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MERRA AIRS AQUA : Gridded Monthly Time-Mean Observation minus Forecast (omf) Values V001 at GES DISC

Entry ID: GES_DISC_MA_AIRS_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

(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 AIRS 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: 0.000167 Decimal degrees
Longitude Resolution: 0.000167 Decimal degrees
Horizontal Resolution Range: 1 meter - < 30 meters

Science Keywords

BIOLOGICAL CLASSIFICATION > ANIMALS/VERTEBRATES > BIRDS > DUCKS/GEESE/SWANS
BIOLOGICAL CLASSIFICATION > ANIMALS/VERTEBRATES > BIRDS > LOONS

ISO Topic Category

BIOTA

BOUNDARIES

Platform

AIRCRAFT

Project

OBIS-SEAMAP > OBIS - Spatial Ecological Analysis of Megavertebrate Populations

Access Constraints ACCESS CONSTRAINTS: Open public unless otherwise noted.

DISTRIBUTION LIABILITY: Not to hold OBIS-SEAMAP liable for errors in the data. While we have made every effort to ensure the quality of the database, we cannot guarantee the accuracy of these datasets. Also please refer to Use Constraints.

Use Constraints Not to use data contained in OBIS-SEAMAP in any publication, product, or commercial application without prior written consent of the original data provider. To cite both the data provider and OBIS-SEAMAP appropriately after approval of use is obtained. Also please refer to Distribution Liability.

Ancillary Keywords

Achichilique pico amarillo

aigles

Data Set Progress

COMPLETE

Data Center

OBIS Spatial Ecological Analysis of Megavertebrate Populations

Data Center URL: http://seamap.env.duke.edu/

Data Center Personnel
Name: PATRICK N. HALPIN Phone: 919-613-8062 Email: phalpin at duke.edu Contact Address: Duke University Box 90328 City: Durham Province or State: NC Postal Code: 27708 Country: USA

Distribution

Distribution Format: Shapefile

Personnel

EI FUJIOKA

Role: TECHNICAL CONTACT

Role: DIF AUTHOR

Phone: 919-613-8021

Fax: 919-684-8741

Email: efujioka at duke.edu

Contact Address:

LSRC A321

Box 90328

City: Durham

Province or State: NC

Postal Code: 27708

Country: USA

GLENN FORD

Role: TECHNICAL CONTACT

Phone: (503) 287-5173

Email: eci at teleport.com

Contact Address:

2735 N.E. Weidler Street

City: Portland

Province or State: OR

Postal Code: 97232

Country: USA

Publications/References

Bonnell, M. L., M. O. Pierson, and G. D. Farrens. 1983. Pinnipeds and Sea Otters of Central and Northern California, 1980-1983: Status, Abundance, and Distribution. Part of Investigator's Final Report, Marine Mammal and Seabird Study, Central and Northern California, Contract No. 14-12-0001-29090. Prepared by Center for Marine Sciences, University of California, Santa Cruz, for the Pacific OCS ...

Creation and Review Dates

DIF Creation Date: 2007-12-28

Last DIF Revision Date: 2010-03-02

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