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MERRA HIRS2 NOAA09 : Gridded Monthly Time-Mean Observation minus Forecast (omf) Values V001 at GES DISC

Entry ID: GES_DISC_MA_HIRS2_NOAA09_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

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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 HIRS2 NOAA09 : 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: 1985-02-01

Location Keywords

GEOGRAPHIC REGION > GLOBAL

Data Resolution

Latitude Resolution: 2/3 degrees
Longitude Resolution: 1/2 degrees
Vertical Resolution: 19 levels
Temporal Resolution: Monthly

Science Keywords

ATMOSPHERE > ATMOSPHERIC PRESSURE > ATMOSPHERIC PRESSURE MEASUREMENTS

ATMOSPHERE > ATMOSPHERIC TEMPERATURE > AIR TEMPERATURE

ISO Topic Category

CLIMATOLOGY/METEOROLOGY/ATMOSPHERE

Platform

T-39 > Rockwell Sabreliner T-39

Instrument

GUST PROBES

HYGROMETERS

Project

EOSDIS > Earth Observing System Data Information System

FIRE > First ISCCP Regional Experiment

Ancillary Keywords

AIR MOTIONS

CLOUD MICROPHYSICS

Originating Center

LARC_DAAC

Data Center

Atmospheric Science Data Center, Science Directorate, Langley Research Center, NASA

Data Center URL: http://eosweb.larc.nasa.gov/
Dataset ID: FIRE_CI2_SABRELINER

Data Center Personnel
Name: ASDC USER SERVICES Phone: 757-864-8656 Email: support-asdc at earthdata.nasa.gov Contact Address: NASA Langley Atmospheric Science Data Center User and Data Services NASA Langley Research Center Mail Stop 157D City: Hampton Province or State: VA Postal Code: 23681-2199 Country: USA

Personnel

ANDREW HEYMSFIELD

Role: INVESTIGATOR

Phone: 303-497-8943

Email: heyms1 at ucar.edu

Contact Address:

P.O. Box 3000

City: Boulder

Province or State: Colorado

Postal Code: 80307

Country: USA

LARRY MILOSHEVICH

Role: TECHNICAL CONTACT

Phone: (303) 497-8963

Email: milo at ncar.ucar.edu

Contact Address:

National Center for Atmospheric Research

MMM Division

P.O. Box 3000

City: Boulder

Province or State: CO

Postal Code: 80307-3000

Country: USA

ASDC USER SERVICES

Role: DIF AUTHOR

Phone: 757-864-8656

Email: support-asdc at earthdata.nasa.gov

Contact Address:

NASA Langley Atmospheric Science Data Center

User and Data Services

NASA Langley Research Center

Mail Stop 157D

City: Hampton

Province or State: VA

Postal Code: 23681-2199

Country: USA

Publications/References

Cox, et al., 1987. "FIRE - The First ISCCP Regional Experiment," Bull.
Amer. Meteor. Soc., 68, 114-118.

Starr, D.O'C., 1987. "A Cirrus-Cloud Experiment: Intensive Field
Observations Planned for FIRE," Bull. Am. Met. Soc., 68, 119-124.

Sorlie, S., February 1993. "Langley DAAC Handbook." NASA Langley
Research Center, Hampton, Virginia.

Extended Metadata Properties

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Creation and Review Dates

Last DIF Revision Date: 2009-09-14

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