RADIAL WIND SUPER-OBS FROM THE WSR-88D RADARS FOR USE IN THE NCEP
OPERATIONAL ASSIMILATION SYSTEM

Jordan C. Alpert and V. Krishna Kumar

NOAA/NWS/NCEP, Camp Springs, Maryland 20746

**ABSTRACT**

The spatial and temporal densities of WSR-88D raw radar radial wind
represent a rich source of high resolution observations for initializing
numerical weather prediction (NWP) models. Number range CHAPTERA
characteristic of these observations, in terms of NWP, 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 to a central site was confined to
data feeds that filter the resolution and precision of that available
from each radar. 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 WSR-88D 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-ob
make little or no impact on the EDAS variables and subsequent forecasts.
The level 2.5 super-ob product show improved precipitation threat scores
as well as reduction in RMS and bias height and winds particularly in
the upper troposphere. Meso-scale precipitation patterns benefit from
the super-ob level 2.5 product and even more so when greater weight is
given to the high resolution/precision observations in the few cases
studied. Direct transmission of raw level 2 radar data to a central
site, and its use, is now eminent, however, 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**

WSR-88D NEXRAD radars number 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 weather forecasters in issuing
weather service Warnings and Watches to citizens about dangerous weather
and its location. The spatial and temporal densities of WSR-88D raw
radar radial wind represent a rich source of high resolution
observations and radial wind from these Doppler radars is an observed
measurement that can be assimilated in operational numerical weather
prediction models (NWP). For more than a decade, the WSR-88D has played
an important role in the improvement of short-range forecasts and
warnings for severe thunderstorms, tornadoes and flash floods, but
operational models had to wait the equivalent amount of time to obtain
the benefit of these high resolution observations in real time. NOAA's
National Centers for Environmental Prediction (NCEP), where the model
development for the weather service takes place, 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 NOAA operational models was in
a demonstration project at the 1996 Olympics in Atlanta, to aid the
forecasters at the weather forecast office who had the responsibility
for issuing weather warnings for various 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. 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. Impact from the addition of NIDS data into
the regional model was not clear from the experiments that were run
during the demonstrations. This provided the motivation for this report
which was to compare higher resolution/precision radar returns with what
was available commercially at the time. The technology available, for
example, CPU resources, limited the large amount of data that the radar
generated that could be transmitted as well as the computations needed
to perform tasks such as locating the returns in the model's coordinates
compared to azimuth, radius and antenna tilt. A practical and cost
effective way to transmit the information content of the high resolution
radar observations was 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.

This technique can be used because 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 effect whatever data compression
the ensemble of fresh observations allow 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 applied to operational analysis at the NCEP
Operational assimilation system for subsets of the WSR-88D radar radial
wind observations from the NIDS. NIDS was a way that public users and
the National Weather Service Centers were able to receive NEXRAD data.
The NIDS contract expired on 31 December 2000, and NWS now assumes the
responsibility of distributing the NEXRAD data. In both cases, a
truncated set of radar data products was transmitted, which we will
refer to as NIDS compared to new level 2 data feed to a central site to
begin in 2005. In terms of radial wind, the NIDS data transmission and
the subsequent data transmission of the weather service truncated the
number of antenna tilts, data precision and radial resolution to 4
antenna tilts of a possible 16, 4 bits (15 levels of amplitude) of data
precision compared to 16 bits (65,536 levels of amplitude), and 1 km
along a radial line of a possible ¼ 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 WSR-88D 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 (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 we chose to confine this report
to radial winds where the forward model is well defined. We note that
beginning in 2005, with rapid improvements in information technology,
direct transfer of all radar data to a central site is scheduled, so NWP
models can take advantage of the complete set of radar products and
antenna tilts. This data feed will include new quality control measures
because the entire matrix of events will be available before averaging
is done so more complex quality control can be applied (Liu 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. The super-ob described here-in
was placed in the July 2003 (Ferrier et al., 2003) implementation of the
ETA meso-scale regional model now called the North American Model
(Rogers et al., 2004).

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

The full-resolution WSR-88D base radial wind data provides sufficient
data amounts for statistically significant averaging. The super-ob
product is programmed at each WSR-88D site using the Open Systems RPG
(Radar Product Generator) to control all aspects of the calculation. The
Open RPG is the system which operates between the Open Radar Data
Acquisition (RDA) system and more sophisticated display devices, such as
the National Weather Service\'s Advanced Weather Interactive Processing
System (AWIPS). The Open RDA collects data from the WSR-88D 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 will be super-obs (observations) of radar radial winds and known
in this report as level 2.5. These will be compared with the NIDS radar
radial winds referred to as level 3.0 returns as well as a No-radar
radial wind case in analysis and forecast experiments using the NCEP ETA
Data Assimilation System (EDAS) 3-dimensional variational (3DVAR)
analysis and meso-ETA forecast model in its operational configuration
with resolution of 12 km. Information on current and past operational
analysis and meso-ETA models configurations can be found at web sites
<http://www.emc.ncep.noaa.gov/mmb/mesoscale.html>. The 3DVAR is ideally
suited for assimilating Doppler radar wind data because the radial wind
super-obs are a scalar having only one component and can not be uniquely
determined. To find the conventional wind (a vector), additional
information is needed, for example, an overlapping second (dual-Doppler)
radar beam. The variational procedure requires a forward model, which is
known, to project the models winds onto the radar observation locations,
a linear process. In effect this utilizes the models knowledge of
dynamics and physics to project the observations onto the 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, are
shown in Table 1. The default settings indicate that at each elevation
angle, a wedge shape volume of 6 azimuth degrees by 5 km along a radius,
averaged over a time of 60 minutes define each super-ob cell. Each of
the super-obs contain no less than 50 points and no cell would extend
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 Open System RPG (ORPG). The
range of possible values will allow for the super-ob product to adjust
to different analysis resolution requirements as they occur. All the
WSR-88D installations will contribute super-ob data by this process to
initialize the cycling analysis system, with identical settings. The
data transmission precision of the super-ob (mean) radial wind is 0.01
m/s as described in the "Interface Control Document for the RPG\*".
Other improvements in the level 2.5 data over the level 3.0 are the
transmission of a maximum of 15 antenna tilt levels compared to four
levels in the level 3 data. The standard deviation of the mean radial
wind super-ob is transmitted as well as the standard deviation in units
of m/s.

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

\* Interface Control Document for the RPG to Class 1 User prepared by
WSR-88D Radar Operations Center, Document Number 262001E, 29 December
2002, 135pp.

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 where the arrow direction is toward or away from the radar and colors,
as well as arrow length indicates the speed of the return. The Data for
this example is from KATX, Seattle, WA radar, at antenna tilt of 0.4
degrees, from May 28, 2002 at 1800Z and represents a typical return. The
characteristic pattern of radar radial returns is apparent and there are
large areas that do not have returns. At larger distances from the radar
the radial wind magnitude is seen to increase southwest and northeast of
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 number of super-ob returns near the
radar has not been reduced at this time although there is code to
accomplish this and may be done in the future. The radar returns close
to the radar antenna can be in error due to electronic gating
deficiencies so the level 2.5 super-obs shown in Fig 1 will, in their
smallest radius, have averaged some of this data that should have been
screened out. By the time the super-ob radius exceeds 5 km, the inner
most super-ob location, the WSR88D radar will have 20 independent
returns, each at ¼ km radius, at each azimuth therefore, we believe that
this is not an important factor in the tests following. 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. Each point in Fig 2a is the result of the super-ob at a
particular azimuth and the next 5 km of radius increasing its height as
the radial distance increases and then clockwise from north around with
increasing azimuth. As the radar beam extends to larger radial distances
from the radar, the height of the returns increases as shown as spikes
in Fig 2a until either there are no more returns along a particular
radius or the radius reaches the default maximum of 100km (Table1). 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 as a 16 level value and 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 computes a simulated
observation from model variables) is just

**u**~radial~ = **u** **cos** θ + **v** **sin** θ (1)

where (u,v) are the components of the real wind at the same height as
the super-ob and θ is the azimuth. 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 alone 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 m/s near point
200 to 10 m/s 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 in Fig 2.

Radial wind speeds, for each radar beam elevation angle, in Fig 2a
(elevation angle = 0.4), sometimes appear as outliers compared with
their neighbors (Fig2c). For example, point 108 at (lat, lon), (48.85,
-121.632) have 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, located on Fig 1, show radial wind magnitudes of -14.12
m/s and -12.58m/s on Fig 2c. These are outliers that are outside the
range of common values. The returns for these super-obs are expected to
have larger wind magnitudes with increased elevation as they are further
away from the radar. The variance for these points is low, 2 m/s, for
each point indicating the influence of ground clutter. 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 can be useful in determining the quality of the
returns. The quality control of the super-ob data in the analysis
program may also be able to utilize the reflectivity to determine
locations of severe convection (and vertical motion) which could cause
errors in the radial wind returns but this will not be discussed here.
The standard deviation can also be large at points that have less than 1
m/s winds 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 WSR-88D
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
reported as the location, in time, of a particular super-ob cell from
the reported base time. Several scans occur within each time window
(defaults shown in Table 1). The time deviation parameter is transmitted
with each super-ob report accounting for the number of raw returns
weighted in, time, during the time window. Close to the radar, the raw
returns of radial wind are sufficient to create super-obs under the
criterion of Table 1, even though the super-ob area grows smaller.

Analyzed regular winds at a height of 0.4 km from the NCEP Global
Forecast System (GFS) Spectral Statistical Interpolation (SSI) Analysis
of May 28, 2002 at 18Z 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. It is
plotted at 1 degree in the figure. We attempt to use the SSI analysis as
a proxy for observed winds in the radar area because the number of wind
observations depicted around the radar is few. This is done with the
knowledge that a global 1x1 degree analysis at a particular height level
is not a definitive way to verify these high resolution observations
however it serves as a coarse check on the validity of the super-ob
returns. The black cross hair in Fig 4 is located at the Seattle, WA
radar location. The regular winds shown in Fig 4 are at a height of 0.4
km which was interpolated vertically by the SSI analysis. 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. In viewing the analyzed regular winds, composed
of wind components (u,v), in Fig 2c, one must keep in mind the
definition of radial wind from eq 1 is a scalar, where θ is the angle
between a line from the radar directed at 0 degrees azimuth (north) and
the radial wind azimuth. There is one direction where the regular wind
will be identical to the radial wind and regular winds perpendicular to
this will have zero radial wind. The direction of the regular wind north
and west of the radar in Fig 4 is southwest at 15 knots. The radial wind
along a line northwest of the radar, line up in the same direction. The
radial wind speeds in the northeast quadrant vary from 5-15 knots close
in as shown by the blue and green wind arrows in Fig 1. To the east of
the radar, the wind magnitudes are far less, 0-5 knots. This is because
of the radial wind definition and the winds in this region being more
southerly and of lower magnitude (Fig 4) so their component in the
radial direction will be 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. That we show
consistent magnitudes of the radial wind along the SE - NW direction as
the observed winds is a necessary condition for acceptance of the data
by the analysis system. The analyzed regular winds in the southeast and
northwest quadrants show changes that are reflected in the radial wind
pattern as a turning of the regular wind direction from south to
southwesterly as shown in Fig 2c.

The number of returns from radar radial wind varies with the amount of
precipitation, increasing with more precipitation compared to clear air.
The minimum number of returns required to make a super-ob, as shown in
Table 1, is 50. Even when the air is clear, some super-obs can be
created close to the radars as they are sensitive to motions in the
clear atmosphere. This number of super-obs is increased with increased
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 (Fig 2b). 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 give to a super-ob observation.

The quality control used in the EDAS for the super-ob products (level
2.5 and 3) is very simple. One quality control element of the super-ob
ingest into the EDAS analysis is the vertical component of the radar
beam width is assumed to increase with radar range at a rate of 20 m
km-1. This is roughly 20% larger than the actual beam width in order to
account for some uncertainty in beam propagation. Winds at all model
levels (in coordinates) 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 straight-line fits of the radial wind
observations. All winds out to the maximum range, as set in Table 1,
(presently set at 100 km) of the radar are used. Quality marks of wind
observations derived separately from independent VAD (velocity-azimuth
display) 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
quality control efforts were developed for the NIDS level 3 data and
should considered preliminary. The level 2.5 super-ob also uses these
same quality controls. However, more sophisticated QC algorithms
developed by Zhang et al., (2005) and Liu and Xu, (2005), which are
currently under development for NCEP operations with the full level 2
data set. 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 m s-1, or (3) it fails the same gross
checks used for conventional winds.

In addition, 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 an added inflate factor:

> Weights (p) α 1/(Inflate parameter X error (p)) (2)

The NIDS 3.0 super-ob was assigned an inflate parameter of 3.
Experiments will be shown when the level 2.5 super-ob inflate parameter
is 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 3.0. We will show a case study in section 3 where
we compare the influence of changing the inflate parameter to 2 for the
level 2.5 super-ob. In addition, using the standard deviation of the
super-ob as the observation error estimate, gives information for
quality control to the analysis system for each observation event.

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

To test the impact of the level 2.5 super-ob on the regional model,
three experiments were performed with the NCEP 12 km, meso-ETA regional
model (Ferrier et al., 2003 and Rogers et al., 2004). Each experiment
included a separate analysis of 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 28OCT12Z, 2003.
All conventional and non-conventional observations were assimilated. In
addition, radar radial wind observations were assimilated over a 3-h
window, centered about 12Z. Case I is the level 2.5 super-ob radar
radial wind, Case II is run with the level 3 super-ob and Case III has
no radar radial wind data present as indicated in Table 2.

The mean sea level pressure map in Fig 5 is representative of the
synoptic situation and shows a trough of low pressure in the central US
extending to northeastern US. On this same figure, the 12-hr forecast of
precipitation is shown indicating the areas that had precipitation,
since more returns should give a greater number of super-obs compared
with clear air returns in these areas. Also seen is approaching low
pressure trough into the great plains as well as associated
precipitation on a northwest-southeast axis.

There are not sufficient verifying observations to directly check the
impact of radial wind experiments. We are interested in comparing the
impact of higher precision level 2.5 super-ob with the lower precision
level 3.0 NIDS type super-obs. We show a case study which is a single
experiment and as such has the limitation of not being statistically
significant. We employ the case study approach to show the impact of the
different super-ob generating methods. For example, our hypothesis is
that the result of the lower precision super-ob from NIDS type
observations will not be successfully drawn for in the analysis compared
to the higher precision super-ob 2.5 observations. We first take the
difference, super-ob 2.5 minus super-ob 3.0 (NIDS type), and then the
super-ob 3.0 (NIDS type) minus No Radar radial wind to see the impact of
both super-obs. The difference plots in Fig 6 and 7 respectively show,
vector wind and magnitudes from independent EDAS analyses for Case I
minus Case II and Case II minus Case III. The 2.5 minus 3.0 (Fig 6)
differences are of small scale, the largest difference being generally
greater than 1 m/s, are located in areas along fronts (East Coast) or
low pressure (along the Northern US). The wind vector differences are
shown and are large in the same areas. The larger difference value in
the Southwest US is likely from extrapolation into mountains which are
higher than 700 hPa. The difference vectors are sensitive to small
differences between the wind field cases. In Fig. 7, the magnitude range
is from -2.5 to 1.5 m/s compared to Fig 6 which shows a magnitude
difference range of -4 to 1.5 m/s, excluding mountainous regions. Areas
of larger magnitude and area extent are shown in Fig 6 while in Fig 7
there are smaller area size and magnitude differences between the level
3 and no radial winds. This indicates that the level 3 super-ob (Case 2)
observations were not "drawn" for as much in the analysis compared with
the level 2.5 super-ob. This is confirmed in the difference plot of
level 2.5 minus no radial wind (not shown).

The initial condition from each of the three experiment analyses was
integrated with the meso-ETA model, at 12 km resolution, producing a
48-h forecast for each case. Shown is the (36-h) forecasted wind
differences between the level 2.5 super-ob and level 3.0 (NIDS) radar
radial wind present (Case I - Case II) in Fig 8 and the (36-h)
forecasted wind differences between the level 3 super-ob and no radar
radial wind present (Case II - Case III) in Fig 9. We continue to use
the 700 hPa level as representative. The largest changes appear for the
level 2.5 (Case 1 - Case 2) which had magnitude differences ranging from
-7 to 5 m/s compared to the level 3 minus no radial wind (Case 2 - Case
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 appear more random. The main differences
between the level 2.5 super-ob and the level 3 super-ob observations are
the precision in the delivered radial wind values (0.05 m/s compared to
5 m/s, respectively) and the extra antenna tilt angles (16 compared to
4, respectively). The prominent differences seen in Fig 8 are in the
vicinity of synoptically active regions as depicted by the precipitation
areas in Fig 5.

The number of super-ob winds with wind error (as measured by the
standard deviation within each super-ob area) within a 1 m/s range for
the level 2.5 super-ob and the level 3 NIDS type wind error 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 m/s standard deviation so Table 3 does not
show the results for wind magnitudes less than 2 m/s. However, the
number of super-obs with standard deviation less than 2 m/s is small,
for example, as shown on Fig 2b. The Table shows that the wind errors,
measured in this way, have increasing error of wind magnitude value as
the wind speed range increases. However, most of the level 2.5 super-obs
appear within 2 m/s error. If one compares this with the level 3 (NIDS)
experiment one finds that the number of winds with wind error peaks at 3
m/s instead of the 2 m/s as shown in Table 2.

The radar radial wind observations that are analyzed by the EDAS system
uses past forecasts as error statistics to gain the knowledge necessary
for the model to project the radial wind onto the analyzed wind field.
The definition of radial wind (known as the forward model) is a scalar
as shown in eqn 1 and there is only one direction where 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.

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

The 32-km NCEP EDAS analysis with ETA parallel forecast model containing
the NIDS (Level 3.0 noted as ETAW) super-ob control is compared with a
super-ob (Level 2.5 noted as ETAY) integration with no NIDS super-ob
data. The experiments are 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
forecast. All the integrations are averaged and equitable threat score
and bias for 24-h accumulated precipitation and RMS errors are shown for
height. Precipitation is shown in Fig 10 for 48-h forecast and in Fig
11, for 60-h forecasts. The 48-h super-ob level 2.5 forecast
precipitation threat scores 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 at least 2500.
The precipitation bias 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.
Precipitation is a derived quantity depending on many complex physical
interactions, therefore small improvements in this quantity implies the
level 2.5 super-ob precision improvement is making an impact on the EDAS
analysis and forecast system.

The RMS height error of the two experiments versus RAOBS over the CONUS
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 3.0
super-ob (NIDS). The improvement in RMS is more pronounced at upper
levels but this reflects the larger height values at these levels. At
shorter forecast times, the low level RMS do not show improvement. The
height bias error versus RAOBS over the CONUS is shown in Fig 13. Upper
levels show a significant reduction in the bias but at low levels there
is a small increase in the bias error. 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 at all
levels particularly over upper levels.

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

The NCEP 3-D variational 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 will be weighted
according to its known error when the analysis system analyzes the
observations. The super-ob standard deviation is calculated, at each
radar, in each grid cell and 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 eqn 2. The inflate
parameter from eqn 2 is assigned the value of 3 for the NIDS level 3
super-ob which is also used for the ETA operations of the level 2.5
super-ob when it was implemented in July of 2003. Since the level 2.5
data is obtained from higher resolution radar returns and not filtered
(from ¼ 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 m/s vs 9. m/s), 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
study is the first step to motivate an extensive investigation to
operationally change the level 2.5 influence through the inflate
parameter. The horizontal correlation length for the super-ob are set to
be several degrees, based on the NIDS level 3 errors and unchanged for
the level 2.5 super-ob. More work will have to be done to find the
proper setting for the level 2.5 observations.

Fig 2b shows typical standard deviations for the 0.4 elevation antenna
tilt at the Seattle, Washington radar. The values range from 0 to 15 (or
greater) in units of m/s and with an inflate parameter of 3 which can
produce a weighting from 0 to 45. A gross toss (removal) of a super-ob
is applied for standard deviation values greater than 6 m/s (weights ≥
21). After July of 2003, 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. Since the level 2.5 data is constructed at each
radar with ¼ km radius and transmitted at higher precision to a central
site, we hypothesize that the inflate parameter can be reduced so the
EDAS will draw toward 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. Two consecutive days (29th and 30th,
SEP2004) were integrated for 48 hours each and compared with the NIDS
level 3.0 control experiment. One can not draw definitive statistical
conclusions from only two experiments. However decreasing the inflate
parameter will cause the EDAS to draw more closely to the radial wind
observations and we can show the potential impact to the analysis.

For this case study the synoptic situation on 01OCT2005 shows that there
are two light to moderate precipitation patterns or bands oriented
northeast to southwest through Nebraska and South Dakota as well as on
the OK-TX state line. We show an enlarged excerpt of the total ETA
domain from 30-45N and 110-90W. The verifying 12-km ETA , 24-h forecast
of accumulated precipitation, at 12Z 01OCT2005, is shown in Fig 14 when
the inflate parameter is set to 2 (using level 2.5 super-ob winds).
These precipitation patterns will be used to highlight the differences
between the experiments. The level 2.5 super-ob experiment does not
utilize level 3 (NIDS type) radial winds, none are present, therefore,
we compare to the ETA 12-km Operational run with NIDS level 3 super-ob
winds, and inflate parameter set to 3, as shown in Fig 15. Generally the
lower setting of the inflate parameter, which translates to the EDAS
analysis giving more weight to the level 2.5 super-ob winds, compared to
the other data types, has produced enhanced precipitation for moderate
amounts (1/2"). Earlier experiments showed that the NIDS level 3 impact
(Figs 6 & 7 and Figs 8 & 9) by itself was negligible. 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. To see this in
more detail, the precipitation difference, Fig 14 minus Fig 15 is shown
in Fig 16 where the enhanced precipitation values extend well into north
TX and OK with smaller enhancements in the panhandle of TX, and in the
northern pattern near 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) arises 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 NE-CO-WY region (41N,104W) are isolated and not
in a dipole pattern like the differences found in the OK-TX area. 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 ETA precipitation forecasts (Fig
15 and 16), on 3-hr boundaries, are added to compare with the 24-h
accumulated observed values. The precipitation forecasts in Fig 15 and
16 can be verified directly with the observed values 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, for example,
at 2ΔX scales as well as the effects of the convective scheme, so the
model may be closer to 30-km or more resolution even though the grid
spacing is 12-km. However, we should still see similarities in the
precipitation patterns. For example, the two banded patterns, at the
OK-TX line and the northern pattern from NE-CO-WY extending northeast
through SD. The main features are captured by both experiments but in
both patterns the observed maximum precipitation is about double (see
the 1" and ½" contour at the OK-TX line in Fig 18) that was forecasted
(Fig 14 and 15) and the 2" contours in the observed TX panhandle and
near the NE-CO-WY intersection are too small scale for the ETA model to
resolve. The level 2.5 forecast precipitation case 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 ¼" to ½". A forecast experiment with the
level 2.5 super-ob, same initial conditions with the inflate parameter
set to 3 is shown as the difference field in Fig 18 (inflate 2 minus
inflate 3). This shows the impact of the analysis system with more
influence from the level 2.5 super-ob compared to other observation
types. The precipitation amounts 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 where the inflate parameter was set to 2 and the results
compared with the operational run with level 3 (NIDS) super-ob and
inflate parameter set to 3. This figure (Fig 19) shows that the
super-ob, level 2.5 experiment has precipitation amounts (Inflate 2)
than the operational NIDS case in most regions particularly near the
OK-TX border. 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 but with the level 2.5 super-ob (not shown)
fall in between that shown by the operations and the level 2.5 super-ob
with inflate 2 experiment. This is a similar result to that indicated by
Fig 18 comparing an inflate parameters set to 2 and 3 for the level 2.5
super-ob. By 48 hours, the large scale phase error in the precipitation
forecasts are growing as indicated by the dipole structure in the
difference plot (Fig 19 compared with Fig 16).

**6. SUMMARY AND CONCLUSIONS**

A radial wind product at all NEXRAD radars was constructed and
operationally run 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 compute burden and
bandwidth needed to assimilate high resolution radar radial winds into
operational NWP models. This was done simply by averaging many
observations into one at the radars and transmitting the result to a
central site at higher precision with ephemeris 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 m/s
precision and 4 antenna tilts over 1km radial distance. The level 2.5
super-ob product has the advantage of calculating the super-ob averaging
at the radar with ¼ km radial distance, 9-16 antenna tilts for each
radar scan and a capable precision of ±0.002 delivered 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 (NIDS type) winds and
the level 2.5 experimental runs assimilating the super-ob calculated at
the radars as described above. We think that the reason we did not see
impact with the level 3 data was that the precision of the level 3
observations was not sufficient to provide winds that would project
closely enough onto the analyzed wind field (from other sources) so they
are rejected by the analysis system. 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 from rain gauges were used to verify
another 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 as one case "doth not a statistic make", but details
in the verifying precipitation field that correspond to model forecasts
indicate predictive capability. We point out that 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 level precipitation amount and location improved
compared to the high resolution verifying rain gauge observations. 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. There are no
other 3-dimensional observations that can supply high resolution
information for meso-scale models. 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 fro
use in the north American and weather research and forecast models.

We feel the level 2.5 data is obtained from higher resolution radar
returns and not filtered (from ¼ 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 m/s vs 9. m/s), the level 2.5 super-ob may
deserve to be treated with higher confidence in the EDAS. This study is
the first step to motivate an extensive 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
eminent, 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 writing 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 also need to thank Fred Toepfer for improving the
manuscript and his successful efforts with AWIPS administration on our
behalf. 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 WSR-88D radar radial winds for
use in the NCEP operational assimilation system, 19 Conf IIPS, Long
Beach, Amer. Meteor. Soc., P1.51

Alpert, J. C., V. K. Kumar and Y. Song, 2004: Improved super-ob radar
radial wind precision for the NCEP data assimilation and forecast
system, 20^th^ Conf on Weather Analysis and Forecasting/16^th^ Conf NWP,
Seattle, WA. PJ9.2

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

The WSR-88D rainfall algorithm. Weather and Forecasting,**13**, 377-395.

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: Changers 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.

Liu S. 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., J. Purser, E. Rogers, and Y. Lin. 1996: The regional 3D
variational analysis for the ETA model. Preprints. 11^th^ Conf. on NWP.,
Norfolk, VA., *Amer. Meteor. Soc*. 454-455.

Purser R. J., Parrish D.,Masutani M., 2000: Meterorological
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 mesoscale
modeling system: Final ETA model/analysis changes and preliminary
experiments using the WRF-NMM, 11Conf. 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, W&F, **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*

  ----------------------- ----------------------- -----------------------
  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 Study of 28OCT12Z, 2003

  --------------- -------------------------------------------------------
  CASE I          The level 2.5 Super-ob radar radial wind

  CASE II         The level 3 super-ob (NIDS type feed only)

  CASE III        No radar radial wind data present
  --------------- -------------------------------------------------------

# FIGURES

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

Fig 1. Radar radial (scalar) wind super-obs speed and location from
station KATX in Seattle, WA on May 28, 2002. Arrows are toward or away
from the radar with the color and arrow length indicating the speed 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 \[m/s\], (c) Super-ob radar radial (mean) wind
> \[m/s\].

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

> Fig 3. Super-ob radar radial wind location \[longitude & latitude\]
> for each observation the same as in Fig 1.

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

Fig 4. Analyzed regular Wind barbs (blue) at 0.4 km height from the NCEP
Global Forecast System Spectral Statistical Interpolation Analysis of
May 28, 2002 at 18Z. The cross is at the Seattle, WA radar location.

![](media/image7.wmf) *Fig 5. Eta 12-h forecast MSLP (hPa) and12 -h
accumulated precipitation rate\[mm/12-hr\] verifying 29OCT 2003 00Z
including level 2.5 super-ob (Case I).*

![](media/image8.wmf)

*Fig 6. Difference in the analysis (00) of wind magnitude between the
level 2.5 (Case I) and level 3 super-ob (Case 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. Difference in the analysis (00) of wind magnitude between the
level 3 (Case 2) and No radial wind(Case 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. Difference in the Eta forecasts at 36-h of wind magnitude
between the level 2.5 (Case I) and level 3 super-ob (Case 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. Difference in the Eta forecasts at 36-h of wind magnitude
between the level 3 (Case 2) and No radial wind(Case 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 8JUN2004 --
20JUN2004 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 3.0 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 8JUN2004 --
20JUN2004 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 3.0 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, 8JUN2004 -- 20JUN2004 (24 forecats). The dash
line is the radial wind super-ob Level 2.5 with no NIDS data experiment
and the solid line is the Eta control with NIDS level 3.0 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, 8JUN2004 -- 20JUN2004 (24 forecats). The dash line is
the radial wind super-ob Level 2.5 with no NIDS data experiment and the
solid line is the Eta control with NIDS level 3.0 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 01OCT 2004 12Z 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 01OCT 2004 12Z (Operations at this
time contains super-ob level 3.0 (NIDS type) and inflate parameter set
to 3).*

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

*Fig 16. Difference of the Eta super-ob 2.5 and Operational Eta 24-h
forecasts of 24-h accumulated precipitation \[inches\] verifying 01OCT
2004 12Z (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 12Z 01 October 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 01OCT 2004 12Z.*

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

*Fig 19. Difference of the Eta super-ob 2.5 and Operational Eta 48-h
forecast of 24-h accumulated precipitation \[inches\] verifying 01OCT
2004 12Z.*