MA_HIRS2_NOAA08_OBS_001

Gridded Monthly Time-Mean Observation (obs) Values 0.5 x 0.667 degree V001 (MA_HIRS2_NOAA08_OBS) at GES DISC

Version 001 eng
  • 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.
  • Version Description
    This is the first version of this data produced

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    Data Dates
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    Last Revision
    Processing Level

    4

    Quality
    Collection Progress

    Complete

Related URLs
    Science Keywords
    • EARTH SCIENCE
    • ATMOSPHERE
    • ALTITUDE
    • BAROMETRIC ALTITUDE
    • EARTH SCIENCE
    • LAND SURFACE
    • TOPOGRAPHY
    • TERRAIN ELEVATION
    • EARTH SCIENCE
    • LAND SURFACE
    • LAND USE/LAND COVER
    • LAND USE/LAND COVER CLASSIFICATION
Other Descriptive Keywords
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ISO Topic Categories
  • CLIMATOLOGY/METEOROLOGY/ATMOSPHERE
  • ENVIRONMENT
  • ELEVATION
  • GEOSCIENTIFIC INFORMATION
  • IMAGERY/BASE MAPS/EARTH COVER
Additional Attributes
  • Data Granularity
    STRING
    The time coverage of individual data granules.
    Resolution Range Begin Range End Unit of Measure Accuracy

    gov.nasa.gsfc.disc

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Acquisition Information Fields:

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Platforms
  • Models/Analyses
    MERRA
    Instruments
    • NOT APPLICABLE
Projects
Project Short Name Campaigns Project Dates
MERRA TIME-MEAN OBSERVATION DATA No campaigns listed. No dates provided.

Temporal Information Fields:

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Spatial Information Fields:

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Location Keywords
    • GEOGRAPHIC REGION
    • GLOBAL

Data Centers

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  • NASA/GSFC/SED/ESD/GCDC/GESDISC
    ARCHIVER
    Goddard Earth Sciences Data and Information Services Center (formerly Goddard DAAC), Global Change Data Center, Earth Sciences Division, Science and Exploration Directorate, Goddard Space Flight Center, NASA

    This data center does not have any addresses listed.

    This data center does not have any contact mechanisms listed.

    https://disc.gsfc.nasa.gov/
    • Home Page

Data Contacts

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  • DANA
    Metadata Author
    DANA OSTRENGA

    Code 610.2 Bldg 32 Rm S151
    NASA Goddard Space Flight Center
    Greenbelt, MD 21230

    • Email
    • 301-614-5475
    • 301-614-5268
  • ARLINDO
    Investigator
    ARLINDO DASILVA

    NASA/Goddard Space Flight Center
    Global Modeling and Assimilation Office
    Code 610.1
    Greenbelt, MD 20771

  • GLOBAL MODELING AND ASSIMILATION OFFICE
    Technical Contact
    GLOBAL MODELING AND ASSIMILATION OFFICE

    NASA Goddard Space Flight Center
    Code 610.1
    Greenbelt, MD 20771

  • GLOBAL MODELING AND ASSIMILATION OFFICE
    Data Center Contact
    GLOBAL MODELING AND ASSIMILATION OFFICE

    NASA/GSFC/SED/ESD/GCDC/GESDISC

    Goddard Earth Sciences Data and Information Services Center
    Code 610.2
    NASA Goddard Space Flight Center
    Greenbelt, MD 20771

    • Email
    • 301-614-5224
    • 301-614-5268

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