The potential of high-density observations for numerical weather prediction: A study with simulated observations

The skill of numerical weather prediction depends to a large extent upon the quantity of globally available observations. Only a fraction of the available observations (especially high-density observations) is used in current operational assimilation systems. In this paper, the potential of high-density observations is studied in a practical four-dimensional variational assimilation context. Two individual meteorological situations are used to examine the impact of different observation densities on the analysis and the forecast. A series of observing-system simulation experiments are performed. Both direct observations (temperature and surface pressure) and indirect observations (radiance) are simulated, with uncorrelated or correlated errors. In general, it is verified that a small reduction (increase) of the initial error in a sensitive area can produce a considerable improvement (degradation) of the targeted forecast. In particular, the results show that increasing the observation density for the uncorrelated-error case can generally improve the analysis and the forecast. However, for correlated observation errors and the use of a diagonal observation-error covariance matrix in the assimilation, an increase in the observation number such that the error correlation between two adjacent observations becomes greater than a threshold value (around 0.2) degrades the analysis and the forecast. Posterior diagnostics of the sub-optimality of the assimilation scheme for correlated observation errors are analysed. Finally, it is shown that a risk of using high-density observations and poor vertical resolution is that deficiencies in the background-error statistics can lead to unrealistic analysis increments at some levels where no observations are present, and so produce a degradation of the analysis at these levels.