A relationship between busted European forecasts, a Rockies trough, and storms over eastern North America suggests the importance of improving quality and use of observations, model depiction of convective systems, and representation of uncertainties.

SUMMARYThe influence matrix is used in ordinary least-squares applications for monitoring statistical multipleregression analyses. Concepts related to the influence matrix provide diagnostics on the influence of individual data on the analysis-the analysis change that would occur by leaving one observation out, and the effective information content (degrees of freedom for signal) in any sub-set of the analysed data. In this paper, the corresponding concepts have been derived in the context of linear statistical data assimilation in numerical weather prediction. An approximate method to compute the diagonal elements of the influence matrix (the selfsensitivities) has been developed for a large-dimension variational data assimilation system (the four-dimensional variational system of the European Centre for Medium-Range Weather Forecasts). Results show that, in the boreal spring 2003 operational system, 15% of the global influence is due to the assimilated observations in any one analysis, and the complementary 85% is the influence of the prior (background) information, a short-range forecast containing information from earlier assimilated observations. About 25% of the observational information is currently provided by surface-based observing systems, and 75% by satellite systems.Low-influence data points usually occur in data-rich areas, while high-influence data points are in data-sparse areas or in dynamically active regions. Background-error correlations also play an important role: high correlation diminishes the observation influence and amplifies the importance of the surrounding real and pseudo observations (prior information in observation space). Incorrect specifications of background and observation-error covariance matrices can be identified, interpreted and better understood by the use of influence-matrix diagnostics for the variety of observation types and observed variables used in the data assimilation system.

This paper describes the use of forecast sensitivity to observations as a diagnostic tool to monitor the observation impact on the 24-hour forecast range. In particular, the forecast error is provided by the control experiments (using all observations available) of two sets of observing system experiments performed at ECMWF, a month in summer 2006 and a month in winter 2007, respectively. In such a way, the observation data impact obtained with the forecast sensitivity is compared with the observing system experiment's data impact; differences and similarities are highlighted. Globally, the assimilated observations decrease the forecast error; locally, some poor performances are detected that are related either to the data quality or to the suboptimality of the data assimilation system. It is also found that the synoptic situation can affect the measurements or can produce areas of large field variability that the assimilation system cannot model correctly.

We have evaluated the incidence of lupus erythematosus (LE)-specific skin disease in 186 patients with LE, seen retrospectively over a 10-year period at our Dermatology Department and determined the correlation of LE-nonspecific skin disease in patients with systemic involvement. Chronic cutaneous LE (CCLE) with classical discoid lesions (localized, 70%; generalized, 30%) was the most common cutaneous manifestation (72.5%). Subacute cutaneous LE (SCLE) represented only 8% of LE skin disease (annular-polycyclic type, 73%; papulo-squamous type, 27%). Acute cutaneous LE (ACLE) was detected in 15% of our patients: the butterfly erythema was the most frequent skin lesion (96%) while only one case of bullous LE and one case of widespread maculo-papular eruption in association with malar erythema were demonstrated. In 8 patients no LE-specific skin lesions (lupus sine lupo) were found. LE-nonspecific skin lesions were found in 31% of our patients with systemic LE (SLE): Raynaud's phenomenon was found in 23/58 (39.6%), cutaneous small vessel leukocytoclastic vasculitis in 8/58 (13.7%), nonscarring alopecia in 18/58 (31%), lupus pernio in 6/58 (10.3%), hemorrhagic lesions in 4/58 (6.8%), livedo reticularis in 5/58 (8.6%), mucosal ulcers in 3/58 (5.1%) and periungual telangiectasia in 12/58 (20.6%) SLE patients. LE-nonspecific skin lesions are detected only in patients with SLE and usually in the active phases of the disease.