NORTH AMERICAN REGIONAL REANALYSIS

Fedor Mesinger\*, Geoff DiMego+, Eugenia Kalnay#, Perry Shafran\~,
Wesley Ebisuzaki\^, Dusan Jovic\~, Jack Woollen\~, Kenneth Mitchell+,
Eric Rogers+, Michael Ek\*, Yun Fan¬, Robert Grumbine+,

Wayne Higgins\^, Hong Li#, Ying Lin+, Geoff Manikin+, David Parrish+,
and Wei Shi¬

\* NCEP/EMC and UCAR; + NCEP/EMC; \# Univ. Maryland; \~ NCEP/EMC and
SAIC/GSO; \^ NCEP/CPC;

¬ NCEP/CPC and RSIS

Draft, 16 November 2004, to be submitted to the Bulletin of the American
Meteorological Society

**1. INTRODUCTION**

The NCEP North American Regional Reanalysis (NARR) is a long-term,
consistent, high-resolution climate data set for the North American
domain. It covers the 25-year period 1979-2003, and is being continued
in near-real time as the Regional Climate Data Assimilation System,
R-CDAS. After several years of development, most of the production was
successfully completed during May-September 2003, taking advantage of
the window of availability of the previously \"production\" NCEP IBM ASP
supercomputer, and using four parallel streams to carry it out during
this limited time. Most of the remaining NARR tasks have subsequently
been completed, including processing of the complete 25-year period. A
few residual tasks are still in progress at the time of this writing,
including the archiving activities.

The NARR was developed as a major improvement upon the earlier NCEP/NCAR
Global Reanalysis (GR1, Kalnay et al, 1996; Kistler et al, 2000), in
both resolution and accuracy. The NCEP/DOE Global Reanalysis (GR2,
Kanamitsu et al, 2002) is used to provide boundary conditions, but the
NARR takes advantage of the use of the regional Eta model including the
many advances that have been made in the Eta regional modeling and data
assimilation systems since the GR system's starting time of 1995. Some
of the most important improvements are direct assimilation of radiances,
the use of additional sources of data (Table 2), improved data
processing, and several Eta model developments, particularly those
associated with the GCIP-funded initiatives in hydrological research,
assimilation of precipitation, land-atmosphere coupling, and
improvements to the Noah

--------------------------------

\* *Corresponding author address:* Fedor Mesinger, NCEP/EMC, 5200 Auth
Road, Room 207, Camp Springs, MD 20746-4304.

E-mail: fedor.mesinger@noaa.gov

land surface model, which is the land-model sub-component of the
Regional Reanalysis (Mitchell et al. 2003; Ek et al. 2003; Berbery et
al. 2003).

The NARR should help answer questions about the variability of water in
weather and climate, in particular as it concerns U.S. precipitation
patterns. To that end, a special effort was made to output all "native"
(Eta) grid time-integrated quantities of water budget. We expect that
the NARR should have a good representation of extreme events, such as
floods and droughts, and should interface well with hydrological models.

Our results -- first those of preliminary pilot runs at 80 km horizontal
resolution and 38 layers in the vertical, and later those of most of the
"production" results, at 32 km/45 layer resolution \-- have been
reported on in a sequence of conference papers. The last of those is
Mesinger et al. (2004); note that its revised version is available at
<http://wwwt.emc.ncep.noaa.gov/mmb/rreanl/index.html>.

In all of these earlier reports, the assimilation of precipitation
during the reanalysis was found to be very successful, obtaining model
precipitation quite similar to the analyzed precipitation. Temperature
and vector wind rms fits to rawinsondes were considerably improved over
those of the GR throughout the troposphere, both in January and in July,
and in the analyses as well as in the first guess fields. Significant
improvements in the 2-m temperatures and 10-m winds were seen as well.

Subsequently, in addition to completing our 25-year production period,
we have also built the system for and started the near real-time
continuation of the NARR, following the practice of the "Climate Data
Assimilation System", the real time continuation of the GR. A basic
requirement underlying reanalysis efforts is of course minimization of
inhomogeneities as much as possible without detrimentally affecting the
results. However, important inhomogeneities which we could not avoid are
those in our precipitation input fields. One is the switch from the
"unified" Climate Prediction Center (CPC) precipitation analyses over
the Continental United States (ConUS) area, used until 1998, to
observations from real-time gauges only, starting in 1999 (Higgins et
al. 2000). Another is the change in precipitation analyses used over
southern portions of the oceans starting in January 2003, when we
switched to our current near real-time system. We give more details on
the two new systems, CMAP and CMORPH, in the following sections.

As was the case with the GR, the NARR includes free forecasts performed
at regular intervals, useful for predictability studies. We have chosen
to do these forecasts every 2.5 days, out to 72 h in order to have free
forecasts alternatively initialized at 0000 and 1200 UTC, with a 12-h
overlap period. This would be useful to estimate spin-up in the first 12
h. The free forecasts use GR2 forecast (not reanalysis) lateral boundary
conditions, in order to simulate the forecast skill that would be
attainable in operational conditions using the same system.

The project has been supported for 6 years by the NOAA Office of Global
Programs (OGP), with a reduced support in the originally unplanned for
sixth year. A Scientific Advisory Panel chaired by John Roads and
reporting to OGP has provided valuable and continued guidance to the
NARR project.

This is our first open literature documentation of the project, and the
first report after the completion of the processing of the planned 25
years. In section 2 we summarize the system and the data used. In
section 3 we give a description of the precipitation, upper air, near
surface, and land-surface results obtained and compare the fits to
observations with those of the global reanalysis. A brief summary of the
near real-time continuation of the project, R-CDAS, is given in section
4. Section 5 summarizes the data sets produced, archiving systems
established, and archiving activities in progress or planned. Section 6
contains our concluding comments. Appendix A contains a more extensive
documentation of the NARR data sets, and Appendix B a list of acronyms
used. A DVD accompanying this issue includes samples of results, and
provides additional information useful to potential NARR users. A
companion paper (Rutledge et al., 2004) will describe the data retrieval
system in place at the main data distribution center, NCEP and NOAA's
Climate Data Center (NCDC).

**2. REANALYSIS SYSTEM AND DATA USED**

The NARR System is essentially the same as the Eta Model and 3D-Var Data
Assimilation System (EDAS), operational in April 2003 when the NARR
system was frozen (Rogers and DiMego,

ftp://ftp.ncep.noaa.gov/pub/emc/wd20er/caftimay01/v3_document.htm, and
http://www.emc.ncep.noaa.gov/mmb/research/eta.log.html), except for a
few differences. They include horizontal/vertical resolution, the use of
the Zhao et al. (1997) \-- as opposed to the later

"Ferrier" \-- cloud microphysics, and the use of a number of additional
data sources (Tables 1 and 2). The system is fully cycled, with a 3-h
forecast from the previous cycle serving as the first guess for the next
cycle.

The 32 km/45 layer resolution used for the NARR production runs is the
same as that of the operational Eta prior to September 2000, but the
domain is that of the current operational Eta, including North America
and parts of Atlantic and Pacific, and encompassing 106° x 80° of
rotated longitude x latitude. The NARR domain and topography are shown
in Fig. 1, and the daily climatologies used are listed in Table 3.

A number of "fixed fields" are used as input to the land-surface model:
land mask (land or water), vegetation type, soil type, surface slope
type, snow-free albedo, maximum snow albedo, surface roughness, soil
column bottom temperature, and the number of root zone soil layers (Ek
et al. 2003; Mitchell et al. 2004).

The data used in the production runs includes most of the observations
used in the Global Reanalysis, as listed in Table 1. The only GR data
not used in the NARR are temperature retrievals since they were replaced
by the use of radiances. Additional data sets used or improved in the
NARR are summarized in Table 2 and discussed further below.

a\. Precipitation. The assimilation of observed precipitation is by far
the most important data addition to the NARR. The successful
assimilation of these observations, converted into latent heat (Lin et
al. 1999, see also section 3) ensures that the model precipitation
during the assimilation is close to that observed, and therefore that
the hydrological cycle is more realistic than it would be otherwise.
Over the ConUS area, Mexico,

Table 1. Data used in both the NCEP/NCAR Global Reanalysis and in the
North American Regional Reanalysis

  ---------------------- --------------------------- ---------------------
  Dataset                Observed variable           Source

  Rawinsondes            Temperature, wind, moisture NCEP/NCAR Global
                                                     Reanalysis (GR)

  Dropsondes             Same as above               GR

  Pibals                 Wind                        GR

  Aircraft               Temperature and wind        GR

  Surface                Pressure                    GR

  Geostationary          Cloud drift wind            GR
  satellites                                         
  ---------------------- --------------------------- ---------------------

Table 2. Data added or improved upon for the North American Regional
Reanalysis

+-------------------+-----------------------------+-------------------+
| Dataset           | Details                     | Source            |
+-------------------+-----------------------------+-------------------+
| Precipitation,    | CONUS (with PRISM), Mexico, | NCEP/CPC,         |
|                   |                             |                   |
| disaggregated     | Canada, CMAP over oceans    | Canada, Mexico    |
| into hours        | (\<42.5°N)                  |                   |
+-------------------+-----------------------------+-------------------+
| TOVS-1B radiances | Temperature, precipitable   | NESDIS            |
|                   |                             |                   |
|                   | water over oceans           |                   |
+-------------------+-----------------------------+-------------------+
| NCEP Surface      | Wind, moisture              | GR                |
+-------------------+-----------------------------+-------------------+
| TDL Surface       | Pressure, wind, moisture    | NCAR              |
+-------------------+-----------------------------+-------------------+
| COADS             | Ship and buoy data          | NCEP/EMC          |
+-------------------+-----------------------------+-------------------+
| Air Force Snow    | Snow depth                  | COLA and NCEP/EMC |
+-------------------+-----------------------------+-------------------+
| SST               | 1-degree Reynolds, with     | NCEP/EMC, GLERL   |
|                   |                             |                   |
|                   | Great Lakes SSTs            |                   |
+-------------------+-----------------------------+-------------------+
| Sea and lake ice  | Contains data on            | NCEP/EMC, GLERL,  |
|                   |                             |                   |
|                   | Canadian lakes, and Great   | Canadian Ice      |
|                   | Lakes                       | Center            |
+-------------------+-----------------------------+-------------------+
| Tropical cyclones | Locations used for blocking | Lawrence          |
|                   |                             | Livermore         |
|                   | CMAP precipitation          |                   |
|                   |                             | National          |
|                   |                             | Laboratory        |
+-------------------+-----------------------------+-------------------+

Table 3. Daily climatologies used in the North American Regional
Reanalysis

  ------------------------------ ----------------------------- ----------
  Dataset                        Used for                      Source

  Green vegetation fraction      Initialization of vegetation  GR

  Baseline snow-free albedo      Initialization of albedo      GR
  ------------------------------ ----------------------------- ----------

![](media/image1.png){width="5.706944444444445in"
height="4.027083333333334in"}

Fig. 1. The NCEP Regional Reanalysis domain and its 32 km/45 layer
topography.

and Canada, the precipitation data assimilated are 24-h rain gauge data
disaggregated into hourly bins. Over ConUS the disaggregation is
performed based on hourly precipitation data (HPD), using an inverse
distance scheme, and the Parameter-elevation Regressions on Independent
Slopes Model (PRISM, Daly et al, 1994) known as \"mountain mapper\".
Over Mexico and Canada, the disaggregation is based on the GR2
(Kanamitsu et al. 2002) forecasts of precipitation. Over the oceans,
south of 27.5^o^N, CPC Merged Analysis of Precipitation (CMAP) pentad
data (Xie and Arkin 1997) are used, also disaggregated into hourly using
the GR2 precipitation forecasts. North of 42.5^o^N, where the CMAP data
is known to be increasingly less reliable, there is no assimilation of
precipitation. Over a 15^o^ latitude belt centered at 35^o^N there is a
linear transition from full precipitation assimilation south of this
blending belt, to no assimilation north of it. Moreover, over tropical
cyclones, with locations prescribed from Fiorino (2002), there is no
assimilation of precipitation since CMAP pentad data do not have
adequate time resolution to be useful for very heavy precipitation.

b\. TOVS-1b radiances (instead of the NESDIS TOVS retrievals used in GR1
and GR2);

c\. Profilers and Vertical Azimuth Display (VAD) winds;

d\. Land surface wind (10 m) and moisture (2 m). We have made extensive
tests on the impact of the assimilation of land surface data additional
to surface pressure. In these tests, assimilation of land surface wind
and moisture was found helpful, and thus was implemented in our
production runs. Assimilation of land surface (2 m) temperature was
found to be significantly detrimental to our tropospheric fits to
rawinsondes, and therefore was not done. This issue is further discussed
on the FAQ section in our NARR web page
[http://wwwt.emc.ncep.noaa.gov/mmb/rreanl](http://wwwt.emc.ncep.noaa.gov/mmb/ rreanl).

e\. Lake surface: Ice cover (Grumbine, personal communication), and lake
temperature, to the extent available are assimilated, as opposed to the
global SST used in the GR. For lakes for which temperature is not
available, the temperature was assumed to be the same as that of nearby
lakes.

f\. SST and sea ice: these data were used in the GR but improved
processing was developed for the NARR (Stokes, Grumbine, personal
communications).

A more detailed discussion of the NARR data is presented in Shafran et
al. (2004), also available on our web page.

**3. RESULTS**

Given that the Global Reanalysis data have been available for almost a
decade, an obvious goal of the NARR, in addition to higher resolution,
was to provide a more realistic and accurate data set over North
America. We will now compare NARR precipitation monthly averages to
observations, and the fit of NARR and GR to rawinsonde and near surface
observations. The section will end with a brief look at the land surface
data obtained.

In presenting the precipitation results of our pilot and preliminary
runs, we compared monthly totals for January and July of the NARR
precipitation with those of the "observed" (i.e., analyzed)
precipitation assimilated into the NARR, as well as with those of the
GR. We have found an excellent agreement of the NARR with the analyzed
precipitation over areas with assimilation in the January and July
months for all the years that we examined. For illustration, we present
winter and summer examples of particular interest, in which extreme
events occurred. These are January results for a year of a strong El
Niño, 1997, and the difference between flood months in 1993, and drought
months in 1988 (e.g., Altshuler et al. 2002).

In Fig. 2 we compare the NARR precipitation for January 1997, the El
Niño case, with the analyzed precipitation. The comparison shows that
over land there is an extremely high agreement between NARR and observed
precipitation, even over the complex western topography. It should be
recalled that the model does not assimilate precipitation directly but
instead latent heat information derived from observations, and that from
this forcing the model produces the NARR precipitation (Lin et al.
1999). Thus, it was not obvious that it was possible to achieve such
exceedingly good agreement over land. Over the oceans, the agreement is
very good in southern latitudes, and toward more northerly latitudes
where the assimilation is gradually transitioned out, the agreement is
still reasonable in magnitude, but not in the detailed distribution. The
tendency of the NARR to generate visibly weaker maxima over cyclonic
regions of the northern Atlantic, seen in Fig. 2, has been found to be
also characteristic of other months. Given that the NARR was clearly
meant to address primarily the North American land, this is not seen as
a significant weakness. On the other hand, the satellite-based
precipitation over oceans, as stated, is not fully to be trusted either.

For a summer example of precipitation we present the difference between
the June, July of the flood year of 1993, and the drought year of 1988.
The monthly average of this difference for observations and NARR is
shown in Fig. 3. Once again, the agreement over land is extraordinarily
good, down to very small-scale detail. This is true not only for the
Midwestern maxima, but for the details of minor maxima and minima over
land. Over oceans, the agreement is also very good, since systematic
underestimations such as those shown in the winter if any are not
apparent as they are canceled out when taking the difference between two
summers. The figure indicates a high degree of reliability in the NARR
estimation of interannual variability in precipitation.

While the realistic precipitation will be very helpful for hydrologic
and near surface variables, and in particular soil wetness, the accuracy
of model variables in the troposphere, especially winds and
temperatures, is a primary indication of the performance of the overall
system. In Mesinger et al. (2004), we compared 24-year January and July
averages of temperature and vector wind rms fits to rawinsondes as
functions of pressure, with those of the GR. We found that the advantage
of the NARR over the GR was quite large, especially for winds, and
greater for the analysis than for the first guess. However our
temperature plots were affected by an inadvertent temperature
"de-virtualization". Corrected plots for only the latest 5 of the 24
years are shown in the revised version of that paper, available on our
web site.

We now have averages of rms fits to rawinsondes for both temperatures
and vector winds for all of the 25 years, and they are shown in Fig 4.
In the figure, NARR rms fits to rawinsondes as functions of pressure are
shown, dashed lines, for temperature (upper panels), and for vector wind
(lower panels), for January (left panels) and July (right panels). The
same fits for the GR are shown as solid lines. As before, our
verification domain for these upper-air plots as well as for the
near-surface plots to follow is the so-called grid 212, encompassing
most of Mexico to the south and up to a considerable fraction of Canada
to the north. But one should note that rms fits shown are not averages
over the domain but over the observations available, so that regions
with more observations will be weighted more accordingly.

![](media/image2.png){width="5.052777777777778in"
height="3.8513888888888888in"}

![](media/image3.png){width="5.0625in" height="3.8513888888888888in"}

Fig 2. \"Observed\" (analyzed) precipitation assimilated by the NARR
over land and over southern parts of the oceans (see text), and NARR
precipitation, averaged for January 1997 (inches/month). White indicates
no available observations.

![](media/image4.png){width="-2.4618055555555554in"
height="-3.8673611111111112in"}

![](media/image5.png){width="-2.4756944444444446in"
height="-3.863888888888889in"}

Fig. 3. "Observed" precipitation, assimilated by the NARR over land and
over southern parts of the oceans (see text), and NARR precipitation,
June, July 1993 minus June, July 1988 (inches/month)

> ![](media/image6.png){width="3.2256944444444446in"
> height="2.636111111111111in"}![](media/image7.png){width="3.2in"
> height="2.7194444444444446in"}![](media/image8.png){width="3.2256944444444446in"
> height="2.6243055555555554in"}![](media/image9.png){width="3.2in"
> height="2.7194444444444446in"}

Fig. 4. RMS fits to rawinsondes as a function of pressure, for
temperature (upper panels), and for vector wind (lower panels), for
January (left panels) and July (right panels), average over 1991-2003
for temperature, and over 1979-2003 for wind. NARR: dashed lines, GR:
solid lines.

Typically, regarding rawinsonde reports, about 105-109 sites would have
reports on any one day within the grid 212. Of those, most, about 90,
would come from the ConUS area, with about 60 from its eastern and
Plains areas, and about 30 from the predominantly mountainous U.S. west.

NARR fits to rawinsondes are seen to be considerably better than those
of the GR for both temperatures and winds, and in both January and July.
The advantage of the NARR is greater in January than in July, and larger
for winds than for temperatures.

Before turning attention to the first guess fits, we note that the fits
of the analysis to the observations, shown in Fig. 4, are influenced by
both the estimation of the background and observation error covariances,
and by the degree of balance imposed on the analysis. The fit will be
better the weaker the balance constraint imposed in the analysis scheme.
The fit of the first guess to the observations is for that reason
generally considered a better independent validation of the quality of
the analysis system. For example, the changes implemented in the
operational Eta 3D-Var in May 2001 (web site given in section 2)
resulted in improved NARR fits to rawinsondes in the first guess (3-h
forecasts) but made them worse in the analysis. We therefore compare the
NARR and GR first guess fits to data, fits prior to entering the 3D-Var
analysis. From a practical point of view, most users of the NARR will
want to use the analyses for the variables that are analyzed, but will
use the first guess for non-

![](media/image10.png){width="3.290277777777778in"
height="2.547222222222222in"}

![](media/image11.png){width="3.3in" height="2.6972222222222224in"}

![](media/image12.png){width="3.2979166666666666in"
height="2.5590277777777777in"}

![](media/image13.png){width="3.1847222222222222in"
height="2.6659722222222224in"}

Fig. 5. Same as Fig. 4 but for the first guess.

analyzed fields such as surface fluxes. We have accordingly produced the
so-called "merged" NARR files, a mix of the two, as will be explained in
our Appendix A.

The NARR first guess fits to rawinsondes for our 25 years, shown in Fig.
5, are overall still considerably better than those of the GR, even
though the improvement is smaller than for the analysis fields.
Generally, improvements are large near the surface and at the tropopause
levels, and are somewhat smaller in the lower troposphere. Specifically,
for the temperature, the NARR first guess fits in January at 700 mb are
only marginally better than those of the GR, and in July between about
500 to 750 mb they are even slightly worse. This appears to be caused by
somewhat of a bias problem of the NARR (not shown), reaching a value on
the order of --0.5°K at 700 mb, compared to hardly any bias in the lower
troposphere of the GR. The fits of the first guess winds in the NARR, on
the other hand, are significantly better than in the GR at all levels,
especially in January, and in particular at the upper troposphere --
both just as it was for the analyses.

With respect to near-surface variables, 2-m temperatures and 10-m winds,
we show January and July 1988. Only the first guess results are
presented, because there are no GR analyses available for these fields.
Recall that over land surface winds but not temperatures are
assimilated. We display in Fig. 6 the bias and the rms fits of the first
guess 2-m temperature for both the NARR (dashed lines) and the GR (solid
lines), as functions of time. The results shown are averages for all the
surface stations of the domain 212 that have passed the quality control
test. The results

![](media/image14.png){width="3.265972222222222in"
height="2.640972222222222in"}

![](media/image15.png){width="3.265972222222222in"
height="2.640972222222222in"}

![](media/image16.png){width="3.252083333333333in"
height="2.6729166666666666in"}![](media/image17.png){width="3.252083333333333in"
height="2.6729166666666666in"}

Fig 6. Bias (top) and RMS (bottom) of the first guess 2-m temperatures
fits to observations for the NARR (dashed lines) and the GR (solid
lines), for January 1988 (left) and July 1988 (right) as functions of
time.

indicate that the NARR 2-m temperature biases are generally smaller and
have less of a diurnal cycle than the GR, both in the winter and in the
summer. The rms errors are also smaller for the NARR than for the GR,
especially in winter; and the diurnal amplitude in the rms fit to
observations -- a problem of the GR in July -- is also considerably
smaller.

Fig. 7 displays the corresponding plots of the first guess 10-m vector
wind biases and rms fits for the same two months. The NARR has a slight
negative bias in both winter and summer. A considerable positive bias is
displayed by the GR in January, on the order of 1-2 m/s. This carries
over into the rms results, contributing to a large rms advantage of more
than 1 m/s of the NARR over the GR in January. In July, despite no
obvious bias advantage, the NARR rms is still smaller than that of the
GR.

One advantage of the NARR compared to GR is its higher temporal
resolution, 3 vs 6 h. Not only are analyses and first guess fields
available at shorter time intervals, but also a considerable fraction of
the data are being assimilated at times closer to the observation time.
But two additional factors should also be considered: the shorter 3-h
interval reduces the time for model errors to grow (an advantage) but
also allows less time for the gravity waves created by the initial
imbalance to settle down (a disadvantage). The two factors can have an
opposite effect in terms of the NARR first guess fitting better the
observations. We run experiments aimed at finding out which of the two
effects might be dominant:

![](media/image18.png){width="3.2909722222222224in" height="2.61875in"}

![](media/image19.png){width="3.28125in" height="2.640972222222222in"}

![](media/image20.png){width="3.2597222222222224in"
height="2.6666666666666665in"}![](media/image21.png){width="3.252083333333333in"
height="2.6729166666666666in"}

Fig. 7. Same as Fig. 6 but for the 10-m winds

January, April, July and October 2002 were rerun with each of the 3-h
forecast segments extended to 6 h, and fits to rawinsondes of the thus
obtained 6-h NARR first guess fields were then compared against those of
the 3-h fits. The 3 and 6-h fits were remarkably similar, but in

most cases the 6-h fits were slightly smaller. Thus, as far as the
comparisons with the GR first guess fits of Fig. 5 are concerned, the 3
vs 6-h difference appears to have a negligible impact.

(Ken will add/ change): The good precipitation patterns produced in the
NARR by the assimilation of observed precipitation provide vastly
improved precipitation forcing for the Noah LSM component compared to
the GR\'s. The Noah LSM used in NARR closely follows that described and
evaluated in both the coupled Eta/Noah study of Ek et al. (2003) and the
uncoupled North American Land Data Assimilation System (NLDAS) study of
Mitchell et al. (2004). The Noah LSM simulates soil temperature and soil
moisture (including frozen) in four soil layers of 10, 30, 60, and 100
cm thickness. The surface infiltration scheme accounts for subgrid
variability in soil moisture and precipitation. The surface evaporation
includes evaporation from the soil, transpiration from the vegetation
canopy, evaporation of dew/frost or canopy-intercepted precipitation,
and snow sublimation. The Noah LSM simulates snowpack states of water
content, density and fractional coverage via the processes of
sublimation, snowfall, and snowmelt and the snowpack surface energy
fluxes of radiation, sensible/latent heat flux, subsurface heat flux,
and phase-change heat sources/sinks. In the NARR, the snowpack depth is
updated daily from the daily global snow depth analysis (47-km) of the
U.S. Air Force, known as SNODEP. This daily update increment is the
minimum needed to achieve a NARR snow depth within a factor of two of
the Air Force snow depth. Being conscious of the notoriously slow
spin-up time of soil moisture, in setting up the four stream processing
of the NARR we have been careful to allow for the first stream's initial
time of 3 months prior to the official beginning of the NARR data set,
and for a long, 15-months, overlap spin-up time at the junctures of the
streams. All of these should have contributed to the generation of a
high quality land-surface subset of the NARR; this has been confirmed by
our inspection of various LSM NARR results.

Finally, it should be noted that a wealth of additional summary-type
NARR results is available on our web site within the CPC-produced
"climatology" of the NARR, and also, in particular as it concerns
moisture transport processes, in Mo et al. (2005).

**4. WORK IN PROGRESS: R-CDAS, AND DATA ARCHIVING ACTIVITIES**

The NARR project originally aimed to produce a "retrospective"
reanalysis of 25 years, 1979-2003, and to have it continue as a
near-real time system starting with 1 January 2004. After we finished
the retrospective processing to the end of November 2002, we faced a
number of obstacles due to the fact that some of the data sets that we
used were no longer available. Two major obstacles were unavailability
of CMAP precipitation analysis over oceans, and unavailability of gauge
precipitation observations over Canada. Other data sets that we were not
able to procure in the same form as used retrospectively were specially
processed ("Grumbine") sea ice and GLERL Great Lakes ice, beyond
November 2002; and Great Lakes SSTs, beyond December 2002. Yet another
problem was unavailability of data on the ice cover of the Canadian
lakes.

We have therefore decided to process 2003, and for most of the variables
listed above also December 2002, using our real-time system. This
system, R-CDAS, Regional Climate Data Assimilation System, is identical
to the one used in the retrospective NARR except for the following. For
precipitation analyses over oceans we use the newly developed
precipitation monitoring system CMORPH (Joyce et al. 2004). As a
replacement of precipitation observations from Canada starting in
January 2003 we use the model produced precipitation north of the area
analyzed by CPC in real time, which is an area somewhat greater than
ConUS; at the northern boundary we use a blending similar to that used
in the retrospective mode over oceans. For ocean ice cover as of
December 2002 we use the daily NESDIS Interactive Multisensor Snow and
Ice Mapping System (IMS) data
(http://www.ssd.noaa.gov/PS/SNOW/ims.html). And finally, for the Great
Lakes and Canadian lakes ice we use climatology.

We have compared the new system to 2002 for continuity and are running
it as R-CDAS. Porting it to the current NCEP mainframe computer is in
progress, with responsibility for its daily running to be taken over by
CPC.

During the intensive effort to complete the 23+ years of the NARR
processing on the NCEP IBM supercomputer previously used for production,
datasets had to be moved directly into the mass-storage system at NCEP.
The production of monthly means and other data forms that facilitate the
use of the data followed and was completed in early 2004. Four archiving
centers plan to host various subsets of the NARR data. They are NCDC,
NCAR, San Diego Supercomputing Center (SDSC) and the University of
Maryland. These centers have different storage resources at hand and
will be making different portions of the total NARR database available
at their institutions. Our plan is to make the (spell out) NOMADS
facility at NCDC a major distributor of NARR data. To handle the data
volumes, the next version of the NOMADS software should allow subsetting
by user specification of region, time, level, field, and resolution.
This will likely require four unique processing streams to extract the
proper data and prepare it to take maximum advantage of the individual
storage possibilities available. Specifically, since none of the
archiving centers have the ability to have on-line both the analysis and
the first guess GRIB (also referred to as AWIPS) files, \"merged\" AWIPS
files have been produced, containing analysis and some of the first
guess fields.

Additional outreach efforts are in progress or planned. Several NARR
papers (Shafran et al. 2004 on the data used; Ebisuzaki et al. 2004 on
the archiving and data access, and Ek et al. on the land
surface/boundary layer issues) are available on our web page,
http://wwwt.emc.ncep.noaa.gov/mmb/rreanl. About 1000 copies of a CDROM
with 24 years of NARR maps were distributed at the AMS 2004 Annual
Meeting, and a Users Workshop is scheduled to be held at the 2005 Annual
Meeting. Additional information is available at the web page above,
including instructions how to access the data that have been posted at
various archiving centers.

Comments on the NARR results posted or other related questions are most
welcome and are hereby solicited.

**5. CONCLUDING COMMENTS**

The results summarized we believe confirm that the objectives set out at
the beginning of the Regional Reanalysis project, to create a long-term,
consistent, high-resolution climate data set for the North American
domain, as a major improvement upon the earlier global reanalysis data
sets in both resolution and accuracy, have been fully met. Regarding
accuracy, not only have the near-surface temperatures and winds been
shown as closer to the observations than those of the GR, as probably
would have been widely expected; but clear and quite significant
improvements in winds and temperatures throughout the troposphere have
been demonstrated as well.

As to the degree of improvements, near surface, one might be inclined
not to be much impressed by the improvements in the 10-m winds, given
that the RR has assimilated the 10-m winds while the GR did not. But the
2-m temperatures, which neither of the two assimilated, can be looked
upon as an independent verification of the reanalysis skill, so that
improvements, quite considerable in winter, are worth being noted. But
perhaps the strongest indication of the overall quality of the product
are the winds in the upper troposphere. Improved fits to raobs by about
a third of that of the GR throughout the troposphere, including those at
the jet stream levels, may well be more than one might have hoped for.
Given that jet stream level winds if anything describe primarily the
largest atmospheric scales, this could be seen as a result going beyond
the widespread downscaling concept when it comes to the use of limited
area models. Another result worth noting is that of the improvements
over the GR being greater in winter than they have been in summer.

On the other hand, some of the RR features that were expected to be
beneficial failed to be confirmed as such. While no harm was documented
from direct assimilation of radiances, no evidence of benefit was noted
either. It is suspected that this is due to the relatively low top of
the Eta Model used, of 25 mb. Higher temporal frequency of the RR
compared to that of the GR, 3 vs 6 h, respectively, was also not
demonstrated as increasing the accuracy of the RR. Issues such as these
are hoped to be understood better as studies of the system components
and features are presumably advanced in years to come.

Just as well, there also have been weaknesses noted that require
studying before they are understood better. The most conspicuous of
these is the very systematic excessive strength of the Gulf of
California low level jet (GCLLJ) in summer (Mo et al. 2005), with the
largest differences compared to various observational evidence over the
northern Gulf of California. This is an important issue in view of the
North American Monsoon Experiment (NAME) activities and is at the time
of this writing under investigation.

We are encouraged by the widespread use of the RR data for a variety of
applications already at this early time, and hope to be able to learn
from as well as be helpful to users and their projects. At the time of
this writing we are looking forward to our first major opportunity in
that sense, NARR Users Workshop at the coming AMS Annual Meeting in San
Diego, in January 2005.

*Acknowledgements*. We are most grateful to the team that carried out
the GR1 project, whose footsteps we have followed. Bringing a project of
this magnitude to completion would not have been possible without the
extraordinary NCEP work environment, and the strong support and
suggestions of the NCEP director, Louis Uccellini, the NCEP/EMC
director, Stephen Lord, and the NCEP/CPC director, Jim Laver. No lesser
credit should go to NOAA/OGP who funded the project, and whose program
directors, Mark Eakin, Richard Lawford and Ken Mooney provided us with
guidance in terms of the mechanics of the effort, as well as trust
regarding the eventual high quality of the product to be obtained. The
project's Scientific Advisory Committee, chaired by John Roads, and
including Anna Barros, Lance Bosart, Mike Fiorino, Roy Jenne, Dennis
Lettenmaier, Ed Miles, Roger Pulwarty, Eugene Rasmusson, and Greg
Tripoli, provided us not only useful guidance but also strong support
and encouragement. Hugo Berbery, of the University of Maryland, made us
aware of numerous essential features of the developing NARR with his
careful and insightful studies of the EDAS and NARR preliminary results.
Huug van den Dool, of NCEP/CPC, provided unfailing assistance with a
variety of our precipitation processing efforts. Numerous other
colleagues, too many to mention, gave us useful advice, and are hereby
acknowledged as well.

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Appendix A1

(from Wesley, pasted)

NARR Output

The complete NARR archive is approximately 80 TB and includes,

input observations

input observations with QC marks and differences from analyses and 1^st^
guess

> input analyses: sea surface temperature, snow, sea ice, observed
> precipitation
>
> plots of observations locations, QC
>
> plots of fits of analyses to observations
>
> plots of fits to global reanalysis
>
> plots of analyses
>
> fixed fields such as land-sea, vegetation, soil type, orographic
> height
>
> 3 day forecasts every 2 ½ days

analyses and 3 hour forecasts (1^st^ guess) in 3 different formats
(model restart,

GRIB format on model grid, GRIB format on Lambert conformal grid)

The bulk of the 80 TB is taken by the forecasts and analyses on the 3
different variations of the analyses and 1^st^ guess fields. Experience
from earlier reanalyses suggests that a majority of users only want the
analyses and flux quantities (e.g., precipitation, latent heat, OLR)
from the 1^st^ guess. Demand for the forecasts is expected to be small.
See the Appendix for the analysis and flux variables..

The analyses and fluxes are available in 3 different formats. The
formats differ by grid type (model and Lambert conformal) and by format
(binary restart, GRIB). The model grid is unsupported by many
visualization programs as the wind and mass points are staggered. The
binary restart file has a non-standard format and is much larger than
the GRIB files because it contains much information for restarting the
model. Consequently the Lambert conformal (AWIPS) GRIB data would be the
best suited for most users. A \'merged\' data set based on the
analyses+fluxes on the AWIPS grid is approximately 5 TB (60 MB every 3
hours). We expect that this merged dataset (Appendix ##) along with some
of the smaller data sets will satisfy most users.

The data in the merged file is similar to that from the operational ETA
model; the data is in GRIB format, on a Lambert-conformal grid and most
of the fields are common to both the operational eta and NARR data sets.
However, there are some subtle differences. In the operational eta, the
winds components are grid relative and need to be rotated to produce the
usual earth-relative winds. In the NARR, the rotation has already been
done. Another difference is that NARR uses only one GRIB table (see
appendix ##) because some software doesn\'t support multiple GRIB
tables. (The operational ETA uses three GRIB tables.)

There are many software packages that handle GRIB files. We have tested
and use the following,

GrADS: http://grads.iges.org/grads/

visualization and calculation tool (linux, windows, etc)

grib2ctl: http://wesley.ncep.noaa.gov/grib2ctl.html

makes control files for GrADS, updated for NARR (linux, windows, etc)

copygb: ftp://wesley.ncep.noaa.gov/pub/copygb/copygb-g95-linux.tgz

convert lambert-conformal grids to other grids, port for NARR (linux)

wgrib: http://wesley.ncep.noaa.gov/wgrib.html

inventory and decode GRIB files, updated for NARR (linux, windows, etc)

Distribution of Data

NCAR (National Center for Atmospheric Research), NCDC (National Climatic
Data Center) and SDSC (San Diego Supercomputer Center at UCSD) have
agreed to distribute the NARR merged dataset. These centers have their
communities that they support; however, NCDC is using NOMADS (reference
##) for distributing the data over the internet making the data freely
and widely available. Of course bandwidth limitations are factor in such
an approach, so one needs to avoid the mindset of \"how do I get all the
data\" to \"how do I use the web services to only get the data that I
need.\" (See Rutledge et al (2005) and Ebisuzaki et al (2004) for more
details.) In order to keep the resources used per request at a
manageable amount, NCDC-NOMADS set policies to limit requests that uses
large amounts of disk space, CPU time and I/O time. For current
information, see the NARR home page,
http://wwwt.emc.ncep.noaa.gov/mmb/rreanl/index.html.

Appendix A2

The merged data set consists of two files 8x daily analyses. The main
file contains the analyses, accumulations and averages. The \"b\" file
contains a few 3 hour forecasts that are helpful in doing the
hydrological budget calculations.

Main Merged File

Name time level comments

4LFTX anl 180 Best (4-layer) lifted index

ACPCP acc sfc Convective precipitation

ALBDO anl sfc Albedo

APCP acc sfc Total precipitation

APCPN acc sfc Total precipitation (nearest grid point)

BGRUN acc sfc Subsurface runoff (baseflow)

BMIXL anl hl1 Blackadar\'s mixing length scale

CAPE anl 180 Convective available potential energy

CAPE anl sfc Convective available potential energy

CCOND anl sfc Canopy conductance

CD anl sfc Surface drag coefficient

CDCON ave col Convective cloud cover

CDLYR ave col Non-convective cloud

CFRZR 3hr sfc Categorical freezing rain

CICEP 3hr sfc Categorical ice pellets

CIN anl 180 Convective inhibition

also sfc

CLWMR anl prs Cloud water

CNWAT anl sfc Plant canopy surface water

CRAIN 3hr sfc Categorical rain

CSNOW 3hr sfc Categorical snow

DLWRF ave sfc Downward long wave radiation flux

DPT anl 2m Dew point temp.

DSWRF ave sfc Downward short wave radiation flux

EVP acc sfc Evaporation

FRICV anl sfc Surface friction velocity

GFLUX ave sfc Ground Heat Flux

HCDC 3hr hcl High level cloud cover

HGT anl prs Geopotential height

also 0C, hl1, mwl, trop, cld base, cld

top, max wind,

HLCY anl 3000 Storm relative helicity

HPBL anl Planetary boundary layer height

ICMR anl prs Ice mixing ratio

LCDC 3hr lcl Low level cloud cover

LFTX anl Surface lifted index

LHTFL ave sfc Latent heat flux

MCDC 3hr mcl Middle level cloud cover

MCONV anl slay Horizontal moisture divergence

MCONV anl hl1 Horizontal moisture divergence

MSLET anl Mean sea level pressure (ETA model)

MSTAV anl 100 Moisture availability

PEVAP acc sfc Potential evaporation

POT anl 10m Potential temperature

also 30m, hl1, sfc

PRATE 3hr sfc Precipitation rate

PRES anl 2m Pressure

also 30m, cld base, cld top, cond level,

hl1, max wind, sfc, trop,

PRESN anl sfc Pressure (nearest grid point)

PRMSL anl MSL Pressure reduced to MSL

PWAT anl col Precipitable water

RCQ anl sfc Humidity parameter in canopy conductance

RCS anl sfc Solar parameter in canopy conductance

RCSOL anl sfc Soil moisture para. canopy conductance

RCT anl sfc Temperature para. canopy conductance

RH anl 0C Relative humidity

also 2m, hl1

SFEXC anl sfc Exchange coefficient

SHTFL ave sfc Sensible heat flux

SNOD anl sfc Snow depth

SNOHF ave sfc Snow phase-change heat flux

SNOM acc sfc Snow melt

SNOC anl sfc Snow cover

SOILL anl soil Liquid volumetric soil moisture

(non-frozen)

SOILM anl soil Soil moisture content

SPFH anl prs Specific humidity

also 2m, 10m, 30m, slay, hl1

SSRUN acc sfc Surface runoff (non-infiltrating)

TCDC 3hr col Total cloud cover

TKE anl hl1 Turbulent Kinetic Energy

also prs (only 1000-600 hPa)

TMP anl prs Temperature

also 2m, 10m, 30m, slay, hl1, sfc trop

TSOIL anl soil Soil temperature

UGRD anl prs Zonal wind (earth relative, AWIP grid)

also 10m, 30m, slay, hl1,max wind, trop

ULWRF ave top Upward long wave radiation flux

ULWRF ave sfc Upward long wave radiation flux

USTM anl 6000 u-component of storm motion

USWRF ave top Upward short wave radiation flux

USWRF ave sfc Upward short wave radiation flux

VEG anl sfc Vegetation

VGRD anl prs Zonal wind (earth relative on AWIP grid)

also 10m, 30m, slay, hl1,max wind, trop

VIS anl sfc Visibility

VSTM anl 6000 v-component of storm motion

VVEL anl prs Pressure vertical velocity

also slay, hl1

VWSH anl trop Vertical speed shear

WCCONV acc 2l Water condensate flux convergence

WCINC acc 2l water condensate added by precipitation

assimilation

WCUFLX acc 2l Water condensate zonal flux

WCVFLX acc 2l Water condensate meridional flux

WEASD anl sfc Accumulated snow

WVCONV acc 2l Water vapor flux convergence

WVINC acc 2l Water vapor added by precipitation

assimilation

WVUFLX acc 2l Water vapor zonal flux

WVVFLX acc 2l Water vapor meridional flux

\"B\" Merged Data File

Name time level comments

DLWRF fcst sfc Downward long wave radiation flux

DSWRF fcst sfc Downward short wave radiation flux

GFLUX fcst sfc Ground heat flux

LHTFL fcst sfc Latent heat flux

PWAT fcst col Precipitable water

SHTFL fcst sfc Sensible heat flux

ULWRF fcst sfc Upward long wave radiation flux

USWRF fcst sfc Upward short wave radiation flux

WEASD fcst sfc Accumulated snow \[kg/m\^2\]

Layer Definitions:

0C = Freezing level

100 = 0-100 cm below ground

10m = 10 meters above ground

180 = 0-180 hPa above ground

2l = atmospheric column, top of atmosphere-700 hPa

2m = 2 meters above ground

3000 = 0-3000 m above ground

6000 = 0-6000 m above the ground

acc = accumulation from a 0-3 hour forecast

anl = analyses

ave = average from a 0-3 hour forecast

col = atmospheric column

fcst = 3 hour forecast

hcl = high cloud layer

hl1 = hybrid level 1

prs = 1000, 975, 950, 925, 900, 875, 850, 825, 800, 775,

750, 725,700, 650, 600, 550, 500, 450, 400, 350, 300,

275, 250, 225, 200, 175, 150, 125, 100 hPa

slay = 0-30, 30-60, 60-90, 90-120, 120-150, 150-180 hPa

above ground

soil = 0-10, 10-40, 40-100, 100-200 cm below the surface

top = nominal top of the atmosphere

APPENDIX B: List of Acronyms