[Personnel: Last_Name='OSTRENGA', First_Name='DANA']
MERRA AIRS AQUA : Gridded Monthly Time-Mean Observation (obs) Values V001 at GES DISCEntry ID: GES_DISC_MA_AIRS_AQUA_OBS_V001
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.
Data Set Citation
Dataset Originator/Creator: Washington Dept. of Fish and Wildlife
Dataset Title: WADFW Puget Sound Ambient Monitoring Program Winter 99
Dataset Series Name: OBIS-SEAMAP
Dataset Release Date: 2006-05-12 12:44:02-04
Dataset Release Place: http://seamap.env.duke.edu/
Dataset Publisher: OBIS-SEAMAP
Data Presentation Form: vector digital dataOnline Resource: http://wdfw.wa.gov/
This data set description is a member of a collection. The collection is described in
Start Date: 1998-12-09Stop Date: 1999-02-19
Latitude Resolution: 0.000167 Decimal degrees
Longitude Resolution: 0.000167 Decimal degrees
Horizontal Resolution Range: 1 meter - < 30 meters
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
Use Constraints 1. Not to use data contained in OBIS-SEAMAP in any publication, product, or
commercial application without prior written consent of the original data
provider. 2. To cite both the data provider and OBIS-SEAMAP appropriately after
approval of use is obtained. 3. 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.
Data Set Progress
Distribution Format: Shapefile
Role: TECHNICAL CONTACT
Role: DIF AUTHOR
Email: efujioka at duke.edu
LSRC A321 Box 90328
Province or State: NC
Postal Code: 27708
Role: TECHNICAL CONTACT
Phone: (360) 902-1718
Email: philip.bloch at wadnr.gov
Washington State Department of Natural Resources P.O. Box 47001
Province or State: WA
Postal Code: 98504-7001
Creation and Review Dates
DIF Creation Date: 2007-04-13
Last DIF Revision Date: 2008-01-13