• Adaptive observations;
  • Extratropical cyclones;
  • Singular vectors


The potential to improve short-range forecasts of two extratropical storms by using supplementary observations in regions lacking accurate observations is investigated. In the idealized framework used here, a control and a truth experiment are selected from a set of forecasts initialized with analyses from different numerical weatherprediction centres. Synthetic soundings of wind and temperature are created from the truth experiment and are assimilated with four-dimensional variational analysis using the operational observation-error estimates for radiosondes and the initial condition of the control experiment as background. Through multiple analysis/forecast experiments we obtain a nonlinear estimate of the optimal zone for observing (OZO); that is the zone in which the use of a given number of supplementary observations leads to the largest reduction in forecast error. We evaluate targeting techniques based on either total-energy singular vectors (TESVs) or on Hessian singular vectors (HSVs) by comparison with the OZO and by comparison with experiments in which the same amount of supplementary observations are distributed in an untargeted manner, namely with a random distribution scheme (RDS).

Overall, the HSV targeting is superior to the TESV targeting in the two cases. In one case there is a significant difference between the target regions determined with TESVs and HSVs. The HSV-based observing strategy resembles the OZO in terms of the observing region and the achieved forecast-error reduction. With the RDS, the forecast error is variable and likely to be larger than the forecast error obtained with singular-vector targeting. Experiments with target regions of different sizes show that supplementary observations in an area of about 3×106 km2 are required to achieve a significant forecast improvement. A two-dimensional sampling pattern with soundings spaced at a distance of about 1–2 times the horizontal correlation length-scale of the backgrounderror estimate appears very efficient. In additional impact experiments for one case, observations were perturbed with noise to represent observational error. The perturbations are almost as likely to improve the forecast as to worsen it compared with the forecast using unperturbed observations. Copyright © 2002 Royal Meteorological Society.