RADIAL WIND SUPER-OBS FROM THE WSR88D RADARS IN THE NCEP OPERATIONAL
ASSIMILATION SYSTEM

Jordan C. Alpert and V. Krishnakumar

NOAA/NWS/NCEP, Camp Springs, Maryland 20746

Email: <Jordan.alpert@noaa.gov>

Krishna.Kumar@noaa.gov

> Fax: 301-763-8545
>
> Tel: 301-763-8000 x7205

**\_\_\_\_\_\_\_\_**

Corresponding author address: Jordan C. Alpert, NCEP/EMC, Rm. 204, 5200
Auth Road, Camp Springs, MD 20746.

E-mail: jordan.alpert@noaa.gov

RADIAL WIND SUPER-OBS FROM THE WSR88D RADARS IN THE NCEP OPERATIONAL
ASSIMILATION SYSTEM

**ABSTRACT**

The spatial and temporal densities of WSR88D raw radar radial wind
represent a rich source of high resolution observations for initializing
numerical weather prediction (NWP) models. A characteristic of these
observations is the presence of a significant degree of redundant
information leading to potential improvement by constructing averages,
called super-obs. In the past, transmission of the radar radial wind
from each radar to a central site was confined to data feeds that filter
the resolution and precision of that available. We call this data feed
type level 3. The precision and information content of the radial wind
can be improved if data at each radar site is directly utilized at the
highest resolution and precision found at the WSR88D radar and then
transmitted to a central site for processing in assimilation systems. In
addition, with data compression from using super-obs, the volume of data
is reduced allowing quality control information to be included in the
data transmission. We call the super-ob product from each WSR88D radar,
level 2.5. Parallel, operational runs, and case studies of the impact of
the level 2.5 radar radial wind super-ob on the NCEP operational 12-km
ETA data assimilation (EDAS) and forecast system, are compared with
NEXRAD level 3 radial wind observations which are spatially filtered and
delivered at reduced precision. From the cases studied, we show that the
level 3 super-obs make little or no impact on the ETA data analysis and
subsequent forecasts. The assimilation of the level 2.5 super-ob product
in the EDAS and forecast system shows improved precipitation threat
scores as well as reduction in RMS and bias height errors particularly
in the upper troposphere. Meso-scale precipitation patterns benefit from
the level 2.5 super-obs, and even more so when greater weight is given
to these high resolution/precision observations in the few cases
studied. Direct transmission of raw level 2 radar data to a central site
and its use are now imminent but this study shows that the level 2.5
product can be used as an operational benchmark to compare with new
quality control and assimilation schemes.

**1. INTRODUCTION**

WSR88D NEXRAD radars include 158 deployed operational Doppler devices
throughout the United States and represent superior resolution and
improved observation accuracy compared to past radar systems. The
observations from these radars aid National Weather Service (NWS)
forecasters in issuing warnings and watches to citizens about dangerous
weather. The spatial and temporal densities of WSR88D raw radial wind
data represent a rich source of high resolution observations that can be
assimilated in operational numerical weather prediction (NWP) models.
For more than a decade, the WSR88D has played an important role in the
improvement of short-range nowcasts as well as warnings for severe
thunderstorms, tornadoes and flash floods, but until recently
operational models did not obtain the benefit of these high resolution
observations in real time. NOAA's National Centers for Environmental
Prediction (NCEP), pointed out that high resolution observations were
needed in real time for high resolution regional models coming online
for the next millennium. The first time radar radial winds were
assimilated into NCEP operational models was in a demonstration project
at the 1996 Olympics in Atlanta[^1], to aid the forecasters at the
weather forecast office who had the responsibility for issuing weather
warnings for Olympic events. For this demonstration, a commercial radar
data feed was used from the NEXRAD Information Dissemination Service
(NIDS) which delivered a truncated resolution and precision compared to
the radar returns at each individual radar[^2].

That such data was not supplied at a central site, capable of
operational model ingest, was more a problem with the level of
information technology in the advanced weather and interactive
processing (AWIPS) system. The impact from the addition of NIDS data
into the regional model was not clear from the experiments that were run
at the time of the 1996 Atlanta Olympic demonstration. This provides a
motivation to compare the impact of higher resolution/precision radar
returns with NIDS delivered observations available a decade ago. The
technology available then, for example, band width, limited the large
amount of data the radar generated that could be transmitted. The
computations needed to perform pre-processing tasks such as converting
radar return locations to model coordinates were limited by CPU
resources. A practical and cost-effective way to transmit the
information content of the high resolution radar observations is to use
an averaging technique to reduce the data set size at the radar and
utilize the computers located at each radar station to do the necessary
computations. This is an early use of a parallel system of many
distributed (potentially 158 across the CONUS) processors acting on the
same software program independently at the same time and delivering the
results to a central site. The reduction in the data size from the
averaging technique at each radar was 1500 to 1 (Istok et. al, 2003).

A characteristic of these radar observations is the significant degree
of redundant information present in the radar returns. Redundant
observations impose a burden on an operational assimilation system since
each datum is processed with repetitive interpolations from the analysis
grid to its location and back again. This effort is carried out for each
datum regardless of the information that can be attributed to it in the
overall assimilation. The time and storage expended on mutually
redundant data could be better spent on improving other aspects of the
assimilation (Purser, et al., 2000). Therefore, it is desirable to
maximize whatever data compression the ensemble of fresh observations
allows, while minimizing any degradation of the information content. The
term for a surrogate datum which replaces several partially redundant
actual data is a \"super observation\" or \"super-ob\".

Super-obs have been assimilated in the NCEP operational analysis system
for subsets of the WSR88D radar radial wind observations from the NIDS
commercial data feed. NIDS provided a way for public users and NWS to
receive NEXRAD data. The NIDS contract expired on 31 December 2000, and
NWS assumed the responsibility of distributing the NEXRAD data for
operations, the content of which remained the same. In terms of radial
wind, the NIDS data transmission truncated the number of antenna tilts
to 4 from a possible 16, decreased data to 4 bits (15 levels of
amplitude) compared to 16 bits (65,536 levels of amplitude) of
precision, and truncated radial resolution to 1 km along a radial line
of a possible 0.25 km that is available at the radar site.

The precision and information content of the NIDS radial wind is
improved if data at each radar site is directly utilized at the
resolution and precision of the WSR88D radar. We construct a super-ob at
each radar site, acting on the complete set of radar data in several
volume scans, and then deliver the reduced data set to a central site
with higher precision as described in the reports of Alpert et. al
(2003, 2004). The radar wind super-ob takes the results of radar scans
and averages data points within a prescribed time and spatial
3-dimensional volume before transmitting reports to a central collection
of radial wind data. One could create super-obs using the reflectivity
and spectrum width in addition to the radial wind super-ob, however, use
of these quantities require an independent forward model so this study
confines itself to radial winds for which the forward model is well
defined. We note that beginning in 2005, with rapid improvements in
information technology, direct transfer of all raw radar (level 2) data
to a central site is scheduled, so that models can take advantage of the
complete set of radar data and antenna tilts. This data feed will
include more complex quality control measures because the entire matrix
of events will be available before averaging is done (Liu and Xu, 2005
and Zhang et. al 2005). The super-ob product reported here will remain
useful as a benchmark and backup system to compare with the level 2 data
which may be ready for operations within a year.

**2. DESCRIPTION OF THE SUPER-OB**

The full-resolution WSR88D base radial wind data provides sufficient
data amounts for statistically significant averaging. The super-ob
product is programmed at each WSR88D site using the open systems Radar
Product Generator (RPG) to control all aspects of the calculation. The
open RPG is the system which operates between the Radar Data Acquisition
(RDA) system and more sophisticated display devices, such as the
National Weather Service\'s Advanced Weather Interactive Processing
System (AWIPS). The RDA collects data from the WSR88D radar and forwards
base data products to the open RPG. These base data products consist of
reflectivity, radial wind, and spectrum width. The open RPG creates the
special purpose products from the base data and forwards them to other
systems for display or for further processing. Super-ob is one of the
newly enabled, enhanced products under the open RPG. The new product is
super-obs of radar radial winds and known in this study as level 2.5.
These will be compared with the NIDS radar radial winds referred to as
level 3 as well as a no-radar radial wind case in analysis and forecast
experiments using the NCEP ETA Data Assimilation System (EDAS) which is
a 3-dimensional variational (3DVAR) analysis system (Parrish et al.
1996). Forecasts are from the meso-ETA model, in its operational
configuration with a resolution of 12 km[^3]. The level 3 super-obs were
placed in the July 2003 implementation of the ETA meso-scale regional
model (Ferrier et al., 2003). The level 2.5 super-ob described here was
implemented in the operational ETA model renamed as the North American
Mesoscale (NAM) model in the spring bundle of April, 2005 (Dimego and
Rogers, 2005, Rogers et al., 2004). In this paper, we refer to the NAM
as the meso-ETA.

3DVAR is ideally suited for assimilating Doppler radar wind data because
the radial wind super-obs are a scalar having only one component and the
vector wind can not be uniquely determined. To find both wind components
(u,v), additional information is needed. The variational procedure
requires a forward model to project the models winds onto the radar
observation locations, a linear process. In effect the assimilation
(inverse) process utilizes the models knowledge, through dynamics and
physics from its forecast error statistics, to project the scalar radial
wind observations onto the model wind vectors.

Adaptable parameters for super-ob are the time window, cell range size,
cell azimuth size, maximum range and minimum number of points. The
values of these parameters, which define the super-ob averaging in time
and space, are shown in Table 1. The default settings indicate that at
each elevation angle, a wedge shaped volume of 6 azimuth degrees by 5 km
along a radius, averaged over a time of 60 minutes, define each super-ob
cell. The processing of the radial wind observations at each radar
requires one to choose time averaging and a super-ob cell size to
encompass a large enough area and time to include enough observations to
insure a good average and to minimize the dependency between adjacent
super-obs. The projection of the radial wind field in the observation
space can be calculated in the 3DVAR analysis from the orthogonalization
of the observation forward model (Liu, et. al, 2005). Each of the
super-obs contains no fewer than 50 points, and no cell extends past 100
km, as the radar beam width becomes too wide, and returns become less
certain at larger distances from the radar. These adaptable parameter
values are programmed through the RPG. The range of possible values,
shown in Table 1, allows for the super-ob product to adjust to different
analysis resolution requirements as they occur. All the WSR88D sites
will create a super-ob data product by this process and transmit it to a
central site. The data transmission precision of the super-ob (time
mean) radial wind is

0.01 m/s as described in the "Interface Control Document for the
RPG"[^4]. Other improvements in the level 2.5 data over the level 3 are
the transmission of a maximum of 15 antenna tilt levels compared to four
levels. The super-ob product is potentially a time average of many
volume scans which depend on the number that can fit into the Table 1
time window interval value. This number varies with the radar volume
coverage pattern (VCP) mode. The standard deviation about the mean
within each cell is calculated from the time and spatial averaging is
included in units of ms^-1^.

A number of tests have been made of the radar system within the Systems
Engineering Center at the NOAA/NWS Office of Science and Technology. An
example of the radial wind scalar field is shown on a vector plot in
Fig.1. The data for this example is from KATX, Seattle, WA radar, at an
antenna tilt of 0.4 degrees, from May 28, 2002 at 1800 UTC and
represents a typical return. The characteristic pattern of radar radial
returns is apparent, and there are large areas that do not have returns.
The radial wind magnitude is seen to increase southwest and northeast at
larger distances from the radar. In a clear air test at station KCRI,
the Norman, OK test radar, (not shown) the coverage was more complete
and typically between 700 and 1000 super-ob returns out of a possible
1200 given the default parameters indicated in Table 1.

The radar returns close to the radar antenna can have errors due to
electronic gating deficiencies, so the level 2.5 super-obs shown in Fig.
1 will have averaged some of this data that should have been screened
out from its smallest radius. The super-ob returns near the radar have
not been filtered, but this task may be done in the future and should
not affect the results of the experiments. Fig. 2 represents a one
dimensional representation of the super-ob returns shown in Fig. 1 as a
function of azimuth and radius. Fig. 2a shows the height variation
plotted for the super-ob data set according to the first observation
which is along 0 degrees north, closest to the radar, extending out to
larger radii from the radar, and then clockwise around in azimuth for
the remaining returns in the report, in this case over 700 super-obs for
a complete circle of azimuth. For example, the first line segment in
Fig. 2a coincides approximately with 0 azimuth pointing local north
(Fig. 1) and radius of \~2.5 km and the next point along that radius,
7.5 km further out, as radial distance increases and continuing
clockwise from north around with increasing azimuth of 6 degrees. As the
radar beam extends to larger radial distances from the radar, the height
of the returns increases as shown by spikes in Fig. 2a until either
there are no more returns along a particular radius or the radius
reaches the default maximum of 100km (Table 1). In the case of the
super-ob radial wind shown in Fig. 1 for the lowest elevation angle, the
height ranges from about 250 m to 2 km as shown in Fig. 2a. The radial
wind standard deviation is calculated within each super-ob, 6 degree of
azimuth by 5 km radius, and transmitted in the super-ob record with a
precision of 1 ms^-1^ as shown in Fig. 2b.

Because this analysis was initially created to assimilate Doppler radar
winds, all winds are treated as line-of-sight winds. A conventional wind
observation becomes two line-of-sight observations, one along a north
pointing line (earth v component) and one along an east pointing line
(earth u component). All line-of-sight winds, radar or conventional, are
then assigned the angle that each observation line-of-sight makes with
the ETA grid x-axis. The forward model (which estimates the observed
quantity from model variables) is just

u~radial~ = u cos + v sin , (1)

where (u,v) are the components of the real wind at the same height as
the super-ob and is the azimuth. This equation reduces from the
3-dimensional system (Parrish, 2005) with the vertical motion set to
zero and at constant elevation angle. Fig. 2c shows each super-ob radial
wind plotted along a particular radius and clockwise for each 6 degrees
of azimuth, therefore, a sinusoidal pattern of the radial wind results
due to the radial wind definition (1). Each successive super-ob moves
out along the radii with increasing beam height causing a range of
super-ob wind magnitudes along each azimuth. The super-ob radial wind
returns labeled "mean wind" in Fig. 2c range from a few ms^-1^ near
point 200 to 10 ms^-1^ near point 650. At any radial distance from the
radar, observing the returns in increasing azimuth will result in a
characteristic sinusoidal pattern. The range of wind values superimposed
on the sinusoid is from varying heights and location of the return as
well as the result of natural variance in the winds in the radar
vicinity due to synoptic conditions. However, a number of super-ob wind
values in Fig. 2c extend beyond the normal range as defined by its
neighbors. One can scan this data set, and examine values outside the
expected range of wind, and compare the standard deviation, height and
the location of a return in question.

A few radial wind speeds in Fig. 1 (elevation angle = 0.4), sometimes
appear as outliers compared with their neighbors (Fig. 2c). For example,
point 108 at (lat, lon), (48.85, -121.632) has a super-ob wind of 12.1
m/s at a height of 1549 m. The standard deviation corresponding to this
point on Fig. 2b is among the lower values that may arise from a
preponderance of ground clutter. Points 458 and 474 at (47.263,
-123.238) and (47.579, -123.110) respectively (see SW quadrant of Fig.
1), show radial wind magnitudes of -14.12 m/s and -12.58ms^-1^ (Fig. 2c)
which are outliers being larger than their neighbors. The returns for
these super-obs are expected to have larger wind magnitudes with
increased elevation (Fig. 2a) as they are further away from the radar.

An example of a high standard deviation return is shown for point 589 at
(48.039, -123.789) with a standard deviation of 12 m/s and wind
magnitude of -10.33 m/s. The height for point 589 is 1572 m which makes
it among the larger distances from the radar and higher height for this
radar elevation angle. Fig. 2b shows a moderate spike at point 589 in
line with the increased height in Fig. 2a and the increased negative
super-ob wind. In this case, the value is suspect because of the large
standard deviation in the radar radial winds making up the super-ob. One
may expect a larger standard deviation of radial wind values because of
the widening of the radar beam width with increasing distance from the
radar as well as with increased wind speeds at higher altitudes. When
the radar beam grows wider with increasing distance from the radar, the
vertical position of the return can not be determined with precision.
The standard deviation is useful in determining the quality of the
returns. The quality control of the super-ob data in the analysis
program may be able to utilize the reflectivity to determine locations
of severe convection (and vertical motion) which could cause errors in
the radial wind and this is under consideration for assimilating level 2
raw returns. The standard deviation can be large at points that have
less than 1 ms^-1^ wind magnitudes such as points 630, 638, and 639 and
634. In this case these were among the nearly calm values located north
and west of the radar (Fig. 1).

Fig. 3 illustrates the location of each super-ob. The centroid of WSR88D
returns in space is calculated before super-ob averaging and made part
of the transmitted header as the location in longitude and latitude of
each super-ob value. This is the reason the super-ob longitude/latitude
locations, shown by a "+" in Fig. 3, or the tails of the vectors in Fig.
1, are not orientated in exact concentric circles. A time deviation is
also reported as the valid time of a particular super-ob cell as the
average of several scans occurring within each time window (defaults
shown in Table 1).

Analyzed conventional winds at a height of 0.4 km interpolated from the
NCEP Global Forecast System (GFS) Spectral Statistical Interpolation
(SSI, Parrish and Derber, 1992) valid 1800 UTC 28 May 2002, are shown in
Fig. 4. The wind barbs are displayed over the same domain and time as
the super-ob radial wind observations in Fig. 1. The operational SSI,
used here as an independent (low resolution) analysis, uses all
available remote and conventional observations to create the analysis at
a resolution (spectral triangular truncation T254) of less than 1 degree
of latitude and longitude. The SSI analysis is used as a proxy for
observed winds in the radar area because the number of wind observations
available around the radar is few. This is done with the knowledge that
a global ˚ x ˚ analysis at a particular height level is not a definitive
way to verify these high resolution observations, but it serves as a
coarse check on the validity of the super-ob returns. The super-ob
radial winds located close to the radar are at or near a height of 0.4
km as shown in Fig. 2a therefore, one can compare Fig. 4 with Fig. 1.
Given the definition of radial wind from eq. 1, there is one direction
where the analyzed wind will be identical to the radial wind, and
ideally, winds perpendicular to this will have zero radial wind. The
direction of the analyzed wind north and west of the radar in Fig. 4 is
southwesterly at \~15 m/s. The radial wind along a line in the northwest
quadrant in Fig. 1, lines up in the same direction as analyzed winds
shown in the same quadrant in Fig. 4. To the east of the radar, the
radial wind magnitudes are far less, 0-5 ms^-1^. This is because the
radial wind in this region is perpendicular to the analyzed southerly
wind (Fig. 4), so according to the definition (eq. 1) the component in
the radial direction is small. The analyzed winds in the southwest
quadrant tend toward larger magnitudes as do the radial winds shown on
Fig. 1. Plots at 1 km and 1.5 km levels of regular analyzed winds show
that winds at higher elevations in this area have increased magnitudes
even though the angle of the wind (southerly) and the radial wind are at
large angles (not shown). The regular winds in the southeast quadrant
are SSE with similar magnitudes to the radial wind. Overall the analyzed
winds show changes that are reflected in the radial wind pattern as
shown in Fig. 1 and Fig. 4.

The number of radar returns, and therefore the corresponding super-obs,
vary with the amount of precipitation, increasing with more
precipitation. Convective regions can distort the returns, and these are
filtered by observing the standard deviation of the returns which is
transmitted with each super-ob. The super-ob standard deviation is used
as a proxy for the error in the radial wind measurement. The larger the
standard deviation, the smaller the weight the analysis system will
apply to a super-ob. Even in clear air mode, super-obs are successfully
created as the radars are sensitive to Doppler motions.

The quality control used in the EDAS for the super-ob products is the
same for level 2.5 and level 3. One quality control element of the
super-ob ingest into the EDAS analysis is that the vertical component of
the radar beam width is assumed to increase with radar range at a rate
of 20 m/km. This is roughly 20% larger than the actual beam width and is
done to account for the uncertainty in beam propagation. Winds at all
model vertical levels, intercepted by the radar beam, are adjusted so
that the observation is as close as possible to an acceptable range of
wind speeds derived from linear fits of the radial wind observations.
All winds within the maximum range, as set in Table 1, (presently set at
100 km) are used. Quality marks of wind observations derived separately
from independent velocity-azimuth display (VAD) observations are
co-located in the vertical in 500-m bins with the corresponding radial
winds. The radial winds are not used if there is no VAD observation or
if it fails a quality-control (QC) check. This approach combines the QC
algorithm for removing radial winds contaminated by birds with other
checks used on the VAD winds. These QC algorithms were developed for the
NIDS level 3 data and should be considered preliminary (Parrish and
Purser, 1998). In addition, the radial winds are not used if (1) the
beam envelope extends below the ETA model terrain height, (2) the super
observation error is larger than 6 ms^-1^, or (3) it fails the same
gross checks used for conventional winds. Sophisticated QC algorithms
developed by Zhang et al., (2005) and Liu and Xu, (2005), are currently
under development for NCEP operations with the full level 2 data set.

The analysis contains an "inflate" parameter which magnifies the
observation error relative to other observation types at each model
level. The weights for any observation type are proportional to the
inverse of the observation errors at each model level, *p*, as discussed
by Zapotocny et al. (2000) with the added inflate factor:

> Weights (*p*) Inflate parameter X error (*p*)). (2)

The NIDS level 3 super-ob are both assigned an inflate parameter of 3.
Experiments will be shown for which the level 2.5 super-ob inflate
parameter is set to 3 and reduced to 2, since this data has more
precision. The level 2.5 super-ob radial winds were operationally
implemented with the same inflate parameter as the NIDS level 3. In
addition, using the standard deviation of the super-ob as the
observation error estimate, gives information for quality control in the
analysis system for each observation event. For example, in strong
convection, the super-ob will suffer from a larger standard deviation in
the raw returns that composed it which will diminish the weighting in
the analysis system relative to other observations.

**3. RESULTS FROM ETA MODEL IMPACT EXPERIMENTS**

In order to test the impact of the level 2.5 super-ob on regional model
forecasts, three experiments were performed with the NCEP 12 km,
meso-ETA regional model. Each experiment included a separate analysis
from the EDAS cycling 3-dimensional variational analysis system (Parrish
et al. 1996, and Wu et al., 2002) starting 12 hours before the initial
condition time of 1200 UTC 28 Oct 2003. All conventional and
non-conventional observations that were available in the operations were
assimilated in these experiments. In addition, the operational model run
contains the assimilated level 3 super-ob over a 3-hr window therefore,
the operational run will be the control run for experiments. Experiments
are conducted to show the impact of assimilating the level 2.5 super-ob,
which we designate as Exp. 1. Exp. 2 is the operational run that
includes the level 3 super-ob, and Exp. 3 has no radar radial wind data
present as indicated in Table 2.

The mean sea level pressure map and precipitation in Fig. 5 are 12-h
forecasts which are representative of the synoptic situation. We use a
12-hr forecast for convenience to show generally the areas where
precipitation is indicated, and the synoptic situation of the
experiments. In the central US there are two major low pressure systems,
one extending to northwestern US and a second over the Great Lakes.
There is also an inverted trough over the eastern US coast. This figure
also shows the 12-h forecast of precipitation to indicate regions with
more radar returns and a likely corresponding greater number of
super-obs compared to regions with clear air returns. An approaching low
pressure system and associated precipitation having a
northwest-southeast orientation can be seen to have been forecasted to
be approaching the Great Plains. These precipitation patterns move to
the south and east and provide areas where the radar responds in its
precipitation mode, usually providing more than the normal number of
super-obs through 36-h.

There are not sufficient verifying observations to compare the high
resolution super-ob data directly using co-located conventional
observations like rawindsondes because of scale differences. To compare
the impact of higher precision level 2.5 super-obs with the lower
precision level 3 super-obs, a case study approach is employed to show
the impact of the different super-ob generating methods. The lower
precision super-ob, from level 3 observations, may not have as much
influence on the analysis due to ultimately larger deviations from the
guess wind field compared to the other observations. The higher
precision level 2.5 super-obs should have less error and therefore have
more influence in the analysis by virtue of being closer to other
observations. We first show the vector wind difference and magnitudes
between level 2.5 (Exp. 1), minus level 3 (Exp. 2), in Fig. 6, and then
the level 3 minus no radar radial wind (Exp. 3) to see the impact of
both super-obs in Fig. 7. The level 2.5 minus level 3 (Fig. 6)
differences are of small scale, the largest difference being generally
greater than 1 ms^-1^, are located in areas along the inverted trough
(East Coast), low pressure (from the Great Lakes through the Northwest),
and an area in the Southwest mountains. The magnitude and aerial extent
are larger in Fig. 6 than in Fig. 7 implying that the level 3 super-obs
(Exp. 2) have little or no impact in the analysis compared with the
level 2.5 super-obs. This is confirmed in the difference plot of level
2.5 minus no radial wind (not shown).

The analyses from each of the three experiments were used to initialize
separate meso-ETA forecasts at 12 km resolution, resulting in a 48-h
forecast for each experiment. Shown is the (36-h) forecasted wind
differences between the level 2.5 super-ob and level 3 super-ob (Exp. 1
minus Exp. 2) in Fig. 8 and the (36-h) forecasted wind differences
between the level 3 super-ob and no radar radial wind present (Exp. 2
minus Exp. 3) in Fig. 9. We use the 700 hPa as the lowest representative
level above most mountains. The largest changes appear for the level 2.5
(Exp. 1 minus Exp. 2) which had wind magnitude differences ranging from
-7 to 5 ms^-1^ compared to the differences between level 3 and no radar
integration (Exp. 2 minus Exp. 3), ranging from -2.5 to 3.5. Thus, by
the 36-h forecast, the influence of the level 2.5 is double that of the
level 3 super-ob. The shape of the differences appear as filament-like
areas in the level 2.5 super-ob, while the level 3 differences are
barely discernable and appear random. The main differences between the
level 2.5 super-ob and the level 3 super-ob are the precision in the
delivered wind values (0.05 ms^-1^ compared to 5 ms^-1^, respectively)
and the extra antenna tilt angles (16 compared to 4, respectively). The
largest differences seen in Fig. 8 appear in regions of precipitation as
shown in Fig. 5 for the 12-h and the 36-h (not shown).

The number of super-ob winds with wind magnitude error (as measured by
the standard deviation for each super-ob cell) within a 1 ms^-1^ range
for the level 2.5 and level 3 super-obs is shown in Table 3. The simple
quality control used for the level 2.5 and 3 super-obs has a cut-off of
2 ms^-1^ standard deviation, so Table 3 does not show the results for
errors less than 2 ms^-1^. However, the number of super-obs with
standard deviation less than 1 ms^-1^, is typically small (Fig. 2b).
Most of the level 2.5 super-obs appear within 2 ms^-1^ error where as
the level 3 super-ob errors peak at 3 ms^-1^ as shown in Table 2. Also
note that the number of level 2.5 super-obs is typically 5 times that of
the level 3[^5].

The radar radial wind observations that are analyzed by the EDAS system
use knowledge of past forecasts to calculate error statistics for the
dependant variables. The knowledge necessary for the analysis to project
the radial wind onto the analyzed wind field comes through the adjoint
of the observation operator or the adjoint of the forward model. The
definition of radial wind is a scalar as shown in eq. 1, and there is
only one direction for which the regular wind will be identical to the
radial wind. We can not uniquely compose the radial winds into regular
wind vectors. The EDAS analysis uses the information of how the regular
winds are related to the radial winds to make an optimal decision of how
the observed radial wind projects onto the regular winds and other
variables.

**4. VERIFICATION WITH THE ETA32-KM MODEL**

The 32-km NCEP EDAS analysis with ETA parallel forecast model containing
only the level 3 super-ob (noted as ETAW, control) is compared with only
level 2.5 super-ob (noted as ETAY, parallel) integration. The
experiments were run twice daily at 00Z and 12Z for twelve days starting
from June 8, 2004 to June 20, 2004 for 24 integrations to 60-h
forecasts. Equitable threat score and bias for 24-h accumulated
precipitation and RMS height errors are shown (only 1200 UTC cycles are
used in 48-h forecasts and only 0000 UTC cycles are used in 60-h
forecasts). The 48-h super-ob level 2.5 forecast precipitation threat
scores (Fig. 10) show a small improvement at 0.01 and 0.1 inch
thresholds and at the 1.5 inch threshold over the control run. It is
slightly inferior at the 0.75 inch threshold. Except for the 1.5 inch
threshold, the number of events is significant exceeding 1000 events for
1 inch categories and less. The precipitation bias score is unchanged.
The number of events for larger thresholds is too small to be
statistically significant. Extending the ETA model precipitation
forecasts to 60-h, shows that there is improvement of bias and equitable
threat scores for all thresholds for the level 2.5 experiment.
Precipitation is a sensitive parameter, dependent on complex physical
interactions, so small threat score enhancements imply the level 2.5
super-ob precision improvements are making an impact on the EDAS
analysis and forecast system.

The RMS height error versus RAOBS over the CONUS for the two experiments
is shown in Fig. 12 as a function of pressure level. The level 2.5
super-ob shows smaller errors at all levels compared to the level 3
super-ob (Fig. 12). Fig. 13 shows the height bias errors as a function
of pressure against RAOBS over the CONUS from ETA-32 60-h forecasts for
the two experiments. A significant reduction in the bias is seen at
upper levels, but there is actually a small increase in the bias errors
at low levels. At shorter forecast times, the low level RMS errors do
not show improvement (not shown) and this is also the case for the RMS
vector wind error versus RAOBS (not shown). For shorter forecast times,
this pattern persists, but the bias differences at the lower levels are
insignificant. For the cases presented, the presence of the level 2.5
super-obs provides a beneficial impact in precipitation threat scores as
well as reduction in RMS and bias errors in geopotential height,
particularly over upper levels.

**5. CASE STUDY OF EDAS ANALYSIS INFLATE PARAMETER IMPACT**

The NCEP 3DVAR analysis of the EDAS performs a regional analysis adapted
to the ETA model in operations at the time of these experiments. Each
observation type used in the analysis has a certain weight relative to
the error of other data types. For example, the errors assigned to
rawinsonde temperatures at various pressure levels are approximately 1
K. Each competing observation type is weighted according to its
estimated error. The super-ob standard deviation is calculated at each
radar cell and is provided in the transmission of the super-ob product.
The standard deviation in each super-ob cell is taken to be proportional
to the error in the radial wind super-ob. The analysis weight of the
super-ob is set as shown in eq. 2. The inflate parameter from eq 2
(section 2) is assigned the value of 3 for the level 3 super-ob which is
the ETA control experiment. Since the level 2.5 data is obtained from
higher resolution radar returns and not filtered (from 0.25 km to 1km)
as in the level 3 case, and maintain high precision values during level
2.5 transmission compared to the level 3 super-ob (0.01 m/s vs 9.
ms^-1^), the level 2.5 super-ob may deserve to be treated with higher
confidence in the EDAS. In addition, the level 2.5 super-ob benefits
from the full compliment of up to 16 antenna tilt levels, which
correspond with an increase in the information content of the level 2.5
observation data set relative to other data types in the EDAS. This
motivates a more extensive investigation to operationally change the
level 2.5 influence through the inflate parameter.

The standard deviation values (Fig. 2b) range from 0 to 15 in units of
ms^-1^ and with an inflate parameter of 3, can produce weights that
range from 0 to 45 (eq 2). A gross toss (removal) of a super-ob is
applied for standard deviation values greater than 6 ms^-1^ or weight ≥
21. Since the level 2.5 data is constructed at each radar with 0.25 km
radius and transmitted at higher precision to a central site, a
reduction of the inflate parameter should cause the analysis to draw
closer (by giving more weight) to the level 2.5 super-ob radial wind
observations. Two experiments were run to study the influence of the
inflate parameter for the level 2.5 super-ob and a comparison was made
with the operational control (level 3 super-ob with inflate parameter
set to 3). The two experiments consist of consecutive initial conditions
(29 and 30, September 2004) integrated for 48 hours each. One can not
draw definitive statistical conclusions from only two experiments in
terms of statistics as shown in the last section however, a comparison
of precipitation patterns between the experiments can determine whether
the impact from the changes are significant. Precipitation patterns will
be used to highlight the differences between the experiments.

The relevant synoptic situation on 1200 UTC 01 Oct 2004 corresponding to
these experiments (not shown) has two light to moderate precipitation
bands oriented northeast to southwest through Nebraska and South Dakota
as well as on the OK-TX state line (Fig. 17). Fig. 14 and 15 show an
enlarged excerpt of the total ETA domain from 30-45N and 110-90W. The
12-km ETA, 24-h forecast of accumulated precipitation, valid at 1200 UTC
01 Oct 2004, is shown in Fig. 14 when the inflate parameter is set to 2
and using the level 2.5 super-ob. Fig. 15 shows the operational control,
with the level 3 super-ob, and inflate parameter set to 3. Setting the
inflate parameter to 2 with the level 2.5 super-ob has enhanced
precipitation for moderate amounts for example, the maxima in Figs 14
and 15. The enhanced precipitation amounts seen with the inflate
parameter set to 2, are small in horizontal scale and extend south of
the OK-TX line as well as in the tri-section of the state boundaries of
NE-CO-WY. The precipitation difference (Fig. 14 minus Fig. 15) shown in
Fig. 16, indicates the most significant precipitation differences over
north TX and OK, with smaller maxima in the TX panhandle, and in the
tri-state region (NE-CO-WY). These patterns are isolated, indicating
that they are from precipitation amount increases rather than from phase
displacement of the banded patterns. For example, differences in the
precipitation banded pattern in SD (centered at 44N,100W) arise from
forecast phase error as shown by the dipole pattern in the difference
plot. As forecast times lengthen, the larger scale phase error of the
model increases and will overcome the small scale effects we intend to
study. The larger precipitation difference values in the tri-state
region (41N,104W) are isolated and not in a dipole pattern like the
differences found in the OK-TX area at 48-h forecast (Fig. 19). The
level 2.5 super-ob experiment enhanced the precipitation amounts in both
regions.

The verification for this experiment is the observed 24-h accumulated
10-km RFC mosaic precipitation observations from rain gauges (Fulton et
al., 1998) and is shown in Fig. 17. The patterns from the observed 10-km
rain gauges are far more detailed than the 12-km ETA forecasts. This is
expected as the rain gauges are analyzed from extremely high resolution
data while the 12 km ETA model has various filters and other
non-meteorological forcing and hence smoother than the rain gauge
observations. There are two prominent precipitation bands at the OK-TX
line and a northern pattern from NE-CO-WY tri-state area extending
northeast through SD. The main features are captured by both
experiments, but the observed maximum precipitation is about double (see
the 1" and 0.5" contour at the OK-TX line in Fig. 17) that was
forecasted (Fig. 14 and 15), and the 2" contours in the observed TX
panhandle and near the tri-state area (in Fig. 17) intersection are too
small scale for the ETA model to resolve. The level 2.5 experiment
forecast precipitation enhances the precipitation amount over the
operational level 3, which is evident in the difference field of Fig.
16, significantly, for both above mentioned precipitation areas by about
a 0.25" to 0.5". A forecast experiment with the only difference being a
change of the inflate parameter from 2 to 3 using the level 2.5 super-ob
was run, and the impact to the precipitation forecasts is shown in Fig.
18. This shows the impact of the analysis system with more influence
from the level 2.5 super-ob compared to other conventional observation
types. The precipitation differences are positive through out most of
the domain indicating that more influence from the super-ob observations
produce precipitation in the direction of what is observed. This has to
be taken in the context of a single case study in view of the non-linear
nature of convective schemes.

A 48-h forecast started from initial conditions 24-h earlier so that the
verifying times are the same, was run with the 24-h accumulated
precipitation difference shown in Fig. 19. This run is identical to the
previous one in which the inflate parameter was set to 2 and the results
compared with the operational run with level 3 super-ob and inflate
parameter set to 3. The level 2.5 super-ob has precipitation amounts
(inflate 2) greater than the operational level 3 super-ob in most
regions particularly near the OK-TX border (Fig. 19). This is in the
correct direction compared to the high resolution rain gauge
observations shown in Fig. 17. Experiments with the inflate parameter
set to 3 with the level 2.5 super-ob and with these initial conditions
(not shown) at 48-h fall in between that shown in Fig. 19. This is a
similar result to that indicated by Fig. 18 showing further improvement
by giving more weight to the level 2.5 super-obs. By 48 hours, the large
scale phase errors in the precipitation forecasts are growing as
indicated by the dipole structure in the difference plots (Fig. 19
compared with Fig. 16).

**6. SUMMARY AND CONCLUSIONS**

A radial wind product at all NEXRAD radars was added to NCEP operations
to reduce the degree of redundant information in the radar returns,
increase the precision of the radial wind values delivered to a central
site and decrease the computational burden and bandwidth needed to
assimilate high resolution radar radial winds into operational NWP
models. This was done by simply averaging many observations into one at
each radar site and transmitting the result to a central site at higher
precision with navigation data. We have used the term super-ob for the
averaging and distinguish between level 3 (NIDS type) data feed and a
level 2.5 super-ob product. The level 3 super-ob has 15 levels (4 bits)
of radial wind to span the ±50 m/s possible of radar wind values which
are filtered from the raw radar data to 1 ms^-1^ precision and 4 antenna
tilts over 1km radial distance. The level 2.5 super-ob product have the
advantage of calculating the super-ob averaging at the radar with 0.25
km radial resolution, 9-16 antenna tilts for each radar scan and a
capable precision of transmitting the results at ±0.002 ms^-1^ to a
central site. In addition, the super-ob transmission includes the
standard deviation within the averaging area which is used as error
information for quality control in the operational analysis system.

A case study with the ETA-12km model comparing the level 3 with the
level 2.5 super-ob showed that there was almost no impact to the
analyzed wind fields (700hPa shown) for the level 3 data. Two weeks of
twice daily runs with the ETA-32 km version showed improvement from the
level 2.5 super-ob for upper level height and wind fields and
precipitation threat scores. The model experiments were identical except
for the operational control including the level 3 winds and the level
2.5 experimental runs assimilating the super-ob calculated at the radars
as described above. There is some indication that the lack of impact for
the level 3 observations is that the precision is not sufficient to
provide winds that project closely enough onto the analyzed wind field
compared to other sources. This will happen because the observation
error is larger due to lower precision of the transmitted radar
observations. Also the larger number of antenna tilts in the level 2.5
radar composed super-obs will provide more observations to radial winds
compared to the level 3. Improving the super-ob precision by having
access to the raw radar data, appropriately filtering the redundant data
which allows the communications bandwidth to transmit higher precision
values and more information from the extra tilts in each radar volume
scan, provides a superior information content to the analysis system.

High resolution precipitation data from rain gauges was used to verify a
case study comparing the level 3 and level 2.5 radial wind super-ob
forecasts. Definitive conclusions can not be drawn from a single case
study but details in the verifying precipitation field that correspond
to model forecasts indicate predictive capability. The details of the
precipitation patterns show improved predictability for both the
orientation and amount for the level 2.5 super-ob when only the level 3
observations are assimilated over regions where heavier rain events
appear. In addition, when the analysis was forced to take into account
the level 2.5 super-ob and draw more for these observations, the 24-h
forecast details in precipitation amount and location improved. How well
the analysis system will project the radial wind information onto the
analyzed wind is determined by the error in the observations. The
standard deviation of the super-ob area averaging is used to calculate
observational error estimates. We obtain an error estimate for each
super-ob by assuming that the standard deviation is inversely
proportional to the weights. We studied the result of more or less
reliable super-ob observations by comparing a parameter to inflate the
errors of the super-ob relative to other observation types. The results
showed that a lower error estimate for the level 2.5 super-ob enhanced
(improved) precipitation amount compared to the verifying independent
rain gauge observations.

The indication that model forecasts might utilize more influence from
the level 2.5 super-ob by lowering the inflate parameter giving these
observations more weight compared to other observation types, can be
tested by many experiments in an operational environment. The
improvements in quality control afforded from having the raw radar data
in one place as well as the ability to act on the entire raw radar data
set will undoubtedly improve the accuracy of high resolution models.
Efforts are under way at NCEP to utilize a comprehensive quality control
of radial winds as well as reflectivity with the level 2 raw radar data
for use in the North American and Weather Research and Forecast
models[^6].

Since the level 2.5 data is obtained from higher resolution radar
returns and not filtered (from 0.25 km to 1km) as in the level 3 case,
and maintained high precision values during level 2.5 transmission
compared to the level 3 super-ob (0.01 ms^-1^ vs 9. ms^-1^), the level
2.5 super-ob may deserve to be treated with higher confidence in the
EDAS. After April of 2005, the level 2.5 super-ob was implemented in
EDAS/ETA operations, but if the level 2.5 super-ob is not present, then
the NIDS super-obs are used. However, the inflate parameter settings
continue to be set at 3. This study is the first step to motivate an
investigation to operationally change the level 2.5 influence through
the inflate parameter. With the use and delivery of all the raw radar to
a central site, which is imminent, the super-ob 2.5 product can serve as
a bench mark to test improvements in quality control and other radar
enhancements.

ACKNOWLEDGEMENT

The authors wish to thank Yukuan Song for contributing to the open RPG
code now operating at the radar sites. The team from NOAA's Office of
Science and Technology: Melanie Taylor, Peter Pickard, Michael Istok and
Yukuan Song were instrumental in the implementation phase of the
super-ob program into the operational stream. We thank Dave Parrish for
many discussions on the EDAS analysis system, Eric Rogers for conducting
the ETA 32-km parallel model and William Facey for his role in the NIDS
data implementation. We thank the anonymous internal reviewers and Keith
Brill for their constructive suggestions which have improved the
manuscript. Krishna Kumar would like to thank Maxine Brown for her
encouragement.

REFERENCES

Alpert, J.C.,P. Pickard, Y. Song., M. Taylor, W.B. Facey, M. Istok, and
D.F. Parrish, 2003: A Super-ob for the WSR88D radar radial winds for use
in the NCEP operational assimilation system, *Preprint , Nineteenth
Conference on Interactive Information and Processing Systems*, Long
Beach, Amer. Meteor. Soc., P1.51.

\_\_\_\_\_, V. K. Kumar and Y. Song, 2004: Improved super-ob radar
radial wind precision for the NCEP data assimilation and forecast
system, *Preprint, Twentieth Conference on Weather Analysis and
Forecasting/Sixteenth Conference on Numerical Weather Prediction*,
Seattle, Amer. Meteor. Soc., PJ9.2.

DiMego, G. and E. Rogers, 2005: Spring 2005 upgrade package for North
American Mesoscale (NAM) decision brief., pdf document available at
http://www.emc.ncep.noaa.gov/mmb/Sprint2005.NAMUpgrade.pdf

Ferrier, B, Y., Lin, D, Parrish, M. Pondeca, E. Rogers, G.Manikin, M.
Ek, M. Hart, G, DiMego, K. Mitchell, and H.-Y Chuang, 2003: Changes to
the NCEP Meso ETA analysis and forecast system: Modified cloud
microphysics, assimilation of GOES cloud-top pressure, assimilation of
NEXRAD 88D radial wind velocity data., *NWS Tech. Proc. Bull.*,
available at http://www.emc.ncep.noaa.gov/mmb/tpb.spring03.tpb.htm.

Fulton, R.A., J.P. Breidenbach, D.J. Seo, D.A. Miller, and T. O\'Bannon,
1998:

The WSR88D rainfall algorithm. Weather and Forecasting,**13**, 377-395.

Istok, M. J., R. Elvander, R. Saffle, and J. Roe 2003: NEXRAD product
improvement---implementing new science, Preprint, 31^st^ International
Conference on Radar Meteorology, Seattle, Amer. Meteor. Soc., P5B.1.

Liu S., M. Xue, J. Gao and D. Parrish 2005: Analysis and impact of
super-obbed

Doppler radial velocity in the NCEP grid-point statistical interpolation
(GSI)

analysis system, Preprint, Seventeenth Conference on Numerical Weather

Prediction, Washington DC, Amer. Meteor. Soc., 13A.4.

\_\_\_\_\_ and Q. Xu, 2005: Identifying Doppler velocity contamination
caused by migrating birds. Part II: Bayes identification and probability
tests, *J. Atmos. Oceanic Technol*. (In press)

Parrish, D. , 2005: Assimilation strategy for level 2 radar winds.
*Presentation, 1^st^ GSI User Orientation.* Camp Spring, MD \[available
online at http://www.emc.ncep
.noaa.gov/gmb/treadon/gsi/documents/presentations/1st_gsi_orientation\].

Parrish D. and J. Derber, 1992: The National Meteorological Center's
spectral statistical interpolation analysis system*, Mon. Wea. Rev.,*
**120**, 1747-1763.

\_\_\_\_\_ and J. Purser, 1998: Anisotropic covariances in 3D-Var:
Application to hurricane Doppler radar observations. *Proc. HIRLAM 4
Workshop on variational analysis in limited area models*, Meteo-France,
Toulouse.

\_\_\_\_\_, J. Purser, E. Rogers, and Y. Lin. 1996: The regional 3D
variational analysis for the ETA model. *Preprints, Eleventh Conference
on Numerical Weather Prediction,* Norfolk, VA., *Amer. Meteor. Soc*.
454-455.

Purser R. J., Parrish D.,Masutani M., 2000: Meteorological Observational
Data Compression; an Alternative to Conventional \"SUPER-OBBING\", NCEP
Office Note #430, 13pp.

Rogers, E., Y. Lin, K. Mitchell, W. Wu, B. Ferrier, G. Gayno, M.
Pondeca, M. Pyle, V. Wong and M. Ek, 2004: The NCEP North American
meso-scale modeling system: Final ETA model/analysis changes and
preliminary experiments using the WRF-NMM, *Preprint,* *Eleventh
Conference on Numerical Weather Prediction,* Norfolk, VA., Amer. Meteor.
Soc., 4B.5, 55-65.

Wu, W.-S., R. J. Purser, and D. Parrish, 2002: Three dimensional
variational analysis with spatially inhomogeneous covariances*. Mon.
Wea. Rev*. **130**, 2905-2916.

Zapotocny, T. H. and S. J. Nieman, W. P. Menzel, J. P. Nelson III, J.A.
Jung, E. Rogers, D. F. Parrish, G.J.DiMego, M. Baldwin and T.J. Schmit,
2000, A case study of the sensitivity of the Eta Data Assimilation
System, *Wea. Forecasting*, **15**, 603-621.

Zhang P., S. Liu and Q. Xu, 2005: Identifying Doppler velocity
contamination caused by migrating birds. Part I: Feature extraction and
quantification, *J. Atmos. Oceanic Technol*. (In press)

TABLES

Table 1 Adaptable parameters for the Super-ob product.

Table 2 Summary of Eta regional model case studies

Table 3 Number of super-ob for level 2.5 and level 3 (NIDS) with
standard deviation in a 1 ms^-1^ range for all locations:

Table 1. Adaptable parameters for the Super-ob product*.*

  ----------------------- ----------------------- -----------------------
  Parameter               Default                 Range

  Time Window             60 minutes              \[5-90 min\]

  Cell Range Size         5 km                    \[1-10 km \]

  Cell Azimuth Size       6 degrees               \[2-12 deg\]

  Maximum Range           100 km                  \[60-230km\]

  Minimum Number of       50 unitless             \[20-200\]
  points required                                 
  ----------------------- ----------------------- -----------------------

Table 2 Summary of Eta regional model case studies.

  --------------- -------------------------------------------------------
  Exp. 1          The level 2.5 Super-ob radar radial wind

  Exp. 2          The level 3 super-ob (NIDS type feed only)

  Exp. 3          No radar radial wind data present
  --------------- -------------------------------------------------------

# FIGURES

Fig. 1. Radar radial (scalar) wind super-obs magnitude (ms^-1^) and
location from station KATX in Seattle, WA on 1800 UTC 28 May 2002.
Arrows are toward or away from the radar with the color and arrow length
indicating the magnitude of the return.

Fig. 2. (a) Super-ob height \[m\] for each observation beginning at 0
azimuth (north) and extending clockwise through larger radii every 6
degrees for the same time as in Fig. 1, (b) Super-ob radial wind
standard deviation \[ms^-1^\], (c) Super-ob radar radial wind
\[ms^-1^\].

Fig. 3. Longitude -- latitude section of super-ob radar radial winds for
each observation shown in Fig. 1.

Fig. 4. Analyzed regular wind barbs (ms^-1^) at 1 degree resolution at
0.4 km height from the NCEP Global Forecast System Spectral Statistical
Interpolation Analysis of 1800 UTC 28 May 2002. The cross is at the
Seattle, WA radar location.

Fig. 5. Eta 12-h forecast MSLP (hPa) and12 -h accumulated precipitation
\[kgm^-2^\] verifying 0000 UTC 29 Oct 2003 including level 2.5 super-ob
(Exp. I).

Fig. 6. Wind magnitude differences in the analysis (f00) between the
level 2.5 (Exp. I) and level 3 super-ob (Exp. 2) are shown in the color
fill contours, and the arrows show the vector difference of the wind
fields at 700 hPa.

Fig. 7. Wind magnitude differences in the analysis (f00) between the
level 3 (Exp. 2) and no radial wind (Exp. 3) are shown in the color fill
contours, and the arrows show the vector difference of the wind fields
at 700 hPa.

Fig. 8. Wind magnitude differences in the Eta forecasts at 36-h between
the level 2.5 (Exp. I) and level 3 super-ob (Exp. 2) are shown in the
color fill contours, and the arrows show the vector difference of the
wind fields at 700 hPa.

Fig. 9. Wind magnitude differences in the Eta forecasts at 36-h between
the level 3 (Exp. 2) and no radial wind (Exp. 3) are shown in the color
fill contours, and the arrows show the vector difference of the wind
fields at 700 hPa.

Fig 10. 24-h accumulated precipitation equitable threat score (upper)
and bias (lower) from Eta 32-km 48-h forecasts from 8 Jun 2004 -- 20 Jun
2004 for various thresholds in inches. The solid line (+) are the radial
wind super-ob level 2.5 experiment and the dash is the Eta control (▲)
with NIDS level 3 super-obs.

Fig 11. 24-h accumulated precipitation equitable threat score (upper)
and bias (lower) from Eta 32-km 60-h forecasts from 8 Jun 2004 -- 20 Jun
2004 for various thresholds in inches. The solid line (+) are the radial
wind super-ob level 2.5 experiment and the dash is the Eta control (▲)
with NIDS level 3 super-obs.

Fig 12. Root mean square height errors against RAOBS over the CONUS from
Eta 32-km 60-h forecasts, 8 Jun 2004 -- 20 Jun 2004 (24 forecasts). The
dash line is the radial wind super-ob level 2.5 and the solid line is
the Eta control with NIDS level 3 super-obs.

Fig. 13. Height bias errors against RAOBS over the CONUS from Eta 32-km
60-h forecasts, 8 Jun 2004 -- 20 Jun 2004 (24 forecasts). The dash line
is the radial wind super-ob level 2.5 and the solid line is the Eta
control with NIDS level 3 super-obs.

Fig 14. 12-km Eta 24-h forecast of 24-h accumulated precipitation
\[inches\] verifying 1200 UTC 01 Oct 2004 with level 2.5 super-ob and
inflate parameter set to 2.

Fig 15. Operational 12-km Eta 24-h forecast of 24-h accumulated
precipitation \[inches\] verifying 1200 UTC 01 Oct 2004.

Fig 16. Difference of the Eta super-ob 2.5 with inflate set to 2 and
operational (with level 3 super-ob and inflate set to 3) Eta 24-h
forecasts at 24-h accumulated precipitation \[inches\] verifying 1200
UTC 01 Oct 2004 (As in Fig 14 minus Fig 15).

Fig 17. Observed 24-hr precipitation from rain gauges for the period
ending at 1200 UTC 01 Oct 2004. Values are in tenths of inches.

Fig 18. Difference of the 12-km Eta super-ob 2.5 with inflate parameter
set to 2 and inflate parameter set to 3 of 24-h forecasts of 24-h
accumulated precipitation \[inches\] verifying 1200 UTC 01 Oct 2004.

Fig 19. Difference of the Eta super-ob 2.5 (with inflate set to 2) and
operational (with level 3 super-ob and inflate set to 3) Eta 48-h
forecast of 24-h accumulated precipitation \[inches\] verifying 1200 UTC
01 Oct 2004.

![](media/image1.wmf){width="5.9125in" height="6.375in"}

Fig 1. Radar radial (scalar) wind super-obs magnitude (ms^-1^) and
location from station KATX in Seattle, WA on 1800 UTC 28 May 2002.
Arrows are toward or away from the radar with the color and arrow length
indicating the magnitude of the return.

> ![](media/image2.wmf){width="6.373622047244094in"
> height="2.798031496062992in"}![](media/image3.wmf){width="6.373622047244094in"
> height="2.9236220472440944in"}![](media/image4.wmf){width="6.373622047244094in"
> height="2.9236220472440944in"}
>
> Fig 2. (a) Super-ob height \[m\] for each observation beginning at 0
> azimuth (north) and extending clockwise through larger radii every 6
> degrees for the same time as in Fig 1, (b) Super-ob radial wind
> standard deviation \[ms^-1^\], (c) Super-ob radar radial wind
> \[ms^-1^\].

![](media/image5.wmf){width="6.39375in" height="6.458333333333333in"}

> Fig 3. Longitude -- latitude section of super-ob radar radial winds
> for each observation shown in Fig 1.

![](media/image6.wmf){width="4.5in" height="4.4840277777777775in"}

Fig. 4. Analyzed regular wind barbs (m/s) at 1 degree resolution at 0.4
km height from the NCEP Global Forecast System Spectral Statistical
Interpolation Analysis of 1800 UTC 28 May 2002. The cross is at the
Seattle, WA radar location.

![](media/image7.wmf)Fig 5. Eta 12-h forecast MSLP (hPa) and12 -h
accumulated precipitation \[kgm^-2^\] verifying 0000 UTC 29 Oct 2003
including level 2.5 super-ob (Exp. I).

![](media/image8.wmf)

Fig 6. Wind magnitude differences in the analysis (f00) between the
level 2.5 (Exp. I) and level 3 super-ob (Exp. 2 ) are shown in the color
fill contours, and the arrows show the vector difference of the wind
fields at 700 hPa

![](media/image9.wmf)

Fig 7. Wind magnitude differences in the analysis (f00) between the
level 3 (Exp. 2) and no radial wind (Exp. 3) are shown in the color fill
contours, and the arrows show the vector difference of the wind fields
at 700 hPa.

![](media/image10.wmf){width="6.249305555555556in"
height="4.720138888888889in"}

Fig 8. Wind magnitude differences in the Eta forecasts at 36-h between
the level 2.5 (Exp. I) and level 3 super-ob (Exp. 2) are shown in the
color fill contours, and the arrows show the vector difference of the
wind fields at 700 hPa.

![](media/image11.wmf){width="6.25in" height="4.686111111111111in"}

Fig 9. Wind magnitude differences in the Eta forecasts at 36-h between
the level 3 (Exp. 2) and no radial wind(Exp. 3) are shown in the color
fill contours, and the arrows show the vector difference of the wind
fields at 700 hPa.

![](media/image12.png){width="7.251388888888889in"
height="5.6097222222222225in"}

Fig 10. 24-h accumulated precipitation equitable threat score (upper)
and bias (lower) from Eta 32-km 48-h forecasts from 8 Jun 2004 -- 20 Jun
2004 for various thresholds in inches. The solid line (+) are the radial
wind super-ob level 2.5 experiment and the dash is the Eta control (▲)
with NIDS level 3 super-obs.

![](media/image13.png){width="7.127777777777778in"
height="6.011111111111111in"}

Fig 11. 24-h accumulated precipitation equitable threat score (upper)
and bias (lower) from Eta 32-km 60-h forecasts from 8 Jun 2004 -- 20 Jun
2004 for various thresholds in inches. The solid line (+) are the radial
wind super-ob level 2.5 experiment and the dash is the Eta control (▲)
with NIDS level 3 super-obs.

![](media/image14.png){width="6.747916666666667in"
height="5.399305555555555in"}

Fig 12. Root mean square height errors against RAOBS over the CONUS from
Eta 32-km 60-h forecasts, 8 Jun 2004 -- 20 Jun 2004 (24 forecasts). The
dash line is the radial wind super-ob level 2.5 and the solid line is
the Eta control with NIDS level 3 super-obs.

![](media/image15.png){width="6.377083333333333in" height="4.4875in"}

Height Bias Error (m)

Fig 13. Height bias errors against RAOBS over the CONUS from Eta 32-km
60-h forecasts, 8 Jun 2004 -- 20 Jun 2004 (24 forecasts). The dash line
is the radial wind super-ob level 2.5 and the solid line is the Eta
control with NIDS level 3 super-obs.

![](media/image16.png){width="6.5in" height="5.29375in"}

Fig 14. 12-km Eta 24-h forecast of 24-h accumulated precipitation
\[inches\] verifying 1200 UTC 01 Oct 2004 with level 2.5 super-ob and
inflate parameter set to 2.

![](media/image17.png){width="6.5in" height="4.875in"}

Fig 15. Operational 12-km Eta 24-h forecast of 24-h accumulated
precipitation \[inches\] verifying 1200 UTC 01 Oct 2004*.*

![](media/image18.png){width="6.5in" height="4.875in"}

Fig 16. Difference of the Eta super-ob 2.5 with inflate set to 2 and
operational (with level 3 super-ob and inflate set to 3) Eta 24-h
forecasts at 24-h accumulated precipitation \[inches\] verifying 1200
UTC 01 Oct 2004 (As in Fig 14 minus Fig 15).

![](media/image19.png){width="6.5in" height="5.585416666666666in"}

Fig 17. Observed 24-hr precipitation from rain gauges for the period
ending at 1200 UTC 01 Oct 2004. Values are in tenths of inches.

![](media/image20.png){width="6.5in" height="4.875in"}

Fig 18. Difference of the 12-km Eta super-ob 2.5 with inflate parameter
set to 2 and inflate parameter set to 3 of 24-h forecasts of 24-h
accumulated precipitation \[inches\] verifying 1200 UTC 01 Oct 2004.

![](media/image21.png){width="6.5in" height="4.875in"}

Fig 19. Difference of the Eta super-ob 2.5 (with inflate set to 2) and
operational (with level 3 super-ob and inflate set to 3) Eta 48-h
forecast of 24-h accumulated precipitation \[inches\] verifying 1200 UTC
01 Oct 2004.

[^1]: Memo dated April 12, 1995, to the director of NCEP from the
    Environmental Modeling Center indicating that NEXRAD observations
    are needed for the NWS modernization.

[^2]: Memo transmitted June 12, 1995, from the NCEP director to the
    director of the NWS, regarding NCEP's requirement for a national
    collection of WSR-88D NEXRAD observations and their delivery to a
    central site, 2pp.

[^3]: Information on current and past operational analysis and meso-ETA
    model configurations can be found at
    <http://www.emc.ncep.noaa.gov/mmb/mesoscale.html>.

[^4]: Interface Control Document for the RPG to Class 1 User prepared by
    WSR88D Radar Operations Center, Document Number 262001E, 29 December
    2002, 135pp.

[^5]: An operational real time monitoring web site containing a display
    of observation counts can be found at
    http://www.nco.ncep.noaa.gov/pmb/nwprod/realtime/

[^6]: http://www.wrf-model.org/wrfadmin/publications.php