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Model Bias (model + bias)
Selected AbstractsRegional Climate Models for Hydrological Impact Studies at the Catchment Scale: A Review of Recent Modeling StrategiesGEOGRAPHY COMPASS (ELECTRONIC), Issue 7 2010Claudia Teutschbein This article reviews recent applications of regional climate model (RCM) output for hydrological impact studies. Traditionally, simulations of global climate models (GCMs) have been the basis of impact studies in hydrology. Progress in regional climate modeling has recently made the use of RCM data more attractive, although the application of RCM simulations is challenging due to often considerable biases. The main modeling strategies used in recent studies can be classified into (i) very simple constructed modeling chains with a single RCM (S-RCM approach) and (ii) highly complex and computing-power intensive model systems based on RCM ensembles (E-RCM approach). In the literature many examples for S-RCM can be found, while comprehensive E-RCM studies with consideration of several sources of uncertainties such as different greenhouse gas emission scenarios, GCMs, RCMs and hydrological models are less common. Based on a case study using control-run simulations of fourteen different RCMs for five Swedish catchments, the biases of and the variability between different RCMs are demonstrated. We provide a short overview of possible bias-correction methods and show that inter-RCM variability also has substantial consequences for hydrological impact studies in addition to other sources of uncertainties in the modeling chain. We propose that due to model bias and inter-model variability, the S-RCM approach is not advised and ensembles of RCM simulations (E-RCM) should be used. The application of bias-correction methods is recommended, although one should also be aware that the need for bias corrections adds significantly to uncertainties in modeling climate change impacts. [source] How to account for the lipid effect on carbon stable-isotope ratio (,13C): sample treatment effects and model biasJOURNAL OF FISH BIOLOGY, Issue 4 2008K. Mintenbeck This study investigated the impact of lipid extraction, CaCO3 removal and of both treatments combined on fish tissue ,13C, ,15N and C:N ratio. Furthermore, the suitability of empirical ,13C lipid normalization and correction models was examined. ,15N was affected by lipid extraction (increase of up to 1·65,) and by the combination of both treatments, while acidification alone showed no effect. The observed shift in ,15N represents a significant bias in trophic level estimates, i.e. lipid-extracted samples are not suitable for ,15N analysis. C:N and ,13C were significantly affected by lipid extraction, proportional to initial tissue lipid content. For both variables, rates of change with lipid content (,C:N and ,,13C) were species specific. All tested lipid normalization and correction models produced biased estimates of fish tissue ,13C, probably due to a non-representative database and incorrect assumptions and generalizations the models were based on. Improved models need a priori more extensive and detailed studies of the relationships between lipid content, C:N and ,13C, as well as of the underlying biochemical processes. [source] Phase determination via Sayre-type equations with anomalous-scattering dataACTA CRYSTALLOGRAPHICA SECTION A, Issue 3 2001Jeffrey Roach The necessary background for the analysis of complex-valued electron-density maps is established. Various systems of structure-factor equations of convolutional type akin to Sayre's squaring method equations are tested for agreement on the real and imaginary parts of the electron density as well as approximations thereof. A system of convolutional structure-factor equations holding in a complex-valued electron density generated by two atom types is developed. The scope of application of these equations is determined and it is shown that the equations provide a method of extrapolating high-resolution phases from a low-resolution base phase set without introducing further model bias. Additional applications to phase refinement are explored. [source] Accounting for an imperfect model in 4D-VarTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 621 2006Yannick Tr'emolet Abstract In most operational implementations of four-dimensional variational data assimilation (4D-Var), it is assumed that the model used in the data assimilation process is perfect or, at least, that errors in the model can be neglected when compared to other errors in the system. In this paper, we study how model error could be accounted for in 4D-Var. We present three approaches for the formulation of weak-constraint 4D-Var: estimating explicitly a model-error forcing term, estimating a representation of model bias or, estimating a four-dimensional model state as the control variable. The consequences of these approaches with respect to the implementation and the properties of 4D-Var are discussed. We show that 4D-Var with an additional model-error representation as part of the control variable is essentially an initial-value problem and that its characteristics are very similar to that of strong constraint 4D-Var. Taking the four-dimensional state as the control variable, however, leads to very different properties. In that case, weak-constraint 4D-Var can be interpreted as a coupling between successive strong-constraint assimilation cycles. A possible extension towards long-window 4D-Var and possibilities for evolutions of the data assimilation system are presented. Copyright © 2006 Royal Meteorological Society [source] Ping-pong cross-validation in real space: a method for increasing the phasing power of a partial model without risk of model biasACTA CRYSTALLOGRAPHICA SECTION D, Issue 2 2003John F. Hunt Experimental phases could only be obtained to 4.4,Å resolution for crystals of the SecA translocation ATPase. Density modification of these phases exploiting the 65% solvent content of the crystal produced a map from which an approximate backbone model could be built for 80% of the structure. Combining the phases inferred from this partial model with the MIR phases and repeating the density modification produced an improved map from which a more complete backbone model could be built. However, this procedure converged before yielding a map, that allowed unambiguous sequence assignment for the majority of the protein molecule. In order to avoid the likely model bias associated with a speculative attempt at sequence assignment, a real-space cross-validation procedure was employed to facilitate completion of the crystal structure based on partial model phasing. The protein was partitioned into two disjoint sets of residues. Models in which the side chains were built for residues in one of the two sets were used for phase combination and density modification in order to produce improved electron density for interpretation of residues in the other set that had not been included in the model. Residues in the two sets were therefore omitted from the model in alternation except at sites where the side chain could be identified definitively based on phasing with the other set. This ping-pong cross-validation procedure allowed partial model phasing to be used to complete the crystal structure of SecA without being impeded by model bias. These results show that the structure of a large protein molecule can be solved with exclusively low-resolution experimental phase information based on intensive use of partial model phasing and density modification. Real-space cross-validation can be applied to reduce the risk of model bias associated with partial model phasing, streamlining this approach and expanding its range of applicability. [source] | |||||||||||||