MERRA AMSUB NOAA16 : Gridded Monthly Time-Mean Observation minus Analysis (oma) Values V001 (MA_AMSUB_NOAA16_OMA) at GES DISC
Entry ID: GES_DISC_MA_AMSUB_NOAA16_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 > ON-LINE ARCHIVE
Description: Daily Product direct FTP access.


Link: VIEW PROJECT HOME PAGE
Description: The GMAO home page


Link: VIEW RELATED INFORMATION
Description: The GES DISC Modeling and Assimilation Data and Information Services Center.


Link: VIEW RELATED INFORMATION > USER'S GUIDE
Description: The GMAO File Specification Document


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

Data Set Citation
Dataset Originator/Creator: Global Modeling and Assimilation Office (GMAO)
Dataset Title: MERRA AMSUB NOAA16 : Gridded Monthly Time-Mean Observation minus Analysis (oma) Values
Dataset Series Name: MA_AMSUB_NOAA16_OMA
Dataset Release Place: Greenbelt, MD, USA
Dataset Publisher: Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC)
Version: 001
Data Presentation Form: Digital Science Data
Other Citation Details: http://disc.sci.gsfc.nasa.gov/mdisc/
Online Resource: http://disc.sci.gsfc.nasa.gov/datacollection/MA_AMSUB_NOAA16_OMA_V0...


Temporal Coverage
Start Date: 2001-01-01


Location Keywords
GEOGRAPHIC REGION > GLOBAL


Data Resolution
Latitude Resolution: 1/2 degrees
Longitude Resolution: 2/3 degrees
Vertical Resolution: 5 levels
Temporal Resolution: Monthly


Science Keywords
ATMOSPHERE >ATMOSPHERIC TEMPERATURE    [Definition]


ISO Topic Category
CLIMATOLOGY/METEOROLOGY/ATMOSPHERE


Platform
Models/Analyses    [Information]
MERRA >Modern-Era Retrospective Analysis for Research and Applications


Project
MERRA TIME-MEAN OBSERVATION DATA >MERRA for Research and Applications Gridded Monthly Time-Mean Observation 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: FTP/HTTP
Distribution_Size: approx 0.76MB
Distribution_Format: HDF
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
Fax: 301-614-5268
Email: dana.ostrenga at nasa.gov
Contact Address:
Code 610.2 Bldg 32 Rm S151
NASA Goddard Space Flight Center
City: Greenbelt
Province or State: MD
Postal Code: 21230
Country: USA


MICHAEL BOSILOVICH
Role: INVESTIGATOR
Email: Michael.Bosilovich at nasa.gov
Contact Address:
NASA/Goddard Space Flight Center
Global Modeling and Assimilation Office
Code 610.1
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



Creation and Review Dates
DIF Creation Date: 2015-05-14
Last DIF Revision Date: 2015-06-30

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