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MERRA AMSUA NOAA15 : Gridded Monthly Time-Mean Observation minus Analysis (oma) Values V001 at GES DISC

Entry ID: GES_DISC_MA_AMSUA_NOAA15_OMA_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: GET DATA > ECHO

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

Geographic Coverage

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Spatial coordinates

N: 90.0

S: -90.0

E: 180.0

W: -180.0

Data Set Citation

Dataset Originator/Creator: GMAO

Dataset Title: MERRA AMSUA NOAA15 : Gridded Monthly Time-Mean Observation minus Analysis (oma) 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: 1998-11-01

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

LAND SURFACE > LAND USE/LAND COVER > LAND COVER

LAND SURFACE > TOPOGRAPHY > TERRAIN ELEVATION

ISO Topic Category

CLIMATOLOGY/METEOROLOGY/ATMOSPHERE

Project

EOSDIS > Earth Observing System Data Information System

ESIP > Earth Science Information Partners Program

MERRA TIME-MEAN OBSERVATION DATA > MERRA for Research and Applications Gridded Monthly Time-Mean Obs Dataset

CWIC > CEOS WGISS Integrated Catalog

Access Constraints No constraints

Use Constraints Please cite U.S. Geological Survey and National Science Foundation.

Ancillary Keywords

SEYMOUR ISLAND

ROSS ISLAND

Service Provider

DOI/USGS/USARC > United States Antarctic Resource Center, U.S. Geological Survey, U.S. Department of the Interior

Service Organization URL: http://usarc.usgs.gov/

Service Provider Personnel
Name: CHERYL A. HALLAM Phone: 703-648-4525 Fax: 703-648-4165 Email: challam at usgs.gov Contact Address: U.S. Geological Survey 12201 Sunrise Valley Drive City: Reston Province or State: VA Postal Code: 20192 Country: USA

Distribution

Distribution Media: Online (HTTP)

Distribution Format: HTML

Fees: None

Personnel

CHERYL A. HALLAM

Role: TECHNICAL CONTACT

Phone: 703-648-4525

Fax: 703-648-4165

Email: challam at usgs.gov

Contact Address:

U.S. Geological Survey

12201 Sunrise Valley Drive

City: Reston

Province or State: VA

Postal Code: 20192

Country: USA

TYLER B. STEVENS

Role: SERF AUTHOR

Phone: (301) 614-6898

Fax: 301-614-5268

Email: Tyler.B.Stevens at nasa.gov

Contact Address:

NASA Goddard Space Flight Center

Global Change Master Directory

City: Greenbelt

Province or State: MD

Postal Code: 20771

Country: USA

Extended Metadata Properties

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Creation and Review Dates

SERF Creation Date: 2003-10-23
SERF Last Revision Date: 2013-04-12
Future SERF Review Date: 2004-10-23

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