.5 ms^-1^.

This method of AP mitigation is scored identically to the AP removal
scheme within ITWS. Isaminger et al. (1997) analyzed ten cases that
either exhibited AP only, weather only, or a combination of both.
Overall, this technique is able to properly edit 67.6% of the AP pixels
while it only incorrectly edits 4.1% of weather echoes. Additional
recommendations resulted from this work. One suggestion is to increase
the spectrum width threshold to optimize the trade-off between PEAP and
PEW scores. Another is to utilize a two-dimensional median filter on the
data because the most frequent error for incorrect AP flagging in this
scheme results from range folding problems. In addition, many times a
majority of AP is removed from large AP regions, but a speckled map of

## Is the radar echo within 45 km of the radar and below 1 km in height?

> Yes (Echo in Omit All
>
> Region) No

#### Remove echo

##### 

Is the radar echo above 5 degrees in elevation?

> Yes
>
> No

#### Keep echo

#### Is the radar echo between 45 and 103 km in range and at 0.5° elevation?

> Yes (Echo in Accept If Region)

###### Is the radial velocity ≥1.0 ms^-1^ ***AND*** it's spectrum width ≥0.5 ms^-1^

> Yes No No
>
> (Echo in
>
> Keep echo Remove echo Reject If Region)

###### Is the radial velocity ≥1.0 ms^-1^ ***OR*** it's spectrum width ≥0.5 ms^-1^

> Yes No
>
> Keep echo Remove echo

Figure 2. Flow chart for the LRM-APR technique for AP radar echo
removal.

AP is left remaining. Using a median filter could alleviate the
remaining speckled AP field by discarding values of reflectivity near
zero dBZ.

If AP/GC regions can be sufficiently removed from a WSR-88D reflectivity
field, the WSR-88D (using the comprefl product) could then be used in
conjunction with an ASR-9 radar to remove AP more efficiently within a
TRACON area. However, some concern has been given to the idea of
specifying different sets of rules within pre-specified regions (i.e.,
"Omit All", "Accept If", "Reject If") (personal communication, Tim

O'Bannon). Instead, it may be more useful to apply radial velocity and
spectrum width thresholds throughout a radar domain without
pre-conceived assumptions on where AP/GC may or may not exist.

**3. Current WSR-88D Clutter Suppression and AP Removal.**

Besides the LRM-APR product, two other AP/GC removal and suppression
methods are contained within the WSR-88D system. A clutter suppression
technique is currently incorporated within the Radar Data Acquisition
(RDA) component of the WSR-88D system. The degree of clutter suppression
can be controlled by the radar operator. Within the Radar Product
Generator (RPG), the PPS product (O'Bannon 1998) contains an AP removal
technique to improve the quality of rainfall estimates surrounding
WSR-88Ds.

3.1 *Clutter Suppression within the WSR-88D RDA*

Ground clutter returns typically produce signals with near zero (i.e.,
typically less than 0.5 m s^-1^) radial velocities and narrow (i.e., ≤1
m s^-1^) spectrum widths (Doviak and Zrnic 1993). Thus, filters can be
designed to remove or reduce power return from clutter and decrease its
influence on reflectivity estimates.

All WSR-88Ds contain a clutter suppression technique that is implemented
prior to the calculation of base data estimates. Clutter suppression
filters are designed to reduce signal power from targets containing a
radial velocity near 0 m s^-1^. These filters reduce signal power within
a "notch width" centered about the zero mean radial velocity value
(Figure 3). Power returns from meteorological returns are not adjusted
because only

![](media/image2.png){width="5.998611111111111in"
height="6.145138888888889in"}

Figure 3. **A)** A depiction of the input power for a given range bin.
The clutter signal has a radial velocity centered around zero and the
meteorological signal is offset from zero due to its radial velocity.
**B)** Representation of the clutter filter with a notch width centered
on zero radial velocity. The scale represents the amount of power
reduction, from 0 dB (no power reduction) to -50 dB (maximum power
reduction), applied within the notch width. **C)** A diagram of the
resulting power after the algebraic addition of the signal from A and
the power reduction factor from B (A+B=C). Reproduced from Chrisman et
al. (1995).

signal power from targets with a radial velocity that falls within the
notch width is reduced.

Clutter suppression within the WSR-88D system is accomplished through a
combination of processes. The signal processor uses the Bypass Map,
Default Notch Width Map, and operator-defined Clutter Suppression
Regions to determine where clutter suppression is needed. The Bypass Map
supplies locations of known ground clutter return. The Default Notch
Width Map reduces reflectivity values of clutter targets by 40 dB (Level
2 suppression) and velocity/spectrum width values by 50 db (Level 3
suppression) (Figure 3). The radar operator can control the application
of the Notch Width Map producing additional clutter suppression across
regions specified by beginning and ending ranges and azimuths if she
does not want to use the Default Notch Width Map. The amount of signal
reduction for identified clutter targets can be varied from 30 dB to 50
dB when using the Notch Width Map.

One obvious weakness of this clutter suppression technique is exposed
when precipitation echoes that move perpendicular to the radar beam
(i.e., radial velocity near zero) are suppressed. Figure 4 shows an
example of this when the clutter suppression technique is used
improperly. Reflectivities immediately northwest and southeast of the
radar (Figure 4a) are reduced to near zero along the "zero-line" in the
radial velocity field shown in gray (Figure 4b).

![](media/image3.jpeg){width="5.996527777777778in"
height="2.9381944444444446in"}

Figure 4. A) Reflectivity and B) radial wind fields after improper
application of clutter suppression. The gray region in B) denotes radial
velocities near zero.

Care has to be taken in the amount of suppression that can be applied.
Too little suppression allows the base data estimates to be
contaminated, which can lead to anomalously high rainfall estimates when
high reflectivity AP is retained.

Contaminated radial velocity data can also result, leading to erroneous
Velocity Azimuth Display Wind Profile estimates (Sanders and Vasiloff
1996). However, too much suppression can also severely impact rainfall
estimation by reducing reflectivity estimates within precipitation
regions, thereby reducing rainfall estimates (Margraf 1998; Story 2001).
But, knowledgeable radar operators that can recognize and react to AP/GC
situations should be able to accurately remove AP/GC in most cases
(Margraf 1998; Goss and Chrisman, 1995).

3.2 *AP removal within the Precipitation Processing Subsystem*

Radar-derived rainfall estimates are provided through the Precipitation
Processing Subsystem (PPS) product. The PPS employs a technique that
keys on small, but high reflectivity regions indicative of AP/GC
(O'Bannon 1998). Reflectivity values of $$ 18 dBZ with no more than one
surrounding reflectivity bin of equal or higher reflectivity are
removed. A second threshold of 53 dBZ is used to remove hail
contamination while maintaining high enough rainfall rates to identify
flash flood events. In this case, any bin that exceeds the threshold is
replaced with an average of the surrounding values if none of the
surrounding bins are of equal reflectivity magnitude. Removal of hail
contamination aids precipitation estimates, however, retaining this
information would be beneficial for aviation users.

Although effective clutter suppression procedures can remove a majority
of clutter, at times additional quality control procedures are required
within the PPS (Fulton et al. 1998). Thus, an areal tilt test is used to
remove additional anomalous propagation. If a decrease in total
reflectivity echo area exceeds 75% from the first to the second
elevation angles over an annular area between the range of 40 to 150 km,
the algorithm removes reflectivity information from the lowest of the
two elevation scans. Although primitive, this method of AP removal has
proven very effective in cases where rainfall does not exist within
range of the radar.

However, the tilt test can be very inefficient, particularly in regions
where AP is embedded in precipitation (O'Bannon 1998). In addition, this
test often classifies and then rejects regions of stratiform or
orographic precipitation as AP, causing significant PPS rainfall
under-estimates. Currently, the ROC is planning to replace the PPS AP
removal technique with NCAR's AP Mitigation Scheme discussed in Section
5.

**4. Quantitative Precipitation Estimation at NSSL**

The Quantitative Precipitation Estimation and Segregation Using Multiple
Sensors (QPE-SUMS) technique has been developed at NSSL to accurately
estimate precipitation types and rates using an optimal blend of radar,
satellite, and rain gauge data that is physics-based. Obviously, when
using radar reflectivities to provide rainfall estimation about a radar,
it becomes very important to remove all non-meteorological radar echoes.
Therefore, AP/GC problems can severely restrict the ability to estimate
rainfall in remote areas that do not contain rain gauges.

In order for an AP/GC radar echo to be flagged for removal, two
conditions must be met. First, the radial velocity of the radar echo
must be ≤2.5 ms^-1^. The second condition is a reflectivity rule
containing two parts: 1) the reflectivity must be above a threshold
(currently 30 dBZ) and 2) the vertical gradient in reflectivity must be
≥80% of the reflectivity in the lowest elevation scan. For example, if a
radar echo contains a radial velocity less than 2.5 ms^-1^ and has a
reflectivity value of 30 dBZ, the reflectivity value in the vertically
adjacent elevation scan scan must be 6 dBZ or less for the radar echo to
be removed.

Radar echoes must meet both the radial velocity and reflectivity rules.
Otherwise, valid precipitation radar echoes that move perpendicular to
the radar beam, and do not contain a large vertical reflectivity
gradient, would be removed. Likewise, new convective cells that may
contain a large vertical reflectivity gradient but have radial
velocities above 2.5 m s^-1^ could be removed. In addition, the 30 dBZ
rule allows for removal of embedded, high reflectivity AP/GC within
large stratiform precipitation shields that typically contain
reflectivity values below 30 dBZ (Figures 5 and 6). The 30 dBZ rule can
be effective in situations where AP is contained within precipitation,
which is considered one of the most difficult cases for AP removal.

QPE-SUMS has been tested primarily in Oklahoma, Texas, and Arizona.
WSR-88D radar echoes in Arizona can sometimes provide a challenge to the
AP/GC removal scheme when AP radar echoes result from chaff produced by
military aircraft. Upon release, chaff will advect with the background
environmental wind, which may be greater than 2.5 m s^-1^. Also, many
times chaff may extend through more than one elevation scan and not meet
the vertical gradient of reflectivity condition for removal. Residual
clutter represents another type of AP/GC that QPE-SUMS has difficulty in
removing. After the clutter suppression technique within the WSR-88D
system has been applied, low reflectivity residual clutter can still
remain. Although residual clutter meets the radial velocity rule for
removal, it does not meet the reflectivity rule of being at least 30 dBZ
in magnitude.

Therefore, an additional method of AP/GC removal has been added that
takes advantage of satellite information, which is part of the QPE-SUMS
suite of sensors. Cloud top IR temperature is compared against either
surface temperature observations or numerical weather prediction model
surface temperature data directly below. If the difference

![](media/image4.jpeg){width="4.003472222222222in"
height="3.520138888888889in"}

![](media/image5.jpeg){width="4.003472222222222in"
height="3.5069444444444446in"}

> Figure 5. A) Reflectivity returns for the KIWA WSR-88D containing
> ground clutter immediately around the radar and embedded AP within a
> precipitation shield northwest of the radar. B) Ground clutter and AP
> are removed using the QPE-SUMS AP/GC Mitigation Scheme

![](media/image6.jpeg){width="4.089583333333334in"
height="3.6055555555555556in"}

![](media/image7.jpeg){width="4.003472222222222in"
height="3.5854166666666667in"}

> Figure 6. A) Reflectivity returns for the KDYS WSR-88D containing
> embedded AP within a precipitation shield southeast of the radar. B)
> Embedded AP is removed using the QPE-SUMS AP/GC Mitigation Scheme

between the two temperatures is ≤8 K below the radar echo in question,
then the radar echo is considered to be within a clear-air region (i.e.,
no clouds are present since the two temperatures are so close in
magnitude) and is removed (Figure 7). This method is particularly
effective in the removal of chaff, residual clutter, and other clear-air
echoes as long as clouds are not present.

In Figure 7a, a considerable amount of chaff and residual clutter exists
across the state of Arizona. Figure 7b demonstrates the effectiveness of
the satellite scheme in removing both chaff and residual clutter for
this case where clouds are not located. Figure 8a shows the cloud top
temperatures while Figure 8b is a plot of surface temperature. A cloud
band exists across northeastern Arizona and temperature differences
between cloud top and surface generally exceed 8 K. However, the
southwestern half of Arizona is cloud free and satellite IR temperature
and surface temperature differences are ≤8 K across this region.
Therefore, all reflectivity returns are removed across southwestern
Arizona, alleviating a majority of the chaff and residual clutter
problem. Notice, however, that any chaff and residual clutter that
exists across northeastern Arizona is not removed because conditions are
cloudy, thereby making the satellite method ineffective.

Using a combination of radial velocity, reflectivity, and satellite
data, QPE-SUMS is very efficient at removing AP/GC. The method has
proven robust for removing chaff, residual clutter, clear-air echoes,
and AP both separate from and embedded within precipitation.

![](media/image8.jpeg){width="3.7506944444444446in"
height="3.6930555555555555in"}

![](media/image9.jpeg){width="3.8305555555555557in" height="3.7in"}

> Figure 7. A) Reflectivity returns for a WSR-88D in Arizona that still
> contains chaff and residual ground clutter even after application of
> the QPE-SUMS AP/GC Mitigation Scheme. B) Resulting reflectivity
> returns when the satellite method of AP/GC removal is applied.

![](media/image10.jpeg){width="3.3333333333333335in"
height="2.959722222222222in"}

![](media/image11.jpeg){width="3.3333333333333335in"
height="2.959722222222222in"}

> Figure 8. A) Satellite cloud top IR temperatures. B) Surface
> temperatures.

**5. Next Generation Clutter Suppression and AP Mitigation**

The most ambitious AP/clutter mitigation effort to date has been
undertaken by NCAR's Remote Sensing Facility as funded by the ROC
through the WSR-88D Data Quality Optimization Program (Kessinger et al.
2000). The goals of this program are to improve the quality of the base
data from the WSR-88D systems with special emphasis on the problem of
ground clutter return due to AP conditions. The scheme will also be used
to improve the performance of the WSR-88D PPS product. The AP clutter
mitigation scheme will be included within the WSR-88D Open System
(Saffle and Johnson 1998). Implementation of the AP Mitigation Scheme is
currently planned first for the Open Radar Product Generator Build 2 and
later for the Open Radar Data Acquisition systems.

NCAR's AP clutter mitigation scheme consists of four components:

1)  radar echo classifier

2)  compensation of filtered reflectivity values

3)  clutter filter specification and control

4)  tracking of AP-affected areas

Currently, construction of only components 1) and 2) above has begun.
Work on the radar echo classifier is near completion. One of the
differences between the proposed clutter suppression in this scheme
versus the current clutter suppression technique within WSR-88Ds is the
ability to automatically apply ground clutter filters in regions of AP
without the intervention of a WSR-88D operator. Before this can be
automated, a classification technique that identifies various types of
radar echoes is required. Hence, most of the work to date has centered
around the radar echo classifier (REC) algorithm.

Within REC are three different detection algorithms: the AP Detection
Algorithm (APDA), the Precipitation Detection Algorithm (PDA), and the
Clear Air Detection Algorithm (CADA). The algorithms are based on fuzzy
logic using derived radar fields. These fields are calculated from a
radar's base data of reflectivity (*DBZ*), radial velocity (*V*), and
spectrum width (*SW*) and are computed over *N* data bins within a small
region typically defined as two beams on either side of the current beam
and +1 km (Doppler fields) or +2 km (reflectivity fields) in range from
the current gate. The derived fields include the:

mean reflectivity (MDZ),

$\frac{\sum_{1}^{N}\text{DBZ}}{N}$,

mean radial velocity (MVE),

$\frac{\sum_{1}^{N}V}{N}$,

mean spectrum width (MSW),

$\frac{\sum_{1}^{N}\text{SW}}{N}$,

the standard deviation of the radial velocity (SDVE),

$\left\lbrack \frac{\left( \sum_{i = 1}^{N}{(V_{i} - \overline{V})} \right)^{2}}{(}N - 1) \right\rbrack^{\frac{1}{2}}$,

the standard deviation of spectrum width (SDSW),

$\left\lbrack \frac{\left( \sum_{i = 1}^{N}{(\text{SW}_{i} - \overline{\text{SW}})} \right)^{2}}{(}N - 1) \right\rbrack^{\frac{1}{2}}$,

the "texture" of the reflectivity field (TDBZ),

$\frac{\left( \sum_{i = 2}^{N}{(\text{DBZ}_{i} - \text{DBZ}_{i - 1})^{2}} \right)}{(}N - 1)$,

the "texture" of the radial velocity field (TDVE),

$\frac{\left( \sum_{i = 2}^{N}{(V_{i} - V_{i - 1})^{2}} \right)}{(}N - 1)$,

the "spin" of a feature,

$\left( \sum_{i = 1}^{N}{(\text{DBZ}_{i} - \text{DBZ}_{i + 1})} \right)\text{for}\left( \text{DBZ}_{i} - \text{DBZ}_{i + 1} \right) > \text{DBZ}_{\text{thresh}}$,

and the "sign" of the feature,

$$\begin{array}{r}
\text{for}(\text{DBZ}_{i} - \text{DBZ}_{i + 1}) > 0,\text{then}\begin{matrix}
 & 
\end{matrix}\text{Sign} = \text{Sign} + 1,\text{and} \\
\text{for}(\text{DBZ}_{i} - \text{DBZ}_{i + 1}) < 0,\text{then}\begin{matrix}
 & 
\end{matrix}\text{Sign} = \text{Sign} - 1\text{.}
\end{array}$$

SPIN is expressed as the percentage (0-100%) of the maximum number of
possible gate-to-gate differences in the reflectivity values. A
threshold on the minimum difference is used in the numerator while the
denominator uses all differences. The SIGN feature is similar to SPIN
since it also computes a gate-to-gate difference in the reflectivity.
However, no threshold is used and the values accumulated are either +1.0
or -1.0 depending on the sign of the gate-to-gate reflectivity
difference, rather than the value of the difference. Where reflectivity
values have large gate-to-gate changes over the local region, the SIGN
value will approach 0; in nearly uniform reflectivity regions, SIGN will
approach +1. Values for SIGN are generally between +1 and --1 but values
beyond either endpoint are possible.

A \"membership function\" (Figures 9-11) is applied to the values of the
derived fields to scale them so they match the characteristics of the
echo type under consideration. Membership functions are stepwise linear
functions that scale the derived fields to have values between 1.0 and
-1.0. The membership value for a derived field is multiplied by a
pre-determined, and empirically optimized, weight. This process is
repeated for other membership functions that are summed to produce an
aggregation value (Vivekanandan et al., 1999) for a particular algorithm
(i.e., AP, precipitation, or clear-air). The membership functions used
for each algorithm are given in Figures 9-11. The aggregation values or
output fields from this process are termed \"interest fields\". Interest
fields with a value of 1.0 indicate a high likelihood that the echo type
matches the desired characteristic such as AP, clear-air, or
precipitation. Likewise, an interest value of -1.0 indicates a high
likelihood that the echo type does not match the desired characteristic.

![](media/image12.png){width="4.550694444444445in"
height="7.990972222222222in"}

Figure 9: Membership functions for the APDA.

![](media/image13.png){width="5.198611111111111in" height="8.0in"}

Figure 10: Membership functions for the PDA.

![](media/image14.png){width="4.540972222222222in"
height="7.997222222222222in"}

Figure 11: Membership functions for the CADA.

In order to score the various detection algorithms within REC, an
objective truth field has been devised using NCAR S-Pol radar data. The
polarimetric data are input into a fuzzy-logic, particle identification
(PID) algorithm (Vivekanandan et al., 1999) to identify each echo type.
Once the objective truth field is defined, the REC algorithms are run
and re-evaluated against this new truth field. Additionally, the truth
fields will be used to optimize the membership functions and set the
appropriate weighting for each interest field within each algorithm.

A statistical analysis package is used to construct *2x2* contingency
tables for evaluation of the statistical performance of the algorithms
(Kessinger et al. 2000). The statistical indices calculated are
Probability of Detection (POD), False Alarm Ratio (FAR), Critical
Success Index (CSI), Percent Correct, and the Heidke Skill Score (HSS).
Two baseline data sets, a 151-scan S-Pol data set from Brazil and a
60-scan WSR-88D data set, are used to develop these scores. To examine
algorithm performance based on the type of weather occurring, each radar
scan is placed into one of three categories. The first category is for
precipitation echoes that are separated from any AP/GC; the second is
for precipitation echoes that are mixed with AP/GC; and the third
category is for no precipitation echoes while AP/GC may or may not be
present. In all categories, clear-air returns may or may not be present.
Preliminary results from the 171-scan Severe Thunderstorm
Electrification and Precipitation Study (STEPS) data set is also
available, but the scans have not yet been grouped by weather
categories.

Using these datasets, the REC algorithm can be optimized through the
proper setting of weights associated with each interest field. The
proper setting of weights is obtained by aligning the peaks of the CSI
and HSS skill curves to an interest threshold of 0.5 (Figure 12).

An example of how the REC identifies different regions of radar returns
is shown in Figure 13. This event occurred July 13, 1993 at the WSR-88D
KDDC radar and represents a precipitation echo separated from the
clutter return case. A large southwest-northeast oriented squall line is
moving eastward toward the radar with a preceding gust front. Clear air
return is present ahead of the squall line out to about 80-km range.
Individual algorithm results are shown in Figure 14. In this figure,
results from both 1999 and 2000 versions of the REC are given. The 1999
version of the CADA algorithm is actually more accurate than the 2000
version. The APDA algorithm detects some of the true precipitation echo
as AP, while the PDA algorithm is detecting some clear-air returns as
precipitation. Overall, the PDA and APDA algorithms perform well, but
additional optimizing of the weights associated with the membership
functions is required to fine-tune the results, and should result in
much more accurate depiction of the event.

Work has begun on the compensation of filtered reflectivity values; the
third component of the four-component NCAR AP Mitigation Scheme.
Reflectivity compensation is necessary in any AP mitigation scheme using
clutter filtering. When the clutter filters

are used in weather echoes having low radial velocity, power is also
removed from the weather echoes, resulting in a negative reflectivity
bias. Furthermore, when strong clutter is suppressed, a residue may
remain and cause a positive bias to the radar reflectivity. To obtain
accurate radar-estimated rainfall amounts, the ground clutter
contamination must be removed with clutter filters and the resulting
precipitation reflectivity bias must be corrected.

In the near future, NCAR's AP Mitigation Scheme will also use automated
clutter suppression and AP Clutter Tracking techniques. Automation of
WSR-88D clutter suppression through selective use of clutter filters is
an ultimate goal of the WSR-88D Data Quality Optimization work.
Automation will ease the clutter filter control task for the National
Weather Service (NWS) staff and should provide a clutter filter control
system for all WSR-88D radars. Automation will be achieved in real-time
by using the output from the APDA to augment the clutter filter map
defined for ground clutter (currently called the "bypass map") such that
clutter filters are enabled or disabled where appropriate for each
volume coverage pattern.

**6. University of Washington's Mesoscale Group Radar Quality Control
Algorithm**

Over the past seven years, the Mesoscale Group within the University of
Washington (UW) has developed a general QC algorithm to automate the
removal of non-meteorological echoes for a large radar database
(personal communication, Sandra Yuter). The goal of the QC algorithm is
to remove approximately 95% of non-meteorological radar echoes. The
algorithm uses only reflectivity and radial wind information and is
designed to primarily remove land and sea clutter, second trip echoes,
and biological targets. Sea clutter is simply AP returns immediately
above the surface of the ocean and is transient since ocean waves
propagate. UW's radar database includes cases taken from ships, islands
(over the ocean), and several continental locations, such as Seattle,
WA, Eureka, CA, and over the state of Kansas. The UW algorithm is
optimized relative to the geographical location of the radar by tuning
the thresholds of algorithm parameters.

UW's QC algorithm removes non-meteorological targets through a
three-step process. The first step is primarily effective in removing
clear-air echoes, especially those attributable to biological targets.
The algorithm examines the homogeneity of reflectivity signatures within
a 9-pixel x 9-pixel window. If the reflectivity field is both relatively
homogeneous and does not exceed a reflectivity threshold (i.e., 18 dBZ,
12dBZ, and 10 dBZ for the lowest three elevation scans, respectively),
then the reflectivity echoes are considered clear-air or biological in
nature and are removed.

The second test within the algorithm examines the vertical continuity of
echoes and assumes a vast majority of precipitation returns are
typically present over multiple elevation scans. If a radar echo is not
present over more than one elevation scan it is removed. A larger region
within the higher elevation scan is checked relative to the areal extent
of the radar echo in the lowest elevation scan in order to avoid
removing precipitation echoes that are tilted in the vertical. The
vertical gradient of reflectivity is examined as a third test. Any radar
echo that decreases by 35 dBZ or more from one elevation scan to the
next highest scan is considered AP and is removed.

Figure 15 demonstrates the application of UW's radar quality control
algorithm to a case in which only AP existed. The relatively homogenous
regions of clear-air return extending northeast and southwest of the
radar are removed by step one of the algorithm. The region of higher
reflectivity AP northwest of the radar is removed by step two. The
smaller areas of higher reflectivity are removed through step three so
that all AP is removed from this case.

Figure 16 is a case of AP that coexists with precipitation and contains
a small amount of precipitation embedded within a larger region of AP.
UW's scheme removes most of the AP region northwest of the radar while
leaving the precipitation shield intact to the southeast. Furthermore, a
true precipitation echo located 60 km from the radar near the 310º
radial is not removed because the vertical gradient of reflectivity does
not exceed 35 dBZ.

![](media/image15.jpeg){width="6.0in" height="4.634027777777778in"}

Figure 15. AP removal case using the UW QC algorithm at the KMIA
WSR-88D. All radar echoes in this case are AP.

![](media/image16.jpeg){width="6.0in" height="4.634027777777778in"}

Figure 16. AP removal case using the UW QC algorithm at the KAMA
WSR-88D. A mix of AP and precipitation echoes are present.

**7. Goddard Space Flight Center Radar Quality Control Algorithm**

The Goddard Space Flight Center (GSFC)/Joint Center for Earth Systems
Technology (JCET) is currently engaged in a comprehensive Ground
Validation (GV) program (Ciach et al. 1997) to analyze and QC
meteorological ground-based radar data for TRMM (Kummerow et al. 2000).

JCET is tasked with collecting and processing data from four primary
sites (Melbourne, FL; Houston, TX; Darwin, Australia; and Kwajalein
Atoll, Republic of Marshall Islands) as part of an operational system.
One job of the operational system is to remove non-meteorological radar
echoes so that accurate rainfall estimates are possible.

JCET's QC Algorithm is based on Rosenfeld et al. (1995), which employs
eight adjustable threshold parameters to filter non-meteorological radar
echoes. The thresholds are based on echo height and reflectivity. An
echo is any radar return containing a reflectivity value above
dBZ~noise~ which is 0 dBZ for all radar sites. Radar echoes are analyzed
within a \~5x5 km^2^ window in polar coordinates if any of the following
four criteria are met:

1)  (Z~top~ \< H~3~ or Z~1~$$Z~max~(3km) \<= Z~1~) and (Z~max~(H~1~) \<
    Z~3~)

2)  Z~top~ \< H~2~

3)  Z~max~(1.5 km) \< Z~0~;

4)  Z \> dBZ~noise~ and Z~2~$$Z in the lowest tilt,

+---------+-------+-------+-------+-------+-------+-------+-------+
| Radar   | H~1~  | H~2~  | H~3~  | Z~0~  | Z~1~  | Z~2~  | Z~3~  |
| L       |       |       |       |       |       |       |       |
| ocation |       |       |       |       |       |       |       |
+---------+-------+-------+-------+-------+-------+-------+-------+
| Me      | 3.0   | 3.0   | 1.0   | 12.0  | 12.0  | 40.0  | 20.0  |
| lbourne |       |       |       |       |       |       |       |
+---------+-------+-------+-------+-------+-------+-------+-------+
| Houston | 1.7   | 1.5   | 1.5   | 13.0  | 13.0  | 30.0  | 20.0  |
+---------+-------+-------+-------+-------+-------+-------+-------+
| # Kw    | 2.2   | 3.0   | 3.0   | 12.0  | 12.0  | 15.0  | 20.0  |
| ajalein |       |       |       |       |       |       |       |
+---------+-------+-------+-------+-------+-------+-------+-------+
| Darwin  | 3.0   | 2.0   | 3.0   | 13.0  | 13.0  | 18.0  | 20.0  |
+---------+-------+-------+-------+-------+-------+-------+-------+

Table 2: Current QC algorithm default parameters for the primary TRMM GV
sites. Height thresholds are in units of km and reflectivity thresholds
are in units of dBZ. (Reprinted from Kulie et al. 1999).

where Z~top~ is the echo top height and Z~max~ represents a maximum
reflectivity value at a specified height. Echo height and reflectivity
thresholds are given in Table 2.

Kulie et al. (1999) notes that a wide variety of AP/GC radar echoes can
be removed because of the parameter flexibility. Specifically, the QC
algorithm is effective in removing biological targets (e.g. birds),
shallow, non-embedded AP and clutter specks, and light second-trip and
clear-air echoes. Many moderate to strong false echoes located within
100 km of the radar can be removed, but the algorithm often has
difficulty removing such echoes at longer ranges, especially when
precipitation is present. The algorithm fails to remove
non-meteorological radar echo when embedded in precipitation.

**8. Princeton University Testing of Radar Data Quality Control
Algorithms**

Princeton University has been investigating the development and
evaluation of Radar Data Quality Control (RDQC) algorithms for several
years. The objectives of this work are to develop and evaluate
algorithms for automated identification of radar echo patterns. Part of
this effort involves close collaboration with JCET to improve the
current operational quality control procedures used for radar data
processing of TRMM.

Currently, tasks are centered around: (1) developing an improved RDQC
algorithm, (2) evaluating different RDQC algorithms, (3) comparing their
performance in specific case studies, (4) determining the feasibility of
the algorithms for use in an operational processing environment through
collaboration with JCET, and (5) evaluating the impact of alternative QC
algorithms on rainfall estimates.

A database of AP/GC cases is being compiled at Princeton University
facilitating comparison and development of AP/GC mitigation algorithms.
The collected cases represent situations of (a) AP without
precipitation, (b) AP separated from precipitation, (c) AP embedded in
precipitation, and (d) other features such as clear air echoes, gust
fronts, convergence lines, etc.

Robinson et al. (2001) conducted a comparison of six different RDQC
algorithms. The RDQC algorithms are evaluated and compared based on
their ability to remove AP without removing significant precipitation.

The six RDQC algorithms are:

1)  No QC -- only raw radar data

2)  GSFC default -- automated TRMM GV algorithm (Section 7).

3)  GSFC reprocessed -- height and reflectivity echo removal parameters
    are manually tuned for specific QC concerns beyond the ability of
    GSFC default.

4)  WSR-88D PPS -- AP removal scheme used within the PPS algorithm
    described in Section 3.2.

5)  PU -- QC algorithm developed at Princeton University

6)  NCAR REC -- NCAR's Radar Echo Classifier technique as part of the
    overall AP Mitigation Scheme described in Section 5.

The Princeton University QC algorithm makes use of three-dimensional
reflectivity structure (Steiner and Smith 2001). Radial velocity and
spectrum width information are not used for four different reasons.
First, radial velocity values will not be near zero for sea clutter
since the clutter is moved along by the propagation of ocean waves.
Second, there are instances when contamination of AP in the
second-lowest elevation sweep may exhibit velocities similar to
precipitation echoes even though the reflectivity signature indicates
AP. Third, Steiner et al. (1999) notes that the distribution of spectrum
width data values is significantly broader than radial velocities for AP
echoes, and, thus, are less useful in identifying AP. Fourth, data
processing is kept to a minimum if only reflectivity data is used.
Hence, the PU algorithm uses three pieces of information utilizing
reflectivity information; the vertical extent of radar echoes, the
spatial variability of the reflectivity field, and the vertical gradient
of reflectivity. One unique feature of the algorithm is the use of
reflectivity data from vertically adjacent elevation scans to fill in
gaps in rainfall echo areas left by the removal of AP.

All reflectivity data less than 5 dBZ are removed. A vertical continuity
check removes all reflectivity echoes that exist only at the lowest
elevation scan. Remaining radar echoes are then examined for
reflectivity spatial variability. An 11 by 21 pixel window is created
around the pixel of interest. A reflectivity increase or decrease in the
radial direction within the window by more than 2 dBZ constitutes a spin
change. The number of spin changes is normalized by all potential spin
changes resulting in a percentage of possible spin changes. If this
percentage is smaller than a "spin threshold", the pixel is kept and
considered a valid radar echo. The spin threshold is dependent upon the
reflectivity of the pixel where,

SPIN THRESHOLD = 8 -- (dBZ~pixel~ -- 40)/15.

If the percentage is greater than the spin threshold, a vertical
reflectivity gradient check is made for that pixel. The pixel is
considered AP if the gradient exceeds 10 dBZ/deg.

The six algorithms examined in Robinson et al. (2001) are applied to
AP/GC contaminated WSR-88D radar data from Amarillo, TX and St. Louis,
MO. The Amarillo, TX AP contamination case occurred on May 24, 1995 when
intense AP

![](media/image17.jpeg){width="6.002777777777778in"
height="3.029861111111111in"}

coexists with, but is not embedded within, legitimate precipitation
(Figure 17). The GSFC algorithms, whether using fixed or tunable
threshold parameters, have great

difficulty removing the more intense AP echoes while NCAR, PU, and
WSR-88D PPS are more successful. NCAR greatly reduces the areal coverage
of the intense AP leaving only widespread, isolated pixels of intense
spurious returns. GSFC and PU algorithms are most effective in
maintaining precipitation echoes. Within NCAR, precipitation returns are
reduced as the RDQC algorithms occasionally remove echoes at the edge of
convective cores, which is an artifact of using a fuzzy-logic based
algorithm. WSR-88D accumulations are relatively low since higher tilt
data and resultant weaker reflectivity returns are utilized when AP is
detected.

On July 3, 1997, intense AP embedded within convection surrounded the
St. Louis WSR-88D. In application to this case, the GSFC algorithms have
no effect on embedded AP while NCAR REC is more successful in mitigating
the adverse effects of embedded AP contamination. NCAR REC is able to
remove large areas of intense AP but a significant number of strong
spurious echo pixels still persist. The PU algorithm performs very well
in this particular case by replacing contaminated base-scan data with
higher-tilt information, while maintaining the overall integrity of the
precipitation field.

Based upon the results of this study, evidence suggests that superior
results are obtained from either of the automated NCAR REC or PU
algorithms. Both of these algorithms may be a viable alternative to the
current, labor-intensive GSFC technique as well as the WSR-88D PPS
method. Both the NCAR REC and PU algorithms perform well in removing
strong AP echoes while not removing true precipitation echoes.

**9. Importance of Radar Data Parameters in Removing AP/GC**

Steiner and Smith (1999a,b; 1997; 1996) of the Department of Civil
Engineering and Operations Branch of Princeton University, have
exhaustively reviewed the history of AP mitigation techniques. They have
recently evaluated the performance of a variety of algorithms based on
reflectivity alone, and in combination with radial velocity and spectrum
width information.

Steiner and Smith (1999b; 1997) examine the importance of algorithm
parameters based on a selection of cases that represent situations of
(i) AP echoes without or separated from precipitation, (ii) AP echoes
embedded in precipitation, (iii) precipitation, and (iv) other features
such as clear air echoes, gust fronts, convergence lines, chaff, etc.
The parameters studied are: reflectivity, vertical gradient of
reflectivity, radar echo top height, radial velocity, spectrum width,
texture and spin change of reflectivity as well as the mean and standard
deviation of radial velocity.

As a result, they make the following conclusions:

1)  The information about reflectivity, radial velocity, or spectrum
    width at a given pixel, by itself, is of no value for echo type
    identification. However, when used in combination with other
    parameters, these three observations can be made useful. For
    example, in cases of AP embedded within precipitation, radial
    velocities near zero can identify AP as long as the motion of the
    surrounding precipitation is not primarily tangential to the radar
    beam.

2)  Large vertical gradients of reflectivity are indicative of
    non-raining echoes.

3)  High echo tops indicate precipitation, however, AP clutter may still
    be embedded.

4)  High spin change values appear to signal AP clutter; however this
    parameter alone may not result in clear AP clutter identification.

5)  The spatial (horizontal) variability or texture of the radar
    reflectivity field is a powerful indicator of ground clutter and AP
    echoes. This parameter alone may not result in clear AP clutter
    identification.

6)  The local variability of velocity or spectrum width is not helpful
    with echo type classification.

7)  AP clutter separated from precipitation can be identified based on
    information about the vertical gradient of reflectivity and echo top
    height if an appropriate radar scan strategy is used and AP exists
    at a particular range from the radar.

8)  When AP occurs embedded within precipitation, a combination of
    radial velocity, vertical gradient and spatial variability of
    reflectivity (spin change or texture) may provide enough information
    to guide automated identification.

9)  The worst AP case is sea clutter embedded within precipitation.
    Unfortunately, no combination of investigated parameters provided an
    ability to tackle this problem except possibly through the use of
    satellite information.

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10. **Summary and Implications for WARP's AP Mitigation Technique**

AP/GC echoes can result from several processes including thunderstorm
outflows, nocturnal temperature inversions, advection of warm, dry air
over cool, moist air, ground clutter, clear-air, biological returns,
residual clutter, and man-made chaff released into the atmosphere. AP/GC
mitigation schemes typically use radar information such as reflectivity,
radial wind, spectrum width or statistical measures thereof (i.e., mean
and standard deviation) to remove non-meteorological echoes. In
addition, some techniques utilize "tilt tests" that analyze vertical
gradients of reflectivity or areal extent of reflectivity. Atmospheric
observing platforms other than radar are now being utilized including
satellite, surface observations, and numerical weather prediction model
data. These new sources of data are applied assuming that meteorological
targets do not exist in cloud-free regions of the atmosphere.

Some of the more common problems in removing AP occur in cases of
clear-air echoes, AP embedded within precipitation, residual ground
clutter, and chaff. AP is most difficult to remove when embedded within
a precipitation shield. On the other hand, most AP/GC removal schemes
are very successful when no precipitation is present. For instance, the
AP mitigation technique used within ITWS can remove up to 98% of AP in
precipitation-free cases (Klingle-Wilson 1995). However, that percentage
drops to 80% in situations in which AP is embedded within precipitation.

Table 3 summarizes all the AP/GC mitigation techniques discussed in this
study and the radar parameters each method used to remove the AP/GC.
Table 4 lists different types of AP/GC that can produce
non-meteorological radar echoes and the radar parameters that are most
useful in removing a particular type of AP/GC. Some of the more
important parameters that are used in several techniques, and deemed
useful in removing different types of AP/GC, are radial velocity,
spectrum width, vertical gradient of reflectivity, texture or spatial
variability of the reflectivity field, and satellite cloud top IR
temperatures.

Typically AP/GC echoes contain radial velocities and spectrum widths
near zero. However, Steiner and Smith (1999b; 1997) point out that
radial velocity and spectrum width thresholds should be used in
combination with other parameters to effectively remove AP/GC.
Otherwise, true precipitation echoes can be removed when moving
tangentially with respect to the radar beam. This will impact rainfall
estimates, but more importantly for WARP, meteorological echoes
potentially hazardous to aviation could be removed, or spuriously
attenuated. The vertical gradient of reflectivity is also a particularly
useful parameter since AP typically contains a reflectivity maximum in
the lowest elevation scans and a large vertical reflectivity gradient
above. Meteorological radar echoes, except for thunderstorms that are in
the formative stages of their life cycle, will typically contain
vertical reflectivity gradients much less than AP/GC (Mueller and Sims
1975).

+-------------------------------+--------------------------------------+
| **AP/GC Technique**           | **Radar Parameters Used in Scheme**  |
+-------------------------------+--------------------------------------+
| ## MIT/LL ITWS                | dBZ comparison between 2 radars      |
+-------------------------------+--------------------------------------+
| ###### WSR88D-PPS             | VG of areal dBZ; dBZ thresholds      |
+-------------------------------+--------------------------------------+
| WSR88D-Clutter Suppression    | RV and SW                            |
+-------------------------------+--------------------------------------+
| ## WSR88D-LRM-APR             | RV and SW over preset regions        |
+-------------------------------+--------------------------------------+
| PU                            | dBZ texture, VG, VC                  |
+-------------------------------+--------------------------------------+
| UW                            | dBZ texture, VG, VC                  |
+-------------------------------+--------------------------------------+
| GSFC                          | radar echo top height, dBZ           |
|                               | thresholds                           |
+-------------------------------+--------------------------------------+
| NSSL QPE-SUMS                 | RV, SW, VG, SAT                      |
+-------------------------------+--------------------------------------+
| NCAR-REC                      | MDZ,MSW,MDVE,MDSW,SDVE,              |
|                               |                                      |
|                               | SDSW, TDBZ,TVE,RS,RG                 |
+-------------------------------+--------------------------------------+

Table 3. Listing of AP/GC techniques discussed in this study along with
the radar parameters used within the removal technique. The following is
a key for acronyms within the table:

dBZ=reflectivity SDVE=standard deviation of radial velocity

VG=vertical gradient of reflectivity SDSW=standard deviation of spectrum
width

RV=radial velocity TDBZ=texture of reflectivity

SW=spectrum width TVE=texture of radial velocity

VC=vertical continuity of reflectivity RS=reflectivity spin

MDZ=mean reflectivity RG=reflectivity sign

MSW=mean spectrum width SAT=satellite cloud-top IR temperatures

MDVE=median radial velocity

MDSW=median spectrum width

+-----------------------------+-----------------------------------------+
| **Examples of AP/GC**       | ###### **Useful Radar Parameters**      |
|                             |                                         |
|                             | **needed to alleviate AP/GC**           |
+-----------------------------+-----------------------------------------+
| ## AP only                  | RV, SW, SAT                             |
+-----------------------------+-----------------------------------------+
| ###### AP and Precipitation | RV, SW, and VG                          |
|                             |                                         |
| (but not intermixed)        |                                         |
+-----------------------------+-----------------------------------------+
| AP embedded within          | RV, SW, VG, TDBZ, and possibly VC       |
| precipitation               |                                         |
+-----------------------------+-----------------------------------------+
| Clear-air echoes            | TDBZ, SAT                               |
|                             |                                         |
| (e.g. biological targets)   |                                         |
+-----------------------------+-----------------------------------------+
| Residual clutter            | RV, SW, SAT                             |
+-----------------------------+-----------------------------------------+
| Boundary Layer Features     | VG                                      |
|                             |                                         |
| (e.g. gust front)           |                                         |
+-----------------------------+-----------------------------------------+
| Chaff                       | SAT                                     |
+-----------------------------+-----------------------------------------+

Table 4. Listing of atmospheric examples of AP/GC and the more useful
radar parameters that are needed within AP mitigation techniques to
remove the AP/GC. Same key is used as in Table 3.

A more complex radar parameter, the texture of the reflectivity field,
can be used to accurately identify AP/GC regions. This parameter can be
calculated by different methods and is a measure of the horizontal
homogeneity of the reflectivity field. Within NCAR-REC, the texture of
reflectivity is calculated as the average of the sum of least squared
differences of reflectivity. Within the PU algorithm, reflectivity
texture is calculated by identifying the number of 2 dBZ changes in the
reflectivity field along several radials within a prescribed window.
Reflectivity texture is useful because precipitation fields tend to be
smoother (i.e., more continuous or homogenous) than either clear-air
echoes or AP regions over a wide area.

The Weather and Radar Processor (WARP) program currently uses the
WSR-88D LRM-APR product as truth data to correct for AP within the
composite reflectivity or base reflectivity WSR-88D product. MIT/LL has
developed this AP removal technique using radial velocity and spectrum
width thresholds (Isaminger et al. 1997). One examination of ten cases
that included AP only, weather only, and AP embedded within
precipitation showed that 67.6% of AP is removed using this technique.

However, this method is applied differently over preset regions of the
atmosphere surrounding a radar site (Figure 1). Thus, the scheme is not
flexible enough to be applied over locations containing AP that may
exist outside of the predetermined regions. In addition, this technique
automatically excludes all radar echoes within 45 km of the radar that
are below 1 km in height. Thus, in particular instances, very important
precipitation echo information may be arbitrarily and incorrectly
removed. Furthermore, this scheme uses radial velocity and spectrum
width data only. Therefore, this method can fail by not properly
identifying AP in cases such as thunderstorm outflows or fail by
incorrectly removing precipitation echoes.

While WARP's current AP/GC removal scheme has yet to be tested, it is
likely that it can be improved upon in several different ways. One is to
simply replace WARP's current technique with another more accurate
method. In the comparison of RDQC algorithms made by Robinson et al.
(2001), NCAR's REC performed very well relative to other algorithms.
This scheme will be available through the WSR-88D ORPG within the next
five years as it is currently slated for release within ORPG Build 2
(Kessinger and VanAndel 2001). Preparation should be made for the
integration of NCAR's AP Mitigation scheme (particularly the REC) within
the WSR-88D system in order to take advantage of its superior ability to
detect AP/GC radar echoes.

Until NCAR-REC is available, other alternatives to improving WARP's AP
removal technique could be examined. Unfortunately, there is very little
that can be done to improve on the AP/GC mitigation technique through
the use of the LRM-APR product since the LRM-APR product is calculated
prior to being ingested within the WARP system. Rather than trying to
determine an optimal method of editing the composite reflectivity or
base data products using the LRM-APR product, a more judicious use of
time could be realized in formulating a new AP/GC removal technique.
This is not to imply that the current AP/GC mitigation technique within
WARP will do a poor job at removing most non-meteorological echoes.
However, if a new removal technique is found to be required, there is
little that could be done to improve the quality of the current AP/GC
mitigation technique. In order to improve it's quality, the LRM-APR
product would have to be reformulated or improved upon in some manner.
By the time this could be done, the NCAR REC will become available and
be the preferred product.

A simple, and potentially very effective, technique could be formulated
using radial velocity, spectrum width, vertical gradient of
reflectivity, and texture of reflectivity using data supplied through
the real-time WSR-88D narrowband connection to each WARP system. For
instance, UW, PU, and NCAR REC all show the importance of reflectivity
texture analysis as being accurate in discerning clear-air echoes from
precipitation. When a reflectivity texture parameter is used in
conjunction with vertical gradient of reflectivity as well as radial
velocity and spectrum width, the AP removal process can be fairly
accurate. UW has shown cases where the vertical gradient and texture of
reflectivity can aid the ability to remove AP that coexists with
precipitation echoes (Figure 16). When both of those parameters are used
in combination with radial velocity and spectrum width information,
clear-air echoes and residual clutter can be removed in addition to AP
only regions.

Three sources of AP that remain difficult to remove are chaff, clear-air
echoes existing over multiple elevation scans in close proximity to the
radar, and sea clutter. One effective means to remove these sources of
AP includes the use of satellite cloud top IR temperature information as
formulated within NSSL's QPE-SUMS. Because WARP is already receiving
satellite information, IR cloud-top temperature observations could be
readily available. Tests with QPE-SUMS show the use of satellite can be
particularly effective at removing residual ground clutter and chaff
within cloud-free regions (Figures 7, 8).

Another future AP removal technique that has not been discussed, yet
shows great potential, uses polarimetric radar data. Parameters such as
the cross-correlation coefficient between horizontally and vertically
polarized radar returns, the standard deviation of differential phase,
and the specific differential phase can determine regions of AP very
accurately (Ryzhkov and Zrnic 1998). However, the incorporation of
polarimetric capabilities within the WSR-88D system will likely take
place beyond the five year horizon.

A final consideration when using AP removal techniques is the amount of
computational time required to execute the method. Each AP mitigation
technique described has been implemented (or soon will be) in a
real-time, operational system to provide users with immediate
information. Software code for real-time applications has always been
optimized to reduce CPU usage, and AP mitigation schemes are no
different. For example, the ROC has already incorporated the NCAR REC
method within the WSR-88D system for investigation. Preliminary testing
shows, with the proper software coding, that the addition of the NCAR
REC method only increases the overall computation time for the WSR-88D
RPG by less than a minute (personal communication, Tim O'Bannon).
Likewise, the AP Mitigation scheme used by NSSL's QPE-SUMS takes only
seconds to execute on a Silicon Graphics Origin 2000 workstation. Thus,
most techniques that have been examined do not adversely increase
computational loads.

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