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

Entry ID: GES_DISC_MA_AMSUA_NOAA15_OBS_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

(Click for Interactive Map)

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 (obs) 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

BIOTA

CLIMATOLOGY/METEOROLOGY/ATMOSPHERE

GEOSCIENTIFIC INFORMATION

OCEANS

Project

PAGES > Past Global Changes, IGBP

Ancillary Keywords

paleovegetation

vegetation reconstruction

pollen

Data Set Progress

COMPLETE

Originating Center

NOAA/NCDC

Data Center

World Data Center for Paleoclimatology, Boulder

Data Center URL: http://www.ncdc.noaa.gov/paleo/

Data Center Personnel
Name: WDC/PALEOCLIMATOLOGY, BOULDER Phone: +1 (303) 497-6160 Fax: +1 (303) 497-6513 Email: paleo at noaa.gov Contact Address: World Data Center for Paleoclimatology NOAA/NCDC Code E/GCx3 325 Broadway City: Boulder Province or State: CO Postal Code: 80303 Country: USA

Distribution

Distribution Media: online ftp, www

Distribution Format: ascii

Fees: none

Personnel

JONATHAN T. OVERPECK

Role: INVESTIGATOR

Email: jto at email.arizona.edu

Contact Address:

Institute of the Environment

Department of Geosciences

University of Arizona

City: Tucson

Province or State: AZ

Postal Code: 85721-0077

Country: USA

THOMPSON WEBB III

Role: INVESTIGATOR

Phone: (401) 863-3128

Contact Address:

Department of Geological Sciences

Brown University

City: Providence

Province or State: Rhode Island

Postal Code: 02912-1846

Country: USA

ROBERT S. WEBB

Role: INVESTIGATOR

Phone: (303) 497-6967

Fax: (303) 497-7013

Contact Address:

NOAA Paleoclimatology Program

National Geophysical Data Center

325 Broadway

City: Boulder

Province or State: CO

Postal Code: 80303

Country: USA

Publications/References

Overpeck, J.T., Webb, R.S. and Webb, T. III, 1992, Mapping eastern North
America vegetation change over the past 18,000 years: no-analogs and the
future, Geology, 20, 1071-1074.

Creation and Review Dates

DIF Creation Date: 2004-03-23

Last DIF Revision Date: 2012-08-31

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