Sensitivity of surface temperature analyses to background and observation errors
Daniel Tyndall, University of Utah, Salt Lake City, UT; and J. D. Horel
A local surface analysis (LSA) using a two-dimensional variational method to analyze surface temperature is developed for a limited domain (4° latitude × 4° longitude) in order to evaluate approaches to examine the sensitivity of the temperature analysis to the specification of observation and background errors. This LSA utilizes the 1-h forecast from the Rapid Update Cycle (RUC) downscaled to a 5-km resolution terrain for its background fields, as well as observations from METAR sites as well as various mesonets obtained from the Meteorological Assimilation Data Ingest System.
The observation error variance as a function of broad network categories and error variance and covariance of the downscaled 1-h RUC background fields are estimated using a sample of over 7 million surface temperature observations in the continental United States collected during the period 8 May – 7 June 2008. The ratio of observation to background error variance is found to be between 2 and 3. This ratio is likely even higher in mountainous regions where representativeness errors attributed to the observations are large. The background errors, which are specified as a function of spatial distance, also tend to remain strongly correlated over horizontal distances of approximately 80 km.
Analysis sensitivity to both the ratio of the observation and background error variance and background error decorrelation length scale is examined for a single case (0900 UTC 22 October 2007) using the LSA centered over the Shenandoah Valley of Virginia. This particular case was chosen because a well defined surface inversion is present in the 1200 UTC sounding from that day. Sets of 10 data denial experiments in which 10% of the observations are randomly and uniquely from each analysis are used. The analysis error is estimated by the differences between the withheld observations and the corresponding analyses from which the observations are withheld while the analysis sensitivity to the withheld observations are computed from differences between the control analyses and the analyses from which the observations are withheld. For this case, it is possible to improve analysis accuracy in multiple ways, i.e., by making the analysis less (more) detailed by broadening (shortening) the decorrelation length scales of the background error covariance in combination with increasing (decreasing) the observation to background error variance ratio. These results, not surprisingly, confirm the need to examine analysis sensitivity over many types of synoptic situations and the difficulty in specifying those parameters a priori.
Session 15A, Data Assimilation: RTMA and Rapid Refresh
Thursday, 4 June 2009, 1:30 PM-3:00 PM, Grand Ballroom East
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