1.4

Observational Data Used for Assimilation in the NCEP North American
Regional Reanalysis

Perry C. Shafran\*, Jack Woollen

SAIC/GSO and NCEP/EMC, Camp Springs, MD

Wesley Ebisuzaki

NCEP/CPC, Camp Springs, MD

Wei Shi, Yun Fan

RSIS and NCEP/CPC, Camp Springs, MD

Robert W. Grumbine

NCEP/EMC, Camp Springs, MD

Michael Fennessy

COLA, Calverton, MD

(updated 18 November 2004)

**1. Introduction**

The National Centers for Environmental Prediction (NCEP) North American
Regional Reanalysis (NARR, Mesinger et al, this volume) assimilated a
great deal of observational data. The data used was assimilated into the
analysis, used as boundary conditions, or directly used during the
execution of the Eta model. Here, the data usage and preparation are
described in depth.

Table 1: Data Used in Global Reanalysis and Regional Reanalysis

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

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

  Dropsondes          Same as above             GR

  Pibals              Wind                      GR

  Aircraft            Temperature and wind      GR

  Surface             Pressure                  GR

  Cloud drift         Winds from geostat.sat.   GR
  ------------------- ------------------------- --------------------------

2\. Data Used in Global Reanalysis and Regional Reanalysis

Much of the NARR input dataset was created during the preparation period
of the NCAR/NCEP Reanalysis, also known as the Global Reanalysis (GR).
Most, but not all, of that data was also used in the NARR.

Temperature, winds, and moisture from radiosondes were used in the NARR.
Figure 1 shows a 00Z radiosonde distribution for a typical day. About
100-130

**\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--**

*Corresponding author address:* Perry C. Shafran, SAIC/GSO and NCEP/EMC,
5200 Auth Rd. Rm. 207, Camp Springs, MD 20746; email:
<Perry.Shafran@noaa.gov>

radiosondes were available for assimilation. Also included were
dropsondes, instruments dropped from

airplanes, that also measured temperature, winds, and moisture. Wind
data was used from pibals. Commercial aircraft measured temperature and
winds. While NCEP surface data was available in the GR, it only used the
sea-level pressure in their assimilation, and thus it was available for
usage in the NARR. Also, wind profilers have been available since 1990
and are assimilated from that time on. Vertical Azimuth Display (VAD)
winds have also been available since about 1990 as well. Finally, cloud
drift winds from geostationary satellites also were used during the
creation of the analyses.

> The dataset was basically the same as what was used in the GR.
> Therefore, it required only a small amount of preparation. To make the
> data useful for the NARR, though, the 6-hourly files had to be split
> into 3-hourly files. Also, data outside the Eta domain was cut from
> the file for easy file management.
>
> ![](media/image1.png){width="3.1222222222222222in" height="2.4125in"}

Figure 1: Distribution of radiosondes assimilated in the NARR 1 January
1988.

Table 2: Data Added or Improved Upon for Regional Reanalysis (\*, not
assimilated)

+-------------------+---------------------+---------------------------+
| Dataset           | Details             | Source                    |
+-------------------+---------------------+---------------------------+
| Precipitation,    | CONUS (with PRISM), | NCEP/CPC,                 |
|                   |                     |                           |
| disaggregated     | Mexico, Canada,     | Canada, Mexico            |
|                   | CMAP                |                           |
| into hours        |                     |                           |
|                   | Over oceans (\<35N) |                           |
+-------------------+---------------------+---------------------------+
| TOVS-1B           | Wind over oceans    | NESDIS                    |
|                   |                     |                           |
| radiances         |                     |                           |
+-------------------+---------------------+---------------------------+
| Surface           | Temperature\*,      | GR                        |
|                   | wind,               |                           |
|                   |                     |                           |
|                   | moisture            |                           |
+-------------------+---------------------+---------------------------+
| TDL surface       | Pressure,           | NCAR                      |
|                   | temperature\*,      |                           |
|                   |                     |                           |
|                   | wind, moisture      |                           |
+-------------------+---------------------+---------------------------+
| COADS             | Ship and buoy obs.  | NCEP/EMC                  |
+-------------------+---------------------+---------------------------+
| Air Force snow    | Snow depth          | COLA and NCEP/EMC         |
+-------------------+---------------------+---------------------------+
| SST               | 1-deg. Reynolds,    | NCEP/EMC, GR              |
|                   | with                |                           |
|                   |                     |                           |
|                   | Great Lakes surface |                           |
|                   | temp.               |                           |
+-------------------+---------------------+---------------------------+
| Sea and lake ice  | Includes data on    | NCEP/EMC,GLERL,           |
|                   |                     |                           |
|                   | Canadian, Great     | Canadian Ice Center       |
|                   | Lakes               |                           |
+-------------------+---------------------+---------------------------+
| Tropical          | Locations used for  | LLNL                      |
|                   | blocking            |                           |
| cyclones          |                     |                           |
|                   | CMAP precipitation  |                           |
+-------------------+---------------------+---------------------------+

**3. Precipitation**

Precipitation is used to take advantage of the Eta's precipitation
assimilation (Lin et al, 2001). Precipitation comes from several
sources. Oceanic data comes from the Climate Prediction Center (CPC)
Merged Analysis of Precipitation (CMAP). Land data comes from different
sources for the Continental United States (CONUS), Mexico, and Canada.
CMAP (Xie and Arkin, 1997) is a merged dataset of satellite-based data
and rain gauges (Wei Shi, personal communication). It is a global
2.5-degree dataset that was interpolated onto the Eta grid. This
particular dataset has reliable data only up to about 50 deg N, so no
CMAP data is used there. To make sure that there is no sharp
discontinuity, a blending was added to the Eta code to ease the
influence of the CMAP data within a 15-degree zone centered at 35
degrees N.

The data is packaged in a pentad, and is disaggregated to an hourly
dataset using hourly precipitation weighting based on GR precipitation.
The CMAP dataset was available starting January 1979.

![](media/image2.jpeg){width="3.125in" height="5.059722222222222in"}

Figure 2: Total number of stations ever reported in a) NCDC cooperative
dataset, b) RFC (CPC) cooperative data set, and c) HPD dataset. Figure
from Higgins et al (2000).

CMAP is not reliable in areas of very heavy precipitation (\< 100
mm/day) or near the centers of tropical storms, so in those locations
CMAP is filtered out and the EDAS is allowed to produce its own
precipitation.

The precipitation data for the CONUS is a gage-based daily precipitation
dataset analyzed on a 1/8-degree grid. The CONUS precipitation comes
from a variety of sources: NCDC daily cooperative stations, River
Forecast Center stations, and daily accumulations of the Hourly
Precipitation Data (HPD) set. Fig. 2 shows all the datasets that have
reported. In a typical day, the number of daily reports is 8000 NCDC,
7000 RFC, and 2500 HPD stations (Higgins et al, 2000). This particular
dataset is analyzed using an orographic dataset known as the
Parameter-elevation Regressions on Independent Slopes Model (PRISM, Daly
et al 1994). This allows the effects of mountainous terrain to be more
accurately analyzed for the RR. Also, a least-squares weighting scheme
was used as well. The daily precipitation was disaggregated using the
HPD.

![](media/image3.png){width="3.1222222222222222in" height="2.4125in"}

Figure 3: Distribution of precipitation observations for a typical day
for CONUS, Canada, and Mexico.

Both the Mexico and Canada data were also daily gage-based datasets on
1-degree grids (E. Yarosh, personal communication). These datasets were
disaggregated using the hourly precipitation from the GR. Then all four
of the datasets were applied onto the Eta grid. The borders along
Canada, the United States, and Mexico, were blended together to minimize
the effect of boundaries.

**4. Additional data improved upon for the NARR**

Several other data sources were included that were not used for the GR.
Some of this data include radiances, additional surface data, ship and
buoy data, snow, sea-ice, and sea-surface temperatures.

**4.1 *Radiances***

Radiances, which provide winds and precipitable water data, mainly over
oceans, comes from the National Oceanic and Atmospheric Administration's
(NOAA) TIROS (Television Infrared Operational Satellite) Operational
Vertical Sounder (TOVS, Kidwell 1995). The NARR utilized data from two
instruments of this polar orbiter: the High Resolution Infrared
Radiation Sounder/2 (HIRS/2) and the Microwave Sounding Unit (MSU).
These satellites started receiving data in October 1978. The data for
1978-1997 was collected from the Satellite Active Archive (SAA) of the
National Environmental Satellite Data and Information Services (NESDIS).
These data were collected by scans and were converted into the format
required for the NARR, in 3-hourly blocks. From 1998 to the present, the
data comes from NCEP's operational run history tapes, already in the
format required for usage in the NARR, except that the data had to be
converted from a 6-hourly set to a 3-hourly set. This set was used
instead of the TOVS retrievals that were assimilated in the GR.

**4.2** ***Surface observations***

![](media/image4.png){width="3.1222222222222222in" height="2.4125in"}

Figure 4: Distribution of surface observations assimilated in the NARR 1
January 1988.

![](media/image5.png){width="3.1222222222222222in" height="2.4125in"}

Figure 5: Distribution of ship and buoy observations assimilated in the
NARR 1 January 1988.

Some land-surface observations were available for the Global Reanalysis
but were not used by them. From the GR dataset the NARR assimilated 10-m
winds and 2-m moisture values. The 2-m land temperatures from the GR
dataset were not assimilated in the NARR because testing showed that the
errors of 850-mb and 700-mb winds and temperature were worse when they
were assimilated (Mesinger et al, 2004, this volume).

In addition, another surface dataset was provided by the National
Centers for Atmospheric Research (NCAR). This dataset originated from
the Model Development Laboratory (MDL) and was based on observations
that have been taken since 1976 (R. Jenne, personal communication). The
dataset was merged with the operational NCEP surface dataset. The obs
were carefully checked against the existing surface dataset to ensure
that there were no duplicates (J. Woollen, personal communication). When
a duplicate was noted, the MDL observation was used and the NCEP
observation was thrown out.

For the ocean, the NARR used an updated version of the Comprehensive
Ocean-Atmosphere Data Set (COADS, Woodruff et al, 1993). This dataset
contains temperatures, winds, moisture, and pressure from ships. and
buoys. The new dataset was finished up to 1997, so for the period
1998-present the GR existing ship and buoy dataset was used.

**4.3 *Sea and lake ice***

Sea ice comes from a variety of sources. The main data over the oceans
come from a satellite-based ice sensor that was interpolated onto the
Eta 32-km grid (Grumbine 1996). This data is a sea-ice concentration
between 0 and 100% of the gridpoint. For the NARR's purposes, if a
gridpoint contained 50% or more sea ice, it was considered an ice point,
otherwise, it was considered a water point.

For the Great Lakes, a digital ice dataset was available from the Great
Lakes Environmental Research Laboratory (GLERL, R. Assel, personal
communication) up to 1978-2000 and applied to the Eta grid. For 2001 and
beyond, a 5-year climatology (1995-2000) was calculated and applied.

Canadian ice data came from the Canadian Ice Service (CIS). The CIS
provided data on a per-lake basis, not by gridpoint. A comparison to the
Eta's land-sea mask and an atlas provided the gridpoints representative
on each lake. This comparison listed the gridpoints for all the lakes in
Canada, some which were not available by the CIS. For those available by
the CIS, the value given for each lake was applied to every gridpoint on
that lake. For those lakes not available by the CIS, the value of the
nearest CIS-provided lake was applied to those gridpoints. For inland
bodies of water that are near coastlines, the oceanic value was applied.
The dataset had data available from 1995 to 2002, with a daily
climatology calculated for 1978-1994. The incorporation of ice from
these three sources provided a complete history of ice for all
water-based points.

**4.4 *Sea-surface temperatures***

Sea-surface temperatures (SSTs) also come from a variety of sources. The
main source is a 1-degree Reynolds dataset analyzed using a optimal
interpolation algorithm, available from 1981 to the present (Reynolds et
al 2002). Prior to 1981, the SSTs originate from a from a reconstructed
SST dataset using COADS (Smith and Reynolds 2003). These datasets were
interpolated between the Pacific and Atlantic Oceans. Inland lakes are
not properly represented by this interpolation. Using the previously
calculated ice data set, if a Canadian lake point is ice, it is given a
value of 273.15 K, the freezing point of ice. If it is not ice, it is
left alone. For the Great Lakes, the SSTs were provided by GLERL (R.
Assel, personal communication) and applied to the Eta grid.

For the Gulf of California, in order to more closely approximate the
warming of the waters, a patch was added. This patch takes the mean
value of the gulf near Guaymas, Mexico, and applies that value to the
entire gulf. The mean value for the first of the month is interpolated
temporally to the proper day of the month. Unfortunately, a bug in the
code prevented this temporal interpolation, so the result was the
temperature for the first of the month was erroneously applied for the
entire month. This bug was corrected in runs starting 1 April 2004.

The Great Salt Lake was also represented by a climatological value
throughout the period.

**4.5 *Snow cover***

A snow cover data set comes from an Air Force snowfall set (Hall 1986).
This dataset originated on a global 512x512 grid and was interpolated to
the Eta grid over land. The snowfall from 1979-1999 was supplied by the
Center for Ocean-Land-Atmosphere Studies (COLA). The data from
1999-present was available from NCEP's run history tapes.

**4.6 *Tropical cyclones***

Finally, the locations of tropical cyclones were useful (Fiorino 2002).
The CMAP precipitation dataset is not reliable near tropical cyclones,
and the Eta model produces better precipitation near the cyclones. The
only use of the tropical cyclones locations was to determine where to
block the CMAP in their vicinity.

Table 3: Daily climatologies

+------------------------------------+--------------------+-----------+
| Dataset                            | Use                | Source    |
+------------------------------------+--------------------+-----------+
| Green vegetation fraction          | Initialization of  | EDAS      |
|                                    |                    |           |
|                                    | Vegetation         |           |
+------------------------------------+--------------------+-----------+
| Baseline snow-free albedo          | Initialization of  | EDAS      |
|                                    |                    |           |
|                                    | Albedo             |           |
+------------------------------------+--------------------+-----------+

**5. DAILY CLIMATOLOGIES**

Two daily climatologies were used to initialize the NARR runs once per
day. The baseline snow-free albedo and the vegetative fraction (a
monthly field) are fixed fields were updated daily during the NARR run.
These are the same fixed fields that are used by the EDAS.

**6. FIXED FIELDS**

Finally, several fixed fields were used and remained constant throughout
the entire NARR period. The list of variables follows:

Land mask (land=1, sea=0)

Vegetation type (index, 1-13)

Soil type (index, 1-9)

Surface slope type (index, 1-9)

Snow-free albedo (%)

Maximum snow albedo (%)

Surface roughness (m)

Soil column bottom temperature (K)

Number of root zone soil layers (non-dimensional)

**7. DATA CHANGES FOR REAL-TIME R-CDAS**

Most of the input data sets are the same for the Regional Climate Data
Assimilation System (R-CDAS) as they were for the retrospective NARR,
but there were several changes. This section highlights the items that
are different.

For precipitation, the datasets changed starting 1 January 2003 and on
into the R-CDAS period. Oceanic precipitation is provided by a
half-hourly satellite dataset known as CMORPH (CPC Morphing Technique).
This dataset goes up to 60 deg N, north of which there is no
assimilation. The Mexican precipitation is supplied by CPC's merged
US/Mexico precipitation dataset. Finally, the CONUS precipitation is
supplied by the North American Land Data Assimilation System (NLDAS).
Since the availability of Canadian precipitation data sets is too late
to be considered real-time, there is no precipitation assimilation in
Canada during 2003 and the R-CDAS period.

Sea-ice also has a different dataset. The oceanic sea-ice is supplied by
NCEP's Ice Mapping System (IMS), a 25-km ice data set that is
interpolated to the Eta grid. For the Canadian and Great Lakes, the ice
field applied is a calculated daily climatology of the input fields
between Dec. 1978 and Nov. 2002. The climatology becomes the input
starting 1 January 2003 and into the R-CDAS period.

For SSTs the oceanic SSTs are still supplied by the Reynolds dataset,
and the Canadian SST values are still calculated as before. The only
difference is the Great Lakes SSTs come from a 14-km 2DVAR SST dataset.
The patch for the Gulf of California is also still in use during the
R-CDAS period.

**8. REFERENCES**

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statistical-topographic model for mapping climatological precipitation
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ECMWF 40-year Reanalysis (ERA- 40). *25th Conf. on Hurricanes and
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Grumbine, R. W., 1996: Automated passive microwave

> sea ice concentration analysis at NCEP. DOC/NOAA/NWS/NCEP/EMC/OMB
> Technical Note 120, 13 pp.

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