1.1 DAO TOGA COARE IOP 4-D Timeseries Dataset Guide

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1.5 Abstract:

The Data Assimilation Office (DAO) at NASA Goddard Space Flight Center has produced a four-month (1 November 1992 - 28 February 1993) gridded global assimilated dataset to aid in the study of physical processes and multiscale interactions during the Tropical Ocean Global Atmosphere (TOGA) Coupled Ocean Atmosphere Response Experiment (COARE) Intensive Observing Period (IOP). These data are archived at the Goddard Distributed Active Archive Center (DAAC). The analyses incorporate rawinsonde reports, satellite retrievals of geopotential thickness, cloud-motion winds, and aircraft, ship and rocketsonde reports. At the lower boundary, the assimilating atmospheric general circulation model (AGCM) is constrained by the observed sea surface temperature, and climatological soil moisture. All available (meaning, that received over the GTS) wind and sea level pressure data collected during the TOGA COARE IOP were incorporated into this assimilation. Please note, however, that all TOGA COARE moisture and temperature data were withheld from this assimilation. We will be producing a reanalysis of TOGA COARE IOP utilizing ALL TOGA COARE data as soon as the level 2b data become available in the late spring 1995.

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Relevant references concerning the DAO assimilation system (GEOS-1 DAS)

1.6 Table of Contents

2. Investigators:

Investigator(s) Name and Title:

Name:Arthur Hou
Addresses:
DATA ASSIMILATION OFFICE  code 910.3
NASA/Goddard Space Flight Center
Greenbelt MD 20771
Telephone Numbers:301-286-3594
Electronic Mail Address:
hou@dao.gsfc.nasa.gov

Name:David V. Ledvina
Addresses:
DATA ASSIMILATION OFFICE  code 910.3
NASA/Goddard Space Flight Center
Greenbelt MD 20771
Telephone Numbers:301-805-7955
Electronic Mail Address:
ledvina@dao.gsfc.nasa.gov

3. Dataset Information

Dataset Identification

DAO 4-D timeseries subset of a TOGA COARE IOP assimilation

Introduction:

The data are a subset of a global four-month assimilated dataset produced at the Data Assimilation Office (DAO) of the Goddard Laboratory for Atmospheres with a non-varying assimilation system. The model is called the Goddard Earth Observing System (GEOS-1). The data are organized in a timeseries format to facilitate the computation of statistics spanning the entire four months. In an attempt to make the data as accessible as possible, the data have been partitioned into a geographical box surrounding the TOGA COARE domain (26S-26N; 40E-100W). The large array of available prognostic and diagnostic fields represent a comprehensive description of the atmospheric evolution throughout the IOP. Data are currently available for the time period 1 November 1992 through 28 February 1993.

Objectives/Purpose

The analysis of historical data using an assimilation system is important to enable researchers to study atmospheric variability and potential short-term climate change. The analysis is simplified by using a non-varying system since the researcher need not account for spurious changes resulting from changes to the assimilation system. The DAO's primary mission is the development of the tools necessary to produce research-quality assimilated data sets [NRC, 1991]. The mission of the DAO is unique because it is the quality and the utility of the assimilated data, rather than the forecast, that measures the success of the effort. Ultimately, the assimilation system developed in this effort will be used to assimilate the satellite and other upper-air and surface-based measurements of the Earth system which will become available at the turn of the century from the Earth Observing System (EOS) program.

The DAO has produced this benchmark TOGA COARE dataset using version 1 of the GEOS-1 assimilation system. The objectives in producing these data are threefold. First, it is believed that the absence of spin-up in the hydrological cycle and the use of a fixed assimilation system will make these data extremely useful for a wide range of process studies. Secondly, these data will serve as the benchmark against which future assimilations, which test the impact of new data types, will be compared. Thirdly, making the data available to the larger scientific community will attract valuable feedback on the quality and limitations of the assimilated data. This feedback will help guide future development of the system.

Summary of Parameters:

The data are organized somewhat loosely according to whether they are from a PROGNOSTIC or DIAGNOSTIC calculation in the AGCM (see Glossary or Calculated Variables). This naming convention is mainly for historical reasons, and as more parameters have been added, this is no longer strictly accurate. The parameters included in this dataset are grouped under seven data products. Each parameter is described further in the Parameter Table, and in more detail in the document: geos1.0_gcm.doc.ps.

DATA PRODUCTS:

  1. SURFACE PROGNOSTICS

    These parameters consist of various surface boundary conditions, prognostic and diagnostic surface quantities and vertically integrated/averaged fields. The fields are made available every six hours, though it should be noted the boundary condition fields are interpolated linearly in time from monthly mean or climatological (surface wetness) fields.

    • surface geopotential height
    • surface albedo
    • ground wetness from off-line bucket model [Schemm et al, 1992]
    • surface pressure minus top of the atmosphere pressure (10mb)
    • surface ground temperature
    • sea level pressure
    • water=1, land=2, permanent ice=3, sea ice=4 flags
    • vertically integrated (barotropic) zonal wind
    • vertically integrated (barotropic) meridional wind

  2. UPPER AIR PROGNOSTICS

    These parameters are "instantaneous snapshots" reported at 6 hour intervals. Each product is reported as a full three-dimensional field at 18 pressure levels (1000, 950, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20mb).

    • zonal wind speed
    • meridional wind speed
    • geopotential height
    • temperature profiles
    • specific humidity profiles
    • turbulent kinetic energy
    • standard deviation of the height error

  3. DIAGNOSTICS: Precip/Evap

    These parameters are 3-hour averages, reported at the endpoint of the 3-hour interval.

    • surface pressure minus top of the atmosphere pressure (10mb)
    • total precipitation
    • convective precipitation
    • surface evaporation
    • vertically averaged zonal wind * specific humidity (U*Q)
    • vertically averaged meridional wind * specific humidity (V*Q)
    • vertically averaged zonal wind * temperature (U*T)
    • vertically averaged meridional wind * temperature (V*T)
    • vertically integrated ("total") precipitable water

  4. DIAGNOSTICS: Momentum/Heat Flux

    These parameters are 3-hour averages, reported at the endpoint of the 3-hour interval.

    • surface pressure minus top of the atmosphere pressure (10mb)
    • zonal momentum surface stress
    • meridional momentum surface stress
    • surface flux of sensible heat
    • surface drag coefficient for temperature and specific humidity
    • surface drag coefficient for winds
    • surface wind speed
    • friction velocity
    • surface roughness
    • planetary boundary layer depth

  5. DIAGNOSTICS: Radiation

    These parameters are 3-hour averages, reported at the endpoint of the 3-hour interval.

    • surface pressure minus top of the atmosphere pressure (10mb)
    • net upward LW radiation at the surface
    • net downward SW radiation at the surface
    • outgoing longwave radiation
    • outgoing longwave radiation clear sky
    • surface longwave radiation clear sky
    • incident SW radiation at the top of the atmosphere
    • outgoing shortwave radiation
    • outgoing shortwave radiation clear sky
    • surface SW radiation clear sky
    • 2-dimensional total cloud fraction

  6. DIAGNOSTICS: Near Surface

    These parameters are 3-hour averages, reported at the endpoint of the 3-hour interval.

    • surface pressure minus top of the atmosphere pressure (10mb)
    • ground temperature
    • surface air temperature
    • surface saturation specific humidity
    • surface pressure tendency
    • zonal wind at 2m
    • meridional wind at 2m
    • temperature at 2m
    • specific humidity at 2m
    • zonal wind at 10m
    • meridional wind at 10m
    • temperature at 10m
    • specific humidity at 10m

  7. UPPER AIR DIAGNOSTICS

    These parameters are 6-hour averages, reported at the midpoint of the 6-hour interval. Each product is reported as a full three-dimensional field at 18 pressure levels (1000, 950, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20mb).

    • zonal momentum changes due to turbulence
    • meridional momentum changes due to turbulence
    • temperature changes due to turbulence
    • moisture changes due to turbulence
    • temperature changes due to moist processes
    • moisture changes due to moist processes
    • temperature changes due to LW radiation
    • temperature changes due to SW radiation
    • vertical velocity
    • filling of negative specific humidity

Each parameter is described further in the Parameter Table, and in more detail in the document: geos1.0_gcm.doc.ps. We request that if a user discovers additional problems that the user will alert the DAO by e-mail at:

toga@dao.gsfc.nasa.gov

4. Theory of Measurements:

The assimilated data are a synthesis of measurements and short-term model forecasts. In the GEOS-1 system this is accomplished using Optimal Interpolation (OI), in which observations of the following variables are used:

The Optimal Interpolation (OI) Scheme

RESOLUTION

The tropospheric version of the OI analysis scheme being used for the control assimilation has been carried out at a horizontal resolution of 2 degree latitude by 2.5 degree longitude at 14 upper-air pressure levels (20, 30, 50, 70, 100, 150, 200, 250, 300, 400, 500, 700, 850, 1000 mb) and at sea level. The analysis increments are computed every 6 hours using observations from a +/- 3 hour data window centered on the analysis times (00, 06, 12, and 18 UTC). The innovation vector (observation minus background forecast) used as input to the OI is computed using a single forecast valid at the analysis time.

THE ASSIMILATION

The OI scheme is multivariate in geopotential height and winds and employs a damped cosine function for the horizontal correlation of model prediction error. The height-wind cross-correlation model is geostrophic and scaled to zero at the equator. The multivariate surface analysis scheme over the oceans adopts an Ekman balance for the pressure-wind analysis. The moisture analysis for mixing ratio employs only rawinsonde moisture data. Although, it should be noted, that all TOGA COARE upper air moisture data was withheld from the assimilation. All grid point analyses are done using up to 75 nearby observations from within a radius of 1600 km.

The assimilation system does not include an initialization scheme and relies on the damping properties of a Matsuno time differencing scheme to control initial imbalances generated by the insertion of observations. However, the initial imbalances and spinup have been greatly reduced over earlier versions by the introduction of an incremental analysis update (IAU) procedure [Bloom et al., 1991]. As shown in the figure of the IAU procedure, iau graphic

standard OI analysis increments are computed at the analysis times (0, 6, 12, 18Z). The increments are then inserted gradually into the AGCM by rerunning the forecast and adding a fraction of the increment at each model time step. Over the 6 hour period centered at the analysis time the full effect of the increment is realized. The assimilation thus effectively consists of a continuous AGCM forecast with additional heat, momentum, moisture and mass source terms updated every 6 hours from observations. It is the output of this IAU procedure that makes up the dataset provided here. An important difference between the IAU scheme and the usual Newtonian nudging procedure is that the IAU forcing terms are held constant over the insertion period, while in Newtonian nudging they are proportional to the difference between a target analysis and the instantaneous current model state.

BUDGET CALCULATIONS

The implication of the IAU procedure for performing budget (e.g., moisture or heat) calculations with the assimilated data is that, in order to balance the budget, one must include the analysis increments and the filling of negative specific humidities as additional forcing terms. If the assimilating model had no bias the mean analysis increments would, of course, be zero and the increments would have no contribution to the mean budget equations. The current model (GEOS-1), does have a bias, and while the initial timeseries dataset provided by the DAAC does not include the analysis increments, they (the increments) can be obtained as the residual of the other terms in the budget equations. The filling of negative specific humidities (QFILL) are provided in the timeseries.

5. Equipment:

Instrument Description:

Collection Environment:

Data were collected from globally deployed in situ and remote observation platforms throughout the assimilation period.

Platform:

  • NOAA polar orbiters (TOVS)
  • ships
  • buoys
  • rawinsondes, dropwindsondes, rocketsondes
  • aircraft winds

Platform Mission Objectives:

The assimilation system synthesizes observations and model first guesses with the intention of producing a consistent and accurate estimate of the climate.

Key Variables:

The following observed variables were ingested into the data assimilation system when and where available:

  • Geopotential thickness
  • Winds
  • Water vapor mixing ratio
  • Sea level pressure
  • Surface winds over the ocean

Note that all diagnostic parameters are calculated from AGCM's physical parameterizations in a manner consistent with the prognostic fields (See Optimal Interpolation).

6. Procedure:

Data Acquisition Methods:

The input observational database is one that has been accumulated at the GLA. These data have been obtained directly from NMC, and do not include data which came in after the cut-off time for the operational NMC system. In addition to this source, some TOVS temperature soundings have come directly from the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data and Information Service (NESDIS), and gaps have been filled with data from the National Climatic Data Center (NCDC) in Ashville, NC. For the global sea level pressure and near surface wind analysis over the oceans, data from surface land synoptic reports (sea level pressure only), ships and buoys are used. The upper-air analyses of height, wind and moisture incorporate the data from rawinsondes, dropwindsondes, rocketsondes, aircraft winds, cloud tracked winds, and thicknesses from the historical TOVS soundings produced by NOAA NESDIS. The satellite heights are computed using a reference level which depends on the analyzed sea level pressure. The only situation where data are used as a proxy for un-collected observations are 1000 mb height observations which are generated above pressure reports from ships. These serve to further couple the surface and upper-air analyses.
For a description of how these data are used in the model, see the description of optimal interpolation.

7. Observations:

Data Notes:

Additional processesing notes available via anonymous ftp to: hera.gsfc.nasa.gov
/pub/assimilation/toga/toga_doc.txt
Details of this dataset can be found in this ASCII file.

/pub/tech_memos/volume_4.ps.Z
Details of the assimilation system (GEOS-1 DAS) can be found in this compressed postscript file.

/pub/gcm/geos1.0_gcm.doc.ps
Details of the diagnostics (how they were computed, etc.) and further information about the GEOS-1 GCM used in the assimilation may be found in this postscript file.

8. Data Granularity:

Each timeseries file contains one month of data, and is considered a DATA GRANULE. Each file consists of a time sequence of either a single three-dimensional upper air parameter (4 times daily), or a collection of several single level or vertically integrated parameters (8 times daily). The data files are named "edvl049.prs.NAME_ss.bYYMMDD.eYYMMDD" where NAME is one of the filenames listed in the following table. Below is a table summarizing the data granularity (for a more detailed description of the parameters, see Parameter Table:

NAME	MBytes	PARAMETERS
SFCPROG	58	PHIS,ALBD,GWET,PSMPTOP,GTMP,SLP,LWI,UBAR,VBAR

UWND	 21	UWND

VWND	 21	VWND

HGHT	 21	HGHT	

TMPU	 21	TMPU

SPHU	 21	SPHU

QQ	 21	QQ

HGHTE	 17	HGHTE

DIAG1	 21	PSMPTOP,PREACC,PRECON,EVAP,VINTUQ,VINTVQ,VINTUT,VINTVT,QINT

DIAG2	 24	PSMPTOP,UFLUX,VFLUX,HFLUX,CT,CU,WINDS,USTAR,Z0,PBL

DIAG3	 26	PSMPTOP,RADLWG,RADSWG,OLR,OLRCLR,LWGCLR,RADSWT,OSR,OSRCLR,SWGCLR,CLDFRC

DIAG4	 31	PSMPTOP,TG,TS,QS,DPDT,U2M,V2M,T2M,Q2M,U10M,V10M,T10M,Q10M

TURBU	 21	TURBU

TURBV	 21	TURBV

TURBT	 21	TURBT

TURBQ	 21	TURBQ

MOISTT	 21	MOISTT

MOISTQ	 21	MOISTQ

RADLW	 21	RADLW

RADSW	 21	RADSW

OMEGA	 21	OMEGA

QFILL	 21	QFILL

9. Data Description:

Spatial Characteristics:

Spatial Coverage:

GLOBAL, A subset is available through the DAAC. It covers the area: 26S-26N; 40E-100W

Spatial Resolution:

The spatial resolution is 2 deg latitude by 2.5 deg longitude for the geographical region bounded by 26S-26N and 40E-100W. There are 18 pressure levels (1000, 950, 900, 850, 800, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20mb) interpolated from the following 20 sigma levels. sigma levels graphic

Grid Description:

There are 89 grid points in the longitude direction with the first grid point at 40E with a grid spacing of 2.5 degrees. There are 27 grid points in the latitude direction with the first grid point at 26S with a grid spacing of 2.0 degrees. There are 18 pressure levels with the bottom level (highest pressure) first.

Temporal Characteristics:

Temporal Coverage:

The temporal coverage of this data set spans the entire TOGA COARE IOP (1 November 1992 to 28 February 1993).

Temporal Coverage Map:

There are no gaps, either temporal or spatial.

Temporal Resolution:

  • PROGNOSTIC fields are sampled 4 times daily at 00Z, 06Z, 12Z, 18Z.
  • DIAGNOSTIC surface or other single level fields are given 8 times daily as time averages over the three hours preceding the time stamp (i.e. at 00Z for 21Z(previous day) to 00Z, at 03Z for 00Z to 03Z, at 06Z for 03Z to 06Z....)
  • DIAGNOSTIC upper air fields are given 4 times daily as time averages over 6 hours centered on the times 00Z, 06Z, 12Z, 18Z.

Data Description:

Parameter/Variable:

See Parameter Table

Variable Description/Definition

See Parameter Table

Unit of Measurement

See Parameter Table

DAO Timeseries PARAMETER TABLE

PROGNOSTIC VARIABLES

Surface Prognostic

NAME UNIT DESCRIPTION PHIS (m/s)^2 surface geopotential height (z * gravity) ALBD 0-1 proportion of solar radiation reflected by the surface (0-1) GWET 0-1 ground wetness from off-line bucket model [Schemm et al, 1992] PSMPTOP mb surface pressure minus top of the atmosphere pressure (PTOP= 10mb) GTMP K surface ground temperature SLP mb sea level pressure LWI flag water=1, land=2, permanent ice=3, sea ice=4 flags UBAR m/s vertically integrated (barotropic) zonal wind VBAR m/s vertically integrated (barotropic) meridional wind

Upper Air Prognostic

NAME UNIT DESCRIPTION UWND m/s zonal wind speed VWND m/s meridional wind speed HGHT m geopotential height TMPU K temperature profiles SPHU g/kg specific humidity profiles QQ (m/s)^2 turbulent kinetic energy HGHTE m std dev of the height error

DIAGNOSTIC VARIABLES

Surface Diag 1 Precip/Evap

NAME UNIT DESCRIPTION PSMPTOP mb surface pressure minus top of the atmosphere pressure (PTOP= 10mb) PREACC mm/dy total precipitation PRECON mm/dy convective precipitation EVAP mm/dy surface evaporation VINTUQ m/s g/kg vertically averaged zonal wind * specific humidity (U*Q) VINTVQ m/s g/kg vertically averaged meridional wind * specific humidity (V*Q) VINTUT m/s K vertically averaged zonal wind * temperature (U*T) VINTVT m/s K vertically averaged meridional wind * temperature (V*T) QINT g/cm^2 vertically integrated ("total")precipitable water

Surface Diag 2 Momentum/Heat Flux

NAME UNIT DESCRIPTION PSMPTOP mb surface pressure minus top of the atmosphere pressure (PTOP= 10mb) UFLUX N/m^2 zonal momentum surface stress VFLUX N/m^2 meridional momentum surface stress HFLUX W/m^2 surface flux of sensible heat CT - surface drag coefficient for temperature and specific humidity CU - surface drag coefficient for winds WINDS m/s surface wind speed USTAR m/s friction velocity Z0 m surface roughness PBL mb planetary boundary layer depth

Surface Diag 3 Radiation

NAME UNIT DESCRIPTION PSMPTOP mb surface pressure minus top of the atmosphere pressure (PTOP= 10mb) RADLWG W/m^2 net upward LW radiation at the surface RADSWG W/m^2 net downward SW radiation at the surface OLR W/m^2 outgoing longwave radiation OLRCLR W/m^2 outgoing longwave radiation clear sky LWGCLR W/m^2 surface longwave radiation clear sky RADSWT W/m^2 incident SW radiation at top of the atmosphere OSR W/m^2 outgoing shortwave radiation OSRCLR W/m^2 outgoing shortwave radiation clear sky SWGCLR W/m^2 surface SW radiation clear sky. CLDFRC 0-1 2-dimensional total cloud fraction (0-1)

Surface Diag 4 Near Surface

NAME UNIT DESCRIPTION PSMPTOP mb surface pressure minus top of the atmosphere pressure (PTOP= 10mb) TG K ground temperature TS K surface air temperature QS g/kg saturation specific humidity at the surface DPDT mb/dy surface pressure tendency U2M m/s zonal wind at 2m V2M m/s meridional wind at 2m T2M K temperature at 2m Q2M kg/kg specific humidity at 2m U10M m/s zonal wind at 10m V10M m/s meridional wind at 10m T10M K temperature at 10m Q10M kg/kg specific humidity at 10m

Upper Air Diagnostic

NAME UNIT DESCRIPTION TURBU m/s/dy zonal momentum changes due to turbulence TURBV m/s/dy meridional momentum changes due to turbulence TURBT K/dy temperature changes due to turbulence TURBQ g/kg/dy moisture changes due to turbulence MOISTT K/dy temperature changes due to moist processes MOISTQ g/kg/dy moisture changes due to moist processes RADLW K/dy temperature changes due to LW radiation RADSW K/dy temperature changes due to SW radiation OMEGA mb/dy vertical velocity QFILL g/kg/dy filling of negative specific humidities
For more detailed information see geos1.0_gcm.doc.ps.

Sample Data Record:

The first Sea Level Pressure record (1 Nov 92, 00Z) is displayed below:

TOGA_SLP graphic

It was created by the fortran read program called: single_level_read.f.

Data Format:

The data representation is ieee 32 bit floating point, written sequentially by FORTRAN 77. There are no header or trailer records.

10. Data Manipulations:

Formulae:

Derivation Techniques and Algorithms:

A detailed guide which describes how the variables were created is available as a postscript document. It can be accessed via anonymous ftp from hera.gsfc.nasa.gov under the directory "pub/gcm". The file is called geos1.0_gcm.doc.ps.

Data Processing Sequence:

Processing Steps (and Datasets):

DYNAMICS

The current tropospheric version of the model (GEOS-1) uses the potential enstrophy and energy-conserving horizontal differencing scheme on a C-grid developed by Sadourney [1975], and further described by Burridge and Haseler [1977]. An explicit leapfrog scheme is used for the time differencing, applying an Assilin [1972] time filter to damp out the computational mode. An 8th order Shapiro filter is applied to the wind, potential temperature and specific humidity to avoid non-linear computational instability. The filter is applied at every time step in such a way that the amplitude of the two-grid interval wave would be reduced by half in two hours. Applying the filter weakly at each time step eliminates the shock that occurred in earlier assimilations by intermittent application of filter. The model also uses a polar Fourier filter to avoid linear instability due to violation of the CFL condition for the Lamb wave and internal gravity waves. This polar filter, however, is applied only to the tendencies of the winds, potential temperature, specific humidity and surface pressure. The model's vertical finite differencing scheme is that of Arakawa and Suarez [1983]. The above dynamics routines are organized into a plug-compatible module called the ARIES/GEOS "dynamical core" developed by M. Suarez and L. Takacs in the Goddard Laboratory for Atmospheres.

RADIATION

The infrared and solar radiation parameterizations follow closely those described by Harshvardhan et al. [1987]. In the longwave water vapor absorption is parameterized as in Chou [1984], the 15 micron band of CO2 as in Chou et al. [1983], and ozone absorption as in Rodgers [1968] with the modifications suggested by Rosenfield et al. [1987]. The shortwave follows Davies [1982], as described in Harshvardhan et al. [1987]. Shortwave absorption by water vapor uses a k-distribution approach as in Lacis and Hansen [1974]. Cloud albedo and transmissivity for the model layers are obtained from specified single-scattering albedo and cloud optical thickness using the delta-Eddington approximation [Joseph et al., 1976; King and Harshvardhan, 1986].

CONVECTION

The penetrative convection originating in the boundary layer is parameterized using the Relaxed Arakawa-Schubert (RAS) scheme [Moorthi and Suarez, 1992], which is a simple and efficient implementation of the Arakawa-Schubert [1974] scheme. Unlike the Arakawa-Schubert scheme, which solves an adjustment problem by considering simultaneous interaction among all possible cloud types, RAS considers only one cloud at a time, and rather than adjusting fully every hour or two, it does a series of partial adjustments that tend to relax the state toward equilibrium. The AGCM also includes a parameterization that models the evaporation of falling convective rain as described in Sud and Molod [1988]. Negative values of specific humidity produced by the finite-differenced advection are filled by borrowing from below.

BOUNDARY LAYER

The planetary boundary layer (PBL) is explicitly resolved in a 2 to 4 layer region. Wind, temperature and humidity profiles in an "extended" surface layer (which can be up to 150m thick), and the turbulent fluxes of heat, moisture, and momentum at the surface are obtained from Monin-Obukov similarity theory by selecting similarity functions that approach the convective limit for unstable profiles and that agree with observations for very stable profiles. Surface roughness lengths are taken as functions of vegetation type over land and as a function of surface stress over water. Turbulent fluxes above the "extended" surfaced layer are computed using the second order closure model of Helfand and Labraga [1988]. In this scheme, the turbulent kinetic energy is a prognostic variable, and the remaining second order moments are diagnosed from it and the atmospheric sounding.

BOUNDARY CONDITIONS

The topography used in GEOS-1 was prepared from the 10 minute topography map of the Navy Fleet Numerical Oceanography Center in Monterey. The 2 degree latitude by 2.5 degree longitude elevation values were obtained by averaging the high resolution values (areas with more than 60% water were considered water points), and then applying a Lanczos [1966] filter. The Lanczos filter was designed to remove small scale structure (it completely removes 2 DX waves) while minimizing the Gibbs phenomena.

This version of the AGCM is run without a land surface model. For the assimilation described here, soil moisture is computed off-line based on a simple bucket model using monthly mean observed surface air temperature and precipitation [Schemm et al., 1992]. The climatology calculated using these monthly means was used in this assimilation run. The snow line and surface albedo are prescribed and vary with the season. The sea surface temperature is updated according to the observed monthly mean values provided by the Climate Analysis Center at NMC and the Center for Ocean, Land and Atmosphere (COLA) at the University of Maryland. Long-term plans call for the incorporation of both land-surface and ocean models.

Calculated Variables:

Since this dataset is the output of a model, technically all variables are calculated. However, the DAO makes a distinction between PROGNOSTIC and DIAGNOSTIC variables (See Glossary). The PROGNOSTIC tend to be those which the model predicts and hence can be adjusted to match observations, when available. The DIAGNOSTIC variables should be considered calculated, in the sense that they are not assimilated from direct observation. They are estimates of the physical processes operating in nature which are generated by the GCM's physical parameterizations in a manner consistent with the observations. For a list of those observed variables which are assimilated, see the KEY VARIABLE list.

11. Errors:

Sources of Error:

The two primary sources of error are observational errors and errors in the first guess (forecast errors).

Quality Assessment:

Confidence Level/Accuracy Judgement:

An important quantity is the OI estimate of the analysis error variance. While this is a rather crude estimate of the true analysis error, it is valuable for assessing the impact of poor data coverage. Maps of these error estimates provide a detailed picture of the regions which have a recent history of poor data coverage.

Measurement Error for Parameters and Variables:

The estimated analysis error, mentioned in the previous paragraph, is provided in the timeseries data as the HEIGHT ERROR FIELD. This parameter is the root mean square of the OI's estimate of the analyzed height error variance.

12. Application of the Dataset:

This dataset serves as a comprehensive description of the atmosphere through the duration of the TOGA COARE IOP. It is intended to aid in the study of physical processes and multiscale interactions, and as an evaluation of the the GEOS-1 analysis system.

13. Dataset Plans:

Description of Future Plans:

A reanalysis of the TOGA COARE IOP will be performed when the entire level 2b dataset becomes available in the late spring 1995.

METHODS

Various improvements are planned for both the assimilating AGCM and the analysis scheme. In the short term the model improvements include a more accurate moisture advection scheme, further improvements to the PBL, convection and radiation parameterizations, including the introduction of a land surface model, and a cloud liquid water scheme. The OI scheme is currently being modified to use a variational approach to solve the OI analysis equations; the method solves the system globally, thus eliminating the need to perform data selection. Longer term developments include the introduction of semi-lagrangian dynamics [Bates et al., 1993], a coupled ocean model, and a simplified Kalman filtering scheme.

14. References:

14.1 Satellite/Instrument/Data Processing Documentation:

Available Documents:

These files should be retrieved via anonymous FTP.

toga_doc.txt This file contains information about this dataset.

volume_4.ps.Z This compressed postscript file contains information about the assimilation system (GEOS-1 DAS).

geos1.0_gcm.doc.ps This file contains information about the gcm and how the diagnostics are calculated.

BAMS Article (available online) decribing the DAO 5-year assimilation.

Schubert, S., R. Rood, J. Pfaendtner, 1993: An Assimilated Dataset for Earth Science Applications, Bull. Am. Met. Soc., 74, 2331-2342.

14.2 Journal Articles and Study Reports:

Arakawa, A., and W. Schubert, 1974: Interaction of a cumulus ensemble with the large-scale environment, Part I, J.. Atmos. Sci., 31, 674-701.

Arakawa, A., and M.J. Suarez, 1983: Vertical differencing of the primitive equations in sigma coordinates. Mon. Wea. Rev., 111, 34-45.

Bates, J. R., S. Moorthi and R. W. Higgins, 1993: A global multilevel atmospheric model using a vector semi-Lagrangian finite-difference scheme. Part I: Adiabatic formulation. Mon. Wea. Rev., 121, 244-263.

Bengtsson, L. and J. Shukla, 1988: Integration of space and in situ observations to study global climate change. Bull. Amer. Meteor. Soc., 69, 1130-1143.

Bloom, S. C., L. L. Takacs and E. Brin, 1991: A scheme to incorporate analysis increments gradually in the GLA assimilation system. Ninth Conference on Numerical Weather Prediction, Denver, CO.

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Chou, M.D.,and L. Peng, 1983: A parameterization of the absorption in the 15m CO2 spectral region with application to climate sensitivity studies. J. Atmos. Sci. , 40, 2183-2192.

Davies, R., 1982: Documentation of the solar radiation parameterization in the GLAS climate model. NASA Tech. Memo. 83961, 57pp., Goddard Space Flight Center, Greenbelt, MD 20771.

Harshvardhan, R. Davies, D.A. Randall, and T.G. Corsetti, 1987: A fast radiation parameterization for atmospheric circulation models. J. Geophys. Res., 92, 1009-1016.

Helfand, H.M. and J.C. Labraga, 1988: Design of a non-singular level 2.5 second-order closure model For the prediction of atmospheric turbulence. J. Atmos. Sci., 45, 113-132.

Helfand, H. M. and S. D. Schubert, 1993: Contribution of the Great Plains low-level jet to the simulated continental moisture budget of the United States. J. Climate, (submitted).

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15. Related Software:

Software Description:

A sample READ program for data sets is:
       PARAMETER (IM=89,JNP=27)
       REAL FIELD(IM,JNP)
       DO 1 ITIMES=1,NTIMES
           DO 2 IXX=1,NXX
               READ(8) FIELD
 2         CONTINUE
 1     CONTINUE 
where:

For each DATA FILE there is an associated TABLE FILE which provides which values to use for these parameters. The TABLE FILE has the same name as the DATA FILE except the part of the name ".prs." is replaced by ".tabl.".

In the TABLE FILE:

A program written by the DAAC to read the upper air data products, based on this template is available: UPPER AIR SAMPLE READ PROGRAM.

Software Access:

Assign statements (for Cray)

1) cray data (iau and restart files)

assign -a $data1                           fort.21


2) ieee data (all other files)

assign -a $data1  -N ieee -F f77           fort.21

16. Data Access:

Contacts for Archive/Data Access Information:

Name:

GSFC DAAC User Services Office

Addresses:

Goddard DAAC code 610.2
NASA/Goddard Space Flight Center 
GREENBELT MD 20771

Telephone Numbers:

TEL:301-614-5224

FAX:301-614-5268

Electronic Mail Address:

daacuso@disc.gsfc.nasa.gov

Archive Identification:

DAO_TOGA_4D_TIMESERIES

17. Glossary of Terms:

ASSIMILATION
Assimilation is the process of combining observations and model first guess fields. See Optimal Interpolation.
DIAGNOSTIC
A Diagnostic parameter is an inference of the processes occuring in the climate system. These parameters are generally not measured, but are calculated by the model's physical parameterizations in a manner consistent with the observations.
GRANULE
A Granule is the smallest sub-division of data. In DAO 4D TIMESERIES data set each granule is either a single parameter at all pressure levels or a collection of surface variables. The temporal coverage of each granule is one month.
PROGNOSTIC
A Prognostic parameter is an atmospheric state variable which the model forecasts. During the assimilation these are the parameters most directly influenced by the observations. See Theory of Measurements for a list of assimilated quantities.
TIMESERIES
The timeseries is a subset of the DAO 5-year analysis project.

18. List of Acronyms:

AGCM     Atmospheric General Circulation Model
COARE    Coupled Ocean-Atmosphere Response Experiment
COLA     Center for Ocean, Land and Atmosphere at the University of Maryland
DAAC     Distributed Active Archive Center
DAO      Data Assimilation Office
ECMWF    European Center for Medium Range Forecasts
EOS      Earth Observing System
GEOS-1   Goddard Earth Observing System version 1 model
GEWEX    Global Energy and Water Cycle Experiment
GLA      Goddard Lab for Atmospheres
GSFC     Goddard Space Flight Center
HIRS2    High-Resolution Infrared Sounder 2
IAU      Incremental Analysis Update
IMS      Information Management System
KF       Kalman Filtering
LW       Longwave (same as IR)
NASA     National Aeronautics and Space Administration
NCAR     National Center for Atmospheric Research
NCDC     National Climatic Data Center
NESDIS   National Environmental Satellite Data and Information Service
NH       Northern Hemisphere
NMC      National Meteorological Center 
NOAA     National Oceanic and Atmospheric Administration
NRC      National Research Council
OI       Optimal Interpolation
PBL      Planetary Boundary Layer
RAS      Relaxed Arakawa-Schubert
RHS      Right Hand Side (e.g. of an equation)
SPADE    Stratospheric Photochemistry, Aerosols, and Dynamics Expedition
STRATAN  Stratospheric version of GEOS-1
SH       Southern Hemisphere
SW       Shortwave (same as VISIBLE)
TIROS    Television Infrared Observing Satellite
TOA      Top Of the Atmosphere
TOGA     Tropical Ocean Global Atmosphere
TOVS     TIROS Operational Vertical Sounder
UARS     Upper Atmosphere Research Satellite 


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