Gridded Monthly Time-Mean Observation minus Forecast (omf) Values 0.5 x 0.667 degree V001 (MA_HIRS3_NOAA15_OMF) at GES DISC

Metadata Updated: May 2, 2019

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.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019

Metadata Source

Harvested from NASA Data.json

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MA_HIRS3_NOAA15_OMF variable

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date May 2, 2019
Unique Identifier C1274764764-GES_DISC
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Citation Global Modeling and Assimilation Office (GMAO). 2011-06-01. MA_HIRS3_NOAA15_OMF. Version 001. Gridded Monthly Time-Mean Observation minus Forecast (omf) Values V001. Greenbelt, MD, USA. Archived by National Aeronautics and Space Administration, U.S. Government, Goddard Earth Sciences Data and Information Services Center (GES DISC). Digital Science Data.
Creator Global Modeling and Assimilation Office (GMAO)
Data Presentation Form Digital Science Data
Datagov Dedupe Retained 20190501230127
Graphic Preview Description MA_HIRS3_NOAA15_OMF variable
Graphic Preview File
Harvest Object Id 621e3bdc-5e64-497e-8c6f-856bbaaea1d5
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2007-06-14
Homepage URL
Language en-US
Metadata Type geospatial
Data Last Modified 2015-07-07
Program Code 026:001
Release Place Greenbelt, MD, USA
Series Name MA_HIRS3_NOAA15_OMF
Source Datajson Identifier True
Source Hash 65df735690368733b385603e1ced12a25ce1bee5
Source Schema Version 1.1
Spatial -180.0 -90.0 180.0 90.0
Temporal 1998-09-01T00:00:00Z/2000-06-30T23:59:59Z

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