Can wavelets improve the representation of forecast error covariances in variational data assimilation?

Two wavelet-based control variable transform schemes are described and are used to model some important features of forecast error statistics for use in variational data assimilation. The first is a conventional wavelet scheme and the other is an approximation of it. Their ability to capture the position and scale-dependent aspects of covariance structures is tested in a two-dimensional latitude-height context. This is done by comparing the covariance structures implied by the wavelet schemes with those found from the explicit forecast error covariance matrix, and with a non-wavelet- based covariance scheme used currently in an operational assimilation scheme. Qualitatively, the wavelet-based schemes show potential at modeling forecast error statistics well without giving preference to either position or scale-dependent aspects. The degree of spectral representation can be controlled by changing the number of spectral bands in the schemes, and the least number of bands that achieves adequate results is found for the model domain used. Evidence is found of a trade-off between the localization of features in positional and spectral spaces when the number of bands is changed. By examining implied covariance diagnostics, the wavelet-based schemes are found, on the whole, to give results that are closer to diagnostics found from the explicit matrix than from the nonwavelet scheme. Even though the nature of the covariances has the right qualities in spectral space, variances are found to be too low at some wavenumbers and vertical correlation length scales are found to be too long at most scales. The wavelet schemes are found to be good at resolving variations in position and scale-dependent horizontal length scales, although the length scales reproduced are usually too short. The second of the wavelet-based schemes is often found to be better than the first in some important respects, but, unlike the first, it has no exact inverse transform.

A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances

Exploitation of Geostationary Earth Radiation Budget data using simulations from a numerical weather prediction model: Methodology and data validation

We describe a new methodology for comparing satellite radiation budget data with a numerical weather prediction (NWP) model. This is applied to data from the Geostationary Earth Radiation Budget (GERB) instrument on Meteosat-8. The methodology brings together, in near-real time, GERB broadband shortwave and longwave fluxes with simulations based on analyses produced by the Met Office global NWP model. Results for the period May 2003 to February 2005 illustrate the progressive improvements in the data products as various initial problems were resolved. In most areas the comparisons reveal systematic errors in the model's representation of surface properties and clouds, which are discussed elsewhere. However, for clear-sky regions over the oceans the model simulations are believed to be sufficiently accurate to allow the quality of the GERB fluxes themselves to be assessed and any changes in time of the performance of the instrument to be identified. Using model and radiosonde profiles of temperature and humidity as input to a single-column version of the model's radiation code, we conduct sensitivity experiments which provide estimates of the expected model errors over the ocean of about ±5–10 W m−2 in clear-sky outgoing longwave radiation (OLR) and ±0.01 in clear-sky albedo. For the more recent data the differences between the observed and modeled OLR and albedo are well within these error estimates. The close agreement between the observed and modeled values, particularly for the most recent period, illustrates the value of the methodology. It also contributes to the validation of the GERB products and increases confidence in the quality of the data, prior to their release.

A cross-calibration of chlorine isotopic measurements and suitability of seawater as the international reference material

A collection of 24 seawaters from various worldwide locations and differing depth was culled to measure their chlorine isotopic composition (delta(37)Cl). These samples cover all the oceans and large seas: Atlantic, Pacific, Indian and Antarctic oceans, Mediterranean and Red seas. This collection includes nine seawaters from three depth profiles down to 4560 mbsl. The standard deviation (2sigma) of the delta(37)Cl of this collection is +/-0.08 parts per thousand, which is in fact as large as our precision of measurement ( +/- 0.10 parts per thousand). Thus, within error, oceanic waters seem to be an homogeneous reservoir. According to our results, any seawater could be representative of Standard Mean Ocean Chloride (SMOC) and could be used as a reference standard. An extended international cross-calibration over a large range of delta(37)Cl has been completed. For this purpose, geological fluid samples of various chemical compositions and a manufactured CH3Cl gas sample, with delta(37)Cl from about -6 parts per thousand to +6 parts per thousand have been compared. Data were collected by gas source isotope ratio mass spectrometry (IRMS) at the Paris, Reading and Utrecht laboratories and by thermal ionization mass spectrometry (TIMS) at the Leeds laboratory. Comparison of IRMS values over the range -5.3 parts per thousand to +1.4 parts per thousand plots on the Y=X line, showing a very good agreement between the three laboratories. On 11 samples, the trend line between Paris and Reading Universities is: delta(37)Cl(Reading)= (1.007 +/- 0.009)delta(37)Cl(Paris) - (0.040 +/- 0.025), with a correlation coefficient: R-2 = 0.999. TIMS values from Leeds University have been compared to IRMS values from Paris University over the range -3.0 parts per thousand to +6.0 parts per thousand. On six samples, the agreement between these two laboratories, using different techniques is good: delta(37)Cl(Leeds)=(1.052 +/- 0.038)delta(37)Cl(Paris) + (0.058 +/- 0.099), with a correlation coefficient: R-2 = 0.995. The present study completes a previous cross-calibration between the Leeds and Reading laboratories to compare TIMS and IRMS results (Anal. Chem. 72 (2000) 2261). Both studies allow a comparison of IRMS and TIMS techniques between delta(37)Cl values from -4.4 parts per thousand to +6.0 parts per thousand and show a good agreement: delta(37)Cl(TIMS)=(1.039 +/- 0.023)delta(37)Cl(IRMS)+(0.059 +/- 0.056), with a correlation coefficient: R-2 = 0.996. Our study shows that, for fluid samples, if chlorine isotopic compositions are near 0 parts per thousand, their measurements either by IRMS or TIMS will give comparable results within less than +/- 0.10 parts per thousand, while for delta(37)Cl values as far as 10 parts per thousand (either positive or negative) from SMOC, both techniques will agree within less than +/- 0.30 parts per thousand. (C) 2004 Elsevier B.V. All rights reserved.

Spatial analysis of the error in a model of soil nitrogen

Models of the dynamics of nitrogen in soil (soil-N) can be used to aid the fertilizer management of a crop. The predictions of soil-N models can be validated by comparison with observed data. Validation generally involves calculating non-spatial statistics of the observations and predictions, such as their means, their mean squared-difference, and their correlation. However, when the model predictions are spatially distributed across a landscape the model requires validation with spatial statistics. There are three reasons for this: (i) the model may be more or less successful at reproducing the variance of the observations at different spatial scales; (ii) the correlation of the predictions with the observations may be different at different spatial scales; (iii) the spatial pattern of model error may be informative. In this study we used a model, parameterized with spatially variable input information about the soil, to predict the mineral-N content of soil in an arable field, and compared the results with observed data. We validated the performance of the N model spatially with a linear mixed model of the observations and model predictions, estimated by residual maximum likelihood. This novel approach allowed us to describe the joint variation of the observations and predictions as: (i) independent random variation that occurred at a fine spatial scale; (ii) correlated random variation that occurred at a coarse spatial scale; (iii) systematic variation associated with a spatial trend. The linear mixed model revealed that, in general, the performance of the N model changed depending on the spatial scale of interest. At the scales associated with random variation, the N model underestimated the variance of the observations, and the predictions were correlated poorly with the observations. At the scale of the trend, the predictions and observations shared a common surface. The spatial pattern of the error of the N model suggested that the observations were affected by the local soil condition, but this was not accounted for by the N model. In summary, the N model would be well-suited to field-scale management of soil nitrogen, but suited poorly to management at finer spatial scales. This information was not apparent with a non-spatial validation. (c),2007 Elsevier B.V. All rights reserved.

Errors associated with the preparation of aseptic products in UK hospital pharmacies: lessons from the national aseptic error reporting scheme

Background Pharmacy aseptic units prepare and supply injectables to minimise risks. The UK National Aseptic Error Reporting Scheme has been collecting data on pharmacy compounding errors, including near-misses, since 2003. Objectives The cumulative reports from January 2004 to December 2007, inclusive, were analysed. Methods The different variables of product types, error types, staff making and detecting errors, stage errors detected, perceived contributory factors, and potential or actual outcomes were presented by cross-tabulation of data. Results A total of 4691 reports were submitted against an estimated 958 532 items made, returning 0.49% as the overall error rate. Most of the errors were detected before reaching patients, with only 24 detected during or after administration. The highest number of reports related to adult cytotoxic preparations (40%) and the most frequently recorded error was a labelling error (34.2%). Errors were mostly detected at first check in assembly area (46.6%). Individual staff error contributed most (78.1%) to overall errors, while errors with paediatric parenteral nutrition appeared to be blamed on low staff levels more than other products were. The majority of errors (68.6%) had no potential patient outcomes attached, while it appeared that paediatric cytotoxic products and paediatric parenteral nutrition were associated with greater levels of perceived patient harm. Conclusions The majority of reports were related to near-misses, and this study highlights scope for examining current arrangements for checking and releasing products, certainly for paediatric cytotoxic and paediatric parenteral nutrition preparations within aseptic units, but in the context of resource and capacity constraints.

Name agreement in picture naming: An ERP study

Name agreement is the extent to which different people agree on a name for a particular picture. Previous studies have found that it takes longer to name low name agreement pictures than high name agreement pictures. To examine the effect of name agreement in the online process of picture naming, we compared event-related potentials (ERPs) recorded whilst 19 healthy, native English speakers silently named pictures which had either high or low name agreement. A series of ERP components was examined: P1 approximately 120ms from picture onset, N1 around 170ms, P2 around 220ms, N2 around 290ms, and P3 around 400ms. Additionally, a late time window from 800 to 900ms was considered. Name agreement had an early effect, starting at P1 and possibly resulting from uncertainty of picture identity, and continuing into N2, possibly resulting from alternative names for pictures. These results support the idea that name agreement affects two consecutive processes: first, object recognition, and second, lexical selection and/or phonological encoding.