A 3‐dimensional variational data assimilation (3D‐Var) scheme for the High Resolution Limited Area Model (HIRLAM) forecasting system is described. The HIRLAM 3D‐Var is based on the minimization of a cost function that consists of one term Jb, which measures the distance between the resulting analysis and a background field, in general a short‐range forecast, and another term J0, which measures the distance between the analysis and the observations. This paper is concerned with the general formulation of the HIRLAM 3D‐Var and with Jb, while the companion paper by Lindskog and co‐workers is concerned with the handling of observations, including the Jo term, and with validation of the 3D‐Var through extended parallel assimilation and forecast experiments. The 3D‐Var minimization requires a pre‐conditioning that is achieved by a transformation of the minimization control variable. This change of variable is designed as an operator approximating an inverse square root of the forecast error covariance matrix in the model space. The main transformations are the subtraction of the geostrophic wind increment, the bi‐Fourier transform, and the projection on vertical eigenvectors. The spectral bi‐Fourier approach allows one to derive non‐separable structure functions in a limited area model, in the form of vertically dependent horizontal spectra and scale‐dependent vertical correlations. Statistics have been accumulated from differences between + 24 h and +48 h HIRLAM forecasts valid at the same time. Results from single observation impact studies as well as results from assimilation cycles using operational observations are presented. It is shown that the HIRLAM 3D‐Var produces assimilation increments in accordance with the applied analysis structure functions, that the fit of the analysis to the observations is in agreement with the assumed error statistics, and that assimilation increments are well balanced. It is also shown that the particular problems associated with the limited area formulation have been solved. These results, together with the results of the companion paper, indicate that the 3D‐Var scheme performs significantly better than the statistical interpolation scheme.

This study elucidates the routes of elimination of ciprofloxacin and its metabolites in two groups of 5 subjects each, one of healthy volunteers, the other of patients with severe renal failure having a creatinine clearance of 12 ml/min (range 8–16 ml/min). Each subject received one dose of 200 mg ciprofloxacin infused intravenously over 30 min. In an effort to recover the total dose administered, all urine and faeces were collected for the 7 days following dosing. Blood was collected at set intervals after dosing. Serum, urine, and faeces were assayed by high-pressure liquid chromatography for ciprofloxacin and metabolites. The ciprofloxacin serum half-life in healthy volunteers was 3.9 ± 0.4 h and in patients with marked renal failure 11.2 + 2.5 h. The total amount of ciprofloxacin recovered in urine fell by a multiple of 3.4 from ± 10.7% in healthy subjects to 19.0 ± 15.9% in patients with renal failure, and the metabolites from 12.2 ± 2.3% in the former group to 5.8 ± 5.1% in the latter. In contrast, the amount of ciprofloxacin eliminated in faeces increased, by a similar factor, from ± 2.6% in healthy subjects to 37.2 ± 12.5 % in patients with renal failure. The amount of metabolites in faeces increased analogously from 7.3 ± 1.6 to 26.2 ± 6.5%. Since ciprofloxacin was administered intravenously and biliary elimination of the drug and its metabolites is negligible, we propose that elimination by faeces is due primarily to transintestinal elimination. This study demonstrates that transintestinal elimination of ciprofloxacin serves as an extrarenal safety factor compensating for reduced elimination by the renal route.

A 3‐dimensional variational data assimilation (3D‐Var) scheme for the High Resolution Limited Area Model (HIRLAM) forecasting system is described. The HIRLAM 3D‐Var is based on the minimisation of a cost function that consists of one term, Jb, which measures the distance between the resulting analysis and a background field, in general a short‐range forecast, and another term, Jo, which measures the distance between the analysis and the observations. This paper is concerned with Jo and the handling of observations, while the companion paper by Gustafsson et al. (2001) is concerned with the general 3D‐Var formulation and with the Jb term. Individual system components, such as the screening of observations and the observation operators, and other issues, such as the parallelisation strategy for the computer code, are described. The functionality of the observation quality control is investigated and the 3D‐Var system is validated through data assimilation and forecast experiments. Results from assimilation and forecast experiments indicate that the 3D‐Var assimilation system performs significantly better than two currently used HIRLAM systems, which are based on statistical interpolation. The use of all significant level data from multilevel observation reports is shown to be one factor contributing to the superiority of the 3D‐Var system. Other contributing factors are most probably the formulation of the analysis as a single global problem, the use of non‐separable structure functions and the variational quality control, which accounts for non‐Gaussian observation errors.

The pharmacokinetics of fosfomycin trometamol has been assessed in 12 healthy volunteers given oral doses of 2, 3, and 4 g of fosfomycin and 3 g intravenously of fosfomycin as fosfomycin sodium, all in the fasting state. The assay was microbiological (Proteus mirabilis ATCC 21100). There was a gradual rise in both peak serum concentrations and total area under the curve by rising oral doses, from 16.0 mg/l and 106.7 mg x h/1, after 2 g to 30.9 mg/l and 189.7 mg x h/1 after 4 g respectively. The serum half-life was 4 h after the oral doses and 2.1 h after the intravenous dose. After the oral doses, the amounts excreted in urine in the active form ranged from 36 to 40% compared to 93% after the intravenous dose. The bioavailability was slightly below 40%. Concentrations in urine covers the usual urinary tract pathogens after oral doses of 2, 3, and 4 g.