The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

Metrics for solar wind prediction models: Comparison of empirical, hybrid, and physics-based schemes with 8 years of L1 observations

Space weather effects on technological systems originate with energy carried from the Sun to the terrestrial environment by the solar wind. In this study, we present results of modeling of solar corona-heliosphere processes to predict solar wind conditions at the L1 Lagrangian point upstream of Earth. In particular we calculate performance metrics for (1) empirical, (2) hybrid empirical/physics-based, and (3) full physics-based coupled corona-heliosphere models over an 8-year period (1995–2002). L1 measurements of the radial solar wind speed are the primary basis for validation of the coronal and heliosphere models studied, though other solar wind parameters are also considered. The models are from the Center for Integrated Space-Weather Modeling (CISM) which has developed a coupled model of the whole Sun-to-Earth system, from the solar photosphere to the terrestrial thermosphere. Simple point-by-point analysis techniques, such as mean-square-error and correlation coefficients, indicate that the empirical coronal-heliosphere model currently gives the best forecast of solar wind speed at 1 AU. A more detailed analysis shows that errors in the physics-based models are predominately the result of small timing offsets to solar wind structures and that the large-scale features of the solar wind are actually well modeled. We suggest that additional “tuning” of the coupling between the coronal and heliosphere models could lead to a significant improvement of their accuracy. Furthermore, we note that the physics-based models accurately capture dynamic effects at solar wind stream interaction regions, such as magnetic field compression, flow deflection, and density buildup, which the empirical scheme cannot.

Predictions of the arrival time of coronal mass ejections at 1AU: an analysis of the causes of errors

Three existing models of Interplanetary Coronal Mass Ejection (ICME) transit between the Sun and the Earth are compared to coronagraph and in situ observations: all three models are found to perform with a similar level of accuracy (i.e. an average error between observed and predicted 1AU transit times of approximately 11 h). To improve long-term space weather prediction, factors influencing CME transit are investigated. Both the removal of the plane of sky projection (as suffered by coronagraph derived speeds of Earth directed CMEs) and the use of observed values of solar wind speed, fail to significantly improve transit time prediction. However, a correlation is found to exist between the late/early arrival of an ICME and the width of the preceding sheath region, suggesting that the error is a geometrical effect that can only be removed by a more accurate determination of a CME trajectory and expansion. The correlation between magnetic field intensity and speed of ejecta at 1AU is also investigated. It is found to be weak in the body of the ICME, but strong in the sheath, if the upstream solar wind conditions are taken into account.

Errors associated with preparation of adult and paediatric parenteral nutrition in UK hospital pharmacies

Rationale: In UK hospitals, the preparation of all total parenteral nutrition (TPN) products must be made in the pharmacy as TPNs are categorised as high-risk injectables (NPSA/2007/20). The National Aseptic Error Reporting Scheme has been collecting data on pharmacy compounding errors in the UK since August 2003. This study reports on types of error associated with the preparation of TPNs, including the stage at which these were identified and potential and actual patient outcomes. Methods: Reports of compounding errors for the period 1/2004 - 3/2007 were analysed on an Excel spreadsheet. Results: Of a total of 3691 compounding error reports, 674 (18%) related to TPN products; 548 adult vs. 126 paediatric. A significantly higher proportion of adult TPNs (28% vs. 13% paediatric) were associated with labelling errors and a significantly higher proportion of paediatric TPNs (25% vs. 15% adult) were associated with incorrect transcriptions (Chi-Square Test; p<0.005). Labelling errors were identified equally by pharmacists (42%) and technicians (48%) with technicians detecting mainly at first check and pharmacists at final check. Transcription errors were identified mainly by technicians (65% vs. 27% pharmacist) at first check. Incorrect drug selection (13%) and calculation errors (9%) were associated with adult and paediatric TPN preparations in the same ratio. One paediatric TPN error detected at first check was considered potentially catastrophic; 31 (5%) errors were considered of major and 38 (6%) of moderate potential consequence. Five errors (2 moderate, 1 minor) were identified during or after administration. Conclusions: While recent UK patient safety initiatives are aimed at improving the safety of injectable medicines in clinical areas, the current study highlights safety problems that exist within pharmacy production units. This could be used in the creation of an error management tool for TPN compounding processes within hospital pharmacies.

The impact of a closed-loop electronic prescribing and administration system on prescribing errors, administration errors and staff time: a before-and-after study

Objectives: To assess the impact of a closed-loop electronic prescribing, automated dispensing, barcode patient identification and electronic medication administration record (EMAR) system on prescribing and administration errors, confirmation of patient identity before administration, and staff time. Design, setting and participants: Before-and-after study in a surgical ward of a teaching hospital, involving patients and staff of that ward. Intervention: Closed-loop electronic prescribing, automated dispensing, barcode patient identification and EMAR system. Main outcome measures: Percentage of new medication orders with a prescribing error, percentage of doses with medication administration errors (MAEs) and percentage given without checking patient identity. Time spent prescribing and providing a ward pharmacy service. Nursing time on medication tasks. Results: Prescribing errors were identified in 3.8% of 2450 medication orders pre-intervention and 2.0% of 2353 orders afterwards (p<0.001; χ2 test). MAEs occurred in 7.0% of 1473 non-intravenous doses pre-intervention and 4.3% of 1139 afterwards (p = 0.005; χ2 test). Patient identity was not checked for 82.6% of 1344 doses pre-intervention and 18.9% of 1291 afterwards (p<0.001; χ2 test). Medical staff required 15 s to prescribe a regular inpatient drug pre-intervention and 39 s afterwards (p = 0.03; t test). Time spent providing a ward pharmacy service increased from 68 min to 98 min each weekday (p = 0.001; t test); 22% of drug charts were unavailable pre-intervention. Time per drug administration round decreased from 50 min to 40 min (p = 0.006; t test); nursing time on medication tasks outside of drug rounds increased from 21.1% to 28.7% (p = 0.006; χ2 test). Conclusions: A closed-loop electronic prescribing, dispensing and barcode patient identification system reduced prescribing errors and MAEs, and increased confirmation of patient identity before administration. Time spent on medication-related tasks increased.

TIGGE: Comparison of the prediction of northern hemisphere extratropical cyclones by different ensemble prediction systems

The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) is a World Weather Research Programme project. One of its main objectives is to enhance collaboration on the development of ensemble prediction between operational centers and universities by increasing the availability of ensemble prediction system (EPS) data for research. This study analyzes the prediction of Northern Hemisphere extratropical cyclones by nine different EPSs archived as part of the TIGGE project for the 6-month time period of 1 February 2008–31 July 2008, which included a sample of 774 cyclones. An objective feature tracking method has been used to identify and track the cyclones along the forecast trajectories. Forecast verification statistics have then been produced [using the European Centre for Medium-Range Weather Forecasts (ECMWF) operational analysis as the truth] for cyclone position, intensity, and propagation speed, showing large differences between the different EPSs. The results show that the ECMWF ensemble mean and control have the highest level of skill for all cyclone properties. The Japanese Meteorological Administration (JMA), the National Centers for Environmental Prediction (NCEP), the Met Office (UKMO), and the Canadian Meteorological Centre (CMC) have 1 day less skill for the position of cyclones throughout the forecast range. The relative performance of the different EPSs remains the same for cyclone intensity except for NCEP, which has larger errors than for position. NCEP, the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), and the Australian Bureau of Meteorology (BoM) all have faster intensity error growth in the earlier part of the forecast. They are also very underdispersive and significantly underpredict intensities, perhaps due to the comparatively low spatial resolutions of these EPSs not being able to accurately model the tilted structure essential to cyclone growth and decay. There is very little difference between the levels of skill of the ensemble mean and control for cyclone position, but the ensemble mean provides an advantage over the control for all EPSs except CPTEC in cyclone intensity and there is an advantage for propagation speed for all EPSs. ECMWF and JMA have an excellent spread–skill relationship for cyclone position. The EPSs are all much more underdispersive for cyclone intensity and propagation speed than for position, with ECMWF and CMC performing best for intensity and CMC performing best for propagation speed. ECMWF is the only EPS to consistently overpredict cyclone intensity, although the bias is small. BoM, NCEP, UKMO, and CPTEC significantly underpredict intensity and, interestingly, all the EPSs underpredict the propagation speed, that is, the cyclones move too slowly on average in all EPSs.

Can 4D-Var use dynamical information from targeted observations of a baroclinic structure?

Targeted observations are generally taken in regions of high baroclinicity, but often show little impact. One plausible explanation is that important dynamical information, such as upshear tilt, is not extracted from the targeted observations by the data assimilation scheme and used to correct initial condition error. This is investigated by generating pseudo targeted observations which contain a singular vector (SV) structure that is not present in the background field or routine observations, i.e. assuming that the background has an initial condition error with tilted growing structure. Experiments were performed for a single case-study with varying numbers of pseudo targeted observations. These were assimilated by the Met Office four-dimensional variational (4D-Var) data assimilation scheme, which uses a 6 h window for observations and background-error covariances calculated using the National Meteorological Centre (NMC) method. The forecasts were run using the operational Met Office Unified Model on a 24 km grid. The results presented clearly demonstrate that a 6 h window 4D-Var system is capable of extracting baroclinic information from a limited set of observations and using it to correct initial condition error. To capture the SV structure well (projection of 0.72 in total energy), 50 sondes over an area of 1×106 km2 were required. When the SV was represented by only eight sondes along an example targeting flight track covering a smaller area, the projection onto the SV structure was lower; the resulting forecast perturbations showed an SV structure with increased tilt and reduced initial energy. The total energy contained in the perturbations decreased as the SV structure was less well described by the set of observations (i.e. as fewer pseudo observations were assimilated). The assimilated perturbation had lower energy than the SV unless the pseudo observations were assimilated with the dropsonde observation errors halved from operational values. Copyright © 2010 Royal Meteorological Society

Errors in "Wavefunctions for the methane molecule"

Two errors in my paper “Wave functions for the methane molecule” [1] are corrected. They concern my f-harmonic approximation to the wave-function in the equilibrium configuration, for which the final expression for the wave function, the energy lowering, and the density function were all in error.

Forecast calibration and combination: a simple Bayesian approach for ENSO

This study presents a new simple approach for combining empirical with raw (i.e., not bias corrected) coupled model ensemble forecasts in order to make more skillful interval forecasts of ENSO. A Bayesian normal model has been used to combine empirical and raw coupled model December SST Niño-3.4 index forecasts started at the end of the preceding July (5-month lead time). The empirical forecasts were obtained by linear regression between December and the preceding July Niño-3.4 index values over the period 1950–2001. Coupled model ensemble forecasts for the period 1987–99 were provided by ECMWF, as part of the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) project. Empirical and raw coupled model ensemble forecasts alone have similar mean absolute error forecast skill score, compared to climatological forecasts, of around 50% over the period 1987–99. The combined forecast gives an increased skill score of 74% and provides a well-calibrated and reliable estimate of forecast uncertainty.

The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator

In this paper we focus on the one year ahead prediction of the electricity peak-demand daily trajectory during the winter season in Central England and Wales. We define a Bayesian hierarchical model for predicting the winter trajectories and present results based on the past observed weather. Thanks to the flexibility of the Bayesian approach, we are able to produce the marginal posterior distributions of all the predictands of interest. This is a fundamental progress with respect to the classical methods. The results are encouraging in both skill and representation of uncertainty. Further extensions are straightforward at least in principle. The main two of those consist in conditioning the weather generator model with respect to additional information like the knowledge of the first part of the winter and/or the seasonal weather forecast. Copyright (C) 2006 John Wiley & Sons, Ltd.

Description of a multifaceted strategy for recruiting general practitioners and community pharmacists to talk about medication errors

Objective: To describe the use of a multifaceted strategy for recruiting general practitioners (GPs) and community pharmacists to talk about medication errors which have resulted in preventable drug-related admissions to hospital. This is a potentially sensitive subject with medicolegal implications. Setting: Four primary care trusts and one teaching hospital in the UK. Method: Letters were mailed to community pharmacists and general practitioners asking for provisional consent to be interviewed and permission to contact them again should a patient be admitted to hospital as a result of a medication error. In addition, GPs were asked for permission to approach their patients should they be admitted to hospital. A multifaceted approach to recruitment was used including gaining support for the study from professional defence agencies and local champions. Key findings: Eighty-five percent (310/385) of GPs and 62% (93/149) of community pharmacists responded to the letters. Eighty-five percent (266/310) of GPs who responded and 81% (75/93) of community pharmacists who responded gave provisional consent to participate in interviews. All GPs (14 out of 14) and community pharmacists (10 out of 10) who were subsequently asked to participate, when patients were admitted to hospital, agreed to be interviewed. Conclusion: The multifaceted approach to recruitment was associated with an impressive response when asking healthcare professionals to be interviewed about medication errors which have resulted in preventable drug-related morbidity.