NWS Technical Procedures Bulletin

RUC20 - The 20-km version of the Rapid Update Cycle

#### **Draft -- 6 April 2002**

Stanley G. Benjamin, John M. Brown, Kevin J. Brundage, Dezso Devenyi,
Georg Grell, Dongsoo Kim, Barry E. Schwartz, Tatiana G. Smirnova, and
Tracy Lorraine Smith, and Stephen S. Weygandt

NOAA/OAR Forecast Systems Laboratory, Boulder, CO

Geoffrey S. Manikin

Environmental Modeling Center, National Centers for Environmental
Prediction, Camp Springs, MD

# **Summary of RUC20 vs. RUC40 (RUC-2) differences - Abstract**

**1. Horizontal resolution**

The RUC20 has a 20-km horizontal resolution, compared to 40 km for the
previous RUC40 (RUC-2). The area covered by the computational grid has
not changed. The RUC20 has a 301x225 horizontal grid, compared to
151x113 for the RUC40.

**2. Vertical resolution**

The RUC20 has 50 computational levels, compared to 40 levels for the
RUC40. The RUC20 continues to use the hybrid isentropic-sigma vertical
coordinate used in previous versions of the RUC.

**3. Improved moist physics**

Improved quantitative precipitation forecasts have been the primary
focus for changes in the RUC20 model, including a major revision in the
MM5/RUC mixed-phase microphysics cloud routine, and a new version of the
Grell convective parameterization with an ensemble approach to closure
and feedback assumptions. The main effect of the microphysics change is
to decrease overforecasting of graupel and ice and to improve the
precipitation type forecast. The new Grell scheme results in
considerable improvement in convective precipitation forecasts from the
RUC.

**4. Assimilation of GOES cloud-top data**

The RUC20 includes a cloud analysis which updates the initial 3-d
cloud/hydrometeor fields by combining cloud-top pressure data from GOES
with the background 1-h RUC hydrometeor field. Cloud clearing and
building is done to improve the initial cloud
water/ice/rain/snow/graupel fields for the RUC.

**5. Better use of observations in analysis**

The RUC20 assimilates near-surface observations more effectively through
improved algorithms for calculating observation-background differences.
Assimilation of surface observations is improved by diagnosing
background forecasts for surface temperature and dewpoint at 2 m and for
winds at 10 m. It is also improved by matching of land-use type between
the background and the observation for near-coastal stations. The RUC20
continues to use an optimal interpolation analysis as in the RUC40 --
implementation of a 3d variational analysis has been deferred.

**6. Improved land-surface physics**

The RUC20 land-surface model is changed from that of the RUC40. It uses
more detailed land-use and soil texture data, in contrast to 1-degree
resolution fields used in the RUC40. It includes improved cold-season
processes (soil freezing/thawing) and a 2-layer snow model. These
changes improve the evolution of surface moisture and temperature and
snow cover, which in turn improve forecasts of surface temperature and
moisture and precipitation.

**7. Lateral boundary conditions**

The RUC40 used lateral boundary conditions specified from the Eta model
initialized every 12 h. The RUC20 adds updates of its lateral boundaries
from the 0600 and 1800 UTC Eta runs.

**8. Improved post-processing**

The RUC20 includes improved diagnostic techniques for 2-m temperature
and dewpoint, 10-m winds, helicity, visibility, convective available
potential energy, and convective inhibition.

**Most significant improvements in RUC20 fields over those from RUC40
(RUC-2)**.

-   **precipitation - both summer and winter** - From improved
    precipitation physics and higher resolution

-   **all surface fields - temperature, moisture, winds** -- Reduced
    bias and RMS error in comparison with METAR observations. From
    improved surface and cloud/precipitation physics and higher
    resolution

-   **upper-level winds and temperatures** - From higher vertical and
    horizontal resolution, better physics

-   **orographically induced precipitation and circulations** - From
    higher horizontal resolution, cloud physics, and better use of
    surface data near mountains.

1.  **INTRODUCTION**

A new version of the Rapid Update Cycle (RUC) is being implemented with
a doubling of horizontal resolution (40km to 20km), an increased number
of vertical computational levels (40 to 50), and improvements in the
analysis and model physical parameterizations. A primary goal with the
20-km RUC (or RUC20) has been improvement in warm-season and cold-season
quantitative precipitation forecasts. Improvements in near-surface
forecasts and cloud forecasts have also been targeted. The RUC20
provides improved forecasts for these variables, as well as for wind,
temperature, and moisture above the surface.

The RUC20 provides improved short-range numerical weather guidance for
general public forecasting as well as for the special short-term needs
of aviation and severe-weather forecasting. The RUC20 continues to
produce new analyses and short-range forecasts on an hourly basis, with
forecasts out to 12 h run every 3 h. The implementation of the RUC20 in
2002 follows previous major implementations of a 60-km 3-h cycle version
in 1994 (Benjamin et al 1994, 1991) and a 40-km 1-h cycle version in
1998 (Benjamin et al 1998).

The uses of the RUC summarized below continue with the implementation of
the RUC20:

-   **Explicit use of short-range forecasts** - The RUC forecasts are
    unique in that they are initialized with very recent data. Thus, in
    most cases, the most recent RUC forecast has been initialized with
    more recent data than other forecast model runs available. Even at
    0000 or 1200 UTC, when other model runs are available, the RUC
    forecasts are useful for comparison over the next 12 h. Although
    there are a vast number of differences between the RUC and other
    NCEP models, the key unique aspects of the RUC are its hybrid
    isentropic vertical coordinate (used in analysis and model), hourly
    data assimilation, and model physics.

-   **Monitoring current conditions with hourly analyses** - Hourly
    analyses are particularly useful when overlaid with hourly satellite
    and radar images, or hourly observations such as from surface
    stations or profilers.

-   **Evaluating trends of longer-range models** - RUC analyses and
    forecasts are useful for evaluation of the short-term predictions of
    the Eta and AVN models.

The users of the RUC include:

-   Aviation Weather Center/NCEP, Kansas City, MO

-   Storm Prediction Center/NCEP, Norman, OK

-   NWS Weather Forecast Offices

-   FAA/DOT, including use for air traffic management and other
    automated tools, and for FAA workstations

-   NASA Space Flight Centers

-   Private sector weather forecast providers

Sections below describe changes in the RUC with the RUC20 implementation
regarding spatial resolution, data assimilation, model, and diagnostics
/ post-processing.

![](media/image1.png){width="5.083464566929134in"
height="4.131102362204724in"}![](media/image2.png){width="5.0in"
height="4.075590551181103in"} Figure 1. Terrain elevation for a) 40-km
RUC-2, b) 20-km RUC20

2.  **SPATIAL RESOLUTION**

The RUC20 occupies the same spatial domain as the previous RUC40 (40-km
RUC-2), as shown in Figs. 1a,b. The RUC20 grid points are still a subset
of the AWIPS Lambert conformal grid (AWIPS/GRIB grid 215 for 20km) used
as a distribution grid by the National Weather Service. Direct use of
the AWIPS grid reduces the number of distribution grids for the RUC. The
AWIPS grid ID for the RUC20 grid is 252, compared to 236 for the RUC40
grid. Thus, the 252 grid for the RUC20 is a subset of the 215 grid. The
RUC20 grid size is 301 x 225 grid points (compared to 151 x 113 for
RUC-2).

***2.a. Horizontal resolution***

The 20-km grid spacing used by the RUC20 allows better resolution of
small-scale terrain variations, leading to improved forecasts of many
topographically induced features, including low-level eddies,
mountain/valley circulations, mountain waves, sea/lake breezes, and
orographic precipitation. It also allows better resolution of land-water
boundaries and other land-surface discontinuities. While the most
significant differences in the terrain resolution of the RUC20 (Fig. 1b)
vs. RUC40 (Fig. 1a) are in the western United States, a number of
important differences are also evident in the eastern part of the
domain.

The surface elevation of the RUC20, as with the RUC40, is defined as a
\"slope envelope\" topography. The standard envelope topography is
defined by adding the sub-grid-scale terrain standard deviation
(calculated from a 10-km terrain field) to the mean value over the grid
box. By contrast, in the slope envelope topography, the terrain standard
deviation is calculated with respect to a plane fit to the
high-resolution topography within each grid box. This gives more
accurate terrain values, especially in sloping areas at the edge of
high-terrain regions. It also avoids a tendency of the standard envelope
topography to project the edge of plateaus too far laterally onto low
terrain regions. Using the slope envelope topography gives lower terrain
elevation at locations such as Denver and Salt Lake City which are
located close to mountain ranges. As shown in Table 1, the RUC20 more
closely matches station elevations in the western United States.

+------------------------+----------------------+---------------------+
| ### Rawinsonde station | **Station elevation  | **Station elevation |
|                        | minus RUC40          | minus RUC20         |
|                        | elevation (m)**      | elevation (m)**     |
+------------------------+----------------------+---------------------+
| Edwards AFB, CA        | 300                  | 41                  |
+------------------------+----------------------+---------------------+
| Denver, CO             | 354                  | 26                  |
+------------------------+----------------------+---------------------+
| Grand Junction, CO     | 679                  | 323                 |
+------------------------+----------------------+---------------------+
| Boise, ID              | 274                  | 253                 |
+------------------------+----------------------+---------------------+
| Great Falls, MT        | 157                  | 29                  |
+------------------------+----------------------+---------------------+
| Reno, NV               | 381                  | 144                 |
+------------------------+----------------------+---------------------+
| Elko, NV               | 352                  | 152                 |
+------------------------+----------------------+---------------------+
| Medford, OR            | 544                  | 346                 |
+------------------------+----------------------+---------------------+
| Salem, OR              | 233                  | 51                  |
+------------------------+----------------------+---------------------+
| Rapid City, SD         | 153                  | 45                  |
+------------------------+----------------------+---------------------+
| Salt Lake City, UT     | 630                  | 438                 |
+------------------------+----------------------+---------------------+
| Riverton, WY           | 225                  | 119                 |
+------------------------+----------------------+---------------------+

Table 1. Terrain elevation difference between station elevation and
interpolated RUC elevation for selected rawinsonde stations in western
United States.

The grid length is 20.317 km at 35 deg N. Due to the varying map-scale
factor from the projection, the actual grid length in RUC20 decreases to
as small as 16 km at the north boundary. The grid length is about 19 km
at 43 deg N. The RUC20 latitude/longitude (and terrain elevation) at
each point in an ASCII file can be downloaded from
[http://ruc.fsl.noaa.gov/MAPS.domain.html](http://maps.fsl.noaa.gov/MAPS.domain.html).
The lower left corner point is (1,1), and the upper right corner point
is (301,225), as shown in Table 2.

An example is shown below (Fig. 2) of the improved orographic effect on
low-level wind circulation comparing 3-h forecasts from RUC20 and RUC40,
both displayed at 40km resolution. The RUC20 shows a better depiction of
the Denver-area cyclonic circulation, strong southerly flow up the San
Luis Valley into southern Colorado near Alamosa, and winds of greater
than 20 knots near higher terrain in central Colorado and south central
Utah. The verifying analysis in Fig. 3 shows that all of these features
appear to be better depicted in the RUC20 3-h forecasts.

![](media/image3.png){width="3.301388888888889in" height="3.5in"}

Figure 2. RUC 3-h surface wind forecasts from a) RUC40 and b) RUC20.
Forecasts valid at 1800 UTC 3 April 2002.

![](media/image5.png){width="3.4319444444444445in"
height="3.626388888888889in"}

Figure 3. Verifying analysis of surface winds at 1800 UTC 3 April 2002
from RUC20 for a) 40km resolution plot and b) 20km resolution plot over
Colorado.

  ----------------- ----------------------- -------------- ---------------
  **RUC20 point**   **AWIPS-212 point**     **Latitude**   **Longitude**

  (1,1)             (23,7)                  16.2810 N      126.1378 W

  (1,225)           (23,119)                54.1731 N      139.8563 W

  (301,1)           (173,7)                 17.3400 N      69.0371 W

  (301,225)         (173,119)               55.4818 N      57.3794 W
  ----------------- ----------------------- -------------- ---------------

Table 2. Latitude/longitude and AWIPS-212 positions of corner points for
RUC20 domain.

b\. Vertical resolution

The RUC20 continues to use the generalized vertical coordinate
configured as a hybrid isentropic-sigma coordinate (Bleck and Benjamin
1993) used in previous versions of the RUC. This coordinate is used for
both the analysis and the forecast model. The RUC hybrid coordinate has
terrain-following layers near the surface with isentropic layers above.
This coordinate has proven to be very advantageous in providing sharper
resolution near fronts and the tropopause (e.g., Benjamin 1989, Johnson
et al. 1993, 2000). Some of the other advantages include:

-   All of the adiabatic component of the vertical motion on the
    isentropic surfaces is captured in flow along the 2-d surfaces.
    Vertical advection through coordinate surfaces, which usually has
    somewhat more truncation error than horizontal advection, is less
    prominent in isentropic/sigma hybrid models than in quasi-horizontal
    coordinate models. This characteristic results in improved moisture
    transport and less precipitation spin-up problem in the first few
    hours of the forecast.

-   Improved conservation of potential vorticity. The potential
    vorticity and tropopause level (based on the 2.0 PV unit surface)
    show very good spatial and temporal coherence in RUC grids (Olsen et
    al 2000).

-   Observation influence in the RUC analysis extends along isentropic
    surfaces, leading to improved air-mass integrity and frontal
    structure. From an isobaric perspective, the RUC isentropic analysis
    is implicitly anisotropic (Benjamin 1989).

The RUC20 has 50 vertical levels, compared to 40 levels in RUC40. Extra
levels are added near the tropopause and lower stratosphere and also in
the lower troposphere. The RUC hybrid coordinate is defined as follows:

\- Each of the 50 levels is assigned a reference virtual potential
temperature (θ~v~) which increases upward (Table 3).

\- The lowest atmospheric level (k=1) is assigned to be at a pressure
corresponding to an elevation of 5 m above the model terrain.

\- Each of the next 49 levels is assigned a minimum pressure thickness
between it and the next level below. This thickness will apply to
coordinate surfaces that end up being in the lower portion of the domain
where the coordinate surfaces are terrain-following. For grid points
with surface elevation near sea level, the minimum pressure thickness is
2.5, 5.0, 7.5, and 10 hPa for the bottom 4 layers, and 15 mb for all
layers above. These minimum pressure thicknesses are reduced over higher
terrain to avoid 'bulges' of sigma layers protruding upward in these
regions.

\- The pressure corresponding to the reference θ~v~ for each (k) level
is determined for each (i,j) column. (For lower θ~v~ values, this
pressure may be determined via extrapolation as beneath the ground.)

\- At this point, there are two choices for the assignment of pressure
to the (i,j,k) grid point, corresponding to:

1)  the reference θ~v~ value (the 'isentropic' definition), and

2)  the minimum pressure spacing, starting at the surface pressure (the
    'sigma' definition)

If the isentropic pressure (1) is less than sigma pressure (2), the grid
point pressure is defined as isentropic, and otherwise as
terrain-following (sigma).

  ------- ------- ------- ------- ------- ------- ------- ------- ------- -------
  224     232     240     245     250     255     260     265     270     273

  276     279     282     285     288     291     294     296     298     300

  302     304     306     308     310     312     314     316     318     320

  322     325     328     331     334     337     340     343     346     349

  352     355     359     365     372     385     400     422     450     500
  ------- ------- ------- ------- ------- ------- ------- ------- ------- -------

Table 3. Reference θ~v~ values (K) for the RUC20 (50 levels).

The maximum θ~v~ value in the RUC20 is 500 K, compared to 450 K for the
RUC40. The 500 K surface is typically found at 45-60 hPa. As with the
RUC40, a greater proportion of the hybrid levels are assigned as
terrain-following in warmer regions and warmer seasons. This is shown in
Figs. 4a,b below.

![](media/image6.png){width="3.3125in" height="3.5in"}

![](media/image7.png){width="3.21875in" height="3.48125in"}

Figure 4. Vertical cross-sections showing RUC native coordinate levels
for a) RUC40 -- 40 levels, and b) RUC20 -- 50 levels. Data are taken
from RUC 12-h forecasts valid at 1200 UTC 2 April 2002. Cross-sections
are oriented from south (Mississippi) on left to north (western Ontario)
on right

In this example (Fig. 4), north-south vertical cross-sections are shown
depicting the pressure at which the RUC native levels are found for a
particular case. The case shown is from April 2002, with the
cross-section extending from Mississippi (on the left) northward through
Wisconsin (center point), across Lake Superior (slightly higher terrain
on each side), and ending in western Ontario. A frontal zone is present
in the middle of the cross-section, where the RUC levels (mostly
isentropic) between 700 and 300 hPa are strongly sloped.

In the RUC20, seven new levels have been added with reference θ~v~
values between 330 K and 500 K. Three new levels with reference θ~v~ in
the 270-290 K range have also been added. In the RUC20 depiction (Fig.
4b), the tropopause is more sharply defined than in the RUC40, and there
are more levels in the stratosphere, resulting from the additional
levels in the upper part of the domain. In the RUC20, the isentropic
levels from 270-355 K are now resolved with no more than 3 K spacing.

**3. FORECAST MODEL CHANGES IN RUC20**

The RUC20 forecast model is similar to the RUC40 forecast model but has
important changes in physical parameterizations and some regarding
numerical approaches. The model continues to be based upon the
generalized vertical coordinate model described by Bleck and Benjamin
(1993). Modifications to a 20-line section of code in the model are
sufficient to modify it from the hybrid isentropic-sigma coordinate
described in section 2.b to either a pure sigma or pure isentropic
model.

***3.a. Basic dynamics/numerics***

First, the basic numerical characteristics of the RUC model are
reviewed, italicized where different in the RUC20 from the RUC40.

-   Arakawa-C staggered horizontal grid (Arakawa and Lamb 1977); u and v
    horizontal wind points offset from mass points to improve numerical
    accuracy.

-   Generalized vertical coordinate equation set and numerics for
    adiabatic part of model following Bleck and Benjamin (1993)

-   No vertical staggering.

-   *Time step is 30 seconds at 20-km resolution.*

-   Positive definite advection schemes used for continuity equation
    (advection of pressure thickness between levels) and for horizontal
    advection (Smolarkiewicz 1983) of virtual potential temperature and
    all vapor and hydrometeor moisture variables.

-   Application of adiabatic digital filter initialization (DFI, Lynch
    and Huang 1992) for 40-min period forward and backward before each
    model start. The use of the DFI in the RUC is important for
    producing a sufficiently "quiet" (reduced gravity wave activity) 1-h
    forecast to allow the 1-h assimilation cycle. *A problem in
    application of digital filter weights is corrected in the RUC20.*

The atmospheric prognostic variables of the RUC20 forecast model are:

-   pressure thickness between levels

-   virtual potential temperature - θ~v~

-   horizontal wind components

-   water vapor mixing ratio

-   cloud water mixing ratio

-   rain water mixing ratio

-   ice mixing ratio

-   snow mixing ratio

-   graupel (rimed snow, frozen rain drops) mixing ratio

-   number concentration for ice particles

-   turbulence kinetic energy

The soil prognostic variables (at six levels) of the RUC-2 forecast
model are:

-   soil temperature

-   soil volumetric moisture content

Other surface-related prognostic variables are snow water equivalent
moisture and snow temperature *(at 2 layers in RUC20*), and canopy
water.

Other differences in the RUC20 vs. RUC40 model numerics or design are as
follows:

-   The order of solution in each time step:

  ----------------------------------- -----------------------------------
  **RUC40**                           **RUC20**

  Continuity                          Continuity

  Horizontal advection of θ~v~ /      Horizontal advection of θ~v~ /
  moisture                            moisture

  Physics (sub-grid-scale             Physics
  parameterizations)                  

  Coordinate adjustment               Momentum

  Momentum                            Coordinate adjustment
  ----------------------------------- -----------------------------------

-   The vertical advection for all variables is now calculated in a
    consistent manner using upstream differencing. The placement of the
    call for coordinate adjustment at the end of the time step allows
    this consistent treatment.

-   More robust and flexible hybrid coordinate algorithm

-   Much improved modularization

-   Use of new version of Scalable Modeling System (SMS) message-passing
    library with non-intrusive compiler directives (Govett et al. 2001).
    The use of the new SMS and improved modularization led to a
    significant reduction in lines of code in the RUC20 model.

***3.b. Physical parameterizations***

**3.b.1. Explicit mixed-phase cloud/moisture processes.**

The RUC20 uses an updated version (Brown et al 2000) of the explicit
microphysics from the NCAR/Penn State MM5 mesoscale model MM5 (level 4,
Reisner et al. 1998). An earlier version of this scheme was also used in
the RUC40. This scheme explicitly predicts mixing ratios for five
hydrometeor species \-- cloud water, rain water, snow, ice, graupel and
also the ice particle number concentration. This explicit mixed-phase
prediction is different than the diagnostic mixed-phase prediction used
in the Eta-12. In the RUC model, all six cloud/hydrometeor variables are
advected horizontally using the positive definite scheme of
Smolarkiewicz (1983) on the isentropic-sigma levels with adaptive
vertical resolution and vertically using upstream differencing (see
section 3.a.). The hydrometeor variables are cycled without modification
in the RUC40 1-h cycle, but modified by GOES cloud-top pressure
assimilation in the RUC20, as described in section 4.

Significant changes to the RUC/MM5 microphysics (Brown et al. 2000) have
been introduced with the RUC20. These changes are designed to address
unreasonable behavior in the RUC40 regarding excessive graupel and lower
than expected amounts of supercooled liquid water. The modifications,
developed jointly by NCAR and FSL, include a different curve for ice
nucleation as a function of temperature (Cooper replacing Fletcher), new
assumed particle size distributions for graupel to reduce the number of
small particles, a modified procedure for graupel formation as a result
of riming of cloud ice, and revisions to calculation of cloud-ice
particle number concentration. These modifications have been successful
in reducing excessive graupel (e.g., Fig. 5) and in improving the
precipitation-type forecast (less sleet) in the RUC20.

![](media/image8.png){width="3.317361111111111in" height="3.625in"}

Figure 5. Graupel and potential temperature in vertical cross-sections
from a) RUC40 and b) RUC20. For 12-h forecasts valid 0300 UTC 5 January
2001. Cross-section is oriented SW (left) to NE (right) across
Washington (Olympic Peninsula) into British Columbia and Alberta.

**3.b.2. Convective parameterization.**

A new convective parameterization (Grell and Devenyi 2001) based on an
ensemble approach is used in the RUC20. This scheme is based on the
Grell (1993) scheme but draws on other schemes by now using an ensemble
of various closure assumptions. The version of the Grell/Devenyi scheme
used in the RUC20 includes the following closures:

-   ![](media/image10.wmf), where CAPE is convective available potential
    energy

-   removal of total CAPE (Kain and Fritsch 1992) in a specified time
    period

-   low-level horizontal moisture convergence

-   low-level mass flux at cloud base

with different parameters applied to each of these closures. In the
RUC20, a total of 108 closure assumptions are used in the Grell/Devenyi
convective scheme. The RUC20 convective scheme also now includes:

-   detrainment of cloud water and cloud ice

-   entrainment of environmental air into the updraft

-   relaxation of stability (convective inhibition) constraints at
    downstream points based on downdraft strength

-   fix of a bug in RUC40 which exaggerated effects of surface processes
    in forcing convection. This bug resulted in too widespread
    convective precipitation over land in summer, especially in the
    southeastern U.S., and widespread light precipitation over warm
    ocean areas.

![](media/image11.png){width="2.5729166666666665in"
height="3.375in"}![](media/image12.png){width="0.5597222222222222in"
height="4.452083333333333in"} Figure 6. Precipitation (in) forecasts
initialized at 0000 UTC 26 March 2002 from a) RUC40 and b) RUC20 for
0900-1200 UTC (9-12 h forecasts). c) Radar summary valid at 1115 UTC
(verification).

The skill of RUC precipitation forecasts is significantly improved with
the RUC20 version, including the Grell/Devenyi ensemble-based convective
parameterization. An example of this improvement is presented in Figs. 6
a,b,c, where Figs. 6a,b are 12-h forecasts of 3-h accumulated
precipitation from the RUC40 and RUC20 respectively, and Fig. 6c is a
radar image in the verifying period. In this case, the RUC20 has
accurately forecast much more intensity than the RUC40 to the southern
end of a convective line, especially in eastern Louisiana and southern
Mississippi. Not only is the intensity improved in the RUC20 forecast,
but also the position of the line is more accurately forecast to be
farther east than the RUC40 forecast, stretching from central Ohio into
northwestern Alabama before bending back to eastern Louisiana.

Improvement in precipitation forecasts from the RUC20 relative to the
RUC40 is also evident in overall precipitation verification statistics
over multi-week periods. Daily verification has been performed using the
NCEP 24-h precipitation analysis against RUC 24-h totals produced by
summing two 12-h forecasts. Two scores traditionally used for
precipitation verification, equitable threat score and bias, are used to
compare RUC20 and RUC40 forecasts. For a period from spring 2002, the
RUC20 has a much higher equitable threat score (Fig. 7a) and bias (Fig.
7b) much closer to 1.0 (preferable) than the RUC40 for almost all
precipitation thresholds. Precipitation verification for a
November-December 2001 cold season period (Benjamin et al 2002a) also
shows a marked improvement for the RUC20 over the RUC40.

![](media/image13.wmf){width="3.125in" height="2.198611111111111in"}

Figure 7. Precipitation verification comparing RUC20 and RUC40
forecasts, a) equitable threat score and b) bias. Verification is
against NCEP 24-h precipitation analysis. For period 20 March -- 15
April 2001.

As with the RUC40, the inclusion of downdrafts in the Grell scheme
results in smaller-scale details in RUC warm season precipitation
patterns than may be evident in that from the Eta model using the
Betts-Miller-Janjic convective parameterization. This same difference in
character of precipitation forecasts is also evident in NCEP/NSSL
experiments comparing the Kain-Fritsch (which also includes downdrafts)
and Betts-Miller-Janjic schemes both within the MesoEta model (e.g.,
Kain et al. 1998).

**3.b.3. Land-surface physics.**

A new version of the RUC land-surface model (LSM) is used in the RUC20,
including accounting for freezing and thawing of soil, and using a
2-layer representation of snow (Smirnova et al. 2000b). This updated LSM
is a refinement of the previous RUC40 version discussed in Smirnova et
al. (1997). Surface (shelter/anemometer level) forecasts are often
critically dependent on accurate estimates of surface fluxes, and in
turn, on reasonably accurate soil moisture and temperature estimates.
The RUC soil model contains heat and moisture transfer equations solved
at 6 levels for each column together with the energy and moisture budget
equations for the ground surface. These budgets are applied to a thin
layer spanning the ground surface and including both the soil and the
atmosphere with corresponding heat capacities and densities. (The budget
formulation is one of the primary differences between the RUC LSM and
LSMs in other operational models.) A treatment of the evapotranspiration
process, developed by Pan and Mahrt (1987), is implemented in the RUC
LSM. When snow cover is present, snow is considered to be an additional
one

![](media/image15.wmf){width="3.375in" height="2.1194444444444445in"}

Figure 8. Diurnal variation of 2-m temperature bias (forecast-obs) in
RUC20 and RUC40 forecasts valid at times on horizontal axis.
Verification against METAR observations in RUC domain east of 105°W. a)
for 6-h forecasts, b) for 12-h forecasts.

![](media/image17.png){width="0.55625in" height="3.0006944444444446in"}

Figure 9. Comparison of 2-m temperature (°F) 12-h forecasts from RUC40
(upper left) and RUC20 (lower left) valid 1200 UTC 22 Feb 2002.
Verification analyses from RUC40 (upper right) and RUC20 (lower right).

or two upper layers of soil, depending on its depth. To provide a more
accurate solution of the energy budget through deeper snow, a snowpack
thicker than 7.5 cm is split up into two layers where the top layer is
set to be 7.5 cm deep, and the energy budget is applied to the top half
of this top layer. A heat budget is also calculated at the boundary
between the snow pack and the soil, allowing melting from the bottom of
the snow layer. Incorporation of a two-layer snow representation into
the land-surface scheme in the RUC20 significantly improves the skin
temperatures in winter, and therefore, also the 2-m temperature
forecasts (Figs. 8, 9).

The accumulation of snow on the ground surface is provided by the
mixed-phase cloud microphysics algorithm of the RUC forecast scheme
(Reisner et al. 1998, Brown et al. 2000, section 3.b.1 of this
document). It predicts the total amount of precipitation and also the
distribution of precipitation between the solid and liquid phase. In the
RUC20, the Grell/Devenyi convective parameterization scheme now also
contributes to the snow accumulation if the surface temperature is at or
below 0° C.

As with the RUC40, the RUC20 continues to cycle volumetric soil moisture
and soil temperature at the 6 model levels, as well as canopy water, and
snow temperature. In the RUC20, cycling of the snow temperature of the
second layer (where needed) is also performed. The RUC continues to be
unique among operational models in its specification of snow cover and
snow water content through cycling (Smirnova et al. 2000b). The 2-layer
snow model in the RUC20 improves this cycling, especially in spring
time, more accurately depicting the snow melting season and spring spike
in total runoff, as shown in 1-dimensional experiments with the RUC LSM
over an 18-year period from a site in Russia (Smirnova et al 2000b).

The RUC20 also uses a different formulation for thermal conductivity
(Johansen 1975, Peters-Lidard 1998) that generally reduces values of
this parameter, especially in near-saturated soils, thereby contributing
to a stronger diurnal cycle. This change helps to correct an inadequate
diurnal cycle (daytime too cool, nighttime too warm) in the RUC40. Fig.
8 shows that the diurnal cycle is better depicted in the RUC20 but that
there is still some remaining tendency for inadequate nighttime cooling.
An example of improved surface temperature forecasts is provided in Fig.
9, where the RUC20 provides more accurate forecasts in the central
plains (cooler), northern Indiana and Ohio (warmer), and central
California (cooler) than the RUC40 for this overnight 12-h forecast
ending at 1200 UTC 22 Feb 2002. Schwartz and Benjamin (2002) show that
the RUC20 provides improved 2-m temperature and 10-m wind forecasts than
the RUC40, especially during daytime.

**3.b.4. Atmospheric radiation.**

The RUC20 continues to use the MM5 atmospheric radiation package (Dudhia
1989, Grell et al. 1994) with additions for attenuation and scattering
by all hydrometeor types. This scheme is a broadband scheme with
separate components for longwave and shortwave radiation. In the RUC20,
the calculation of shortwave radiation is corrected for a 30-min mean
time lag in solar radiation present in the RUC40. This correction helps
to improve morning near-surface temperature forecasts (e.g., Fig. 8
results for forecasts valid at 1500 UTC).

**3.b.5. Turbulent mixing.**

The RUC20 continues, like the RUC40, to prescribe turbulent mixing at
all levels, including the boundary layer, via the explicit turbulence
scheme of Burk and Thompson (1989). This scheme is a level-3.0 scheme,
with explicit forecast of turbulent kinetic energy and three other
turbulence variables. The surface layer mixing continues to be
prescribed by Monin-Obukhov similarity theory, specifically the
three-layer scheme described in Pan et al. (1994). With the
Burk-Thompson scheme, the RUC typically forecasts TKE amounts of 5-20
J/kg in the boundary layer, and also forecasts TKE maxima aloft,
typically localized in frontal zones, corresponding to likely areas for
clear-air turbulence.

**3.b.6. Time splitting for physical parameterizations**

As with other mesoscale models, the RUC gains efficiency by not calling
physical parameterizations at the full frequency of each dynamic time
step. Time truncation errors are, however, incurred by this time
splitting. In the RUC20, the frequency of calls to physical
parameterizations has been increased, as is shown in Table 4. Of these
changes, the one for the cloud microphysics is most significant,
decreasing time truncation errors associated with microphysical
processes and precipitation fallout.

+----------------------------+--------------------+--------------------+
| **Physical                 | **RUC40            | **RUC20            |
| parameterization**         | frequency**        | frequency**        |
|                            |                    |                    |
|                            | **(min)**          | **(min)**          |
+----------------------------+--------------------+--------------------+
| Cloud microphysics         | 10                 | 2                  |
+----------------------------+--------------------+--------------------+
| Convection                 | 5                  | 2                  |
+----------------------------+--------------------+--------------------+
| Turbulence                 | 5                  | 2                  |
+----------------------------+--------------------+--------------------+
| Land-surface               | 5                  | 2                  |
+----------------------------+--------------------+--------------------+
| Shortwave radiation        | 60                 | 30                 |
+----------------------------+--------------------+--------------------+
| Longwave radiation         | 60                 | 60                 |
+----------------------------+--------------------+--------------------+

Table 4. Frequency of calls to physical parameterizations in RUC40 and
RUC20.

The application of tendencies (rate of change to temperature, moisture,
wind, etc.) from the physical parameterizations is also different in
RUC20 vs. RUC40. In RUC40, tendencies from each physics routine except
for radiation were applied with the parameterization time step only when
the parameterization was called instead of being spread evenly over the
interval between calls. This technique, which we inelegantly term
'chunking', results in some shock to the model, although the effects did
not seem harmful. In the RUC20, tendencies are applied at each dynamics
time step, thus avoiding 'chunking'.

**4. CHANGES TO LATERAL AND LOWER BOUNDARY CONDITIONS IN RUC20**

***4.a. Lateral boundary conditions***

With the RUC20, lateral boundary conditions are specified from Eta model
runs made every 6 h. Thus, the lateral boundaries are updated with more
recent data than with RUC40, for which new Eta runs were incorporated
only every 12 h. The output frequency from the Eta used for the RUC
boundary conditions is 3 h. The Eta data used for RUC lateral boundary
conditions is currently from 25-mb 40-km output grids. The Eta model
forecasts are interpolated to the RUC20 domain on its hybrid coordinate
levels. Values of pressure thickness, virtual potential temperature, and
horizontal winds at the edge of the RUC domain (up to 5 grid points from
the boundary) are nudged (Davies 1976) toward the Eta values at each
time step in a model run. For the RUC20, fixes have been made in
application of lateral boundary conditions, resulting in smoother fields
near the boundaries.

It is important to note that since the RUC runs prior to the Eta in
NCEP's operational suite, it uses 'old' boundary condition data for
model forecasts made at 0000 and 1200 UTC. This timing sequence results
in a slight degradation of quality of RUC forecasts near the boundaries
for runs initialized at these times. Tests at FSL in which the RUC runs
at 0000 and 1200 UTC are made *after* Eta boundary conditions are
available at those same times show a clear increase in statistical
forecast skill.

***4.b. Lower boundary conditions***

-   Sea-surface temperature -- Uses same daily analysis as used for Eta
    runs (currently, the 50-km global real-time SST analysis from the
    NCEP/EMC Ocean Modeling Branch). Higher-resolution information for
    the Great Lakes is also incorporated. The RUC's use of SST data is
    set via scripts to follow any changes made for the Eta model.

    -   In the RUC20, a bug has been fixed that was causing 1° lat/lon
        blockiness in the SST used in the RUC40. This blockiness was
        also apparent in the 2-m temperatures over oceans (e.g., Fig.
        10).

    -   Monthly climatological values are used for Great Salt Lake in
        RUC20 but not RUC40 (L. Dunn, personal communication). Time
        interpolation is to date of month.

    -   ![](media/image19.png){width="1.4in"
        height="2.229861111111111in"}![](media/image19.png){width="0.5597222222222222in"
        height="2.379166666666667in"}![](media/image19.png){width="1.5208333333333333in"
        height="2.229861111111111in"}

> Figure 10. 2-m temperature 12-h forecasts from a) RUC40, b) RUC20,
> valid at 1200 UTC 21 Feb 2001

-   Ice cover -- RUC20 uses NESDIS daily ice analysis, same as used by
    Eta model. No change from RUC40.

-   Land use -- RUC20 land-use (Fig. 11b) is taken from USGS 24-class,
    30-second data set used in MM5 and WRF (Weather Research and
    Forecasting) model pre-processing programs. RUC40 (Fig. 11a) used
    old MM4 land-use data with 1° lat/lon resolution, which was blocky
    and caused blocky effect in RUC40 surface fields.

![](media/image20.png){width="2.875in" height="2.240972222222222in"}

Figure 11. Land-use for a) RUC40 and b) RUC20

-   Soil texture -- RUC20 uses much higher resolution information in
    RUC20 than in RUC40. RUC20 soil type data is taken from a global
    30-second, accessible from the WRF pre-processor code.

-   Vegetation fraction -- For both RUC20 and RUC40, this is specified
    from monthly high-resolution (0.144°) data produced from 5-year
    climatology (Gutman and Ignatov 1998) of NDVI (normalized digital
    vegetation index, an AVHRR-based satellite product). This is the
    same data set used by the Eta model. Values are interpolated by date
    of month between monthly values assumed to be valid on the 15^th^ of
    each month.

-   Albedo -- For RUC20, this is also specified from NESDIS monthly
    high-resolution (0.144°) data produced from a 5-year climatology
    (Csiszar and Gutman 1999), and this is the same data set used by Eta
    model. In the RUC40, albedo data was from a much coarser 1° seasonal
    climatology data set.

-   Terrain elevation -- As described in section 2.

**5. ANALYSIS CHANGES IN RUC20**

The RUC20 analysis continues to use an optimal interpolation (OI)
analysis applied on the RUC native hybrid isentropic-sigma levels, but
with some important modifications from the RUC40 OI analysis, as
described below.

\[A 3-dimensional variational (3DVAR) analysis has been developed for
the RUC (Devenyi et al 2001); some further tuning is needed to squeeze
out a little more skill in 3-h forecasts before it can be implemented.
It is hoped that the RUC 3DVAR can be implemented 4-6 months after the
initial RUC20 implementation.\]

***5.a. Assimilation of GOES cloud-top pressure data***

Toward the goal of improved short-range forecasts of cloud/hydrometeors,
icing, and precipitation, an advanced version of the Rapid Update Cycle
(RUC) cloud-top pressure assimilation technique (Benjamin et al 2002b)
has been developed and tested. This improved technique, using GOES
single field-of-view cloud-top pressure and temperature data provided by
NESDIS, is being implemented into operations with the rest of the RUC20.
As described in section 3.b.1, the RUC uses a bulk mixed-phase cloud
microphysics scheme from the NCAR/Penn State MM5 model, with 5
hydrometeor types explicitly forecast (Brown et al. 2000). The
prognostic variables in this scheme are mixing ratios of water vapor,
cloud water, rain water, ice, snow, and graupel, and number
concentration of ice particles. In the RUC40, the initial conditions for
the fields were simply those carried over from the previous 1-h RUC
forecast. In the RUC20 including assimilation of GOES cloud-top data,
these fields are modified each hour as part of the cloud clearing and
cloud building.

The RUC20 cloud/hydrometeor analysis technique is an advanced version of
the procedures previously described by Kim and Benjamin (2001, 2000).
GOES cloud-top pressure data provides information on the horizontal
location of cloudy and cloud-free areas, but not on cloud depth. Also,
unless there are broken layers, it cannot provide information on
multiple cloud layers. Thus, the RUC cloud/hydrometeor assimilation
technique is designed to use this partial information. When GOES data
indicate that no clouds are present, the technique removes any
hydrometeors and reduces water vapor mixing ratio to a sub-saturation
value. When GOES data indicate that cloud is present that is not in the
RUC 1-h forecast at the correct level, cloud water and/or ice is added
in a layer of not more than 50 hPa depth. This layer is also saturated
with respect to water or ice with a linear variation between these two
saturation vapor pressure values in the 248-263 K range.

Other features of the RUC GOES cloud-top assimilation include:

-   Rederivation of cloud-top pressure from GOES cloud-top temperature
    if the original retrieval of cloud-top pressure is closer to the
    ground than 620 hPa. This rederivation of the cloud-top pressure
    uses the RUC 1-h temperature/moisture profile at the nearest grid
    point.

-   Use of single field-of-view GOES data (\~10-km resolution). The
    median values from the fields-of-view around each RUC box are used.
    With this sampling, cloud fraction is calculated in RUC grid
    volumes.

-   Use of stability check to identify possible sub-field-of-view
    variations from small convective clouds that result in inaccurate
    cloud-top temperature and pressure determination.

-   Remove cloud indicators if they only occur at isolated
    (non-contiguous) RUC grid points, again on the presumption that GOES
    may be observing sub-field-of-view clouds.

-   Special handling for marine stratus situations to force cloud-top at
    consistent level with top of marine inversion in RUC background
    profile.

![](media/image22.png){width="3.31875in" height="2.5729166666666665in"}

Figure 12. Cloud-top pressure valid 1200 UTC 9 Dec 2001 for a) RUC40 3-h
forecast, b) RUC20 3-h forecast, c) analysis using NESDIS cloud-top
data. White areas are clear skies.

An example of the impact of GOES cloud-top assimilation on RUC forecasts
is shown in Fig. 12. The RUC diagnostic cloud-top pressure field is
calculated by searching downward from the top of the model at each grid
point until a combined hydrometeor mixing ratio (cloud water, ice, rain,
snow, graupel) of at least 10^-6^ g/g is encountered. (If none is
encountered, conditions are regarded as clear skies.) A 3-h forecast of
cloud-top pressure from the RUC40 (Fig. 12a) shows a general resemblance
to the analysis cloud-top field (Fig. 12c) generated from the NESDIS
cloud product, but the RUC20 3-h forecast (Fig. 12b) shows much better
agreement with the analysis. This improved forecast is due not only to
the cloud-top assimilation (and retention in the subsequent forecast),
but also to other changes in the RUC20 including improved microphysics
and higher resolution.

Ongoing statistical verification has been performed, calculating
correlation coefficients between the NESDIS cloud-top product and RUC40
and RUC20 forecasts of durations from 1 h to 12 h. These statistics
(Benjamin et al 2002b) show that the RUC20 produces improved cloud-top
forecasts over RUC40 not just at 1-h and 3-h projections, but with
similar improvement out to 12-h forecasts.

***5.b. Improved observation pre-processing***

The most important changes in the RUC20 OI analysis are the observation
preprocessing and matching to background values. In the observation
preprocessing, a more flexible, lower-memory, observation array
structure is used in the RUC20 that allows each level of a profile
observation (e.g., rawinsonde, profiler, VAD) to be associated with its
own metadata (position, time, expected error) if necessary. This
structure was developed for the RUC 3DVAR but is also used in the RUC20
OI analysis. It allows for use of varying positions to account for
balloon drift for rawinsonde observations. However, it was decided that
since the effects of time change and position drift largely cancel each
other, to not incorporate balloon drift in the RUC20 analysis.

The following features are implemented in the RUC20 observation
preprocessing to improve the use of observations in the analysis. The
goal of these features is to match the information in the observation
and background as nearly as possible.

-   Surface observations

    -   Calculate 2-m temperature and moisture values and 10-m winds
        from background, instead of simply taking the 5-m background
        values. The result of this is reduced bias in the analysis.

    -   Choose nearest land grid point from background for most surface
        observations over land, but choose nearest water grid point for
        buoy surface observations when calculating
        observation-minus-background values for coastal surface
        stations. This improves the RUC20 analyzed surface fields in
        coastal regions.

    -   Improve use of background model lapse rate to match observations
        and background when the elevation is different. This constrained
        lapse rate reduction is applied for surface temperature
        observations, and the surface moisture observation is
        correspondingly modified such that the original dewpoint
        depression is maintained.

-   Rawinsonde/profiler observations

    -   Use code to preserve observed near-surface structure when
        rawinsonde surface elevation does not match that of model
        background. This logic is similar to that used for surface
        observations.

    -   Use raw level observations now in addition to values
        interpolated to background levels (also used for wind profiler
        and VAD observations).

    -   Prevent use of interpolated values if significant level data not
        present. For profilers, prevent use of interpolated values if
        separation between raw values exceeds 1200 m. This change in the
        RUC20 prevents a RUC40 problem in which unrealistic linearly
        interpolated profiles were used when there were large vertical
        gaps in rawinsonde, profiler or VAD observation profiles.

-   Precipitable water observations

    -   Account for elevation differences between observation and
        background.

***5.c. Modifications to optimal interpolation analysis***\
A detailed description of the RUC OI analysis from the RUC40 is
available in the RUC-2 Technical Procedures Bulletin (Benjamin et al
1998, available from the NWS at <http://205.156.54.206/om/tpb/448.htm>
).

Modifications made in the RUC20 to other aspects of the OI analysis are
listed below.

-   Quality control -- Continues to use OI-based buddy check. In RUC20,
    buddy check now performed for cloud-drift winds and precipitable
    water observations (not in RUC40) and bugs fixed. RUC20 honors NCEP
    observation QC flags, which was not done in RUC40. This means, for
    instance, that quality flags from the NOAA Profiler Hub are now
    being used.

-   Improved observation search strategy allowing much more complete use
    of aircraft ascent/descent profiles than in RUC40.

-   Moisture analysis looping -- In order to force some interconsistency
    in the RUC20 analysis between different moisture observations, a
    two-pass loop is performed. Within each loop, the analysis order is
    as follows: cloud-top observations, precipitable water observations,
    in situ moisture observations. The observation-minus-background
    values are recalculated after each part of the moisture analysis,
    and in situ observations are given the 'last say'.

-   Moisture variable -- changed from condensation pressure in RUC40 to
    natural logarithm of water vapor mixing ratio (ln q). This
    simplifies the variable transformation needed for precipitable water
    analysis and cloud-top assimilation. The variable ln q is conserved
    under motion in adiabatic conditions, considered to be desirable for
    the choice of an analysis variable. The cycled water vapor variable
    in the RUC and prognostic variable in the RUC model continues to be
    water vapor mixing ratio.

-   Constraints applied at end of analysis

    -   A series of top-down and bottom-up lapse rate checks are applied
        which are designed to prevent unrealistic lapse rates from
        occurring in the RUC20 temperature profiles. These checks also
        improve the retention of surface temperature observations under
        conditions of a deep boundary layer. A shallow superadiabatic
        layer near the surface of up to 1.5 K is allowed in these
        checks.

    -   Supersaturation is removed (also performed in RUC40 analysis).

-   NCEP quality control flags for individual observations are used, and
    suspect observations are flagged so that they will not be used in
    the RUC20 analysis.

-   More robust hybrid coordinate adjustment.

The RUC20 OI analysis has been tested extensively at FSL with three
additional new observation types:

-   GPS ground-based precipitable water values (now over 100 in U.S.)

-   915 MHz boundary-layer profilers (about 25 in RUC domain)

-   RASS temperature low-level virtual temperature profiles from
    selected 405 MHz and 915 MHz profilers

Work by FSL and NCEP is nearly complete to make these observations
available to the RUC and other NCEP operational models, and it is likely
that they will be added to the RUC20 within 3 months after its initial
implementation.

**6. RUC20 OUTPUT FILES AND VARIABLES**

***6.a. Output files***

The output files from the RUC20 are essentially the same as those
produced by the RUC40, except that they will be available at both 20-km
and 40-km resolution. The 40-km files are meant to provide 'look-alike'
files so that the change will be relatively transparent to RUC users. A
list of the variables in each of these files is provided at
<http://ruc.fsl.noaa.gov/ruc2vars.html> . The gridded files provided by
the RUC20 are reviewed below:

-   Native (bgrb, bgrb20) files -- 14 3-d variables (no change from
    RUC40) and 46 2-d variables (the last 8 are new, but the first 38
    are identical to those being produced currently by the RUC40).

    -   There are 50 vertical levels in the bgrb files at both 20-km and
        40-km resolution, different from the 40 levels in the RUC40 bgrb
        files.

-   Isobaric (pgrb, pgrb20) files -- 6 3-d variables at 25-mb vertical
    resolution from 1000-100 hPa and 88 2-d variables (surface,
    precipitation, mean-layer values, etc.). No change from RUC40
    variables.

-   Surface (sgrb, sgrb20) files -- 25 2-d variables (surface,
    precipitation, precipitation type, stability indices, etc.). No
    change from RUC40 variables. All fields in the sgrb files are also
    found in the pgrb files.

Improved BUFR data are available from RUC20. Hourly BUFR soundings with
the same format as used for the Eta model are available with the RUC20,
including individual station files. These individual station files (only
\~25-50 KB each) were not available with the RUC40. The hourly output to
12 h is also new with the RUC20. The station list is the same as that
used for the Eta model for stations within the RUC domain. (One small
difference in the BUFR data is that the RUC uses 6 soil levels compared
with 4 levels with Eta BUFR output.) The so-called 'monolithic' files
with all stations and all output times are also available from the
RUC20.

A summary of this information is available at
<http://ruc.fsl.noaa.gov/ruc20.data-access.html> .

***6.b. Changes to GRIB identifiers for RUC20***

When the RUC40 was implemented, some GRIB parameter values were used on
an interim basis until official designations were made. Since the RUC40
implementation, these GRIB parameter values have been officially
assigned. These updated parameter values have now been also been changed
in the RUC20, and changes are listed in the Table 5.

  ----------------------- ----------------------- -----------------------
  **Field**               **Parameter value in    **Parameter value in
                          RUC40**                 RUC20**

  Water vapor mixing      185                     53
  ratio                                           

  Gust wind speed         255                     180

  Soil moisture           207                     199
  availability                                    
  ----------------------- ----------------------- -----------------------

Table 5. Changes in GRIB parameters in RUC20

***6.c. Basic 3-d output variables***

There is no change in the 3-dimensional fields output by the RUC20 for
either bgrb (native) or pgrb (isobaric) fields resulting from
post-processing changes except that isobaric heights from the RUC20 are
smoother due to extra smoothing passes.

## *6.d. RUC 2-d diagnosed variables* 

As with the 3-d fields, the 2-d fields from the RUC20 are different from
those produced by the RUC40 due to all of the analysis, model,
resolution, etc. changes listed above in this document. Below are listed
2-d output variables for which there are significant changes from
changes in diagnostic techniques or for other reasons not previously
addressed in this document.

-   *2-m temperature and dewpoint, and 10-m winds*. Similarity theory is
    used to derive values at these levels rather than the previous
    approximation of simply using the 5-m values. Note that the RUC20
    continues to use a separate topography file (TOPOMINI, recalculated
    for 20km resolution) designed to more closely match METAR elevations
    than the model elevation. The 20-km TOPOMINI matches the METAR
    elevations more closely than the 40-km version. The 2-m temperature
    and dewpoint temperature values from the RUC are *not* from the
    model terrain but are instead reduced to the TOPOMINI elevation.
    Thus, the RUC20 2-m temperature and dewpoint values includes effects
    both from reduction to the TOPOMINI elevation and similarity
    reduction to 2-m above the surface.

-   *convective available potential energy.* Some bug fixes resulting in
    smoother CAPE and CIN (convective inhibition) fields.

-   *helicity* -- corrections to helicity and storm-relative motion
    calculations, including change to Bunkers et al. (2000) formulation.

![](media/image25.png){width="0.44027777777777777in"
height="2.0833333333333335in"}

![](media/image25.png){width="2.5506944444444444in"
height="1.5611111111111111in"}

Figure 13. Visibility (mi) valid at 1200 UTC 30 January 2002. a) RUC40
00-h forecast, b) RUC20 0-h forecast, c) METAR observations.

-   *MAPS mean sea-level pressure* -- Bug fixed for reduction over
    higher terrain, resulting in more coherent SLP patterns than in
    RUC40.

-   *precipitation type* -- Less diagnosis of sleet (ice pellets) in
    RUC20 due to cloud microphysics changes described in section 3.b.1.

-   *visibility* (see Smith et al. 2002, Smirnova et al. 2000b) -- RUC20
    diagnostic changed to use multiple levels near surface for
    hydrometeor and relative humidity and modification in hydrometeor
    and relative humidity effects. An example of an improved visibility
    diagnostic is shown in Fig. 13.

A detailed description of techniques to derive RUC diagnostic variables
is available at <http://ruc.fsl.noaa.gov/vartxt.html> . Some of these
are listed below, and are unchanged from RUC40.

**Relative humidity** - Defined with respect to saturation over water in
the RUC isobaric fields and in the surface relative humidity field.

**Freezing levels** - Two sets of freezing levels are output from RUC,
one searching from the bottom up, and one searching from the top down.
Of course, these two sets will be equivalent under most situations, but
they may sometimes identify multiple freezing levels. The bottom-up
algorithm will return the surface as the freezing level if any of the
bottom three native RUC levels (up to about 50 m above the surface) are
below freezing (per instructions from Aviation Weather Center, which
uses this product). The top-down freezing level returns the first level
at which the temperature goes above freezing searching from the top
downward. For both the top-down and bottom-up algorithms, the freezing
level is actually interpolated between native levels to estimate the
level at which the temperature goes above or below freezing.

**Tropopause Pressure** - Diagnosed from the 2.0 isentropic potential
vorticity unit (PVU) surface. The 2.0 PVU surface is calculated directly
from the native isentropic/sigma RUC grids. First, a 3-d PV field is
calculated in the layers between RUC levels from the native grid. Then,
the PV=2 surface is calculated by interpolating in the layer where PV is
first found to be less than 2.0 searching from the top down in each grid
column. Low tropopause regions correspond to upper-level waves and give
a quasi-3D way to look at upper-level potential vorticity. They also
correspond very well to dry (warm) areas in water vapor satellite
images, since stratospheric air is very dry.

**MAPS mean sea-level pressure** - This reduction is the one used in
previous versions of the RUC. It uses the 700 hPa temperature to
minimize unrepresentative local variations caused by local surface
temperature variations. This reduction is described by Benjamin and
Miller (1990). It has some improvement over the standard reduction
method in mountainous areas and gives geostrophic winds that are more
consistent with observed surface winds.

**3-h surface pressure change** - These fields are determined by
differencing surface pressure fields at valid times separated by 3 h.
Since altimeter setting values (surface pressure) are used in the RUC
analyses, this field reflects the observed 3-h pressure change fairly
closely over areas with surface observations. It is based on the
forecast in data-void regions. The 3-h pressure change field during the
first 3 h of a model forecast often shows some non-physical features,
resulting from gravity wave sloshing in the model. After 3 h, the
pressure change field appears to be quite well-behaved. The
smaller-scale features in this field appear to be very useful for seeing
predicted movement of lows, surges, etc. despite the slosh at the
beginning of the forecast.

**2m temperature, dewpoint temperature** - Temperature and dewpoint
temperatures displayed are extrapolated to a \"minimum\" topography
field to give values more representative of valley stations in
mountainous areas, where surface stations are usually located.

**Precipitation accumulation** - All precipitation values, including the
12-h total, are liquid equivalents, regardless of whether the
precipitation is rain, snow, or frozen.

**Resolvable and sub-grid scale precipitation** -- The Grell family of
convective schemes used in the RUC tends to force grid-scale saturation
in its feedback to temperature and moisture fields. One result of this
is that for the RUC model, some of the precipitation from weather
systems that might be considered to be largely convective will be
reflected in the resolvable-scale precipitation. Thus, the sub-grid
scale precipitation from RUC should *not* be considered equivalent to
\"convective precipitation\".

**Snow accumulation** - Snow accumulations are calculated using a 10 to
1 ratio between snow and liquid water equivalent. Of course, in reality,
the snow to snow liquid water equivalent ratio varies, but the ratio
used here was set at this constant value so that users will know the
water equivalent exactly.

Also, snow accumulation (through the snow liquid water equivalent) is
not diagnosed based on temperature, but is explicitly forecast through
the mixed-phase cloud microphysics in the RUC model.

**Categorical precipitation types - rain/snow/ice pellets/freezing
rain** - These yes/no indicators are calculated from the explicit cloud
microphysics in the RUC model (see section 3.b.1**).** *These values are
not mutually exclusive. More than one value can be yes (1) at a grid
point.* Here is how the diagnostics are done:

> Diagnostic logic for precipitation types

-   Snow

> There are a few ways to get snow.

-   If fall rate for snow mixing ratio at ground is at least 0.2 x
    10^-9^ g/g/second, snow is diagnosed.

-   If fall rate for graupel mixing ratio at ground is \> 1.0 x 10^-9^
    g/g/s and

    -   surface temp is \< 0 deg C, and max rain mixing ratio at any
        level \< 0.05 g/kg or the graupel rate at the surface is less
        than the snow fall rate, snow is diagnosed.

    -   surface temp is between 0 - +2 deg C, snow is diagnosed.

```{=html}
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-   Rain - If the fall rate for rain mixing ratio at ground is at least
    0.01 g/g/second, and the temperature at the surface is \> or = 0 deg
    C, then rain is diagnosed. The temperature used for this diagnosis
    is that at the minimum topography, described above.

-   Freezing rain - Same as for rain, but if the temperature at the
    surface is \< 0 deg C **and** some level above the surface is above
    freezing, freezing rain is diagnosed.

-   Ice pellets - If

    -   the graupel fall rate at the surface is at least 1.0 x 10^-9^
        g/g/s and

    -   the surface temp is \< 0 deg C and the max rain mixing ratio in
        the column is \> 0.05 g/kg and

    -   the graupel fall rate at the surface is greater than that for
        snow mixing ratio,

> then ice pellets are diagnosed.

**CAPE** - Convective available potential energy - indicates energy
available for buoyant parcel from native RUC hybrid-b level with maximum
buoyancy within 300 mb of surface. Before the most buoyant level is
determined, an averaging of potential temperature and water vapor mixing
ratio is done in the lowest seven RUC native levels (about 40 hPa).

**CIN** - Convective inhibition - indicates negative buoyancy in layer
through which a potentially buoyant parcel must be lifted before
becoming positively buoyant.

**Lifted index / Best lifted index** - Lifted index uses the surface
parcel, and best lifted index uses buoyant parcel from the native RUC-2
level with maximum buoyancy within 300 hPa.

**Precipitable water** - Integrated precipitable water vapor from
surface of RUC model to top level (\~50 mb). The precipitable water
calculation is performed by summing the product of the specific humidity
at each level times the mass of each surrounding layer. This mass layer
is bounded by the mid-points between each level, since the native RUC
vertical grid is non-staggered.

**7. STATISTICAL VERIFICATION AGAINST RAWINSONDES**

RUC20 forecast skill was compared with that of the RUC40 for
retrospective periods from February 2001 (cold season, statistics at
http://www.emc.ncep.noaa.gov/mmb/ruc2/oiretrostats/) and July 2001 (warm
season, statistics at
http://www.emc.ncep.noaa.gov/mmb/ruc2/summerretrostats/). In addition,
recent real-time runs provide results from cold season and transition
season periods (statistics at
http://www.emc.ncep.noaa.gov/mmb/ruc2/stats). In general, RUC20 analyses
do not fit rawinsonde data quite as closely at this time as RUC40
analyses. This may be due to improved use of aircraft ascent/descent
data in the case of wind and temperature analyses, and the use of ln q
as a moisture analysis variable in the case of relative humidity.

![](media/image27.png){width="3.375in" height="2.939583333333333in"}

![](media/image28.png){width="3.433333333333333in"
height="2.977777777777778in"}

Figure 14. Verification of RUC40 and RUC20 3-h and 12-h forecasts
against rawinsonde observations. For a) wind, and b) temperature, and
for period 22 January -- 8 February 2002.

For wind forecasts (Fig. 14a), the RUC20 provides some improvement over
the RUC40 for 3-h forecasts (margin 0 -- 0.3 m/s) and for 12-h forecasts
(margin 0.1 -- 0.4 m/s). For temperatures (Fig. 14b), the RUC20 against
gives some improvement by this measure, especially in the lower
troposphere.

**8. REFERENCES**

RUC web page -- [http://ruc.fsl.noaa.gov](http://ruc.fsl.noaa.gov/) -
real time products and a great deal of other information including a
more complete list of references

Arakawa, A., and V.R. Lamb, 1977: Computational design of the basic
dynamical processes of the UCLA general circulation model. Methods in
Computational Physics, Vol. 17, Academic Press, 174-265, 337 pp.

Benjamin, S.G., J.M. Brown, D. Devenyi, G.A. Grell, D. Kim, T.L. Smith,
T.G. Smirnova, B.E. Schwartz, S. Weygandt, K.J. Brundage, and G.S.
Manikin, 2002a: The 20-km Rapid Update Cycle\--Overview and implications
for aviation applications. 10th Conf. on Aviation, Range, and Aerospace
Meteorology, Portland, OR, Amer. Meteor. Soc. (available in PDF from
http://ruc.fsl.noaa.gov under 'RUC pubs')

Benjamin, S.G., D. Kim, and J.M. Brown, 2002b: Cloud/hydrometeor
initialization in the 20-km RUC with GOES and radar data. 10th Conf. on
Aviation, Range, and Aerospace Meteorology, Portland, OR, Amer. Meteor.
Soc. (available in PDF from http://ruc.fsl.noaa.gov under 'RUC pubs')

Benjamin, S.G., J.M. Brown, K.J. Brundage, B.E. Schwartz, T.G. Smirnova,
T.L. Smith, L.L. Morone, 1998: RUC-2 - The Rapid Update Cycle Version 2.
NWS Technical Procedure Bulletin No. 448. NOAA/NWS, 18 pp. \[National
Weather Service, Office of Meteorology, 1325 East-West Highway, Silver
Spring, MD 20910\] Available online at
<http://205.156.54.206/om/tpb/448.htm> ).

Benjamin, S.G., K.J. Brundage, and L.L. Morone, 1994: The Rapid Update
Cycle. Part I: Analysis/model description. Technical Procedures Bulletin
No.\~416, NOAA/NWS, 16 pp. \[National Weather Service, Office of
Meteorology, 1325 East-West Highway, Silver Spring, MD 20910\].

Benjamin, S. G., K. A. Brewster, R. L. Brummer, B. F. Jewett, T. W.
Schlatter, T. L. Smith, and P. A. Stamus, 1991: An isentropic
three-hourly data assimilation system using ACARS aircraft observations.
*Mon. Wea. Rev*., **119**, 888-906.

Benjamin, S. G., and P. A. Miller, 1990: An alternative sea-level
pressure reduction and a statistical comparison of surface geostrophic
wind estimates with observed winds. *Mon. Wea. Rev*., **118**,
2099-2116.

Benjamin, S. G., 1989: An isentropic meso-alpha scale analysis system
and its sensitivity to aircraft and surface observations. *Mon. Wea.
Rev*., **117**, 1586-1603.

Bleck, R., and S.G. Benjamin, 1993. Regional weather prediction with a
model combining terrain-following and isentropic coordinates. Part I:
model description. *Mon. Wea. Rev*., **121**, 1770-1885.

Brown, J.M., T.G. Smirnova, S.G. Benjamin, R. Rasmussen, G. Thompson,
and K. Manning, 2000: Use of a mixed-phase microphysics scheme in the
operational NCEP Rapid Update Cycle. Preprints, 9th Conf. on Aviation,
Range, and Aerospace Meteorology, AMS, Orlando, 100-101.

Bunkers, M.J., B.A. Klimowski, J.W. Zeitler, R.L. Thompson, M.L.
Weisman, 2000: Prediction of supercell motion using a new hodograph
technique. *Wea. Forecast.*, **15**, 61-79.

Burk, S.D., and W.T. Thompson, 1989: A vertically nested regional
numerical prediction model with second-order closure physics. *Mon. Wea.
Rev*., **117**, 2305-2324.

Csiszar, I. and G. Gutman, 1999: Mapping global land surface albedo from
NOAA/AVHRR. *J Geophys. Res.* **104**, 6215-6228.

Davies, H.C., 1976: A lateral boundary formulation for multi-level
prediction models. *Tellus*, **102**, 405-418.

Devenyi, D., S.G. Benjamin, and S.S. Weygandt, 2001: 3DVAR analysis in
the Rapid Update Cycle. 14th Conf.

on Numerical Weather Prediction, Fort Lauderdale, FL, Amer. Meteor.
Soc., J103-J107.

Dudhia, J., 1989: Numerical study of convection observed during the
winter monsoon experiment using a mesoscale two-dimensional model. *J.
Atmos. Sci*., **46**, 3077-3107.

Govett, M.W., D.S. Schaffer, T.Henderson, L.B. Hart, J.P. Edwards, C.S.
Lior and T.L. Lee, 2001: SMS: A directive-based parallelization approach
for shared and distributed memory high performance computers.
Proceedings 9^th^ ECMWF Workshop on the Use of High Performance
Computing in Meteorology, Volume: Developments in TeraComputing, World
Scientific, pp 251-268.

Grell, G.A., 1993: Prognostic evaluation of assumptions used by cumulus
parameterizations. *Mon. Wea. Rev*., **121**, 764-787.

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1994: A description of the
fifth-generation Penn State/NCAR Mesoscale Model (MM5). NCAR Technical
Note, NCAR/TN-398 + STR, 138 pp.

Grell, G.A., and D. Devenyi, 2001: Parameterized convection with
ensemble closure/feedback assumptions. Ninth Conf. on Mesoscale
Processes, Fort Lauderdale, FL, Amer. Meteor. Soc., 12-16.

Gutman, G. and A. Ignatov, 1998: The derivation of green vegetation
fraction from NOAA/AVHRR for use in numerical weather prediction models.
*Int. J. Remote Sens*., **19**, 1533-1543.

Johansen, O., 1975: Thermal conductivity in soils. Ph.D. thesis,
University of Trondheim, 236 pp. (English translation 637, Cold Reg.
Res. and Eng. Lab., Hanover, N.H., 1977).

Johnson, D.R., T.H. Zapotocny, F.M. Reames, B.J. Wolf, and R.B. Pierce,
1993: A comparison of simulated precipitation by hybrid isentropic-sigma
and sigma models. *Mon. Wea. Rev*., **121**, 2088-2114.

Johnson, D.R., A.J. Lenzen, T.H. Zapotocny, and T.K. Schaack, 2000:
Numerical uncertainties in the simulation of reversible isentropic
processes and entropy conservation. *J. Climate,* **13**, 3860-3884.

Kain, J.S., and J.M. Fritsch, 1992: The role of the convective "trigger
function" in numerical forecasts of mesoscale convective systems.
*Meteor. Atmos. Phys*., **49**, 93-106.

Kain, J.S., M.E. Baldwin, D.J. Stensrud, T.L. Black, and G.S. Manikin,
1998: Considerations for the implementation of a convective
parameterization in an operational mesoscale model. Preprints 12^th^
Conf. Num Wea. Pred., AMS, Phoenix, 103-106.

Kim, D., and S.G. Benjamin, 2001: Cloud/hydrometeor initialization for
the 20-km RUC using satellite and radar data. 14^th^ Conf. on Numerical
Weather Prediction, Fort Lauderdale, FL, Amer. Meteor. Soc., J113-J115.

Kim, D., and S.G. Benjamin, 2000: An initial RUC cloud analysis
assimilating GOES cloud-top data. Preprints, 9th Conf. on Aviation,
Range, and Aerospace Meteorology, AMS, Orlando, 522-524.

Lynch, P. and X.-Y. Huang, 1992: Initialization of the HIRLAM model
using a digital filter. *Mon. Wea. Rev.,* **120**, 1019-1034.

Pan, H.-L. and L. Mahrt, 1987: Interaction between soil hydrology and
boundary-layer development. *Bound.-Layer Meteorol.,* **38**, 185-202.

Olsen, M.A., W.A. Gallus, J.L. Stanford, and J.M. Brown, 2000: An
intense Midwestern cyclone: Fine-scale comparisons of model analysis
with TOMS total ozone data. *J. Geophys. Res*., **105**, 20487-20495.

Pan, Z., S.G. Benjamin, J.M. Brown, and T. Smirnova. 1994. Comparative
experiments with MAPS on different parameterization schemes for surface
moisture flux and boundary- layer processes. *Mon. Wea. Rev*. **122**,
449-470.

Peters-Lidard, C. D., E. Blackburn, X. Liang, and E. F. Wood, 1998: The
effect of soil thermal conductivity parmeterization on surface energy
fluxes and temperatures, *J. Atmos. Sci*., **55**, 1209-1224.

Reisner, J., R.M. Rasmussen, and R.T. Bruintjes, 1998: Explicit
forecasting of supercooled liquid water in winter storms using the MM5
mesoscale model. *Quart. J. Roy. Meteor. Soc*., **142**, 1071-1107.

Schwartz, B.E., and S.G. Benjamin, 2002: Verification of RUC surface
forecasts at major U.S. airport hubs. 10th Conf. on Aviation, Range, and
Aerospace Meteorology, Portland, OR, Amer. Meteor. Soc. (available in
PDF from http://ruc.fsl.noaa.gov under 'RUC pubs')

Smirnova, T.G., S.G. Benjamin, and J.M. Brown, 2000: Case study
verification of RUC/MAPS fog and visibility forecasts. Preprints, 9th
Conf. on Aviation, Range, and Aerospace Meteorology, AMS, Orlando,
31-36.

Smirnova, T.G., J.M. Brown, S.G. Benjamin, and D. Kim, 2000:
Parameterization of cold-season processes in the MAPS land-surface
scheme. *J. Geophys. Res.,* **105, D3,** 4077-4086.

Smirnova, T. G., J. M. Brown, and S. G. Benjamin, 1997: Performance of
different soil model configurations in simulating ground surface
temperature and surface fluxes. *Mon. Wea. Rev*., **125**, 1870-1884.

Smith, T.L., and S.G. Benjamin, 2002: Visibility forecasts from the
RUC20. 10th Conf. on Aviation, Range, and Aerospace Meteorology,
Portland, OR, Amer. Meteor. Soc. (available in PDF from
http://ruc.fsl.noaa.gov under 'RUC pubs')

Smolarkiewicz, P.K., 1983: A simple positive-definite advection
transport algorithm. *Mon. Wea. Rev*., **111**, 479-486.

**APPENDIX A. Known or suspected RUC20 biases or deficiencies as of
April 2002 (per FSL)**

-   Some remaining light precipitation bias. Even though the RUC20
    clearly has reduced the dry precipitation bias from the RUC40, some
    of this bias remains (Fig. 7).

-   Weak diurnal cycle. Again, this problem has been considerably
    improved in the RUC20, but it has not disappeared. The RUC20 seems
    to do fairly well for daytime temperatures, but overall, does not
    cool quite enough at night (Fig. 8).

-   Too cold over snow cover. The RUC20 seems to cool off too much over
    snow covered areas. FSL has developed a fix to this problem that
    will be tested further and, if successful, will be implemented
    hopefully over the next several months.

**APPENDIX B. Comments from field users during RUC20 evaluation from
late March to early April 2002.**

##### Fred Mosher -- SOO -- Aviation Weather Center

While the time period for the RUC20 evaluation was short, and the
weather was rather benign during the evaluation period, the evaluation
did show the RUC20 to be a definite improvement over the current RUC2
model. The AWC evaluation focused mainly on the derived hazard fields
(clouds, convection, turbulence, and visibility) rather than the
traditional state of the atmosphere parameters (winds, temperature,
etc.). The cloud tops and the convective cloud tops showed a major
improvement, as did the visibility fields. This shows a definite
improvement in the moisture distribution and the cloud physics
parameterizations within the models, as well as the ability of the RUC20

to better assimilate initial time period meteorological information. We
did not notice any degradation of the forecast skill for any field, and
we did notice big improvements in some fields. Hence the AWC would
recommend that the RUC20 model become the operational NCEP model used
for short term forecasts.

*Steve Weiss -- SOO -- Storm Prediction Center*

Our ability to assess the RUC20 has been tempered somewhat by the
relatively inactive severe weather season so far this spring, however we
have been able to formulate some preliminary assessments based on a
small number of cases so far.  I will focus on the Mar 25, Mar 29, and
Apr 2 severe weather cases and attach some gif images relevant to each
case.  In the gif images \[not shown here\], the RUC40x files refer to
the RUC20 output displayed on a 40 km grid.  In addition, Greg Carbin
has created two web pages that examine 1) a 3 hour forecast of
precipitation valid at 00z Mar 18, and 2) 06z 28 Mar 00hr forecasts of
850 mb wind associated with the low level jet.  These can be found at

1)   <http://www.spc.noaa.gov/staff/carbin/rucrvu/>\
2)   <http://www.spc.noaa.gov/staff/carbin/rucrvu2/>

In Greg\'s first case, the RUC20 appears to overforecast the development
of a precipitation along a front across the TN valley into AR, with
radar showing that an elevated band of convection north of the front
(and RUC20 forecast) is the primary precipitation activity at the
verifying time.  In his second, case, he observed that the RUC20 depicts
850 mb winds that are much weaker than observed by profilers and radar
VWP. (There is some question regarding a possible influence of birds
and/or insects in the profiler/VAD winds, especially near the center of
the 850 mb low where you might expect weaker winds.)   In both cases,
the RUC40 appeared to be better than the RUC20.  If you have the data
available, it would be good to look back at these cases. \[FSL note:
This case is a bird contamination flagging issue. The RUC40 does not use
the Profiler Hub flags, and so it let through profiler observations that
the RUC20 did not use since it honors the Profiler Hub flags.\]

Our assessment focus has been primarily on short range forecasts of
moisture, instability, and precipitation in support of our short range
severe weather forecast mission.  Overall, we have found no persistent
evidence suggesting that the RUC20 should not be implemented as
scheduled on April 16.  The higher model resolution in the RUC20 seems
to develop mesoscale features in the precipitation and vertical velocity
fields that appear more realistic than the RUC40, even when viewed at
identical display resolutions.  In addition, our small sample indicates
the forecasts of MUCAPE are better from the RUC20 than the RUC40,
although aspects of low level temperature and dew point profiles from
one case (Mar 29) raise interesting questions concerning the evolution
of the afternoon boundary layer.  Given the small number of cases we
have seen, we plan to continue evaluating the RUC20 during this storm
season in order to gain a better understanding of its strengths and
weaknesses as it relates to convective forecasting issues.  As always,
we appreciate the opportunity to participate in the pre-implementation
evaluation.

Mar 25\...15z runs with forecasts valid at 00z and 03z\
A weak surface low was forecast to move into central AR during the
afternoon, and both RUC20 and RUC40 showed a similar scenario that
verified well by 00z.  The RUC20 predicted higher CAPE into central AR
compared to the RUC40 (1000-1500 j/kg versus around 500 j/kg) and the
stronger CAPE forecast also verified better.  Both RUC versions
predicted 3 hourly precipitation developing near the front from western
TN across AR into parts of LA and east TX by 00z and continuing through
03z.  Although precipitation did develop along the corridor predicted,
both models were too fast in developing storms southward into east TX. 
The RUC20 700-500 mb mean vertical velocity and 3 hourly precipitation
forecasts exhibited more detailed structures that appeared to relate
better to the actual convective development when compared to the RUC40
forecasts.

Mar 29\...12z runs with forecasts valid 00z\
On this day, there were two severe threat areas: 1) morning elevated
severe storms moving eastward from MO toward the OH valley were expected
to develop southward into the warm sector over AR/TN during the
afternoon, and 2) new convection was expected to develop over west/north
central TX during the late afternoon or evening as moisture returned
northwestward across TX in advance of a strong upper low moving toward
the southern Rockies.

Both models were similar in predicting surface dew points over the lower
MS valley region although the 12 hour forecast from the RUC20 was
considered slightly better.  Across TX both models did not transport
surface moisture fast enough into southwest and central TX, with the
RUC40 worse than the RUC20.  This resulted in not enough instability
being forecast into central and southwest TX by both models.  Overall,
the instability predicted over the lower MS valley region by the RUC20
was \"in the ballpark\", and better than that from the RUC40 (see below
for more discussion of sounding profiles).

12 hour forecasts of 3 hourly precipitation were similar from both
models but the RUC20 showed more realistic details in structure and
location when compared to observed radar images over the OH and lower TN
valleys.  Unlike the RUC40, the RUC20 also developed precipitation over
a small part of southwest TX by 00z.  Although deficient in coverage,
the RUC20 forecast was more in agreement with the severe storms that had
developed by that time over parts of southwest/west central TX.

We also looked closely at model forecast soundings constructed from 25
mb vertical grids, and compared the model forecasts with observed
soundings at LIT, SHV, and JAN.  (There was precipitation occurring at
BNA by 00z, so this sounding may not be representative of the
preconvective environment.)  In all cases, the models were able to
accurately predict the general vertical structure in the warm sector
showing a warm, moist boundary layer overlaid by an inversion based in
the 800-850 mb layer, with drier conditions above the inversion before
moistening again in the middle and upper levels.  The forecast inversion
was not as sharp as in the observed soundings, but this may be partially
related to the use of 25 mb vertical grids which can smooth out some of
the details between vertical levels.  In all cases the RUC20 appeared to
produce a boundary layer that was cooler and more moist than the
observed boundary layers.  The RUC40 forecast soundings were
characterized by low level temperature profiles similar to observed
profiles, but moisture was greater than forecast (similar to the
RUC20).  As a result, the RUC20 moisture/temperature errors tended to
compensate for each other and forecast MUCAPE values were closer to the
observed values, whereas the RUC40 MUCAPE values were much higher than
observed.   Here is a small table with forecast and observed MUCAPE
values from two raob sites at 00z 30 Mar computed from NSHARP:

Location    RUC20    RUC40     Raob\
SHV            2303        3869        2831\
LIT              2708        3541        1879\
(JAN observed sounding was a short run - observed MUCAPE could not be
computed)

Apr 02\...12z run with forecasts valid 00z\
There was a slight risk of severe thunderstorms across parts of AR/west
TN is the day 2 and day 1 outlooks.  Moisture was forecast to return
northward ahead of an advancing cold front, with an axis of instability
forecast by the RUC40 and RUC20 during the afternoon.  A primary
question was determining whether or not thunderstorms would develop
along the front during the afternoon.  Both versions of the RUC
indicated little in the way of precipitation by 00z, although the RUC20
showed a better defined axis of upward vertical motion in the 700-500 mb
layer north of the surface front location.  The lack of precipitation
verified quite well, as thunderstorms failed to develop across the
area.  In this case, the forecast soundings were quite close to the
observed sounding at LIT, including boundary layer profiles of
temperature and moisture.

##### Tim Garner -- NWS Spaceflight Meteorology Group (SMG), Johnson Space Flight Center, Houston, TX

I filled out the on-line form concerning the RUC for a forecast on 25
March for the Edwards AFB and White Sands areas. The RUC20 properly
simulated that the mountains east of White Sands would block the
progress of a cold

front. Low level winds on either side of the Tularosa Basin (where White
Sands is) were simulated quite well. Flow inside the basin during the
day was quite light and variable so it was hard to ascertain how well
the model

performed. In general that day it did an admirable job simulating the
low level winds in southern California.

I looked in more detail on the 27th when I used the 06Z and 12Z RUC20
runs as the primary tool for a landing simulation that we were working.
The RUC20 appeared to be the only model (including NGM and AVN MOS) that

forecasted a sea breeze in Florida. I have saved a series of GIF images
from that day if you or Stan would like to see them. The forecast
verified quite well. I had to fend off a lot of questions from some of
the NASA

users as to whether or not I was sure the winds would change. The RUC20
was almost spot on with the 10m winds. It did seem to overdo the
precipitation in Florida later that afternoon, but I didn\'t stick
around much after 21Z to

see how well it did. This is a great improvement. I remember how poorly
the RUC low level winds were over Florida when it first came out. The
early RUC was so disappointing that we lost so much confidence in it
that we

rarely used it.

As far as precipitation forecasts go, neither Tim Oram or I have noticed
whether it has been any better or worse than the RUC40.

*Pablo Santos -- SOO, Miami, FL*

We have been using the model operationally for almost two weeks. Weather
has been quite active for us particularly during the afternoons this
whole week. I used the model myself operationally for two days

last week and I have gotten feedback from 2 forecasters so far. So far
the model is proving to be a very good mesoscale guidance tool. It picks
up the sea breeze development but not as well as the Eta 12 although we
might attribute that to resolution \[using 40km display\] and the fact
the we are looking at the Eta 12 in AWIPS through the D2D which gives us
a lot of control over the display properties. The precipitation field
forecast is turning out to be pretty good also although we do not
concentrate much on QPF but rather the when and where. In this area it
seems to be hand on hand with the Eta12. Although it is to early and
soon to tell given how long we have had it, you can tell data from the
FSL Mesoscale data networks is going into it, and hence FAWN (Florida
Agricultural Weather

Network) (am I right?). It seems it produces better analysis fields to
begin with that guidance we obtain from NCEP. Again, this is something I
cannot conclude for certain until I get the data in AWIPS and am able to
sample to grid.

The great advantage with this model is how frequently it updates. It
really provides us with an excellent tool in the scale of hours when
rapidly developing/weakening Florida type convection occurs. That to us
is invaluable.

*Chris Buonanno -- SOO, Little Rock, AR*

Our office has often utilized the precipitation forecasts from the
RUC20. We have found these forecasts to be particularly useful during
the 6-18 h time frame, to help determine areal coverage (or lack of),
and quantitative

precipitation amounts during convective situations. We have noted that
overall locations of forecast precipitation from the RUC20 seem to be
improved to that from the RUC40. We have also noted during several
recent events that the RUC20 correctly forecasted a lack of
precipitation during situations where convective inhibition limited the
extent of convection.