MERRA AMSUA AQUA : Gridded Monthly Time-Mean Observation minus Forecast (omf) Values V001 at GES DISC
Entry ID: GES_DISC_MA_AMSUA_AQUA_OMF_V001

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Summary
Abstract: The differences between the observations and the forecast background used for the analysis (the innovations or O-F for short) and those between the observations and the final analysis (O-A) are by-products of any assimilation system and provide information about the quality of the analysis and the impact of the observations. Innovations have been traditionally used to diagnose observation, background and analysis errors at observation locations (Hollingsworth and Lonnberg 1989; Dee and da Silva 1999). At the most simplistic level, innovation variances can be used as an upper bound on background errors, which are, in turn, an upper bound on the analysis errors. With more processing (and the assumption of optimality), the O-F and O-A statistics can be used to estimate observation, background and analysis errors (Desroziers et al. 2005). They can also be used to estimate the systematic and random errors in the analysis fields. Unfortunately, such data are usually not readily available with reanalysis products. With MERRA, however, a gridded version of the observations and innovations used in the assimilation process is being made available. The dataset allows the user to conveniently perform investigations related to the observing system and to calculate error estimates. Da Silva (2011) provides an overview and analysis of these datasets for MERRA.

The innovations may be thought of as the correction to the background required by a given instrument, while the analysis increment (A-F) is the consolidated correction once all instruments, observation errors, and background errors have been taken into consideration. The extent to which the O-F statistics for the various instruments are similar to the A-F statistics reflects the degree of homogeneity of the observing system as a whole. Using the joint probability density function (PDF) of innovations and analysis increments, da Silva (2011) introduces the concepts of the effective gain (by analogy with the Kalman gain) and the contextual bias. In brief, the effective gain for an observation is a measure of how much the assimilation system has drawn to that type of observation, while the contextual bias is a measure of the degree of agreement between a given observation type and all other observations assimilated.

With MERRAs gridded observation and innovation data sets, a wealth of information is available for examination of the quality of the analyses and how the different observations impact the analyses and interact with each other. Such examinations can be conducted regionally or globally and should provide useful information for the next generation of reanalyses.

Related URL
Link: VIEW PROJECT HOME PAGE
Description: The GES DISC Data Modelling and Assimilation web site.


Link: VIEW RELATED INFORMATION
Description: The GMAO home page


Geographic Coverage
 N: 90.0 S: -90.0  E: 180.0  W: -180.0

Data Set Citation
Dataset Originator/Creator: GMAO
Dataset Title: MERRA AMSUA AQUA : Gridded Monthly Time-Mean Observation minus Forecast (omf) Values
Dataset Release Date: June 1, 2011
Dataset Release Place: NASA Goddard Space Flight Center
Version: 001
Data Presentation Form: Digital Science Data
Online Resource: http://disc.sci.gsfc.nasa.gov/MDISC/


Temporal Coverage
Start Date: 2002-10-01


Location Keywords
GEOGRAPHIC REGION > GLOBAL


Data Resolution
Latitude Resolution: 2/3 degrees
Longitude Resolution: 1/2 degrees
Vertical Resolution: 15 levels
Temporal Resolution: Monthly


Science Keywords
ATMOSPHERE >ALTITUDE >BAROMETRIC ALTITUDE    [Definition]
LAND SURFACE >LAND USE/LAND COVER >LAND COVER    [Definition]
LAND SURFACE >TOPOGRAPHY >TERRAIN ELEVATION    [Definition]


ISO Topic Category
CLIMATOLOGY/METEOROLOGY/ATMOSPHERE


Project
EOSDIS >Earth Observing System Data Information System    [Information]
ESIP >Earth Science Information Partners Program    [Information]
MERRA TIME-MEAN OBSERVATION DATA >MERRA for Research and Applications Gridded Monthly Time-Mean Obs Dataset    [Information]


Keywords
TB
EOSDIS


Data Set Progress
COMPLETE


Data Center
Goddard Earth Sciences Data and Information Services Center (formerly Goddard DAAC), Global Change Data Center, Earth Sciences Division, Science and Exploration Directorate, Goddard Space Flight Center, NASA    [Information]
Data Center URL: http://disc.gsfc.nasa.gov/

Data Center Personnel
Name: GES DISC HELP DESK SUPPORT GROUP
Phone: 301-614-5224
Fax: 301-614-5268
Email: gsfc-help-disc at lists.nasa.gov
Contact Address:
Goddard Earth Sciences Data and Information Services Center
Code 610.2
NASA Goddard Space Flight Center
City: Greenbelt
Province or State: MD
Postal Code: 20771
Country: USA



Distribution
Distribution_Media: Online (FTP)
Distribution_Format: HDF (Grid)
Fees: Free


Personnel
GLOBAL MODELING AND ASSIMILATION OFFICE
Role: TECHNICAL CONTACT
Phone: 301-614-6142
Email: data at gmao.gsfc.nasa.gov
Contact Address:
NASA Goddard Space Flight Center
Code 610.1
City: Greenbelt
Province or State: MD
Postal Code: 20771
Country: USA


DANA OSTRENGA
Role: DIF AUTHOR
Phone: 301-614-5475
Email: dana.m.ostrenga at nasa.gov
Contact Address:
NASA Goddard Space Flight Center
Code 610.2
City: Greenbelt
Province or State: MD
Postal Code: 20771
Country: USA


ARLINDO DASILVA
Role: INVESTIGATOR
Email: Arlindo.Dasilva at nasa.gov
Contact Address:
NASA/Goddard Space Flight Center
Data Assimilation Office
City: Greenbelt
Province or State: MD
Postal Code: 20771
Country: USA


Publications/References
(),

Creation and Review Dates
DIF Creation Date: 2007-06-14
Last DIF Revision Date: 2011-06-21

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