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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.)
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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

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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

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:

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

NASA GSFC Goddard DAAC cidc site
NASAGoddardGDAACCIDC

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