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

Abstract

AbstractThe 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. Copyright © 2003 Royal Meteorological Society