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[CIDC FTP Data]
[DAO IDC Data on FTP]
Data Access
DAO 4D Assimilation Monthly Mean Subset Data
One layer diagnostic products (radiation, surface temp.,
precip., etc.)
Surface prognostic products (surface pressure, etc.)
Upper air prognostic products (U & V wind, temp., etc.)
[rule]
Readme Contents
Data Set Overview
Sponsor
Original Archive
Future Updates
The Data
Characteristics
Source
The Files
Format
Name and Directory Information
Companion Software
The Science
Theoretical Basis of Data
Processing Sequence and Algorithms
Scientific Potential of Data
Validation of Data
Contacts
Points of Contact
References
[rule]
Data Set Overview
This is a subset of the Data Assimilation Office's (DAO) monthly
mean data set. The DAO monthly mean data set, in turn, is based on
the DAO's full multi-year assimilation. Data Assimilation is the
process of ingesting observations (horizontal winds, temperatures,
dew point temperatures, etc.) into a model of the Earth system.
The current product, GEOS-1, uses meteorological observations and
an atmospheric model (Schubert et al., 1995). The data is ingested
at six hour intervals. The result is a comprehensive and
dynamically consistent dataset which represents the best estimate
of the state of the atmosphere at that time. The assimilation
process fills data voids with model predictions and provides a
suite of data-constrained estimates of unobserved quantities such
as vertical motion, radiative fluxes, and precipitation. Those
pondering the use of these data should look at the Validation of
Data Section.
This data set provides global data determined on a 2.5 x 2 degree
latitude- longitude grid for 26 fields. The data has been
regridded to a 2 x 2 degree latitude-longitude grid. Five of these
fields are given at eight pressure levels; the rest are surface
values, or vertically integrated values.
Sponsor
The production and distribution of this data set are funded by
NASA's Earth Science enterprise. The data are not copyrighted;
however, we request that when you publish data or results using
these data please acknowledge as follows:
The authors wish to thank the Data Assimilation Office
at the Goddard Space Flight Center, Greenbelt, MD,
20771, for producing these data, and Goddard's
Distributed Active Archive Center for distributing the
data. These activities are sponsored as part of NASA's
Earth Science enterprise.
Original Archive
The full Monthly Means of the DAO's GEOS-1 Multiyear Assimilation
are available via anonymous FTP from the DAAC.
The monthly mean data set was produced at the Data Assimilation
Office (DAO) at NASA GSFC.
Information about other assimilation datasets can be found in the
list of assimilation parameters and the list of assimilation
datasets.
Future Updates
This data set may be extended, based on user interest.
The Data
Characteristics
* Parameters: This subset of the monthly means contains the
following 26 parameters. Click on any parameter name to get
to the data.
Parameter Description Units Typical
Range
Surface Prognostic Products
surface pressure
psptop minus ptop (where hPa 520 to
ptop=10 hPa) 1020
slp sea level pressure hPa 965 to
1040
land:2,
lwi water:1,permanent unitless 1 to 4
ice:3 sea ice:4
flags
One Layer Diagnostic Products
net upward
radlwg longwave radiation W/m**2 -15 to
at ground 200
net downward
radswg shortwave W/m**2 0 to
radiation at 375
ground
preacc total mm/day 0 to 27
precipitation
winds surface wind speed m/s 1 to 17
tg ground temperature K 190 to
317
olr outgoing longwave W/m**2 78 to
radiation 350
incident shortwave
radswt radiation at top W/m**2 0 to
of atmosphere 550
evap surface mm/day -9 to
evaporation 11
ustar surface stress m/s 0.030
velocity to 0.9
z0 surface roughness m 0 to
2.653
pbl planetary boundary hPa 7 to
layer depth 440
osr outgoing shortwave W/m**2 0 to
radiation 400
vertically
vintuq averaged uwnd * (m/s)(g/kg) -50 to
sphu 65
vertically
vintvq averaged vwnd * (m/s)(g/kg) -30 to
sphu 40
2-dimensional
cldfrc total cloud unitless 0 to 1
fraction
qint precipitable water g/cm**2 0 to 6
t2m temperature at 2 K 195 to
meters 312
q2m specific humidity kg/kg 0 to
at 2 meters 0.025
Upper Air Prognostic Products
zonal wind at
uwnd eight pressure m/s -25 to
levels 65*
meridional wind at
vwnd eight pressure m/s -20 to
levels 25*
geopotential
hght height at eight m 5 to
pressure levels 12500*
temperature at
tmpu eight pressure K 195 to
levels 310*
specific humidity
sphu at eight pressure g/kg 0.005
levels to 20*
* range is for all pressure levels
* Temporal Coverage: March 1980 - November 1993
* Temporal Resolution: All gridded values are monthly means
* Spatial Coverage: Global
* Horizontal Resolution: 2 degree x 2 degree, grid point data
(180 x 91 values per level, proceeding west to east and then
north to south)
* Vertical Resolution: 21 parameters are single-layer, 5
parameters contain data on 8 pressure levels (1000, 950, 900,
850, 700, 500, 300 200 mb)
The DAO monthly mean data has 144 grid points in the longitude
direction with the first grid point at the dateline and with a
grid spacing of 2.5 degrees. This subset of the DAO monthly mean
data were regridded so that they have 180 grid points in the
longitude direction with the first grid point at the dateline and
with a grid spacing of 2 degrees. There are 91 grid points in the
latitude direction with the first grid point at the north pole and
with a grid spacing of 2.0 degrees. This is the same as in the
original DAO data except that the orientation of the data was
reversed from south-north (original DAO data) to north-south
(subset of DAO data).
Source
The Data Assimilation Office's assimilated data are a synthesis of
observations and short-term model forecasts. DAO receives the same
observational data that the National Centers for Environmental
Prediction (NCEP, formerly NMC) receives operationally. Some gaps
in the operational data were filled using the somewhat more
complete data available after the fact from the National Climatic
Data Center (NCDC) and the National Center for Atmospheric
Research (NCAR). The observational data were collected from global
in situ and remote observations throughout the assimilation
period. The platforms used to collect observations are:
1. Tiros Operational Vertical Sounder (TOVS) (NOAA/NESDIS
thickness retrievals)
2. Ships and Buoys
3. Surface synoptic reports over land
4. Rawinsondes and dropwindsondes
5. Aircraft (wind measurements)
6. Cloud-motion winds (from GOES satellite)
Sources 1, 4, 5, and 6 are used in the upper air analyses of
height and wind, while the moisture analysis uses only rawinsonde
reports. Sources 2 and 3 are used to determine sea level pressure
and near-surface wind analysis over oceans.
The remote observations, Sources 1 and 6, provide much of the data
in regions where in situ data are sparse; for example, over
oceans.
At the lower boundary, the assimilating General Circulation Model
(GCM) is constrained by the monthly mean observed sea surface
temperature and soil moisture derived from monthly mean observed
surface air temperature and precipitation fields.
More information on the DAO GEOS-1 Multiyear Assimilation can be
found in the DAAC Guide Document for the DAO's GEOS-1 Multiyear
Assimilation datasets, or in greater detail in the DAO Technical
Report Series.
The Files
Format
Compressed:
The data files have been compressed using Lempel-Ziv coding. Files
with a .gz ending are compressed versions of the .bin file. When
decompressing the files use the -N option so that the original
.bin file name ending is restored. For additional information on
decompression see aareadme file in the directory:
software/decompression/
Uncompressed:
* File Size: There are 26 data files for each monthly average.
21 of these data files are for single layer parameters. These
files are 65520 bytes each, and contain 16380 (180 x 91)
floating point values. The other 5 parameters (uwnd, vwnd,
hght, tmpu, and sphu) are provided on 8 pressure levels.
These files are 524160 bytes each, and contain 131040 values
each (8 x 180 x 91).
* Data Format: IEEE floating point
* Headers, trailers, and delimiters: none
* Fill value: -999.9
* Data Ordering: Starting from global position (180W, 90N),
these data are read as 91 rows of 180 data values each,
proceeding west east and then from north to south. Where a
quantity is given at multiple levels (uwnd, vwnd, hght, tmpu,
and sphu) the data in the file contain the lowest level first
then proceeds sequentially up to the highest level. For
example, for uwnd, the first 65520 bytes represent the zonal
wind at 1000 mb, the next 65520 bytes the zonal wind at 950
mb, and so on.
Name and Directory Information
Naming Convention
The file naming convention for this data set is
assim54a.VVVVVV.NLTGRR.[YYMM].DDD
where
assim54a.= parent data set
VVVVVV = name of parameter, as given in the Data
Characteristics section
N = Number of vertical levels (1 or 8)
L = Type of vertical level, p for pressure, s for surface
T = Timestep indicator. 'm' indicates monthly data
G = Grid used. 'd' indicates 2x2 degree grid
RR = Region of Earth. 'gg' indicates global data
YY = year
MM = month
DDD = File type (.gz=compressed, .bin=binary, .ctl=GrADS
control file)
Example: assim54a.sphu.8pmdgg.8503.bin
NOTE: When decompressing the data files be sure to use the -N
option. This will restore the original .bin filename. For
additional information on decompression see the format section of
this readme and the aareadme file in the directory:
software/decompression/
Directory Path
/data/inter_disc/assim_atmo_dyn/ddddddd/pppppp/yyyy
where
ddddddd is data type
surf_prog = Surface prognostic data
one_layer_diag = One layer diagnostic data
upper_air_prog = upper air prognostic data
pppppp is parameter
yyyy is year
Links to each parameter's data directory are provided in the Data
Characteristics section of this document.
Companion Software
Several software packages have been made available on the CIDC
CD-ROM set. The Grid Analysis and Display System (GrADS) is an
interactive desktop tool that is currently in use worldwide for
the analysis and display of earth science data. GrADS meta-data
files (.ctl) have been supplied for each of the data sets. A GrADS
gui interface has been created for use with the CIDC data. See the
GrADS document for information on how to use the gui interface.
Decompression software for PC and Macintosh platforms have been
supplied for datasets which are compressed on the CIDC CD-ROM set.
For additional information on the decompression software see the
aareadme file in the directory:
software/decompression/
Sample programs in FORTRAN, C and IDL languages have also been
made available to read these data. You may also acquire this
software by accessing the software/read_cidc_sftwr directory on
each of the CIDC CD-ROMs
The Science
Theoretical Basis of Data
Modern weather forecasting and climate study, global circulation
models (GCMs) contain a large number of inter-related parameters
such as winds, atmospheric temperature profiles, clouds,
precipitation, and atmospheric water vapor and radiation.
Equations describing the physics of the atmosphere are used to
constrain these various parameters into an internally self
consistent model of the real atmosphere. The limited accuracy of
the input data and approximations in the equations keep the models
from being perfect mirrors of nature. The accuracy is improved
somewhat if instead of predicting the future they are used to
analyze past weather conditions. Satellite based soundings of
atmospheric temperature and water vapor profiles by multi spectral
channel sensors can also be used to define the atmospheric cloud
and radiation fields. Both the global circulation models and the
multi channel soundings can thus produce a large number of
atmospheric parameters, but in both cases some of them will be
more accurate than others.
Goddard 4D Assimilated Data: The Data Assimilation Office (DAO) at
Goddard Space Flight Center produced a multi year global
assimilated data set with version 1 of the Goddard Earth Observing
System Data Assimilation System (GEOS-DAS-1). This systrem is
commonly abreviated as GEOS-1 (Schubert et al., 1993). By use of
the geophysical equations of atmospheric motion, observational
measurements were blended with climate data to produce a self
consistent model of the dynamic atmosphere. The assimilation
process fills data voids with model predictions and provides a
suite of data-constrained estimates of unobserved quantities such
as vertical motion, radiative fluxes, and precipitation. In
contrast, when atmospheric parameters are individually measured
they are not necessarily consistent with one another because of
temporal and spatial measurement differences and gaps and various
experimental errors and biases. The reanalysis was motivated by
the fact that operational data assimilation systems undergo
frequent updates that introduce spurious climate signals in the
analysis output. One of the main goals of this project was to
produce a research quality data set suitable for the study of
general Earth science problems such as climate variability,
atmospheric chemistry, stratosphere-troposphere exchange, and
surface processes. This current data set is also considered a
critical benchmark for further development of the GEOS system,
thus feedback from the general Earth Science community is deemed
vital.
Schubert et al. (1995) discuss both the advantages and possible
shortcomings of the present class of assimilated data products.
The greatest potential benefit of assimilation systems for climate
studies is that they can provide essentially time continuous
global estimates of all the relevant parameters at the full
resolution of the assimilating geophysical model. ... climate
applications place new demands on the quality of the parameterized
physical processes in the assimilating geophysical models. For
example, accurate and consistent estimates of such quantities as
precipitation, cloudiness, and surface fluxes require a degree of
veracity in the physical parameterizations and a level of
sophistication in the analysis techniques that the current systems
are just beginning to achieve. ... Assimilated data products can
be approximately grouped into two categories: those (primarily
prognostic) fields that are directly assimilated (e.g. winds and
specific humidity) and those (primarily diagnostic) fields that
are generated from the various physical parameterizations. The
former are the quantities which are strongly constrained by the
observations and ,where these are available, are only marginally
impacted by errors in the model ... The quality of the latter
fields depends strongly both on the accuracy of the physical
parameterizations and the quality of the observations. Of course,
in regions where observations are sparse, all estimates will be
dominated by the model's first guess field.
The Subset of Monthly Means data described in this document is one
of several subsets of the 4-D Assimilated Data Set. These data are
a subset of the monthly means generated from the DAO's 6 hourly
assimilated analyses to learn about related data sets see the list
of assimilation data sets or the list of assimilation parameters.
Processing Sequence and Algorithms
DAO produced the data on a 2 x 2.5 degree grid. The input
(forcing) parameters are introduced into the model every six hours
and the products are saved on a three hourly or six hourly basis
depending on the parameter; monthly diurnal means were calculated
from these. The upper air prognostic fields are saved every 6
hours as instantaneous quantities. All the upper air diagnostics
are saved 4 times daily as 6 hour averages centered on the output
time. The single level and vertically-integrated fields are saved
every 3 hours accumulated over the previous 3 hours. The global
circulation model (GCM) has twenty pressure levels called sigma
levels since a normalized pressure parameter, sigma, is used. For
normal distribution, the products are translated to 18 standard
pressure levels from 1000 hPa to 20 hPa (Schubert et al., 1995).
Monthly mean values are derived from these to facilitate inter
annual studies. We selected monthly means at eight pressure
levels, 1000 hPa to 200 hPa, for inclusion in this interdiscipline
data collection.
To make the data commensurable with the standard Inderdisciplinary
Data Collection 1-degree latitude by 1-degree logitude world grid,
the DAAC regridded the monthly data to a 2 x 2 degree grid from 89
S to 89 N. latitude and retained the 1-degree polar caps. We were
requested not to regrid to a 1 x 1 degree grid because the Data
Assimilation Office has plans to produce its own 1 x 1 degree GEOS
product in the future. Our regridding to 1 x 1 might thus have
caused some confusion.
Regridding was accomplished by implementing the following steps.
1. Every data value in each latitude band was replicated by the
target number of grid cells in a latitude band within the
final output data file, 180, and assigned to a temporary
array. Each original latitude band had 144 data values which
when replicated 180 times produced a temporary array of 25920
data values for that latitude band.
2. The first 144 (temporary array) data values were compared
against the fill value for these data. Any values that were
not fill values were then summed, and a count of data value
and fill value occurrence was kept.
3. A test for fill value occurrence was performed. If fill value
constituted 50% or more of contributing values then the fill
value was assigned to that grid cell. Otherwise, the average
was computed for the target grid cell from only those points
constituting data values. When assigning fill values, a new
fill value was used to provide greater uniformity with other
existing data sets held at the Goddard DAAC.
4. Steps 2 and 3 were repeated for the next 144 values within
the temporary array until all values were summed, tested for
fill value occurrence, and assigned to a target grid cell.
5. Steps 2, 3, and 4 were repeated for each of the next 90
latitude bands.
Scientific Potential of Data
These data are well suited for climate research, since they are
produced by a fixed assimilation system designed to minimize
spinup in the hydrological cycle. By using a nonvarying system,
variability resulting from algorithm change is eliminated and
geophysical variability can be more confidently isolated.
Validation of Data
The DAO has compared selected output from this assimilation with
various other analyses, including European Centre for medium-range
Weather Forecasts (ECMWF) analyses, and with gridded (i.e.
interpolated) observational data sets. The primary strength of the
GEOS-1 assimilation system lies in its ability to capture many of
the key climate variations associated with El Nino and La Nina
events, monsoons, droughts and other low frequency variations. A
number of shorter term fluctuations are also well represented in
the assimilation. These are primarily associated with fluctuations
in the zonal wind and/or the boundary layer winds and surface
stresses. Over land, these results indicate that the performance
of the GCM's planetary boundary layer (PBL) parameterization
generates very realistic wind fields, since the GEOS-DAS
assimilates few wind observations below 850 hPa. Over the oceans,
the results suggest that both the surface wind/pressure analysis
and the PBL parameterization are performing well.
The following climate mean quantities are generally consistent
with available verifying observations, and/or are consistent or
better than found in other analyses:
1. The climate mean and seasonal evolution of the basic
prognostic fields appear to be well captured in the GEOS
analysis. Differences with ECMWF analyses over the Northern
Hemisphere land masses are small. The largest differences
occur over the tropics, and the Southern Hemisphere oceans,
where observations are sparse and model bias is apparently
playing a role (more on this below).
2. The clear sky longwave flux and albedo are in good agreement
with ERBE measurements.
3. The general patterns of tropical convection and their
seasonal evolution are consistent with available
observations, but details of local maxima and amplitudes are
not.
4. GEOS-1 wind stress fields have been employed to force an
ocean model in the North Pacific with some success,
particularly in producing the subpolar circulation.
The greatest deficiencies in the GEOS-1 products are tied to
biases in the humidity and cloud fields. There are several reasons
for this. Moisture biases of the GCM are clearly playing a role,
as well as, deficiencies in how the available moisture
observations (currently only radiosonde) are being assimilated.
One of the most disturbing aspects of the results is the manner in
which the observations and model first guess appear to generate
spurious feedbacks. A number of DAO development activities are
geared to addressing these deficiencies. There are various
problems with the precipitation, and near surface temperature and
humidity fields. Over land, these include substantial errors in
the diurnal cycle. Some of these appear to be tied to the
convective parameterization and should be remedied with the
introduction of the changes under way.. Improvements to the
diurnal cycle and longer term impacts of soil moisture variations
must await the introduction of a land surface model (currently
being implemented).
Sample problems include: a much too wet upper troposphere (300
hPa) over the Pacific Ocean compared with available observations;
low level coastal stratiform clouds are underestimated; longwave
and shortwave cloud radiative forcing tend to be overestimated
over the intertropical convergence zone, and underestimated over
middle-latitude storm tracks; Summertime precipitation over
eastern North America is overestimated; too much rain over
continental Europe and northern Asia in July and too little over
the Mediterranean during January.
For more information, see Volume 6 of the DAO's Technical Report
Series on Global Modeling and Data Assimilation (which is
available in postscipt format from the DAO's Technical Report
Series web page). Interested users should also look at the DAO
documents Summary of Strengths and Weaknesses of the GEOS-1 Data
Assimilation Products.
Contacts
Points of Contact
For information about or assistance in using any DAAC data,
contact
EOS Distributed Active Archive Center (DAAC)
Code 902.2
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: daacuso@daac.gsfc.nasa.gov
301-614-5224 (voice)
301-614-5268 (fax)
For information about the original data archive, contact
The Data Assimilation Office
Code 910.3
NASA Goddard Space Flight Center
Greenbelt, Maryland 20771
Internet: data@dao.gsfc.nasa.gov
References
Molod, A., H. M. Helfand and L. Takacs, 1997: The climatology of
parameterized physical processes in the GEOS-1 GCM and their
impact on the GEOS-1 data assimilation system, J. Climate (to be
published). For a preprint, try
ftp://dao.gsfc.nasa.gov/pub/papers/molod/modelpaper.ps.Z or
contact the authors at molod@dao.gsfc.nasa.gov (Andrea Molod)
Schubert, S. D., J. Pjaendtner, and R. Rood, 1993. An assimilated
data set for Earth science applications. Bull. Am. Met. Soc., 74:,
2331-2342.
Schubert, S., C.-K. Park, C.-Y. Wu, W. Higgins, Y. Kondratyeva, A.
Molod, L. Takacs, M. Seablom, and R. Rood, 1995: A multiyear
assimilation with the GEOS-1 System. Overview and Results, Vol. 6
of Technical report series on global modeling and data
assimilation, M. J. Suarez, Ed., NASA T. M. 104606, Vol. 6, 201
pp.
Schubert, S. D., J. Pjaendtner, and R. Rood, 1993. An assimilated
data set for Earth science applications. Bull. Am. Met. Soc.,
74:2331-2342.
DAO Technical Report Series
DAO's Summary of Strengths and Weaknesses of the GEOS-1 Data
Assimilation Products
Documentation for the Monthly Means of the DAO's GEOS-1 Multiyear
Assimilation
Goddard DAAC Atmospheric Dynamics Site
DAO Home Page
------------------------------------------------------------------------
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NASA Goddard GDAAC CIDC
Last update:Tue Sep 30 16:15:04 EDT 1997
Page Author: Edward Hartnett -- ejh@larry.gsfc.nasa.gov
Web Curator: Daniel Ziskin -- ziskin@daac.gsfc.nasa.gov
NASA official: Paul Chan, DAAC Manager -- chan@daac.gsfc.nasa.gov
