Abstract
AbstractWith the development of convection-permitting numerical weather prediction the efficient use of high-resolution observations in data assimilation is becoming increasingly important. The operational assimilation of these observations, such as Doppler radar radial winds (DRWs), is now common, although to avoid violating the assumption of uncorrelated observation errors the observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast requires the introduction of the full, potentially correlated, error statistics. In this work, observation error statistics are calculated for the DRWs that are assimilated into the Met Office high-resolution U.K. model (UKV) using a diagnostic that makes use of statistical averages of observation-minus-background and observation-minus-analysis residuals. This is the first in-depth study using the diagnostic to estimate both horizontal and along-beam observation error statistics. The new results obtained show that the DRW error standard deviations are similar to those used operationally and increase as the observation height increases. Surprisingly, the estimated observation error correlation length scales are longer than the operational thinning distance. They are dependent both on the height of the observation and on the distance of the observation away from the radar. Further tests show that the long correlations cannot be attributed to the background error covariance matrix used in the assimilation, although they are, in part, a result of using superobservations and a simplified observation operator. The inclusion of correlated error statistics in the assimilation allows less thinning of the data and hence better use of the high-resolution observations.
Highlights
6 With the recent development of convection permitting numerical weather prediction (NWP), 7 such as the Met Office UK variable resolution (UKV) model (Lean et al 2008; Tang et al 2013), 8 the assimilation of observations that have high frequency both in space and time has become in9 creasingly important (Park and Zupanski 2003; Dance 2004; Sun et al 2014; Ballard et al 2016; Clark et al 2015)
Currently at the Met Office the error statistics associated with Doppler radar radial winds (DRWs) are assumed uncorrelated (Simonin et al 2014)
67 Here we present the first in-depth study using the diagnostic of Desroziers et al (2005) to calcu68 late observation error statistics for the DRWs assimilated into the Met Office high resolution UK 69 (UKV) model
Summary
MetOffice@Reading, Meteorology Building, University of Reading, Reading, Berkshire, S. Observation error statistics are calculated for the DRWs that are assimilated into the Met Office high-resolution UK model using a diagnostic that makes use of statistical aver ages of observation-minus-background and observation-minus-analysis resid uals. This is the first in-depth study using the diagnostic to estimate both hor izontal and along-beam observation error statistics. The 20 estimated observation error correlation length-scales are longer than the op erational thinning distance They are dependent both on the height of the ob servation and on the distance of the observation away from the radar. The inclusion of correlated error statistics in the assimilation allows less thinning of the data and better use of the high-resolution observations
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