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.

Analytical potentials for triatomic molecules IX. The prediction of anharmonic force constants from potential energy surfaces based on harmonic force fields and dissociation energies for SO2 and O3

Analytical potential energy functions which are valid at all dissociation limits have been derived for the ground states of SO2 and O3. The procedure involves minimizing the errors between the observed vibrational spectra and spectra calculated by a variational procedure. Good agreement is obtained between the observed and calculated spectra for both molecules. Comparisons are made between anharmonic force fields, previously determined from the spectral data, and the force fields obtained by differentiating the derived analytical functions at the equilibrium configurations.

A simple theoretical model for the intensification of tropical cyclones and polar lows

A simple theoretical model for the intensification of tropical cyclones and polar lows is developed using a minimal set of physical assumptions. These disturbances are assumed to be balanced systems intensifying through the WISHE (Wind-Induced Surface Heat Exchange) intensification mechanism, driven by surface fluxes of heat and moisture into an atmosphere which is neutral to moist convection. The equation set is linearized about a resting basic state and solved as an initial-value problem. A system is predicted to intensify with an exponential perturbation growth rate scaled by the radial gradient of an efficiency parameter which crudely represents the effects of unsaturated processes. The form of this efficiency parameter is assumed to be defined by initial conditions, dependent on the nature of a pre-existing vortex required to precondition the atmosphere to a state in which the vortex can intensify. Evaluation of the simple model using a primitive-equation, nonlinear numerical model provides support for the prediction of exponential perturbation growth. Good agreement is found between the simple and numerical models for the sensitivities of the measured growth rate to various parameters, including surface roughness, the rate of transfer of heat and moisture from the ocean surface, and the scale for the growing vortex.

Singular vector ensemble forecasting systems and the prediction of flow dependent uncertainty

The ECMWF ensemble weather forecasts are generated by perturbing the initial conditions of the forecast using a subset of the singular vectors of the linearised propagator. Previous results show that when creating probabilistic forecasts from this ensemble better forecasts are obtained if the mean of the spread and the variability of the spread are calibrated separately. We show results from a simple linear model that suggest that this may be a generic property for all singular vector based ensemble forecasting systems based on only a subset of the full set of singular vectors.

Numerical simulation of the 12 May 1997 CME Event: The role of magnetic reconnection

We perform a numerical study of the evolution of a Coronal Mass Ejection (CME) and its interaction with the coronal magnetic field based on the 12 May 1997, CME event using a global MagnetoHydroDynamic (MHD) model for the solar corona. The ambient solar wind steady-state solution is driven by photospheric magnetic field data, while the solar eruption is obtained by superimposing an unstable flux rope onto the steady-state solution. During the initial stage of CME expansion, the core flux rope reconnects with the neighboring field, which facilitates lateral expansion of the CME footprint in the low corona. The flux rope field also reconnects with the oppositely orientated overlying magnetic field in the manner of the breakout model. During this stage of the eruption, the simulated CME rotates counter-clockwise to achieve an orientation that is in agreement with the interplanetary flux rope observed at 1 AU. A significant component of the CME that expands into interplanetary space comprises one of the side lobes created mainly as a result of reconnection with the overlying field. Within 3 hours, reconnection effectively modifies the CME connectivity from the initial condition where both footpoints are rooted in the active region to a situation where one footpoint is displaced into the quiet Sun, at a significant distance (≈1R ) from the original source region. The expansion and rotation due to interaction with the overlying magnetic field stops when the CME reaches the outer edge of the helmet streamer belt, where the field is organized on a global scale. The simulation thus offers a new view of the role reconnection plays in rotating a CME flux rope and transporting its footpoints while preserving its core structure.

Whole crop cereals 2. Prediction of apparent digestibility and energy value from in vitro digestion techniques and near infrared reflectance spectroscopy and of chemical composition by near infrared reflectance spectroscopy

Samples of whole crop wheat (WCW, n = 134) and whole crop barley (WCB, n = 16) were collected from commercial farms in the UK over a 2-year period (2003/2004 and 2004/2005). Near infrared reflectance spectroscopy (NIRS) was compared with laboratory and in vitro digestibility measures to predict digestible organic matter in the dry matter (DOMD) and metabolisable energy (ME) contents measured in vivo using sheep. Spectral models using the mean spectra of two scans were compared with those using individual spectra (duplicate spectra). Overall NIRS accurately predicted the concentration of chemical components in whole crop cereals apart from crude protein. ammonia-nitrogen, water-soluble carbohydrates, fermentation acids and solubility values. In addition. the spectral models had higher prediction power for in vivo DOMD and ME than chemical components or in vitro digestion methods. Overall there Was a benefit from the use of duplicate spectra rather than mean spectra and this was especially so for predicting in vivo DOMD and ME where the sample population size was smaller. The spectral models derived deal equally well with WCW and WCB and Would he of considerable practical value allowing rapid determination of nutritive value of these forages before their use in diets of productive animals. (C) 2008 Elsevier B.V. All rights reserved.

Near infrared reflectance spectroscopy (NIRS) to predict biological parameters of maize silage: effects of particle comminution, oven drying temperature and the presence of residual moisture

Maize silage nutritive quality is routinely determined by near infrared reflectance spectroscopy (NIRS). However, little is known about the impact of sample preparation on the accuracy of the calibration to predict biological traits. A sample population of 48 maize silages representing a wide range of physiological maturities was used in a study to determine the impact of different sample preparation procedures (i.e., drying regimes; the presence or absence of residual moisture; the degree of particle comminution) on resultant NIR prediction statistics. All silages were scanned using a total of 12 combinations of sample pre-treatments. Each sample preparation combination was subjected to three multivariate regression techniques to give a total of 36 predictions per biological trait. Increased sample preparations procedure, relative to scanning the unprocessed whole plant (WP) material, always resulted in a numerical minimisation of model statistics. However, the ability of each of the treatments to significantly minimise the model statistics differed. Particle comminution was the most important factor, oven-drying regime was intermediate, and residual moisture presence was the least important. Models to predict various biological parameters of maize silage will be improved if material is subjected to a high degree of particle comminution (i.e., having been passed through a 1 mm screen) and developed on plant material previously dried at 60 degrees C. The extra effort in terms of time and cost required to remove sample residual moisture cannot be justified. (c) 2005 Elsevier B.V. All rights reserved.

Prediction of cottonseed longevity

The objective of this work was to determine the viability equation constants for cottonseed and to detect the occurrence and depletion of hardseededness. Three seedlots of Brazilian cultivars IAC-19 and IAC-20 were tested, using 12 moisture content levels, ranging from 2.2 to 21.7% and three storage temperatures, 40, 50 and 65 degrees C. Seed moisture content level was reached from the initial value (around 8.8%) either by rehydration, in a closed container, or by drying in desiccators containing silica gel, both at 20 degrees C. Twelve seed subsamples for each moisture content/temperature treatment were sealed in laminated aluminium-foil packets and stored in incubators at those temperatures, until complete survival curves were obtained. Seed equilibrium relative humidity was recorded. Hardseededness was detected at moisture content levels below 6% and its releasing was achieved either naturally, during storage period, or artificially through seed coat removal. The viability equation quantified the response of seed longevity to storage environment well with K-E = 9.240, C-W = 5.190, C-H = 0.03965 and C-Q = 0.000426. The lower limit estimated for application of this equation at 65 degrees C was 3.6% moisture content.

A GIS-based empirical model for vegetation prediction in Lefka Ori, Crete

The aim of the study was to establish and verify a predictive vegetation model for plant community distribution in the alti-Mediterranean zone of the Lefka Ori massif, western Crete. Based on previous work three variables were identified as significant determinants of plant community distribution, namely altitude, slope angle and geomorphic landform. The response of four community types against these variables was tested using classification trees analysis in order to model community type occurrence. V-fold cross-validation plots were used to determine the length of the best fitting tree. The final 9node tree selected, classified correctly 92.5% of the samples. The results were used to provide decision rules for the construction of a spatial model for each community type. The model was implemented within a Geographical Information System (GIS) to predict the distribution of each community type in the study site. The evaluation of the model in the field using an error matrix gave an overall accuracy of 71%. The user's accuracy was higher for the Crepis-Cirsium (100%) and Telephium-Herniaria community type (66.7%) and relatively lower for the Peucedanum-Alyssum and Dianthus-Lomelosia community types (63.2% and 62.5%, respectively). Misclassification and field validation points to the need for improved geomorphological mapping and suggests the presence of transitional communities between existing community types.

Influence-matrix diagnostic of a data assimilation system

The influence matrix is used in ordinary least-squares applications for monitoring statistical multiple-regression 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 self-sensitivities) 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. Copyright © 2004 Royal Meteorological Society

Prediction of global and local model quality in CASP8 using the ModFOLD server

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0-an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/.

Intrinsic disorder prediction from the analysis of multiple protein fold recognition models

Motivation: Intrinsic protein disorder is functionally implicated in numerous biological roles and is, therefore, ubiquitous in proteins from all three kingdoms of life. Determining the disordered regions in proteins presents a challenge for experimental methods and so recently there has been much focus on the development of improved predictive methods. In this article, a novel technique for disorder prediction, called DISOclust, is described, which is based on the analysis of multiple protein fold recognition models. The DISOclust method is rigorously benchmarked against the top.ve methods from the CASP7 experiment. In addition, the optimal consensus of the tested methods is determined and the added value from each method is quantified. Results: The DISOclust method is shown to add the most value to a simple consensus of methods, even in the absence of target sequence homology to known structures. A simple consensus of methods that includes DISOclust can significantly outperform all of the previous individual methods tested.

Numerical abundance of invasive ants and monopolisation of exudate-producing resources - a chicken and egg situation

1. Invasive ants commonly reach abnormally high abundances and have severe impacts on the ecosystems they invade. Current invasion theory recognises that not only negative interactions, such as natural enemy release, but positive interactions, such as facilitation, are important in causing this increased abundance. 2. For invasive ants, facilitation can occur through mutualism with exudate-producing plants and insects. To obtain such partnerships, however, invaders must first displace native ants, whose communities are highly structured around such resources. 3. By manipulating the abundance of an invasive ant relative to a native, we show that a minimum threshold abundance exists for invasive ants to monopolise exudate-producing resources. In addition, we show that behavioural dominance is context dependent and varies with spatial location and numerical abundance. 4. Thus, we suggest a 'facilitation-threshold' hypothesis of ant invasion, whereby a minimum abundance of invasive ants is required before facilitation and behavioural dominance can drive abundance rapidly upwards through positive feedback.

Constrained models of evolution lead to improved prediction of functional linkage from correlated gain and loss of genes

Motivation: We compare phylogenetic approaches for inferring functional gene links. The approaches detect independent instances of the correlated gain and loss of pairs of genes from species' genomes. We investigate the effect on results of basing evidence of correlations on two phylogenetic approaches, Dollo parsminony and maximum likelihood (ML). We further examine the effect of constraining the ML model by fixing the rate of gene gain at a low value, rather than estimating it from the data. Results: We detect correlated evolution among a test set of pairs of yeast (Saccharomyces cerevisiae) genes, with a case study of 21 eukaryotic genomes and test data derived from known yeast protein complexes. If the rate at which genes are gained is constrained to be low, ML achieves by far the best results at detecting known functional links. The model then has fewer parameters but it is more realistic by preventing genes from being gained more than once. Availability: BayesTraits by M. Pagel and A. Meade, and a script to configure and repeatedly launch it by D. Barker and M. Pagel, are available at http://www.evolution.reading.ac.uk .