A comparison of ensemble-transform Kalman-filter targeting guidance with ECMWF and NRL total-energy singular-vector guidance

Ongoing adaptive observing programmes strive to improve short- and medium-range forecasts of winter weather over populated areas. Aircraft equipped with Global Positioning System (GPS) dropwindsondes are directed over ordinarily data-sparse regions (oceans) to collect observations to enhance the subsequent operational analysis-forecast cycle. Two objective techniques that have been used to identify optimal 'target regions' are the ensemble-transform Kalman filter (ET KF) and the singular-vector technique. The similarities and differences between targeting guidance based on the ET KF and total-energy singular vectors (TESVs) are assessed for ten cases during the North Pacific Experiment (NORPEX). TESVs are computed at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Naval Research Laboratory (NRL) using their respective global models, and the ET KF uses 25 ECMWF ensemble perturbations. Using measures based on (i) rankings of aircraft flight tracks and (ii) spatial similarities between targeting guidance maps, the main finding is that (a) the ET KF guidance is reasonably correlated with TESV guidance for at least seven of the ten NORPEX cases. Other findings include: (b) the ECMWF and NRL TESV targets sometimes differ significantly, (c) the ET KF and TESV guidance maps often display different optimal locations for targeting on smaller scales, but larger-scale aspects are usually similar, (d) the ET KF generally identifies larger regions over which useful observations can be taken compared with the SV technique, and (e) regions that are deemed effective for targeting by both techniques often correspond to baroclinic zones. The ET KF and SV techniques may identify similar regions for targeting if locations of large ensemble-based analysis-error variance coincide with areas where rapid perturbation growth occurs. On the other hand, they may identify different targeting locations for the following two reasons. First, the ET KF implicitly accounts for error correlation length-scales in its predictions of forecast-error variance reduction produced by any set of targeted observations. Hence, it can identify locations for targeted observations that are distant from the regions of high analysis sensitivity that are selected for targeting by the SV technique. Second, ET KF estimates of analysis-error variance are constrained to a subspace of evolved ensemble perturbations and are, therefore, rank deficient.