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MERRA CONVENTIONAL OMA : Gridded Daily Time-Mean Observation minus Analysis (oma) Values V001 at GES DISC

Entry ID: GES_DISC_MA_CONV_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: 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 CONVENTIONAL OMA : Gridded Daily 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: 1979-01-01

Location Keywords

CONTINENT > NORTH AMERICA > UNITED STATES OF AMERICA

Science Keywords

AGRICULTURE > ANIMAL SCIENCE > ANIMAL DISEASES/DISORDERS/PESTS > WEST NILE VIRUS

HUMAN DIMENSIONS > PUBLIC HEALTH > DISEASES/EPIDEMICS > WEST NILE VIRUS

BIOLOGICAL CLASSIFICATION > VIRUSES

ISO Topic Category

FARMING

HEALTH

Access Constraints None

Use Constraints None

Ancillary Keywords

West Nile Virus

maps

Data Set Progress

COMPLETE

Originating Center

Data Center

West Nile Virus, National Wildlife Health Center, Biological Resources Division, USGS, U.S. Department of the Interior

Data Center URL: http://www.nwhc.usgs.gov/research/west_nile/west_nile.html
Dataset ID: usa_human_nov_30

Data Center Personnel
Name: EMI SAITO Phone: 608-270-2456 Email: esaito at usgs.gov Contact Address: USGS National Wildlife Health Center 6006 Schroeder Road City: Madison Province or State: Wisconsin Postal Code: 53711 Country: USA

Distribution

Fees: none

Personnel

DVBID

Role: TECHNICAL CONTACT

Phone: (970) 221-6400

Email: dvbid at cdc.gov

Contact Address:

Division of Vector-Borne Infectious Diseases

Centers for Disease Control and Prevention

P.O. Box 2087

City: Fort Collins

Province or State: Colorado

Postal Code: 80522

Country: USA

TYLER B. STEVENS

Role: DIF 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

Creation and Review Dates

DIF Creation Date: 2002-12-30

Last DIF Revision Date: 2007-03-06

Future DIF Review Date: 2003-12-30

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