Changes to the NCEP Operational "Early" Eta Analysis
/ Forecast System
Eric Rogers, Michael Baldwin, Thomas Black, Keith Brill, Fei
Chen, Geoffrey DiMego, Joseph Gerrity, Geoffrey Manikin, Fedor Mesinger,
Kenneth Mitchell, David Parrish, Qingyun Zhao
Environmental Modeling Center, National Centers for Environmental
The National Centers for Environmental Prediction (NCEP) "Early"
Eta analysis and forecasting system was operationally implemented in June
1993, replacing the Limited-Area Mesh Model in providing early forecast
guidance over North America (Black et al., 1993, Rogers et al. 1995). The
June 1993 system was comprised of 1) an Eta Regional Optimum Interpolation
(OI, DiMego, 1988) analysis using a first guess from the NCEP Global Data
Assimilation System (GDAS), and 2) a 48-h Eta model forecast at a resolution
of 80-km with 38 vertical levels.
In 1995 two major improvements in mesoscale forecast guidance from NCEP
were implemented. First, NCEP began twice-daily forecasts of a 29-km, 50
level eta model (Eta-29, Black, 1994) over the contiguous United States
and adjacent areas in March 1995. A 33-h forecast from the Eta-29 is run
from 0300 and 1500 UTC initial conditions after a 3-h spin-up cycle. Second,
in October 1995 NCEP implemented the Eta-48 system to replace the 80-km
Early Eta (Rogers et al., 1996) which included an increase in horizontal
resolution to 48-km, and the use of the Eta Data Assimilation System (EDAS)
to produce an analysis and first guess more consistent with the forecast
model than that obtained from the GDAS. Model physics were enhanced in
the 1995 upgrade with the introduction of a cloud prediction scheme (Zhao
et al., 1997) and in January 1996 with a new land-surface model (Chen et
al., 1996), a new viscous sublayer parameterization (Janjic 1996a) and
a revised version of the Mellor-Yamada level 2.5 turbulence scheme (Janjic,
1996b). Examination of model surface temperature / moisture during 1996
showed that the skin temperature generally showed too large a diurnal cycle.
Several factors contributed to this problem, including insufficient absorption
of shortwave radiation, underestimates of cloud, and a negative bias in
surface evaporation. To address these concerns several modifications to
the model radiation package and land-surface model (Black et al., 1997,
Betts et al., 1997) were implemented in the Eta-48 and Eta-29 systems in
Recent studies (Baldwin and Black, 1996; Schneider et al. 1996; McDonald
et al., 1998) have shown that the Eta-29 is a valuable tool in predicting
mesoscale features, such as orographic influences on precipitation and
downslope wind events. Feedback from NWS field forecasters to Environmental
Prediction Center (EMC) scientists supports this perception, but since
the Eta-29 runs about 2-3 hours after the Eta-48, Eta-29 forecasts usually
arrive too late to NWSFO meteorologists to impact their short-range forecasts.
In an effort to improve the quality and timeliness of NCEP's mesoscale
forecast guidance over North America, a series of enhancements to the Early
Eta system is currently undergoing testing prior to operational implementation:
- Increase in resolution from 48-km / 38 levels to 32-km / 45 levels with little change in the size of the horizontal domain
- Replacement of the regional OI analysis with a regional 3-d variational analysis (3DVAR; Parrish et al., 1996) designed for use with the Eta model
- Use of a "partial" continuous EDAS cycle in which soil parameters (temperature / moisture) and cloud water are obtained from the previous EDAS cycle instead of the GDAS (Rogers et al., 1996, 1998), accompanied by an increase in the number of soil layers in the Eta model land-surface physics package from two to four
- Modification of the Eta model's computation of the master length scale
for vertical turbulent transport
The changes listed above will be discussed in detail in Section 2. Impact
of these changes on model performance will follow in Section 3. A discussion
of future plans for the Early Eta are described in Section 4.
2. CHANGES TO THE OPERATIONAL ETA SYSTEM
2.1 Horizontal and vertical resolution
Fig. 1 shows the horizontal domains of
the Eta-48, Eta-29 and the new 32-km grid (called Eta-32), while the vertical
resolution of the eta layers in the three configurations is shown in Fig.
2. The choice of 32-km / 45 levels to replace the current 48-km / 38 level
configuration represents a compromise among several factors:
- Increase the resolution of the Early Eta system to be as close as possible to the Eta-29
- Keep the model horizontal domain size nearly the same as the current 48-km grid
- Design the new Early Eta configuration so that it can be run using
NCEP's current computing resources, and that operational products from
the Early Eta (e.g., AFOS, facsimile charts, FOUS) are available to forecasters
at the same time as they are currently disseminated
The resulting 32-km horizontal domain covers all of North America as
did the 48-km grid. The eastern boundary of the 32-km grid is close to
that of the 48-km, in order to capture as much of the tropical Atlantic
as possible and to keep Puerto Rico inside the domain. Similar reasoning
was employed at the northern boundary for Alaska. The biggest difference
is seen along the western boundary, where Hawaii, although still inside
the 32-km grid domain, is much closer to the boundary than with the 48-km
In order to partially alleviate any potential problem caused by a closer
western boundary, two changes were made to the processing of the NCEP Aviation
(AVN) model forecasts used as lateral boundary conditions. In the 48-km
system, the EDAS / Early Eta computes boundary tendencies every 6-h from
the 12-h old AVN forecast. The Eta-29 computes boundary tendencies
every 3-h from the current AVN forecast. In the Eta-32, boundary
tendencies will be computed every 3-h from the previous AVN forecast. Since
the AVN is now run every 6-h (Iredell and Caplan, 1997), tendencies computed
from the "off-time" AVN (0600 and 1800 UTC start time) will be
used for the last 6-h of the EDAS and for the 48-h forecast.
The vertical structure of the Eta-48 is shown in Fig.
2a, the Eta-29 in Fig. 2b, and the
Eta-32 in Fig. 2c. As with the horizontal
grid, the choice of 45 vertical layers represents a compromise between
the 38 levels in the Eta-48 and the 50 levels in the Eta-29. In order to
allow the Eta-32 to better resolve low-level mesoscale structures in mountainous
areas (like the western United States and Alaska) most of the extra model
levels were added below 700 mb.
An example in model terrain differences between the Eta-29, Eta-32,
and Eta-48 model grids over northern California is shown in Fig.
3. The Eta model uses step-mountain orography, in which after interpolation
to the eta native grid (a semi-staggered Arakawa e-grid), the step-mountain
is raised or lowered to the closest vertical interface. Since the 3 models
depicted in Fig. 3 have a different vertical
structure, the steps will not be at the same elevation in each model.
As expected, the Eta-29 and Eta-32 model orography shows considerably
more detail than the the Eta-48, particularly in differentiating between
the Sierra Nevada and Cascade ranges in northern California. One obvious
difference between the Eta-29 and Eta-32 models is in the depiction of
the Great Basin in northern Nevada. The Eta-29 terrain shows most of the
region at one elevation, while in the Eta-32 this region is depicted on
3 different steps. This difference is due to a modification of the Eta
model orography algorithm toward one that is more "valley-favoring"
(Mesinger, 1997, personal communication) that adds more small-scale detail.
The impact of the increase in resolution to 32-km / 45 levels is seen
in Table 1, which shows the observed elevation, and the terrain height
in both the Eta-48 and Eta-32 at selected rawinsonde stations in the western
United States. Of the 13 stations listed, 12 moved closer to the observed
elevation. Although grid-cell mean terrain height, by definition, will
continue to be higher than the elevation of individual stations (usually
located in valleys), the differences will be less pronounced with the new
algorithm described above.
Table 1 - The Impact of the Increase in Resolution
|Station name||WMO Station ID||Observed Elevation (m)||Eta-48 terrain height (m)||Eta-32 terrain height (m)|
|Great Falls, MT||72776||1130||1252||1194|
|Salt Lake City, UT||72572||1288||1743||1839|
|Grand Junction, CO||72476||1475||2321||2213|
2.2 The regional 3-dimensional variational analysis
A regional 3DVAR analysis has been developed for use in the EDAS (Parrish
et al., 1996) as a replacement for the Eta OI analysis system. It is patterned
after the NCEP global spectral statistical interpolation analysis (Parrish
and Derber, 1992) with several differences. First, the background error
statistics are simulated in grid space instead of model space, using a
recursive filter (Hayden and Purser, 1995). Second, there are no fast and
slow variables. Approximate balance is maintained only through a weak constraint
on the thermal wind. Also, the primary analysis variables in the regional
3DVAR (as in the regional OI) are at observation locations, not model grid
Like the OI analysis, the 3DVAR analyzes wind and specific humidity.
While the OI analyzes geopotential height (and infers the temperature analysis
from the analyzed thickness) the regional 3DVAR analyzes both height and
temperature. Therefore, it can easily assimilate "isolated" temperature
observations, such as the high density ACARS temperature data. The 3DVAR
analysis can also assimilate other data types (such as radial velocities
from NEXRAD radars, and direct radiances from GOES or polar orbiting satellites)
which are not easily adapted for use in the NCEP regional OI analysis.
From 1996 onward the 3DVAR analysis has undergone extensive parallel
testing in an 80-km EDAS, with an 80-km Eta OI EDAS as control. Fig.
4 shows the RMS temperature error versus rawinsonde data of the OI
and 3DVAR analyses at the end of the 12-h EDAS cycle (valid at 0000 and
1200 UTC) for October 1996. The 3DVAR analysis has lower RMS errors at
most of the mandatory pressure levels. The greatest differences are seen
above 250 mb, where one would expect the use of ACARS temperature data
to have the greatest impact. A similar signal was seen in the RMS wind
and specific humidity errors (not shown). RMS temperature errors for the
12-h through 48-h forecasts (not shown) had a similar signal, with the
greatest impact from the 3DVAR analysis seen between 300 and 150 mb.
The equitable threat score (ETS) and bias score for 80-km Eta model
forecasts from the OI and 3DVAR analyses for August - November 1996 are
shown in Fig. 5. These charts show that
the 3DVAR has a small positive impact on precipitation forecasts at nearly
The 3DVAR will use all the data types that are used in the OI analysis
- Rawinsonde mass and wind
- Pibal winds
- Wind profilers
- Surface land temperature and moisture
- Oceanic surface data (ships and buoys)
- Aircraft winds
- Satellite cloud-drift winds
- Oceanic TOVS thickness retrievals
- GOES and SSM/I precipitable water retrievals
With the operational implementation of the 3DVAR analysis in the Eta-32
system, the following additional data types will be used which were not
analyzed by the OI analysis:
- ACARS temperature data
- Surface winds over land
- VAD winds from NEXRAD
- SSM/I oceanic surface winds
- Tropical cyclone bogus data
Additional data types, such as the NEXRAD radial velocities and GOES radiance data, will be included in the future.
2.3 Continuously cycled EDAS / 4-layer soil model
The EDAS which runs as part of the Eta-48 system is a 12-h assimilation
with 3-h analysis updates, initialized from the GDAS. Initial values of
soil parameters are obtained from the GDAS. The latter has had significant
soil moisture biases (Wu et al., 1997), owing to biases in the 6-h global
model forecast of precipitation and solar insolation. Most notable is the
GDAS positive soil moisture bias in the southeast United States during
the warm season. In the Eta-48 and Eta-29 model codes, an arbitrary adjustment
of the input GDAS soil moisture is applied at the beginning of the EDAS
to mitigate the aforementioned bias.
EMC has developed the capability to run the EDAS in continuous cycling mode, in which the previous EDAS run is used to initialize the next cycle (Rogers et al., 1996). The option exists to run either "full" cycling, in which all variables (atmospheric winds/temperature/moisture plus soil moisture / temperature, cloud water, and turbulent kinetic energy (TKE)) are cycled, or "partial" cycling, in which the soil, cloud, and TKE parameters are cycled but the first guess atmospheric state variables are obtained from the GDAS.
Although experiments at 80-km resolution (Rogers et al., 1996) show
no adverse impact from full cycling and minor positive impact on quantitative
precipitation skill scores, NCEP has decided that with the multitude of
changes described above, a more conservative approach to cycling would
be prudent. Therefore, in this package of changes the EDAS has been modified
to run in partial cycling mode. The soil moisture pattern which evolves
from the continuous EDAS is used to initialize all Eta model forecasts.
Since the EDAS/Eta model has lower precipitation biases than the GDAS (Mesinger,
1996), use of a cycled EDAS will lead to improved soil moisture. An improved
depiction of soil moisture during the model integration leads to a better
simulation of the surface processes (fluxes, evaporation, etc) with positive
impact on 2-m temperature and humidity, boundary layer profiles, convective
indices and precipitation. Further improvements are anticipated in future
Early Eta upgrades when the cycled EDAS is modified to assimilate hourly
precipitation (Lin et al., 1998) and cloud observations (Zhao et al., 1998).
As part of the Eta-32 the number of soil layers is increased from two
to four. In the Eta-48 and Eta-29, the original choice of two layers and
their thicknesses (top layer of 10 cm and bottom layer of 190 cm) was driven
by the fact that the initial soil moisture for the 48-km EDAS was taken
from the GDAS, which has the same soil layer configuration. The 190 cm
bottom layer physically acted as a deep root zone for transpiration through
the vegetation canopy. Experience with both the GDAS and EDAS (Betts et
al., 1996; Betts et al., 1997) showed that this very deep root zone tends
to have a too long drying time scale (months), owing to its a) large water
capacity (due to its thickness) and b) spatially uniform deep root extent.
In order to capture the multiple time scales of the observed drying cycle
of surface evaporation in various geographic regions (i.e. days for direct
evaporation from the top layer in sparsely vegetated regions, weeks for
shallow rooted vegetation in top 1/2 meter, months for deep rooted vegetation
in top 1-2 meters), the cited studies recommended adding additional soil
layers. We chose four layers with thicknesses of 10, 30, 60, and 100 cm,
and added the flexibility for the rooting depth to span anywhere from 1
to 4 of these layers. Finally, the additional layers are a necessary pre-requisite
for the addition of the physics of frozen soil, which will be included
in a future upgrade.
An example of the difference in the near-surface layer volumetric soil
moisture between the 48-km and 32-km EDAS is shown in Fig.
6. The soil moisture shown in the 32-km EDAS had been cycling for 2
weeks. The 32-km EDAS soil moisture shows more small scale structure than
the 48-km, which is due to a) sharper precipitation gradients, b) more
soil layers, and c) more spatially variable soil and vegetation types in
the EDAS than the GDAS. Most noteworthy is the sharper soil "dry line"
across Texas, Oklahoma, and Kansas in the cycled EDAS. Significant differences
are seen over New England and Florida which are due to the retention of
soil moisture in the 32-km system caused by EDAS precipitation during the
2 weeks of continuous cycling.
2.4 Eta Model Physics / Dynamics modifications
Two changes to the Eta model's physics / dynamics were made as part
of this upgrade. The first concerns the computation of the master length
scale which is needed for the vertical turbulence transport via the model's
Mellor-Yamada scheme (Janjic, 1990). In the January 1996 upgrade to the
Early and Meso Eta described in section 1, the master length scale is calculated
at model interfaces. In the Eta-32 upgrade, this procedure has been changed
so that the master length scale is computed at the mid-point of the model
layers, and the interface value is computed by averaging the two nearest
layer values. If one imagines eddies within a model layer to be of a characteristic
size appropriate to the layer thickness, and if one accords each layer
an equal role in the turbulent exchange between two layers, then averaging
of the master length scales of two adjacent layers appears to be the more
justified approach to the problem.
The second change involves a modification to the main driver that calls
the primary subroutines in the Eta model code. The Eta model uses the split
explicit approach of integration which means that the fundamental variables
are updated with new tendencies after each major dynamical or physical
process has been described. Previously, the order of events in the dynamics
were as follows: (1) the mass field was updated by the continuity equation;
(2) the mass and wind fields were updated by horizontal and vertical advection;
(3) the winds were updated by inertial gravity wave adjustment. Steps 1
and 3 comprise the so-called adjustment process. In the new driver in the
32-km upgrade, step 3 immediately follows step 1 so that both the mass
and wind fields are updated by the adjustment process before the advection
step begins. This change removes fictitious gravity waves that occasionally
appeared in the forecasts, as illustrated in the Eta-29 example given in
Fig. 7a and Fig.
3. VERIFICATION RESULTS
Results described below are taken from a parallel test of the Eta-32
system which was started at 1200 UTC 27 October 1997. Due to limited weekday
computing resources at NCEP, a 48-h forecast from the Eta-32 was run at
0000 UTC only during Monday-Friday, with 1200 UTC runs added during the
3.1 Objective Scores
3.1.1 Grid-to-Observation Verification - EDAS first guess
One measure of the relative performance of the Eta-32 EDAS is to measure
the fit of the "on-time" first guess (used by the 3DVAR or OI
analysis from which the 48-h forecast is run) at 0000 and 1200 UTC. The
height (500 and 250 mb, Fig. 8a), temperature
(850 and 250 mb, Fig. 8b), and vector wind
(950 and 250 mb, Fig. 8c) RMS errors against
all observations for the Eta-48 EDAS and Eta-32 EDAS are shown in Fig.
8 for the period 27 October - 23 November 1997. The lower and middle tropospheric
errors in the Eta-32 EDAS are consistently smaller for the entire period,
indicating that the Eta-32 EDAS with the 3DVAR analysis provides a better
first guess to the on-time analysis than the Eta-48 EDAS with the OI analysis.
The Eta-32 250 mb wind errors tend to be lower than the Eta-48 for most
cycles but the differences between the two are smaller than seen at 950
mb. The 250 mb height RMS error traces show no clear signal, with a nearly
50-50 split between the Eta-32 and Eta-48. It is not clear why the signal
seen at 500 mb is not repeated at 250 mb; a closer examination of the daily
numbers (not shown) did not reveal any diurnal signal. Additionally, comparisons
between concurrent 80-km OI and 3DVAR cycled EDAS parallel runs show a
similar pattern (not shown), indicating that both resolution difference
and continuous cycling are not the cause of the observed error patterns
at 250 mb. The only source of upper tropospheric mass data over the U.S.
at asynoptic times is the high-density ACARS temperature observations.
Therefore, it is possible that by only having aircraft temperature data
available for the off-time analyses in the 3DVAR EDAS, one could have a
worse fit to the 250 mb height data (which consists solely of rawinsondes)
at 0000 and 1200 UTC than in an OI EDAS which did not use aircraft temperature
The ETS and bias score of 24-h accumulated precipitation over the contiguous
United States for the operational Eta-48 and the Eta-32 are depicted in
Fig. 9 for the period 29 October - 24 November
1997. A total of 35 cases were used in this sample. At the highest thresholds
above 1 inch the Eta-32 forecasts are more skillful, while at the lowest
thresholds the Eta-48 forecasts are better. The bias scores indicate that
the lower ETS at the rain-no rain threshold for the Eta-32 is due to overprediction
of light rain amounts (which is obvious from a subjective evaluation of
daily forecasts). Since the bias scores for the Eta-32 at the high thresholds
are lower than the Eta-48 bias scores, the superiority of the Eta-32 forecast
at these thresholds (based on the equitable threat score) is due not to
overprediction but to an improved forecast of these heavy rain events by
3.1.3 Grid-to-Observation Verification : Surface data
As part of its routine output the Eta model produces hourly forecast
soundings and surface parameters at over 600 stations, in which the nearest
model grid point is used as the profile for the given station. Verification
of this model surface data from the Eta-48 and Eta-32 forecasts against
all coincident surface observations for 3-15 November 1997 is shown in
Fig. 10. Initially, the average 2-m temperature
from Eta-32 is worse than the Eta-48, but by the 3-h forecast it is closer
to observations. The hourly trace of 2-m temperature for the 3-h through
48-h shows that the Eta-32 reduces the error seen in the Eta-48 temperature
trace, with a significant reduction in the root-mean-square error (0.59
deg K for the Eta-32 versus 1.08 deg K for the Eta-48). This trend is reversed
in the 2-m specific humidity trace, in which the average value from the
Eta-32 is roughly 0.2 g/kg higher at most forecast hours. The average 10-m
vector wind RMS error in the Eta-32 is slightly lower then the Eta-48 throughout
the forecast, in spite of the 12% reduction in this error at 00-h, probably
due to the 3DVAR analysis used in the Eta-32 system, which uses surface
winds over land.
It is not clear why the Eta-32 has a worse fit to the 2-m moisture data
and to the initial 2-m temperature data, given the improved fit to lower
and upper tropospheric temperatures in the Eta-32 EDAS shown in Fig.
8b. It is possible that the treatment of surface temperature and moisture
data in the 3DVAR analysis needs some refinement.
3.2 Two Case Studies
3.2.1 16-17 November 1997 : California Rains
Fig. 11 shows the 24-h observed precipitation
across Northern California valid at 1200 UTC 17 November 1997. The precipitation
data were obtained from the Office of Hydrology River Forecast Center rain
gauge database. The precipitation is associated with a frontal boundary
which moved onshore into Northern California around 1200 UTC 16 November
and moved northward into Oregon and Washington during the next 12-24 hours.
There is considerable variation in precipitation amounts reflecting orographic
influences. One maximum is seen along the coast, undoubtedly the result
of upslope flow caused by the Coastal Range with amounts as high as 94
mm / 24 h along the coast near 40N, 124W. The precipitation minimum inland
in the Sacramento River valley is probably caused by downslope conditions.
A second maximum is observed further inland, reflecting upslope flow caused
by the Sierra Nevada Range (highest amounts around 40 mm observed northwest
of Lake Tahoe (39N, 120W)) and the Cascade Range, with many stations around
Shasta Lake (near 41N, 122W) reporting over 40 mm / 24 h.
Fig. 12 shows the 0-24 h forecast of
accumulated precipitation from the Eta-29, Eta-32, and the Eta-48 valid
at 1200 UTC 17 November 1997. Although none of the three models were able
to predict the extreme amounts observed, there are significant differences
between them. The Eta-48 model predicted the highest amounts (> 36 mm)
north of Cape Mendocino (located at 40.5N 124.5W in Fig.
11), which appears to be an overprediction based on the observed precipitation
seen north of 40N in Fig. 11. Both the Eta-29 and the Eta-32 predicted
more widespread rains > 24 mm along the coast between Cape Mendocino
and San Francisco then did the Eta-48 model, but none were able to predict
more extreme amounts > 40 mm observed at several stations in this region.
The Eta-32 forecast predicts more coastal precipitation than the Meso Eta,
which is an improvement near and south of Cape Mendocino but an overprediction
further north. It is obvious that the two higher resolution models have
considerably more detail in the inland precipitation pattern then the Eta-48.
All three models predict two distinct upslope maxima along the Sierra Nevada
and Cascade Ranges, with the Eta-29 and Eta-32 forecasting greater small-scale
detail. Additionally, the Eta-32 and Eta-29 both did better than the Eta-48
in predicting lower precipitation amounts between the Coastal and inland
ranges. Although Eta-32 did not make a perfect forecast, it is clear that
is has the ability to predict orographically-driven mesoscale structure
in the precipitation field as well as the Eta-29.
3.2.2 20 November 1997 : Oceanic Cyclogenesis
Fig. 13 shows the NCEP GDAS analysis
of sea level pressure valid at 0000 UTC 20 November 1997 with surface observations
of wind and sea level pressure. The frontal wave with central pressure
of ~1010 mb at 0000 UTC 20 November deepened into a mature cyclone with
a central pressure below 1000 mb by 1200 UTC 20 November (not shown) as
it tracked northeastward towards the Canadian Maritimes. This deepening
was in response to a strong 500 mb short-wave trough (not shown) which
caused 12-h height falls of 80-140 m across eastern New England and Nova
Fig. 14 shows the 48-h forecasts of
sea level pressure from the Eta-48 (ETA) and the Eta-32 upgrade (PARA32)
valid at 0000 UTC 20 November 1997, while Fig.
15 shows the 24-h forecasts from these two models valid at 0000 UTC
20 November 1997. These charts reveal that for two successive forecast
cycles the operational 48-km Eta model was 12-h too fast in predicting
the rapid deepening, as well as predicting development closer to the U.S.
coast then was observed.
Given the many changes associated with the Eta-32 upgrade, it is not clear based on the above charts alone which component is responsible for this improved cyclone forecast. Concurrently with the Eta-32 parallel, EMC is running two 80-km EDAS parallel runs with the 3DVAR and OI analyses, which are identical to the 32-km system (partial cycling, 4-layer soil model) except for the use of the Eta OI analysis in one of the parallels. When the forecasts from these 80-km runs were examined (not shown), the 80-km 3DVAR parallel did better than the 80-km OI parallel. Thus, it appears that the use of the 3DVAR analysis is the reason the Eta-32 system produced a better forecast of this case of oceanic cyclogenesis then the Eta-48.
4. CONCLUSIONS AND FUTURE PLANS
This implementation will see no changes made to any of the products
created from the Early Eta forecast suite which are available via facsimile
and AFOS. Existing Early Eta products on the NWS Office of Systems Operations
(OSO) server (IP address = 18.104.22.168, or on the World Wide Web at http://www.nws.noaa.gov/oso/ftpgate.shtml)
and the NCEP anonymous ftp server (nic.fb4.noaa.gov) will not change.
Since the Eta-32 system will eventually replace the Eta-29 (see below)
those products unique to the Eta-29 will be produced by the Eta-32. Further
information on the availability of output from the Eta-32 can be found
under frequently asked questions to the Mesoscale Modeling Branch on the
EMC homepage (http=//nic.fb4.noaa.gov:8000/research/FAQ-eta.html).
NCEP will also generate two new grids from the Eta-32: 1) a new 32-km
Lambert conic-conformal grid (AWIPS grid #221) covering the entire computational
domain of the 32-km Eta (~97000 grid points); and 2) a grid covering the
same area as the 32-km Lambert conformal grid but with 1/6th the resolution
(AWIPS grid #222, 188-km resolution with ~ 3000 grid points). Current users
of grid #104 (90-km polar stereographic over North America) who use this
grid because it covers the computational grid of the Eta-48 are encouraged
to move towards using either of these new Lambert conformal grids. Initially
the new Lambert conformal grid will be accessible on the NCEP side of the
This initial implementation of the Eta-32 is intended to replace the
Eta-48 EDAS and 48-h forecast from 0000 UTC and 1200 UTC initial conditions.
The 0300 / 1500 UTC Eta-29 system will still run using the system described
by Black (1994). Within 6-9 months after this implementation NCEP intends
to make two further changes to the Eta forecast suite:
- Include off-time Eta-32 forecasts (initially out to 18-h) from 0600 UTC and 1800 UTC initial conditions. When the off-time Eta-32 forecasts are in place the 0300 and 1500 UTC runs of the Eta-29 will be turned off
- Modify the Eta model convective parameterization scheme to better predict convective precipitation, especially in mountainous regions like the western United States, and along the Gulf of Mexico and southeast Atlantic coastal area
Baldwin, M. E., and T. L. Black, 1996: Precipitation forecasting experiments
in the western U.S. with NCEP's mesoscale Eta model. Preprints, 11th AMS
Conference on Numerical Weather Prediction, Norfolk, VA, 19-23 August 1996.
Betts, A. K., S. -Y. Hong, and H. L. Pan, 1996: Comparison of NCEP/NCAR
reanalysis with 1987 FIFE data. Mon. Wea. Rev., 124, 1480-1498.
-------, F. Chen, K. E. Mitchell, and Z. I. Janjic, 1997: Assessment
of the land surface and boundary layer models in two operational versions
of the NCEP Eta model using FIFE data. Mon. Wea. Rev., 125,
Black, T. L., 1994: The new NMC mesoscale Eta model : Description and
forecast examples. Wea. Forecasting, 9, 265-278.
-------, D. G. Deaven, and G. J. DiMego, 1993: The step-mountain eta-coordinate
model : 80-km "early" version and objective verifications. NWS
Technical Procedures Bulletin 412, NOAA/NWS, 31 pp. [ Available from National
Weather Service, Office of Meteorology, 1325 East-West Highway, Silver
Spring, MD 20910 ]
-------, and coauthors, 1997 : Changes to the Eta forecast systems.
NWS Technical Procedures Bulletin 421, NOAA/NWS, 31 pp. [ Available from
National Weather Service, Office of Meteorology, 1325 East-West Highway,
Silver Spring, MD 20910 ]
Chen, F. and coauthors, 1996 : Modeling of land-surface evaporation
by four schemes and comparison with FIFE results. J. Geophys. Res.,
DiMego, G. J., 1988 : The National Meteorological Center Regional Analysis
System. Mon. Wea. Rev., 116, 977-1000.
Hayden, C. M., and R. J. Purser, 1995: Recursive filter objective analysis
of meteorological field: applications to NESDIS operational processing.
J. Appl. Met., 34, 3-15.
Iredell, M, and P. Caplan, 1997 : Four-times-daily runs of the AVN Model.
NWS Technical Procedures Bulletin 442, NOAA/NWS, 31 pp. [ Available from
National Weather Service, Office of Meteorology, 1325 East-West Highway,
Silver Spring, MD 20910 ]
Janjic, Z., 1990 : The step-mountain coordinate: Physical package. Mon.
Wea. Rev., 118, 1429-1443.
-------, 1996a : The surface parameterization in the NCEP Eta model.
Research Activities in Atmospheric and Oceanic Modelling. CAS/JSC
Working Group on Numerical Experimentation, WMO, 440 pp. [ Available from
WMO, CP No. 2300, CH-1211, Geneva 2, Switzerland ]
-------, 1996b : The Mellor-Yamada level 2.5 turbulence closure scheme
in the NCEP Eta model. Preprints, 11th AMS Conference on Numerical Weather
Prediction, Norfolk, VA, 19-23 August 1996.
Lin, Y., K. E. Mitchell, E. Rogers, and M. E. Baldwin, 1998: Assimilation of real-time multi-sensor hourly precipitation observations into the NCEP Eta model. Preprints, 12th AMS Conference on Numerical Weather Prediction, Phoenix, AZ, 12-16 January 1998.
McDonald, B. E., J. D. Horel, J. Stiff, and W. J. Steenburgh, 1998:
Observations and simulations of three downslope wind events over the northern
Wasatch mountains. Preprints, 12th AMS Conference on Numerical Weather
Prediction, Phoenix, AZ, 12-16 January 1998.
Mesinger, F., 1996 : Improvements in quantitative precipitation forecasts
with the Eta regional and mesoscale models at the National Centers for
Environmental Prediction. Preprints, 11th AMS Conference on Numerical Weather
Prediction, Norfolk, VA, 19-23 August 1996.
Parrish, D. F., and J. Derber, 1992: The National Meteorological Center's
spectral statistical interpolation analysis system. Mon. Wea. Rev.,
-------, J. Purser, E. Rogers, and Y. Lin, 1996 : The regional 3d-variational
analysis for the Eta model. Preprints, 11th AMS Conference on Numerical
Weather Prediction, Norfolk, VA, 19-23 August 1996.
Rogers, E., D. G. Deaven, and G. J. DiMego, 1995: The regional analysis
system for the operational Eta model : Original 80-km configuration and
future changes. Wea. Forecasting, 10, 810-825.
-------, T. L. Black, D. G. Deaven, G. J. DiMego, Q. Zhao, M. Baldwin,
N. W. Junker, and Y. Lin, 1996 : Changes to the operational "early"
eta analysis / forecast system at the National Centers for Environmental
Prediction. Wea. Forecasting, 11, 391-413.
-------, D. Parrish, Y. Lin, G. DiMego, 1996 : The NCEP Eta Data Assimilation
System : Tests with a regional 3-d variational analysis and continuous
cycling. Preprints, 11th AMS Conference on Numerical Weather Prediction,
Norfolk, VA, 19-23 August 1996.
-------, D. Parrish, G. DiMego, 1998 : Data assimilation experiments
with the regional 3-d variational analysis at the National Centers for
Environmental Prediction. Preprints, 12th AMS Conference on Numerical Weather
Prediction, Phoenix, AZ, 12-16 January 1998.
Schneider, R. S., N. W. Junker, M. T. Eckert, and T. M. Considine, 1996
: The performance of the 29 km Meso Eta model in support of forecasting
in the Hydrometeorological Prediction Center. Preprints, 11th AMS Conference
on Numerical Weather Prediction, Norfolk, VA, 19-23 August 1996.
Wu, W.-S., M. Iredell, S. Saha, and P. Caplan, 1997 : Changes to the
1997 NCEP operational MRF model analysis/forecast system. NWS Technical
Procedures Bulletin 443, NOAA/NWS, 31 pp. [ Available from National Weather
Service, Office of Meteorology, 1325 East-West Highway, Silver Spring,
MD 20910 ]
Zhao, Q., T. L. Black, and M. E. Baldwin, 1997 : Implementation of the
cloud prediction scheme in the Eta model at NCEP. Wea. Forecasting,
-------, and coauthors, 1998 : Assimilating cloud and precipitation
observations in the Eta model to improve cloud and precipitation forecasts.
Preprints, 12th AMS Conference on Numerical Weather Prediction, Phoenix,
AZ, 12-16 January 1998.