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.

Force constants and dipole‐moment derivatives of molecules from perturbed Hartree–Fock calculations

General expressions for the force constants and dipole‐moment derivatives of molecules are derived, and the problems arising in their practical application are reviewed. Great emphasis is placed on the use of the Hartree–Fock function as an approximate wavefunction, and a number of its properties are discussed and re‐emphasised. The main content of this paper is the development of a perturbed Hartree–Fock theory that makes possible the direct calculation of force constants and dipole‐moment derivatives from SCF–MO wavefunctions. Essentially the theory yields ∂ϕi / ∂RJα, the derivative of an MO with respect to a nuclear coordinate.

Force constants and dipole moment derivatives of molecules from perturbed Hartree-Fock calculations II. Applications to limited basis set SCF-MO wavefunctions

The perturbed Hartree–Fock theory developed in the preceding paper is applied to LiH, BH, and HF, using limited basis‐set SCF–MO wavefunctions derived by previous workers. The calculated values for the force constant ke and the dipole‐moment derivative μ(1) are (experimental values in parentheses): LiH, ke  =  1.618(1.026)mdyn/Å,μ(1)  =  −18.77(−2.0±0.3)D/ÅBH,ke  =  5.199(3.032)mdyn/Å,μ(1)  =  −1.03(−)D/Å;HF,ke  =  12.90(9.651)mdyn/Å,μ(1)  =  −2.15(+1.50)D/Å. The values of the force on the proton were calculated exactly and according to the Hellmann–Feynman theorem in each case, and the discrepancies show that none of the wavefunctions used are close to the Hartree–Fock limit, so that the large errors in ke and μ(1) are not surprising. However no difficulties arose in the perturbed Hartree–Fock calculation, so that the application of the theory to more accurate wavefunctions appears quite feasible.

Rapid synthesis of carbohydrate derivatives, including mimetics of c-linked disaccharides and c-Linked aza disaccharides, using the Hetero-Diels−Alder Reaction

In this work we demonstrate the value of performing a Hetero Diels-Alder reaction (HDAR) between Danishefsky’s diene and a range of aldehydes or imines, under microwave irradiation. By using a range of aldehydes and imines, including those derived from carbohydrates, access to functionalised 2,3-dihydro-4H-pyran-4-ones or 2,3-dihydro-4-pyridinones in good to excellent synthetic yields is possible. A particular strength of the methodology is its ability to access mimetics of C-linked disaccharides and C-linked aza disaccharides, targets of current therapeutic interest, in a rapid, convenient and diastereoselective manner. The effect of high pressure on the HDARs involving carbohydrate derived aldehydes and imines is also explored, with enhancement in yields occurring for the aldehyde substrates. Finally, HDARs using carbohydrate derived ketones, enones and enals are described under a range of conditions. Optimum results were obtained under high pressure conditions, with highly functionalized carbohydrate derivatives being afforded, in good yields, in this way.

Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

Simulation of crop yields using ERA-40: Limits to skill and nonstationarity in weather-yield relationships

Reanalysis data provide an excellent test bed for impacts prediction systems. because they represent an upper limit on the skill of climate models. Indian groundnut (Arachis hypogaea L.) yields have been simulated using the General Large-Area Model (GLAM) for annual crops and the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-yr reanalysis (ERA-40). The ability of ERA-40 to represent the Indian summer monsoon has been examined. The ability of GLAM. when driven with daily ERA-40 data, to model both observed yields and observed relationships between subseasonal weather and yield has been assessed. Mean yields "were simulated well across much of India. Correlations between observed and modeled yields, where these are significant. are comparable to correlations between observed yields and ERA-40 rainfall. Uncertainties due to the input planting window, crop duration, and weather data have been examined. A reduction in the root-mean-square error of simulated yields was achieved by applying bias correction techniques to the precipitation. The stability of the relationship between weather and yield over time has been examined. Weather-yield correlations vary on decadal time scales. and this has direct implications for the accuracy of yield simulations. Analysis of the skewness of both detrended yields and precipitation suggest that nonclimatic factors are partly responsible for this nonstationarity. Evidence from other studies, including data on cereal and pulse yields, indicates that this result is not particular to groundnut yield. The detection and modeling of nonstationary weather-yield relationships emerges from this study as an important part of the process of understanding and predicting the impacts of climate variability and change on crop yields.

Modeling of relationships between weather and Septoria tritici epidemics on winter wheat: A critical approach

Two models for predicting Septoria tritici on winter wheat (cv. Ri-band) were developed using a program based on an iterative search of correlations between disease severity and weather. Data from four consecutive cropping seasons (1993/94 until 1996/97) at nine sites throughout England were used. A qualitative model predicted the presence or absence of Septoria tritici (at a 5% severity threshold within the top three leaf layers) using winter temperature (January/February) and wind speed to about the first node detectable growth stage. For sites above the disease threshold, a quantitative model predicted severity of Septoria tritici using rainfall during stern elongation. A test statistic was derived to test the validity of the iterative search used to obtain both models. This statistic was used in combination with bootstrap analyses in which the search program was rerun using weather data from previous years, therefore uncorrelated with the disease data, to investigate how likely correlations such as the ones found in our models would have been in the absence of genuine relationships.