Sensitivity of surface temperature analyses to specification of background and observation error covariances
Creators: name: Tyndall, Daniel Paul
Publisher: name: University of Utah
Year: 2012
Subjects: Climatology
Earth temperature

description: A two-dimensional variational analysis for surface temperature is developed for a limited domain (4° latitude x 4° longitude) in order to evaluate approaches to efficiently examine the sensitivity of similar variational analysis systems to the specification of observation and background errors. This work is intended to help facilitate improvements to the operational Real-Time Mesoscale Analysis (RTMA) developed by the National Centers for Environmental Prediction. The local surface analysis (LSA) uses the same  projection and terrain (on a roughly 5-km x 5-km grid), background fields derived from  1-h forecasts of the Rapid Update Cycle (RUC) downscaled to that grid, and observation  assets used by the RTMA.  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, which is higher than that  used operationally by the RTMA. This ratio is likely even higher in mountainous regions  where the representativeness errors attributed to the observations are large. The  background errors also tend to remain more strongly correlated over longer horizontal  distances than those specified operationally for the RTMA. A two-dimensional variational analysis for surface temperature is developed for a  limited domain (4° latitude x 4° longitude) in order to evaluate approaches to efficiently  examine the sensitivity of similar variational analysis systems to the specification of  observation and background errors. This work is intended to help facilitate improvements  to the operational Real-Time Mesoscale Analysis (RTMA) developed by the National  Centers for Environmental Prediction. The local surface analysis (LSA) uses the same  projection and terrain (on a roughly 5-km x 5-km grid), background fields derived from  1-h forecasts of the Rapid Update Cycle (RUC) downscaled to that grid, and observation  assets used by the RTMA.  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, which is higher than that  used operationally by the RTMA. This ratio is likely even higher in mountainous regions  where the representativeness errors attributed to the observations are large. The  background errors also tend to remain more strongly correlated over longer horizontal  distances than those specified operationally for the RTMA.A two-dimensional variational analysis for surface temperature is developed for a  limited domain (4° latitude x 4° longitude) in order to evaluate approaches to efficiently  examine the sensitivity of similar variational analysis systems to the specification of  observation and background errors. This work is intended to help facilitate improvements  to the operational Real-Time Mesoscale Analysis (RTMA) developed by the National  Centers for Environmental Prediction. The local surface analysis (LSA) uses the same  projection and terrain (on a roughly 5-km x 5-km grid), background fields derived from  1-h forecasts of the Rapid Update Cycle (RUC) downscaled to that grid, and observation  assets used by the RTMA.  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, which is higher than that  used operationally by the RTMA. This ratio is likely even higher in mountainous regions  where the representativeness errors attributed to the observations are large. The  background errors also tend to remain more strongly correlated over longer horizontal  distances than those specified operationally for the RTMA. 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. The RTMA surface temperature analysis for that case exhibited several  unrealistic features in that region as a result of a pronounced surface-based temperature  inversion. Sets of 10 data denial experiments in which 10% of the observations are  withheld 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 is computed from the differences between 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.