MERRA AMSUA AQUA : Gridded Monthly Time-Mean Observation minus Analysis (oma) Values

Federal Geographic Data Committee (FGDC) Metadata:

• Identification_Information
• Data_Quality_Information
• Spatial_Reference_Information
• Distribution_Information
• Metadata_Reference_Information

Identification_Information:

Citation:

Citation_Information:

Originator: GMAO

Publication_Date: June 1, 2011

Title: MERRA AMSUA AQUA : Gridded Monthly Time-Mean Observation minus Analysis (oma) Values

Edition: 001

Geospatial_Data_Presentation_Form: Digital Science Data

Series_Information:

Series_Name:

Issue_Identification:

Publication_Information:

Publication_Place: NASA Goddard Space Flight Center

Publisher:

Other_Citation_Details:

Online_Linkage: http://disc.sci.gsfc.nasa.gov/MDISC/

Description:

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.

Purpose:

Not Available

Supplemental_Information:

REFERENCE:

Time_Period_of_Content:

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Beginning_Date: 20021001

Ending_Date: Unknown

Currentness_Reference:Unknown

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Progress: Complete

Maintenance_and_Update_Frequency: As needed

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West_Bounding_Coordinate: -180.0

East_Bounding_Coordinate: 180.0

North_Bounding_Coordinate: 90.0

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Theme_Keyword: EARTH SCIENCE > ATMOSPHERE > ALTITUDE > BAROMETRIC ALTITUDE

Theme_Keyword: EARTH SCIENCE > LAND SURFACE > LAND USE/LAND COVER > LAND COVER

Theme_Keyword: EARTH SCIENCE > LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION

Theme_Keyword: EOSDIS > EARTH OBSERVING SYSTEM DATA INFORMATION SYSTEM

Theme_Keyword: ESIP > EARTH SCIENCE INFORMATION PARTNERS PROGRAM

Theme_Keyword: MERRA TIME-MEAN OBSERVATION DATA > MERRA FOR RESEARCH AND APPLICATIONS GRIDDED MONTHLY TIME-MEAN OBS DATASET

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Temporal_Keyword: Monthly

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Contact_Position: TECHNICAL CONTACT

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Address: NASA Goddard Space Flight Center

Address: Code 610.1

City: Greenbelt

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Contact_Organization: NASA/GSFC/SED/ESD/GCDC/GESDISC > 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

Contact_Person: GES DISC HELP DESK SUPPORT GROUP

Contact_Position: DATA CENTER CONTACT

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Address_Type: Mailing and Physical Address

Address: Goddard Earth Sciences Data and Information Services Center

Address: Code 610.2

Address: NASA Goddard Space Flight Center

City: Greenbelt

State_or_Province: MD

Postal_Code: 20771

Country: USA

Contact_Voice_Telephone: 301-614-5224

Contact_Facsimile_Telephone: 301-614-5268

Contact_Electronic_Mail_Address: gsfc-help-disc@lists.nasa.gov

Resource_Description: GES_DISC_MA_AMSUA_AQUA_OMA_V001

Distribution_Liability:

Not Available

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Refine your granule search with ECHO's next generation Earth Science discovery tool (Reverb) using information from this record.

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http://disc.sci.gsfc.nasa.gov/MDISC/

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The GES DISC Data Modelling and Assimilation web site.

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http://gmao.gsfc.nasa.gov/

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Fees: Free

Metadata_Reference_Information:

Metadata_Date: 20070614

Metadata_Review_Date: 20110621

Metadata_Contact:

Contact_Information:

Contact_Person_Primary:

Contact_Person: DANA OSTRENGA

Contact_Position: DIF AUTHOR

Contact_Address:

Address_Type: Mailing and Physical Address

Address: NASA Goddard Space Flight Center

Address: Code 610.2

City: Greenbelt

State_or_Province: MD

Postal_Code: 20771

Country: USA

Contact_Voice_Telephone: 301-614-5475

Contact_Electronic_Mail_Address: dana.m.ostrenga@nasa.gov

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Metadata_Standard_Version: FGDC-STD-001-1998

Metadata_Time_Convention: local time